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@@ -2,7 +2,8 @@ import numpy as np
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import pandas as pd
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import plotly.graph_objects as go
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-import plotly.io as pio
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+import plotly.io as pio
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+
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pio.kaleido.scope.mathjax = None
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import seaborn as sns
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@@ -14,6 +15,9 @@ parser = argparse.ArgumentParser()
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parser.add_argument("--input")
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parser.add_argument("--suffix")
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parser.add_argument("--compact", action="store_true", default=False)
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+parser.add_argument("--transparent", action="store_true", default=False)
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+parser.add_argument("--highlight-in", default=list(), nargs="+")
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+parser.add_argument("--highlight-out", default=list(), nargs="+")
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args = parser.parse_args()
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samples = np.load(opj(args.input, f"ei_samples_{args.suffix}.npz"))
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@@ -34,20 +38,39 @@ print(colors)
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total_incoming = samples["counts"].mean(axis=0).sum(axis=1)
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total_outcoming = samples["counts"].mean(axis=0).sum(axis=0)
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-incoming_labels = [f"{topics[i]} ({100*total_incoming[i]:.0f}%)" for i in range(n_topics)]
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-outcoming_labels = [f"{topics[i]} ({100*total_outcoming[i]:.0f}%)" for i in range(n_topics)]
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+incoming_labels = [
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+ f"{topics[i]} ({100*total_incoming[i]:.0f}%)" for i in range(n_topics)
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+]
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+outcoming_labels = [
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+ f"{topics[i]} ({100*total_outcoming[i]:.0f}%)" for i in range(n_topics)
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+]
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for i in range(n_topics):
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for j in range(n_topics):
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v = samples["counts"][:, i, j].mean()
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- x = samples["counts"].mean(axis=0)[i,:].sum()
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- y = samples["counts"].mean(axis=0)[:,j].sum()
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+ x = samples["counts"].mean(axis=0)[i, :].sum()
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+ y = samples["counts"].mean(axis=0)[:, j].sum()
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value.append(v)
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source.append(i)
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target.append(j + n_topics)
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- color.append("rgba(100,100,100,0.2)" if v >= x*y else "rgba(200,200,200,0.02)")
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+
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+ highlight = (
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+ (len(args.highlight_in) > 0 and topics[i] in args.highlight_in)
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+ or (len(args.highlight_out) > 0 and topics[j] in args.highlight_out)
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+ or (
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+ (not args.transparent)
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+ and (len(args.highlight_in + args.highlight_out) == 0)
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+ )
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+ ) and (v >= x * y)
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+
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+ # print(topics[i], topics[j])
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+ # print((len(args.highlight_in) > 0 and topics[i] in args.highlight_in))
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+ # print((len(args.highlight_out) > 0 and topics[j] in args.highlight_out))
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+ # print((len(args.highlight_in + args.highlight_out) == 0))
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+
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+ color.append("rgba(100,100,100,0.3)" if highlight else "rgba(200,200,200,0.02)")
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fig = go.Figure(
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data=[
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@@ -75,53 +98,80 @@ fig.update_layout(
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width=650 if args.compact else 800,
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height=600,
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)
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-#fig.show()
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-fig.write_image(opj(args.input, f"sankey_control_{args.suffix}{'_compact' if args.compact else ''}.pdf"), width=650 if args.compact else 800, height=600)
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-
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-transfers = pd.DataFrame([
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- {
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- "from": topics[i],
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- "to": topics[j],
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- "magnitude": 100*samples["counts"].mean(axis=0)[i,j],
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- "ratio": samples["counts"].mean(axis=0)[i,j]/samples["counts"].mean(axis=0)[i,:].sum()
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- }
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- for i in range(n_topics)
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- for j in range(n_topics)
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-])
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-
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-
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-latex = transfers[transfers["from"]!=transfers["to"]].sort_values("magnitude", ascending=False).head(10).to_latex(
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- columns=["from", "to", "magnitude"],
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- header=["Origin research area", "Target research area", "Magnitude"],
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- index=False,
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- multirow=True,
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- multicolumn=True,
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- column_format='b{0.4\\textwidth}|b{0.4\\textwidth}|c',
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- escape=False,
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- float_format=lambda x: f"{x:.2f}",
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- caption="Largest transfers across research areas.",
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- label="table:largest_transfers"
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+# fig.show()
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+
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+highlights = f"{'_'.join(args.highlight_in)}{'_'.join(args.highlight_out)}".replace(
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+ "/", "_"
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+).replace("&", "_").replace(" ", "").lower()
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+if len(highlights):
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+ highlights = f"_{highlights}"
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+
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+fig.write_image(
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+ opj(
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+ args.input,
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+ f"sankey_control_{args.suffix}{'_compact' if args.compact else ''}{'_transparent' if args.transparent else ''}{highlights}.pdf",
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+ ),
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+ width=650 if args.compact else 800,
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+ height=600,
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+)
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+
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+transfers = pd.DataFrame(
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+ [
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+ {
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+ "from": topics[i],
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+ "to": topics[j],
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+ "magnitude": 100 * samples["counts"].mean(axis=0)[i, j],
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+ "ratio": samples["counts"].mean(axis=0)[i, j]
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+ / samples["counts"].mean(axis=0)[i, :].sum(),
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+ }
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+ for i in range(n_topics)
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+ for j in range(n_topics)
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+ ]
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+)
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+
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+
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+latex = (
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+ transfers[transfers["from"] != transfers["to"]]
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+ .sort_values("magnitude", ascending=False)
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+ .head(10)
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+ .to_latex(
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+ columns=["from", "to", "magnitude"],
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+ header=["Origin research area", "Target research area", "Magnitude"],
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+ index=False,
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+ multirow=True,
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+ multicolumn=True,
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+ column_format="b{0.4\\textwidth}|b{0.4\\textwidth}|c",
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+ escape=False,
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+ float_format=lambda x: f"{x:.2f}",
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+ caption="Largest transfers across research areas.",
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+ label="table:largest_transfers",
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+ )
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)
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-latex = latex.replace('\\\\\n', '\\\\ \\hline\n')
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+latex = latex.replace("\\\\\n", "\\\\ \\hline\n")
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with open(opj(args.input, "largest_transfers.tex"), "w+") as fp:
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fp.write(latex)
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-latex = transfers[transfers["from"]==transfers["to"]].sort_values("ratio", ascending=False).head(10).to_latex(
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- columns=["from", "ratio"],
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- header=["Research area", "Conservatism"],
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- index=False,
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- multirow=True,
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- multicolumn=True,
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- column_format='b{0.4\\textwidth}|b{0.4\\textwidth}|c',
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- escape=False,
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- float_format=lambda x: f"{x:.2f}",
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- caption="Most conservative research areas.",
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- label="table:most_conservative"
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+latex = (
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+ transfers[transfers["from"] == transfers["to"]]
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+ .sort_values("ratio", ascending=False)
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+ .head(10)
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+ .to_latex(
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+ columns=["from", "ratio"],
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+ header=["Research area", "Conservatism"],
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+ index=False,
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+ multirow=True,
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+ multicolumn=True,
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+ column_format="b{0.4\\textwidth}|b{0.4\\textwidth}|c",
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+ escape=False,
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+ float_format=lambda x: f"{x:.2f}",
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+ caption="Most conservative research areas.",
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+ label="table:most_conservative",
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+ )
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)
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-latex = latex.replace('\\\\\n', '\\\\ \\hline\n')
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+latex = latex.replace("\\\\\n", "\\\\ \\hline\n")
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with open(opj(args.input, "most_conservative.tex"), "w+") as fp:
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fp.write(latex)
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