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-from pathlib import Path
-import numpy as np
-import os
-from matplotlib import pyplot as plt
-import matplotlib as mpl
-from time import time
-from munch import Munch
-import pickle
-from itertools import count
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-from FKMC.general import shapes, smooth
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-%matplotlib inline
-np.seterr(all='warn')
-textwidth = 6.268
-mpl.rcParams['figure.dpi'] = 70
-default_figargs = dict(figsize = (textwidth,textwidth))
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-import logging
-mpl_logger = logging.getLogger('matplotlib')
-mpl_logger.setLevel(logging.WARNING)
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-In [2]:
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-with open('/data/users/tch14/pickled_data/TU_phase_data.pickle', 'rb') as file:
- TU_phase_obs = pickle.load(file)
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-f, axes = plt.subplots(1,6, figsize = (25,5), gridspec_kw = dict(wspace = 0.25, hspace = 0.25))
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-binder = 0
-norm = mpl.colors.Normalize(vmin=0, vmax=1)
-#TU_data.hints.Mf_moments == ('Ns', 'repeats', 'Us', 'Ts', 'moment', 'MCstep')
-M2 = TU_phase_obs.Mf_moments[-1, :, :, :, 2].mean(axis = 0).T
-M4 = TU_phase_obs.Mf_moments[-1, :, :, :, 4].mean(axis = 0).T
-Y = M2**2 / M4 if binder else M2
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-pcol = axes[0].pcolormesh(TU_phase_obs.Us, TU_phase_obs.Ts, Y, cmap="RdBu_r", norm = norm, linewidth=0, rasterized = True)
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-def get_nearby_index(sorted_list, value):
- i = np.searchsorted(sorted_list, value)
- return sorted_list[i], i
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-T, T_i = get_nearby_index(TU_phase_obs.Ts, 1.8)
-U, U_i = get_nearby_index(TU_phase_obs.Us, 5)
-print(U, T)
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-axes[0].plot([U,], [T], color = 'k', marker = 'o')
-axes[1].plot([U,], [T], color = 'k', marker = 'o')
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-E_bins = TU_phase_obs.E_bins
-e = 0.5
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-for e in [0.001, 0.01, 0.1, 1]:
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- gap_indices = (-e < E_bins[:-1]) & (E_bins[:-1] < e)
- gap_state_count = TU_phase_obs.DOS[-1, :, :, :, :].mean(axis=0)[:, :, gap_indices].sum(axis = -1)
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- pcol = axes[1].contour(TU_phase_obs.Us, TU_phase_obs.Ts, gap_state_count, cmap="RdBu_r", levels = [0.2,], label = 'e = {e}')
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-axes[1].legend()
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-#pcol = axes[1].pcolormesh(TU_phase_obs.Us, TU_phase_obs.Ts, gap_state_count, cmap="RdBu_r", linewidth=0, rasterized = True)
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-for i, N in zip(count(), TU_phase_obs.Ns):
- if N < 16: continue
- IPR = TU_phase_obs.IPR[i, :, U_i, T_i, :].mean(axis = 0)
- DOS = TU_phase_obs.DOS[i, :, U_i, T_i, :].mean(axis = 0)
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- axes[3].plot(E_bins[:-1], DOS, label = f'N = {N}')
- axes[3].set(xlim = (-2*e, 2*e))
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- #smooth by a value dependant on the size
- IPR = smooth(IPR, scale = 32/N, axis = -1)
- DOS = smooth(DOS, scale = 32/N, axis = -1)
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- axes[2].plot(E_bins[:-1], DOS, label = f'N = {N}')
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- axes[4].plot(E_bins[:-1], IPR)
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-energy_cuts = [0, 2, 3, 4]
-cut_colors = 'rgbky'
-energy_cuts_exact, energy_cuts_i = np.array([get_nearby_index(TU_phase_obs.E_bins, E) for E in energy_cuts]).T
-axes[4].vlines(energy_cuts_exact, colors = cut_colors, linestyle= 'dashed', ymin = 0, ymax = 0.1)
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-axes[2].legend()
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-from matplotlib.animation import FuncAnimation
-from IPython.display import HTML, display
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-def lerp(start, end, lerp): return start * (1 - lerp) + lerp * end
-T_bot = 0.5
-T_top = 4
-U_left = 2
-U_right = 8
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-##how wide to make the central energy interval
-e = 0.3
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-##where to make cuts for the scaling lines
-energy_cuts = [0, 2, 3, 4]
-cut_colors = 'rgbky'
-energy_cuts_exact, energy_cuts_i = np.array([get_nearby_index(TU_phase_obs.E_bins, E) for E in energy_cuts]).T
-energy_cuts_i = energy_cuts_i.astype(int)
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-def move_shape(i):
- if i < 0.25:
- j = i / 0.25
- T = lerp(T_bot, T_top, j)
- U = U_left
- return T, U
- if i < 0.5:
- j = (i-0.25) / 0.25
- T = T_top
- U = lerp(U_left, U_right, j)
- return T, U
- if i < 0.75:
- j = (i-0.5) / 0.25
- T = lerp(T_top, T_bot, j)
- U = U_right
- return T, U
- else:
- j = (i-0.75) / 0.25
- T = T_bot
- U = lerp(U_right, U_left, j)
- return T, U
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-f, axes = plt.subplots(3,2, figsize = (10,15), gridspec_kw = dict(wspace = 0.25, hspace = 0.25))
-axes = axes.flatten()
-axes[[0,1,2,3,4,5]] = axes[:]
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-### plot the phase diagrams
-binder = 0
-norm = mpl.colors.Normalize(vmin=0, vmax=1)
-#TU_data.hints.Mf_moments == ('Ns', 'repeats', 'Us', 'Ts', 'moment', 'MCstep')
-M2 = TU_phase_obs.Mf_moments[-1, :, :, :, 2].mean(axis = 0).T
-M4 = TU_phase_obs.Mf_moments[-1, :, :, :, 4].mean(axis = 0).T
-Y = M2**2 / M4 if binder else M2
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-pcol = axes[0].pcolormesh(TU_phase_obs.Us, TU_phase_obs.Ts, Y, cmap="RdBu_r", norm = norm, linewidth=0, rasterized = True)
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-E_bins = TU_phase_obs.E_bins
-axes[2].vlines((-e, e), ymin = 0, ymax = 900)
-axes[3].vlines((-e, e), ymin = 0, ymax = 800)
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-axes[0].set(title = 'Order Parameter')
-axes[1].set(title = '# of states near E = 0')
-axes[2].set(title = 'DOS')
-axes[3].set(title = 'Zoom on DOS around E = 0')
-axes[4].set(title = 'IPR')
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-axes[3].set(xlim = (-2*e, 2*e), ylim = (0, 500))
-axes[4].set(ylim = (0, 0.1))
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-gap_indices = (-e < E_bins[:-1]) & (E_bins[:-1] < e)
-gap_state_count = TU_phase_obs.DOS[-1, :, :, :, :].mean(axis=0)[:, :, gap_indices].sum(axis = -1)
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-pcol = axes[1].pcolormesh(TU_phase_obs.Us, TU_phase_obs.Ts, gap_state_count, cmap="RdBu_r", linewidth=0, rasterized = True)
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-#axes[0,0].set(xlim = (-5,5), ylabel = 'DOS\nT')
-#axes[1,0].set(ylabel = 'IPR\nT', xlabel = '$\omega$')
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-#axes[0,1].set(ylabel = 'DOS')
-#axes[1,1].set(ylabel = 'IPR', xlabel = '$\omega$')
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-legends = [None, None]
-points = [None, None]
-IPR_vlines = [None for _ in energy_cuts_exact]
-E_lines = [None for _ in TU_phase_obs.Ns]
-E_lines2 = [None for _ in TU_phase_obs.Ns]
-IPR_lines = [None for _ in TU_phase_obs.Ns]
-E_cut_lines = [None for _ in energy_cuts]
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-#plot all the stuff that needs to change and save references to them
-def init():
- global legends
- #text = ax.text(0,1.05, f' ', fontsize = 15, transform=ax.transAxes)
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- T, T_i = get_nearby_index(TU_phase_obs.Ts, 0)
- U, U_i = get_nearby_index(TU_phase_obs.Us, 0)
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- points[0], = axes[0].plot([U,], [T], color = 'k', marker = 'o')
- points[1], = axes[1].plot([U,], [T], color = 'k', marker = 'o')
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- for i, E_i, E, colour in zip(count(), energy_cuts_i, energy_cuts_exact, cut_colors):
- print(U_i, T_i, E_i)
- IPR_cut = TU_phase_obs.IPR[:, :, U_i, T_i, E_i].mean(axis = 1)
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- E_cut_lines[i], = axes[5].plot(TU_phase_obs.Ns, IPR_cut, color = colour, label = f'E = {E}', marker = 'o', linestyle = 'None')
- IPR_vlines[i], = axes[4].plot([E,E], [0,900], color = colour, linestyle= 'dashed')
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- #IPR_lines = []
- #E_lines = []
- #for i, N in enumerate(o.Ns):
- #E_line, = axes[0, 1].plot(o.E_bins[1:], o.sE[i][T_select, :], label = f'N = {N}')
- #IPR_line, = axes[1, 1].plot(o.E_bins[1: ], o.sI[i][T_select, :])
- #E_lines.append(E_line)
- #IPR_lines.append(IPR_line)
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- for i, N in zip(count(), TU_phase_obs.Ns):
- if N < 16: continue
- IPR = TU_phase_obs.IPR[i, :, U_i, T_i, :].mean(axis = 0)
- DOS = TU_phase_obs.DOS[i, :, U_i, T_i, :].mean(axis = 0)
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- #smooth by a value dependant on the size
- IPR = smooth(IPR, scale = 32/N, axis = -1)
- DOS = smooth(DOS, scale = 32/N, axis = -1)
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- E_lines[i], = axes[2].plot(E_bins[:-1], 0*DOS, label = f'N = {N}')
- E_lines2[i], = axes[3].plot(E_bins[:-1], 0*DOS, label = f'N = {N}')
- IPR_lines[i], = axes[4].plot(E_bins[:-1], 0*IPR)
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- if legends[0] == None: legends[0] = axes[2].legend(loc = 'upper left')
- if legends[1] == None: legends[1] = axes[5].legend()
- return np.concatenate([points, E_lines, IPR_lines, IPR_vlines, E_cut_lines, [legend,legend2]])
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-def update(frame):
- T, U = move_shape(frame)
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- T, T_i = get_nearby_index(TU_phase_obs.Ts, T)
- U, U_i = get_nearby_index(TU_phase_obs.Us, U)
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- for p in points: p.set_data((U, ), (T, ))
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- for i, N in zip(count(), TU_phase_obs.Ns):
- if N < 16: continue
- IPR = TU_phase_obs.IPR[i, :, U_i, T_i, :].mean(axis = 0)
- DOS = TU_phase_obs.DOS[i, :, U_i, T_i, :].mean(axis = 0)
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- #smooth by a value dependant on the size
- sIPR = smooth(IPR, scale = 32/N, axis = -1)
- sDOS = smooth(DOS, scale = 32/N, axis = -1)
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- E_lines[i].set_data(E_bins[:-1], sDOS)
- E_lines2[i].set_data(E_bins[:-1], DOS) #not smoothed!
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- IPR_lines[i].set_data(E_bins[:-1], sIPR)
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- for i, E_i, E, colour in zip(count(), energy_cuts_i, energy_cuts_exact, cut_colors):
- IPR_cut = TU_phase_obs.IPR[:, :, U_i, T_i, E_i].mean(axis = 1)
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- E_cut_lines[i].set_data(TU_phase_obs.Ns, IPR_cut)
- #IPR_vlines[i].set_data([E,E], [0,900])
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- return np.concatenate([points, E_lines, E_lines2, IPR_lines, E_cut_lines])
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-frames = np.linspace(0,1,120)
-frames = np.linspace(0,1,12)
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-interval = 40000 /len(frames)
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-ani = FuncAnimation(f, update,
- frames=frames,
- init_func=init,
- blit=False,
- repeat_delay = 1000,
- interval = interval,
- )
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-HTML(ani.to_html5_video())
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-%%time
-with open("TU_sweep.html", "w") as f:
- print(ani.to_html5_video(), file=f)
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-ani.save('TU_sweep.mp4', dpi = 300)
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