def freeze_frames(num_frames, current_frame):
"""Freeze frames for num_frames
:num_frames: Int - Number of frames frozen
:current_frame: Int
:returns: Int - current_frame
"""
for _ in range(1, num_frames + 1):
current_frame += 1
print("\n====================================")
print(f"On freeze frame: {current_frame}")
illu.save_figure(f"frames/ising/{current_frame:03}")
pdf_frames.append(f"plots/frames/ising/{current_frame:03}.pdf")
return current_frame
:sites: 1xn array of Site Objects
:returns: [[min_x, max_x], [min_y, max_y]]
"""
pass
if __name__ == '__main__':
fig, ax = plt.subplots()
system_size = 128
dimension = 2
# Color of flux
c = const.COLOR_MAP["black"]
# plot_setup(system_size)
# Load graph
sites = ani.process_graph_file(f"ising/large_cluster/largest_cluster",
dimension)
ax.axis('off')
# movegraph = 128 * 50
movegraph = 90
# Plot all flux
for site in sites:
site.weights = [w / 2 for w in site.weights]
site.i = site.i + movegraph
site.neighbours = [n + movegraph for n in site.neighbours]
site.plot_arrows_to_neighbours(ax, system_size, color=c, style='-')
illu.save_figure(f"largest_cluster_testing_nolattice")
x, y = 6, 6
s = 2.5
color = "#d64d4d"
figname = "percolatingFlux"
illu.plot_lattice(x, y, size=1)
# Simple loop cluster
illu.fill_with_arrows([2, 3], [5, 3], size=s, color=color)
illu.fill_with_arrows([5, 3], [5, 5], size=s, color=color)
illu.fill_with_arrows([5, 5], [4, 5], size=s, color=color)
illu.fill_with_arrows([4, 5], [4, 4], size=s, color=color)
illu.fill_with_arrows([4, 4], [2, 4], size=s, color=color)
illu.fill_with_arrows([2, 4], [2, 3], size=s, color=color)
# Loop cluster with a link with 2 flux quanta
illu.fill_with_arrows([0, 2], [1, 2], size=s, color=color)
illu.fill_with_arrows([1, 2], [1, 3], size=s, color=color)
illu.fill_with_arrows([1, 3], [0, 3], size=2.5 * s, color=color)
illu.fill_with_arrows([0, 3], [0, 4], size=s, color=color)
illu.fill_with_arrows([0, 4], [1, 4], size=s, color=color)
illu.fill_with_arrows([1, 4], [1, 3], size=s, color=color)
illu.fill_with_arrows([0, 3], [0, 2], size=s, color=color)
# Percolating cluster
illu.fill_with_arrows([-1, 1], [x + 1, 1], size=s, color=color)
# plt.show()
illu.save_figure(figname)
# plt.savefig(f"./plots/{figname}.{form}",\
# bbox_inches='tight', format=form, dpi=1200)
Author: Simon Rydell
Python Version: 3.7
'''
import matplotlib.pyplot as plt
import helpers.illustrations as illu
fig = plt.figure()
ax = fig.add_subplot(111, aspect='equal')
circle = plt.Circle((0.5, 0.5), 0.3, color="#202020")
ax.add_artist(circle)
plt.grid(color='k', linestyle='-', linewidth=1)
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.annotate(r"$\}$",
fontsize=60,
rotation=-90,
xy=(0.2154, 0.00),
xycoords='figure fraction')
plt.annotate(r"$\epsilon$",
fontsize=20,
xy=(0.265, 0.),
xycoords='figure fraction')
illu.save_figure("circle_within_boxes")
# plt.show()
simulation_data = process_file("./data/loop_lengths128x128.txt")
clean_processed_data(simulation_data, exclude_system_size=[])
# plot_syssize_vs_looplength(simulation_data)
errorbar_data = {}
for size in simulation_data:
errorbar_data[size] = [
np.average(simulation_data[size]),
np.std(simulation_data[size]),
len(simulation_data[size])
]
calc.plot_errorbars(
errorbar_data,
# label=r"$\bar{D}_H \pm \sigma_{\bar{D}_H}$",
color=const.COLOR_MAP["black"])
plot_syssize_vs_fit(simulation_data)
plt.title("Maximum loop length on a 2D Ising lattice")
plt.xlabel("Linear system size")
plt.ylabel("Loop length")
plt.legend()
savefig = False
if savefig:
illu.save_figure(f"maximum_loop_length_for_2D_Ising")
plt.show()
ax.scatter(*site.convert_to_xyz(site.i, system_size),
c=const.COLOR_MAP["red"],
s=3)
# site.plot_arrows_to_neighbours(ax, system_size, color=c)
pdf_frames = []
theta = 30
current_frame = -1
max_angle = 360
for angle in np.arange(0, max_angle, 1):
current_frame += 1
print("\n=========================")
print(f"On frame number: {current_frame}")
# Set viewing angle
ax.view_init(theta, angle)
illu.save_figure(f"frames/xy/largest_cluster/{current_frame:03}")
pdf_frames.append(
f"plots/frames/xy/largest_cluster/{current_frame:03}.pdf")
# ax.cla()
output_file = "plots/3dxy_largest_cluster_test"
print(f"Merging the pdfs into {output_file}.pdf")
ani.merge_pdfs(pdf_frames, output_file)
# print(f"Creating the gif {output_file}.gif")
# ani.pdf2gif(output_file, output_file)
# colors = ["#966842", "#f44747", "#eedc31", "#7fdb6a", "#0e68ce"]
plt.loglog(xdata, fit_function(xdata, *opt_pars),\
c="#0e68ce",\
label=fr"$\propto L^{{ {opt_pars[1]:.2f} \pm {stderr_pars[opt_pars[1]]:.3f} }}$")
# plt.loglog(xdata, fit_function(xdata, *[0.51, .25]),\
# c="#7fdb6a", label=r"$\propto L^{{ 2 - \eta_{Ising} }}$")
# plt.loglog(xdata, fit_function(xdata, *[0.35, 0]),\
# c="#f44747", label=r"$\propto L^{{ 2 }}$")
print(f"exponent = {opt_pars[1]:.6} +- {stderr_pars[opt_pars[1]]:.4}")
if __name__ == '__main__':
simulation_data = process_file("./data/total_loop_length.txt")
clean_processed_data(simulation_data, exclude_system_size=[])
# plot_hist_of_error(simulation_data)
plot_setup()
plot_syssize_vs_looplength(simulation_data)
plot_syssize_vs_fit(simulation_data)
plt.legend()
savefig = False
if savefig:
illu.save_figure("total_loop_length128x128Ising")
# plt.savefig(f"./plots/total_loop_length128x128Ising.png",\
# bbox_inches='tight')
plt.show()
for frame in range(227, last_simulation_frame + 1):
# for frame in range(348, last_simulation_frame + 1):
print("\n====================================")
print(f"On frame: {frame}")
plot_setup(system_size)
# Load graph
sites = ani.process_graph_file(f"ising/{frame}", dimension)
# Plot all flux
for site in sites:
site.plot_arrows_to_neighbours(ax, system_size, color=c, style='-')
illu.save_figure(f"frames/ising/{frame:03}")
pdf_frames.append(f"plots/frames/ising/{frame:03}.pdf")
if frame == last_simulation_frame:
frame = freeze_frames(stop_frames, frame)
# Clear axis for next plot
ax.cla()
largest_worm = calc.process_file("data/animation/ising/largest_worm",
"xxxx", r"(\d*)")
plot_setup(system_size)
plot_largest_worm(largest_worm, sites, system_size, ax)
print("Freeze frames for largest_worm")
import matplotlib.pyplot as plt
import helpers.illustrations as illu
import matplotlib.patches as patches
import random
fig = plt.figure()
ax = fig.add_subplot(111, aspect='equal')
ax.set_xticklabels([])
ax.set_yticklabels([])
# 4 small boxes
l = 30
for x in range(l):
for y in range(l):
xy = (x, y)
c = random.choice([
"#202020", "#202020", "#202020", "#FFFFFF", "#FFFFFF", "#FFFFFF",
"#FFFFFF"
])
ax.add_patch(patches.Rectangle(xy, 1, 1, facecolor=c, fill=True))
ax.set_xlim([-1, l + 1])
ax.set_ylim([-1, l + 1])
ax.axis('off')
# plt.grid(color='k', linestyle='-', linewidth=1)
illu.save_figure("hk_grid")
# plt.show()
if __name__ == '__main__':
simulation_data = process_file("./data/susceptibility.txt")
clean_processed_data(simulation_data, exclude_system_size=[])
# plot_hist_of_error(simulation_data)
plot_setup()
# plot_syssize_vs_susc(simulation_data)
errorbar_data = {}
for size in simulation_data:
errorbar_data[size] = [np.average(simulation_data[size]),
np.std(simulation_data[size]),
len(simulation_data[size])]
calc.plot_errorbars(errorbar_data,
label=r"$\bar{\chi} \pm \sigma_{\bar{\chi}}$",
color=const.COLOR_MAP["black"])
plot_syssize_vs_fit(simulation_data)
# plt.xticks(list(simulation_data.keys()),
# list(simulation_data.keys()))
plt.legend()
savefig = True
if savefig:
illu.save_figure("susceptibility128x128Ising")
# plt.show()
