Source code for networkx.drawing.nx_pylab

"""
**********
Matplotlib
**********

Draw networks with matplotlib.

Examples
--------
>>> G = nx.complete_graph(5)
>>> nx.draw(G)

See Also
--------
 - :doc:`matplotlib <matplotlib:index>`
 - :func:`matplotlib.pyplot.scatter`
 - :obj:`matplotlib.patches.FancyArrowPatch`
"""
from numbers import Number
import networkx as nx
from networkx.drawing.layout import (
    shell_layout,
    circular_layout,
    kamada_kawai_layout,
    spectral_layout,
    spring_layout,
    random_layout,
    planar_layout,
)
import warnings

__all__ = [
    "draw",
    "draw_networkx",
    "draw_networkx_nodes",
    "draw_networkx_edges",
    "draw_networkx_labels",
    "draw_networkx_edge_labels",
    "draw_circular",
    "draw_kamada_kawai",
    "draw_random",
    "draw_spectral",
    "draw_spring",
    "draw_planar",
    "draw_shell",
]


[docs]def draw(G, pos=None, ax=None, **kwds): """Draw the graph G with Matplotlib. Draw the graph as a simple representation with no node labels or edge labels and using the full Matplotlib figure area and no axis labels by default. See draw_networkx() for more full-featured drawing that allows title, axis labels etc. Parameters ---------- G : graph A networkx graph pos : dictionary, optional A dictionary with nodes as keys and positions as values. If not specified a spring layout positioning will be computed. See :py:mod:`networkx.drawing.layout` for functions that compute node positions. ax : Matplotlib Axes object, optional Draw the graph in specified Matplotlib axes. kwds : optional keywords See networkx.draw_networkx() for a description of optional keywords. Examples -------- >>> G = nx.dodecahedral_graph() >>> nx.draw(G) >>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout See Also -------- draw_networkx draw_networkx_nodes draw_networkx_edges draw_networkx_labels draw_networkx_edge_labels Notes ----- This function has the same name as pylab.draw and pyplot.draw so beware when using `from networkx import *` since you might overwrite the pylab.draw function. With pyplot use >>> import matplotlib.pyplot as plt >>> G = nx.dodecahedral_graph() >>> nx.draw(G) # networkx draw() >>> plt.draw() # pyplot draw() Also see the NetworkX drawing examples at https://networkx.org/documentation/latest/auto_examples/index.html """ import matplotlib.pyplot as plt if ax is None: cf = plt.gcf() else: cf = ax.get_figure() cf.set_facecolor("w") if ax is None: if cf._axstack() is None: ax = cf.add_axes((0, 0, 1, 1)) else: ax = cf.gca() if "with_labels" not in kwds: kwds["with_labels"] = "labels" in kwds draw_networkx(G, pos=pos, ax=ax, **kwds) ax.set_axis_off() plt.draw_if_interactive() return
[docs]def draw_networkx(G, pos=None, arrows=None, with_labels=True, **kwds): r"""Draw the graph G using Matplotlib. Draw the graph with Matplotlib with options for node positions, labeling, titles, and many other drawing features. See draw() for simple drawing without labels or axes. Parameters ---------- G : graph A networkx graph pos : dictionary, optional A dictionary with nodes as keys and positions as values. If not specified a spring layout positioning will be computed. See :py:mod:`networkx.drawing.layout` for functions that compute node positions. arrows : bool or None, optional (default=None) If `None`, directed graphs draw arrowheads with `~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges via `~matplotlib.collections.LineCollection` for speed. If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish). If `False`, draw edges using LineCollection (linear and fast). For directed graphs, if True draw arrowheads. Note: Arrows will be the same color as edges. arrowstyle : str (default='-\|>') For directed graphs, choose the style of the arrowsheads. See `matplotlib.patches.ArrowStyle` for more options. arrowsize : int or list (default=10) For directed graphs, choose the size of the arrow head's length and width. A list of values can be passed in to assign a different size for arrow head's length and width. See `matplotlib.patches.FancyArrowPatch` for attribute `mutation_scale` for more info. with_labels : bool (default=True) Set to True to draw labels on the nodes. ax : Matplotlib Axes object, optional Draw the graph in the specified Matplotlib axes. nodelist : list (default=list(G)) Draw only specified nodes edgelist : list (default=list(G.edges())) Draw only specified edges node_size : scalar or array (default=300) Size of nodes. If an array is specified it must be the same length as nodelist. node_color : color or array of colors (default='#1f78b4') Node color. Can be a single color or a sequence of colors with the same length as nodelist. Color can be string or rgb (or rgba) tuple of floats from 0-1. If numeric values are specified they will be mapped to colors using the cmap and vmin,vmax parameters. See matplotlib.scatter for more details. node_shape : string (default='o') The shape of the node. Specification is as matplotlib.scatter marker, one of 'so^>v<dph8'. alpha : float or None (default=None) The node and edge transparency cmap : Matplotlib colormap, optional Colormap for mapping intensities of nodes vmin,vmax : float, optional Minimum and maximum for node colormap scaling linewidths : scalar or sequence (default=1.0) Line width of symbol border width : float or array of floats (default=1.0) Line width of edges edge_color : color or array of colors (default='k') Edge color. Can be a single color or a sequence of colors with the same length as edgelist. Color can be string or rgb (or rgba) tuple of floats from 0-1. If numeric values are specified they will be mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters. edge_cmap : Matplotlib colormap, optional Colormap for mapping intensities of edges edge_vmin,edge_vmax : floats, optional Minimum and maximum for edge colormap scaling style : string (default=solid line) Edge line style e.g.: '-', '--', '-.', ':' or words like 'solid' or 'dashed'. (See `matplotlib.patches.FancyArrowPatch`: `linestyle`) labels : dictionary (default=None) Node labels in a dictionary of text labels keyed by node font_size : int (default=12 for nodes, 10 for edges) Font size for text labels font_color : string (default='k' black) Font color string font_weight : string (default='normal') Font weight font_family : string (default='sans-serif') Font family label : string, optional Label for graph legend kwds : optional keywords See networkx.draw_networkx_nodes(), networkx.draw_networkx_edges(), and networkx.draw_networkx_labels() for a description of optional keywords. Notes ----- For directed graphs, arrows are drawn at the head end. Arrows can be turned off with keyword arrows=False. Examples -------- >>> G = nx.dodecahedral_graph() >>> nx.draw(G) >>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout >>> import matplotlib.pyplot as plt >>> limits = plt.axis("off") # turn off axis Also see the NetworkX drawing examples at https://networkx.org/documentation/latest/auto_examples/index.html See Also -------- draw draw_networkx_nodes draw_networkx_edges draw_networkx_labels draw_networkx_edge_labels """ import matplotlib.pyplot as plt valid_node_kwds = ( "nodelist", "node_size", "node_color", "node_shape", "alpha", "cmap", "vmin", "vmax", "ax", "linewidths", "edgecolors", "label", ) valid_edge_kwds = ( "edgelist", "width", "edge_color", "style", "alpha", "arrowstyle", "arrowsize", "edge_cmap", "edge_vmin", "edge_vmax", "ax", "label", "node_size", "nodelist", "node_shape", "connectionstyle", "min_source_margin", "min_target_margin", ) valid_label_kwds = ( "labels", "font_size", "font_color", "font_family", "font_weight", "alpha", "bbox", "ax", "horizontalalignment", "verticalalignment", ) valid_kwds = valid_node_kwds + valid_edge_kwds + valid_label_kwds if any([k not in valid_kwds for k in kwds]): invalid_args = ", ".join([k for k in kwds if k not in valid_kwds]) raise ValueError(f"Received invalid argument(s): {invalid_args}") node_kwds = {k: v for k, v in kwds.items() if k in valid_node_kwds} edge_kwds = {k: v for k, v in kwds.items() if k in valid_edge_kwds} label_kwds = {k: v for k, v in kwds.items() if k in valid_label_kwds} if pos is None: pos = nx.drawing.spring_layout(G) # default to spring layout draw_networkx_nodes(G, pos, **node_kwds) draw_networkx_edges(G, pos, arrows=arrows, **edge_kwds) if with_labels: draw_networkx_labels(G, pos, **label_kwds) plt.draw_if_interactive()
[docs]def draw_networkx_nodes( G, pos, nodelist=None, node_size=300, node_color="#1f78b4", node_shape="o", alpha=None, cmap=None, vmin=None, vmax=None, ax=None, linewidths=None, edgecolors=None, label=None, margins=None, ): """Draw the nodes of the graph G. This draws only the nodes of the graph G. Parameters ---------- G : graph A networkx graph pos : dictionary A dictionary with nodes as keys and positions as values. Positions should be sequences of length 2. ax : Matplotlib Axes object, optional Draw the graph in the specified Matplotlib axes. nodelist : list (default list(G)) Draw only specified nodes node_size : scalar or array (default=300) Size of nodes. If an array it must be the same length as nodelist. node_color : color or array of colors (default='#1f78b4') Node color. Can be a single color or a sequence of colors with the same length as nodelist. Color can be string or rgb (or rgba) tuple of floats from 0-1. If numeric values are specified they will be mapped to colors using the cmap and vmin,vmax parameters. See matplotlib.scatter for more details. node_shape : string (default='o') The shape of the node. Specification is as matplotlib.scatter marker, one of 'so^>v<dph8'. alpha : float or array of floats (default=None) The node transparency. This can be a single alpha value, in which case it will be applied to all the nodes of color. Otherwise, if it is an array, the elements of alpha will be applied to the colors in order (cycling through alpha multiple times if necessary). cmap : Matplotlib colormap (default=None) Colormap for mapping intensities of nodes vmin,vmax : floats or None (default=None) Minimum and maximum for node colormap scaling linewidths : [None | scalar | sequence] (default=1.0) Line width of symbol border edgecolors : [None | scalar | sequence] (default = node_color) Colors of node borders label : [None | string] Label for legend margins : float or 2-tuple, optional Sets the padding for axis autoscaling. Increase margin to prevent clipping for nodes that are near the edges of an image. Values should be in the range ``[0, 1]``. See :meth:`matplotlib.axes.Axes.margins` for details. The default is `None`, which uses the Matplotlib default. Returns ------- matplotlib.collections.PathCollection `PathCollection` of the nodes. Examples -------- >>> G = nx.dodecahedral_graph() >>> nodes = nx.draw_networkx_nodes(G, pos=nx.spring_layout(G)) Also see the NetworkX drawing examples at https://networkx.org/documentation/latest/auto_examples/index.html See Also -------- draw draw_networkx draw_networkx_edges draw_networkx_labels draw_networkx_edge_labels """ from collections.abc import Iterable import numpy as np import matplotlib as mpl import matplotlib.collections # call as mpl.collections import matplotlib.pyplot as plt if ax is None: ax = plt.gca() if nodelist is None: nodelist = list(G) if len(nodelist) == 0: # empty nodelist, no drawing return mpl.collections.PathCollection(None) try: xy = np.asarray([pos[v] for v in nodelist]) except KeyError as err: raise nx.NetworkXError(f"Node {err} has no position.") from err if isinstance(alpha, Iterable): node_color = apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax) alpha = None node_collection = ax.scatter( xy[:, 0], xy[:, 1], s=node_size, c=node_color, marker=node_shape, cmap=cmap, vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths, edgecolors=edgecolors, label=label, ) ax.tick_params( axis="both", which="both", bottom=False, left=False, labelbottom=False, labelleft=False, ) if margins is not None: if isinstance(margins, Iterable): ax.margins(*margins) else: ax.margins(margins) node_collection.set_zorder(2) return node_collection
[docs]def draw_networkx_edges( G, pos, edgelist=None, width=1.0, edge_color="k", style="solid", alpha=None, arrowstyle="-|>", arrowsize=10, edge_cmap=None, edge_vmin=None, edge_vmax=None, ax=None, arrows=None, label=None, node_size=300, nodelist=None, node_shape="o", connectionstyle="arc3", min_source_margin=0, min_target_margin=0, ): r"""Draw the edges of the graph G. This draws only the edges of the graph G. Parameters ---------- G : graph A networkx graph pos : dictionary A dictionary with nodes as keys and positions as values. Positions should be sequences of length 2. edgelist : collection of edge tuples (default=G.edges()) Draw only specified edges width : float or array of floats (default=1.0) Line width of edges edge_color : color or array of colors (default='k') Edge color. Can be a single color or a sequence of colors with the same length as edgelist. Color can be string or rgb (or rgba) tuple of floats from 0-1. If numeric values are specified they will be mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters. style : string or array of strings (default='solid') Edge line style e.g.: '-', '--', '-.', ':' or words like 'solid' or 'dashed'. Can be a single style or a sequence of styles with the same length as the edge list. If less styles than edges are given the styles will cycle. If more styles than edges are given the styles will be used sequentially and not be exhausted. Also, `(offset, onoffseq)` tuples can be used as style instead of a strings. (See `matplotlib.patches.FancyArrowPatch`: `linestyle`) alpha : float or None (default=None) The edge transparency edge_cmap : Matplotlib colormap, optional Colormap for mapping intensities of edges edge_vmin,edge_vmax : floats, optional Minimum and maximum for edge colormap scaling ax : Matplotlib Axes object, optional Draw the graph in the specified Matplotlib axes. arrows : bool or None, optional (default=None) If `None`, directed graphs draw arrowheads with `~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges via `~matplotlib.collections.LineCollection` for speed. If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish). If `False`, draw edges using LineCollection (linear and fast). Note: Arrowheads will be the same color as edges. arrowstyle : str (default='-\|>') For directed graphs and `arrows==True` defaults to '-\|>', See `matplotlib.patches.ArrowStyle` for more options. arrowsize : int (default=10) For directed graphs, choose the size of the arrow head's length and width. See `matplotlib.patches.FancyArrowPatch` for attribute `mutation_scale` for more info. connectionstyle : string (default="arc3") Pass the connectionstyle parameter to create curved arc of rounding radius rad. For example, connectionstyle='arc3,rad=0.2'. See `matplotlib.patches.ConnectionStyle` and `matplotlib.patches.FancyArrowPatch` for more info. node_size : scalar or array (default=300) Size of nodes. Though the nodes are not drawn with this function, the node size is used in determining edge positioning. nodelist : list, optional (default=G.nodes()) This provides the node order for the `node_size` array (if it is an array). node_shape : string (default='o') The marker used for nodes, used in determining edge positioning. Specification is as a `matplotlib.markers` marker, e.g. one of 'so^>v<dph8'. label : None or string Label for legend min_source_margin : int (default=0) The minimum margin (gap) at the begining of the edge at the source. min_target_margin : int (default=0) The minimum margin (gap) at the end of the edge at the target. Returns ------- matplotlib.colections.LineCollection or a list of matplotlib.patches.FancyArrowPatch If ``arrows=True``, a list of FancyArrowPatches is returned. If ``arrows=False``, a LineCollection is returned. If ``arrows=None`` (the default), then a LineCollection is returned if `G` is undirected, otherwise returns a list of FancyArrowPatches. Notes ----- For directed graphs, arrows are drawn at the head end. Arrows can be turned off with keyword arrows=False or by passing an arrowstyle without an arrow on the end. Be sure to include `node_size` as a keyword argument; arrows are drawn considering the size of nodes. Self-loops are always drawn with `~matplotlib.patches.FancyArrowPatch` regardless of the value of `arrows` or whether `G` is directed. When ``arrows=False`` or ``arrows=None`` and `G` is undirected, the FancyArrowPatches corresponding to the self-loops are not explicitly returned. They should instead be accessed via the ``Axes.patches`` attribute (see examples). Examples -------- >>> G = nx.dodecahedral_graph() >>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G)) >>> G = nx.DiGraph() >>> G.add_edges_from([(1, 2), (1, 3), (2, 3)]) >>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G)) >>> alphas = [0.3, 0.4, 0.5] >>> for i, arc in enumerate(arcs): # change alpha values of arcs ... arc.set_alpha(alphas[i]) The FancyArrowPatches corresponding to self-loops are not always returned, but can always be accessed via the ``patches`` attribute of the `matplotlib.Axes` object. >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots() >>> G = nx.Graph([(0, 1), (0, 0)]) # Self-loop at node 0 >>> edge_collection = nx.draw_networkx_edges(G, pos=nx.circular_layout(G), ax=ax) >>> self_loop_fap = ax.patches[0] Also see the NetworkX drawing examples at https://networkx.org/documentation/latest/auto_examples/index.html See Also -------- draw draw_networkx draw_networkx_nodes draw_networkx_labels draw_networkx_edge_labels """ import numpy as np import matplotlib as mpl import matplotlib.colors # call as mpl.colors import matplotlib.patches # call as mpl.patches import matplotlib.collections # call as mpl.collections import matplotlib.path # call as mpl.path import matplotlib.pyplot as plt # The default behavior is to use LineCollection to draw edges for # undirected graphs (for performance reasons) and use FancyArrowPatches # for directed graphs. # The `arrows` keyword can be used to override the default behavior use_linecollection = not G.is_directed() if arrows in (True, False): use_linecollection = not arrows if ax is None: ax = plt.gca() if edgelist is None: edgelist = list(G.edges()) if len(edgelist) == 0: # no edges! return [] if nodelist is None: nodelist = list(G.nodes()) # FancyArrowPatch handles color=None different from LineCollection if edge_color is None: edge_color = "k" # set edge positions edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist]) # Check if edge_color is an array of floats and map to edge_cmap. # This is the only case handled differently from matplotlib if ( np.iterable(edge_color) and (len(edge_color) == len(edge_pos)) and np.alltrue([isinstance(c, Number) for c in edge_color]) ): if edge_cmap is not None: assert isinstance(edge_cmap, mpl.colors.Colormap) else: edge_cmap = plt.get_cmap() if edge_vmin is None: edge_vmin = min(edge_color) if edge_vmax is None: edge_vmax = max(edge_color) color_normal = mpl.colors.Normalize(vmin=edge_vmin, vmax=edge_vmax) edge_color = [edge_cmap(color_normal(e)) for e in edge_color] def _draw_networkx_edges_line_collection(): edge_collection = mpl.collections.LineCollection( edge_pos, colors=edge_color, linewidths=width, antialiaseds=(1,), linestyle=style, alpha=alpha, ) edge_collection.set_cmap(edge_cmap) edge_collection.set_clim(edge_vmin, edge_vmax) edge_collection.set_zorder(1) # edges go behind nodes edge_collection.set_label(label) ax.add_collection(edge_collection) return edge_collection def _draw_networkx_edges_fancy_arrow_patch(): # Note: Waiting for someone to implement arrow to intersection with # marker. Meanwhile, this works well for polygons with more than 4 # sides and circle. def to_marker_edge(marker_size, marker): if marker in "s^>v<d": # `large` markers need extra space return np.sqrt(2 * marker_size) / 2 else: return np.sqrt(marker_size) / 2 # Draw arrows with `matplotlib.patches.FancyarrowPatch` arrow_collection = [] if isinstance(arrowsize, list): if len(arrowsize) != len(edge_pos): raise ValueError("arrowsize should have the same length as edgelist") else: mutation_scale = arrowsize # scale factor of arrow head base_connection_style = mpl.patches.ConnectionStyle(connectionstyle) # Fallback for self-loop scale. Left outside of _connectionstyle so it is # only computed once max_nodesize = np.array(node_size).max() def _connectionstyle(posA, posB, *args, **kwargs): # check if we need to do a self-loop if np.all(posA == posB): # Self-loops are scaled by view extent, except in cases the extent # is 0, e.g. for a single node. In this case, fall back to scaling # by the maximum node size selfloop_ht = 0.005 * max_nodesize if h == 0 else h # this is called with _screen space_ values so covert back # to data space data_loc = ax.transData.inverted().transform(posA) v_shift = 0.1 * selfloop_ht h_shift = v_shift * 0.5 # put the top of the loop first so arrow is not hidden by node path = [ # 1 data_loc + np.asarray([0, v_shift]), # 4 4 4 data_loc + np.asarray([h_shift, v_shift]), data_loc + np.asarray([h_shift, 0]), data_loc, # 4 4 4 data_loc + np.asarray([-h_shift, 0]), data_loc + np.asarray([-h_shift, v_shift]), data_loc + np.asarray([0, v_shift]), ] ret = mpl.path.Path(ax.transData.transform(path), [1, 4, 4, 4, 4, 4, 4]) # if not, fall back to the user specified behavior else: ret = base_connection_style(posA, posB, *args, **kwargs) return ret # FancyArrowPatch doesn't handle color strings arrow_colors = mpl.colors.colorConverter.to_rgba_array(edge_color, alpha) for i, (src, dst) in enumerate(edge_pos): x1, y1 = src x2, y2 = dst shrink_source = 0 # space from source to tail shrink_target = 0 # space from head to target if isinstance(arrowsize, list): # Scale each factor of each arrow based on arrowsize list mutation_scale = arrowsize[i] if np.iterable(node_size): # many node sizes source, target = edgelist[i][:2] source_node_size = node_size[nodelist.index(source)] target_node_size = node_size[nodelist.index(target)] shrink_source = to_marker_edge(source_node_size, node_shape) shrink_target = to_marker_edge(target_node_size, node_shape) else: shrink_source = shrink_target = to_marker_edge(node_size, node_shape) if shrink_source < min_source_margin: shrink_source = min_source_margin if shrink_target < min_target_margin: shrink_target = min_target_margin if len(arrow_colors) == len(edge_pos): arrow_color = arrow_colors[i] elif len(arrow_colors) == 1: arrow_color = arrow_colors[0] else: # Cycle through colors arrow_color = arrow_colors[i % len(arrow_colors)] if np.iterable(width): if len(width) == len(edge_pos): line_width = width[i] else: line_width = width[i % len(width)] else: line_width = width if ( np.iterable(style) and not isinstance(style, str) and not isinstance(style, tuple) ): if len(style) == len(edge_pos): linestyle = style[i] else: # Cycle through styles linestyle = style[i % len(style)] else: linestyle = style arrow = mpl.patches.FancyArrowPatch( (x1, y1), (x2, y2), arrowstyle=arrowstyle, shrinkA=shrink_source, shrinkB=shrink_target, mutation_scale=mutation_scale, color=arrow_color, linewidth=line_width, connectionstyle=_connectionstyle, linestyle=linestyle, zorder=1, ) # arrows go behind nodes arrow_collection.append(arrow) ax.add_patch(arrow) return arrow_collection # compute initial view minx = np.amin(np.ravel(edge_pos[:, :, 0])) maxx = np.amax(np.ravel(edge_pos[:, :, 0])) miny = np.amin(np.ravel(edge_pos[:, :, 1])) maxy = np.amax(np.ravel(edge_pos[:, :, 1])) w = maxx - minx h = maxy - miny # Draw the edges if use_linecollection: edge_viz_obj = _draw_networkx_edges_line_collection() # Make sure selfloop edges are also drawn selfloops_to_draw = [loop for loop in nx.selfloop_edges(G) if loop in edgelist] if selfloops_to_draw: edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in selfloops_to_draw]) arrowstyle = "-" _draw_networkx_edges_fancy_arrow_patch() else: edge_viz_obj = _draw_networkx_edges_fancy_arrow_patch() # update view after drawing padx, pady = 0.05 * w, 0.05 * h corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady) ax.update_datalim(corners) ax.autoscale_view() ax.tick_params( axis="both", which="both", bottom=False, left=False, labelbottom=False, labelleft=False, ) return edge_viz_obj
[docs]def draw_networkx_labels( G, pos, labels=None, font_size=12, font_color="k", font_family="sans-serif", font_weight="normal", alpha=None, bbox=None, horizontalalignment="center", verticalalignment="center", ax=None, clip_on=True, ): """Draw node labels on the graph G. Parameters ---------- G : graph A networkx graph pos : dictionary A dictionary with nodes as keys and positions as values. Positions should be sequences of length 2. labels : dictionary (default={n: n for n in G}) Node labels in a dictionary of text labels keyed by node. Node-keys in labels should appear as keys in `pos`. If needed use: `{n:lab for n,lab in labels.items() if n in pos}` font_size : int (default=12) Font size for text labels font_color : string (default='k' black) Font color string font_weight : string (default='normal') Font weight font_family : string (default='sans-serif') Font family alpha : float or None (default=None) The text transparency bbox : Matplotlib bbox, (default is Matplotlib's ax.text default) Specify text box properties (e.g. shape, color etc.) for node labels. horizontalalignment : string (default='center') Horizontal alignment {'center', 'right', 'left'} verticalalignment : string (default='center') Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'} ax : Matplotlib Axes object, optional Draw the graph in the specified Matplotlib axes. clip_on : bool (default=True) Turn on clipping of node labels at axis boundaries Returns ------- dict `dict` of labels keyed on the nodes Examples -------- >>> G = nx.dodecahedral_graph() >>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G)) Also see the NetworkX drawing examples at https://networkx.org/documentation/latest/auto_examples/index.html See Also -------- draw draw_networkx draw_networkx_nodes draw_networkx_edges draw_networkx_edge_labels """ import matplotlib.pyplot as plt if ax is None: ax = plt.gca() if labels is None: labels = {n: n for n in G.nodes()} text_items = {} # there is no text collection so we'll fake one for n, label in labels.items(): (x, y) = pos[n] if not isinstance(label, str): label = str(label) # this makes "1" and 1 labeled the same t = ax.text( x, y, label, size=font_size, color=font_color, family=font_family, weight=font_weight, alpha=alpha, horizontalalignment=horizontalalignment, verticalalignment=verticalalignment, transform=ax.transData, bbox=bbox, clip_on=clip_on, ) text_items[n] = t ax.tick_params( axis="both", which="both", bottom=False, left=False, labelbottom=False, labelleft=False, ) return text_items
[docs]def draw_networkx_edge_labels( G, pos, edge_labels=None, label_pos=0.5, font_size=10, font_color="k", font_family="sans-serif", font_weight="normal", alpha=None, bbox=None, horizontalalignment="center", verticalalignment="center", ax=None, rotate=True, clip_on=True, ): """Draw edge labels. Parameters ---------- G : graph A networkx graph pos : dictionary A dictionary with nodes as keys and positions as values. Positions should be sequences of length 2. edge_labels : dictionary (default={}) Edge labels in a dictionary of labels keyed by edge two-tuple. Only labels for the keys in the dictionary are drawn. label_pos : float (default=0.5) Position of edge label along edge (0=head, 0.5=center, 1=tail) font_size : int (default=10) Font size for text labels font_color : string (default='k' black) Font color string font_weight : string (default='normal') Font weight font_family : string (default='sans-serif') Font family alpha : float or None (default=None) The text transparency bbox : Matplotlib bbox, optional Specify text box properties (e.g. shape, color etc.) for edge labels. Default is {boxstyle='round', ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)}. horizontalalignment : string (default='center') Horizontal alignment {'center', 'right', 'left'} verticalalignment : string (default='center') Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'} ax : Matplotlib Axes object, optional Draw the graph in the specified Matplotlib axes. rotate : bool (deafult=True) Rotate edge labels to lie parallel to edges clip_on : bool (default=True) Turn on clipping of edge labels at axis boundaries Returns ------- dict `dict` of labels keyed by edge Examples -------- >>> G = nx.dodecahedral_graph() >>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G)) Also see the NetworkX drawing examples at https://networkx.org/documentation/latest/auto_examples/index.html See Also -------- draw draw_networkx draw_networkx_nodes draw_networkx_edges draw_networkx_labels """ import matplotlib.pyplot as plt import numpy as np if ax is None: ax = plt.gca() if edge_labels is None: labels = {(u, v): d for u, v, d in G.edges(data=True)} else: labels = edge_labels text_items = {} for (n1, n2), label in labels.items(): (x1, y1) = pos[n1] (x2, y2) = pos[n2] (x, y) = ( x1 * label_pos + x2 * (1.0 - label_pos), y1 * label_pos + y2 * (1.0 - label_pos), ) if rotate: # in degrees angle = np.arctan2(y2 - y1, x2 - x1) / (2.0 * np.pi) * 360 # make label orientation "right-side-up" if angle > 90: angle -= 180 if angle < -90: angle += 180 # transform data coordinate angle to screen coordinate angle xy = np.array((x, y)) trans_angle = ax.transData.transform_angles( np.array((angle,)), xy.reshape((1, 2)) )[0] else: trans_angle = 0.0 # use default box of white with white border if bbox is None: bbox = dict(boxstyle="round", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)) if not isinstance(label, str): label = str(label) # this makes "1" and 1 labeled the same t = ax.text( x, y, label, size=font_size, color=font_color, family=font_family, weight=font_weight, alpha=alpha, horizontalalignment=horizontalalignment, verticalalignment=verticalalignment, rotation=trans_angle, transform=ax.transData, bbox=bbox, zorder=1, clip_on=clip_on, ) text_items[(n1, n2)] = t ax.tick_params( axis="both", which="both", bottom=False, left=False, labelbottom=False, labelleft=False, ) return text_items
[docs]def draw_circular(G, **kwargs): """Draw the graph G with a circular layout. Parameters ---------- G : graph A networkx graph kwargs : optional keywords See networkx.draw_networkx() for a description of optional keywords, with the exception of the pos parameter which is not used by this function. """ draw(G, circular_layout(G), **kwargs)
[docs]def draw_kamada_kawai(G, **kwargs): """Draw the graph G with a Kamada-Kawai force-directed layout. Parameters ---------- G : graph A networkx graph kwargs : optional keywords See networkx.draw_networkx() for a description of optional keywords, with the exception of the pos parameter which is not used by this function. """ draw(G, kamada_kawai_layout(G), **kwargs)
[docs]def draw_random(G, **kwargs): """Draw the graph G with a random layout. Parameters ---------- G : graph A networkx graph kwargs : optional keywords See networkx.draw_networkx() for a description of optional keywords, with the exception of the pos parameter which is not used by this function. """ draw(G, random_layout(G), **kwargs)
[docs]def draw_spectral(G, **kwargs): """Draw the graph G with a spectral 2D layout. Using the unnormalized Laplacian, the layout shows possible clusters of nodes which are an approximation of the ratio cut. The positions are the entries of the second and third eigenvectors corresponding to the ascending eigenvalues starting from the second one. Parameters ---------- G : graph A networkx graph kwargs : optional keywords See networkx.draw_networkx() for a description of optional keywords, with the exception of the pos parameter which is not used by this function. """ draw(G, spectral_layout(G), **kwargs)
[docs]def draw_spring(G, **kwargs): """Draw the graph G with a spring layout. Parameters ---------- G : graph A networkx graph kwargs : optional keywords See networkx.draw_networkx() for a description of optional keywords, with the exception of the pos parameter which is not used by this function. """ draw(G, spring_layout(G), **kwargs)
[docs]def draw_shell(G, **kwargs): """Draw networkx graph with shell layout. Parameters ---------- G : graph A networkx graph kwargs : optional keywords See networkx.draw_networkx() for a description of optional keywords, with the exception of the pos parameter which is not used by this function. """ nlist = kwargs.get("nlist", None) if nlist is not None: del kwargs["nlist"] draw(G, shell_layout(G, nlist=nlist), **kwargs)
[docs]def draw_planar(G, **kwargs): """Draw a planar networkx graph with planar layout. Parameters ---------- G : graph A planar networkx graph kwargs : optional keywords See networkx.draw_networkx() for a description of optional keywords, with the exception of the pos parameter which is not used by this function. """ draw(G, planar_layout(G), **kwargs)
def apply_alpha(colors, alpha, elem_list, cmap=None, vmin=None, vmax=None): """Apply an alpha (or list of alphas) to the colors provided. Parameters ---------- colors : color string or array of floats (default='r') Color of element. Can be a single color format string, or a sequence of colors with the same length as nodelist. If numeric values are specified they will be mapped to colors using the cmap and vmin,vmax parameters. See matplotlib.scatter for more details. alpha : float or array of floats Alpha values for elements. This can be a single alpha value, in which case it will be applied to all the elements of color. Otherwise, if it is an array, the elements of alpha will be applied to the colors in order (cycling through alpha multiple times if necessary). elem_list : array of networkx objects The list of elements which are being colored. These could be nodes, edges or labels. cmap : matplotlib colormap Color map for use if colors is a list of floats corresponding to points on a color mapping. vmin, vmax : float Minimum and maximum values for normalizing colors if a colormap is used Returns ------- rgba_colors : numpy ndarray Array containing RGBA format values for each of the node colours. """ from itertools import islice, cycle import numpy as np import matplotlib as mpl import matplotlib.colors # call as mpl.colors import matplotlib.cm # call as mpl.cm # If we have been provided with a list of numbers as long as elem_list, # apply the color mapping. if len(colors) == len(elem_list) and isinstance(colors[0], Number): mapper = mpl.cm.ScalarMappable(cmap=cmap) mapper.set_clim(vmin, vmax) rgba_colors = mapper.to_rgba(colors) # Otherwise, convert colors to matplotlib's RGB using the colorConverter # object. These are converted to numpy ndarrays to be consistent with the # to_rgba method of ScalarMappable. else: try: rgba_colors = np.array([mpl.colors.colorConverter.to_rgba(colors)]) except ValueError: rgba_colors = np.array( [mpl.colors.colorConverter.to_rgba(color) for color in colors] ) # Set the final column of the rgba_colors to have the relevant alpha values try: # If alpha is longer than the number of colors, resize to the number of # elements. Also, if rgba_colors.size (the number of elements of # rgba_colors) is the same as the number of elements, resize the array, # to avoid it being interpreted as a colormap by scatter() if len(alpha) > len(rgba_colors) or rgba_colors.size == len(elem_list): rgba_colors = np.resize(rgba_colors, (len(elem_list), 4)) rgba_colors[1:, 0] = rgba_colors[0, 0] rgba_colors[1:, 1] = rgba_colors[0, 1] rgba_colors[1:, 2] = rgba_colors[0, 2] rgba_colors[:, 3] = list(islice(cycle(alpha), len(rgba_colors))) except TypeError: rgba_colors[:, -1] = alpha return rgba_colors