adaptation_specialization_material/plots/ei_sankey.py

import numpy as np
import pandas as pd

import plotly.graph_objects as go
import plotly.io as pio

pio.kaleido.scope.mathjax = None

import seaborn as sns

import argparse
from os.path import join as opj

parser = argparse.ArgumentParser()
parser.add_argument("--input")
parser.add_argument("--suffix")
parser.add_argument("--compact", action="store_true", default=False)
parser.add_argument("--transparent", action="store_true", default=False)
parser.add_argument("--highlight-in", default=list(), nargs="+")
parser.add_argument("--highlight-out", default=list(), nargs="+")
parser.add_argument("--mle", action="store_true", default=False)
args = parser.parse_args()

format = "mle" if args.mle else "samples"
samples = np.load(opj(args.input, f"ei_{format}_{args.suffix}.npz"))
topics = pd.read_csv(opj(args.input, "topics.csv"))
topics["label"] = topics["label"].str.replace("\\&", "&", regex=False)
topics = topics[~topics["label"].str.contains("Junk")]["label"].tolist()

if args.mle:
    n_topics = samples["counts"].shape[0]
else:
    n_topics = samples["counts"].shape[1]

source = []
target = []
value = []
color = []
colors = sns.color_palette("hls", n_topics).as_hex()

print(colors)

if args.mle:
    total_incoming = samples["counts"].sum(axis=1)
    total_outcoming = samples["counts"].sum(axis=0)
else:
    total_incoming = samples["counts"].mean(axis=0).sum(axis=1)
    total_outcoming = samples["counts"].mean(axis=0).sum(axis=0)

incoming_labels = [
    f"{topics[i]} ({100*total_incoming[i]:.0f}%)" for i in range(n_topics)
]
outcoming_labels = [
    f"{topics[i]} ({100*total_outcoming[i]:.0f}%)" for i in range(n_topics)
]

for i in range(n_topics):
    for j in range(n_topics):
        if args.mle:
            v = samples["counts"][i, j]
            x = samples["counts"][i, :].sum()
            y = samples["counts"][:, j].sum()
        else:
            v = samples["counts"][:, i, j].mean()
            x = samples["counts"].mean(axis=0)[i, :].sum()
            y = samples["counts"].mean(axis=0)[:, j].sum()

        value.append(v)
        source.append(i)
        target.append(j + n_topics)

        highlight = (
            (len(args.highlight_in) > 0 and topics[i] in args.highlight_in)
            or (len(args.highlight_out) > 0 and topics[j] in args.highlight_out)
            or (
                (not args.transparent)
                and (len(args.highlight_in + args.highlight_out) == 0)
            )
        ) and (v >= x * y)

        # print(topics[i], topics[j])
        # print((len(args.highlight_in) > 0 and topics[i] in args.highlight_in))
        # print((len(args.highlight_out) > 0 and topics[j] in args.highlight_out))
        # print((len(args.highlight_in + args.highlight_out) == 0))

        color.append("rgba(100,100,100,0.3)" if highlight else "rgba(200,200,200,0.02)")

fig = go.Figure(
    data=[
        go.Sankey(
            node=dict(
                pad=7,
                thickness=20,
                line=dict(color="black", width=0.5),
                label=topics + topics,
                color=colors + colors,
            ),
            link=dict(
                source=source,  # indices correspond to labels, eg A1, A2, A1, B1, ...
                target=target,
                value=value,
                color=color,
            ),
        )
    ]
)

fig.update_layout(font_size=13)
fig.update_layout(
    autosize=False,
    width=650 if args.compact else 800,
    height=600,
)
# fig.show()

highlights = f"{'_'.join(args.highlight_in)}{'_'.join(args.highlight_out)}".replace(
    "/", "_"
).replace("&", "_").replace(" ", "").lower()
if len(highlights):
    highlights = f"_{highlights}"

fig.write_image(
    opj(
        args.input,
        f"sankey_control_{args.suffix}{'_compact' if args.compact else ''}{'_transparent' if args.transparent else ''}{highlights}.pdf",
    ),
    width=650 if args.compact else 800,
    height=600,
)

transfers = pd.DataFrame(
    [
        {
            "from": topics[i],
            "to": topics[j],
            "magnitude": 100 * samples["counts"].mean(axis=0)[i, j],
            "ratio": samples["counts"].mean(axis=0)[i, j]
            / samples["counts"].mean(axis=0)[i, :].sum(),
        }
        for i in range(n_topics)
        for j in range(n_topics)
    ]
)


latex = (
    transfers[transfers["from"] != transfers["to"]]
    .sort_values("magnitude", ascending=False)
    .head(10)
    .to_latex(
        columns=["from", "to", "magnitude"],
        header=["Origin research area", "Target research area", "Magnitude"],
        index=False,
        multirow=True,
        multicolumn=True,
        column_format="b{0.4\\textwidth}|b{0.4\\textwidth}|c",
        escape=False,
        float_format=lambda x: f"{x:.2f}",
        caption="Largest transfers across research areas.",
        label="table:largest_transfers",
    )
)

latex = latex.replace("\\\\\n", "\\\\ \\hline\n")

with open(opj(args.input, "largest_transfers.tex"), "w+") as fp:
    fp.write(latex)

latex = (
    transfers[transfers["from"] == transfers["to"]]
    .sort_values("ratio", ascending=False)
    .head(10)
    .to_latex(
        columns=["from", "ratio"],
        header=["Research area", "Conservatism"],
        index=False,
        multirow=True,
        multicolumn=True,
        column_format="b{0.4\\textwidth}|b{0.4\\textwidth}|c",
        escape=False,
        float_format=lambda x: f"{x:.2f}",
        caption="Most conservative research areas.",
        label="table:most_conservative",
    )
)

latex = latex.replace("\\\\\n", "\\\\ \\hline\n")

with open(opj(args.input, "most_conservative.tex"), "w+") as fp:
    fp.write(latex)