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- import pandas as pd
- import numpy as np
- import tomotopy as tp
- import matplotlib
- from matplotlib import pyplot as plt
- matplotlib.use("pgf")
- matplotlib.rcParams.update(
- {
- "pgf.texsystem": "xelatex",
- "font.family": "serif",
- "font.serif": "Times New Roman",
- "text.usetex": True,
- "pgf.rcfonts": False,
- }
- )
- import seaborn as sns
- import textwrap
- def reverse_colourmap(cmap, name):
- """
- In:
- cmap, name
- Out:
- my_cmap_r
- Explanation:
- t[0] goes from 0 to 1
- row i: x y0 y1 -> t[0] t[1] t[2]
- /
- /
- row i+1: x y0 y1 -> t[n] t[1] t[2]
- so the inverse should do the same:
- row i+1: x y1 y0 -> 1-t[0] t[2] t[1]
- /
- /
- row i: x y1 y0 -> 1-t[n] t[2] t[1]
- """
- reverse = []
- k = []
- for key in cmap._segmentdata:
- k.append(key)
- channel = cmap._segmentdata[key]
- data = []
- for t in channel:
- data.append((1-t[0],t[2],t[1]))
- reverse.append(sorted(data))
- LinearL = dict(zip(k,reverse))
- my_cmap_r = matplotlib.colors.LinearSegmentedColormap(name, LinearL)
- return my_cmap_r
- orig_cmap = matplotlib.cm.RdBu
- mdl = tp.CTModel.load("output/hep-ct-75-0.1-0.001-130000-20/model")
- correlations = mdl.get_correlations()
- usages = pd.read_csv('output/supersymmetry_usages.csv')
- usages = usages.groupby("term").agg(topic=("topic", lambda x: x.tolist()))
- topics = usages.loc["supersymmetry"]["topic"] + [t for t in usages.loc["susy"]["topic"] if t not in usages.loc["supersymmetry"]["topic"]]
- descriptions = pd.read_csv("output/hep-ct-75-0.1-0.001-130000-20/descriptions.csv")
- labels = descriptions.loc[topics]["description"].tolist()
- submatrix = correlations[topics,:][:,topics]
- for i in range(submatrix.shape[0]):
- submatrix[i,i] = np.nan
- w = textwrap.TextWrapper(width=20,break_long_words=False,replace_whitespace=False)
- wlabels = ["\n".join(words) for words in map(w.wrap, labels)]
- # shrunk_cmap = shiftedColorMap(orig_cmap, start=-submatrix.max(), midpoint=0, stop=+submatrix.max(), name='shrunk')
- reverse_cmap = reverse_colourmap(orig_cmap, "reverse")
- sns.heatmap(submatrix, xticklabels = wlabels, yticklabels = wlabels, annot=True, fmt=".2f", cmap=reverse_cmap, center=0, vmin=-1, vmax=1)
- plt.savefig("plots/susy_correlations.pdf", bbox_inches="tight")
- plt.savefig("plots/susy_correlations.pgf", bbox_inches="tight")
- plt.savefig("plots/susy_correlations.eps", bbox_inches="tight")
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