192 lines
5.0 KiB
Python
192 lines
5.0 KiB
Python
from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy.stats import linregress
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def plot_phase_transition_alt(indir, outdir):
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files = [
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"size_20.txt",
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"size_40.txt",
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"size_60.txt",
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"size_80.txt",
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"size_100.txt",
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"size_500.txt",
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]
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labels = ["L = 20", "L = 40", "L = 60", "L = 80", "L = 100", "L = 500"]
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figure1, ax1 = plt.subplots()
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figure2, ax2 = plt.subplots()
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figure3, ax3 = plt.subplots()
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figure4, ax4 = plt.subplots()
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figure5, ax5 = plt.subplots()
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# For linear regression
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L = []
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Tc = []
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size = 20
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for file, label in zip(files, labels):
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t = []
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e = []
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m = []
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CV = []
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X = []
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# Append the lattice size
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L.append(size)
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size += 20
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with open(Path(indir, file)) as f:
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lines = f.readlines()
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for line in lines:
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l = line.strip().split(",")
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t.append(float(l[0]))
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e.append(float(l[1]))
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m.append(float(l[2]))
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CV.append(float(l[3]))
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X.append(float(l[4]))
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# Append the critical temp for the current lattice size
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Tc.append(t[X.index(max(X))])
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ax1.plot(t, e, label=label)
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ax2.plot(t, m, label=label)
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ax3.plot(t, CV, label=label)
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ax4.plot(t, X, label=label)
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inv_L = list(map(lambda x: 1 / x, L))
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# Attempt linear regression
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x = np.linspace(0, 1 / 20, 1001)
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regression = linregress(inv_L, Tc)
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f = lambda x: regression[0] * x + regression[1]
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ax5.scatter(inv_L, Tc)
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ax5.plot(x, f(x), label=f"m = {regression[0]}, i = {regression[1]}")
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figure1.legend()
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figure2.legend()
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figure3.legend()
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figure4.legend()
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figure5.legend()
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figure1.savefig(Path(outdir, "energy.pdf"))
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figure2.savefig(Path(outdir, "magnetization.pdf"))
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figure3.savefig(Path(outdir, "heat_capacity.pdf"))
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figure4.savefig(Path(outdir, "susceptibility.pdf"))
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figure5.savefig(Path(outdir, "linreg.pdf"))
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plt.close(figure1)
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plt.close(figure2)
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plt.close(figure3)
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plt.close(figure4)
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plt.close(figure5)
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def plot_phase_transition(indir, outdir):
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files = [
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"size_20.txt",
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"size_40.txt",
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"size_60.txt",
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"size_80.txt",
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"size_100.txt",
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]
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labels = [
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"L = 20",
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"L = 40",
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"L = 60",
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"L = 80",
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"L = 100",
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]
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figure1, ax1 = plt.subplots()
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figure2, ax2 = plt.subplots()
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figure3, ax3 = plt.subplots()
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figure4, ax4 = plt.subplots()
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figure5, ax5 = plt.subplots()
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# For linear regression
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L = []
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Tc = []
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size = 20
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for file, label in zip(files, labels):
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t = []
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e = []
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m = []
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CV = []
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X = []
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# Append the lattice size
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L.append(size)
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size += 20
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with open(Path(indir, file)) as f:
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lines = f.readlines()
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for line in lines:
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l = line.strip().split(",")
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t.append(float(l[0]))
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e.append(float(l[1]))
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m.append(float(l[2]))
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CV.append(float(l[3]))
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X.append(float(l[4]))
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# Append the critical temp for the current lattice size
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Tc.append(t[X.index(max(X))])
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ax1.plot(t, e, label=label)
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ax2.plot(t, m, label=label)
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ax3.plot(t, CV, label=label)
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ax4.plot(t, X, label=label)
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inv_L = list(map(lambda x: 1 / x, L))
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# Attempt linear regression
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x = np.linspace(0, 1 / 20, 1001)
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regression = linregress(inv_L, Tc)
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f = lambda x: regression[0] * x + regression[1]
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ax5.scatter(inv_L, Tc)
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ax5.plot(x, f(x), label=f"m = {regression[0]}, i = {regression[1]}")
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figure1.legend()
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figure2.legend()
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figure3.legend()
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figure4.legend()
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figure5.legend()
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figure1.savefig(Path(outdir, "energy.pdf"))
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figure2.savefig(Path(outdir, "magnetization.pdf"))
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figure3.savefig(Path(outdir, "heat_capacity.pdf"))
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figure4.savefig(Path(outdir, "susceptibility.pdf"))
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figure5.savefig(Path(outdir, "linreg.pdf"))
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plt.close(figure1)
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plt.close(figure2)
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plt.close(figure3)
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plt.close(figure4)
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plt.close(figure5)
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if __name__ == "__main__":
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plot_phase_transition_alt(
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"fox_output/phase_transition/wide/10M/",
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"latex/images/phase_transition/fox/wide/10M/",
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)
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plot_phase_transition(
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"fox_output/phase_transition/wide/1M/",
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"latex/images/phase_transition/fox/wide/1M/",
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)
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plot_phase_transition(
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"fox_output/phase_transition/narrow/10M/",
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"latex/images/phase_transition/fox/narrow/10M/",
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)
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plot_phase_transition(
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"output/phase_transition/", "latex/images/phase_transition/hp/"
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)
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plot_phase_transition(
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"output/phase_transition/", "latex/images/phase_transition/hp/"
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)
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plot_phase_transition(
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"output/phase_transition/",
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"latex/images/phase_transition/hp/",
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)
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