def _():
kwargs["save"] = None
kwargs["show"] = False
if "cmap" not in kwargs:
kwargs["cmap"] = cm.viridis
cols = len(lin_names) if ncols is None else ncols
nrows = ceil(len(lin_names) / cols)
fig, axes = plt.subplots(
nrows,
cols,
figsize=(7 * cols, 4 * nrows) if figsize is None else figsize,
constrained_layout=True,
dpi=dpi,
)
# fig.tight_layout() can't use this because colorbar.make_axes fails
i = 0
axes = [axes] if not isinstance(axes, np.ndarray) else np.ravel(axes)
vmin, vmax = np.inf, -np.inf
if basis is not None:
kwargs["basis"] = basis
kwargs["scatter_flag"] = True
kwargs["color"] = cluster_key
for i, (ax, lineage_name) in enumerate(zip(axes, lin_names)):
colors = [v[0][i] for v in d.values()]
kwargs["ax"] = ax
kwargs["colors"] = tuple(colors)
kwargs["title"] = f"{dir_prefix} {lineage_name}"
vmin = np.min(colors + [vmin])
vmax = np.max(colors + [vmax])
paga(adata, **kwargs)
if cbar:
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
cax, _ = mpl.colorbar.make_axes(ax, aspect=60)
_ = mpl.colorbar.ColorbarBase(
cax,
ticks=np.linspace(norm.vmin, norm.vmax, 5),
norm=norm,
cmap=kwargs["cmap"],
label="average absorption probability",
)
for ax in axes[i + 1:]: # noqa
ax.remove()
return fig
def _():
colors = list(adata.obsm[lk][:, lin_names].colors)
colors = {
i: odict(zip(colors, mean))
for i, (mean, _) in enumerate(d.values())
}
fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
fig.tight_layout()
kwargs["ax"] = ax
kwargs["show"] = False
kwargs["colorbar"] = False # has to be disabled
kwargs["show"] = False
kwargs["node_colors"] = colors
kwargs.pop("save", None) # we will handle saving
kwargs["transitions"] = kwargs.get("transitions",
"transitions_confidence")
if "legend_loc" in kwargs:
orig_ll = kwargs["legend_loc"]
if orig_ll != "on data":
kwargs["legend_loc"] = "none" # we will handle legend
else:
orig_ll = None
kwargs["legend_loc"] = "on data"
if basis is not None:
kwargs["basis"] = basis
kwargs["scatter_flag"] = True
kwargs["color"] = cluster_key
ax = paga(adata, **kwargs)
ax.set_title(kwargs.get("title", cluster_key))
if basis is not None and orig_ll not in ("none", "on data", None):
handles = []
for cluster_name, color in zip(
adata.obs[f"{cluster_key}"].cat.categories,
adata.uns[f"{cluster_key}_colors"],
):
handles += [ax.scatter([], [], label=cluster_name, c=color)]
first_legend = _position_legend(
ax,
legend_loc=orig_ll,
handles=handles,
**{k: v
for k, v in legend_kwargs.items() if k != "loc"},
title=cluster_key,
)
fig.add_artist(first_legend)
if legend_kwargs.get("loc", None) not in ("none", "on data", None):
# we need to use these, because scvelo can have its own handles and
# they would be plotted here
handles = []
for lineage_name, color in zip(lin_names, colors[0].keys()):
handles += [ax.scatter([], [], label=lineage_name, c=color)]
if len(colors[0].keys()) != len(adata.obsm[lk].names):
handles += [ax.scatter([], [], label="Rest", c="grey")]
second_legend = _position_legend(
ax,
legend_loc=legend_kwargs["loc"],
handles=handles,
**{k: v
for k, v in legend_kwargs.items() if k != "loc"},
title=points,
)
fig.add_artist(second_legend)
return fig