# code adapted from https://github.com/lmcinnes/umap/blob/7e051d8f3c4adca90ca81eb45f6a9d1372c076cf/umap/plot.py
import warnings
from matplotlib import patches
from matplotlib.lines import Line2D
import numpy as np
import pandas as pd
from pandas.api.types import is_categorical_dtype
import anndata
from numbers import Number
import matplotlib.cm
from matplotlib.axes import Axes
from anndata import AnnData
from typing import Union, Optional
from ..configuration import _themes, reset_rcParams
from .utils import (
despline_all,
deaxis_all,
_select_font_color,
arrowed_spines,
is_gene_name,
is_cell_anno_column,
is_list_of_lists,
is_layer_keys,
_matplotlib_points,
_datashade_points,
save_fig,
)
from ..preprocessing.utils import (
gen_rotation_2d,
affine_transform,
)
from ..tools.utils import (
update_dict,
get_mapper,
flatten,
)
from ..tools.moments import calc_1nd_moment
from ..dynamo_logger import main_info, main_debug, main_warning
from ..docrep import DocstringProcessor
docstrings = DocstringProcessor()
[docs]@docstrings.get_sectionsf("scatters")
def scatters(
adata: AnnData,
basis: str = "umap",
x: int = 0,
y: int = 1,
color: str = "ntr",
layer: str = "X",
highlights: Optional[list] = None,
labels: Optional[list] = None,
values: Optional[list] = None,
theme: Optional[str] = None,
cmap: Optional[str] = None,
color_key: Union[dict, list] = None,
color_key_cmap: Optional[str] = None,
background: Optional[str] = None,
ncols: int = 4,
pointsize: Union[None, float] = None,
figsize: tuple = (6, 4),
show_legend="on data",
use_smoothed: bool = True,
aggregate: Optional[str] = None,
show_arrowed_spines: bool = False,
ax: Optional[matplotlib.axes.Axes] = None,
sort: str = "raw",
save_show_or_return: str = "show",
save_kwargs: dict = {},
return_all: bool = False,
add_gamma_fit: bool = False,
frontier: bool = False,
contour: bool = False,
ccmap: Optional[str] = None,
alpha: float = 0.1,
calpha: float = 0.4,
sym_c: bool = False,
smooth: bool = False,
dpi: int = 100,
inset_dict: dict = {},
marker: str = None,
group: str = None,
add_group_gamma_fit=False,
affine_transform_degree=0,
affine_transform_A=None,
affine_transform_b=None,
stack_colors=False,
stack_colors_threshold=0.001,
stack_colors_title="stacked colors",
stack_colors_legend_size=2,
**kwargs,
) -> Union[None, Axes]:
"""Plot an embedding as points. Currently this only works
for 2D embeddings. While there are many optional parameters
to further control and tailor the plotting, you need only
pass in the trained/fit umap model to get results. This plot
utility will attempt to do the hard work of avoiding
overplotting issues, and make it easy to automatically
colour points by a categorical labelling or numeric values.
This method is intended to be used within a Jupyter
notebook with ``%matplotlib inline``.
Parameters
----------
adata: :class:`~anndata.AnnData`
an Annodata object
basis: `str`
The reduced dimension.
x: `int` (default: `0`)
The column index of the low dimensional embedding for the x-axis.
y: `int` (default: `1`)
The column index of the low dimensional embedding for the y-axis.
color: `string` (default: `ntr`)
Any column names or gene expression, etc. that will be used for coloring cells.
layer: `str` (default: `X`)
The layer of data to use for the scatter plot.
highlights: `list` (default: None)
Which color group will be highlighted. if highligts is a list of lists - each list is relate to each color
element.
labels: array, shape (n_samples,) (optional, default None)
An array of labels (assumed integer or categorical),
one for each data sample.
This will be used for coloring the points in
the plot according to their label. Note that
this option is mutually exclusive to the ``values``
option.
values: array, shape (n_samples,) (optional, default None)
An array of values (assumed float or continuous),
one for each sample.
This will be used for coloring the points in
the plot according to a colorscale associated
to the total range of values. Note that this
option is mutually exclusive to the ``labels``
option.
theme: string (optional, default None)
A color theme to use for plotting. A small set of
predefined themes are provided which have relatively
good aesthetics. Available themes are:
* 'blue'
* 'red'
* 'green'
* 'inferno'
* 'fire'
* 'viridis'
* 'darkblue'
* 'darkred'
* 'darkgreen'
cmap: string (optional, default 'Blues')
The name of a matplotlib colormap to use for coloring
or shading points. If no labels or values are passed
this will be used for shading points according to
density (largely only of relevance for very large
datasets). If values are passed this will be used for
shading according the value. Note that if theme
is passed then this value will be overridden by the
corresponding option of the theme.
color_key: dict or array, shape (n_categories) (optional, default None)
A way to assign colors to categoricals. This can either be
an explicit dict mapping labels to colors (as strings of form
'#RRGGBB'), or an array like object providing one color for
each distinct category being provided in ``labels``. Either
way this mapping will be used to color points according to
the label. Note that if theme
is passed then this value will be overridden by the
corresponding option of the theme.
color_key_cmap:
The name of a matplotlib colormap to use for categorical coloring.
If an explicit ``color_key`` is not given a color mapping for
categories can be generated from the label list and selecting
a matching list of colors from the given colormap. Note
that if theme
is passed then this value will be overridden by the
corresponding option of the theme.
background: string or None (optional, default 'None`)
The color of the background. Usually this will be either
'white' or 'black', but any color name will work. Ideally
one wants to match this appropriately to the colors being
used for points etc. This is one of the things that themes
handle for you. Note that if theme
is passed then this value will be overridden by the
corresponding option of the theme.
ncols: int (optional, default `4`)
Number of columns for the figure.
pointsize: `None` or `float` (default: None)
The scale of the point size. Actual point cell size is calculated as `500.0 / np.sqrt(adata.shape[0]) *
pointsize`
figsize: `None` or `[float, float]` (default: None)
The width and height of a figure.
show_legend: bool (optional, default True)
Whether to display a legend of the labels
use_smoothed: bool (optional, default True)
Whether to use smoothed values (i.e. M_s / M_u instead of spliced / unspliced, etc.).
aggregate: `str` or `None` (default: `None`)
The column in adata.obs that will be used to aggregate data points.
show_arrowed_spines: bool (optional, default False)
Whether to show a pair of arrowed spines representing the basis of the scatter is currently using.
ax: `matplotlib.Axis` (optional, default `None`)
The matplotlib axes object where new plots will be added to. Only applicable to drawing a single component.
sort: `str` (optional, default `raw`)
The method to reorder data so that high values points will be on top of background points. Can be one of
{'raw', 'abs', 'neg'}, i.e. sorted by raw data, sort by absolute values or sort by negative values.
save_show_or_return: `str` {'save', 'show', 'return'} (default: `show`)
Whether to save, show or return the figure. If "both", it will save and plot the figure at the same time. If
"all", the figure will be saved, displayed and the associated axis and other object will be return.
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": 'scatter', "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.
return_all: `bool` (default: `False`)
Whether to return all the scatter related variables. Default is False.
add_gamma_fit: `bool` (default: `False`)
Whether to add the line of the gamma fitting. This will automatically turn on if `basis` points to gene
names and those genes have went through gamma fitting.
frontier: `bool` (default: `False`)
Whether to add the frontier. Scatter plots can be enhanced by using transparency (alpha) in order to show
area of high density and multiple scatter plots can be used to delineate a frontier. See matplotlib tips &
tricks cheatsheet (https://github.com/matplotlib/cheatsheets). Originally inspired by figures from scEU-seq
paper: https://science.sciencemag.org/content/367/6482/1151. If `contour` is set to be True, `frontier`
will be ignored as `contour` also add an outlier for data points.
contour: `bool` (default: `False`)
Whether to add an countor on top of scatter plots. We use tricontourf to plot contour for non-gridded data.
The shapely package was used to create a polygon of the concave hull of the scatters. With the polygon we
then check if the mean of the triangulated points are within the polygon and use this as our condition to
form the mask to create the contour. We also add the polygon shape as a frontier of the data point (similar
to when setting `frontier = True`). When the color of the data points is continuous, we will use the same
cmap as for the scatter points by default, when color is categorical, no contour will be drawn but just the
polygon. cmap can be set with `ccmap` argument. See below. This has recently changed to use seaborn's
kdeplot.
ccmap: `str` or `None` (default: `None`)
The name of a matplotlib colormap to use for coloring or shading points the contour. See above.
calpha: `float` (default: `0.4`)
Contour alpha value passed into sns.kdeplot. The value should be inbetween [0, 1]
sym_c: `bool` (default: `False`)
Whether do you want to make the limits of continuous color to be symmetric, normally this should be used for
plotting velocity, jacobian, curl, divergence or other types of data with both positive or negative values.
smooth: `bool` or `int` (default: `False`)
Whether do you want to further smooth data and how much smoothing do you want. If it is `False`, no
smoothing will be applied. If `True`, smoothing based on one step diffusion of connectivity matrix
(`.uns['moment_cnn'] will be applied. If a number larger than 1, smoothing will based on `smooth` steps of
diffusion.
dpi: `float`, (default: 100.0)
The resolution of the figure in dots-per-inch. Dots per inches (dpi) determines how many pixels the figure
comprises. dpi is different from ppi or points per inches. Note that most elements like lines, markers,
texts have a size given in points so you can convert the points to inches. Matplotlib figures use Points per
inch (ppi) of 72. A line with thickness 1 point will be 1./72. inch wide. A text with fontsize 12 points
will be 12./72. inch heigh. Of course if you change the figure size in inches, points will not change, so a
larger figure in inches still has the same size of the elements.Changing the figure size is thus like taking
a piece of paper of a different size. Doing so, would of course not change the width of the line drawn with
the same pen. On the other hand, changing the dpi scales those elements. At 72 dpi, a line of 1 point size
is one pixel strong. At 144 dpi, this line is 2 pixels strong. A larger dpi will therefore act like a
magnifying glass. All elements are scaled by the magnifying power of the lens. see more details at answer 2
by @ImportanceOfBeingErnest:
https://stackoverflow.com/questions/47633546/relationship-between-dpi-and-figure-size
inset_dict: `dict` (default: {})
A dictionary of parameters in inset_ax. Example, something like {"width": "5%", "height": "50%", "loc":
'lower left', "bbox_to_anchor": (0.85, 0.90, 0.145, 0.145), "bbox_transform": ax.transAxes, "borderpad": 0}
See more details at https://matplotlib.org/api/_as_gen/mpl_toolkits.axes_grid1.inset_locator.inset_axes.html
or https://stackoverflow.com/questions/39803385/what-does-a-4-element-tuple-argument-for-bbox-to-anchor-mean
-in-matplotlib
marker: `str` (default: None)
The marker style. marker can be either an instance of the class or the text shorthand for a particular
marker. See matplotlib.markers for more information about marker styles.
affine_transform_degree:
Transform coordinates of points according to some degree.
affine_transform_A:
Coefficients in affine transformation Ax + b. 2D for now.
affine_transform_b:
Bias in affine transformation Ax + b.
stack_colors:
Whether to stack all color on the same ax passed above.
Currently only support 18 sequential matplotlib default cmaps assigning to different color groups.
(#colors should be smaller than 18, reuse if #colors > 18. To-do: generate cmaps according to #colors)
stack_colors_threshold:
A threshold for filtering points values < threshold when drawing each color.
E.g. if you do not want points with values < 1 showing up on axis, set threshold to be 1
stack_colors_title:
The title for the stack_color plot.
stack_colors_legend_size:
Control the legend size in stack color plot.
kwargs:
Additional arguments passed to plt.scatters.
Returns
-------
result:
Either None or a matplotlib axis with the relevant plot displayed.
If you are using a notbooks and have ``%matplotlib inline`` set
then this will simply display inline.
"""
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib.colors import to_hex
if calpha < 0 or calpha > 1:
main_warning(
"calpha=%f is invalid (smaller than 0 or larger than 1) and may cause potential issues. Please check."
% (calpha)
)
group_colors = ["b", "g", "r", "c", "m", "y", "k", "w"]
sequential_cmaps = [
"Greys",
"Purples",
"Blues",
"Greens",
"Oranges",
"Reds",
"YlOrBr",
"YlOrRd",
"OrRd",
"PuRd",
"RdPu",
"BuPu",
"GnBu",
"PuBu",
"YlGnBu",
"PuBuGn",
"BuGn",
"YlGn",
]
stack_legend_handles = []
if stack_colors:
color_key = None
def _get_adata_color(adata, cur_l, cur_c):
if cur_l in ["protein", "X_protein"]:
_color = adata.obsm[cur_l].loc[cur_c, :]
elif cur_l == "X":
_color = adata.obs_vector(cur_c, layer=None)
else:
_color = adata.obs_vector(cur_c, layer=cur_l)
return _color
if not (affine_transform_degree is None):
affine_transform_A = gen_rotation_2d(affine_transform_degree)
affine_transform_b = 0
if contour:
frontier = False
if background is None:
_background = rcParams.get("figure.facecolor")
_background = to_hex(_background) if type(_background) is tuple else _background
# if save_show_or_return != 'save': set_figure_params('dynamo', background=_background)
else:
_background = background
# if save_show_or_return != 'save': set_figure_params('dynamo', background=_background)
x, y = (
[x] if type(x) in [int, str] else x,
[y]
if type(y)
in [
int,
str,
]
else y,
)
if all([is_gene_name(adata, i) for i in basis]):
if x[0] not in ["M_s", "X_spliced", "M_t", "X_total", "spliced", "total"] and y[0] not in [
"M_u",
"X_unspliced",
"M_n",
"X_new",
"unspliced",
"new",
]:
if "M_t" in adata.layers.keys() and "M_n" in adata.layers.keys():
x, y = ["M_t"], ["M_n"]
elif "X_total" in adata.layers.keys() and "X_new" in adata.layers.keys():
x, y = ["X_total"], ["X_new"]
elif "M_s" in adata.layers.keys() and "M_u" in adata.layers.keys():
x, y = ["M_s"], ["M_u"]
elif "X_spliced" in adata.layers.keys() and "X_unspliced" in adata.layers.keys():
x, y = ["X_spliced"], ["X_unspliced"]
elif "spliced" in adata.layers.keys() and "unspliced" in adata.layers.keys():
x, y = ["spliced"], ["unspliced"]
elif "total" in adata.layers.keys() and "new" in adata.layers.keys():
x, y = ["total"], ["new"]
else:
raise ValueError(
"your adata oject is corrupted. Please make sure it has at least one of the following "
"pair of layers:"
"'M_s', 'X_spliced', 'M_t', 'X_total', 'spliced', 'total' and "
"'M_u', 'X_unspliced', 'M_n', 'X_new', 'unspliced', 'new'. "
)
if use_smoothed:
mapper = get_mapper()
# check color, layer, basis -> convert to list
if type(color) is str:
color = [color]
if type(layer) is str:
layer = [layer]
if type(basis) is str:
basis = [basis]
if stack_colors and len(color) > len(sequential_cmaps):
main_warning(
"#color: %d passed in is greater than #sequential cmaps: %d, will reuse sequential maps"
% (len(color), len(sequential_cmaps))
)
main_warning("You should consider decreasing your #color")
n_c, n_l, n_b, n_x, n_y = (
1 if color is None else len(color),
1 if layer is None else len(layer),
1 if basis is None else len(basis),
1 if x is None else 1 if type(x) in [anndata._core.views.ArrayView, np.ndarray] else len(x),
# check whether it is an array
1 if y is None else 1 if type(y) in [anndata._core.views.ArrayView, np.ndarray] else len(y),
# check whether it is an array
)
if pointsize is None:
point_size = 16000.0 / np.sqrt(adata.shape[0])
else:
point_size = 16000.0 / np.sqrt(adata.shape[0]) * pointsize
scatter_kwargs = dict(
alpha=alpha,
s=point_size,
edgecolor=None,
linewidth=0,
rasterized=True,
marker=marker,
) # (0, 0, 0, 1)
if kwargs is not None:
scatter_kwargs.update(kwargs)
font_color = _select_font_color(_background)
total_panels, ncols = (
n_c * n_l * n_b * n_x * n_y,
min(max([n_c, n_l, n_b, n_x, n_y]), ncols),
)
nrow, ncol = int(np.ceil(total_panels / ncols)), ncols
if figsize is None:
figsize = plt.rcParams["figsize"]
if total_panels >= 1 and ax is None:
plt.figure(
None,
(figsize[0] * ncol, figsize[1] * nrow),
facecolor=_background,
dpi=dpi,
)
gs = plt.GridSpec(nrow, ncol, wspace=0.12)
ax_index = 0
axes_list, color_list = [], []
color_out = None
def _plot_basis_layer(cur_b, cur_l):
"""a helper function for plotting a specific basis/layer data
Parameters
----------
cur_b :
current basis
cur_l :
current layer
"""
nonlocal adata, x, y, _background, cmap, color_out, labels, values, ax, sym_c, scatter_kwargs, ax_index
if cur_l in ["acceleration", "curvature", "divergence", "velocity_S", "velocity_T"]:
cur_l_smoothed = cur_l
cmap, sym_c = "bwr", True # To-do: maybe use other divergent color map in future
else:
if use_smoothed:
cur_l_smoothed = cur_l if cur_l.startswith("M_") | cur_l.startswith("velocity") else mapper[cur_l]
if cur_l.startswith("velocity"):
cmap, sym_c = "bwr", True
prefix = cur_l + "_" if any([key == cur_l + "_" + cur_b for key in adata.obsm.keys()]) else "X_"
# if prefix + cur_b in adata.obsm.keys():
# if type(x) != str and type(y) != str:
# x_, y_ = (
# adata.obsm[prefix + cur_b][:, int(x)],
# adata.obsm[prefix + cur_b][:, int(y)],
# )
# else:
# continue
if stack_colors:
_stack_background_adata_indices = np.ones(len(adata), dtype=bool)
for cur_c in color:
main_debug("coloring scatter of cur_c: %s" % str(cur_c))
if not stack_colors:
cur_title = cur_c
else:
cur_title = stack_colors_title
_color = _get_adata_color(adata, cur_l, cur_c)
# select data rows based on stack color thresholding
_values = values
if stack_colors:
main_debug("Subsetting adata by stack_colors")
_adata = adata[_color > stack_colors_threshold]
_stack_background_adata_indices = np.logical_and(
_stack_background_adata_indices, (_color < stack_colors_threshold)
)
if values:
_values = values[_color > stack_colors_threshold]
_color = _color[_color > stack_colors_threshold]
main_debug("stack colors: _adata len after thresholding by color value: %d" % (len(_adata)))
if len(_color) == 0:
main_info("skipping color %s because no point of %s is above threshold" % (cur_c, cur_c))
continue
else:
_adata = adata
if hasattr(x, "__len__") and hasattr(y, "__len__"):
x, y = list(x), list(y)
elif (
type(x) in [anndata._core.views.ArrayView, np.ndarray]
and type(y) in [anndata._core.views.ArrayView, np.ndarray]
and len(x) == _adata.n_obs
and len(y) == _adata.n_obs
):
x, y = [x], [y]
for cur_x, cur_y in zip(x, y): # here x / y are arrays
main_debug("handling coordinates, cur_x: %s, cur_y: %s" % (cur_x, cur_y))
if type(cur_x) is int and type(cur_y) is int:
points = pd.DataFrame(
{
cur_b + "_0": _adata.obsm[prefix + cur_b][:, cur_x],
cur_b + "_1": _adata.obsm[prefix + cur_b][:, cur_y],
}
)
points.columns = [cur_b + "_0", cur_b + "_1"]
elif is_gene_name(_adata, cur_x) and is_gene_name(_adata, cur_y):
points = pd.DataFrame(
{
cur_x: _adata.obs_vector(k=cur_x, layer=None)
if cur_l_smoothed == "X"
else _adata.obs_vector(k=cur_x, layer=cur_l_smoothed),
cur_y: _adata.obs_vector(k=cur_y, layer=None)
if cur_l_smoothed == "X"
else _adata.obs_vector(k=cur_y, layer=cur_l_smoothed),
}
)
# points = points.loc[(points > 0).sum(1) > 1, :]
points.columns = [
cur_x + " (" + cur_l_smoothed + ")",
cur_y + " (" + cur_l_smoothed + ")",
]
cur_title = cur_x + " VS " + cur_y
elif is_cell_anno_column(_adata, cur_x) and is_cell_anno_column(_adata, cur_y):
points = pd.DataFrame(
{
cur_x: _adata.obs_vector(cur_x),
cur_y: _adata.obs_vector(cur_y),
}
)
points.columns = [cur_x, cur_y]
cur_title = cur_x + " VS " + cur_y
elif is_cell_anno_column(_adata, cur_x) and is_gene_name(_adata, cur_y):
points = pd.DataFrame(
{
cur_x: _adata.obs_vector(cur_x),
cur_y: _adata.obs_vector(k=cur_y, layer=None)
if cur_l_smoothed == "X"
else _adata.obs_vector(k=cur_y, layer=cur_l_smoothed),
}
)
# points = points.loc[points.iloc[:, 1] > 0, :]
points.columns = [
cur_x,
cur_y + " (" + cur_l_smoothed + ")",
]
cur_title = cur_y
elif is_gene_name(_adata, cur_x) and is_cell_anno_column(_adata, cur_y):
points = pd.DataFrame(
{
cur_x: _adata.obs_vector(k=cur_x, layer=None)
if cur_l_smoothed == "X"
else _adata.obs_vector(k=cur_x, layer=cur_l_smoothed),
cur_y: _adata.obs_vector(cur_y),
}
)
# points = points.loc[points.iloc[:, 0] > 0, :]
points.columns = [
cur_x + " (" + cur_l_smoothed + ")",
cur_y,
]
cur_title = cur_x
elif is_layer_keys(_adata, cur_x) and is_layer_keys(_adata, cur_y):
cur_x_, cur_y_ = (
_adata[:, cur_b].layers[cur_x],
_adata[:, cur_b].layers[cur_y],
)
points = pd.DataFrame({cur_x: flatten(cur_x_), cur_y: flatten(cur_y_)})
# points = points.loc[points.iloc[:, 0] > 0, :]
points.columns = [cur_x, cur_y]
cur_title = cur_b
elif type(cur_x) in [anndata._core.views.ArrayView, np.ndarray] and type(cur_y) in [
anndata._core.views.ArrayView,
np.ndarray,
]:
points = pd.DataFrame({"x": flatten(cur_x), "y": flatten(cur_y)})
points.columns = ["x", "y"]
cur_title = cur_b
else:
raise Exception("Make sure your `x` and `y` are integers, gene names, column names in .obs, etc.")
if aggregate is not None:
groups, uniq_grp = (
_adata.obs[aggregate],
list(_adata.obs[aggregate].unique()),
)
group_color, group_median = (
np.zeros((1, len(uniq_grp))).flatten()
if isinstance(_color[0], Number)
else np.zeros((1, len(uniq_grp))).astype("str").flatten(),
np.zeros((len(uniq_grp), 2)),
)
grp_size = _adata.obs[aggregate].value_counts()[uniq_grp].values
scatter_kwargs = (
{"s": grp_size} if scatter_kwargs is None else update_dict(scatter_kwargs, {"s": grp_size})
)
for ind, cur_grp in enumerate(uniq_grp):
group_median[ind, :] = np.nanmedian(
points.iloc[np.where(groups == cur_grp)[0], :2],
0,
)
if isinstance(_color[0], Number):
group_color[ind] = np.nanmedian(np.array(_color)[np.where(groups == cur_grp)[0]])
else:
group_color[ind] = pd.Series(_color)[np.where(groups == cur_grp)[0]].value_counts().index[0]
points, _color = (
pd.DataFrame(
group_median,
index=uniq_grp,
columns=points.columns,
),
group_color,
)
# https://stackoverflow.com/questions/4187185/how-can-i-check-if-my-python-object-is-a-number
# answer from Boris.
is_not_continuous = not isinstance(_color[0], Number) or _color.dtype.name == "category"
if is_not_continuous:
labels = np.asarray(_color) if is_categorical_dtype(_color) else _color
if theme is None:
if _background in ["#ffffff", "black"]:
_theme_ = "glasbey_dark"
else:
_theme_ = "glasbey_white"
else:
_theme_ = theme
else:
_values = _color
if theme is None:
if _background in ["#ffffff", "black"]:
_theme_ = "inferno" if cur_l != "velocity" else "div_blue_black_red"
else:
_theme_ = "viridis" if not cur_l.startswith("velocity") else "div_blue_red"
else:
_theme_ = theme
_cmap = _themes[_theme_]["cmap"] if cmap is None else cmap
if stack_colors:
main_debug("stack colors: changing cmap")
_cmap = sequential_cmaps[ax_index % len(sequential_cmaps)]
max_color = matplotlib.cm.get_cmap(_cmap)(float("inf"))
legend_circle = Line2D(
[0],
[0],
marker="o",
color="w",
markerfacecolor=max_color,
label=cur_c,
markersize=stack_colors_legend_size,
)
stack_legend_handles.append(legend_circle)
_color_key_cmap = _themes[_theme_]["color_key_cmap"] if color_key_cmap is None else color_key_cmap
_background = _themes[_theme_]["background"] if _background is None else _background
if labels is not None and values is not None:
raise ValueError("Conflicting options; only one of labels or values should be set")
if total_panels > 1 and not stack_colors:
ax = plt.subplot(gs[ax_index])
ax_index += 1
# if highligts is a list of lists - each list is relate to each color element
if highlights is not None:
if is_list_of_lists(highlights):
_highlights = highlights[color.index(cur_c)]
_highlights = _highlights if all([i in _color for i in _highlights]) else None
else:
_highlights = highlights if all([i in _color for i in highlights]) else None
if smooth and not is_not_continuous:
main_debug("smooth and not continuous")
knn = _adata.obsp["moments_con"]
values = (
calc_1nd_moment(values, knn)[0]
if smooth in [1, True]
else calc_1nd_moment(values, knn ** smooth)[0]
)
if affine_transform_A is None or affine_transform_b is None:
point_coords = points.values
else:
point_coords = affine_transform(points.values, affine_transform_A, affine_transform_b)
if points.shape[0] <= figsize[0] * figsize[1] * 100000:
main_debug("drawing with _matplotlib_points function")
ax, color_out = _matplotlib_points(
# points.values,
point_coords,
ax,
labels,
_values,
highlights,
_cmap,
color_key,
_color_key_cmap,
_background,
figsize[0],
figsize[1],
show_legend,
sort=sort,
frontier=frontier,
contour=contour,
ccmap=ccmap,
calpha=calpha,
sym_c=sym_c,
inset_dict=inset_dict,
**scatter_kwargs,
)
else:
main_debug("drawing with _datashade_points function")
ax = _datashade_points(
# points.values,
point_coords,
ax,
labels,
values,
highlights,
_cmap,
color_key,
_color_key_cmap,
_background,
figsize[0],
figsize[1],
show_legend,
sort=sort,
frontier=frontier,
contour=contour,
ccmap=ccmap,
calpha=calpha,
sym_c=sym_c,
**scatter_kwargs,
)
if ax_index == 1 and show_arrowed_spines:
arrowed_spines(ax, points.columns[:2], _background)
else:
despline_all(ax)
deaxis_all(ax)
ax.set_title(cur_title)
axes_list.append(ax)
color_list.append(color_out)
labels, values = None, None # reset labels and values
if add_gamma_fit and cur_b in _adata.var_names[_adata.var.use_for_dynamics]:
xnew = np.linspace(
points.iloc[:, 0].min(),
points.iloc[:, 0].max() * 0.80,
)
k_name = "gamma_k" if _adata.uns["dynamics"]["experiment_type"] == "one-shot" else "gamma"
if k_name in _adata.var.columns:
if not ("gamma_b" in _adata.var.columns) or all(_adata.var.gamma_b.isna()):
_adata.var.loc[:, "gamma_b"] = 0
ax.plot(
xnew,
xnew * _adata[:, cur_b].var.loc[:, k_name].unique()
+ _adata[:, cur_b].var.loc[:, "gamma_b"].unique(),
dashes=[6, 2],
c=font_color,
)
else:
raise Exception(
"_adata does not seem to have %s column. Velocity estimation is required "
"before running this function." % k_name
)
if group is not None and add_group_gamma_fit and cur_b in _adata.var_names[_adata.var.use_for_dynamics]:
cell_groups = _adata.obs[group]
unique_groups = np.unique(cell_groups)
k_suffix = "gamma_k" if _adata.uns["dynamics"]["experiment_type"] == "one-shot" else "gamma"
for group_idx, cur_group in enumerate(unique_groups):
group_k_name = group + "_" + cur_group + "_" + k_suffix
group_adata = _adata[_adata.obs[group] == cur_group]
group_points = points.iloc[np.array(_adata.obs[group] == cur_group)]
group_b_key = group + "_" + cur_group + "_" + "gamma_b"
group_xnew = np.linspace(
group_points.iloc[:, 0].min(),
group_points.iloc[:, 0].max() * 0.90,
)
group_ynew = (
group_xnew * group_adata[:, cur_b].var.loc[:, group_k_name].unique()
+ group_adata[:, cur_b].var.loc[:, group_b_key].unique()
)
ax.annotate(group + "_" + cur_group, xy=(group_xnew[-1], group_ynew[-1]))
if group_k_name in group_adata.var.columns:
if not (group_b_key in group_adata.var.columns) or all(group_adata.var[group_b_key].isna()):
group_adata.var.loc[:, group_b_key] = 0
main_info("No %s found, setting all bias terms to zero" % group_b_key)
ax.plot(
group_xnew,
group_ynew,
dashes=[6, 2],
c=group_colors[group_idx % len(group_colors)],
)
else:
raise Exception(
"_adata does not seem to have %s column. Velocity estimation is required "
"before running this function." % group_k_name
)
# add legends according to colors and cmaps
# collected during for loop above
if stack_colors:
ax.legend(handles=stack_legend_handles, loc="upper right", prop={"size": stack_colors_legend_size})
for cur_b in basis:
for cur_l in layer:
main_debug("Plotting basis:%s, layer: %s" % (str(basis), str(layer)))
main_debug("colors: %s" % (str(color)))
_plot_basis_layer(cur_b, cur_l)
if save_show_or_return in ["save", "both", "all"]:
s_kwargs = {
"path": None,
"prefix": "scatters",
"dpi": None,
"ext": "pdf",
"transparent": True,
"close": True,
"verbose": True,
}
s_kwargs = update_dict(s_kwargs, save_kwargs)
save_fig(**s_kwargs)
if background is not None:
reset_rcParams()
elif save_show_or_return in ["show", "both", "all"]:
if show_legend:
plt.subplots_adjust(right=0.85)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
plt.tight_layout()
plt.show()
if background is not None:
reset_rcParams()
elif save_show_or_return in ["return", "all"]:
if background is not None:
reset_rcParams()
if return_all:
return (axes_list, color_list, font_color) if total_panels > 1 else (ax, color_out, font_color)
else:
return axes_list if total_panels > 1 else ax