iplotx

https://iplotx.readthedocs.io/en/latest/
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iplotx is a Python library for visualizing graphs, networks, and trees using matplotlib, offering...
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### Quick Start with networkx

Source: https://iplotx.readthedocs.io/en/latest/_sources/installing.md.txt

Visualize a graph using iplotx with the networkx library. Requires networkx to be installed.

```python
import networkx as nx
import iplotx as ipx

g = nx.Graph([(0, 1), (1, 2), (2, 3), (3, 4), (4, 0)])
layout = nx.layout.circular_layout(g)
ipx.network(g, layout)
```

--------------------------------

### Quick Start with igraph

Source: https://iplotx.readthedocs.io/en/latest/_sources/installing.md.txt

Visualize a graph using iplotx with the igraph library. Requires igraph to be installed.

```python
import igraph as ig
import iplotx as ipx

g = ig.Graph.Ring(5)
layout = g.layout("circle").coords
ipx.network(g, layout)
```

--------------------------------

### Install iplotx with ilayoutx

Source: https://iplotx.readthedocs.io/en/latest/_sources/installing.md.txt

Install iplotx along with ilayoutx for network layout computations.

```bash
pip install iplotx ilayoutx
```

--------------------------------

### Install iplotx

Source: https://iplotx.readthedocs.io/en/latest/_sources/installing.md.txt

Install the iplotx library using pip.

```bash
pip install iplotx
```

--------------------------------

### Showcase Network Plot Elements

Source: https://iplotx.readthedocs.io/en/latest/gallery/style/plot_elements.html

This example displays a network graph with various styling options applied to vertices, edges, and labels. It requires matplotlib and iplotx to be installed.

```python
import matplotlib.pyplot as plt
import iplotx as ipx

network = {
    "edges": [(0, 1), (1, 2), (2, 3), (3, 0), (2, 2)],
    "directed": True,
}
layout = [(0, 0), (1, 0), (1, 1), (0, 1)]

fig, ax = plt.subplots(figsize=(4.3, 4.3))
ipx.network(
    network,
    layout,
    ax=ax,
    vertex_labels=["Vertex\nlabel"] + [""] * 3,
    edge_labels=["", "", "Edge\nlabel", "", ""],
    vertex_label_bbox_edgecolor="k",
    vertex_label_bbox_linewidth=1.5,
    vertex_label_bbox_facecolor="steelblue",
    vertex_label_bbox_alpha=0.5,
    edge_label_bbox_edgecolor="k",
    edge_label_bbox_linewidth=1.5,
    edge_label_bbox_facecolor="tomato",
    edge_color=["k", "tomato", "k", "k", "k"],
    edge_arrow_color=["k", "darkred", "k", "k", "k"],
    edge_linewidth=[2, 4, 2, 2, 2],
    vertex_facecolor=["w"] * 3 + ["steelblue"],
    vertex_edgecolor="k",
    vertex_linewidth=2,
    vertex_size=[50, 25, 25, 50],
    vertex_marker="o",
    vertex_label_color="k",
    edge_curved=True,
    edge_tension=[0, 1.25, 0, 3.5],
    margins=(0, 0.08),
)


```

--------------------------------

### Basic Network Plotting with iplotx

Source: https://iplotx.readthedocs.io/en/latest/_sources/gallery/index.rst.txt

This example demonstrates how to plot a simple graph using iplotx.

```python
...
```

--------------------------------

### Visualize Branch Support

Source: https://iplotx.readthedocs.io/en/latest/_downloads/ab47bbf2156acc6f1b3ebb544a401c2f/plot_support.ipynb

Basic example to visualize branch support values on a phylogenetic tree. Requires cogent3 and matplotlib.

```python
import cogent3
import matplotlib.pyplot as plt
import iplotx as ipx

tree = cogent3.load_tree("data/tree-with-support.json")

fig, ax = plt.subplots(figsize=(5, 4))
art = ipx.tree(
    tree,
    ax=ax,
    leaf_labels=True,
    style="cogent3",
    show_support=True,
)
```

--------------------------------

### Plotting a Simple Network with iplotx

Source: https://iplotx.readthedocs.io/en/latest/providers.html

Demonstrates how to visualize a network using a simple dictionary-based data provider and a layout dictionary. This example does not require external network analysis libraries.

```python
import iplotx as ipx
network = {
    "edges": [
        ("alice", "bob"),
        ("bob", "jago"),
        ("alice", "jago"),
    ]
}
layout = {
    "alice": (0, 0),
    "bob": (1, 1),
    "jago": (1, 0),
}
ipx.plot(
    network,
    layout,
)

```

--------------------------------

### Plotting a Simple Network with iplotx

Source: https://iplotx.readthedocs.io/en/latest/_sources/providers.md.txt

Demonstrates how to ingest and visualize a network using a simple dictionary format and a predefined layout. This example requires matplotlib, numpy, and pandas.

```python
import iplotx as ipx
network = {
    "edges": [
        ("alice", "bob"),
        ("bob", "jago"),
        ("alice", "jago"),
    ]
}
layout = {
    "alice": (0, 0),
    "bob": (1, 1),
    "jago": (1, 0),
}
ipx.plot(
    network,
    layout,
)
```

--------------------------------

### Basic Branch Support Visualization

Source: https://iplotx.readthedocs.io/en/latest/gallery/tree/plot_support.html

This snippet shows the basic usage of `iplotx.tree` to display branch support values. Ensure you have `cogent3` and `matplotlib` installed and a tree file with support data.

```python
import cogent3
import matplotlib.pyplot as plt
import iplotx as ipx

tree = cogent3.load_tree("data/tree-with-support.json")

fig, ax = plt.subplots(figsize=(5, 4))
art = ipx.tree(
    tree,
    ax=ax,
    leaf_labels=True,
    style="cogent3",
    show_support=True,
)


```

--------------------------------

### Create and Plot a Simple Path Graph

Source: https://iplotx.readthedocs.io/en/latest/_downloads/ae81a22c794f5dd9bb945db79b137431/plot_simple_path.ipynb

Generates a path graph using networkx and visualizes it with iplotx. Ensure networkx and iplotx are installed and imported. The layout is seeded for reproducible results.

```python
import networkx as nx
import iplotx as ipx

G = nx.path_graph(8)
pos = nx.spring_layout(G, seed=47)  # Seed layout for reproducibility
ipx.network(G, layout=pos)
```

--------------------------------

### Basic Cogent3 Tree Layout with iplotx

Source: https://iplotx.readthedocs.io/en/latest/_downloads/ebaee9f96d315bd17e843740077d33ce/plot_cogent3_layouts.ipynb

Reproduces a standard cogent3 tree layout using iplotx and matplotlib. Ensure cogent3 and iplotx are installed.

```python
import cogent3
import matplotlib.pyplot as plt
import iplotx as ipx

tree = cogent3.load_tree("data/tree-with-support.json")

fig, ax = plt.subplots(figsize=(5, 4))
art = ipx.tree(
    tree,
    ax=ax,
    leaf_labels=True,
    style="cogent3",
)
```

--------------------------------

### Create and Visualize Network with Patches

Source: https://iplotx.readthedocs.io/en/latest/gallery/network_science/plot_redblack.html

Use this snippet to create a network graph with iplotx and overlay it with custom matplotlib patches. Ensure iplotx and matplotlib are installed.

```python
import numpy as np
import iplotx as ipx
import matplotlib.pyplot as plt

# We will use a network laid out onto a square grid
gsize = 5  # Grid size
layout = [(i % gsize, i // gsize) for i in range(gsize**2)]

# Two possible colors
colorlist = np.array(["tomato", "#111"])

# Assign node colors at random
node_colors = colorlist[np.random.randint(2, size=gsize**2)]

# Connect two neighboring nodes if they have the same color
edges = []
for inode in range(gsize**2):
    i = inode // gsize
    j = inode % gsize
    # Four possible edges
    cands = []
    if i != 0:
        cands.append(inode - gsize)
    if j != 0:
        cands.append(inode - 1)
    if i != gsize - 1:
        cands.append(inode + gsize)
    if j != gsize - 1:
        cands.append(inode + 1)
    for inode2 in cands:
        if node_colors[inode] == node_colors[inode2]:
            if (inode2, inode) not in edges:
                edges.append((inode, inode2))

# Build the network using our zero-dependency data structure
network = {
    "edges": edges,
    "nodes": list(range(gsize**2)),
}

# Visualise the network
fig, ax = plt.subplots(figsize=(5, 5))
ipx.network(
    network,
    layout=layout,
    ax=ax,
    vertex_size=8,
    vertex_marker='d',
)

# Add square patches centered on each node
for i in range(gsize):
    for j in range(gsize):
        inode = i * gsize + j
        ax.add_patch(plt.Rectangle(
            (j-0.5, i-0.5),
            1,
            1,
            facecolor=node_colors[inode],
            edgecolor="none",
            alpha=0.4,
            zorder=0,
        ))

```

--------------------------------

### Basic Cogent3 Tree Layout

Source: https://iplotx.readthedocs.io/en/latest/gallery/tree/plot_cogent3_layouts.html

Reproduces a standard cogent3 tree layout using iplotx with matplotlib. Ensure cogent3 and matplotlib are installed.

```python
import cogent3
import matplotlib.pyplot as plt
import iplotx as ipx

tree = cogent3.load_tree("data/tree-with-support.json")

fig, ax = plt.subplots(figsize=(5, 4))
art = ipx.tree(
    tree,
    ax=ax,
    leaf_labels=True,
    style="cogent3",
)

```

--------------------------------

### Directed Graph with Opposite Half Arrows (|")

Source: https://iplotx.readthedocs.io/en/latest/_downloads/4befb45017dbf57dbc78b49693a2924c/plot_halfarrows.ipynb

This example demonstrates using the opposite half arrow marker ('|\') by adjusting the `edge_paralleloffset` to a positive value. This creates a visual effect on the other side of the edge compared to the previous example. Use this when you need to visually distinguish opposing directions on the same edge.

```python
fig, ax = plt.subplots(figsize=(3, 2.7))
ipx.network(
    g,
    layout=layout,
    ax=ax,
    edge_arrow_marker="|\\",
    edge_paralleloffset=7,
    edge_arrow_width=15,
)
```

--------------------------------

### Plot Cogent3 Tree with Custom Leaf Labels and Margins

Source: https://iplotx.readthedocs.io/en/latest/_downloads/ccf5f25334782fc9fe12bdab03b91aaf/plot_cogent3_tree.ipynb

Plots a cogent3 tree with custom leaf labels and adjusted horizontal margins. This example utilizes matplotlib for figure and axes setup, and requires iplotx for plotting.

```python
leaf_labels = {leaf: f"Species {i + 1}" for i, leaf in enumerate(tree.tips())}
fig, ax = plt.subplots(figsize=(5, 4))
ipx.plotting.tree(
    tree,
    ax=ax,
    vertex_labels=leaf_labels,
    vertex_label_hmargin=10,
)
ax.set_xlim(-0.2, 1.5)
```

--------------------------------

### Plot Minimum Spanning Tree with Non-Rotating Labels

Source: https://iplotx.readthedocs.io/en/latest/gallery/network_science/plot_minimum_spanning_trees.html

This snippet visualizes the same graph and minimum spanning tree as the previous example, but with edge labels set to not rotate. This can be useful for improving readability in certain layouts. Ensure igraph and iplotx are installed.

```python
ipx.network(
    g,
    layout=layout,
    edge_labels=g.es["weight"],
    style={
        "vertex": {
            "facecolor": "lightblue",
            "edgecolor": "black",
            "linewidth": 1,
        },
        "edge": {
            "linewidth": g.es["width"],
            "label": {
                "rotate": False,
                "bbox": {
                    "facecolor": "white",
                    "edgecolor": "none",
                },
            },
        },
    },
)


```

--------------------------------

### Plot Radial ETE4 Tree with iplotx

Source: https://iplotx.readthedocs.io/en/latest/_downloads/d12e10c1d169b29546615014f2d6dc6a/plot_ete4.ipynb

Plots a radial layout of an ETE4 tree using iplotx. This example customizes the layout orientation, start angle, span, and styling for edges, leaves, and leaf edges. Leaf labels are also customized.

```python
# sphinx_gallery_thumbnail_number = 2
ipx.plotting.tree(
    tree,
    layout="radial",
    layout_orientation="right",
    style=[
        "tree",
        {
            "edge": {
                "color": "deeppink",
                "linewidth": 4,
            },
            "layout": {
                "start": -180,
                "span": 180,
            },
            "leaf": {
                "label": {
                    "hmargin": 15,
                }
            },
            "leafedge": {
                "color": "purple",
                "linewidth": 2,
            },
        },
    ],
    leaf_labels={leaf: str(i + 1) for i, leaf in enumerate(tree.leaves())},
    aspect=1,
    margin=0.1,
)
```

--------------------------------

### get_texts

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the texts of the labels.

```APIDOC
## get_texts

### Description
Get the texts of the labels.

### Method
`get_texts() -> list[str]`

### Returns
- A list of strings, where each string is the text of a label.

```

--------------------------------

### Plotting a Simple NetworkX Graph

Source: https://iplotx.readthedocs.io/en/latest/_sources/gallery/network_science/index.rst.txt

Demonstrates how to create and visualize a basic graph using NetworkX.

```python
import networkx as nx
import matplotlib.pyplot as plt

G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4)])

pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_color='skyblue', edge_color='gray', node_size=500, font_size=10)
plt.title("Simple NetworkX Graph")
plt.show()
```

--------------------------------

### get_text

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the text of the labels.

```APIDOC
## get_text

### Description
Get the texts of the labels.

### Method
`get_text() -> str`

### Returns
- The text of the labels.

```

--------------------------------

### get_tensions

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the tension for the edges.

```APIDOC
## get_tensions()

### Description
Get the tension for the edges.

### Returns
The tension for the edges. If None, the edges are straight.
```

--------------------------------

### Streamlined Double Tree Plotting with `ipx.doubletree`

Source: https://iplotx.readthedocs.io/en/latest/_downloads/a7e412d92468d874cf4616f46df09a11/plot_double_tree.ipynb

This example utilizes the `ipx.doubletree` function for a more concise way to plot two trees facing each other. It allows passing keyword arguments for left and right tree configurations separately. Note the adjusted coordinates for connecting lines due to the centered layout.

```python
fig, ax = plt.subplots(figsize=(9, 4))
ipx.doubletree(
    tree1,
    tree2,
    ax=ax,
    kwargs_left=dict(
        leaf_deep=True,
        edge_color="tomato",
    ),
    kwargs_right=dict(
        leaf_deep=True,
        edge_color="steelblue",
    ),
    gap=2,
    aspect=1,
)

# Add lines connecting corresponding leaves
# NOTE: The layout is horizontally centered now
matches = [
    (0, 0),
    (1, 4),
    (2, 5),
    (3, 2),
    (4, 1),
    (5, 5),
]
for y1, y2 in matches:
    ax.plot(
        [-0.3, 1.3], [y1, y2], color="gray", linewidth=2,
    )
```

--------------------------------

### Network Plot with Style Context Manager

Source: https://iplotx.readthedocs.io/en/latest/_downloads/9137a05a2287cb684755c2ae08e9c463/plot_loops.ipynb

This example shows how to apply styles to a network plot using a style context manager. Styles defined within the `with` block are applied to the network plot generated inside it.

```python
with ipx.style.context(
    vertex_size=30,
    vertex_facecolor="lightblue",
    vertex_edgecolor="none",
    vertex_label_color="black",
    edge_linestyle=linestyle,
    edge_paralleloffset=0,
    edge_looptension=3.5,
    edge_arrow_marker="|>",
):
    ipx.network(
        G,
        layout=pos,
        vertex_labels=True,
    )
```

--------------------------------

### get_tension

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the tension for the edges.

```APIDOC
## get_tension()

### Description
Get the tension for the edges.

### Returns
The tension for the edges. If None, the edges are straight.
```

--------------------------------

### Per-Edge Angular Styling with defaultdict

Source: https://iplotx.readthedocs.io/en/latest/_downloads/3f6ccaf51acdd77ff4048719d9eaa22d/plot_angular_waypoints.ipynb

This example shows how to apply angular styling on a per-edge basis using a defaultdict. It allows specific edges to have different waypoint behaviors, with a default fallback for unspecified edges.

```python
from collections import defaultdict

ipx.tree(
    tree,
    edge_waypoints=defaultdict(lambda: True, {tree.clade[0, 1]: False}),
    title="Mixed\n(per-edge)",
)
```

--------------------------------

### get_ports

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the ports for all edges.

```APIDOC
## get_ports()

### Description
Get the ports for all edges.

### Returns
The ports for the edges, as a pair of strings or None for each edge. If None, it means all edges are free.
```

--------------------------------

### get_looptension

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the tension for loops.

```APIDOC
## get_looptension()

### Description
Get the tension for loops.

### Returns
The tension for loops. If None, the default is 2.5.
```

--------------------------------

### get_rotations

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the rotations of the labels in radians.

```APIDOC
## get_rotations

### Description
Get the rotations of the labels in radians.

### Method
`get_rotations() -> ndarray`

### Returns
- A numpy ndarray containing the label rotations in radians.

```

--------------------------------

### Apply Styles to Network Graph Elements

Source: https://iplotx.readthedocs.io/en/latest/_sources/style.md.txt

Demonstrates how to apply custom styles to specific graph elements like vertices using dictionaries. This example shows setting vertex sizes based on their IDs.

```python
import networkx as nx
import iplotx as ipx

G = nx.Graph([(0, 1)])
ipx.network(
    G,
    vertex_size={0: 20, 1: 30},
)
```

--------------------------------

### get_offsets

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the positions (offsets) of the labels.

```APIDOC
## get_offsets

### Description
Get the positions (offsets) of the labels.

### Method
`get_offsets(_with_margins : bool = False_) -> ndarray`

### Parameters
#### with_margins
- **with_margins** (bool) - Whether to include margins. Defaults to False.

### Returns
- A numpy ndarray containing the label offsets.

```

--------------------------------

### get_datalim

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the data limits of the labels.

```APIDOC
## get_datalim

### Description
Get the data limits of the labels.

### Method
`get_datalim(_transData =None_) -> Bbox`

### Parameters
#### transData
- **transData**: Optional transform data.

### Returns
- The data limits as a Bbox object.

```

--------------------------------

### Access and Print the Default Style

Source: https://iplotx.readthedocs.io/en/latest/_sources/style.md.txt

This snippet demonstrates how to import and print the default style configuration provided by iplotx.

```python
from iplotx.style import default as default_style
print(default_style)
```

--------------------------------

### Apply Styles using Keyword Arguments

Source: https://iplotx.readthedocs.io/en/latest/gallery/basic/plot_loops.html

This snippet shows how to apply styles directly as keyword arguments to the `ipx.network` function, offering a more direct and readable approach for styling graph elements.

```python
ipx.network(
    G,
    layout=pos,
    vertex_labels=True,
    vertex_size=30,
    vertex_facecolor="lightblue",
    vertex_edgecolor="none",
    vertex_label_color="black",
    edge_linestyle=linestyle,
    edge_paralleloffset=0,
    edge_looptension=3.5,
    edge_arrow_marker=">",
)


```

--------------------------------

### get_loopmaxangle

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the maximum angle for loops.

```APIDOC
## get_loopmaxangle()

### Description
Get the maximum angle for loops.

### Returns
The maximum angle in degrees that a loop can take. If None, the default is 60.
```

--------------------------------

### Node Labels and Colors Customization

Source: https://iplotx.readthedocs.io/en/latest/_sources/gallery/network_science/index.rst.txt

Demonstrates how to customize node labels and colors for better graph visualization.

```python
import networkx as nx
import matplotlib.pyplot as plt

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4)])

# Define node labels and colors
labels = {1: 'A', 2: 'B', 3: 'C', 4: 'D'}
colors = ['red', 'blue', 'green', 'purple']

# Get positions for drawing
pos = nx.spring_layout(G)

# Draw the graph with custom labels and colors
nx.draw(G, pos, labels=labels, node_color=colors, with_labels=True, node_size=500, font_size=10, edge_color='gray')
plt.title("Node Labels and Colors")
plt.show()
```

--------------------------------

### get_curved

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets whether the edges are curved or not.

```APIDOC
## get_curved()

### Description
Get whether the edges are curved or not.

### Returns
A bool that is True if the edges are curved, False if they are straight.
```

--------------------------------

### get_datalims_children

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the data limits of the children of this artist.

```APIDOC
## get_datalims_children

### Description
Get the data limits of the children of this artist.

### Method
`get_datalims_children(_transData =None_) -> Sequence[Bbox]`

### Parameters
#### transData
- **transData**: Optional transform data.

### Returns
- A sequence of Bbox objects representing the data limits of the children.

```

--------------------------------

### Displaying Network with Vertex Markers

Source: https://iplotx.readthedocs.io/en/latest/gallery/style/plot_vertexmarkers.html

This snippet shows how to create a network graph and apply different vertex markers, sizes, and colors. It utilizes igraph for graph creation and iplotx for visualization, setting up a star layout and custom color mapping.

```python
import igraph as ig
import numpy as np
import iplotx as ipx

markers = ["o", "s", "^", "v", "<", ">", "d", "e", "p", "h", "8", "*"]
n = len(markers)
g = ig.Graph(
    edges=[(0, i + 1) for i in range(n)],
    directed=False,
)

# Create star layout
layout = {i + 1: (np.cos(2 * np.pi / n * i), np.sin(2 * np.pi / n * i)) for i in range(n)}
layout[0] = (0, 0)

# Colors
colors = [
    "k",
    "#8dd3c7",
    "#ffffb3",
    "#bebada",
    "#fb8072",
    "#80b1d3",
    "#fdb462",
    "#b3de69",
    "#fccde5",
    "#bf5b17",
    "#666666",
    "#000000",
    "#bbbbbb",
]
colors = {i: colors[i] for i in range(n + 1)}

ipx.network(
    g,
    layout=layout,
    vertex_marker=markers,
    vertex_size={i: 20 * bool(i) for i in range(n + 1)},
    vertex_facecolor=colors,
    vertex_edgecolor="black",
    aspect=1,
)

```

--------------------------------

### get_offset

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the offset for parallel straight edges.

```APIDOC
## get_offset()

### Description
Get the offset for parallel straight edges.

### Returns
The offset in points for parallel straight edges. If None, the default is 3.
```

--------------------------------

### Customize Branch Support Label Placement

Source: https://iplotx.readthedocs.io/en/latest/_downloads/ab47bbf2156acc6f1b3ebb544a401c2f/plot_support.ipynb

Demonstrates how to adjust the placement and alignment of branch support labels using margin and alignment parameters. Two subplots show different configurations.

```python
fig, axs = plt.subplots(2, 1, figsize=(5, 9))
art = ipx.tree(
    tree,
    ax=axs[0],
    leaf_labels=True,
    style="cogent3",
    show_support=True,
    vertex_label_hmargin=-1,
    vertex_label_vmargin=5,
    vertex_label_horizontalalignment="right",
    title="Move top left, right-aligned",
)
art = ipx.tree(
    tree,
    ax=axs[1],
    leaf_labels=True,
    style="cogent3",
    show_support=True,
    vertex_label_hmargin=+1,
    vertex_label_vmargin=0,
    vertex_label_horizontalalignment="left",
    title="Move center right, left-aligned",
)
```

--------------------------------

### VertexCollection.axes

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Gets the Axes instance the artist resides in.

```APIDOC
## axes

### Description
The ~.axes.Axes instance the artist resides in, or _None_.

### Property
axes
```

--------------------------------

### Force Leaf Edges and Customize Appearance

Source: https://iplotx.readthedocs.io/en/latest/_downloads/1282670c6ec70d6181c0dc748cc2773e/plot_leafedges.ipynb

This example demonstrates how to force the plotting of leaf edges even without leaf labels, and how to customize their color, linewidth, and linestyle. Use this for explicit visual emphasis on leaf connections.

```python
ipx.plotting.tree(
    tree,
    leaf_deep=True,
    leafedge_color="tomato",
    leafedge_linewidth=2,
    leafedge_linestyle="-.",
)
```

--------------------------------

### get_sizes_dpi

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Get arrow sizes (max of width and height) scaled by dpi.

```APIDOC
## get_sizes_dpi

### Description
Get arrow sizes (max of width and height) scaled by dpi.

### Method
GET (or equivalent for object method call)

### Endpoint
N/A (Object method)

### Parameters
None
```

--------------------------------

### Styling Groups with iplotx

Source: https://iplotx.readthedocs.io/en/latest/gallery/basic/plot_grouping.html

Style groups by specifying colors, line widths, and padding. This example demonstrates how to customize the appearance of group hulls, including face color, edge color, line width, and vertex padding.

```python
ipx.network(
    network=g,
    layout="circle",
    grouping={
        "singleton": {0},
        "doublet": {1, 2},
        "multiplet": {3, 4, 5, 6, 7},
    },
    vertex_size=18,
    vertex_labels=True,
    vertex_label_color="white",
    grouping_facecolor=["purple", "deeppink", "tomato"],
    grouping_edgecolor="black",
    grouping_linewidth=2,
    grouping_vertexpadding=21,
    network_zorder=2,
    grouping_zorder=1,
)
plt.gca().set_aspect(1.0)

```

--------------------------------

### get_sizes

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Get arrow sizes (max of width and height), not scaled by dpi.

```APIDOC
## get_sizes

### Description
Get arrow sizes (max of width and height), not scaled by dpi.

### Method
GET (or equivalent for object method call)

### Endpoint
N/A (Object method)

### Parameters
None
```

--------------------------------

### Graph Creation and Shortest Path Calculation

Source: https://iplotx.readthedocs.io/en/latest/gallery/network_science/plot_shortest_path.html

This snippet sets up a graph with nodes and weighted edges, then calculates the shortest path between two specified nodes using the 'weight' attribute. It also prepares edge attributes for visualization, coloring the shortest path edges red.

```python
import networkx as nx
import matplotlib.pyplot as plt
import iplotx as ipx

G = nx.Graph()
G.add_nodes_from(["A", "B", "C", "D", "E", "F", "G", "H"])
G.add_edge("A", "B", weight=4)
G.add_edge("A", "H", weight=8)
G.add_edge("B", "C", weight=8)
G.add_edge("B", "H", weight=11)
G.add_edge("C", "D", weight=7)
G.add_edge("C", "F", weight=4)
G.add_edge("C", "I", weight=2)
G.add_edge("D", "E", weight=9)
G.add_edge("D", "F", weight=14)
G.add_edge("E", "F", weight=10)
G.add_edge("F", "G", weight=2)
G.add_edge("G", "H", weight=1)
G.add_edge("G", "I", weight=6)
G.add_edge("H", "I", weight=7)

# Find the shortest path from node A to node E
path = nx.shortest_path(G, "A", "E", weight="weight")

# Create a list of edges in the shortest path
path_edges = list(zip(path, path[1:]))
x.set_node_attributes(G, nx.spring_layout(G, seed=37), "pos")
x.set_edge_attributes(
    G,
    {
        (u, v): {
            "color": (
                "red" if (u, v) in path_edges or tuple(reversed((u, v))) in path_edges else "black"
            ),
            "label": d["weight"],
        }
        for u, v, d in G.edges(data=True)
    },
)

fig, ax = plt.subplots()
ipx.plot(
    G,
    ax=ax,
    layout="pos",
    vertex_labels=True,
    edge_labels=True,
)
```

--------------------------------

### Visualize a simple tree with SimpleTree

Source: https://iplotx.readthedocs.io/en/latest/gallery/zero_dependency/plot_simpletreedataprovider.html

Use this snippet to visualize a tree structure using iplotx's SimpleTree. This is useful for projects that do not require external tree libraries or for educational demonstrations. Ensure iplotx is installed.

```python
import iplotx as ipx

tree = {
    "children": (
        {},
        {
            "children": (
                {
                    "children": (
                        {"branch_length": 1.5},
                        {}
                    )
                },
                {
                    "children": (
                        {},
                        {}
                    )
                }
            )
        }
    )
}

# Convert to our simple tree data structure
tree = ipx.ingest.providers.tree.simple.SimpleTree.from_dict(tree)

ipx.tree(
    tree,
)


```

--------------------------------

### get_size

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Get arrow sizes (max of width and height), not scaled by dpi.

```APIDOC
## get_size

### Description
Get arrow sizes (max of width and height), not scaled by dpi.

### Method
GET (or equivalent for object method call)

### Endpoint
N/A (Object method)

### Parameters
None
```

--------------------------------

### get_offset_transform

Source: https://iplotx.readthedocs.io/en/latest/api/artists.html

Get offset transform for the edge arrows. This sets the tip of each arrow.

```APIDOC
## get_offset_transform

### Description
Get offset transform for the edge arrows. This sets the tip of each arrow.

### Method
GET (or equivalent for object method call)

### Endpoint
N/A (Object method)

### Parameters
None
```

--------------------------------

### Plotting an Undirected Ring Graph with Various Styles

Source: https://iplotx.readthedocs.io/en/latest/gallery/style/plot_multistyle.html

This snippet plots an undirected ring graph using the same list of iplotx styles as the previous example. It applies each style to a separate subplot, allowing for a direct comparison of how styles affect undirected graphs. Similar to the directed graph example, vertex labels and margins are conditionally applied.

```python
g = ig.Graph.Ring(9, directed=False)

fig, axs = plt.subplots(5, 2, figsize=(7.5, 20))
for style, ax in zip(styles, axs.ravel()):
    ipx.plot(
        g,
        ax=ax,
        layout="circle",
        style=style,
        vertex_labels=style in ("default", "hollow", "unicorn", "networkx", "igraph"),
        title=style,
        margins=0.1 if style == "unicorn" else 0,
    )


```

--------------------------------

### Create a Style Context in iplotx

Source: https://iplotx.readthedocs.io/en/latest/api/style.html

Use this function to create a style context for iplotx. It accepts a single style specification or a list of specifications, which are applied in order. Additional keyword arguments can override styles at the end.

```python
iplotx.style.context(_style : str | dict[str, Any] | Sequence[str | dict[str, Any]] | Iterable[str | dict[str, Any]] | None = None_, _** kwargs_)
```
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