def plot_per_day(data: dict[str, dict[datetime, int]],
sort: bool = True,
cut: float | None = None):
# convert to list of tuples to be able to sort
data = list(data.items())
if sort:
# sort them such that the largest at the last time step gets plotted first and
# the colors are in a nice order
last_vals = [
list(vals.values())[-1] - list(vals.values())[-2]
for _, vals in data
]
data = [data[i] for i in _argsort(last_vals)[::-1]]
if cut is not None:
# cut those files where the latest data is less than cut*max_latest
last_vals = [
list(vals.values())[-1] - list(vals.values())[-2]
for _, vals in data
]
max_overall = max(last_vals)
data = [(tag, vals) for (tag, vals), last_val in zip(data, last_vals)
if last_val > cut * max_overall]
times = []
values = []
labels = []
for tag, vals in data:
t = list(vals.keys())
v = list(vals.values())
times.append(_get_middle_times(t))
values.append(_get_avg_per_day(t, v))
labels.append(tag)
# start plotting from the 0 before the first value
for j, (tm, val) in enumerate(zip(times, values)):
for i, x in enumerate(val):
if x > 0:
k = max(i - 1, 0)
break
times[j] = tm[k:]
values[j] = val[k:]
n = len(times)
for k, (time, vals, label) in enumerate(zip(times, values, labels)):
plt.plot(time, vals, label=label, zorder=n - k)
matplotx.line_labels()
return plt
def plot_scalar(
y: np.ndarray,
x: np.ndarray = None,
label: str = None,
xlabel: str = None,
ylabel: str = None,
fig_axes: FigAxes = None,
outfile: os.PathLike = None,
**kwargs,
) -> FigAxes:
assert len(y.shape) == 1
if x is None:
x = np.arange(len(y))
if fig_axes is None:
fig, ax = plt.subplots()
else:
fig, ax = fig_axes
_ = ax.plot(x, y, label=label, **kwargs)
if xlabel is not None:
_ = ax.set_xlabel(xlabel)
if ylabel is not None:
_ = ax.set_ylabel(ylabel)
if label is not None:
_ = matplotx.line_labels()
if outfile is not None:
savefig(fig, outfile)
return fig, ax
def plot_leapfrogs(
y: np.ndarray,
x: np.ndarray = None,
fig_axes: FigAxes = None,
xlabel: str = None,
ylabel: str = None,
outfile: os.PathLike = None,
) -> FigAxes:
assert len(y.shape) == 3
if fig_axes is None:
fig, ax = plt.subplots()
else:
fig, ax = fig_axes
if x is None:
x = np.arange(y.shape[0])
# y.shape = [ndraws, nleapfrog, nchains]
nlf = y.shape[1]
yavg = y.mean(-1)
cmap = plt.get_cmap('viridis')
colors = {n: cmap(n / nlf) for n in range(nlf)}
for lf in range(nlf):
_ = ax.plot(x, yavg[:, lf], color=colors[lf], label=f'{lf}')
_ = matplotx.line_labels()
if xlabel is not None:
_ = ax.set_xlabel(xlabel)
if ylabel is not None:
_ = ax.set_ylabel(ylabel)
if outfile is not None:
savefig(fig, outfile)
return fig, ax
def plot_dataArray(
self,
val: xr.DataArray,
key: str = None,
therm_frac: float = 0.,
num_chains: int = 0,
title: str = None,
outdir: str = None,
subplots_kwargs: dict[str, Any] = None,
plot_kwargs: dict[str, Any] = None,
) -> tuple:
plot_kwargs = {} if plot_kwargs is None else plot_kwargs
subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
figsize = subplots_kwargs.get('figsize', set_size())
subplots_kwargs.update({'figsize': figsize})
subfigs = None
# tmp = val[0]
arr = val.values # shape: [nchains, ndraws]
steps = np.arange(arr.shape[0])
if therm_frac > 0:
drop = int(therm_frac * arr.shape[0])
arr = arr[drop:]
steps = steps[drop:]
if len(arr.shape) == 2:
_ = subplots_kwargs.pop('constrained_layout', True)
figsize = (3 * figsize[0], 1.5 * figsize[1])
fig = plt.figure(figsize=figsize, constrained_layout=True)
subfigs = fig.subfigures(1, 2)
gs_kw = {'width_ratios': [1.33, 0.33]}
(ax, ax1) = subfigs[1].subplots(1,
2,
sharey=True,
gridspec_kw=gs_kw)
ax.grid(alpha=0.2)
ax1.grid(False)
color = plot_kwargs.get('color', None)
label = r'$\langle$' + f' {key} ' + r'$\rangle$'
ax.plot(steps,
arr.mean(-1),
lw=1.5 * LW,
label=label,
**plot_kwargs)
sns.kdeplot(y=arr.flatten(), ax=ax1, color=color, shade=True)
ax1.set_xticks([])
ax1.set_xticklabels([])
sns.despine(ax=ax, top=True, right=True)
sns.despine(ax=ax1, top=True, right=True, left=True, bottom=True)
ax1.set_xlabel('')
# _ = subfigs[1].subplots_adjust(wspace=-0.75)
axes = (ax, ax1)
ax0 = subfigs[0].subplots(1, 1)
im = val.plot(ax=ax0)
im.colorbar.set_label(key)
sns.despine(subfigs[0])
ax0.plot(steps, arr.mean(0), lw=2., color=color)
for idx in range(min(num_chains, arr.shape[0])):
ax0.plot(steps, arr[idx, :], lw=1., alpha=0.7, color=color)
else:
if len(arr.shape) == 1:
fig, ax = plt.subplots(**subplots_kwargs)
ax.plot(steps, arr, **plot_kwargs)
axes = ax
elif len(arr.shape) == 3:
fig, ax = plt.subplots(**subplots_kwargs)
cmap = plt.get_cmap('viridis')
nlf = arr.shape[1]
for idx in range(nlf):
y = arr[:, idx, :].mean(-1)
pkwargs = {
'color': cmap(idx / nlf),
'label': f'{idx}',
}
ax.plot(steps, y, **pkwargs)
axes = ax
else:
raise ValueError('Unexpected shape encountered')
ax.set_ylabel(key)
if num_chains > 0 and len(arr.shape) > 1:
lw = LW / 2.
for idx in range(min(num_chains, arr.shape[1])):
# ax = subfigs[0].subplots(1, 1)
# plot values of invidual chains, arr[:, idx]
# where arr[:, idx].shape = [ndraws, 1]
ax.plot(steps,
arr[:, idx],
alpha=0.5,
lw=lw / 2.,
**plot_kwargs)
matplotx.line_labels()
ax.set_xlabel('draw')
if title is not None:
fig.suptitle(title)
if outdir is not None:
plt.savefig(Path(outdir).joinpath(f'{key}.svg'),
dpi=400,
bbox_inches='tight')
return (fig, subfigs, axes)
def plot(
self,
val: torch.Tensor,
key: str = None,
therm_frac: float = 0.,
num_chains: int = 0,
title: str = None,
outdir: str = None,
subplots_kwargs: dict[str, Any] = None,
plot_kwargs: dict[str, Any] = None,
):
plot_kwargs = {} if plot_kwargs is None else plot_kwargs
subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
figsize = subplots_kwargs.get('figsize', set_size())
subplots_kwargs.update({'figsize': figsize})
tmp = val[0]
if isinstance(tmp, torch.Tensor):
arr = val.detach().numpy()
elif isinstance(tmp, float):
arr = np.array(val)
else:
try:
arr = np.array([np.array(i) for i in val])
except (AttributeError, ValueError) as exc:
raise exc
subfigs = None
steps = np.arange(arr.shape[0])
if therm_frac > 0:
drop = int(therm_frac * arr.shape[0])
arr = arr[drop:]
steps = steps[drop:]
if len(arr.shape) == 2:
_ = subplots_kwargs.pop('constrained_layout', True)
figsize = (3 * figsize[0], 1.5 * figsize[1])
fig = plt.figure(figsize=figsize, constrained_layout=True)
# subfigs = fig.subfigures((1, 2), wspace=0.01)#, width_ratios=[1., 1.5])
subfigs = fig.subfigures(
1, 2) #, wspace=0.1)#, width_ratios=[1., 1.5])
gs_kw = {'width_ratios': [1.33, 0.33]}
(ax, ax1) = subfigs[1].subplots(1,
2,
sharey=True,
gridspec_kw=gs_kw)
ax.grid(alpha=0.2)
ax1.grid(False)
# (ax, ax1) = fig.subfigures(1, 1).subplots(1, 2)
# gs = fig.add_gridspec(ncols=3, nrows=1, width_ratios=[1.5, 1., 1.5])
color = plot_kwargs.get('color', None)
label = r'$\langle$' + f' {key} ' + r'$\rangle$'
ax.plot(steps,
arr.mean(-1),
lw=1.5 * LW,
label=label,
**plot_kwargs)
sns.kdeplot(y=arr.flatten(), ax=ax1, color=color, shade=True)
ax1.set_xticks([])
ax1.set_xticklabels([])
# ax1.set_yticks([])
# ax1.set_yticklabels([])
sns.despine(ax=ax, top=True, right=True)
sns.despine(ax=ax1, top=True, right=True, left=True, bottom=True)
# ax.legend(loc='best', frameon=False)
ax1.set_xlabel('')
# ax1.set_ylabel('')
# ax.set_yticks(ax.get_yticks())
# ax.set_yticklabels(ax.get_yticklabels())
# ax.set_ylabel(key)
# _ = subfigs[1].subplots_adjust(wspace=-0.75)
axes = (ax, ax1)
else:
if len(arr.shape) == 1:
fig, ax = plt.subplots(**subplots_kwargs)
ax.plot(steps, arr, **plot_kwargs)
axes = ax
elif len(arr.shape) == 3:
fig, ax = plt.subplots(**subplots_kwargs)
for idx in range(arr.shape[1]):
ax.plot(steps,
arr[:, idx, :].mean(-1),
label='idx',
**plot_kwargs)
axes = ax
else:
raise ValueError('Unexpected shape encountered')
ax.set_ylabel(key)
if num_chains > 0 and len(arr.shape) > 1:
lw = LW / 2.
for idx in range(min(num_chains, arr.shape[1])):
# plot values of invidual chains, arr[:, idx]
# where arr[:, idx].shape = [ndraws, 1]
ax.plot(steps,
arr[:, idx],
alpha=0.5,
lw=lw / 2.,
**plot_kwargs)
matplotx.line_labels()
ax.set_xlabel('draw')
if title is not None:
fig.suptitle(title)
if outdir is not None:
plt.savefig(Path(outdir).joinpath(f'{key}.svg'),
dpi=400,
bbox_inches='tight')
return fig, subfigs, axes
def plot( # noqa: C901
self,
time_unit: str = "s",
relative_to: int | None = None,
logx: str | bool = "auto",
logy: str | bool = "auto",
):
if logx == "auto":
# Check if the x values are approximately equally spaced in log
if np.any(self.n_range <= 0):
logx = False
else:
log_n_range = np.log(self.n_range)
linlog = np.linspace(log_n_range[0], log_n_range[-1],
len(log_n_range))
# don't consider first and last, they are equal anyway
rel_diff = (log_n_range - linlog)[1:-1] / log_n_range[1:-1]
logx = np.all(np.abs(rel_diff) <= 0.1)
if logy == "auto":
if relative_to is not None:
logy = False
elif self.flop is not None:
logy = False
else:
logy = logx
if logx and logy:
plotfun = plt.loglog
elif logx:
plotfun = plt.semilogx
elif logy:
plotfun = plt.semilogy
else:
plotfun = plt.plot
if self.flop is None:
if relative_to is None:
# Set time unit of plots.
# Allowed values: ("s", "ms", "us", "ns", "auto")
if time_unit == "auto":
time_unit = _auto_time_unit(np.min(self.timings_s))
else:
assert time_unit in si_time, "Provided `time_unit` is not valid"
scaled_timings = self.timings_s / si_time[time_unit]
ylabel = f"Runtime [{time_unit}]"
else:
scaled_timings = self.timings_s / self.timings_s[relative_to]
ylabel = f"Runtime\nrelative to {self.labels[relative_to]}"
for t, label in zip(scaled_timings, self.labels):
plotfun(self.n_range, t, label=label)
matplotx.ylabel_top(ylabel)
else:
if relative_to is None:
flops = self.flop / self.timings_s
plt.title("FLOPS")
else:
flops = self.timings_s[relative_to] / self.timings_s
plt.title(f"FLOPS relative to {self.labels[relative_to]}")
for fl, label in zip(flops, self.labels):
plotfun(self.n_range, fl, label=label)
if self.xlabel:
plt.xlabel(self.xlabel)
if self.title:
plt.title(self.title)
if relative_to is not None and not logy:
plt.gca().set_ylim(bottom=0)
matplotx.line_labels()
def plot_metric(
val: np.ndarray,
key: str = None,
therm_frac: float = 0.,
num_chains: int = 0,
title: str = None,
outdir: os.PathLike = None,
subplots_kwargs: dict[str, Any] = None,
plot_kwargs: dict[str, Any] = None,
ext: str = 'png',
) -> tuple:
plot_kwargs = {} if plot_kwargs is None else plot_kwargs
subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
figsize = subplots_kwargs.get('figsize', set_size())
subplots_kwargs.update({'figsize': figsize})
# tmp = val[0]
arr = np.array(val)
subfigs = None
steps = np.arange(arr.shape[0])
if therm_frac > 0:
drop = int(therm_frac * arr.shape[0])
arr = arr[drop:]
steps = steps[drop:]
# arr.shape = [draws, chains]
if len(arr.shape) == 2:
_ = subplots_kwargs.pop('constrained_layout', True)
figsize = (3 * figsize[0], 1.5 * figsize[1])
fig = plt.figure(figsize=figsize, constrained_layout=True)
subfigs = fig.subfigures(1, 2)
gs_kw = {'width_ratios': [1.33, 0.33]}
(ax, ax1) = subfigs[1].subplots(1, 2, sharey=True,
gridspec_kw=gs_kw)
ax.grid(alpha=0.2)
ax1.grid(False)
color = plot_kwargs.get('color', 'C0')
label = r'$\langle$' + f' {key} ' + r'$\rangle$'
ax.plot(steps, arr.mean(-1), lw=1.5*LW, label=label, **plot_kwargs)
if num_chains > 0:
for chain in range(min((num_chains, arr.shape[1]))):
plot_kwargs.update({'label': None})
ax.plot(steps, arr[:, chain], lw=LW/2., **plot_kwargs)
sns.kdeplot(y=arr.flatten(), ax=ax1, color=color, shade=True)
ax1.set_xticks([])
ax1.set_xticklabels([])
sns.despine(ax=ax, top=True, right=True)
sns.despine(ax=ax1, top=True, right=True, left=True, bottom=True)
ax1.set_xlabel('')
axes = (ax, ax1)
else:
# arr.shape = [draws]
if len(arr.shape) == 1:
fig, ax = plt.subplots(**subplots_kwargs)
ax.plot(steps, arr, **plot_kwargs)
axes = ax
# arr.shape = [draws, nleapfrog, chains]
elif len(arr.shape) == 3:
fig, ax = plt.subplots(**subplots_kwargs)
cmap = plt.get_cmap('viridis', lut=arr.shape[1])
_ = plot_kwargs.pop('color', None)
for idx in range(arr.shape[1]):
y = arr[:, idx]
color = cmap(idx / y.shape[1])
if len(y.shape) == 2:
# TOO: Plot chains
if num_chains > 0:
for idx in range(min((num_chains, y.shape[1]))):
ax.plot(steps, y[:, idx], color=color,
lw=LW/4., alpha=0.7, **plot_kwargs)
ax.plot(steps, y.mean(-1), color=color,
label=f'{idx}', **plot_kwargs)
else:
ax.plot(steps, y, color=color,
label=f'{idx}', **plot_kwargs)
axes = ax
else:
raise ValueError('Unexpected shape encountered')
ax.set_ylabel(key)
if num_chains > 0 and len(arr.shape) > 1:
lw = LW / 2.
for idx in range(min(num_chains, arr.shape[1])):
# plot values of invidual chains, arr[:, idx]
# where arr[:, idx].shape = [ndraws, 1]
ax.plot(steps, arr[:, idx], alpha=0.5, lw=lw/2., **plot_kwargs)
matplotx.line_labels()
ax.set_xlabel('draw')
if title is not None:
fig.suptitle(title)
if outdir is not None:
outfile = Path(outdir).joinpath(f'{key}.{ext}')
if not outfile.is_file():
plt.savefig(Path(outdir).joinpath(f'{key}.{ext}'),
dpi=400, bbox_inches='tight')
return fig, subfigs, axes
def plot_dataArray(
val: xr.DataArray,
key: str = None,
therm_frac: float = 0.,
num_chains: int = 0,
title: str = None,
outdir: str = None,
subplots_kwargs: dict[str, Any] = None,
plot_kwargs: dict[str, Any] = None,
) -> tuple:
plot_kwargs = {} if plot_kwargs is None else plot_kwargs
subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
figsize = subplots_kwargs.get('figsize', set_size())
subplots_kwargs.update({'figsize': figsize})
subfigs = None
if key == 'dt':
therm_frac = 0.2
# tmp = val[0]
arr = val.values # shape: [nchains, ndraws]
steps = np.arange(len(val.coords['draw']))
if therm_frac > 0:
drop = int(therm_frac * arr.shape[0])
arr = arr[drop:]
steps = steps[drop:]
if len(arr.shape) == 2:
_ = subplots_kwargs.pop('constrained_layout', True)
figsize = (3 * figsize[0], 1.5 * figsize[1])
fig = plt.figure(figsize=figsize, constrained_layout=True)
subfigs = fig.subfigures(1, 2)
gs_kw = {'width_ratios': [1.33, 0.33]}
(ax, ax1) = subfigs[1].subplots(1, 2, sharey=True,
gridspec_kw=gs_kw)
ax.grid(alpha=0.2)
ax1.grid(False)
vmin = np.min(val)
vmax = np.max(val)
cmap = None
if vmin < 0 < vmax:
# BWR: uniform cmap from blue -> white == 0 -> red
color = '#FF5252' if val.mean() > 0 else '#007DFF'
cmap = 'RdBu_r'
elif 0 < vmin < vmax:
# viridis: uniform cmap from 0 < blue -> green -> yellow
cmap = 'mako'
# color = '#4DC26B'
# color = '#B2DD2D'
color = '#3FB5AD'
else:
color = plot_kwargs.get('color', f'C{np.random.randint(4)}')
label = r'$\langle$' + f' {key} ' + r'$\rangle$'
ax.plot(steps, val.mean('chain'),
label=label, lw=1.5*LW, **plot_kwargs)
sns.kdeplot(y=arr.flatten(), ax=ax1, color=color, shade=True)
ax1.set_xticks([])
ax1.set_xticklabels([])
sns.despine(ax=ax, top=True, right=True)
sns.despine(ax=ax1, top=True, right=True, left=True, bottom=True)
ax1.set_xlabel('')
# _ = subfigs[1].subplots_adjust(wspace=-0.75)
axes = (ax, ax1)
ax0 = subfigs[0].subplots(1, 1)
val = val.dropna('chain')
nchains = min((num_chains, len(val.coords['chain'])))
_ = xr.plot.pcolormesh(val, 'draw', 'chain',
ax=ax0, robust=True,
cmap=cmap, add_colorbar=True)
if key is not None and 'eps' in key:
_ = ax0.set_ylabel('leapfrog')
sns.despine(subfigs[0])
plt.autoscale(enable=True, axis=ax0)
ax.plot(steps, arr.mean(0), lw=2., color='k', label='avg')
for idx in range(min(num_chains, arr.shape[0])):
ax.plot(steps, arr[idx, :], lw=1., alpha=0.7, color=color)
ax.legend(loc='best')
else:
if len(arr.shape) == 1:
fig, ax = plt.subplots(**subplots_kwargs)
ax.plot(steps, arr, **plot_kwargs)
axes = ax
elif len(arr.shape) == 3:
fig, ax = plt.subplots(**subplots_kwargs)
cmap = plt.get_cmap('viridis')
y = val.mean('chain')
for idx in range(len(val.coords['leapfrog'])):
pkwargs = {
'color': cmap(idx / len(val.coords['leapfrog'])),
'label': f'{idx}',
}
ax.plot(steps, y[idx], **pkwargs)
axes = ax
else:
raise ValueError('Unexpected shape encountered')
ax.set_ylabel(key)
# if num_chains > 0 and len(arr.shape) > 1:
# lw = LW / 2.
# #for idx in range(min(num_chains, arr.shape[1])):
# nchains = len(val.coords['chains'])
# for idx in range(min(nchains, num_chains)):
# # ax = subfigs[0].subplots(1, 1)
# # plot values of invidual chains, arr[:, idx]
# # where arr[:, idx].shape = [ndraws, 1]
# ax.plot(steps, val
# alpha=0.5, lw=lw/2., **plot_kwargs)
matplotx.line_labels()
ax.set_xlabel('draw')
if title is not None:
fig.suptitle(title)
if outdir is not None:
plt.savefig(Path(outdir).joinpath(f'{key}.svg'),
dpi=400, bbox_inches='tight')
return (fig, subfigs, axes)
def plot(
self,
val: list | np.ndarray | tf.Tensor,
key: str = None,
therm_frac: float = 0.,
num_chains: int = 0,
title: str = None,
outdir: str = None,
subplots_kwargs: dict[str, Any] = None,
plot_kwargs: dict[str, Any] = None,
ext: str = 'svg',
):
plot_kwargs = {} if plot_kwargs is None else plot_kwargs
subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
figsize = subplots_kwargs.get('figsize', set_size())
subplots_kwargs.update({'figsize': figsize})
key = '' if key is None else key
label = ' '.join([r'$\langle$', f'{key}', r'$\rangle$'])
if isinstance(val[0], tf.Tensor):
arr = val.numpy()
elif isinstance(val[0], float):
arr = np.array(val)
else:
try:
arr = np.array([np.array(i) for i in val])
except (AttributeError, ValueError) as exc:
raise exc
subfigs = None
steps = np.arange(arr.shape[0])
if therm_frac > 0:
drop = int(therm_frac * arr.shape[0])
arr = arr[drop:]
steps = steps[drop:]
if len(arr.shape) == 2:
_ = subplots_kwargs.pop('constrained_layout', True)
figsize = (3 * figsize[0], 1.5 * figsize[1])
fig = plt.figure(figsize=figsize, constrained_layout=True)
subfigs = fig.subfigures(1, 2)
# , wspace=0.1)#, width_ratios=[1., 1.5])
gs_kw = {'width_ratios': [1.33, 0.33]}
(ax, ax1) = subfigs[1].subplots(1,
2,
sharey=True,
gridspec_kw=gs_kw)
ax.grid(alpha=0.2)
ax1.grid(False)
color = plot_kwargs.get('color', None)
ax.plot(steps,
arr.mean(-1),
lw=1.5 * LW,
label=label,
**plot_kwargs)
sns.kdeplot(y=arr.flatten(), ax=ax1, color=color, shade=True)
ax1.set_xticks([])
ax1.set_xticklabels([])
sns.despine(ax=ax, top=True, right=True)
sns.despine(ax=ax1, top=True, right=True, left=True, bottom=True)
ax1.set_xlabel('')
ax1.set_ylabel('')
axes = (ax, ax1)
matplotx.line_labels(ax=ax)
else:
if len(arr.shape) == 1:
fig, ax = plt.subplots(**subplots_kwargs)
ax.plot(steps, arr, label=label, **plot_kwargs)
axes = ax
matplotx.line_labels(ax=ax)
elif len(arr.shape) == 3:
fig, ax = plt.subplots(**subplots_kwargs)
for idx in range(arr.shape[1]):
if idx == 0:
ax.plot(steps,
arr[:, idx, :].mean(-1),
label=label,
**plot_kwargs)
else:
ax.plot(steps, arr[:, idx, :].mean(-1), **plot_kwargs)
axes = ax
matplotx.line_labels(ax=ax)
else:
raise ValueError('Unexpected shape encountered')
ax.set_ylabel(key)
if num_chains > 0 and len(arr.shape) > 1:
for idx in range(min(num_chains, arr.shape[1])):
# plot values of invidual chains, arr[:, idx]
# where arr[:, idx].shape = [ndraws, 1]
ax.plot(steps,
arr[:, idx],
alpha=0.4,
lw=LW / 5.,
**plot_kwargs)
ax.set_xlabel('draw')
if title is not None:
fig.suptitle(title)
if outdir is not None:
fig.savefig(Path(outdir).joinpath(f'{key}.{ext}'))
return fig, subfigs, axes