from ..tools.utils import update_dict
from .utils import save_fig
[docs]def show_landscape(adata,
Xgrid,
Ygrid,
Zgrid,
basis="umap",
save_show_or_return='show',
save_kwargs={},
):
"""Plot the quasi-potential landscape.
Parameters
----------
adata: :class:`~anndata.AnnData`
AnnData object that contains Xgrid, Ygrid and Zgrid data for visualizing potential landscape.
Xgrid: `numpy.ndarray`
x-coordinates of the Grid produced from the meshgrid function.
Ygrid: `numpy.ndarray`
y-coordinates of the Grid produced from the meshgrid function.
Zgrid: `numpy.ndarray`
z-coordinates or potential at each of the x/y coordinate.
basis: `str` (default: umap)
The method of dimension reduction. By default it is trimap. Currently it is not checked with Xgrid and Ygrid.
save_show_or_return: {'show', 'save', 'return'} (default: `show`)
Whether to save, show or return the figure.
save_kwargs: `dict` (default: `{}`)
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function
will use the {"path": None, "prefix": 'show_landscape', "dpi": None, "ext": 'pdf', "transparent": True, "close":
True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that properly modify those keys
according to your needs.
Returns
-------
A 3D plot showing the quasi-potential of each cell state.
"""
if "grid_Pot_" + basis in adata.uns.keys():
Xgrid_, Ygrid_, Zgrid_ = (
adata.uns["grid_Pot_" + basis]["Xgrid"],
adata.uns["grid_Pot_" + basis]["Ygrid"],
adata.uns["grid_Pot_" + basis]["Zgrid"],
)
Xgrid = Xgrid_ if Xgrid is None else Xgrid
Ygrid = Ygrid_ if Ygrid is None else Ygrid
Zgrid = Zgrid_ if Zgrid is None else Zgrid
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from matplotlib.colors import LightSource
fig = plt.figure()
ax = fig.gca(projection="3d")
# Plot the surface.
ls = LightSource(azdeg=0, altdeg=65)
# Shade data, creating an rgb array.
rgb = ls.shade(Zgrid, plt.cm.RdYlBu)
surf = ax.plot_surface(
Xgrid,
Ygrid,
Zgrid,
cmap=cm.coolwarm,
rstride=1,
cstride=1,
facecolors=rgb,
linewidth=0,
antialiased=False,
)
# Customize the z axis.
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter("%.02f"))
# Add a color bar which maps values to colors.
# fig.colorbar(surf, shrink=0.5, aspect=5)
ax.set_xlabel(basis + "_1")
ax.set_ylabel(basis + "_2")
ax.set_zlabel("U")
if save_show_or_return == "save":
s_kwargs = {"path": None, "prefix": 'show_landscape', "dpi": None,
"ext": 'pdf', "transparent": True, "close": True, "verbose": True}
s_kwargs = update_dict(s_kwargs, save_kwargs)
save_fig(**s_kwargs)
elif save_show_or_return == "show":
plt.tight_layout()
plt.show()
elif save_show_or_return == "return":
return ax
# show_pseudopot(Xgrid, Ygrid, Zgrid)
# % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# % % -- Plot selected paths on pot. surface --
# % path_spacing = 4;
# % hold on;
# % for n_path = 1:numPaths
# % if ( ((mod(x_path(n_path, 1), path_spacing) == 0) && (mod(y_path(n_path, 1), path_spacing) == 0)) ...
# % || ((mod(y_path(n_path, 1), path_spacing) == 0) && (mod(x_path(n_path, 1), path_spacing) == 0)) )
# %
# % % % *** To generate log-log surface ***
# % % x_path(n_path, :) = x_path(n_path, :) + 0.1;
# % % y_path(n_path, :) = y_path(n_path, :) + 0.1;
# % % % ***
# %
# % if (path_tag(n_path) == 1)
# % plot3(x_path(n_path, :), y_path(n_path, :), pot_path(n_path, :) ...
# % , '-r' , 'MarkerSize', 1) % plot paths
# % elseif (path_tag(n_path) == 2)
# % plot3(x_path(n_path, :), y_path(n_path, :), pot_path(n_path, :) ...
# % , '-b' , 'MarkerSize', 1) % plot paths
# % elseif (path_tag(n_path) == 3)
# % plot3(x_path(n_path, :), y_path(n_path, :), pot_path(n_path, :) ...
# % , '-g' , 'MarkerSize', 1) % plot paths
# % end
# % hold on;
# %
# % end
# % end