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mnns.plotting

Functions to plot nanowire networks.

Functions:

Name Description
draw_NWN

Draw the given nanowire network as a networkx graph. JDA drawing is more
plot_NWN

Plots a given nanowire network and returns the figure and axes.

draw_NWN 

draw_NWN(
    NWN: Graph,
    figsize: tuple = None,
    font_size: int = 8,
    node_labels: ndarray = None,
    fmt: str = ".2f",
    edge_colors: ndarray = None,
    cbar_label: str = "Colorbar",
    cmap=plt.cm.RdYlBu_r,
) -> tuple[Figure, Axes]

Draw the given nanowire network as a networkx graph. JDA drawing is more detailed as nodes can be given spacial locations. With MNR drawing, nodes will have random locations.

Parameters:

Name Type Description Default
NWN Graph

Nanowire network to draw.

 required
figsize tuple

Figure size to be passed to plt.subplots.

 None
font_size int

Font size to be passed to nx.draw.

 8
node_labels ndarray

If supplied, these values will be display as node labels instead of the names of the nodes.

 None
fmt str

String formatting for node labels. Only used if sol is passed. Default: ".2f".

 '.2f'
edge_colors ndarray

List of values to color the edges. Edges are assumed to be in the same order as NWN.edges.

 None
cbar_label str

Label for the colorbar.

 'Colorbar'
cmap colormap

Matplotlib color map to use for the edges.

 RdYlBu_r

Returns:

Name Type Description
fig Figure

Figure object of the plot.
ax Axes

Axes object of the plot.
Source code in mnns/plotting.py 
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def draw_NWN(
    NWN: nx.Graph,
    figsize: tuple = None,
    font_size: int = 8,
    node_labels: np.ndarray = None,
    fmt: str = ".2f",
    edge_colors: np.ndarray = None,
    cbar_label: str = "Colorbar",
    cmap=plt.cm.RdYlBu_r,
) -> tuple[Figure, Axes]:
    """
    Draw the given nanowire network as a networkx graph. JDA drawing is more
    detailed as nodes can be given spacial locations. With MNR drawing, nodes
    will have random locations.

    Parameters
    ----------
    NWN : Graph
        Nanowire network to draw.

    figsize : tuple, optional
        Figure size to be passed to `plt.subplots`.

    font_size : int, optional
        Font size to be passed to `nx.draw`.

    node_labels : ndarray, optional
        If supplied, these values will be display as node labels
        instead of the names of the nodes.

    fmt : str, optional
        String formatting for node labels. Only used if sol is passed.
        Default: ".2f".

    edge_colors : ndarray, optional
        List of values to color the edges. Edges are assumed to be in the
        same order as `NWN.edges`.

    cbar_label : str, optional
        Label for the colorbar.

    cmap : colormap, optional
        Matplotlib color map to use for the edges.

    Returns
    -------
    fig : Figure
        Figure object of the plot.

    ax : Axes
        Axes object of the plot.

    """
    fig, ax = plt.subplots(figsize=figsize)

    if NWN.graph["type"] == "JDA":
        kwargs = dict()

        # Nodes are placed at the center of the wire
        kwargs.update(
            {
                "pos": {
                    (i,): np.asarray(*NWN.graph["lines"][i].centroid.coords)
                    for i in range(NWN.graph["wire_num"])
                }
            }
        )

        # Label node voltages if sol is given, else just label as nodes numbers
        if node_labels is not None:
            kwargs.update(
                {
                    "labels": {
                        (key,): f"{value:{fmt}}"
                        for key, value in zip(
                            range(NWN.graph["wire_num"]), node_labels
                        )
                    }
                }
            )
        else:
            kwargs.update(
                {"labels": {(i,): i for i in range(NWN.graph["wire_num"])}}
            )

        # Add edges colors if weights are passed
        if edge_colors is not None:
            kwargs.update(
                {
                    "edgelist": NWN.edges,
                    "edge_color": edge_colors,
                    "edge_cmap": cmap,
                }
            )

            # Add a colorbar to the network plot
            norm = mpl.colors.Normalize(
                vmin=np.nanmin(edge_colors), vmax=np.nanmax(edge_colors)
            )

            cax = fig.add_axes([0.95, 0.2, 0.02, 0.6])
            cb = mpl.colorbar.ColorbarBase(cax, norm=norm, cmap=cmap)
            cb.set_label(cbar_label)

        else:
            kwargs.update({"edge_color": "r"})

        # Add node formatting
        kwargs.update({"ax": ax, "font_size": font_size, "node_size": 40})

        nx.draw(NWN, **kwargs)

    elif NWN.graph["type"] == "MNR":
        kwargs = {}
        if node_labels is not None:
            labels = {
                node: f"{value:{fmt}}"
                for node, value in zip(sorted(NWN.nodes()), node_labels)
            }
            kwargs.update({"labels": labels})
        else:
            kwargs.update({"with_labels": True})

        nx.draw(
            NWN,
            ax=ax,
            node_size=40,
            font_size=font_size,
            edge_color="r",
            **kwargs,
        )

    else:
        raise ValueError("Nanowire network has invalid type.")

    return fig, ax

plot_NWN 

plot_NWN(
    NWN: Graph,
    intersections: bool = True,
    rnd_color: bool = False,
    scaled: bool = False,
    grid: bool = True,
    xlabel: str = "",
    ylabel: str = "",
) -> tuple[Figure, Axes]

Plots a given nanowire network and returns the figure and axes.

Parameters:

Name Type Description Default
NWN Graph

Nanowire network to plot.

 required
intersections bool

Whether or not to scatter plot the intersections as well. Defaults to true.

 True
rnd_color bool

Whether or not to randomize the colors of the plotted lines. Defaults to false.

 False
scaled bool

Whether or not to scale the plot by the characteristic values of the given nanowire network. Defaults to False.

 False
grid bool

Grid lines on plot. Defaults to true.

 True
xlabel str

x label string.

 ''
ylabel str

y label string.

 ''

Returns:

Name Type Description
fig Figure

Figure object of the plot.
ax Axes

Axes object of the plot.
Source code in mnns/plotting.py 
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def plot_NWN(
    NWN: nx.Graph,
    intersections: bool = True,
    rnd_color: bool = False,
    scaled: bool = False,
    grid: bool = True,
    xlabel: str = "",
    ylabel: str = "",
) -> tuple[Figure, Axes]:
    """
    Plots a given nanowire network and returns the figure and axes.

    Parameters
    ----------
    NWN : Graph
        Nanowire network to plot.

    intersections : bool, optional
        Whether or not to scatter plot the intersections as well.
        Defaults to true.

    rnd_color : bool, optional
        Whether or not to randomize the colors of the plotted lines.
        Defaults to false.

    scaled: bool, optional
        Whether or not to scale the plot by the characteristic values of the
        given nanowire network. Defaults to False.

    grid: bool, optional
        Grid lines on plot. Defaults to true.

    xlabel: str, optional
        x label string.

    ylabel: str, optional
        y label string.

    Returns
    -------
    fig : Figure
        Figure object of the plot.

    ax : Axes
        Axes object of the plot.

    """
    fig, ax = plt.subplots(figsize=(8, 6))
    l0 = NWN.graph["units"]["l0"]

    # Plot intersection plots if required
    if intersections:
        ax.scatter(
            *np.array(
                [(point.x, point.y) for point in NWN.graph["loc"].values()]
            ).T,
            zorder=10,
            s=5,
            c="blue",
        )

    # Defaults to blue and pink lines, else random colors are used.
    if rnd_color:
        for i in range(NWN.graph["wire_num"]):
            ax.plot(*np.asarray(NWN.graph["lines"][i].coords).T)
    else:
        for i in range(NWN.graph["wire_num"]):
            if (i,) in NWN.graph["electrode_list"]:
                ax.plot(
                    *np.asarray(NWN.graph["lines"][i].coords).T,
                    c="xkcd:light blue",
                )
            else:
                ax.plot(*np.asarray(NWN.graph["lines"][i].coords).T, c="pink")

    # Scale axes according to the characteristic values
    if scaled:
        ax.xaxis.set_major_formatter(
            ticker.FuncFormatter(lambda x, pos: f"{x * l0:.3g}")
        )
        ax.yaxis.set_major_formatter(
            ticker.FuncFormatter(lambda y, pos: f"{y * l0:.3g}")
        )

    # Other attributes
    if grid:
        ax.grid(alpha=0.25)
    if xlabel:
        ax.set_xlabel(xlabel)
    if ylabel:
        ax.set_ylabel(ylabel)

    return fig, ax