A Jupyter widget using sigma.js and graphology to render interactive networks directly within the result of a notebook cell.
ipysigma has been designed to work with either networkx or igraph.
ipysigma lets you customize a large number of the graph's visual variables such as: node color, size, label, border, halo, pictogram, shape and edge color, size, type, label etc.
For an exhaustive list of what visual variables you may tweak, check the "Available visual variables" part of the documentation.
ipysigma is also able to display synchronized & interactive "small multiples" of a same graph to easily compare some of its features.
You can install using pip:
pip install ipysigma
You will also need to install either networkx or igraph.
If you are using an older version of Jupyter, or if the extension does not appear to be installed automatically, you might also need to run some nbextension/labextension commands likewise:
# Try one/all of those for jupyter notebook:
jupyter nbextension enable --py --sys-prefix ipysigma
jupyter nbextension enable --py --user ipysigma
jupyter nbextension enable --py --system ipysigma
# Try one/all of those for jupyter lab:
jupyter labextension enable ipysigma
jupyter labextension enable ipysigma user
jupyter labextension enable ipysigma sys-prefix
If you want to use ipysigma on Google Colab, you will need to enable widget output using the following code:
from google.colab import output
output.enable_custom_widget_manager()
Remember you can always install packages in Colab by executing the following command in a cell:
!pip install networkx ipysigma
Using networkx
import networkx as nx
from ipysigma import Sigma
# Importing a gexf graph
g = nx.read_gexf('./my-graph.gexf')
# Displaying the graph with a size mapped on degree and
# a color mapped on a categorical attribute of the nodes
Sigma(g, node_size=g.degree, node_color='category')
Using igraph
import igraph as ig
from ipysigma import Sigma
# Generating a graph
g = ig.Graph.Famous('Zachary')
# Displaying the graph with a size mapped on degree and
# a color mapped on node betweenness centrality, using
# a continuous color scale named "Viridis"
Sigma(g, node_size=g.degree, node_color=g.betweenness(), node_color_gradient='Viridis')
ipysigma is able to compute metrics on the widget side using graphology. As such, you can ask it to compute e.g. a Louvain partitioning if you don't want or cannot do it on the python side.
For more information about available metrics and how to specify them, check this part of the documentation.
Sigma(g, node_metrics=["louvain"], node_color="louvain")
# Renaming the target attribute
Sigma(g, node_metrics={"community": "louvain"}, node_color="community")
# Passing custom parameters
Sigma(
g,
node_metrics={"community": {"name": "louvain", "resolution": 1.5}},
node_color="community"
)
Use networkx metrics:
import networkx as nx
g = nx.path_graph(5)
Sigma(g, node_size=nx.eigenvector_centrality(g))
Use igraph metrics:
import igraph as ig
g = ig.Graph.GRG(5, 0.5)
Sigma(g, node_size=g.pagerank(), node_color=g.connected_components())
Use custom metrics:
import networkx as nx
def even_or_odd(node):
return node % 2 == 0
g = nx.path_graph(5)
Sigma(g, node_color=even_or_odd)
Read this for an exhaustive list of what can be used as visual variables.
Converting tabular data to a graph is not obvious. So for this, we advise to use helper functions found in our other library python pelote.
In this first example, we create a graph from a DataFrame of edges:
import pandas as pd
from pelote import edges_table_to_graph
# Alice invited Bob and Chloe. Bob invited Chloe twice.
df = pd.DataFrame({
"hosts": ["Alice", "Alice", "Bob", "Bob"],
"guests": ["Bob", "Chloe", "Chloe", "Chloe"]
})
g = edges_table_to_graph(
df,
edge_source_col="hosts",
edge_target_col="guests",
count_rows_as_weight=True,
directed=True
)
Sigma(g, edge_size='weight')
Using pelote again, you can also create a bipartite network (students and their professors, for example) with the table_to_bipartite_graph function:
import pandas as pd
from pelote import table_to_bipartite_graph
df = pd.DataFrame({
"professor": ["A", "A", "A", "B", "B", "B", "B"],
"student": ["C", "D", "E", "C", "F", "G", "H"],
})
g = table_to_bipartite_graph(df, 'student', 'professor', node_part_attr='status')
Sigma(g, node_color='status', default_node_size=10, show_all_labels=True)
Let's say we have a graph of websites that we categorized by type and language and we want to compare the distribution of those categories on the graph's topology. We could use node color for language and border color for type but you will quickly see that this is probably not readable.
To solve this kind of problems and enable its users to easily compare multiple features of a graph, ipysigma exposes a SigmaGrid widget that arranges multiple synchronized views of the same graph on a grid:
from ipysigma import SigmaGrid
# Views to display can be specified through the `views` kwarg, expecting
# a list of dicts of keyword arguments to give to the underlying Sigma widgets:
SigmaGrid(g, views=[
{"node_color": "type"},
{"node_color": "type"}
])
# You can do the same by using the `#.add` method of the grid to
# dynamically add views:
SigmaGrid(g).add(node_color="lang").add(node_color="type")
# Any kwarg passed to the grid directly will be used by all of the views.
# This is useful to avoid repetition:
SigmaGrid(g, node_size=g.degree, views=[
{"node_color": "type"},
{"node_color": "type"}
])
# You can of course display more than 2 views
# By default the grid has 2 columns and will wrap to new rows,
# but you can change the number of columns using the `columns` kwarg:
SigmaGrid(g, columns=3, views=[
{"node_size": g.degree},
{"node_size": g.in_degree},
{"node_size": g.out_degree}
])
If you want comprehensive examples of the widget's visual variables being used, you can read the notebooks found here, which serve as functional tests to the library.
There are several ways to specify what you want to use as visual variables (read this for a detailed explanation).
Here is the exhaustive list of what is possible:
Name of a node or edge attribute
# Let's say your nodes have a "lang" attribute, we can use its modalities as values for
# a categorical color palette:
Sigma(g, node_color='lang')
Node or edge mapping
# You can store the data in a mapping, e.g. a dictionary, likewise:
node_lang = {'node1': 'en', 'node2': 'fr', ...}
Sigma(g, node_color=node_lang)
# For edges, the mapping's key must be a 2-tuple containing source & target nodes.
# Note that for undirected graphs, the order of nodes in the tuple
# does not make any difference as both will work.
edge_type = {('node1', 'node2'): 'LIKES', ('node2', 'node3'): 'LOVES'}
Arbitrary iterable
# Any arbitrary iterable such as generators, ranges, numpy vectors,
# pandas series etc. will work. The only requirement is that they should
# follow the order of iteration of nodes or edges in the graph, so we may
# align the data properly.
# Creating a 0 to n generic label for my nodes
Sigma(g, node_label=range(len(g)))
# Random size for my edges
Sigma(g, edge_size=(random() for _ in g.edges))
# Numpy vector
Sigma(g, node_size=np.random.rand(len(g)))
# Pandas series
Sigma(g, edge_size=df.edge_weights)
Partition
# A partition, complete or not, but not overlapping, of nodes or edges:
# Must be a list of lists or a list of sets.
communities = [{2, 3, 6}, {0, 1}, {4, 6}]
Sigma(g, node_color=communities)
networkx/igraph degree view
# Mapping node size on degree is as simple as:
Sigma(g, node_size=g.degree)
igraph VertexClustering
# IGraph community detection / clustering methods return a VertexClustering object
Sigma(g, node_color=g.connected_components())
Sigma(g, node_color=g.community_multilevel())
Arbitrary callable
# Creating a label for my nodes
Sigma(g, node_label=lambda node: 'Label of ' + str(node))
# Using edge weight as size only for some source nodes
Sigma(g, edge_size=lambda u, v, a: attr['weight'] if g.nodes[u]['part'] == 'main' else 1)
# Node callables will be given the following arguments:
# 1. node key
# 2. node attributes
# Edge callables will be given the following arguments:
# 1. source node key
# 2. target node key
# 3. edge attributes
# Note that given callables may choose to take any number of those arguments.
# For instance, the first example only uses the first argument but still works.
Set
# A set will be understood as a binary partition with nodes or edges being
# in it or outside it. This will be mapped to a boolean value, with `True`
# meaning the node or edge was in the partition.
# This will display the nodes 1, 5 and 6 in a color, and all the other ones
# in a different color.
Sigma(g, node_color={1, 5, 6})
ipysigma lets its users tweak a large number of visual variables. They all work through a similar variety of keyword arguments given to the Sigma widget.
In ipysigma visual variables can be given:
kwargs naming rationale
To be able to be drawn on screen, every visual variable must use values that have a meaning for the the widget's visual representation. For colors, it might be a HTML color name such as #fa65ea or cyan. For sizes, it might be a number of pixels etc.
If you know what you are doing and want to give ipysigma the same "raw" values as those expected by the visual representation directly, all variables have kwargs starting by raw_, such as raw_node_color.
But if you want ipysigma to map your arbitrary values to a suitable visual representation, all variables have a kwarg without any prefix, for instance node_color.
In which case, if you use categorical data, ipysigma can generate or use palettes to map the category values to e.g. colors on screen. You can always customize the palette or mapping using a kwarg suffixed with _palette or _mapping such as node_color_palette or node_shape_mapping.
And if you use numerical data, then values will be mapped to an output range, usually in pixels, that can be configured with a kwarg suffixed with _range such as node_size_range. Similarly, if you want to map numerical data to a gradient of colors, you will find kwarg suffixed with _gradient such as node_color_gradient.
Sometimes, some values might fall out of the represented domain, such as non-numerical values for continuous variables, or categories outside of the colors available in the given palette. In which case there always exists a kwarg prefixed with default_, such as default_node_color. A neat trick is also to use those kwargs as a way to indicate a constant value if you want all your edges to have the same color for instance, or your nodes to have the same size in pixels.
Finally, it's usually possible to tweak the way numerical values will be mapped from their original domain to the visual one. This is what you do, for instance, when you choose to use a logarithmic scale on a chart to better visualize a specific distribution. Similarly, relevant ipysigma visual variables give access to a kwarg suffixed _scale, such as node_color_scale that lets you easily switch from a linear to a logarithmic or power scale etc. (for more information about this, check this in the next part of the documentation).
To summarize, let's finish with two exhaustive examples: node color & node size.
Categorical or continuous variable: node color as an example
node_color is a categorical variable. Hence, given values will be mapped to suitable colors, from a palette generated automatically for you. If you want your data to be interpreted as continuous instead, you will need to give a gradient to the variable through node_color_gradient.ipysigma utilities suit your particular use-case.default_ kwargs always expect a value that will be used in the final representation, so here a CSS color, that will be used if a node category is not found in the color palette or if a node value is not numerical and we are using a gradient.ipysigma uses iwanthue to automatically generate fitting color palettes for the categories present in the given data. But sometimes you might want to customize the colors used. In which case this kwarg expects either a dictionary mapping category values to a CSS color such as {'en': 'blue, 'fr': 'red'} or the name of a categorical color scheme from d3-scale-chromatic such as Tableau10 or RdYlBu for instance.("yellow", "red") or the name of a continuous color gradient from d3-scale-chromatic such as Inferno or YlGn for instance.node_color_gradient and want to apply a nonlinear scale to the given data, you can pass the name of the scale to use such as log or a 2-tuple containing the name of the scale and an optional param such as the scale's base in the case of a logarithmic scale. Here is a binary log scale for instance: ("log", 2).Continuous variable: node size as an example
node_size_scale to a range in pixels given to node_size_range before being used on screen.log or a 2-tuple containing the name of the scale and an optional param such as the scale's base in the case of a logarithmic scale. Here is a binary log scale for instance: ("log", 2).1 pixel and 25 pixels, we would give it (1, 25). Note that most visual variables have a default range and this kwarg can usually be omitted if the defaults suit you.For a comprehensive view of the available visual variables, the values they expect and how they can be customized, read this next part of the documentation.
Available scales
e by default).e by default)..2 by default).2 by default).All the _scale kwargs can take the following:
node_size_scale=None.node_size_scale="log".node_size_scale=("log", 2).Color palettes
By default, color palettes are generated for you by ipysigma using iwanthue. ipysigma will first count the number of distinct categories to represent, sort them by frequency and generate a palette of up to 10 colors for the most used ones. The other one will use the default one given to the relevant default_ kwarg such as default_node_color for instance.
Note that this maximum number of 10 can be increased using the max_categorical_colors kwarg.
Note also that the palette generation is seeded using the mapped attribute name in the data so that the palette is always the same (if the name and the category count remains the same), but is different from one attribute to the other.
If you don't want ipysigma to generate color palettes for you, you can give your own palette through the relevant _palette kwarg such as node_color_palette, or use some d3-scale-chromatic one (they have names starting with scheme).
Here is the full list of those palettes supported by ipysigma: Accent, Blues, BrBG, BuGn, BuPu, Category10, Dark2, GnBu, Greens, Greys, OrRd, Oranges, PRGn, Paired, Pastel1, Pastel2, PiYG, PuBu, PuBuGn, PuOr, PuRd, Purples, RdBu, RdGy, RdPu, RdYlBu, RdYlGn, Reds, Set1, Set2, Set3, Spectral, Tableau10, YlGn, YlGnBu, YlOrBr, YlOrRd.
Color gradients
Color gradients can be defined as a range from "lowest" to "highest" color, e.g. ("yellow", "red).
They can also be taken from any d3-scale-chromatic continuous gradient (they have names starting with interpolate).
Here is the full list of those gradients supported by ipysigma: Blues, BrBG, BuGn, BuPu, Cividis, Cool, CubehelixDefault, GnBu, Greens, Greys, Inferno, Magma, OrRd, Oranges, PRGn, PiYG, Plasma, PuBu, PuBuGn, PuOr, PuRd, Purples, Rainbow, RdBu, RdGy, RdPu, RdYlBu, RdYlGn, Reds, Sinebow, Spectral, Turbo, Viridis, Warm, YlGn, YlGnBu, YlOrBr, YlOrRd.
Since ipysigma is using graphology, it can also draw from its library of graph theory metrics.
As such, the node_metrics enables you to ask your widget to compute node metrics on its own and use to map the result on any visual variable.
Here is how you can specify metrics to be computed:
# node_metrics expects an iterable of metrics to compute:
Sigma(g, node_metrics=["louvain"], node_color="louvain")
# They can be specified by name, but you can also specify through
# a dictionary if you need parameters for the metrics:
Sigma(g, node_metrics=[{"name": "louvain", "resolution": 1.5}], node_color="louvain")
# You can also give a dictionary mapping resulting attribute name to
# the metric to compute if you don't want to map the result on an attribute
# having the same name as the metric:
Sigma(g, node_metrics={"community": "louvain"}, node_color="community")
Sigma(g, node_metrics={"community": {"name": "louvain", "resolution": 1.5}}, node_color="community")
Available node metrics & their parameters
1]: resolution parameter.Labels are costly to render and can negate the benefit of using a WebGL renderer such as sigma.js to render interactive graphs. As such, sigma.js relies on a constant size grid to select the "worthiest" labels to display, after taking camera zoom into account.
You can tweak the parameters of this grid using label_grid_cell_size and label_density. Decreasing the first one or increasing the second one will result in more labels being displayed.
Also, by default, the label of a node is displayed only if its size in pixels is larger than a threshold. You can change that threshold using the label_rendered_size_threshold kwarg.
Finally, if you don't want to deal with all this nonsense and just want to display all labels because you know what you are doing and don't care about performance, you can just use show_all_labels=True instead.
When ipysigma generates palettes for you, it only uses up to 10 colors by default. This number can be increased using the max_categorical_colors kwarg. For more information about palette generation, read this part of the documentation.
Some designer told me (while holding a baseball bat) that it is unwise to have more than 10 categorical colors because you won't be able to distinguish them anymore. My hands are tied. Don't ask me to change this.
node_color does not expect colors per se but arbitrary data that will be mapped to a suitable color palette for you. If you want to give colors directly, use raw_node_color instead. For more information about the visual variables kwarg naming rationale, read this part of the documentation.
Don't forget to turn off the layout when it has converged (the pause button on the left). There is no convincing way to automatically detect when layout has converged so we must rely on you, the user, to indicate when it's done.
If you want to start the layout automatically when instantiating the widget and make sure it will automatically stop after, say, 10 seconds, use start_layout=10.
Your GPU can only render so many webgl canvases in your browser tabs. So if you created too many widgets (this depends on the specifics of your computer and graphics card), it may gracefully deal with the situation by erasing the graph (but not the labels since those are rendered using 2d canvases) or by glitching to death.
Use default_edge_type="curve", node_border_color_from="node", label_size=g.degree and label_font="cursive" and you should have a dazzling Gephi graph.
Type
Categorical or continuous.
Raw values
HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)
Related kwargs
Type
Continuous.
Raw values
A percentage of color saturation. Examples: 0.1, 0.96.
Related kwargs
Type
Continuous.
Raw values
A node size, i.e. a circle radius, in pixels, with default camera (not zoomed nor unzoomed).
Related kwargs
Type
Raw only.
Raw values
A text label.
Related kwargs
Type
Continuous.
Raw values
A font size for the label text, in pixels.
Related kwargs
Type
Categorical.
Raw values
HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)
Related kwargs
Type
Continuous.
Raw values
A border size, in pixels, with default camera (not zoomed nor unzoomed).
Note that this border size will be added to the node's radius.
Related kwargs
Notes
Borders are only shown on screen if a node_border_size OR a node_border_ratio AND a node_border_color are defined.
Type
Continuous.
Raw values
A border ratio, in percentage, with default camera (not zoomed nor unzoomed).
Note that this border ratio will eat the node's size.
Related kwargs
Notes
Borders are only shown on screen if a node_border_size OR a node_border_ratio AND a node_border_color are defined.
Type
Categorical or continuous.
Raw values
HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)
Related kwargs
Notes
Borders are only shown on screen if a node_border_size OR a node_border_ratio AND a node_border_color are defined.
Type
Categorical.
Raw values
The name of any Google Material Icon as listed here (the name must be lowercase and snake_case, e.g. the name "Arrow Drop Done" should be given to ipysigma as arrow_drop_done).
Alternatively, one can also give urls of publicly accessible svg icons such as https://fonts.gstatic.com/s/i/short-term/release/materialsymbolsoutlined/arrow_drop_down/default/48px.svg
Related kwargs
Notes
Pictograms are only shown on screen if node_pictogram AND node_pictogram_color are defined.
Type
Categorical.
Raw values
HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)
Related kwargs
Notes
Pictograms are only shown on screen if node_pictogram AND node_pictogram_color are defined.
Type
Categorical.
Raw values
The name of a supported shape such as: circle, triangle, square, pentagon, star, hexagon, heart or cloud.
Alternatively, if you are feeling adventurous, it can also be the name of any Google Material Icon as listed here (the name must be lowercase and snake_case, e.g. the name "Arrow Drop Done" should be given to ipysigma as arrow_drop_done).
Finally, one can also give urls of publicly accessible svg icons such as https://fonts.gstatic.com/s/i/short-term/release/materialsymbolsoutlined/arrow_drop_down/default/48px.svg
Related kwargs
Note
Node shapes cannot be used with borders nor pictograms nor halos, as of yet.
Type
Continuous.
Raw values
A halo size offset in pixels, with default camera (not zoomed nor unzoomed). The full halo radius will therefore be its size + its node's radius.
Related kwargs
Type
Categorical or continuous.
Raw values
HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)
Related kwargs
Type
Categorical or continuous.
Raw values
HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)
Related kwargs
Type
Name of renderer to use to draw the graph's edges. One of arrow, triangle, rectangle, line or curve.
Usually defaults to rectangle for undirected graphs and arrow for directed graphs, or curve if edge_curveness is activated.
It cannot be mapped to some edge attribute or data as of yet.
Related_kwargs
Type
Continuous.
Raw values
An edge thickness in pixels, with default camera (not zoomed nor unzoomed).
Related kwargs
Type
Continuous.
Raw values
A percentage. Note that it can go beyond 1 and that 0 will make the edge disappear.
Related kwargs
Type
Raw only.
Raw values
A text label.
Related kwargs
Arguments
None - name of the graph.500 - height of the widget container in pixels."white" - css color to use as the graph's background.None - raw css height. Can be useful in some html embedding scenarios. Only use this if you know what you are doing.False - whether to automatically start the layout algorithm when mounting the widget. If a number is given instead, the layout algorithm will start and automatically stop after this many seconds.None - node metrics to be computed by graphology by the widget's JavaScript code. Currently only supports "louvain" for community detection.None - settings for the ForceAtlas2 layout (listed here: https://graphology.github.io/standard-library/layout-forceatlas2#settings.False - whether to allow user to click on edges to display their information. This can have a performance cost on larger graphs.True - whether to process gexf files viz data for node & edges.10 - max number of colors to be generated for a categorical palette. Categories, ordered by frequency, over this maximum will use the default color.False - whether to hide the information panel to the right of the widget.False - whether to hide the search bar to the right of the widget.False - whether to hide the edges when the graph is being moved. This can be useful to improve performance when the graph is too large.SigmaGrid when able, it will handle this advanced aspect of the widget for you.("layout", "camera", "selection", "hover") - Names of targets to synchronize through the sync_key kwarg. Targets include "layout", "camera", "selection" and "hover".{"x": 0.5, "y": 0.5, "ratio": 1, "angle": 0} - Initial state for the widget's camera (which can be retrieved using the #.get_camera_state method).None - Key of the initially selected node in the widget (can be retrieved using the #.get_selected_node method).None - (source, target) tuple of the initially selected edge in the widget (can be retrieved using the #.get_selected_edge method).None - list of selected node category values (can be retrieved using the #.get_selected_node_category_values method).None - list of selected edge category values (can be retrieved using the #.get_selected_edge_category_values method)."sans-serif" - font to be used with labels.1 - number of labels to display per grid cell for default camera zoom.250 - size in pixels of a square cell in the label selection grid.None - minimum actual rendered size (after camera zoom operations) a node must have on screen for its label to be allowed to be displayed. If None, the threshold will be inferred based on the maximum node size of your graph.False - macro setting making sure most, if not all, labels get displayed on screen. Might have an impact on performance with larger graphs.None - node positions, expressed as a {node: {x, y} mapping.None - data to be used as categorical or continuous node color."color" - raw data (css colors) to be used for node colors.None - gradient of colors to map to, for instance: (("yellow", "red")), or name of a d3 continuous color scale (found here: https://github.com/d3/d3-scale-chromatic#readme), for instance: "Viridis". If given, node color will be interpreted as continuous rather than categorical.None - scale to use for node color. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme)."#999" - default color for nodes.None - data to be used as continuous node color saturation.None - raw data (percentage) to be used for node color saturation.None - scale to use for node color saturation. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.(0, 1) - range of percentages to map to, for instance: (0, 0.7).None - default color saturation for nodes.None - data to be used as categorical or continuous node border color."color" - raw data (css colors) to be used for node border colors.None - gradient of colors to map to, for instance: (("yellow", "red")), or name of a d3 continuous color scale (found here: https://github.com/d3/d3-scale-chromatic#readme), for instance: "Viridis". If given, node border color will be interpreted as continuous rather than categorical.None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme).None - optionally select node border color from the following options: "node".None - data to be used as continuous node border size.None - raw data (size in pixels) to be used for node border sizes.(1, 5) - range of sizes in pixels to map to, for instance: (1, 15).1 - default size for node borders.None - data to be used as continuous node border ratio.None - raw data (ratio in pixels) to be used for node border ratios.(0.1, 0.5) - range of ratios in pixels to map to, for instance: (1, 15).0.1 - default ratio for node borders.None - raw data (pictogram name, as found here: https://fonts.google.com/icons or publicly accessible svg icon url) to be used for node pictograms.None - default pictogram for nodes.None - data to be used as categorical or continuous node pictogram color."color" - raw data (css colors) to be used for node pictogram colors.None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme)."black" - default color for node pictograms.None - data to be used as categorical data to be mapped to node shapes.None - raw data (shape name, or pictogram name as found here: https://fonts.google.com/icons or publicly accessible svg icon url) to be used as node shapes.None - default shape for nodes.None - data to be used as categorical or continuous node halo color."color" - raw data (css colors) to be used for node halo colors.None - gradient of colors to map to, for instance: (("yellow", "red")), or name of a d3 continuous color scale (found here: https://github.com/d3/d3-scale-chromatic#readme), for instance: "Viridis". If given, node halo color will be interpreted as continuous rather than categorical.None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme)."red" - default color for node halos.None - data to be used as continuous node halo size.None - raw data (size in pixels) to be used for node halo sizes.(0, 20) - range of sizes in pixels to map to, for instance: (1, 15).0 - default size for node halos."size" - data to be used as continuous node size.None - raw data (size in pixels) to be used for node sizes.(3, 15) - range of sizes in pixels to map to, for instance: (1, 15).None - scale to use for node size. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.None - default size for nodes.None - data to be used as node label."label" - raw data (label string) to be used for node labels.None - default label for nodes.None - data to be used as continuous node label size.None - raw data (size in pixels) to be used for node label sizes.(8, 25) - range of sizes in pixels to map to, for instance: (1, 15).12 - default size for node labels.None - data to be used as categorical or continuous node label color.None - raw data (css colors) to be used for node label colors.None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme)."black" - default color for node labels.None - numerical data used to sort nodes before rendering. Nodes having a higher zindex will be drawn on top of nodes having a lower one.None - data to be used as categorical or continuous edge color."color" - raw data (css colors) to be used for edge colors.None - gradient of colors to map to, for instance: (("yellow", "red")), or name of a d3 continuous color scale (found here: https://github.com/d3/d3-scale-chromatic#readme), for instance: "Viridis". If given, edge color will be interpreted as continuous rather than categorical.None - scale to use for edge color. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme)."#999" - default color for edges.None - default type used to draw edges. Can be selected from "rectangle", "line", "curve", "arrow" & "triangle". Will raise if "arrow" or "triangle" is selected with an undirected graph. If None, will default to "rectangle"."size" - data to be used as continuous edge size.None - raw data (size in pixels) to be used for edge sizes.(3, 15) - range of sizes in pixels to map to, for instance: (1, 15).None - scale to use for edge size. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.None - default size for edges.0.25 - curveness factor for edges when default_edge_type is "curve".None - data to be used as edge label."label" - raw data (label string) to be used for edge labels.None - numerical data used to sort egdes before rendering. Egdes having a higher zindex will be drawn on top of egdes having a lower one.Method returning the layout of the graph, i.e. the current node positions in the widget, as a dict mapping nodes to their {x, y} coordinates.
Method returning the current camera state of the widget, as a {x, y, ratio, angle} dict.
Method returning the currently selected node if any or None.
Method returning the currently selected edge as a (source, target) tuple if any or None.
Method returning a set of currently selected node category values or None.
Method returning a set of currently selected edge category values or None.
Method rendering the widget as an rasterized image in the resulting cell.
Method rendering the widget as a standalone HTML file that can be hosted statically elsewhere.
Arguments
Static method that can be used to override some default values of the Sigma class kwargs.
Arguments
Static method taking the same kwargs as Sigma and rendering the widget as a standalone HTML file that can be hosted statically elsewhere.
Arguments
False]: whether to display the widget by taking up the full space of the screen. If False, will follow the given height.Sigma.Arguments
2 - maximum number of views to display in a line.None - synchronization key to use. If not given, one will be automatically generated by the grid.None - list of kwarg dicts that will be used to instantiate the underlying Sigma views as an alternative to using the #.add method.Method one can use as an alternative or combined to SigmaGrid constructor's views kwarg to add a new Sigma view to the grid. It takes any argument taken by Sigma and returns self for easy chaining.
SigmaGrid(g, node_color='category').add(node_size=g.degree).add(node_size='occurrences')
Guillaume Plique. (2022). ipysigma, A Jupyter widget using sigma.js to render interactive networks. Zenodo. https://doi.org/10.5281/zenodo.7446059
FAQs
A Jupyter widget using sigma.js to render interactive networks.
We found that ipysigma demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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