def main():
if len(sys.argv) > 1:
mode = sys.argv[1]
else:
mode = 'sim'
minrad = 0.2
maxrad = 0.4
radstep = 0.05
numsteps = int((maxrad - minrad) / radstep + 1.5)
steps = np.linspace(minrad, maxrad, num=numsteps, endpoint=True)
for i, radius in enumerate(steps):
log.info(
"running simulation with {0:n} radius steps:\n{1}".format(
numsteps, steps) +
'\n ### current step: #{0} ({1}) ###\n'.format(i + 1, radius))
sim = TriHoles2D(
material='SiN',
radius=radius,
numbands=4, #8,
k_interpolation=5, #31,
resolution=16,
mesh_size=7,
runmode=mode,
num_processors=2,
save_field_patterns=True,
convert_field_patterns=True)
if not sim:
log.error('an error occurred during simulation. See the .out file')
return
# load te mode band data:
fname = path.join(sim.workingdir, sim.jobname + '_tefreqs.csv')
data = np.loadtxt(fname, delimiter=',', skiprows=1)
gapbands = get_gap_bands(data[:, 5:])
# maybe there is no gap?
if len(gapbands) == 0:
gap = 0
elif gapbands[0][0] != 1:
# it must be a gap between band 1 and 2
gap = 0
else:
gap = gapbands[0][3]
# save gap sizes to file (first TE gap):
with open("gaps.dat", "a") as f:
f.write("{0}\t{1}\n".format(radius, gap))
log.info(' ##### radius={0} - success! #####\n\n'.format(radius))
# reset logger; the next stuff logged is going to next step's file:
log.reset_logger()
data = np.loadtxt('gaps.dat')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(data[:, 0], data[:, 1], 'o-')
fig.savefig('gaps.png')
def draw_bands(jobname,
modes,
x_axis_hint=default_x_axis_hint,
custom_plotter=None,
title='',
crop_y=True,
band_gaps=True,
light_cone=False,
projected_bands=False,
mask_proj_bands_above_light_line=False,
add_epsilon_as_inset=False,
color_by_parity=False,
interactive_mode=False):
"""Plot dispersion relation of all bands calculated along all k
vectors.
:param jobname: The band data is loaded from previously saved .csv
files. (filenames: [*jobname* + '_' + *mode* + 'freqs.csv' for mode
in modes])
:param modes: see *jobname*
:param x_axis_hint: gives a hint on which kind of ticks and labels
should be shown on the x-axis and provides the data needed.
*x_axis_hint* can be one of the following:
-- integer number:
The axis' labels will be the 3D k-vectors. The number
denotes the number of major ticks and labels distributed on
the axis.
-- [integer, format-string]:
Same as above, but the labels are formatted with the
format-string - this gives the possibility to only show one
of the three vector components, e.g. the string "{2}" to
only show the k-vector's z-component. The axis title will be
inferred from the format-string.
-- KSpace object:
This must be a KSpace object created with point_labels.
These labels usually denote the high symmetry or crititical
points, and they will be shown on the axis.
-- CustomAxisFormatter object:
This gives the possibility to completely customize the
x-axis' tick positions, tick labels and axis label. If the
CustomAxisFormatter's hover data have not been set, it will
be set here with the k-vectors read from the .csv file.
:param custom_plotter: If you want to add the graph to an existing
figure, supply a BandPlotter with *custom_plotter*, otherwise
(default: custom_plotter=None) a new BandPlotter is created and
returned.
:param title: the subplot's title.
:param crop_y: If this is true (default), the y-axis (frequency)
will be limited so that only frequency values are shown where all
bands are known. Alternatively, a numeric value of *crop_y* denotes
the upper frequency value where the plot will be cropped, or if
*crop_y* is a 2-tuple, it denotes the minimum and maximum y-value.
:param band_gaps: If True, draw the band gaps (with colored boxes).
:param light_cone: If True, add a light cone and crop the bandgaps
at the light line. If the simulation was run on a substrate,
light_cone must be the index of the refraction of this substrate.
The light cone will then be scaled accordingly.
:param projected_bands: If True, add bands that span a range of
frequencies to the plot. Bandgaps are not drawn in this case. The
data needed will be read from previously saved .csv files
(filenames: [ jobname* + '_' + *mode* + '_projected.csv' for mode in
modes]).
:param mask_proj_bands_above_light_line: If *projected_bands* is
True, some projected bands might be completely above the light line.
These bands will not be drawn, if *mask_proj_bands_above_light_line*
is True.
:param add_epsilon_as_inset: epsilon,png will be added as inset. See
defaults.py for parameters like size and location.
:param color_by_parity: Specify 'y' or 'z' to color the plot lines
with the data taken from the parity files
<jobname>_<mode>[z/y]parity.csv.
:param interactive_mode: This is useful if the plot is not intended for
saving, but for showing on the screen. Then defaults.default_onclick()
will be called if the user clicks on a graph in the figure.
:return: a created BandPlotter instance (if *custom_plotter*) was
None, or the *custom_plotter*.
"""
if custom_plotter is None:
plotter = BandPlotter(figure_size=defaults.fig_size)
else:
plotter = custom_plotter
plotter.next_plot()
refr_index = 1 if isinstance(light_cone, bool) else light_cone
x_axis_formatter = None
if isinstance(x_axis_hint, axis_formatter.CustomAxisFormatter):
# use the supplied CustomAxisFormatter:
x_axis_formatter = x_axis_hint
elif isinstance(x_axis_hint, KSpace):
# make a KSpaceAxisFormatter instance from kspace object:
x_axis_formatter = axis_formatter.KSpaceAxisFormatter(x_axis_hint)
elif isinstance(x_axis_hint, int):
# make a standard KVectorAxisFormatter with supplied number of ticks:
x_axis_formatter = axis_formatter.KVectorAxisFormatter(x_axis_hint)
else:
num = 0
hintlen = 0
if hasattr(x_axis_hint, '__len__'):
hintlen = len(x_axis_hint)
else:
# no sequence
try:
# is this a number?
num = int(x_axis_hint)
except ValueError:
# no number
pass
if hintlen > 1 and (isinstance(x_axis_hint[0], int)
and hasattr(x_axis_hint[1], 'format')):
# Supplied a list with at least an int and format_str.
# Use all items in list as KVectorAxisFormatter arguments:
x_axis_formatter = axis_formatter.KVectorAxisFormatter(
*x_axis_hint)
elif num > 0:
# make a standard KVectorAxisFormatter with supplied number
# of ticks:
x_axis_formatter = axis_formatter.KVectorAxisFormatter(num)
if x_axis_formatter is None:
log.warning('draw_bands: Did not understand x_axis_hint, '
'using default.')
x_axis_formatter = axis_formatter.KVectorAxisFormatter(
default_x_axis_hint)
for i, mode in enumerate(modes):
fname = '{0}_{1}freqs.csv'.format(jobname, mode)
data = loadtxt(fname, delimiter=',', skiprows=1)
# add hover data:
if x_axis_formatter._hover_func_is_default:
x_axis_formatter.set_hover_data(data[:, 1:4])
parities = None
if color_by_parity:
# try to load parity file:
fname = '{0}_{1}{2}parity.csv'.format(jobname, mode,
color_by_parity)
try:
# load data, ignore first column with band numbers:
parities = loadtxt(fname, delimiter=',')[:, 1:]
except IOError:
parities = None
plotter.plot_bands(data[:, 5:],
data[:, 1:5],
formatstr=defaults.draw_bands_formatstr,
x_axis_formatter=x_axis_formatter,
label=mode.upper(),
crop_y=crop_y,
picker=interactive_mode * 3,
color_by_parity=parities,
**defaults.draw_bands_kwargs)
if projected_bands:
fname = '{0}_{1}_projected.csv'.format(jobname, mode)
with np.warnings.catch_warnings():
# ignore numpy warning if the file does not contain any
# data, just return empty ndarray:
np.warnings.simplefilter("ignore")
projdata = loadtxt(fname, delimiter=',', ndmin=2)
if projdata.shape[0] != 0:
if projdata.shape[1] % 2 != 0:
# knums added in first column, drop it:
projdata = projdata[:, 1:]
if light_cone and mask_proj_bands_above_light_line:
# ignore projected bands above the light line:
mask = np.zeros_like(projdata, dtype=np.bool)
numbands = projdata.shape[1] // 2
for i in range(numbands):
if (projdata[:, 2 * i] >
data[:, 4] / refr_index).all():
mask[:, 2 * i:2 * i + 2] = True
projdata = np.ma.array(projdata, mask=mask)
if projdata.shape[0] == 1 and data.shape[0] != 1:
# the same band ranges for all k-vecs:
newproj = np.empty((data.shape[0], projdata.shape[1]))
newproj[None, :] = projdata
projdata = newproj
plotter.add_continuum_bands(projdata)
if band_gaps:
if light_cone:
gapbands = get_gap_bands(data[:, 5:],
light_line=data[:, 4] / refr_index)
else:
gapbands = get_gap_bands(data[:, 5:])
for band in gapbands:
plotter.add_band_gap_rectangle(
band[1],
band[2],
light_line=data[:, 4] / refr_index if light_cone else None)
if light_cone:
plotter.add_light_cone(refr_index)
plotter.set_plot_title(title)
if len(modes) > 1 or (len(modes) == 1 and modes[0] != ''):
plotter.add_legend()
if color_by_parity:
plotter.add_color_bar_for_parity(parity_direction=color_by_parity)
if add_epsilon_as_inset:
fname = path.join(path.split(jobname)[0], 'epsilon.png')
if path.isfile(fname):
plotter.add_image_inset(fname,
loc=defaults.epsilon_inset_location,
zoom=defaults.epsilon_inset_zoom,
transpose=defaults.epsilon_inset_transpose)
return plotter
def main():
if len(sys.argv) > 1:
mode=sys.argv[1]
else:
mode='sim'
minrad = 0.2
maxrad = 0.4
radstep = 0.05
numsteps = int((maxrad - minrad) / radstep + 1.5)
steps = np.linspace(minrad, maxrad, num=numsteps, endpoint=True)
for i, radius in enumerate(steps):
log.info("running simulation with {0:n} radius steps:\n{1}".format(
numsteps, steps) +
'\n ### current step: #{0} ({1}) ###\n'.format(i+1, radius))
sim = TriHolesSlab3D(
material='SiN',
radius=radius,
thickness=0.8,
numbands=4,#8,
k_interpolation=5,#31,
resolution=16,#32,
mesh_size=3,#7,
supercell_z=6,
runmode=mode,
num_processors=2,
save_field_patterns=True,
convert_field_patterns=True)
if not sim:
log.error('an error occurred during simulation. See the .out file')
return
# load zeven mode band data:
fname = path.join(sim.workingdir, sim.jobname + '_zevenfreqs.csv')
data = np.loadtxt(fname, delimiter=',', skiprows=1)
gapbands = get_gap_bands(data[:, 5:], light_line=data[:, 4])
# maybe there is no gap?
if len(gapbands) == 0:
gap = 0
elif gapbands[0][0] != 1:
# it must be a gap between band 1 and 2
gap = 0
else:
gap = gapbands[0][3]
# save gap sizes to file (first TE gap):
with open("gaps.dat", "a") as f:
f.write("{0}\t{1}\n".format(radius, gap))
log.info(' ##### radius={0} - success! #####\n\n'.format(radius))
# reset logger; the next stuff logged is going to next step's file:
log.reset_logger()
data = np.loadtxt('gaps.dat')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(data[:,0], data[:,1], 'o-')
fig.savefig('gaps.png')
def main():
if len(sys.argv) > 1:
mode=sys.argv[1]
else:
mode='sim'
minstep = 1
maxstep = 8.0
stepsize = 0.5
numsteps = int((maxstep - minstep) / stepsize + 1.5)
steps = np.linspace(minstep, maxstep, num=numsteps, endpoint=True)
# save previous step's data, needed for comparison with current data:
prev_step_data = None
for i, step in enumerate(steps):
log.info("running simulation with {0:n} supercell height steps:\n{1}".format(
numsteps, steps) +
'\n ### current step: #{0} ({1}) ###\n'.format(i+1, step))
### create and run simulation (according to mode) ###
sim = TriHolesSlab3D(
material='SiN',
radius=0.375,
thickness=0.8,
numbands=8,
k_interpolation=31,
resolution=32,
mesh_size=7,
supercell_z=step,
runmode=mode,
num_processors=8,
save_field_patterns=True,
convert_field_patterns=True,
job_name_suffix='_sct{0:03.0f}'.format(step*10),
bands_title_appendix=', supercell thickness={0:02.1f}'.format(step))
if not sim:
log.error('an error occurred during simulation. See the .out file')
return
### load some data ###
# load zeven mode band data:
fname = path.join(sim.workingdir, sim.jobname + '_zevenfreqs.csv')
data = np.loadtxt(fname, delimiter=',', skiprows=1)
gapbands = get_gap_bands(data[:, 5:], light_line=data[:, 4])
### save comparison data to file ###
# If this is not the first step, we have previous data to compare
# with:
if prev_step_data is not None:
sums = []
# compare the first 3 zeven bands:
for j in range(3):
sums.append(
sum_of_squares(
prev_step_data[:, 5 + j],
data[:, 5 + j],
data[:, 4]
)
)
with open("sum_of_squares.dat", "a") as f:
f.write('\t'.join([str(step)] + map(str, sums)) + '\n')
# save data for next iteration:
prev_step_data = data
### save the gap to file ###
# maybe there is no gap?
if len(gapbands) == 0:
gap = 0
elif gapbands[0][0] != 1:
# it must be a gap between band 1 and 2
gap = 0
else:
gap = gapbands[0][3]
# save gap sizes to file (first z-even gap):
with open("gaps.dat", "a") as f:
f.write("{0}\t{1}\n".format(step, gap))
log.info(' ##### step={0} - success! #####\n\n'.format(step))
# reset logger; the next stuff logged is going to next step's file:
log.reset_logger()
### finally, save some plots ###
data = np.loadtxt('sum_of_squares.dat')
fig = plt.figure()
ax = fig.add_subplot(111)
# make double-logarithmic plot for every band:
for i in range(data.shape[1] -1):
ax.loglog(data[:,0], data[:,i + 1], 'o-',
label='band {0}'.format(i+1))
compdata = np.power(data[:,0], -2)
ax.loglog(data[:,0], compdata, '.:', label='$x^{-2}$')
compdata = np.power(data[:,0], -4)
ax.loglog(data[:,0], compdata, '.:', label='$x^{-4}$')
compdata = np.power(data[:,0], -6)
ax.loglog(data[:,0], compdata, '.:', label='$x^{-6}$')
plt.legend()
plt.title('sum of squared differences between simulation steps')
fig.savefig('sum_of_squares.png')
data = np.loadtxt('gaps.dat')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(data[:,0], data[:,1], 'o-')
plt.title('First z-even band gap for each simulation step')
fig.savefig('gaps.png')
def draw_bands(
jobname, modes, x_axis_hint=default_x_axis_hint,
custom_plotter=None, title='', crop_y=True,
band_gaps=True, light_cone=False, projected_bands=False,
mask_proj_bands_above_light_line=False,
add_epsilon_as_inset=False, color_by_parity=False,
interactive_mode=False):
"""Plot dispersion relation of all bands calculated along all k
vectors.
:param jobname: The band data is loaded from previously saved .csv
files. (filenames: [*jobname* + '_' + *mode* + 'freqs.csv' for mode
in modes])
:param modes: see *jobname*
:param x_axis_hint: gives a hint on which kind of ticks and labels
should be shown on the x-axis and provides the data needed.
*x_axis_hint* can be one of the following:
-- integer number:
The axis' labels will be the 3D k-vectors. The number
denotes the number of major ticks and labels distributed on
the axis.
-- [integer, format-string]:
Same as above, but the labels are formatted with the
format-string - this gives the possibility to only show one
of the three vector components, e.g. the string "{2}" to
only show the k-vector's z-component. The axis title will be
inferred from the format-string.
-- KSpace object:
This must be a KSpace object created with point_labels.
These labels usually denote the high symmetry or crititical
points, and they will be shown on the axis.
-- CustomAxisFormatter object:
This gives the possibility to completely customize the
x-axis' tick positions, tick labels and axis label. If the
CustomAxisFormatter's hover data have not been set, it will
be set here with the k-vectors read from the .csv file.
:param custom_plotter: If you want to add the graph to an existing
figure, supply a BandPlotter with *custom_plotter*, otherwise
(default: custom_plotter=None) a new BandPlotter is created and
returned.
:param title: the subplot's title.
:param crop_y: If this is true (default), the y-axis (frequency)
will be limited so that only frequency values are shown where all
bands are known. Alternatively, a numeric value of *crop_y* denotes
the upper frequency value where the plot will be cropped, or if
*crop_y* is a 2-tuple, it denotes the minimum and maximum y-value.
:param band_gaps: If True, draw the band gaps (with colored boxes).
:param light_cone: If True, add a light cone and crop the bandgaps
at the light line. If the simulation was run on a substrate,
light_cone must be the index of the refraction of this substrate.
The light cone will then be scaled accordingly.
:param projected_bands: If True, add bands that span a range of
frequencies to the plot. Bandgaps are not drawn in this case. The
data needed will be read from previously saved .csv files
(filenames: [ jobname* + '_' + *mode* + '_projected.csv' for mode in
modes]).
:param mask_proj_bands_above_light_line: If *projected_bands* is
True, some projected bands might be completely above the light line.
These bands will not be drawn, if *mask_proj_bands_above_light_line*
is True.
:param add_epsilon_as_inset: epsilon,png will be added as inset. See
defaults.py for parameters like size and location.
:param color_by_parity: Specify 'y' or 'z' to color the plot lines
with the data taken from the parity files
<jobname>_<mode>[z/y]parity.csv.
:param interactive_mode: This is useful if the plot is not intended for
saving, but for showing on the screen. Then defaults.default_onclick()
will be called if the user clicks on a graph in the figure.
:return: a created BandPlotter instance (if *custom_plotter*) was
None, or the *custom_plotter*.
"""
if custom_plotter is None:
plotter = BandPlotter(figure_size=defaults.fig_size)
else:
plotter = custom_plotter
plotter.next_plot()
refr_index = 1 if isinstance(light_cone, bool) else light_cone
x_axis_formatter = None
if isinstance(x_axis_hint, axis_formatter.CustomAxisFormatter):
# use the supplied CustomAxisFormatter:
x_axis_formatter = x_axis_hint
elif isinstance(x_axis_hint, KSpace):
# make a KSpaceAxisFormatter instance from kspace object:
x_axis_formatter = axis_formatter.KSpaceAxisFormatter(x_axis_hint)
elif isinstance(x_axis_hint, int):
# make a standard KVectorAxisFormatter with supplied number of ticks:
x_axis_formatter = axis_formatter.KVectorAxisFormatter(x_axis_hint)
else:
num = 0
hintlen = 0
if hasattr(x_axis_hint, '__len__'):
hintlen = len(x_axis_hint)
else:
# no sequence
try:
# is this a number?
num = int(x_axis_hint)
except ValueError:
# no number
pass
if hintlen > 1 and (isinstance(x_axis_hint[0], int) and
hasattr(x_axis_hint[1], 'format')):
# Supplied a list with at least an int and format_str.
# Use all items in list as KVectorAxisFormatter arguments:
x_axis_formatter = axis_formatter.KVectorAxisFormatter(
*x_axis_hint)
elif num > 0:
# make a standard KVectorAxisFormatter with supplied number
# of ticks:
x_axis_formatter = axis_formatter.KVectorAxisFormatter(num)
if x_axis_formatter is None:
log.warning('draw_bands: Did not understand x_axis_hint, '
'using default.')
x_axis_formatter = axis_formatter.KVectorAxisFormatter(
default_x_axis_hint)
for i, mode in enumerate(modes):
fname = '{0}_{1}freqs.csv'.format(jobname, mode)
data = loadtxt(fname, delimiter=',', skiprows=1)
# add hover data:
if x_axis_formatter._hover_func_is_default:
x_axis_formatter.set_hover_data(data[:, 1:4])
parities = None
if color_by_parity:
# try to load parity file:
fname = '{0}_{1}{2}parity.csv'.format(
jobname, mode, color_by_parity)
try:
# load data, ignore first column with band numbers:
parities = loadtxt(fname, delimiter=',')[:, 1:]
except IOError:
parities = None
plotter.plot_bands(
data[:, 5:], data[:, 1:5],
formatstr=defaults.draw_bands_formatstr,
x_axis_formatter=x_axis_formatter,
label=mode.upper(),
crop_y=crop_y,
picker = interactive_mode * 3,
color_by_parity=parities,
**defaults.draw_bands_kwargs)
if projected_bands:
fname = '{0}_{1}_projected.csv'.format(jobname, mode)
with np.warnings.catch_warnings():
# ignore numpy warning if the file does not contain any
# data, just return empty ndarray:
np.warnings.simplefilter("ignore")
projdata = loadtxt(fname, delimiter=',', ndmin=2)
if projdata.shape[0] != 0:
if projdata.shape[1] % 2 != 0:
# knums added in first column, drop it:
projdata = projdata[:, 1:]
if light_cone and mask_proj_bands_above_light_line:
# ignore projected bands above the light line:
mask = np.zeros_like(projdata, dtype=np.bool)
numbands = projdata.shape[1] // 2
for i in range(numbands):
if (projdata[:, 2 * i] >
data[:, 4] / refr_index).all():
mask[:, 2 * i:2 * i + 2] = True
projdata = np.ma.array(projdata, mask=mask)
if projdata.shape[0] == 1 and data.shape[0] != 1:
# the same band ranges for all k-vecs:
newproj = np.empty((data.shape[0], projdata.shape[1]))
newproj[None, :] = projdata
projdata = newproj
plotter.add_continuum_bands(projdata)
if band_gaps:
if light_cone:
gapbands = get_gap_bands(
data[:, 5:], light_line=data[:, 4] / refr_index)
else:
gapbands = get_gap_bands(data[:, 5:])
for band in gapbands:
bsize = 2 * (band[2] - band[1]) / (band[2] + band[1])
if bsize >= defaults.min_gapsize:
plotter.add_band_gap_rectangle(
band[1], band[2],
light_line= \
data[:,4] / refr_index if light_cone else None)
if light_cone:
plotter.add_light_cone(refr_index)
plotter.set_plot_title(title)
if len(modes) > 1 or (len(modes) == 1 and modes[0]!=''):
plotter.add_legend()
if color_by_parity:
plotter.add_color_bar_for_parity(parity_direction=color_by_parity)
if add_epsilon_as_inset:
fname = path.join(path.split(jobname)[0], 'epsilon.png')
if path.isfile(fname):
plotter.add_image_inset(
fname,
loc=defaults.epsilon_inset_location,
zoom=defaults.epsilon_inset_zoom,
transpose=defaults.epsilon_inset_transpose
)
return plotter