def plot_smoothed(
ax: plt.axis,
timesteps: Iterable[int],
values: Sequence[float], smoothed_values: float,
label: str,
alpha: float = 0.2) \
-> None:
line = ax.plot(timesteps, smoothed_values, label=label)[0]
clr = line.get_color()
ax.plot(timesteps, values, color=clr, alpha=alpha)
def plot_peaks(axis: plt.axis, collection: PeakCollection) -> plt.axis:
for i, peak_data in enumerate(collection):
if i > 1000: # safety break
break
if peak_data.relevant:
axis.plot(peak_data.get_x,
peak_data.get_y,
'x',
color='r',
alpha=1)
return axis
def plot_percentiles(
ax: plt.axis, df: pd.DataFrame, percentiles: Tuple[float, float],
alpha: float) -> None:
assert 0 <= alpha <= 1
assert len(percentiles) == 2 and \
0 <= percentiles[0] <= 1 and \
0 <= percentiles[1] <= 1, "percentiles must be between 0 and 1"
pct_low = df.quantile(percentiles[0], axis=1)
pct_high = df.quantile(percentiles[1], axis=1)
ax.fill_between(df.index, pct_low, pct_high, alpha=alpha)
def draw(self, ax: plt.axis):
ax.scatter(
*self.p0,
s=200,
lw=1,
ec=[0.3, 0.3, 0.3],
color=self.color,
zorder=100,
)
if self.last:
ax.scatter(
*self.p1,
s=200,
lw=1,
ec=[0.3, 0.3, 0.3],
color=self.color,
zorder=100,
)
ax.plot(
[self.p0[0], self.p1[0]],
[self.p0[1], self.p1[1]],
lw=6,
color=[0.3, 0.3, 0.3],
zorder=98,
)
ax.plot(
[self.p0[0], self.p1[0]],
[self.p0[1], self.p1[1]],
lw=5,
color=self.color,
zorder=99,
)
def plot_areas_society_progress(ax: plt.axis, time_array: List,
society_snapshot: Dict,
society_progress: Dict):
previous_status = None
ax.set_xlabel('time [days]')
ax.set_ylabel('population percentage')
for st in Status:
society_progress[st.name].append(society_snapshot[st.name])
if previous_status:
lower_limit = society_progress[previous_status.name]
else:
lower_limit = [0]
ax.fill_between(x=time_array,
y1=lower_limit,
y2=society_progress[st.name],
color=st.value,
label=st.name,
alpha=0.25)
ax.text(x=time_array[-1],
y=1 / 2 * (lower_limit[-1] + society_snapshot[st.name]),
s=r"{0:.2f}".format(society_snapshot[st.name] -
lower_limit[-1]),
size=10,
color=st.value)
previous_status = st
def plot_table_params(ax: plt.axis, params_dict: Dict):
cells = [[i] for i in params_dict.values()]
ax.axis('off')
table = ax.table(cellText=cells,
cellLoc='center',
rowLabels=list(params_dict.keys()),
rowLoc='center',
colWidths=[0.1],
loc='center')
table.auto_set_font_size(False)
table.set_fontsize(10)
def plot_tracking_linearized(
tracking: Union[dict, pd.DataFrame],
ax: plt.axis = None,
plot: bool = True,
**kwargs,
):
ax = ax or plt.subplots(figsize=(9, 9))[1]
x = tracking["global_coord"]
y = np.linspace(1, 0, len(x))
if not plot:
ax.scatter(x, y, **kwargs)
else:
ax.plot(x, y, **kwargs)
def draw_obstacle(ax: plt.axis, obst_pts, *args, **kwargs):
obst_x = np.float32((obst_pts[0], obst_pts[2]))
obst_y = np.float32((obst_pts[1], obst_pts[3]))
p_min = np.float32((np.min(obst_x), np.min(obst_y)))
p_max = np.float32((np.max(obst_x), np.max(obst_y)))
h = p_max[1] - p_min[1]
w = p_max[0] - p_min[0]
patch = patches.Rectangle(p_min,
w,
h,
linewidth=1,
edgecolor='k',
facecolor='k')
ax.add_patch(patch)
return patch
def display_overlay(rgb_image: np.ndarray, binary_mask: np.ndarray, ax: plt.axis, check_mask=False):
"""
Display version of RGB_image where `False` values in binary_mask are darkened.
:param rgb_image:
:param binary_mask:
:param ax:
:param check_mask:
:return:
"""
if check_mask and binary_mask.dtype is not bool:
warn("`binary_mask` is non-boolean, attempting conversion.")
binary_mask = binary_mask.astype(bool)
overlay = rgb_image.copy()
overlay[~binary_mask] = overlay[~binary_mask] // 2 # Darken the color of the original image where mask is False
ax.imshow(overlay)
def plot_bouts_x(
tracking_data: pd.DataFrame,
bouts: pd.DataFrame,
ax: plt.axis,
variable: str,
color: str = blue_grey,
**kwargs,
):
"""
Plots a variable from the tracking data for each bout
"""
for i, bout in bouts.iterrows():
ax.plot(
tracking_data[variable][bout.start_frame:bout.end_frame],
color=color,
**kwargs,
)
def plot_cluster_sizes(cluster_labels: list,
ax: plt.axis = None) -> np.ndarray:
"""
Plots cluster sizes using a histogram and returns a list of most frequent
cluster sizes.
Parameters
----------
cluster_labels : list
List of cluster labels
ax : plt.axis
Matplotlib axis (default None)
Returns
-------
most_common_cluster_sizes : np.ndarray
Numpy array containing the most common cluster sizes
"""
if ax is None:
_, ax = plt.subplots()
# Print cluster size ratio (max / min)
labels_unique, labels_counts = np.unique(cluster_labels,
return_counts=True)
cluster_sizes, cluster_size_counts = np.unique(labels_counts,
return_counts=True)
num_clusters = len(labels_unique)
max_cluster_size = max(labels_counts)
min_cluster_size = min(labels_counts)
cluster_size_ratio = max_cluster_size / min_cluster_size
print(
f"{num_clusters} clusters: max={max_cluster_size}, min={min_cluster_size}, ratio={cluster_size_ratio}"
)
# Plot distribution of cluster sizes
sns.histplot(labels_counts, bins=max_cluster_size, ax=ax)
ax.set_xlabel("Cluster size")
ax.set_ylabel("Number of words in cluster")
plt.show()
# Sort cluster sizes by frequency
most_common_cluster_sizes = cluster_sizes[np.argsort(cluster_size_counts)
[::-1]]
return most_common_cluster_sizes
def plot_bouts_x_by_y(
tracking_data: pd.DataFrame,
bouts: pd.DataFrame,
ax: plt.axis,
x: str,
y: str,
color: str = blue_grey,
**kwargs,
):
"""
Plots two tracking variables one against the other for each bout
"""
for i, bout in bouts.iterrows():
ax.plot(
tracking_data[x][bout.start_frame:bout.end_frame],
tracking_data[y][bout.start_frame:bout.end_frame],
color=color,
**kwargs,
)
def mpl_plot(self,
ax: plt.axis = None,
temp_unit: str = "GK",
**kwargs) -> plt.axis:
ax = ax or plt.gca()
t = np.logspace(-2, 1, 1000)
if temp_unit is "GK":
ax.loglog(
t,
self.rate(t),
color=self.color,
label="Reaclib-" + self.label + " " + self.__str__(),
**kwargs,
)
elif temp_unit is "KeV":
ax.loglog(
Temperature(t).kev,
self.rate(t),
color=self.color,
label=self.label + " " + self.__str__(),
**kwargs,
)
ax.legend()
ax = super().mpl_plot(ax=ax, temp_unit=temp_unit)
return ax
def plot_tensorboard(
ax: plt.axis,
tag: str,
tb_data: Iterable[Iterable[Iterable[Path]]],
names: Iterable[str],
percentiles: Optional[Tuple[float, float]] = (0.25, 0.75),
alpha: float = 0.1,
use_data_cache: bool = False,
data_cache_fname: str = ".rlgear_data.p",
show_same_num_timesteps: bool = False,
max_step: Optional[int] = None) -> None:
if use_data_cache:
with open(data_cache_fname, 'rb') as f:
out_dfs = pickle.load(f)
else:
out_dfs = []
for grouped_files in tb_data:
out_dfs.append(tb_to_df(
grouped_files,
tag, max_step=max_step, only_complete_data=True))
with open(data_cache_fname, 'wb') as f:
pickle.dump(out_dfs, f)
if show_same_num_timesteps:
shorten_dfs(out_dfs)
for name, df in zip(names, out_dfs):
ax.plot(df.index, df.mean(axis=1), label=name)
if percentiles:
plot_percentiles(ax, df, percentiles, alpha)
# https://stackoverflow.com/a/25750438
ax.xaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
ax.set_xlabel('Training Step')
plt.tight_layout()
def plot_heatmap_2d(
data: Union[dict, pd.DataFrame],
key: str = None,
ax: plt.axis = None,
x_key: str = "x",
y_key: str = "y",
cmap: str = "inferno",
vmin: int = 0,
vmax: int = None,
gridsize: int = 30,
mincnt: int = 1,
**kwargs,
):
# bin data in 2d
try:
ax.hexbin(
data[x_key],
data[y_key],
data[key] if key is not None else None,
cmap=cmap,
gridsize=gridsize,
vmin=vmin,
vmax=vmax,
mincnt=mincnt,
**kwargs,
)
except ValueError: # likely the data was nested arrays
ax.hexbin(
np.hstack(data[x_key]),
np.hstack(data[y_key]),
np.hstack(data[key]) if key is not None else None,
cmap=cmap,
gridsize=gridsize,
vmin=vmin,
vmax=vmax,
mincnt=mincnt,
**kwargs,
)
def _sub_plot_single_from_array(self, ax:plt.axis, data:np.array, x_title:str, title:str, legend=None):
'''
Create a sub-plot for a given column.
Parameters
----------
ax : plt.axis
figure axis
data : np.array
data to plot
'''
if legend == None:
ax.plot(data,linestyle='-',linewidth=1)
else:
ax.plot(data,linestyle='-',linewidth=1,label=legend)
ax.set_xlabel(x_title)
ax.set_title(title)
def _sub_plot_multiple(self, axis:plt.axis, columns:tuple):
'''
Create a sub-plot for the given columns.
Parameters
----------
ax : plt.axis
figure axis
columns : tuple
column names
'''
title = ''
for col in columns:
axis.plot(self.m_data_df[col], label=col,linestyle='-',linewidth=0.1)
title += col + ', '
axis.set_title(title)
axis.set_xlabel('Rollouts')
axis.legend()
def plot_tracking_xy(
tracking: Union[dict, pd.DataFrame],
key: str = None,
skip_frames: int = 1,
ax: plt.axis = None,
plot: bool = False,
**kwargs,
):
ax = ax or plt.subplots(figsize=(9, 9))[1]
if key is None:
if not plot:
ax.scatter(
tracking["x"][::skip_frames],
tracking["y"][::skip_frames],
color=[0.3, 0.3, 0.3],
**kwargs,
)
else:
ax.plot(
tracking["x"][::skip_frames],
tracking["y"][::skip_frames],
**kwargs,
)
else:
ax.scatter(
tracking["x"][::skip_frames],
tracking["y"][::skip_frames],
c=tracking[key][::skip_frames],
**kwargs,
)
if "orientation" in key or "angle" in key:
# draw arrows to mark the angles/colors mapping
angles = np.linspace(0, 2 * np.pi, 16)
x = 2 * np.cos(angles[::-1] + np.pi / 2) + 25
y = 2 * np.sin(angles + np.pi / 2) + 2
ax.scatter(x,
y,
s=80,
zorder=50,
c=np.degrees(angles),
alpha=1,
**kwargs)
def plot_bouts_1d(
tracking: Union[dict, pd.DataFrame],
bouts: pd.DataFrame,
ax: plt.axis,
direction: bool = None,
zorder: int = 100,
lw: float = 2,
alpha: float = 1,
**kwargs,
):
# select bouts by direction
if direction is not None:
bouts = bouts.loc[bouts.direction == direction]
# get coords
x = tracking["global_coord"]
y = np.linspace(1, 0, len(x))
# plot
for i, bout in bouts.iterrows():
_x = x[bout.start_frame:bout.end_frame]
_y = y[bout.start_frame:bout.end_frame]
ax.plot(
_x,
_y,
color=colors.bout_direction_colors[bout.direction],
zorder=zorder,
lw=lw,
alpha=alpha,
**kwargs,
)
ax.scatter(
_x[0],
_y[0],
color="white",
lw=1,
ec=colors.bout_direction_colors[bout.direction],
s=30,
zorder=101,
alpha=0.85,
**kwargs,
)
ax.scatter(
_x[-1],
_y[-1],
color=[0.2, 0.2, 0.2],
lw=1,
ec=colors.bout_direction_colors[bout.direction],
s=30,
zorder=101,
alpha=0.85,
**kwargs,
)
def plot_lines_society_progress(ax: plt.axis, time_array: List,
society_snapshot: Dict,
society_progress: Dict):
ax.set_xlabel('time [days]')
ax.set_ylabel('population percentage')
for st in Status:
society_progress[st.name].append(society_snapshot[st.name] /
society_snapshot["Total"])
ax.plot(time_array,
society_progress[st.name],
c=st.value,
ls='-',
label=st.name,
alpha=0.5)
ax.text(x=time_array[-1],
y=society_progress[st.name][-1],
s=r"{0:.2f}".format(society_progress[st.name][-1]),
size=10,
color=st.value)
def get_auto_ylims(ax: plt.axis,
hMC,
*,
hdata=None,
log_y=False,
yaxis_scale='auto'
) -> Tuple[Union[int, float], Union[int, float]]:
"""Return the minimum and maximum values for the y axis. If yaxis_scale is 'auto', get a value such that the
histogram stays in bounds of the plot, and the legend does not overlap with the histogram.
"""
ylims = ax.get_ylim()
ymin = ylims[0]
if yaxis_scale == 'auto':
if not isinstance(hMC[0], float):
hMC = hMC[-1]
labelFraction = 1 / 4
legendFraction = 1 / 3
maxBelowLabel = max(hMC[:len(hMC) * 2 // 3])
maxBelowLegend = max(hMC[len(hMC) * 2 // 3:])
if hdata is not None:
maxBelowLabel = max(maxBelowLabel,
max(hdata[:len(hdata) * 2 // 3]))
maxBelowLegend = max(maxBelowLegend,
max(hdata[len(hdata) * 2 // 3:]))
if log_y:
ymax = max(maxBelowLabel**(1 / (1 - labelFraction)),
maxBelowLegend**(1 / (1 - legendFraction)))
else:
ymax = max(maxBelowLabel / (1 - labelFraction),
maxBelowLegend / (1 - legendFraction))
else:
if log_y:
ymax = ylims[1]**yaxis_scale
else:
ymax = ylims[1] * yaxis_scale
return ymin, ymax
def _sub_plot_single(self, ax:plt.axis, column:str, legend=None):
'''
Create a sub-plot for a given column.
Parameters
----------
ax : :plt.axis
figure axis
column : str
column name
'''
if legend == None:
ax.plot(self.m_data_df[column],linestyle='-',linewidth=0.1)
else:
ax.plot(self.m_data_df[column],linestyle='-',linewidth=0.1,label=legend)
ax.set_xlabel('Rollouts')
if False and column == 'Num_timesteps':
column += ' (approx. run time: {0:.2f} hours)'.format(self.estimated_run_time())
ax.set_title(column)
def plot_balls_errors(
x: np.ndarray,
y: np.ndarray,
yerr: np.ndarray,
ax: plt.axis,
s: int = 150,
colors: Union[list, str] = None,
):
"""
Given a serires of XY values and Y errors it plots a scatter for each XY point and a line
to mark each Y error
"""
if colors is None:
colors = [blue_grey] * len(x)
elif isinstance(colors, str):
colors = [colors] * len(x)
ax.scatter(x, y, s=s, c=colors, zorder=100, lw=1, ec=[0.3, 0.3, 0.3])
ax.plot(x, y, lw=3, color=colors[0], zorder=-1)
if yerr is not None:
for n in range(len(x)):
ax.plot(
[x[n], x[n]],
[y[n] - yerr[n], y[n] + yerr[n]],
lw=4,
color=[0.3, 0.3, 0.3],
zorder=96,
solid_capstyle="round",
)
ax.plot(
[x[n], x[n]],
[y[n] - yerr[n], y[n] + yerr[n]],
lw=2,
color=colors[n],
zorder=98,
solid_capstyle="round",
)
def plot_probe_electrodes(
rsites: pd.DataFrame,
ax: plt.axis,
TARGETS: list = [],
annotate_every: Union[int, bool] = 5,
x_shift: bool = True,
s: int = 25,
lw: float = 0.25,
x_pos_delta: float = 0,
):
x = np.ones(len(rsites)) * 1.025 + x_pos_delta
if x_shift:
x[::2] = 1.025 + x_pos_delta - 0.05
x[2::4] = 1.025 + x_pos_delta - 0.025
x[1::4] = 1.025 + x_pos_delta + 0.025
else:
x = (x_pos_delta +
np.tile([0.975, 1.025, 1.0, 1.05], np.int(np.ceil(
len(rsites) / 4)))[:len(rsites)])
if TARGETS is not None:
colors = [
rs.color if rs.brain_region in TARGETS else
([0.3, 0.3, 0.3] if rs.brain_region in ("unknown",
"OUT") else blue_grey)
for i, rs in rsites.iterrows()
]
else:
colors = [rs.color for i, rs in rsites.iterrows()]
ax.scatter(
x,
rsites.probe_coordinates,
s=s,
lw=lw,
ec=[0.3, 0.3, 0.3],
marker="s",
c=colors,
)
if annotate_every:
for i in np.arange(len(x))[::annotate_every]:
ax.annotate(
f"{rsites.site_id.iloc[i]} - {rsites.brain_region.iloc[i]}",
(0.6, rsites.probe_coordinates.iloc[i]),
color=colors[i],
)
ax.set(xlim=[0.5, 1.25], ylabel="probe coordinates (um)")
def plot_raster(
spikes: np.ndarray,
events: Union[np.ndarray, list],
ax: plt.axis,
window: int = 120,
s=5,
color=grey_darker,
kde: bool = True,
bw: int = 6,
**kwargs,
):
"""
Plots a raster plot of spikes aligned to timestamps events
It assumes that all event and spike times are in frames and framerate is 60
"""
half_window = window / 2
yticks_step = int(np.ceil(len(events) / 8)) if len(events) > 8 else 2
X = []
for n, event in enumerate(events):
event_spikes = (spikes[(spikes >= event - half_window)
& (spikes <= event + half_window)] - event)
X.extend(list(event_spikes))
y = np.ones_like(event_spikes) * n
ax.scatter(event_spikes, y, s=5, color=color, **kwargs)
ax.axvline(0, ls=":", color="k", lw=0.75)
# plot KDE
if kde:
raise NotImplementedError("KDE env setup incorrect")
# plot_kde(
# ax=ax,
# z=-len(events) / 4,
# data=X,
# normto=len(events) / 5,
# color=blue_grey_dark,
# kde_kwargs=dict(bw=bw, cut=0),
# alpha=0.6,
# invert=False,
# )
# set x axis properties
ax.set(
yticks=np.arange(0, len(events), yticks_step),
ylabel="event number",
**get_window_ticks(window),
)
def draw_model_prediction(fig: plt.figure,
ax: plt.axis,
model: nn.Module,
gw: int = 0,
min_point: (float, float) = (0, 0),
max_point: (float, float) = (500, 500),
resolution: float = 1,
apply_ploss: bool = True,
colorbar: bool = True,
detection_flag: bool = False,
detection_threshold: float = -140,
device='cpu',
*args,
**kwargs):
x_mesh, y_mesh = np.mgrid[min_point[0]:max_point[0]:resolution,
min_point[1]:max_point[1]:resolution]
x = x_mesh.ravel()
y = y_mesh.ravel()
data_x = np.stack([x, y], axis=1).astype(np.float32)
if detection_flag is False:
z = model(torch.from_numpy(data_x).to(device),
apply_ploss).detach()[:, gw, 0].cpu().numpy()
else:
#z = model(torch.from_numpy(data_x).to(device), apply_ploss).detach()[:, gw, 0].cpu().numpy()
z = model(torch.from_numpy(data_x).to(device),
apply_ploss).detach()[:, gw, :].cpu().numpy()
sub_z1 = z[:, 1]
sub_z = z[:, 0]
sub_z[sub_z1 < 0.9] = detection_threshold
z = z[:, 0]
z = z.reshape((int(max_point[0] - min_point[0]),
int(max_point[1] - min_point[1]))).transpose()
z[z < detection_threshold] = detection_threshold
im = ax.imshow(z, norm=colors.PowerNorm(gamma=2))
if colorbar:
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)
fig.colorbar(im, cax=cax, orientation='vertical', label='dBm')
return im
def set_inset_spectrum(axis: plt.axis, data: np.ndarray, current_index: int,
peak_collection: PeakCollection) -> plt.axis:
axis.plot(data) # Show intensity spectrum
axis = plot_peaks(axis=axis, collection=peak_collection) # Show peaks
y_bot, y_top = axis.get_ylim()
text_height = y_bot + 0.6 * (y_top - y_bot) # Data coordinates
_margin = 50
x_lim = [
max(0, current_index - _margin),
min(len(data) - 1, current_index + _margin)
]
# Plot clusters
# for cluster in peak_collection.get_clusters:
# bound_left, bound_right = cluster.get_value_slice
# if bound_right > x_lim[0] or bound_left < x_lim[1]:
# axis.axvspan(bound_left, bound_right, alpha=0.5, color='green')
# if x_lim[0] < cluster.get_avg_x < x_lim[1]:
# axis.text(x=cluster.get_avg_x, y=text_height, s=r'$\tilde{m}$'+f'={cluster.get_transverse_mode_id}')
axis.axvline(x=current_index, color='r')
axis.set_xlim(x_lim)
return axis
def add_descriptions_to_plot(
ax: plt.axis,
experiment: Union[str, None] = None,
luminosity: Union[str, None] = None,
additional_info: Union[str, None] = None,
):
ax.set_title(experiment,
loc="left",
fontdict={
'size': 16,
'style': 'normal',
'weight': 'bold'
})
ax.set_title(luminosity, loc="right")
ax.annotate(additional_info, (0.02, 0.98),
xytext=(4, -4),
xycoords='axes fraction',
textcoords='offset points',
fontweight='bold',
ha='left',
va='top')
def mpl_plot(self,
ax: plt.axis = None,
temp_unit: str = "GK",
**kwargs) -> plt.axis:
"""
Parameters
----------
ax : mpl.Axis
mpl Axis to plot onto, if none provided get current axis is used.
temp_unit : str {"Gk", "KeV"}
Tempreture units for x axis.
kwargs : key word args for mpl.plot
"""
ax = ax or plt.gca()
ax.set_title("Reaction Rate")
ax.set_ylabel(r"Rate ($cm^3\;mol^{-1}\;sec^{-1}$)")
if temp_unit is "GK":
ax.set_xlabel("Temperature ($GK$)")
return ax
def plot_aligned(
x: np.ndarray,
indices: Union[np.ndarray, list],
ax: plt.axis,
mode: str,
window: int = 120,
mean_kwargs: dict = None,
**kwargs,
):
"""
Given a 1d array and a series of indices it plots the values of
the array aligned to the timestamps.
"""
pre, aft = int(window / 2), int(window / 2)
mean_kwargs = mean_kwargs or dict(lw=4, zorder=100, color=pink_dark)
# get plotting params
if mode == "pre":
pre_c, pre_lw = kwargs.pop("color", "salmon"), kwargs.pop("lw", 2)
aft_c, aft_lw = blue_grey, 1
ax.axvspan(0, pre, fc=blue, alpha=0.25, zorder=-20)
else:
aft_c, aft_lw = kwargs.pop("color", "salmon"), kwargs.pop("lw", 2)
pre_c, pre_lw = blue_grey, 1
ax.axvspan(aft, window, fc=blue, alpha=0.25, zorder=-20)
# plot each trace
X = [] # collect data to plot mean
for idx in indices:
x_pre = x[idx - pre:idx]
x_aft = x[idx - 1:idx + aft]
if len(x_pre) != pre or len(x_aft) != aft + 1:
logger.warning(f"Could not plot data aligned to index: {idx}")
continue
X.append(x[idx - pre:idx + aft])
ax.plot(x_pre, color=pre_c, lw=pre_lw, **kwargs)
ax.plot(np.arange(aft + 1) + aft,
x_aft,
color=aft_c,
lw=aft_lw,
**kwargs)
# plot mean and line
X = np.vstack(X)
plot_mean_and_error(np.mean(X, axis=0), np.std(X, axis=0), ax,
**mean_kwargs)
ax.axvline(pre, lw=2, color=blue_grey_dark, zorder=-1)
ax.set(**get_window_ticks(window, shifted=False))
