129 lines
3.3 KiB
Python
129 lines
3.3 KiB
Python
import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import glob
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import scipy.interpolate as ip
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plt.style.use(["style", "colors", "two_column"])
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def check_percentage(p1, p2):
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plt.figure()
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plt.plot(p1, p2)
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def merge(files):
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merge = []
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plt.figure()
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for file in files:
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print(file)
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data = np.load(file, allow_pickle=True)
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old_percentage = data["percentage"]
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w_percentage = data["w_percentage"]
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# check_percentage(old_percentage, w_percentage)
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percentage = old_percentage
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out = []
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for o in ["out_1", "out_2", "out_3", "out_4"]:
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out.append(np.array(data[o]))
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print(out)
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out = np.array(out)[:, :, 0]
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summe = np.max(np.sum(out, axis=0))
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out = out / summe
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merge.append(out)
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plt.plot(out[0, :], "r")
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plt.plot(out[3, :], "b")
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plt.plot(out[2, :], "g")
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all = sum(merge)
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summe = np.max(np.sum(all, axis=0))
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all = all / summe
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plt.plot(all[0, :], "k")
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plt.plot(all[3, :], "k")
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plt.plot(all[2, :], "k")
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percentage = 1-percentage
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return percentage, all
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def debug(percentage, out):
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plt.figure()
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for o in out:
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plt.plot(percentage, o)
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plt.plot(percentage, out[0, :], "k")
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plt.plot(percentage, out[3, :], "k")
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plt.plot(percentage, out[2, :], "k")
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def stacked_plot(percentage, out, title=""):
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plt.figure()
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stacks = plt.stackplot(percentage, out[[0, 3, 1, 2]], colors=[
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"w"], ls="solid", ec="k")
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hatches = ["/", "", "\\", "\\"]
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for stack, hatch in zip(stacks, hatches):
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stack.set_hatch(hatch)
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plt.xlabel("Metallic Phase (%)")
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plt.ylabel("normalized Intensity ")
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plt.ylim([0.4, 1])
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plt.xlim([0., 1])
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plt.tight_layout()
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plt.text(0.1, 0.9, "monoclinic", backgroundcolor="w")
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plt.text(0.6, 0.5, "rutile", backgroundcolor="w")
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plt.text(0.35, 0.75, "diffusive", backgroundcolor="w")
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plt.title(title)
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plt.savefig("intens.png")
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plt.savefig("intens.pdf")
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def time_scale(p, o):
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rut_perc = o[0]
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rut_perc = rut_perc - np.min(rut_perc)
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rut_perc /= np.max(rut_perc)
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mono_perc = -o[2]
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mono_perc = mono_perc - np.min(mono_perc)
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mono_perc /= np.max(mono_perc)
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# cs_rut = ip.CubicSpline(p[::-1], rut_perc[::-1])
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# cs_mono = ip.CubicSpline(p[::-1], mono_perc[::-1])
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cs_rut = ip.interp1d(p[::-1], rut_perc[::-1])
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cs_mono = ip.interp1d(p[::-1], mono_perc[::-1])
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plt.figure()
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ph = np.linspace(0.01, 0.99, 100)
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plt.plot(ph, cs_rut(ph))
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plt.plot(ph, cs_mono(ph))
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time = np.linspace(0.01, 3, 1000)
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phy_phase = np.exp(-time)
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rut_phase = cs_rut(phy_phase)
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mono_phase = cs_mono(phy_phase)
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plt.figure()
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plt.plot(time, phy_phase, "k:", label="corr.")
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plt.plot(time, rut_phase, label="rut.")
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plt.plot(time, mono_phase, label="mono")
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plt.xlabel("time (a.u.)")
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plt.ylabel("Metallic Phase (%)")
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plt.legend()
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plt.tight_layout()
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plt.savefig("timescale.png")
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plt.savefig("timescale.pdf")
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def read_file(file):
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files = np.load("./merged.npz")
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p = files["p"]
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o = files["o"]
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return p, o
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if __name__ == "__main__":
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p, o = merge(sys.argv[1:])
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np.savez("merged.npz", p=p, o=o)
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# eval_data_print(f)
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stacked_plot(p, o)
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time_scale(p, o)
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plt.show()
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