def make_subfigure(self, fig_funcs, layout=(5, 4), close=True):
#todo figure out how to iterate properly
n = np.product(layout)
chunks = grouper(n, fig_funcs)
w = str(1 / layout[1])
pbar = tqdm(total=len(fig_funcs))
for chunk in chunks:
with self.doc.create(pyl.Figure(position='ht')) as tex_fig:
for i, fig_func in enumerate(chunk):
if fig_func is None:
continue
with self.doc.create(
pyl.SubFigure(
position='b',
width=pyl.NoEscape(w +
r'\linewidth'))) as subfig:
fig = fig_func()
file_path = self._save_fig(
fig,
bbox_inches='tight') # todo access these kwargs
if close:
plt.close(fig)
subfig.add_image(file_path,
width=pyl.NoEscape(r'\linewidth'))
if i % layout[1] == layout[1] - 1:
self.doc.append('\n')
pbar.update(1)
self.doc.append(pyl.NewPage())
def createFig(self,
imagePath,
caption,
width=tex.NoEscape(r'0.5\linewidth')):
fig = tex.SubFigure(position='b', width=width)
fig.add_image(str(imagePath), width=tex.NoEscape(r'\linewidth'))
fig.add_caption(caption)
return fig
def test_subfigure(self):
fig = plt.figure()
plt.plot([2,3,42,1])
file_path = self._save_fig(fig)
with self.doc.create(pyl.Figure(position='h!')) as kittens:
w = str(0.25)
for i in range(8):
with self.doc.create(pyl.SubFigure(
position='b',
width=pyl.NoEscape(w + r'\linewidth'))) as left_kitten:
left_kitten.add_image(file_path,
width=pyl.NoEscape(r'\linewidth'))
left_kitten.add_caption(f'Kitten on the {i}')
if i % 4 == 3:
self.doc.append('\n')
kittens.add_caption("Two kittens")
def profile_likelihood(self, pl_directory, size=0.3, num_per_row=3):
"""
compile any pdf, jpg and png files
in directory into a latex pdf document
:return:
"""
if isinstance(self.file_types, str):
self.file_types = [self.file_types]
files = self.search_recursive(pl_directory)
if files is []:
raise errors.InputError(
'''Cannot locate pdf, jpg or png files in "{}". Please
give the full path to where your model selection results are
plotted.
'''.format(pl_directory))
doc = pylatex.Document(self.filename, documentclass='article')
for k, v in list(files.items()):
assert isinstance(v, list)
if v is not None:
with doc.create(
pylatex.Figure(
position='htbp!',
width=pylatex.NoEscape(r'\linewidth'))) as fig:
# [fig.add_image(i) for i in v]
for i in range(len(v)):
with doc.create(
pylatex.SubFigure(width=pylatex.NoEscape(
str(size) + r'\linewidth'))) as sub:
sub.add_image(v[i])
sub.add_caption(os.path.split(v[i])[1])
if i is not 0 and i % num_per_row is 0:
doc.append(pylatex.NoEscape(r'\break'))
# sub.add_label(i)
# # fig.add_caption(os.path.join(*Path(v[i]).parts[-3:-1]))
doc.append(pylatex.NoEscape(r'\hfill'))
fig.add_caption(os.path.split(k)[1])
doc.generate_pdf()
LOG.info('PDF generated at "{}"'.format(doc.default_filepath))
def prepare_document(self, directory, subdirs=False, file_types='.png'):
"""
compile any pdf, jpg and png files
in directory into a latex pdf document
:return:
"""
if subdirs not in [True, False]:
raise errors.InputError(
'subdirs argument should be either True or False')
if isinstance(file_types, str):
file_types = [file_types]
if subdirs:
files = self.search_recursive(directory)
else:
files = self.search()
if files is []:
raise errors.InputError(
'''Cannot locate pdf, jpg or png files in "{}". Please
give the full path to where your model selection results are
plotted.
'''.format(directory))
doc = pylatex.Document(self.filename, documentclass='article')
for k, v in list(files.items()):
assert isinstance(v, list)
if v is not None:
with doc.create(
pylatex.Section(os.path.join(*Path(k).parts[-1:]))):
doc.append(k)
doc.append(pylatex.NoEscape(r'\\*'))
if len(v) == 1:
with doc.create(
pylatex.Figure(position='htbp!',
width=pylatex.NoEscape(
r'0.3\linewidth'))) as fig:
fig.add_image(v[0])
fig.add_caption(
os.path.join(*Path(v[0]).parts[-2:]))
# fig.add_label(v[0])
else:
with doc.create(
pylatex.Figure(
position='htbp!',
width=pylatex.NoEscape(r'\linewidth'),
)) as fig:
for i in range(len(v)):
with doc.create(
pylatex.SubFigure(
width=pylatex.NoEscape(
r'0.3\linewidth'))) as sub:
sub.add_image(v[i])
# sub.add_caption('')
if i % 3 == 0:
doc.append(pylatex.NoEscape(r'\break'))
# sub.add_label(i)
fig.add_caption(
os.path.join(*Path(v[i]).parts[-3:-1]))
doc.append(pylatex.NoEscape(r'\hfill'))
doc.generate_pdf()
LOG.info('PDF generated at "{}"'.format(doc.default_filepath))
def _add_fits(self,
doc,
fit_log,
fit_name,
operator_set,
ratio,
gap=False,
const=False):
with doc.create(pylatex.Center()) as centered:
if gap and const and util.ERR_PREC <= 4:
cols = "X[c] X[c] X[4,c] X[4,c] X[4,c] X[4,c] X[2,c] X[2,c] X[2,c] X[c]"
elif (gap or const) and util.ERR_PREC <= 4:
cols = "X[c] X[c] X[4,c] X[4,c] X[4,c] X[2,c] X[2,c] X[2,c] X[c]"
else:
cols = "X[c] X[c] X[4,c] X[4,c] X[2,c] X[2,c] X[2,c] X[c]"
with centered.create(pylatex.LongTabu(
cols, to=r"\linewidth")) as fit_table:
if ratio:
energy_header = r"$a_t \Delta E_{\rm fit}$"
else:
energy_header = r"$a_t E_{\rm fit}$"
header_row = [
pylatex.NoEscape(r"$t_{\rm min}$"),
pylatex.NoEscape(r"$t_{\rm max}$"),
pylatex.NoEscape(energy_header),
"A",
pylatex.NoEscape(r"$\chi^2 / \text{dof}$"),
pylatex.NoEscape(r"$p$-value"),
pylatex.NoEscape(r"$t_{\rm exc}$"),
pylatex.NoEscape(r"$\sigma_{\rm cut}$"),
]
if const and util.ERR_PREC <= 4:
header_row.insert(4, pylatex.NoEscape(r"const"))
if gap and util.ERR_PREC <= 4:
header_row.insert(4, pylatex.NoEscape(r"$a \Delta$"))
fit_table.add_row(header_row, mapper=[pylatex.utils.bold])
fit_table.add_hline()
fit_table.end_table_header()
for fit_info, fit_result in fit_log.fits.items():
value_row = [
fit_info.tmin,
fit_info.tmax,
fit_result.energy,
fit_result.amplitude,
round(fit_result.chisq, 2),
round(fit_result.quality, 2),
fit_info.exclude_times,
round(fit_info.noise_cutoff, 1),
]
if const and util.ERR_PREC <= 4:
value_row.insert(4, fit_result.const)
if gap and util.ERR_PREC <= 4:
value_row.insert(4, fit_result.gap)
fit_table.add_row(value_row)
if self.fit_plots:
for fit_info1, fit_info2 in zip(*[iter(fit_log.fits.keys())] * 2):
fit_plot1 = self.fit_plotfile(operator_set,
fit_name,
fit_info1,
extension=util.PlotExtension.pdf)
fit_plot2 = self.fit_plotfile(operator_set,
fit_name,
fit_info2,
extension=util.PlotExtension.pdf)
with doc.create(pylatex.Figure(position='H')) as fig:
with doc.create(
pylatex.SubFigure(position='b',
width=pylatex.NoEscape(
r'0.5\linewidth'))) as fig1:
caption = f"{fit_info1.model.short_name}, $t_{{\\rm fit}} = {fit_info1.tmin}, {fit_info1.tmax}$"
if fit_info1.ratio:
caption += " Ratio"
if fit_info1.exclude_times:
caption += f" $t_{{\\rm exc}}$ = {fit_info1.exclude_times}"
if fit_info1.noise_cutoff:
caption += f" $\sigma_{{\\rm cut}} = {fit_info1.noise_cutoff}$"
util.add_image(fig1,
self.results_dir,
fit_plot1,
width="1.0",
caption=caption)
with doc.create(
pylatex.SubFigure(position='b',
width=pylatex.NoEscape(
r'0.5\linewidth'))) as fig2:
caption = f"{fit_info2.model.short_name}, $t_{{\\rm fit}} = {fit_info2.tmin}, {fit_info2.tmax}$"
if fit_info1.ratio:
caption += " Ratio"
if fit_info2.exclude_times:
caption += f" $t_{{\\rm exc}}$ = {fit_info2.exclude_times}"
if fit_info2.noise_cutoff:
caption += f" $\sigma_{{\\rm cut}} = {fit_info2.noise_cutoff}$"
util.add_image(fig2,
self.results_dir,
fit_plot2,
width="1.0",
caption=caption)
if len(fit_log.fits) % 2 != 0:
fit_info = list(fit_log.fits.keys())[-1]
fit_plot = self.fit_plotfile(operator_set,
fit_name,
fit_info,
extension=util.PlotExtension.pdf)
with doc.create(pylatex.Figure(position='H')) as fig:
caption = f"{fit_info.model.short_name}, $t_{{\\rm fit}} = {fit_info.tmin}, {fit_info.tmax}$"
if fit_info.ratio:
caption += " Ratio"
if fit_info.exclude_times:
caption += f" $t_{{\\rm exc}}$ = {fit_info.exclude_times}"
if fit_info.noise_cutoff:
caption += f" $\sigma_{{\\rm cut}} = {fit_info.noise_cutoff}$"
util.add_image(fig,
self.results_dir,
fit_plot,
width="0.5",
caption=caption)
doc.append(pylatex.NoEscape(r"\newpage"))
def _add_tmins(self, doc, fit_infos, fit_name, operator_set, ratio):
for fit_info1, fit_info2 in zip(*[iter(fit_infos)] * 2):
fit_plot1 = self.tmin_fit_plotfile(
operator_set,
fit_name,
fit_info1,
extension=util.PlotExtension.pdf)
fit_plot2 = self.tmin_fit_plotfile(
operator_set,
fit_name,
fit_info2,
extension=util.PlotExtension.pdf)
with doc.create(pylatex.Figure(position='H')) as fig:
with doc.create(
pylatex.SubFigure(
position='b',
width=pylatex.NoEscape(r'0.5\linewidth'))) as fig1:
caption = f"{fit_info1.model.short_name}, $t_{{\\rm max}} = {fit_info1.tmax}$"
if fit_info1.ratio:
caption += " Ratio"
if fit_info1.exclude_times:
caption += f" $t_{{\\rm exc}}$ = {fit_info1.exclude_times}"
util.add_image(fig1,
self.results_dir,
fit_plot1,
width="1.0",
caption=caption)
with doc.create(
pylatex.SubFigure(
position='b',
width=pylatex.NoEscape(r'0.5\linewidth'))) as fig2:
caption = f"{fit_info2.model.short_name}, $t_{{\\rm max}} = {fit_info2.tmax}$"
if fit_info1.ratio:
caption += " Ratio"
if fit_info2.exclude_times:
caption += f" $t_{{\\rm exc}}$ = {fit_info2.exclude_times}"
util.add_image(fig2,
self.results_dir,
fit_plot2,
width="1.0",
caption=caption)
if len(fit_infos) % 2 != 0:
fit_info = list(fit_infos)[-1]
fit_plot = self.tmin_fit_plotfile(operator_set,
fit_name,
fit_info,
extension=util.PlotExtension.pdf)
with doc.create(pylatex.Figure(position='H')) as fig:
caption = f"{fit_info.model.short_name}, $t_{{\\rm max}} = {fit_info.tmax}$"
if fit_info.ratio:
caption += " Ratio"
if fit_info.exclude_times:
caption += f" $t_{{\\rm exc}}$ = {fit_info.exclude_times}"
util.add_image(fig,
self.results_dir,
fit_plot,
width="0.5",
caption=caption)
doc.append(pylatex.NoEscape(r"\newpage"))
def _add_data(doc: pl.Document, ds: Dataset, nr: NonRedundantization,
meth: MLMethod):
name = f'{ds.name}_{nr.name}_{meth.name}'
directory = ds.name
aVp_graph = f'{name}.jpg'
angle_dist_graph = f'{name}_angledistribution.jpg'
error_dist_graph = f'{name}_errordistribution.jpg'
sqerror_graph = f'{name}_sqerror_vs_actual.jpg'
stats_csv_all = f'{name}_stats_all.csv'
stats_csv_out = f'{name}_stats_out.csv'
actualVpred_file = os.path.join(directory, aVp_graph)
ang_dist_file = os.path.join(directory, angle_dist_graph)
error_dist_file = os.path.join(directory, error_dist_graph)
sqerror_file = os.path.join(directory, sqerror_graph)
df_all = pd.read_csv(os.path.join(directory, stats_csv_all))
df_out = pd.read_csv(os.path.join(directory, stats_csv_out))
with doc.create(pl.Section(f'Method: {ds.name}, {nr.name}, {meth.name}')):
with doc.create(pl.Subsection('Summary of method:')):
doc.append(f'Dataset: {ds.name}')
doc.append(f'\nNon-redundantization: {nr.name}')
doc.append(f'\nType of machine learning used: {meth.name}')
with doc.create(pl.Subsection('Summary of the data:')):
with doc.create(pl.Figure(position='!htbp')) as actualVpred:
actualVpred.add_image(actualVpred_file, width='300px')
actualVpred.add_caption(
'Graph showing the predicted packing angle against the actual packing angle, when using the above specified methods of non-redundetization and machine learning.'
)
with doc.create(pl.Table(position='!htbp')) as table:
table.add_caption('Summary of results for all data')
table.append(pl.Command('centering'))
table.append(pl.NoEscape(df_all.to_latex(escape=False)))
with doc.create(pl.Table(position='!htbp')) as table:
table.add_caption('Summary of results for outliers.')
table.append(pl.Command('centering'))
table.append(pl.NoEscape(df_out.to_latex(escape=False)))
with doc.create(pl.Figure(position='!htbp')) as graphs:
with doc.create(
pl.SubFigure(position='!htbp',
width=pl.NoEscape(
r'0.30\linewidth'))) as ang_dist_graph:
ang_dist_graph.add_image(ang_dist_file,
width=pl.NoEscape(r'\linewidth'))
ang_dist_graph.add_caption(
'Frequency distribution of the packing angle.')
with doc.create(
pl.SubFigure(position='!htbp',
width=pl.NoEscape(
r'0.33\linewidth'))) as error_dist_graph:
error_dist_graph.add_image(error_dist_file,
width=pl.NoEscape(r'\linewidth'))
error_dist_graph.add_caption(
'Distribution of errors calculated as the difference between the predicted and actual interface angle.'
)
with doc.create(
pl.SubFigure(position='!htbp',
width=pl.NoEscape(
r'0.33\linewidth'))) as sqerror_graph:
sqerror_graph.add_image(sqerror_file,
width=pl.NoEscape(r'\linewidth'))
sqerror_graph.add_caption(
'Squared error in predicted packing angle against actual packing angle.'
)
graphs.add_caption('Graphs for further metrics.')
def add_correlator(doc, task_handler, correlator, name, obs_handler):
operator_src = operator_info.operator.Operator(correlator.getSource())
subtractvev = task_handler.subtractvev and operator_src.channel.vev
if correlator.isSinkSourceSame():
left_pdf_file = task_handler.correlator_plotfile(
correlator, name, extension=PlotExtension.pdf)
right_pdf_file = task_handler.energy_plotfile(
operator_src, name, extension=PlotExtension.pdf)
else:
left_pdf_file = task_handler.correlator_plotfile(
correlator,
name,
complex_arg=sigmond.ComplexArg.RealPart,
extension=PlotExtension.pdf)
right_pdf_file = task_handler.correlator_plotfile(
correlator,
name,
complex_arg=sigmond.ComplexArg.ImaginaryPart,
extension=PlotExtension.pdf)
if correlator.isSinkSourceSame():
left_estimates = sigmond.getCorrelatorEstimates(
obs_handler, correlator, task_handler.hermitian, subtractvev,
sigmond.ComplexArg.RealPart, task_handler.sampling_mode)
right_estimates = sigmond.getEffectiveEnergy(
obs_handler, correlator, task_handler.hermitian, subtractvev,
sigmond.ComplexArg.RealPart, task_handler.sampling_mode,
task_handler.plot_info.timestep,
task_handler.plot_info.eff_energy_type.value, 0.)
else:
left_estimates = sigmond.getCorrelatorEstimates(
obs_handler, correlator, task_handler.hermitian, subtractvev,
sigmond.ComplexArg.RealPart, task_handler.sampling_mode)
right_estimates = sigmond.getCorrelatorEstimates(
obs_handler, correlator, task_handler.hermitian, subtractvev,
sigmond.ComplexArg.ImaginaryPart, task_handler.sampling_mode)
if correlator.isSinkSourceSame():
doc.append(pylatex.NoEscape(rf"Score: {score(left_estimates)}"))
with doc.create(pylatex.Figure(position='H')):
with doc.create(
pylatex.SubFigure(
position='b',
width=pylatex.NoEscape(r'0.5\linewidth'))) as left_fig:
add_image(left_fig,
task_handler.results_dir,
left_pdf_file,
width="1.0")
with doc.create(
pylatex.SubFigure(
position='b',
width=pylatex.NoEscape(r'0.5\linewidth'))) as right_fig:
add_image(right_fig,
task_handler.results_dir,
right_pdf_file,
width="1.0")
if correlator.isSinkSourceSame():
header_row = [
pylatex.NoEscape(r"$t$"),
pylatex.NoEscape(r"$C(t)$"),
pylatex.NoEscape(r"$\delta C(t)$"),
]
if task_handler.plot_info.eff_energy_type.value < 2:
header_row.extend([
pylatex.NoEscape(
rf"$a_t E_{{\rm eff}} (t + {task_handler.plot_info.timestep}/2)$"
),
pylatex.NoEscape(
rf"$\delta a_t E_{{\rm eff}} (t + {task_handler.plot_info.timestep}/2)$"
),
])
else:
header_row.extend([
pylatex.NoEscape(r"$a_t E_{\rm eff} (t)$"),
pylatex.NoEscape(r"$\delta a_t E_{\rm eff} (t)$"),
])
else:
header_row = [
pylatex.NoEscape(r"$t$"),
pylatex.NoEscape(r"$Re C(t)$"),
pylatex.NoEscape(r"$\delta Re C(t)$"),
pylatex.NoEscape(r"$Im C(t)$"),
pylatex.NoEscape(r"$\delta Im C(t)$")
]
with doc.create(pylatex.Center()) as centered:
with centered.create(
pylatex.LongTabu("X[c] X[2,c] X[2,c] X[2,c] X[2,c]",
to=r"\linewidth")) as data_table:
data_table.add_row(header_row, mapper=[pylatex.utils.bold])
data_table.add_hline()
data_table.end_table_header()
for t in sorted(left_estimates.keys()):
left_value = left_estimates[t].getFullEstimate()
left_error = left_estimates[t].getSymmetricError()
left_est = nice_value(left_value, left_error)
left_rel_error = round(left_estimates[t].getRelativeError(), 4)
t_right = t
if correlator.isSinkSourceSame(
) and task_handler.plot_info.eff_energy_type.value < 2:
t_right = t + 0.5 * task_handler.plot_info.timestep
if t_right in right_estimates:
right_value = right_estimates[t_right].getFullEstimate()
right_error = right_estimates[t_right].getSymmetricError()
right_est = nice_value(right_value, right_error)
right_rel_error = round(
right_estimates[t_right].getRelativeError(), 4)
else:
right_est = ""
right_rel_error = ""
row = [
int(t), left_est, left_rel_error, right_est,
right_rel_error
]
data_table.add_row(row)
doc.append(pylatex.NoEscape(r"\newpage"))
def makeInteractionFigure(self, ker, cum, n_terms):
"""
Create figure for interaction analysis, which includes a subfigure of
the latest additive component and a subfigure of posterior of the overall
compositional kernel up to now.
:param ker: latest additive component to be plotted
:type ker: GPCKernel
:param cum: overall compositional kernel up to and including `ker`
:type cum: GPCKernel
:param n_terms: number of additive terms considered in `cum` so far
"""
assert isinstance(ker, GPCKernel), 'Kernel must be of type GPCKernel'
assert isinstance(cum, GPCKernel), 'Kernel must be of type GPCKernel'
doc = self.doc
kerDims = ker.getActiveDims()
cumDims = cum.getActiveDims()
img1Name = 'additive{0}ker'.format(n_terms)
img1Format = '.eps' if len(kerDims) != 3 else '.png'
img1Filename = img1Name + img1Format
ker.draw(os.path.join(self.path, img1Name), active_dims_only=True)
if n_terms == 1 or len(cumDims) > 3:
# Only present current additive component
caption_str = r"Trained classifier on " + dims2text(
kerDims, ker.data) + "."
with doc.create(pl.Figure(position='htbp!')) as fig:
fig.add_image(img1Filename,
width=ut.NoEscape(r'0.7\textwidth'))
fig.add_caption(ut.NoEscape(caption_str))
self.fignum += 1
else:
# Present both current component and cumulative kernel
img2Name = 'additive{0}cum'.format(n_terms)
img2Format = '.eps' if len(cumDims) != 3 else '.png'
img2Filename = img2Name + img2Format
cum.draw(os.path.join(self.path, img2Name), active_dims_only=True)
caption1_str = r"Current additive component involving " + dims2text(
kerDims, ker.data) + "."
caption2_str = r"Previous and current components combined, involving " + dims2text(
cumDims, cum.data) + "."
caption_str = r"Trained classifier on " + dims2text(
cumDims, cum.data) + "."
with doc.create(pl.Figure(position='htbp!')) as fig:
with doc.create(
pl.SubFigure(
position='b',
width=ut.NoEscape(r'0.47\textwidth'))) as subfig1:
subfig1.add_image(img1Filename,
width=ut.NoEscape(r'\textwidth'))
subfig1.add_caption(ut.NoEscape(caption1_str))
doc.append(ut.NoEscape(r'\hfill'))
with doc.create(
pl.SubFigure(
position='b',
width=ut.NoEscape(r'0.47\textwidth'))) as subfig2:
subfig2.add_image(img2Filename,
width=ut.NoEscape(r'\textwidth'))
subfig2.add_caption(ut.NoEscape(caption2_str))
fig.add_caption(ut.NoEscape(caption_str))
self.fignum += 1
def _add_data(doc: pl.Document, dataset):
name = f'{dataset}_NR2_GBReg'
directory = dataset
aVp_graph = f'{name}.jpg'
angle_dist_graph = f'{name}_angledistribution.jpg'
error_dist_graph = f'{name}_errordistribution.jpg'
sqerror_graph = f'{name}_sqerror_vs_actual.jpg'
stats_csv_all = f'{name}_stats_all.csv'
stats_csv_out = f'{name}_stats_out.csv'
actualVpred_file = os.path.join(directory, aVp_graph)
ang_dist_file = os.path.join(directory, angle_dist_graph)
error_dist_file = os.path.join(directory, error_dist_graph)
sqerror_file = os.path.join(directory, sqerror_graph)
df_all = pd.read_csv(os.path.join(directory, stats_csv_all))
df_out = pd.read_csv(os.path.join(directory, stats_csv_out))
with doc.create(pl.Section(f'Results')):
with doc.create(pl.Subsection('Summary of method:')):
doc.append('Trained on PreAF2 dataset.')
doc.append('\n')
doc.append(f'Dataset tested: {dataset}')
doc.append('\n')
doc.append(f'GBR parameters: {gbr_params}.')
doc.append('\n')
with doc.create(pl.Subsection('Summary of the data:')):
with doc.create(pl.Figure(position='!htbp')) as actualVpred:
actualVpred.add_image(actualVpred_file, width='300px')
actualVpred.add_caption(
'Graph showing the predicted packing angle against the actual packing angle.'
)
with doc.create(pl.Table(position='!htbp')) as table:
table.add_caption('Summary of results for all data')
table.append(pl.Command('centering'))
table.append(pl.NoEscape(df_all.to_latex(escape=False)))
with doc.create(pl.Table(position='!htbp')) as table:
table.add_caption('Summary of results for outliers.')
table.append(pl.Command('centering'))
table.append(pl.NoEscape(df_out.to_latex(escape=False)))
with doc.create(pl.Figure(position='!htbp')) as graphs:
with doc.create(
pl.SubFigure(position='!htbp',
width=pl.NoEscape(
r'0.30\linewidth'))) as ang_dist_graph:
ang_dist_graph.add_image(ang_dist_file,
width=pl.NoEscape(r'\linewidth'))
ang_dist_graph.add_caption(
'Frequency distribution of the packing angle.')
with doc.create(
pl.SubFigure(position='!htbp',
width=pl.NoEscape(
r'0.33\linewidth'))) as error_dist_graph:
error_dist_graph.add_image(error_dist_file,
width=pl.NoEscape(r'\linewidth'))
error_dist_graph.add_caption(
'Distribution of errors calculated as the difference between the predicted and actual interface \
angle.')
with doc.create(
pl.SubFigure(position='!htbp',
width=pl.NoEscape(
r'0.33\linewidth'))) as sqerror_graph:
sqerror_graph.add_image(sqerror_file,
width=pl.NoEscape(r'\linewidth'))
sqerror_graph.add_caption(
'Squared error in predicted packing angle against actual packing angle.'
)
graphs.add_caption('Graphs for further metrics.')
