Jean-Sébastien Caux
65 lines
1.5 KiB
Python
Executable File
65 lines
1.5 KiB
Python
Executable File
#! /usr/bin/env python
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"""
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Plot the time-dependent density expectation value in Lieb-Liniger (n-hole wavepacket).
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Setup:
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- ensure matplotlib and numpy are installed in your python environment
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- make this file executable (chmod +x plot_animated.py)
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Usage: ./plot_animated.py [rho file name]
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"""
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import math
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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import numpy as np
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import sys
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filename = str(sys.argv[1])
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data = filename.rpartition('/')[2]
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data = data.partition('c_')[2]
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c = float(data.partition('_')[0])
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data = data.partition('N_')[2]
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N = int(data.partition('_')[0])
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L = float(N)
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data = data.partition('nholes_')[2]
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nholes = int(data.partition('_')[0])
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data = data.partition('width_')[2]
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width = int(data.partition('_')[0])
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data = data.partition('offset_')[2]
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offset = int(data.partition('_')[0])
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data = data.partition('tmax_')[2].partition('.rhoxt')[0]
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tmax = float(data.partition('_')[0])
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x = np.loadtxt(filename.replace('.rhoxt','.x'), unpack=True)
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Nx = x.size
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rho = np.loadtxt(filename, unpack=True)
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Nt = int(rho.size/rho[0].size)
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dt = tmax/(Nt-1)
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fig, ax = plt.subplots()
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artists = []
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for i in range(Nt):
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container = ax.plot(x, rho[i], '.', markersize=4, color='blue')
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artists.append(container)
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ani = animation.ArtistAnimation(fig=fig, artists=artists, interval=100)
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plt.xlabel('x')
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plt.ylabel(r'$\rho(x, t)$')
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plt.title(f"Density expectation value\n{c=}, {N=}, nh={nholes}, w={width}, o={offset}")
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ani.save(filename.replace("rhoxt", "mp4"))
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plt.show()
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