82 lines
2.1 KiB
Python
82 lines
2.1 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import glob
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def eval_data(file):
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data = np.load(file)
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percentage = data["percentage"]
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out = data["out"]
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out = np.array(out)
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print(out.shape)
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fig, all_axs = plt.subplots(2, out.shape[0])
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axs = all_axs[0, :]
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axs2 = all_axs[1, :]
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for o, ax, ax2, lab in zip(out, axs, axs2, ["rutile", "mono_twin", "mono"]):
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# ax.plot(percentage, o/np.max(o, axis=0))
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ax.plot(percentage, o/o[0])
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# ax.plot(percentage, o)
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o = np.mean(o, axis=1)
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o = o/o[0]
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ax2.plot(percentage, o)
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ax2.plot([0, 1], [o[0], o[-1]], "k:")
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ax.set_title(lab)
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if "ising" in file:
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fig.suptitle("Ising")
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else:
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fig.suptitle("Random")
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fig.savefig(f"{file}.png")
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def parse_lists(out):
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lists = []
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for o in out:
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lists.append(np.stack(o))
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max = 0
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for l in lists:
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print(l.shape)
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if max < l.shape[1]:
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max = l.shape[1]
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lists = [np.pad(l, ((0, 0), (0, max-l.shape[1]))) for l in lists]
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for l in lists:
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print(l.shape)
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return np.stack(lists)
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def eval_data_print(file):
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data = np.load(file, allow_pickle=True)
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percentage = data["percentage"]
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out = parse_lists(data["out"])
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out = np.array(out)
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print(out.shape)
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out = out[[0, 2], :, :]
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print(out.shape)
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fig, all_axs = plt.subplots(2, out.shape[0])
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axs = all_axs[0, :]
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axs2 = all_axs[1, :]
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for o, ax, ax2, lab in zip(out, axs, axs2, ["rutile", "monoclinic", "mono"]):
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# ax.plot(percentage, o/np.max(o, axis=0))
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ax.plot(percentage, o/o[0])
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# ax.plot(percentage, o)
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o = np.mean(o, axis=1)
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o = o/o[0]
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ax2.plot(percentage, o)
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ax2.plot([0, 1], [o[0], o[-1]], "k:")
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ax.set_title(lab)
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if "ising" in file:
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fig.suptitle("Ising")
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else:
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fig.suptitle("Random")
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for ax in all_axs.flatten():
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ax.set_xlabel("Rutile Phase")
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ax.set_ylabel("Normalized Intensity")
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plt.tight_layout()
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if __name__ == "__main__":
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for f in glob.glob("*.npz"):
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eval_data_print(f)
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plt.show()
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