def plot_spectrum_with_dos(hlat,
ksys_fracM,
omegacM,
nuM,
ngrid,
Wfig=90,
Hfig=None,
x0frac=0.15,
y0frac=0.1,
wsfrac=0.4,
hs=None,
wsdosfrac=0.3,
vspace=8,
hspace=8,
tspace=10,
FSFS=8,
xlabel=None,
ylabel=None,
title=None):
"""Plot chern spectrum vs ksize (or other variable on x axis), with corresponding DOS plot aligned with y axis
(increasing in frequency).
Parameters
----------
hlat : haldane_lattice
Wfig : int or float
width of the figure in mm
Hfig : int or float or None
Height 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 : matplotlib.pyplot figure instance
ax : matplotlib.pyplot axis instance
"""
# Make figure
fig, ax = initialize_spectrum_with_dos_plot(Wfig=Wfig,
Hfig=Hfig,
x0frac=x0frac,
y0frac=y0frac,
wsfrac=wsfrac,
hs=hs,
wsdosfrac=wsdosfrac,
vspace=vspace,
hspace=hspace,
tspace=tspace,
fontsize=FSFS)
# ax[2].xaxis.set_major_locator( MaxNLocator(nbins = 3) ) #, prune = 'lower') )
# ax[3].xaxis.set_major_locator( MaxNLocator(nbins = 3) )
# Plot sideways DOS colored by PR: http://matplotlib.org/examples/pylab_examples/scatter_hist.html
print 'kpfns: adding ipr to axis...'
hlat.add_ipr_to_ax(ax[0],
ipr=None,
alpha=1.0,
inverse_PR=False,
norm=None,
nbins=75,
fontsize=FSFS,
cbar_ax=ax[2],
vmin=None,
vmax=None,
linewidth=0,
cax_label=r'$p$',
make_cbar=True,
climbars=True,
cbar_labelpad=3,
orientation='horizontal',
cbar_orientation='horizontal',
invert_xaxis=True,
ylabel=r'$D(\omega)$',
xlabel='Oscillation frequency $\omega$',
cbar_nticks=3,
cbar_tickfmt='%0.1f')
leplt.plot_pcolormesh(ksys_fracM,
omegacM,
nuM,
ngrid,
ax=ax[1],
cax=ax[3],
method='nearest',
cmap=cmaps.diverging_cmap(250, 10, l=30),
vmin=-1.0,
vmax=1.0,
title=title,
xlabel=xlabel,
ylabel=ylabel,
cax_label=r'$\nu$',
cbar_labelpad=3,
cbar_orientation='horizontal',
ticks=[-1, 0, 1],
fontsize=FSFS)
# plt.show()
# sys.exit()
# Add title
ax[0].annotate(title,
xy=(0.5, .95),
xycoords='figure fraction',
horizontalalignment='center',
verticalalignment='center')
# Match axes
ax[0].set_ylim(ax[1].get_ylim())
ax[0].xaxis.set_major_locator(MaxNLocator(nbins=2)) # , prune = 'upper') )
ax[1].yaxis.set_label_position("right")
ax[1].yaxis.set_ticks_position('both')
plt.setp(ax[1].get_yticklabels(), visible=False)
ax[2].xaxis.set_label_position("top")
ax[3].xaxis.set_label_position("top")
return fig, ax
def plot_chern_spectrum_on_axis(kcoll, ax, dos_ax, cbar_ipr_ax, cbar_nu_ax, fontsize=8, alpha=1.0,
ipr_vmin=None, ipr_vmax=None, cbar_labelpad=3, invert_xaxis=True,
ipr_cax_label='participation\nratio,' + r' $p$',
chern_cax_label='Chern\nnumber, ' + r'$\nu$'):
"""
Parameters
----------
kcoll : HaldaneCollection instance
The chern collection to add to the plot
ax : matplotlib axis instance
the axis on which to add the spectrum
dos_ax : matplotlib axis instance
the axis on which to add
alpha : float
opacity of the spectrum and DOS added to the plot
Returns
-------
ax, dos_ax
"""
dmyi = 0
for hlat_name in kcoll.cherns:
# Grab a pointer to the haldane_lattice
hlat = kcoll.cherns[hlat_name][0].haldane_lattice
dmyi += 1
if dmyi > 1:
raise RuntimeError('This function takes a HaldaneCollection which is allowed only ONE haldane_lattice-- ' +
'ie, a chern spectrum for a single haldaneLattice instance')
print 'Opening hlat_name = ', hlat_name
ngrid = len(kcoll.cherns[hlat_name][0].chern_finsize)
# Assume all ksize elements are the same size for now
ksys_frac = kcoll.cherns[hlat_name][0].chern_finsize[:, -2]*0.5
ksys_fracM = np.array([ksys_frac for i in range(len(kcoll.cherns[hlat_name]))])
# Build omegacV
omegacV = np.zeros(len(kcoll.cherns[hlat_name]))
nuM = np.zeros((len(kcoll.cherns[hlat_name]), len(ksys_frac)))
for ind in range(len(kcoll.cherns[hlat_name])):
print 'ind = ', ind
cp_ii = kcoll.cherns[hlat_name][ind].cp
omegacV[ind] = cp_ii['omegac']
nuM[ind, :] = kcoll.cherns[hlat_name][ind].chern_finsize[:, -1]
omegacM = omegacV.reshape(len(nuM), 1) * np.ones_like(nuM)
print 'omegacM = ', omegacM
print 'kpfns: adding ipr and chern spectrum to axes...'
print 'Adding chern spectrum to axis: ax = ', ax
leplt.plot_pcolormesh(ksys_fracM, omegacM, nuM, ngrid, ax=ax, cax=cbar_nu_ax, method='nearest',
cmap=cmaps.diverging_cmap(250, 10, l=30),
vmin=-1.0, vmax=1.0, title=None, xlabel=None, ylabel=None, cax_label=chern_cax_label,
cbar_labelpad=cbar_labelpad, cbar_orientation='horizontal', ticks=[-1, 0, 1],
fontsize=fontsize, alpha=alpha)
if dos_ax is not None and dos_ax != 'none':
print 'Adding ipr to DOS axis: dos_ax = ', dos_ax
hlat.add_ipr_to_ax(dos_ax, ipr=None, alpha=alpha, inverse_PR=False,
norm=None, nbins=75, fontsize=fontsize, cbar_ax=cbar_ipr_ax,
vmin=ipr_vmin, vmax=ipr_vmax, linewidth=0, cax_label=ipr_cax_label,
make_cbar=True, climbars=True,
cbar_labelpad=cbar_labelpad, orientation='horizontal', cbar_orientation='horizontal',
invert_xaxis=invert_xaxis, ylabel=None, xlabel=None,
cbar_nticks=2, cbar_tickfmt='%0.1f')
ax.set_ylim(dos_ax.get_ylim())
return ax, dos_ax, cbar_ipr_ax, cbar_nu_ax
def movie_cherns_varyloc(ccoll, title='Chern number calculation for varied positions',
filename='chern_varyloc', rootdir=None, exten='.png', max_boxfrac=None, max_boxsize=None,
xlabel=None, ylabel=None, step=0.5, fracsteps=False, framerate=3):
"""Plot the chern as a function of space for each haldane_lattice examined
Parameters
----------
ccoll : ChernCollection instance
The collection of varyloc chern calcs to make into a movie
title : str
title of the movie
filename : str
the name of the files to save
rootdir : str or None
The cproot directory to use (usually self.cp['rootdir'])
exten : str (.png, .jpg, etc)
file type extension
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
xlabel : str
label for x axis
ylabel : str
label for y axis
step : float (default=1.0)
how far apart to sample kregion vertices in varyloc
fracsteps : bool
framerate : int
The framerate at which to save the movie
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
"""
rad = 1.0
divgmap = cmaps.diverging_cmap(250, 10, l=30)
# plot it
for hlat_name in ccoll.cherns:
hlat = ccoll.cherns[hlat_name][0].haldane_lattice
if hlat.lp['shape'] == 'square':
# get extent of the network from Bounding box
Radius = np.abs(hlat.lp['BBox'][0, 0])
else:
# todo: allow different geometries
pass
# Initialize the figure
h_mm = 90
w_mm = 120
# To get space between subplots, figure out how far away ksize region needs to be, based on first chern
# Compare max ksize to be used with spatial extent of the lattice. If comparable, make hspace large.
# Otherwise, use defaults
ksize = ccoll.cherns[hlat_name][0].chern_finsize[:, 2]
cgll = ccoll.cherns[hlat_name][0].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]))
if max_boxsize is not None:
ksize = ksize[ksize < max_boxsize]
else:
if max_boxfrac is not None:
max_boxsize = max_boxfrac * maxsz
ksize = ksize[ksize < max_boxsize]
else:
ksize = ksize
max_boxsize = np.max(ksize)
if max_boxsize > 0.9 * maxsz:
center0_frac = 0.3
center2_frac = 0.75
elif max_boxsize > 0.65 * maxsz:
center0_frac = 0.35
center2_frac = 0.72
elif max_boxsize > 0.55 * maxsz:
center0_frac = 0.375
center2_frac = 0.71
else:
center0_frac = 0.4
center2_frac = 0.7
fig, ax = initialize_1p5panelcbar_fig(Wfig=w_mm, Hfig=h_mm, wsfrac=0.4, wssfrac=0.4,
center0_frac=center0_frac, center2_frac=center2_frac)
# dimensions of video in pixels
final_h = 720
final_w = 960
actual_dpi = final_h / (float(h_mm) / 25.4)
# Add the network to the figure
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=False, ptcolor=None, figsize='auto',
colormap='BlueBlackRed', bgcolor='#ffffff', axis_off=True, axis_equal=True,
lw=0.2)
# Add title
if title is not None:
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")
ax[2].axis('off')
# Add patches (rectangles from cherns at each site) to the figure
print 'Opening hlat_name = ', hlat_name
done = False
ind = 0
while done is False:
rectps = []
colorL = []
for chernii in ccoll.cherns[hlat_name]:
# Grab small, medium, and large circles
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
xx = float(chernii.cp['poly_offset'].split('/')[0])
yy = float(chernii.cp['poly_offset'].split('/')[1])
nu = chernii.chern_finsize[:, -1]
rad = step
rect = plt.Rectangle((xx-rad*0.5, yy-rad*0.5), rad, rad, ec="none")
colorL.append(nu[ind])
rectps.append(rect)
p = PatchCollection(rectps, cmap=divgmap, alpha=1.0, edgecolors='none')
p.set_array(np.array(np.array(colorL)))
p.set_clim([-1., 1.])
# Add the patches of nu calculations for each site probed
ax[0].add_collection(p)
# Draw the kitaev cartoon in second axis with size ksize[ind]
polygon1, polygon2, polygon3 = kfns.get_kitaev_polygons(ccoll.cp['shape'], ccoll.cp['regalph'],
ccoll.cp['regbeta'], ccoll.cp['reggamma'],
ksize[ind])
patchlist = []
patchlist.append(patches.Polygon(polygon1, color='r'))
patchlist.append(patches.Polygon(polygon2, color='g'))
patchlist.append(patches.Polygon(polygon3, color='b'))
polypatches = PatchCollection(patchlist, cmap=cm.jet, alpha=0.4, zorder=99, linewidths=0.4)
colors = np.linspace(0, 1, 3)[::-1]
polypatches.set_array(np.array(colors))
ax[2].add_collection(polypatches)
ax[2].set_xlim(ax[0].get_xlim())
ax[2].set_ylim(ax[0].get_ylim())
# Save the plot
# make index string
indstr = '_{0:06d}'.format(ind)
hlat_cmesh = kfns.get_cmeshfn(ccoll.cherns[hlat_name][0].haldane_lattice.lp, rootdir=rootdir)
specstr = '_Nks' + '{0:03d}'.format(len(ksize)) + '_step' + sf.float2pstr(step) \
+ '_maxbsz' + sf.float2pstr(max_boxsize)
outdir = hlat_cmesh + '_' + hlat.lp['LatticeTop'] + '_varyloc_stills' + specstr + '/'
fnout = outdir + filename + specstr + indstr + exten
print 'saving figure: ' + fnout
le.ensure_dir(outdir)
fig.savefig(fnout, dpi=actual_dpi*2)
# Save at lower res after antialiasing
f_img = Image.open(fnout)
f_img.resize((final_w, final_h), Image.ANTIALIAS).save(fnout)
# clear patches
p.remove()
polypatches.remove()
# del p
# Update index
ind += 1
if ind == len(ksize):
done = True
# Turn into movie
imgname = outdir + filename + specstr
movname = hlat_cmesh + filename + specstr + '_mov'
subprocess.call(['./ffmpeg', '-framerate', str(int(framerate)), '-i', imgname + '_%06d' + exten, movname + '.mov',
'-vcodec', 'libx264', '-profile:v', 'main', '-crf', '12', '-threads', '0',
'-r', '30', '-pix_fmt', 'yuv420p'])
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