def initialize_1panelcbar_fig(Wfig=90,
Hfig=None,
x0frac=0.15,
y0frac=0.1,
wsfrac=0.4,
hs=None,
vspace=8,
hspace=8,
tspace=10,
fontsize=8):
"""Create new figure with 1 panel and a colorbar, with defaults set for kitaev plots
Parameters
----------
Wfig : width of the figure in mm
x0frac : fraction of Wfig to leave blank left of plot
y0frac : fraction of Wfig to leave blank below plot
wsfrac : fraction of Wfig to make width of subplot
hs : height of subplot in mm. If none, uses ws = wsfrac * Wfig
vspace : vertical space between subplots
hspace : horizontal space btwn subplots
tspace : space above top figure
fontsize : size of text labels, title
Returns
-------
fig
ax
"""
# Make figure
x0 = round(Wfig * x0frac)
y0 = round(Wfig * y0frac)
ws = round(Wfig * wsfrac)
if hs is None:
hs = ws
wscbar = ws * 0.3
hscbar = wscbar * 0.1
if Hfig is None:
Hfig = y0 + hs + vspace + hscbar + tspace
fig = sps.figure_in_mm(Wfig, Hfig)
label_params = dict(size=fontsize, fontweight='normal')
ax = [
sps.axes_in_mm(x0,
y0,
width,
height,
label=part,
label_params=label_params)
for x0, y0, width, height, part in (
[Wfig * 0.5 -
ws * 0.5, y0, ws, hs, ''], # Chern vary hlatparam vs ksize
[Wfig * 0.5 - wscbar, y0 + hs + vspace, wscbar *
2, hscbar, ''] # cbar for chern
)
]
return fig, ax
def initialize_spectrum_with_dos_plot(Wfig=90,
Hfig=None,
x0frac=0.15,
y0frac=0.1,
wsfrac=0.4,
hs=None,
wsdosfrac=0.3,
vspace=8,
hspace=8,
tspace=10,
fontsize=8):
"""Initialize a figure with 4 axes, for plotting chern spectrum with accompanying DOS plot on the side
Returns
-------
fig :
ax : list of 4 matplotlib axis instances
ax[0] is DOS axis, ax[1] is chern spectrum axis, ax[2] is ipr cbar axis, ax[3] is cbar for chern axis
"""
x0 = round(Wfig * x0frac)
y0 = round(Wfig * y0frac)
ws = round(Wfig * wsfrac)
hs = ws
wsDOS = wsdosfrac * ws
hsDOS = hs
wscbar = wsDOS
hscbar = wscbar * 0.1
if Hfig is None:
Hfig = y0 + hs + vspace + hscbar + tspace
fig = sps.figure_in_mm(Wfig, Hfig)
label_params = dict(size=fontsize, fontweight='normal')
ax = [
sps.axes_in_mm(x0,
y0,
width,
height,
label=part,
label_params=label_params)
for x0, y0, width, height, part in (
[x0, y0, wsDOS, hsDOS, ''], # DOS
[x0 + wsDOS + hspace, y0, ws, hs, ''], # Chern omegac vs ksize
[
x0 + wsDOS * 0.5 - wscbar * 0.5, y0 + hs +
vspace, wscbar, hscbar, ''
], # cbar for ipr
[
x0 + wsDOS + hspace + ws * 0.5 - wscbar, y0 + hs +
vspace, wscbar * 2, hscbar, ''
] # cbar for chern
)
]
return fig, ax
def initialize_1p5panelcbar_fig(Wfig=90, Hfig=None, x0frac=0.15, y0frac=0.1, wsfrac=0.4, hs=None,
wssfrac=0.25, hssfrac=None, vspace=8, tspace=10,
fontsize=8, center0_frac=0.35, center2_frac=0.65):
"""Plot a chern figure with a panel below the plot (as in for showing kitaev regions)
Parameters
----------
Wfig : width of the figure in mm
x0frac : fraction of Wfig to leave blank left of plot
y0frac : fraction of Wfig to leave blank below plot
wsfrac : fraction of Wfig to make width of subplot
hs : height of subplot in mm. If none, uses ws = wsfrac * Wfig
vspace : vertical space between subplots
tspace : space above top figure
fontsize : size of text labels, title
Returns
-------
fig
ax
"""
# Make figure
x0 = round(Wfig * x0frac)
y0 = round(Wfig * y0frac)
ws = round(Wfig * wsfrac)
wss = Wfig * wssfrac
if hssfrac is None:
hssfrac = wssfrac
hss = Wfig * hssfrac
if hs is None:
hs = ws
wscbar = ws * 0.3
hscbar = wscbar * 0.1
if Hfig is None:
Hfig = y0 + hs + vspace + hscbar + tspace
fig = sps.figure_in_mm(Wfig, Hfig)
label_params = dict(size=fontsize, fontweight='normal')
ax = [sps.axes_in_mm(x0, y0, width, height, label=part, label_params=label_params)
for x0, y0, width, height, part in (
[Wfig * center0_frac - ws * 0.5, y0, ws, hs, ''], # Chern vary glatparam vs ksize
[Wfig * center0_frac - wscbar, y0 + hs + vspace, wscbar * 2, hscbar, ''], # cbar for chern (DOS)
[Wfig * center2_frac - ws * 0.5, y0, wss, hss, ''] # subplot for showing kitaev region
)]
return fig, ax
def movie_honeycomb_single_frame(deltaz, phi, text=True):
"""
Parameters
----------
deltaz
phi
text
Returns
-------
"""
a1, a2 = bz.find_lattice_vecs(deltaz * np.pi / 180., phi * np.pi / 180.)
b1, b2 = bz.find_bz_lattice_vecs(a1, a2)
R, Ni, Nk, cols, line_cols = lf.honeycomb_sheared_vis(deltaz, phi)
fig = sps.figure_in_mm(90, 90)
ax_lat = sps.axes_in_mm(0, 0, 90, 90)
lf.lattice_plot(R, Ni, Nk, ax_lat, cols, line_cols)
plt.ylim(-2, 1)
plt.xlim(-3, 3)
ax_lat.axis('off')
if text:
ax_lat.text(-2.75,
1.5,
'$\delta$ = %d$^{\circ}$' % deltaz,
fontweight='bold',
color='k',
fontsize=16)
ax_lat.text(-2.75,
1.3,
'$\phi$ = %d$^{\circ}$' % phi,
fontweight='bold',
color='k',
fontsize=16)
return fig, ax_lat
# Make figure
Wfig = 180
x0frac = 0.1
y0frac = 0.1
wsfrac = 0.35
tspace = 12
vspace = 20
hspace = 18
FSFS = 12
x0 = round(Wfig * x0frac)
y0 = round(Wfig * y0frac)
ws = round(Wfig * wsfrac)
hs = ws
Hfig = y0 + 2 * hs + vspace + tspace
fig = sps.figure_in_mm(Wfig, Hfig)
label_params = dict(size=FSFS, fontweight='normal')
ax = [
sps.axes_in_mm(x0,
y0,
width,
height,
label=part,
label_params=label_params)
for x0, y0, width, height, part in (
[x0, y0 + hs + vspace, ws, hs, ''], [x0, y0, ws, hs, ''],
[x0 + ws + hspace, y0, ws, hs, ''],
[x0 + ws + hspace, y0 + hs + vspace, ws, hs, ''])
]
ind = 0
def make_mode_movie(seriesdir,
amp=50,
semilog=True,
freqmin=None,
freqmax=None,
overwrite=False,
percent_max=0.01):
"""
Parameters
----------
seriesdir : str
The full path to the directory with all tracked cines for which to make mode decomposition movies
amp : float
The amplification factor for the displacements in the movie output
semilog : bool
plot the FFT intensity vs frequency in log-normal scale
freqmin : float
The minimum frequency to plot
freqmax : float
The maximum frequency to plot
overwrite : bool
Overwrite the saved images/movies if they exist
Returns
-------
"""
pathlist = dio.find_subdirs('20*', seriesdir)
freqstr = ''
if freqmin is not None:
freqstr += '_minfreq' + sf.float2pstr(freqmin)
if freqmax is not None:
freqstr += '_maxfreq' + sf.float2pstr(freqmax)
for path in pathlist:
movname = path + 'modes_amp{0:0.1f}'.format(amp).replace(
'.', 'p') + freqstr + '.mov'
movexist = glob.glob(movname)
print 'mode_movie: movexist = ', movexist
if not movexist or overwrite:
# If the movie does not exist yet, make it here
print 'building modes for ', path
fn = os.path.join(path, 'com_data.hdf5')
print 'mode_drawing_functions.mode_movie.make_mode_movie(): loading data from ', fn
data = new_mf.load_linked_data_and_window(fn)
tp, fft_x, fft_y, freq = nmf.ffts_and_add(data)
high_power_inds = nmf.find_peaks(tp, percent_max=percent_max)
# Check how many mode images have been done
modespngs = glob.glob(path + 'modes' + freqstr + '/*.png')
modespngs_traces = glob.glob(path + 'modes_traces' + freqstr +
'/*.png')
# If we haven't made images of all the modes, do that here
print 'mode_movie.make_mode_movie(): len(modespngs) = ', len(
modespngs)
print 'mode_movie.make_mode_movie(): len(high_power_inds) = ', len(
high_power_inds)
if len(modespngs) < len(high_power_inds) or overwrite:
# Check if mode pickle is saved
modesfn_traces = path + 'modes_trackes' + freqstr + '.pkl'
globfn = glob.glob(modesfn_traces)
if globfn:
with open(globfn[0], "rb") as fn:
mode_data = cPickle.load(fn)
coords = mode_data['xy']
mode_data = dh.removekey(mode_data, 'xy')
for i in mode_data:
if i % 10 == 0:
print 'mode_movie.make_mode_movie(): Creating mode image #', i
x_traces = mode_data[i][
'x_traces'] # sf.float2pcstr(freq[high_power_inds[i]], ndigits=8)]
y_traces = mode_data[i]['y_traces']
fig = sps.figure_in_mm(120, 155)
ax_mode = sps.axes_in_mm(10, 10, 100, 100)
ax_freq = sps.axes_in_mm(10, 120, 100, 30)
axes = [ax_mode, ax_freq]
new_mf.draw_mode(x_traces,
y_traces,
coords, [freq, tp],
axes,
i,
freq[high_power_inds[i]],
output_dir=os.path.join(
path, 'modes'),
amp=amp,
semilog=semilog)
else:
# The mode data is not saved, so we must create it as we go along
# data = new_mf.load_linked_data_and_window(fn)
coords = np.array([data[2], data[3]]).T
for i in xrange(len(high_power_inds)):
if i % 10 == 0:
print 'mode_movie.make_mode_movie(): Creating mode image #', i
x_traces, y_traces, max_mag = new_mf.get_mode_drawing_data(
fft_x, fft_y, freq, high_power_inds[i])
x_traces = np.array(x_traces)
y_traces = np.array(y_traces)
fig = sps.figure_in_mm(120, 155)
ax_mode = sps.axes_in_mm(10, 10, 100, 100)
ax_freq = sps.axes_in_mm(10, 120, 100, 30)
axes = [ax_mode, ax_freq]
new_mf.draw_mode(x_traces,
y_traces,
coords, [freq, tp],
axes,
i,
freq[high_power_inds[i]],
output_dir=os.path.join(
path, 'modes'),
amp=amp,
semilog=semilog)
# Obtain imagename and moviename, create movie if it doesn't exist
modesfn = glob.glob(path + 'modes' + freqstr + '/*.png')
imagename_split = modesfn[0].split('/')[-1].split('.png')[0]
try:
test = int(imagename_split)
indexsz = len(imagename_split)
imgname = path + 'modes' + freqstr + '/'
except:
print 'mode_movie.make_mode_movie(): imagename_split = ', imagename_split
print 'mode_movie.make_mode_movie(): indexsz = ', len(
imagename_split)
raise RuntimeError(
'Imagename is not just an int -- write code to allow ')
movies.make_movie(imgname,
movname,
indexsz=str(indexsz),
framerate=10)
def plot_cherns_varyloc(ccoll, title='Chern number calculation for varied positions',
filename='chern_varyloc', exten='.pdf', rootdir=None, outdir=None,
max_boxfrac=None, max_boxsize=None,
xlabel=None, ylabel=None, step=0.5, fracsteps=False,
singleksz_frac=None, singleksz=-1.0, maxchern=False,
ax=None, cbar_ax=None, save=True, make_cbar=True, colorz=False,
dpi=600, colormap='divgmap_blue_red'):
"""Plot the chern as a function of space for each haldane_lattice examined. If save==True, saves figure to
Parameters
----------
ccoll : ChernCollection instance
filename : str
name of the file to output as the plot
rootdir : str or None
if specified, dictates the path where the file is stored, with the output directory as
outdir = kfns.get_cmeshfn(ccoll.cherns[hlat_name][0].haldane_lattice.lp, rootdir=rootdir)
max_boxfrac : float
Fraction of spatial extent of the sample to use as maximum bound for kitaev sum
max_boxsize : float or None
If None, uses max_boxfrac * spatial extent of the sample asmax_boxsize
singleksz : float
if positive, plots the spatially-resolved chern number for a single kitaev summation region size, closest to
the supplied value in actual size. Otherwise, draws many rectangles of different sizes for different ksizes.
If positive, IGNORES max_boxfrac and max_boxsize arguments
maxchern : bool
if True, plots the EXTREMAL spatially-resolved chern number for each voxel --> ie the maximum (signed absolute)
value it reaches. Otherwise, draws many rectangles of different sizes for different ksizes.
If True, the function IGNORES max_boxfrac and max_boxsize arguments
ax : axis instance or None
cbar_ax : axis instance or None
save : bool
make_cbar : bool
dpi : int
dots per inch, if exten is '.png'
"""
if colormap == 'divgmap_blue_red':
divgmap = cmaps.diverging_cmap(250, 10, l=30)
elif colormap == 'divgmap_red_blue':
divgmap = cmaps.diverging_cmap(10, 250, l=30)
# plot it
for hlat_name in ccoll.cherns:
rectps = []
colorL = []
print 'all hlats should have same pointer:'
print 'ccoll[hlat_name][0].haldane_lattice = ', ccoll.cherns[hlat_name][0].haldane_lattice
print 'ccoll[hlat_name][1].haldane_lattice = ', ccoll.cherns[hlat_name][1].haldane_lattice
print 'Opening hlat_name = ', hlat_name
if outdir is None:
outdir = hcfns.get_cmeshfn(ccoll.cherns[hlat_name][0].haldane_lattice.lp, rootdir=rootdir)
print 'when saving, will save to ' + outdir + 'filename'
if maxchern:
for chernii in ccoll.cherns[hlat_name]:
# Grab small, medium, and large circles
ksize = chernii.chern_finsize[:, 2]
# Build XYloc_sz_nu from all cherns done on this network
xx = float(chernii.cp['poly_offset'].split('/')[0])
yy = float(chernii.cp['poly_offset'].split('/')[1])
nu = chernii.chern_finsize[:, -1]
ind = np.argmax(np.abs(nu))
rad = step
rect = plt.Rectangle((xx-rad*0.5, yy-rad*0.5), rad, rad, ec="none")
colorL.append(nu[ind])
rectps.append(rect)
elif singleksz > 0:
for chernii in ccoll.cherns[hlat_name]:
# Grab small, medium, and large circles
ksize = chernii.chern_finsize[:, 2]
# Build XYloc_sz_nu from all cherns done on this network
xx = float(chernii.cp['poly_offset'].split('/')[0])
yy = float(chernii.cp['poly_offset'].split('/')[1])
nu = chernii.chern_finsize[:, -1]
ind = np.argmin(np.abs(ksize - singleksz))
# print 'ksize = ', ksize
# print 'singleksz = ', singleksz
# print 'ind = ', ind
rad = step
rect = plt.Rectangle((xx-rad*0.5, yy-rad*0.5), rad, rad, ec="none")
colorL.append(nu[ind])
rectps.append(rect)
elif singleksz_frac > 0:
for chernii in ccoll.cherns[hlat_name]:
# Grab small, medium, and large circles
ksize_frac = chernii.chern_finsize[:, 1]
# Build XYloc_sz_nu from all cherns done on this network
xx = float(chernii.cp['poly_offset'].split('/')[0])
yy = float(chernii.cp['poly_offset'].split('/')[1])
nu = chernii.chern_finsize[:, -1]
ind = np.argmin(np.abs(ksize_frac - singleksz_frac))
# print 'ksize = ', ksize
# print 'singleksz = ', singleksz
# print 'ind = ', ind
rad = step
rect = plt.Rectangle((xx - rad * 0.5, yy - rad * 0.5), rad, rad, ec="none")
colorL.append(nu[ind])
rectps.append(rect)
else:
print 'stacking rectangles in list to add to plot...'
for chernii in ccoll.cherns[hlat_name]:
# Grab small, medium, and large circles
# Note: I used to multiply by 0.5 here: Why did I multiply by 0.5 here? Not sure...
# perhaps before I measured ksize by its diameter (or width) but used radius as an imput for drawing
# a kitaev region.
ksize = chernii.chern_finsize[:, 2]
if max_boxsize is not None:
ksize = ksize[ksize < max_boxsize]
else:
if max_boxfrac is not None:
cgll = chernii.haldane_lattice.lattice
maxsz = max(np.max(cgll.xy[:, 0]) - np.min(cgll.xy[:, 0]),
np.max(cgll.xy[:, 1]) - np.min(cgll.xy[:, 1]))
max_boxsize = max_boxfrac * maxsz
ksize = ksize[ksize < max_boxsize]
else:
ksize = ksize
max_boxsize = np.max(ksize)
# print 'ksize = ', ksize
# print 'max_boxsize = ', max_boxsize
# Build XYloc_sz_nu from all cherns done on this network
xx = float(chernii.cp['poly_offset'].split('/')[0])
yy = float(chernii.cp['poly_offset'].split('/')[1])
nu = chernii.chern_finsize[:, -1]
rectsizes = ksize / np.max(ksize) * step
# Choose which rectangles to draw
if len(ksize) > 30:
# Too many rectangles to add to the plot! Limit the number to keep file size down
inds2use = np.arange(0, len(ksize), int(float(len(ksize))*0.05))[::-1]
else:
inds2use = np.arange(0, len(ksize), 1)[::-1]
# print 'Adding ' + str(len(inds2use)) + ' rectangles...'
# Make a list of the rectangles
for ind in inds2use:
rad = rectsizes[ind]
rect = plt.Rectangle((xx-rad*0.5, yy-rad*0.5), rad, rad, ec="none")
colorL.append(nu[ind])
rectps.append(rect)
print 'Adding patches to figure...'
p = PatchCollection(rectps, cmap=divgmap, alpha=1.0, edgecolors='none')
p.set_array(np.array(np.array(colorL)))
p.set_clim([-1., 1.])
if ax is None:
# Make figure
FSFS = 8
Wfig = 90
x0 = round(Wfig * 0.15)
y0 = round(Wfig * 0.1)
ws = round(Wfig * 0.4)
hs = ws
wsDOS = ws * 0.3
hsDOS = hs
wscbar = wsDOS
hscbar = wscbar * 0.1
vspace = 8 # vertical space btwn subplots
hspace = 8 # horizonl space btwn subplots
tspace = 10 # space above top figure
Hfig = y0 + hs + vspace + hscbar + tspace
fig = sps.figure_in_mm(Wfig, Hfig)
label_params = dict(size=FSFS, fontweight='normal')
ax = [sps.axes_in_mm(x0, y0, width, height, label=part, label_params=label_params)
for x0, y0, width, height, part in (
[Wfig * 0.5 - ws * 0.5, y0, ws, hs, ''], # Chern vary hlatparam vs ksize
[Wfig * 0.5 - wscbar, y0 + hs + vspace, wscbar * 2, hscbar, ''] # cbar for chern
)]
# Add the patches of nu calculations for each site probed
ax[0].add_collection(p)
hlat = ccoll.cherns[hlat_name][0].haldane_lattice
netvis.movie_plot_2D(hlat.lattice.xy, hlat.lattice.BL, 0*hlat.lattice.BL[:, 0],
None, None, ax=ax[0], fig=fig, axcb=None,
xlimv='auto', ylimv='auto', climv=0.1, colorz=colorz, ptcolor=None, figsize='auto',
colormap='BlueBlackRed', bgcolor='#ffffff', axis_off=True, axis_equal=True,
lw=0.2)
# Add title
ax[0].annotate(title, xy=(0.5, .95), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='center')
if xlabel is not None:
ax[0].set_xlabel(xlabel)
if ylabel is not None:
ax[0].set_xlabel(ylabel)
# Position colorbar
sm = plt.cm.ScalarMappable(cmap=divgmap, norm=plt.Normalize(vmin=-1, vmax=1))
# fake up the array of the scalar mappable.
sm._A = []
cbar = plt.colorbar(sm, cax=ax[1], orientation='horizontal', ticks=[-1, 0, 1])
ax[1].set_xlabel(r'$\nu$')
ax[1].xaxis.set_label_position("top")
else:
# Add the patches of nu calculations for each site probed
ax.add_collection(p)
hlat = ccoll.cherns[hlat_name][0].haldane_lattice
netvis.movie_plot_2D(hlat.lattice.xy, hlat.lattice.BL, 0*hlat.lattice.BL[:, 0],
None, None, ax=ax, fig=plt.gcf(), axcb=None,
xlimv='auto', ylimv='auto', climv=0.1, colorz=colorz, ptcolor=None, figsize='auto',
colormap='BlueBlackRed', bgcolor='#ffffff', axis_off=True, axis_equal=True,
lw=0.2)
# Add title
if title is not None and title != 'none':
ax.annotate(title, xy=(0.5, .95), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='center')
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_xlabel(ylabel)
if make_cbar:
# Position colorbar
sm = plt.cm.ScalarMappable(cmap=divgmap, norm=plt.Normalize(vmin=-1, vmax=1))
# fake up the array of the scalar mappable.
sm._A = []
if cbar_ax is not None:
# figure out if cbar_ax is horizontal or vertical
if leplt.cbar_ax_is_vertical(cbar_ax):
cbar = plt.colorbar(sm, cax=cbar_ax, orientation='vertical', ticks=[-1, 0, 1],
label='Chern\n' + r'number, $\nu$')
else:
cbar = plt.colorbar(sm, cax=cbar_ax, orientation='horizontal', ticks=[-1, 0, 1],
label=r'Chern number, $\nu$')
cbar_ax.set_xlabel(r'Chern number, $\nu$')
cbar_ax.xaxis.set_label_position("top")
else:
cbar = plt.colorbar(sm, cax=cbar_ax, orientation='horizontal', ticks=[-1, 0, 1],
label=r'Chern number, $\nu$')
if save:
# Save the plot
# make outdir the hlat_cmesh
print 'kcollpfns: saving figure:\n outdir =', outdir, ' filename = ', filename
# outdir = kfns.get_cmeshfn(ccoll.cherns[hlat_name][0].haldane_lattice.lp, rootdir=rootdir)
if filename == 'chern_varyloc':
filename += '_Nks'+'{0:03d}'.format(len(ksize)) + '_step' + sf.float2pstr(step) + '_maxbsz' +\
sf.float2pstr(max_boxsize) + exten
print 'saving figure: ' + outdir + filename
if exten == '.png':
plt.savefig(outdir + filename, dpi=dpi)
else:
plt.savefig(outdir + filename)
plt.clf()
return ax, cbar
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()