OUTFILE = arguments[0]
plt.style.use('seaborn-paper')
fig = plt.figure()
ax = fig.add_axes([0.15, 0.2, 0.8, 0.75])
pf.plot_arrow_diagram_WM(ax, fixedpoints=True)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.tick_params(labelsize=25)
ax.set_ylabel('$U_{\mathrm{eff}}$', fontsize=25)
ax.set_xlabel('$\Gamma_{\mathrm{eff}}$', fontsize=25)
ax.set_xticks([0, 0.5, 1])
ax.set_yticks([0, 1, 2, 3, 4])
ax.tick_params(labelsize=25)
U_param, GAMMA_param = pf.get_u_gamma()
if OUTFILE != 0:
if OUTFILE == 'folder':
plt.savefig(
"graphs_and_data/arrowflow_and_eigenflow/u-{0:.4f}_gamma-{1:.4f}_UGdiagram.png"
.format(float(U_param), float(GAMMA_param)))
elif OUTFILE == 'def':
plt.savefig("u-{0:.4f}_gamma-{1:.4f}_UGdiagram.png".format(
float(U_param), float(GAMMA_param)))
else:
plt.savefig("{}".format(OUTFILE))
else:
plt.show()
import sys
import py_functions as pf
import matplotlib.pyplot as plt
import numpy as np
arguments = []
for arg in sys.argv[1:]:
arguments.append(arg)
OUTNAME = 0
if len(arguments) >= 1:
OUTNAME = arguments[0]
plt.style.use('seaborn-paper')
fig = plt.figure()
U, G = pf.get_u_gamma()
ax1 = fig.add_axes([0.2, 0.55, 0.55, 0.45])
pf.plot_mag_susc_2(ax1, colour='tab:purple', llabel=f"$U={U}$, $\Gamma={G}$")
ax1.set_xticklabels([])
ax1.set_yticks(np.arange(0, 1, 0.25))
ax2 = fig.add_axes([0.2, 0.15, 0.55, 0.40])
pf.plot_entropy_2(ax2, colour='tab:purple')
ax2.set_yticks(np.arange(0, 4, 1))
ax2.set_xticks(np.exp(np.arange(-20, 1, 4)))
ax2.semilogx()
ax1.legend(loc='upper left',
frameon=True,
facecolor='white',
out_array = nrg_eigenvalues_step - analytic_eigenvalues
return out_array
Ud_array = np.empty(int(N / 2))
Ua_array = np.empty(int(N / 2))
Gd_array = np.empty(int(N / 2))
Ga_array = np.empty(int(N / 2))
nrg_eigenvalues = np.empty((int(N / 2), 6))
nrg_eigenvalues = pf.read_h0_eigenvalues_lowest()
nrg_eigenvalues_step = np.empty(6)
U_param, G_param = pf.get_u_gamma()
starting_values = [U_param, U_param, G_param, G_param]
for n in range(0, int(N / 2)):
nrg_eigenvalues_step = nrg_eigenvalues[n, :]
res = opt.least_squares(F,
starting_values,
bounds=([0, 0, 0, 0], [10, 10, 10, 10]))
Ud_array[n] = res.x[0]
Ua_array[n] = res.x[1]
Gd_array[n] = res.x[2]
Ga_array[n] = res.x[3]
starting_values = res.x
print(res, file=f)