Depedencies

By collecting data on the projects within the WETO portfolio that use other tools within the portfolio, we can get a sense for the software projects that are at the “core” of the capabilities.

This network is divided into two types of connectivity:

1. Direct connections are software that are connected within the code, so they may communicate directly through API’s, share memory, and be distributed as a bundle.

2. Indirect connections are software that require the outputs of other tools in order to construct their inputs. These are connected through workflows rather than code.

Show code cell content

Hide code cell content

from pathlib import Path
import yaml
import networkx as nx

software_attr_dir = Path("..", "..", "software_attributes")
software_database_dir = software_attr_dir / "database"

model_list_inputs = yaml.safe_load( open(software_attr_dir / "database_list.yaml", "r") )
models = model_list_inputs["active"] + model_list_inputs["partial"]

model_attributes_map = {
    model: yaml.safe_load( open( software_database_dir / f"{model}.yaml", "r") )
    for model in models
}

Show code cell content

Hide code cell content

model_connectivity = {}
for model in models:
    model_attributes = model_attributes_map[model]
    if "dependencies" not in model_attributes:
        continue
    model_connectivity[model] = model_attributes["dependencies"]

network_graph = nx.DiGraph()
for model, connections in model_connectivity.items():
    for c in connections:
        weight = 1 if c[1] == "direct" else 2
        network_graph.add_edge(model, c[0], weight=weight)

Show code cell source

Hide code cell source

import matplotlib.pyplot as plt

edges_direct = [(u, v) for (u, v, d) in network_graph.edges(data=True) if d["weight"] == 1]
edges_indirect = [(u, v) for (u, v, d) in network_graph.edges(data=True) if d["weight"] == 2]

# layout = nx.nx_pydot.graphviz_layout(network_graph, prog="neato")
layout = nx.nx_agraph.graphviz_layout(
    network_graph,
    prog="sfdp",
    # args="-Gnormalize=true"
    # args="-GK=0.5"
)
# layout = nx.spring_layout(network_graph, k=2)

# nodes
nx.draw_networkx_nodes(
    network_graph,
    layout,
    # node_size=1000
)

# edges
nx.draw_networkx_edges(
    network_graph,
    layout,
    edgelist=edges_direct,
    width=1,
    # edge_color="b"
)

nx.draw_networkx_edges(
    network_graph,
    layout,
    edgelist=edges_indirect,
    width=1,
    # alpha=0.5,
    # edge_color="b",
    style=":"
    # edge_color="r"
)

# node labels
label_options = {"ec": "k", "fc": "white", "alpha": 0.7}
nx.draw_networkx_labels(
    network_graph,
    layout,
    font_size=8,
    bbox=label_options,
    # font_family="sans-serif"
)
# edge weight labels
# edge_labels = nx.get_edge_attributes(network_graph, "weight")
# nx.draw_networkx_edge_labels(network_graph, layout, edge_labels)

# Title/legend
ax = plt.gca()
font = {"fontname": "Helvetica", "color": "k", "fontweight": "bold", "fontsize": 14}
ax.set_title("WETO Software Portfolio Connectivity", font)

# Change font color for legend
font["color"] = "k"
ax.text(
    0.80,
    0.10,
    "------- Direct",
    horizontalalignment="left",
    transform=ax.transAxes,
    fontdict=font,
)
ax.text(
    0.80,
    0.06,
    "........ Indirect",
    horizontalalignment="left",
    transform=ax.transAxes,
    fontdict=font,
)

ax = plt.gca()
ax.margins(0.08)
plt.axis("off")
plt.tight_layout()
plt.show()

Fontconfig warning: "/usr/share/fontconfig/conf.avail/05-reset-dirs-sample.conf", line 6: unknown element "reset-dirs"
Unable to revert mtime: /usr/share/fonts
Unable to revert mtime: /usr/share/fonts/truetype
Unable to revert mtime: /usr/share/fonts/truetype/dejavu
Unable to revert mtime: /usr/share/fonts/truetype/lato
Unable to revert mtime: /usr/share/fonts/truetype/liberation
Unable to revert mtime: /usr/share/fonts/truetype/noto
Error: remove_overlap: Graphviz not built with triangulation library
findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

findfont: Font family 'Helvetica' not found.

Show code cell source

Hide code cell source

import squarify

# Count the number of projects depending on each project
depending = {model: 0 for model in models}
for model, dependents in model_connectivity.items():
    for d in dependents:
        depending[d[0]] = depending[d[0]] + 1

non_zero = {k:v for k,v in depending.items() if v > 0}
squarify.plot(list(non_zero.values()), label=list(non_zero.keys()))
plt.axis("off")

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[4], line 10
      6     for d in dependents:
      7         depending[d[0]] = depending[d[0]] + 1
      8 
      9 non_zero = {k:v for k,v in depending.items() if v > 0}
---> 10 squarify.plot(list(non_zero.values()), label=list(non_zero.keys()))
     11 plt.axis("off")

File /opt/hostedtoolcache/Python/3.13.14/x64/lib/python3.13/site-packages/squarify/__init__.py:231, in plot(sizes, norm_x, norm_y, color, label, value, ax, pad, bar_kwargs, text_kwargs, **kwargs)
    228     import matplotlib.cm
    229     import random
--> 231     cmap = matplotlib.cm.get_cmap()
    232     color = [cmap(random.random()) for i in range(len(sizes))]
    234 if bar_kwargs is None:

AttributeError: module 'matplotlib.cm' has no attribute 'get_cmap'