def spectrum_hist(self, nbr_bins=61, fs=20, lims=None):
"""
Plot the spectrum.
:param nbr_bins: Default value 101. Number of bins of the histogram.
:param lims: List, lims[0] energy min, lims[1] energy max.
"""
error_handling.empty_ndarray(self.sys.en, 'sys.get_eig')
error_handling.positive_real(nbr_bins, 'nbr_bins')
error_handling.lims(lims)
fig, ax = plt.subplots()
if lims is None:
en_max = np.max(self.sys.en.real)
ind_en = np.ones(self.sys.lat.sites, bool)
ax.set_ylim([-en_max, en_max])
else:
ind_en = np.argwhere((self.sys.en > lims[0]) & (self.sys.en < lims[1]))
ind_en = np.ravel(ind_en)
ax.set_xlim(lims)
en = self.sys.en[ind_en]
n, bins, patches = plt.hist(en, bins=nbr_bins, color='b', alpha=0.8)
ax.set_title('Spectrum', fontsize=fs)
ax.set_xlabel('$E$', fontsize=fs)
ax.set_ylabel('number of states', fontsize=fs)
ax.set_ylim([0, np.max(n)+1])
for label in ax.xaxis.get_majorticklabels():
label.set_fontsize(fs)
for label in ax.yaxis.get_majorticklabels():
label.set_fontsize(fs)
def spectrum_hist(self, nbr_bins=61, fs=20, lims=None):
"""
Plot the spectrum.
:param nbr_bins: Default value 101. Number of bins of the histogram.
:param lims: List, lims[0] energy min, lims[1] energy max.
"""
error_handling.empty_ndarray(self.sys.en, 'sys.get_eig')
error_handling.positive_real(nbr_bins, 'nbr_bins')
error_handling.lims(lims)
fig, ax = plt.subplots()
if lims is None:
en_max = np.max(self.sys.en.real)
ind_en = np.ones(self.sys.lat.sites, bool)
ax.set_ylim([-en_max, en_max])
else:
ind_en = np.argwhere((self.sys.en > lims[0])
& (self.sys.en < lims[1]))
ind_en = np.ravel(ind_en)
ax.set_xlim(lims)
en = self.sys.en[ind_en]
n, bins, patches = plt.hist(en, bins=nbr_bins, color='b', alpha=0.8)
ax.set_title('Spectrum', fontsize=fs)
ax.set_xlabel('$E$', fontsize=fs)
ax.set_ylabel('number of states', fontsize=fs)
ax.set_ylim([0, np.max(n) + 1])
for label in ax.xaxis.get_majorticklabels():
label.set_fontsize(fs)
for label in ax.yaxis.get_majorticklabels():
label.set_fontsize(fs)
def polarization(self,
fig=None,
ax1=None,
ms=10.,
fs=20.,
lims=None,
tag_pola=None,
ind=None):
'''
Plot sublattice polarization.
:param fig: Figure. Default value None. (used by the method spectrum).
:param ax1: Axis. Default value None. (used by the method spectrum).
:param ms: Positive Float. Default value 10. Markersize.
:param fs: Positive Float. Default value 20. Fontsize.
:param lims: List, lims[0] energy min, lims[1] energy max.
:param tag_pola: Binary char. Default value None. Tag of the sublattice.
:param ind: List. Default value None. List of indices. (used in the method spectrum).
:returns:
* **fig** -- Figure.
'''
if fig is None:
error_handling.sys(self.sys)
error_handling.empty_ndarray(self.sys.en, 'sys.get_eig')
error_handling.positive_real(ms, 'ms')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
fig, ax2 = plt.subplots()
ax2 = plt.gca()
if lims is None:
ax2.set_ylim([-0.1, 1.1])
ind = np.ones(self.sys.lat.sites, bool)
else:
ind = (self.sys.en > lims[0]) & (self.sys.en < lims[1])
ax2.set_ylim([lims[0] - 0.1, lims[1] + 0.1])
else:
ax2 = plt.twinx()
error_handling.empty_ndarray(self.sys.pola, 'sys.get_pola')
error_handling.tag(tag_pola, self.sys.lat.tags)
x = np.arange(self.sys.lat.sites)
i_tag = self.sys.lat.tags == tag_pola
ax2.plot(x[ind],
np.ravel(self.sys.pola[ind, i_tag]),
'or',
markersize=(4 * ms) // 5)
str_tag = tag_pola.decode('ascii')
ylabel = '$<' + str_tag.upper() + '|' + str_tag.upper() + '>$'
ax2.set_ylabel(ylabel, fontsize=fs, color='red')
ax2.set_ylim([-0.1, 1.1])
ax2.set_xlim(-0.5, x[ind][-1] + 0.5)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(fs)
for label in ax2.get_yticklabels():
label.set_color('r')
return fig, ax2
def spectrum(self, ms=10, fs=20, lims=None,
tag_pola=None, ipr=None, peterman=None):
'''
Plot spectrum (eigenenergies real part (blue circles),
and sublattice polarization if *pola* not empty (red circles).
:param ms: Default value 10. Markersize.
:param fs: Default value 20. Fontsize.
:param lims: List, lims[0] energy min, lims[1] energy max.
:param tag_pola: Default value None. Binary char. Tag of the sublattice.
:param ipr: Default value None. If True plot the Inverse Partitipation Ration.
:param petermann: Default value None. If True plot the Petermann factor.
:returns:
* **fig** -- Figure.
'''
error_handling.empty_ndarray(self.sys.en, 'sys.get_eig')
error_handling.positive_real(ms, 'ms')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
fig, ax1 = plt.subplots()
ax1 = plt.gca()
x = np.arange(self.sys.lat.sites)
if lims is None:
en_max = np.max(self.sys.en.real)
ax1.set_ylim([-en_max-0.2, en_max+0.2])
ind = np.ones(self.sys.lat.sites, bool)
else:
ind = (self.sys.en > lims[0]) & (self.sys.en < lims[1])
ax1.set_ylim([lims[0]-0.1, lims[1]+0.1])
ax1.plot(x[ind], self.sys.en.real[ind], 'ob', markersize=ms)
ax1.set_title('Spectrum', fontsize=fs)
ax1.set_xlabel('$n$', fontsize=fs)
ax1.set_ylabel('$E_n$', fontsize=fs, color='blue')
for label in ax1.get_yticklabels():
label.set_color('b')
if tag_pola:
error_handling.tag(tag_pola, self.sys.lat.tags)
fig, ax2 = self.polarization(fig=fig, ax1=ax1, ms=ms, fs=fs, tag_pola=tag_pola, ind=ind)
elif ipr:
fig, ax2 = self.ipr(fig=fig, ax1=ax1, ms=ms, fs=fs, ind=ind)
elif peterman:
fig, ax2 = self.petermann(fig=fig, ax1=ax1, ms=ms, fs=fs, ind=ind)
for label in ax1.xaxis.get_majorticklabels():
label.set_fontsize(fs)
for label in ax1.yaxis.get_majorticklabels():
label.set_fontsize(fs)
xa = ax1.get_xaxis()
ax1.set_xlim([x[ind][0]-0.5, x[ind][-1]+0.5])
xa.set_major_locator(plt.MaxNLocator(integer=True))
fig.set_tight_layout(True)
plt.draw()
return fig
def get_intensity_en(self, lims):
'''
Get, if any, the intensity of the sum of the states
between *lims[0]* and *lims[1]*.
:param lims: List. lims[0] energy min, lims[1] energy max.
:returns:
* **intensity** -- Sum of the intensities between (lims[0], lims[1]).
'''
error_handling.empty_ndarray(self.rn, 'sys.get_eig(eigenvec=True)')
error_handling.lims(lims)
ind = np.where((self.en > lims[0]) & (self.en < lims[1]))
ind = np.ravel(ind)
print('{} states between {} and {}'.format(len(ind), lims[0], lims[1]))
return np.sum(self.intensity[:, ind], axis=1)
def get_intensity_en(self, lims):
'''
Get, if any, the intensity of the sum of the states
between *lims[0]* and *lims[1]*.
:param lims: List. lims[0] energy min, lims[1] energy max.
:returns:
* **intensity** -- Sum of the intensities between (lims[0], lims[1]).
'''
error_handling.empty_ndarray(self.rn, 'sys.get_eig(eigenvec=True)')
error_handling.lims(lims)
ind = np.where((self.en > lims[0]) & (self.en < lims[1]))
ind = np.ravel(ind)
print('{} states between {} and {}'.format(len(ind), lims[0], lims[1]))
return np.sum(self.intensity[:, ind], axis=1)
def polarization(self, fig=None, ax1=None, ms=10., fs=20., lims=None,
tag_pola=None, ind=None):
'''
Plot sublattice polarization.
:param fig: Figure. Default value None. (used by the method spectrum).
:param ax1: Axis. Default value None. (used by the method spectrum).
:param ms: Positive Float. Default value 10. Markersize.
:param fs: Positive Float. Default value 20. Fontsize.
:param lims: List, lims[0] energy min, lims[1] energy max.
:param tag_pola: Binary char. Default value None. Tag of the sublattice.
:param ind: List. Default value None. List of indices. (used in the method spectrum).
:returns:
* **fig** -- Figure.
'''
if fig is None:
error_handling.sys(self.sys)
error_handling.empty_ndarray(self.sys.en, 'sys.get_eig')
error_handling.positive_real(ms, 'ms')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
fig, ax2 = plt.subplots()
ax2 = plt.gca()
if lims is None:
ax2.set_ylim([-0.1, 1.1])
ind = np.ones(self.sys.lat.sites, bool)
else:
ind = (self.sys.en > lims[0]) & (self.sys.en < lims[1])
ax2.set_ylim([lims[0]-0.1, lims[1]+0.1])
else:
ax2 = plt.twinx()
error_handling.empty_ndarray(self.sys.pola, 'sys.get_pola')
error_handling.tag(tag_pola, self.sys.lat.tags)
x = np.arange(self.sys.lat.sites)
i_tag = self.sys.lat.tags == tag_pola
ax2.plot(x[ind], np.ravel(self.sys.pola[ind, i_tag]), 'or', markersize=(4*ms)//5)
str_tag = tag_pola.decode('ascii')
ylabel = '$<' + str_tag.upper() + '|' + str_tag.upper() + '>$'
ax2.set_ylabel(ylabel, fontsize=fs, color='red')
ax2.set_ylim([-0.1, 1.1])
ax2.set_xlim(-0.5, x[ind][-1]+0.5)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(fs)
for label in ax2.get_yticklabels():
label.set_color('r')
return fig, ax2
def find_square(self, xlims, ylims):
'''
Private method.
Find hoppings within the square.
:param xlims: List or Tuple. :math:`x` interval.
:param ylims: List or Tuple. :math:`y` interval.
'''
error_handling.lims(xlims)
error_handling.lims(ylims)
in1 = (self.lat.coor['x'][self.hop['i']] >= xlims[0]) & \
(self.lat.coor['y'][self.hop['i']] >= ylims[0]) & \
(self.lat.coor['x'][self.hop['j']] >= xlims[0]) & \
(self.lat.coor['y'][self.hop['j']] >= ylims[0])
in2 = (self.lat.coor['x'][self.hop['i']] <= xlims[1]) & \
(self.lat.coor['y'][self.hop['i']] <= ylims[1]) & \
(self.lat.coor['x'][self.hop['j']] <= xlims[1]) & \
(self.lat.coor['y'][self.hop['j']] <= ylims[1])
return in1 * in2
def find_square(self, xlims, ylims):
'''
Private method.
Find hoppings within the square.
:param xlims: List or Tuple. :math:`x` interval.
:param ylims: List or Tuple. :math:`y` interval.
'''
error_handling.lims(xlims)
error_handling.lims(ylims)
in1 = (self.lat.coor['x'][self.hop['i']] >= xlims[0]) & \
(self.lat.coor['y'][self.hop['i']] >= ylims[0]) & \
(self.lat.coor['x'][self.hop['j']] >= xlims[0]) & \
(self.lat.coor['y'][self.hop['j']] >= ylims[0])
in2 = (self.lat.coor['x'][self.hop['i']] <= xlims[1]) & \
(self.lat.coor['y'][self.hop['i']] <= ylims[1]) & \
(self.lat.coor['x'][self.hop['j']] <= xlims[1]) & \
(self.lat.coor['y'][self.hop['j']] <= ylims[1])
return in1 * in2
def butterfly(self, betas, butterfly, lw=1., fs=20., lims=None, title=''):
'''
Plot energies depending on a parameter.
:param betas: np.array. Parameter values.
:param butterfly: np.array. Eigenvalues.
:param lw: Positive Float. Default value 1. Hopping linewidths.
:param fs: Positive Float. Default value 20. Fontsize.
:param lims: List, lims[0] energy min, lims[1] energy max.
:param title: Default value ''. Figure title.
'''
error_handling.ndarray_empty(betas, 'betas')
error_handling.ndarray_empty(butterfly, 'butterfly')
error_handling.positive_real(lw, 'lw')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
error_handling.string(title, 'title')
i_beta_min = np.argmin(np.abs(betas))
if lims is None:
lims = [butterfly[i_beta_min, 0], butterfly[i_beta_min, -1]]
ind_en = np.argwhere((butterfly[i_beta_min, :] > lims[0])
& (butterfly[i_beta_min, :] < lims[1]))
ind_en = np.ravel(ind_en)
fig, ax = plt.subplots()
plt.title('Energies depending on strain', fontsize=fs)
plt.xlabel(r'$\beta/\beta_{max}$', fontsize=fs)
plt.ylabel('$E$', fontsize=fs)
ax.set_title(title, fontsize=fs)
plt.yticks(np.arange(lims[0], lims[1] + 1, (lims[1] - lims[0]) / 4),
fontsize=fs)
plt.ylim(lims)
beta_max = max(self.sys.betas)
plt.xticks([-beta_max, -0.5 * beta_max, 0, 0.5 * beta_max, beta_max],
fontsize=fs)
ax.set_xticklabels(('-1', '-1/2', '0', '1/2', '1'))
plt.xlim([betas[0], betas[-1]])
for i in ind_en:
plt.plot(betas, butterfly[:, i], 'b', lw=lw)
fig.set_tight_layout(True)
plt.draw()
return fig
def spectrum_complex(self, ms=10., fs=20., lims=None):
'''
Plot complex value eigenenergies, real part (blue circles),
and imaginary part (red circles).
:param ms: Positive Float. Default value 20. Markersize.
:param fs: Positive Float. Default value 20. Font size.
:param lims: List. lims[0] energy min, lims[1] energy max.
:returns:
* **fig** -- Figure.
'''
error_handling.empty_ndarray(self.sys.en, 'sys.get_eig')
error_handling.positive_real(ms, 'ms')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
fig, ax1 = plt.subplots()
ax1 = plt.gca()
x = np.arange(self.sys.lat.sites)
if lims is None:
en_max = np.max(self.sys.en.real)
ax1.set_ylim([-en_max - 0.2, en_max + 0.2])
ind = np.ones(self.sys.lat.sites, bool)
else:
ind = (self.sys.en > lims[0]) & (self.sys.en < lims[1])
ax1.set_ylim([lims[0] - 0.1, lims[1] + 0.1])
ax1.plot(x[ind], self.sys.en.real[ind], 'ob', markersize=ms)
ax1.plot(x[ind], self.sys.en.imag[ind], 'or', markersize=ms)
ax1.set_title('Spectrum', fontsize=fs)
ax1.set_xlabel('$n$', fontsize=fs)
ax1.set_ylabel('Re ' + r'$E_n$' + ', Im ' + r'$E_n$', fontsize=fs)
for label in ax1.xaxis.get_majorticklabels():
label.set_fontsize(fs)
for label in ax1.yaxis.get_majorticklabels():
label.set_fontsize(fs)
xa = ax1.get_xaxis()
ax1.set_xlim([x[ind][0] - 0.1, x[ind][-1] + 0.1])
xa.set_major_locator(plt.MaxNLocator(integer=True))
fig.set_tight_layout(True)
plt.draw()
return fig
def petermann(self, fig=None, ax1=None, ms=10, fs=20, lims=None, ind=None):
'''
Plot Peterman factor.
:param fig: Figure. Default value None. (used by the method spectrum).
:param ax1: Axis. Default value None. (used by the method spectrum).
:param ms: Positive Float. Default value 10. Markersize.
:param fs: Positive Float. Default value 20. Fontsize.
:param lims: List. lims[0] energy min, lims[1] energy max.
:param ind: List. Default value None. List of indices. (used in the method spectrum).
:returns:
* **fig** -- Figure.
'''
if fig is None:
error_handling.sys(self.sys)
error_handling.empty_ndarray(self.sys.ipr, 'sys.get_petermann')
error_handling.positive_real(ms, 'ms')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
fig, ax2 = plt.subplots()
ax2 = plt.gca()
if lims is None:
ind = np.ones(self.sys.lat.sites, bool)
else:
ind = (self.sys.en > lims[0]) & (self.sys.en < lims[1])
else:
ax2 = plt.twinx()
error_handling.empty_ndarray(self.sys.ipr, 'sys.get_ipr')
x = np.arange(self.sys.lat.sites)
ax2.plot(x[ind],
self.sys.petermann[ind],
'or',
markersize=(4 * ms) // 5)
ax2.set_ylabel('K', fontsize=fs, color='red')
ax2.set_xlim(-0.5, x[ind][-1] + 0.5)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(fs)
for label in ax2.get_yticklabels():
label.set_color('r')
return fig, ax2
def spectrum_complex(self, ms=10., fs=20., lims=None):
'''
Plot complex value eigenenergies, real part (blue circles),
and imaginary part (red circles).
:param ms: Positive Float. Default value 20. Markersize.
:param fs: Positive Float. Default value 20. Font size.
:param lims: List. lims[0] energy min, lims[1] energy max.
:returns:
* **fig** -- Figure.
'''
error_handling.empty_ndarray(self.sys.en, 'sys.get_eig')
error_handling.positive_real(ms, 'ms')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
fig, ax1 = plt.subplots()
ax1 = plt.gca()
x = np.arange(self.sys.lat.sites)
if lims is None:
en_max = np.max(self.sys.en.real)
ax1.set_ylim([-en_max-0.2, en_max+0.2])
ind = np.ones(self.sys.lat.sites, bool)
else:
ind = (self.sys.en > lims[0]) & (self.sys.en < lims[1])
ax1.set_ylim([lims[0]-0.1, lims[1]+0.1])
ax1.plot(x[ind], self.sys.en.real[ind], 'ob', markersize=ms)
ax1.plot(x[ind], self.sys.en.imag[ind], 'or', markersize=ms)
ax1.set_title('Spectrum', fontsize=fs)
ax1.set_xlabel('$n$', fontsize=fs)
ax1.set_ylabel('Re '+r'$E_n$'+', Im '+r'$E_n$', fontsize=fs)
for label in ax1.xaxis.get_majorticklabels():
label.set_fontsize(fs)
for label in ax1.yaxis.get_majorticklabels():
label.set_fontsize(fs)
xa = ax1.get_xaxis()
ax1.set_xlim([x[ind][0]-0.1, x[ind][-1]+0.1])
xa.set_major_locator(plt.MaxNLocator(integer=True))
fig.set_tight_layout(True)
plt.draw()
return fig
def butterfly(self, betas, butterfly, lw=1., fs=20., lims=None, title=''):
'''
Plot energies depending on a parameter.
:param betas: np.array. Parameter values.
:param butterfly: np.array. Eigenvalues.
:param lw: Positive Float. Default value 1. Hopping linewidths.
:param fs: Positive Float. Default value 20. Fontsize.
:param lims: List, lims[0] energy min, lims[1] energy max.
:param title: Default value ''. Figure title.
'''
error_handling.ndarray_empty(betas, 'betas')
error_handling.ndarray_empty(butterfly, 'butterfly')
error_handling.positive_real(lw, 'lw')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
error_handling.string(title, 'title')
i_beta_min = np.argmin(np.abs(betas))
if lims is None:
lims = [butterfly[i_beta_min, 0], butterfly[i_beta_min, -1]]
ind_en = np.argwhere((butterfly[i_beta_min, :] > lims[0]) &
(butterfly[i_beta_min, :] < lims[1]))
ind_en = np.ravel(ind_en)
fig, ax = plt.subplots()
plt.title('Energies depending on strain', fontsize=fs)
plt.xlabel(r'$\beta/\beta_{max}$', fontsize=fs)
plt.ylabel('$E$', fontsize=fs)
ax.set_title(title, fontsize=fs)
plt.yticks(np.arange(lims[0], lims[1]+1, (lims[1]-lims[0])/4), fontsize=fs)
plt.ylim(lims)
beta_max = max(self.sys.betas)
plt.xticks([-beta_max, -0.5*beta_max, 0,
0.5*beta_max, beta_max], fontsize=fs)
ax.set_xticklabels(('-1', '-1/2', '0', '1/2', '1'))
plt.xlim([betas[0], betas[-1]])
for i in ind_en:
plt.plot(betas, butterfly[:, i], 'b', lw=lw)
fig.set_tight_layout(True)
plt.draw()
return fig
def petermann(self, fig=None, ax1=None, ms=10, fs=20, lims=None, ind=None):
'''
Plot Peterman factor.
:param fig: Figure. Default value None. (used by the method spectrum).
:param ax1: Axis. Default value None. (used by the method spectrum).
:param ms: Positive Float. Default value 10. Markersize.
:param fs: Positive Float. Default value 20. Fontsize.
:param lims: List. lims[0] energy min, lims[1] energy max.
:param ind: List. Default value None. List of indices. (used in the method spectrum).
:returns:
* **fig** -- Figure.
'''
if fig is None:
error_handling.sys(self.sys)
error_handling.empty_ndarray(self.sys.ipr, 'sys.get_petermann')
error_handling.positive_real(ms, 'ms')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
fig, ax2 = plt.subplots()
ax2 = plt.gca()
if lims is None:
ind = np.ones(self.sys.lat.sites, bool)
else:
ind = (self.sys.en > lims[0]) & (self.sys.en < lims[1])
else:
ax2 = plt.twinx()
error_handling.empty_ndarray(self.sys.ipr, 'sys.get_ipr')
x = np.arange(self.sys.lat.sites)
ax2.plot(x[ind], self.sys.petermann[ind], 'or', markersize=(4*ms)//5)
ax2.set_ylabel( 'K' , fontsize=fs, color='red')
ax2.set_xlim(-0.5, x[ind][-1]+0.5)
for tick in ax2.xaxis.get_major_ticks():
tick.label.set_fontsize(fs)
for label in ax2.get_yticklabels():
label.set_color('r')
return fig, ax2
def spectrum(self,
ms=10,
fs=20,
lims=None,
tag_pola=None,
ipr=None,
peterman=None):
'''
Plot spectrum (eigenenergies real part (blue circles),
and sublattice polarization if *pola* not empty (red circles).
:param ms: Default value 10. Markersize.
:param fs: Default value 20. Fontsize.
:param lims: List, lims[0] energy min, lims[1] energy max.
:param tag_pola: Default value None. Binary char. Tag of the sublattice.
:param ipr: Default value None. If True plot the Inverse Partitipation Ration.
:param petermann: Default value None. If True plot the Petermann factor.
:returns:
* **fig** -- Figure.
'''
error_handling.empty_ndarray(self.sys.en, 'sys.get_eig')
error_handling.positive_real(ms, 'ms')
error_handling.positive_real(fs, 'fs')
error_handling.lims(lims)
fig, ax1 = plt.subplots()
ax1 = plt.gca()
x = np.arange(self.sys.lat.sites)
if lims is None:
en_max = np.max(self.sys.en.real)
ax1.set_ylim([-en_max - 0.2, en_max + 0.2])
ind = np.ones(self.sys.lat.sites, bool)
else:
ind = (self.sys.en > lims[0]) & (self.sys.en < lims[1])
ax1.set_ylim([lims[0] - 0.1, lims[1] + 0.1])
ax1.plot(x[ind], self.sys.en.real[ind], 'ob', markersize=ms)
ax1.set_title('Spectrum', fontsize=fs)
ax1.set_xlabel('$n$', fontsize=fs)
ax1.set_ylabel('$E_n$', fontsize=fs, color='blue')
for label in ax1.get_yticklabels():
label.set_color('b')
if tag_pola:
error_handling.tag(tag_pola, self.sys.lat.tags)
fig, ax2 = self.polarization(fig=fig,
ax1=ax1,
ms=ms,
fs=fs,
tag_pola=tag_pola,
ind=ind)
elif ipr:
fig, ax2 = self.ipr(fig=fig, ax1=ax1, ms=ms, fs=fs, ind=ind)
elif peterman:
fig, ax2 = self.petermann(fig=fig, ax1=ax1, ms=ms, fs=fs, ind=ind)
for label in ax1.xaxis.get_majorticklabels():
label.set_fontsize(fs)
for label in ax1.yaxis.get_majorticklabels():
label.set_fontsize(fs)
xa = ax1.get_xaxis()
ax1.set_xlim([x[ind][0] - 0.5, x[ind][-1] + 0.5])
xa.set_major_locator(plt.MaxNLocator(integer=True))
fig.set_tight_layout(True)
plt.draw()
return fig