def calc_bands(glat, kxy=None, density=100, verbose=True):
"""Compute the band eigenvalues at an unstructured array of kxy points.
Parameters
----------
glat : GyroLattice class instance
the gyro lattice on which to compute the bands
kxy : N x 2 float array or None
the wavevectors (each row is a wavevector) at which to compute the spectrum. If None, will generate a random
collection in the BZ
density : int
the number of evaluation points per unit area of the BZ
verbose : bool
print information to command line
Returns
-------
omegas : #bands x len(kxy) float array
the eigenvalues of the dynamical matrix in descending(?) order
kx, ky :
"""
matk = lambda_matrix_kspace(glat, eps=1e-10)
bzvtcs = glat.lattice.get_bz(attribute=True)
bzarea = dh.polygon_area(bzvtcs)
# Make the kx, ky points to add to current results
if kxy is None:
kxy = dh.generate_random_xy_in_polygon(density * bzarea,
bzvtcs,
sorted=True)
start_time = time.time()
bands = []
for ii in range(len(kxy)):
# time for evaluating point index ii
tpi = []
# Display how much time is left
if ii % 4000 == 1999 and verbose:
end_time = time.time()
tpi.append(abs((start_time - end_time) / (ii + 1)))
total_time = np.mean(tpi) * len(kxy)
printstr = 'Estimated time remaining: ' + '%0.2f s' % (
total_time - (end_time - start_time))
printstr += ', ii = ' + str(ii + 1) + '/' + str(len(kxy))
print printstr
matii = matk([kxy[ii, 0], kxy[ii, 1]])
eigval, eigvect = le.eig_vals_vects(matii)
bands.append(np.imag(eigval))
omegas = np.array(bands)
kx, ky = kxy[:, 0], kxy[:, 1]
return omegas, kx, ky
def c_3D_plot(x, y, z, vertex_points, ax, v_min, v_max):
"""
Parameters
----------
x
y
z
vertex_points
ax
v_min
v_max
Returns
-------
"""
# Create the Triangulation; no triangles so Delaunay triangulation created.
x = np.array(x)
y = np.array(y)
z = np.array(z)
triang = mtri.Triangulation(x, y)
min_radius = 1
# Mask off unwanted triangles.
xmid = x[triang.triangles]
ymid = y[triang.triangles]
vp = np.array([np.array([xmid[i], ymid[i]]).T for i in range(len(xmid))])
areas = np.array([dh.polygon_area(vp[i]) for i in range(len(vp))])
poly_path = Path(vertex_points)
xmid = x[triang.triangles].mean(axis=1)
ymid = y[triang.triangles].mean(axis=1)
mask = np.array([
not poly_path.contains_point([xmid[i], ymid[i]])
for i in range(len(xmid))
])
triang.set_mask(mask)
triang.set_mask(mask)
ax.plot_trisurf(triang,
z,
cmap='cool',
vmin=v_min,
vmax=v_max,
linewidth=0.0,
alpha=1)
def calc_chern(kchern, verbose=True):
"""Compute the chern number using the wedge product of the projector
Parameters
----------
kchern : KChernGyro class instance
kspace Chern calculation class
Returns
-------
"""
# Unpack kchern a bit
cp = kchern.cp
# this is really the only function you need to change to do a different kind of lattice.
# mM, vertex_points, od, fn, ar = lf.honeycomb_sheared(tvals, delta, phi, ons, base_dir)
matk = glatkfns.lambda_matrix_kspace(kchern.gyro_lattice, eps=1e-10)
bzvtcs = kchern.gyro_lattice.lattice.get_bz(attribute=True)
# set the directory for saving images and data. The function above actually creates these directories for you.
# I know this isn't exaclty ideal.
bzarea = dh.polygon_area(bzvtcs)
# Check if a similar results file exists --> new data is appended to the old data, if it exists
savedir = cp['cpmeshfn']
dio.ensure_dir(savedir)
savefn = savedir + 'kspacechern_density{0:07d}.pkl'.format(cp['density'])
globs = sorted(glob.glob(savedir + 'kspacechern_density*.pkl'))
# Obtain the filename with the density that is smaller than the requested one
if globs:
densities = np.array([
int(globfn.split('density')[-1].split('.pkl')[0])
for globfn in globs
])
if (densities < cp['density']).any():
biggestsmall = densities[densities < cp['density']][-1]
smallerfn = savedir + 'kspacechern_density{0:07d}.pkl'.format(
biggestsmall)
else:
smallerfn = False
else:
smallerfn = False
if verbose:
print 'kchern_gyro_fns: looking for saved file ' + savefn
if os.path.isfile(savefn):
if verbose:
print 'kchern_gyro_fns: chern file exists, overwriting: ', savefn
# initialize empty lists to be filled up
kkx, kky = [], []
bands, traces = [], []
npts = int(cp['density'] * bzarea)
elif smallerfn:
if verbose:
print 'kchern_gyro_fns: chern file with smaller density exists, loading to append...'
with open(smallerfn, 'rb') as of:
data = pkl.load(of)
# Convert contents to lists so that we can append to them
bands = list(data['bands'])
kkx = list(data['kx'])
kky = list(data['ky'])
traces = list(data['traces'])
# figure out how many points to append to reach desired density
npts = int(cp['density'] * bzarea) - len(kkx)
else:
# initialize empty lists to be filled up
kkx, kky = [], []
bands, traces = [], []
npts = int(cp['density'] * bzarea)
# Make the kx, ky points to add to current results
kxy = dh.generate_random_xy_in_polygon(npts, bzvtcs, sorted=True)
start_time = time.time()
for ii in range(len(kxy)):
# time for evaluating point index ii
tpi = []
# Display how much time is left
if ii % 4000 == 1999 and verbose:
end_time = time.time()
tpi.append(abs((start_time - end_time) / (ii + 1)))
total_time = np.mean(tpi) * len(kxy)
printstr = 'Estimated time remaining: ' + '%0.2f s' % (
total_time - (end_time - start_time))
printstr += ', ii = ' + str(ii + 1) + '/' + str(len(kxy))
print printstr
eigval, tr = calc_bands(matk, kxy[ii, 0], kxy[ii, 1])
kkx.append(kxy[ii, 0])
kky.append(kxy[ii, 1])
traces.append(tr)
bands.append(eigval)
bands = np.array(bands)
traces = np.array(traces)
kkx = np.array(kkx)
kky = np.array(kky)
bv = []
if verbose:
print 'kchern_gyro_fns: np.shape(bands) = ', np.shape(bands)
print 'kchern_gyro_fns: np.shape(traces) = ', np.shape(traces)
for ii in range(len(bands[0])):
chern = ((1j / (2. * np.pi)) * np.mean(traces[:, ii]) * bzarea
) # chern numbers for bands
bv.append(chern)
dat_dict = {
'kx': kkx,
'ky': kky,
'chern': bv,
'bands': bands,
'traces': traces,
'bzvtcs': bzvtcs
}
kchern.chern = dat_dict
return kchern.chern
def calc_chern(matk,
cp,
lattice,
appendstr=None,
verbose=True,
bz_cutoff=1e14,
kxy=None,
overwrite=False,
signed_norm=False):
"""Compute the chern number using the wedge product of the projector
Parameters
----------
kchern : KChernGyro class instance
kspace Chern calculation class
bz_cutoff : float
if BZ has a dimension larger than bz_cutoff, then we return zero chern number and no bands
Returns
-------
kchern.chern
"""
# this is really the only function you need to change to do a different kind of lattice.
# mM, vertex_points, od, fn, ar = lf.honeycomb_sheared(tvals, delta, phi, ons, base_dir)
# matk = glatkfns.lambda_matrix_kspace(kchern.gyro_lattice, eps=1e-10)
bzvtcs = lattice.get_bz(attribute=True)
# Define the brillouin zone polygon
bzarea = dh.polygon_area(bzvtcs)
# Check if a similar results file exists --> new data is appended to the old data, if it exists
savedir = cp['cpmeshfn']
dio.ensure_dir(savedir)
savefn, cp, search_density_fn = prepare_chern_filename(cp,
appendstr=appendstr)
print 'established savefn: ', cp['savefn']
if kxy is None:
globs = sorted(glob.glob(search_density_fn))
# Obtain the filename with the density that is smaller than the requested one
if globs and not overwrite:
densities = np.array([
int(
globfn.split('density')[-1].split('.pkl')[0].split('_')[0])
for globfn in globs
])
if (densities < cp['density']).any():
biggestsmall = densities[densities < cp['density']][-1]
smallerfn = savedir + 'kspacechern_density{0:07d}'.format(
biggestsmall)
if cp['ortho']:
smallerfn += '_ortho'
if 'deriv_res' in cp:
if cp['deriv_res'] != 1e-5:
smallerfn += '_dres{0:0.3e}'.format(
cp['deriv_res']).replace('-',
'n').replace('.', 'p')
smallerfn += appendstr
smallerfn += '.pkl'
else:
smallerfn = False
else:
smallerfn = False
if verbose:
print 'kchern_gyro_fns: looking for saved file ' + savefn
if os.path.isfile(savefn) and overwrite:
if verbose:
print 'kchern_gyro_fns: chern file exists, overwriting: ', savefn
# initialize empty lists to be filled up
kkx, kky = [], []
bands, traces = [], []
npts = int(cp['density'] * bzarea)
elif os.path.isfile(savefn):
# File of same size exists
chern = load_chern(cp, appendstr=appendstr, verbose=True)
print 'generalized_kchern_fns: loaded chern, returning chern'
return chern
elif smallerfn:
if verbose:
print 'kchern_gyro_fns: chern file with smaller density exists, loading to append...'
with open(smallerfn, 'rb') as of:
data = pkl.load(of)
# Convert contents to lists so that we can append to them
bands = list(data['bands'])
kkx = list(data['kx'])
kky = list(data['ky'])
traces = list(data['traces'])
# figure out how many points to append to reach desired density
npts = int(cp['density'] * bzarea) - len(kkx)
else:
if verbose:
print 'generalized_kchern_fns: no chern file with smaller density exists, computing from scratch...'
# initialize empty lists to be filled up
kkx, kky = [], []
bands, traces = [], []
npts = int(cp['density'] * bzarea)
# Make the kx, ky points to add to current results
# Handle cases where the BZ is too elongated to populate in reasonable time
try:
kxy = dh.generate_random_xy_in_polygon(npts, bzvtcs, sorted=True)
if len(kxy) == 0:
if (bzvtcs > bz_cutoff).any():
print 'The BZ is too large to fill with kvec points'
kkx, kky = np.zeros(10), np.zeros(10)
bv = np.zeros(2 * len(lattice.xy[:, 0]))
bands = np.nan * np.ones(2 * len(lattice.xy[:, 0]))
traces = np.nan * np.ones((2 * len(lattice.xy[:, 0]), 10))
dat_dict = {
'kx': kkx,
'ky': kky,
'chern': bv,
'bands': bands,
'traces': traces,
'bzvtcs': bzvtcs
}
chern = dat_dict
return chern
else:
print 'generalized_kchern_fns: bzvtcs = ', bzvtcs
print 'generalized_kchern_fns: kxy = ', kxy
raise RuntimeError(
'Could not generate random xy in polygon, but BZ is not larger than cutoff in any dim.'
)
except ValueError:
print 'The BZ is too large to fill with kvec points'
if (np.abs(bzvtcs) > bz_cutoff).any():
print 'The BZ is too large to fill with kvec points'
kkx, kky = np.zeros(10), np.zeros(10)
bv = np.zeros(2 * len(lattice.xy[:, 0]))
bands = np.nan * np.ones(2 * len(lattice.xy[:, 0]))
traces = np.nan * np.ones((2 * len(lattice.xy[:, 0]), 10))
dat_dict = {
'kx': kkx,
'ky': kky,
'chern': bv,
'bands': bands,
'traces': traces,
'bzvtcs': bzvtcs
}
chern = dat_dict
return chern
else:
print 'kchern_gyro_fns: bzvtcs = ', bzvtcs
print 'bz_cutoff = ', bz_cutoff
# print 'generalized_kchern_fns: kxy = ', kxy
raise RuntimeError(
'Could not generate random xy in polygon, but BZ is not larger than cutoff in any dim.'
)
dat_dict = {
'kx': kkx,
'ky': kky,
'chern': bv,
'bands': bands,
'traces': traces,
'bzvtcs': bzvtcs
}
else:
# kxy is supplied -- compute at those locations
print 'kxy is supplied, computing berry at supplied points'
kkx, kky = [], []
bands, traces = [], []
start_time = time.time()
for ii in range(len(kxy)):
# time for evaluating point index ii
tpi = []
# Display how much time is left
if ii % 4000 == 1999 and verbose:
end_time = time.time()
tpi.append(abs((start_time - end_time) / (ii + 1)))
total_time = np.mean(tpi) * len(kxy)
printstr = 'Estimated time remaining: ' + '%0.2f s' % (
total_time - (end_time - start_time))
printstr += ', ii = ' + str(ii + 1) + '/' + str(len(kxy))
print printstr
eigval, tr = calc_bands(matk,
kxy[ii, 0],
kxy[ii, 1],
h=cp['deriv_res'],
ortho=cp['ortho'],
signed_norm=signed_norm)
kkx.append(kxy[ii, 0])
kky.append(kxy[ii, 1])
traces.append(tr)
bands.append(eigval)
bands = np.array(bands)
traces = np.array(traces)
kkx = np.array(kkx)
kky = np.array(kky)
bv = []
if verbose:
print 'kchern_gyro_fns: np.shape(bands) = ', np.shape(bands)
print 'kchern_gyro_fns: np.shape(traces) = ', np.shape(traces)
for ii in range(len(bands[0])):
chern = ((1j / (2. * np.pi)) * np.mean(traces[:, ii]) * bzarea
) # chern numbers for bands
bv.append(chern)
dat_dict = {
'kx': kkx,
'ky': kky,
'chern': bv,
'bands': bands,
'traces': traces,
'bzvtcs': bzvtcs
}
chern = dat_dict
return chern
def save_plot(kx, ky, bands, traces, tvals, ons, vertex_points, od):
"""
Parameters
----------
kx
ky
bands
traces
tvals
ons
vertex_points
od
Returns
-------
"""
bz_area = dh.polygon_area(vertex_points)
w = 180
h = 140
label_params = dict(size=12, fontweight='normal')
s = 2
h1 = (h - 3 * s) / 2.
w1 = 0.75 * w
fig = sps.figure_in_mm(w, h)
ax1 = sps.axes_in_mm(0,
h - h1,
w1,
h1,
label=None,
label_params=label_params,
projection='3d')
ax2 = sps.axes_in_mm(0,
h - 2 * h1,
w1,
h1,
label=None,
label_params=label_params,
projection='3d')
ax3 = sps.axes_in_mm(1 * s + w1,
h - 2 * h1,
w - w1 - 3 * s,
2 * h1,
label=None,
label_params=label_params)
ax_lat = sps.axes_in_mm(1 * s + w1,
h - 0.4 * h1,
w - w1 - 3 * s,
0.4 * h1,
label=None,
label_params=label_params)
c1 = '#000000'
c2 = '#B9B9B9'
c3 = '#E51B1B'
c4 = '#18CFCF'
c5 = 'violet'
c6 = 'k'
cc1 = '#96adb4'
cc2 = '#b16566'
cc3 = '#665656'
cc4 = '#9d96b4'
pin = 1
cols = [c1, c2, c3, c4, c5, c6]
ccols = [cc1, cc2, cc3, cc4, c5]
l_cols = [cc1, cc2, cc3, cc4, c5]
ax3.axis([0, 1, 0, 1.])
print len(ons)
xp = [0.025, 0.525, 0.025, .525]
yp = [0.65, 0.65, 0.6, 0.6]
min_l = min([len(xp), len(ons)])
for i in range(len(tvals)):
ax3.text(xp[i],
yp[i] + 0.1,
'$k_%1d$' % (i + 1) + '= $%.2f$' % tvals[i],
style='italic',
color=ccols[i],
fontsize=12)
for i in range(min_l):
ax3.text(xp[i],
yp[i],
'$M_%1d$' % (i + 1) + '$= %.2f$' % ons[i],
style='italic',
color=cols[i],
fontsize=12)
ax3.axis('off')
ax_lat.axis('off')
nb = len(bands[0])
b = []
band_gaps = np.zeros(nb - 1)
ax3.text(0.025,
.55,
'$\mathrm{Chern\/ numbers}$',
fontweight='bold',
color='k',
fontsize=12)
ax3.text(0.025,
.35,
'$\mathrm{Band\/ boundaries}$',
fontweight='bold',
color='k',
fontsize=12)
ax3.text(0.025,
.15,
'$\mathrm{Min.\/differences}$',
fontweight='bold',
color='k',
fontsize=12)
for i in range(int(nb)):
j = i
bv = ((1j / (2 * np.pi)) * np.mean(traces[:, i]) * bz_area)
if j >= nb / 2:
ax1.plot_trisurf(kx,
ky,
bands[:, j],
cmap='cool',
vmin=min(abs(bands.flatten())),
vmax=max(abs(bands.flatten())),
linewidth=0.0,
alpha=1)
ax2.plot_trisurf(kx,
ky,
bands[:, j],
cmap='cool',
vmin=min(abs(bands.flatten())),
vmax=max(abs(bands.flatten())),
linewidth=0.0,
alpha=1)
v_min = min(abs(bands.flatten()))
v_max = max(abs(bands.flatten()))
# c_3D_plot(kx, ky, bands[:,j], vertex_points, ax1, v_min, v_max)
# c_3D_plot(kx, ky, bands[:,j], vertex_points, ax2, v_min, v_max)
ax1.set_ylabel('ky')
ax1.axes.get_xaxis().set_ticks([])
ax2.set_xlabel('kx')
ax2.axes.get_yaxis().set_ticks([])
ax1.set_zlabel('$\Omega$')
ax2.set_zlabel('$\Omega$')
bv = ((1j / (2 * np.pi)) * np.mean(traces[:, i]) * bz_area)
if abs(bv) > 0.7:
cc = 'r'
else:
cc = 'k'
ax3.text(0.025,
.55 - (j - nb / 2 + 1) * 0.04,
'$%0.2f$' % bv,
color=cc,
fontsize=10)
maxtb = max(bands[:, j])
mintb = min(bands[:, j])
ax3.text(0.025,
.35 - (j - nb / 2 + 1) * 0.04,
'$%0.2f$' % mintb,
color='k',
fontsize=10)
ax3.text(0.525,
.35 - (j - nb / 2 + 1) * 0.04,
'$%0.2f$' % maxtb,
color='k',
fontsize=10)
if j > nb / 2:
min_diff = min(bands[:, j] - bands[:, j - 1])
ax3.text(0.025,
.15 - (j - nb / 2) * 0.04,
'$%0.2f$' % min_diff,
color='k',
fontsize=10)
ax1.view_init(elev=0, azim=0.)
ax2.view_init(elev=0, azim=90.)
minx = min(kx)
maxx = max(kx)
miny = min(ky)
maxy = max(ky)
return ax_lat, cols, l_cols, fig
def make_plot(kkx, kky, bands, traces, tvals, ons, pin, num='all'):
"""One of Lisa's functions for plotting chern results with lattice structure"""
w = 180
h = 140
label_params = dict(size=12, fontweight='normal')
s = 2
h1 = (h - 3 * s) / 2.
w1 = 0.75 * w
fig = sps.figure_in_mm(w, h)
ax1 = sps.axes_in_mm(0,
h - h1,
w1,
h1,
label=None,
label_params=label_params,
projection='3d')
ax2 = sps.axes_in_mm(0,
h - 2 * h1,
w1,
h1,
label=None,
label_params=label_params,
projection='3d')
ax3 = sps.axes_in_mm(1 * s + w1,
h - 2 * h1,
w - w1 - 3 * s,
2 * h1,
label=None,
label_params=label_params)
ax_lat = sps.axes_in_mm(1 * s + w1,
h - 0.4 * h1,
w - w1 - 3 * s,
0.4 * h1,
label=None,
label_params=label_params)
c1 = '#000000'
c2 = '#FFFFFF'
c3 = '#E51B1B'
c4 = '#18CFCF'
cc1 = '#96adb4'
cc2 = '#b16566'
cc3 = '#665656'
cc4 = '#9d96b4'
pin = 1
gor = 'random' # grid or random, lines
cols = [c1, c2, c3, c4]
l_cols = [cc1, cc2, cc3, cc4]
mM, vertex_points, dump_dir = lf.alpha_lattice(tvals,
pin,
ons,
ax=ax_lat,
col=cols,
lincol=l_cols)
ax3.axis([0, 1, 0, 1.])
ax3.text(0.025,
.75,
'$k_1 = %.2f$' % tvals[0],
style='italic',
color=cc1,
fontsize=12)
ax3.text(0.525,
.75,
'$k_2 = %.2f$' % tvals[1],
style='italic',
color=cc2,
fontsize=12)
ax3.text(0.025,
.7,
'$k_3 = %.2f$' % tvals[2],
style='italic',
color=cc3,
fontsize=12)
ax3.text(0.525,
.7,
'$k_4 = %.2f$' % tvals[3],
style='italic',
color=cc4,
fontsize=12)
# bbox={'facecolor':'red', 'alpha':0.5, 'pad':10}
ax3.text(0.025,
.65,
'$M_1 = %.2f$' % ons[0],
style='italic',
color=c1,
fontsize=12)
ax3.text(0.525,
.65,
'$M_2 = %.2f$' % ons[1],
style='italic',
color='#B9B9B9',
fontsize=12)
ax3.text(0.025,
.6,
'$M_3 = %.2f$' % ons[2],
style='italic',
color=c3,
fontsize=12)
ax3.text(0.525,
.6,
'$M_4 = %.2f$' % ons[3],
style='italic',
color=c4,
fontsize=12)
# bbox={'facecolor':'red', 'alpha':0.5, 'pad':10}
ax3.axis('off')
ax_lat.axis('off')
nb = len(bands[0])
b = []
bz_area = dh.polygon_area(vertex_points)
band_gaps = np.zeros(nb - 1)
ax3.text(0.025,
.55,
'$\mathrm{Chern\/ numbers}$',
fontweight='bold',
color='k',
fontsize=12)
ax3.text(0.025,
.35,
'$\mathrm{Band\/ boundaries}$',
fontweight='bold',
color='k',
fontsize=12)
ax3.text(0.025,
.15,
'$\mathrm{Min.\/differences}$',
fontweight='bold',
color='k',
fontsize=12)
for i in range(int(nb)):
j = i
# ax.scatter(kkx, kky, 1j*traces[:,j])
if j >= nb / 2:
if num == 'all':
ax1.plot_trisurf(kx[:1000],
ky[:1000],
bands[:, j][:1000],
cmap='cool',
vmin=min(abs(bands.flatten())),
vmax=max(abs(bands.flatten())),
linewidth=0.0)
# ax2.plot_trisurf(kx, ky, bands[:,j], cmap = 'cool', vmin = min(abs(bands.flatten())),
# vmax=max(abs(bands.flatten())), linewidth = 0.0)
elif j == num:
ax2.plot_trisurf(kx[:1000],
ky[:1000],
1j * traces[:, j][:1000],
cmap='cool',
vmin=min(abs(traces[:, j].flatten())),
vmax=max(abs(traces[:, j].flatten())),
linewidth=0.0)
ax1.set_ylabel('ky')
ax1.axes.get_xaxis().set_ticks([])
ax2.set_xlabel('kx')
ax2.axes.get_yaxis().set_ticks([])
ax1.set_zlabel('$\Omega$')
ax2.set_zlabel('$\Omega$')
bv = ((1j / (2 * np.pi)) * np.mean(traces[:, i]) * bz_area)
if abs(bv) > 0.7:
cc = 'r'
else:
cc = 'k'
if j == num:
ax3.text(0.025,
.55 - (j - nb / 2 + 1) * 0.04,
'$%0.2f$' % bv,
color=cc,
fontsize=10)
if j >= nb / 2:
maxtb = max(bands[:, j])
mintb = min(bands[:, j])
ax3.text(0.025,
.35 - (j - nb / 2 + 1) * 0.04,
'$%0.2f$' % mintb,
color='k',
fontsize=10)
ax3.text(0.525,
.35 - (j - nb / 2 + 1) * 0.04,
'$%0.2f$' % maxtb,
color='k',
fontsize=10)
if j > nb / 2:
min_diff = min(bands[:, j] - bands[:, j - 1])
ax3.text(0.025,
.15 - (j - nb / 2) * 0.04,
'$%0.2f$' % min_diff,
color='k',
fontsize=10)
b.append((1j / (2 * np.pi)) * np.mean(traces[:, i]) * bz_area)
ax1.view_init(elev=0, azim=0.)
ax2.view_init(elev=-0, azim=90.)
minx = min(kx)
maxx = max(kx)
miny = min(ky)
maxy = max(ky)
# plt.show()
# plt.savefig(save_dir+'/images/test.png')
s1 = '%0.2f_' % tvals[0]
s2 = '%0.2f_' % tvals[1]
s3 = '%0.2f_' % tvals[2]
s4 = '%0.2f_' % tvals[3]
s5 = '%0.2f_' % ons[0]
s6 = '%0.2f_' % ons[1]
s7 = '%0.2f_' % ons[2]
s8 = '%0.2f_' % ons[3]
plt.show()