import matplotlib.cm as cm
import numpy as np
from matplotlib.colors import is_color_like
from pypesto.util import assign_clusters
# for typehints
from ..C import COLOR
[docs]
def assign_clustered_colors(
vals: np.ndarray, balance_alpha: bool = True, highlight_global: bool = True
):
"""
Cluster and assign colors.
Parameters
----------
vals:
List to be clustered and assigned colors.
balance_alpha:
Flag indicating whether alpha for large clusters should be reduced to
avoid overplotting
highlight_global:
flag indicating whether global optimum should be highlighted
Returns
-------
colors: list of RGBA
One for each element in 'vals'.
"""
# sanity checks
if vals is None or len(vals) == 0:
return []
# assign clusters
clusters, cluster_size = assign_clusters(vals)
# create list of colors, which has the correct shape
n_clusters = 1 + max(clusters) - sum(cluster_size == 1)
# if best value was found more than once: we need one color less
if highlight_global and cluster_size[0] > 1:
n_clusters -= 1
# fill color array from colormap
colormap = cm.ScalarMappable().to_rgba
color_list = colormap(np.linspace(0.0, 1.0, n_clusters))
# best optimum should be colored in red
if highlight_global and cluster_size[0] > 1:
color_list = np.concatenate(([[1.0, 0.0, 0.0, 1.0]], color_list))
# We have clustered the results. However, clusters may have size 1,
# so we need to rearrange the regroup the results into "no_clusters",
# which will be grey, and "real_clusters", which will be colored
# run through the cluster_indices and collect which element
# is in 1-element-clusters, and which is in real clusters
no_clusters = np.where(cluster_size == 1)[0]
real_clusters = np.unique(np.where(cluster_size > 1)[0])
# assign transparency values according to cluster size, if wanted
if balance_alpha:
# set minimal alpha value to avoid non-visible colors
min_alpha = 0.01
# assign neutral color, add 1 for avoiding division by zero
grey = [0.7, 0.7, 0.7, min(1.0, 5.0 / (no_clusters.size + 1.0))]
# reduce alpha level depend on size of each cluster
n_cluster_size = np.delete(cluster_size, no_clusters)
for icluster in range(n_clusters):
color_list[icluster][3] = min(
1.0, max(5.0 / n_cluster_size[icluster], min_alpha)
)
else:
# assign neutral color
grey = [0.7, 0.7, 0.7, 1.0]
# create a color list, prfilled with grey values
colors = np.array([grey] * clusters.size)
# assign colors to real clusters
for icol, iclust in enumerate(real_clusters):
# find indices belonging to the cluster iclust and assign color
ind_of_iclust = np.argwhere(clusters == iclust).flatten()
colors[ind_of_iclust, :] = color_list[icol, :]
# if best value was found only once: replace it with red
if highlight_global and cluster_size[0] == 1:
colors[0] = [1.0, 0.0, 0.0, 1.0]
return colors
[docs]
def assign_colors(
vals: np.ndarray,
colors: COLOR | list[COLOR] | np.ndarray | None = None,
balance_alpha: bool = True,
highlight_global: bool = True,
) -> np.ndarray:
"""
Assign colors or format user specified colors.
Parameters
----------
vals:
List to be clustered and assigned colors.
colors:
list of colors recognized by matplotlib, or single color
balance_alpha:
Flag indicating whether alpha for large clusters should be reduced to
avoid overplotting
highlight_global:
flag indicating whether global optimum should be highlighted
Returns
-------
colors: list of colors recognized by matplotlib
One for each element in 'vals'.
"""
# sanity checks
if vals is None or len(vals) == 0:
return np.array([])
# if the user did not specify any colors:
if colors is None:
return assign_clustered_colors(
vals,
balance_alpha=balance_alpha,
highlight_global=highlight_global,
)
# Get number of elements and use user assigned colors
n_vals = len(vals) if isinstance(vals, list) else vals.size
if is_color_like(colors):
# one color was passed
return np.array([colors] * n_vals)
elif (
isinstance(colors, (list, np.ndarray))
and len(colors) == len(vals)
and sum([is_color_like(c) for c in colors]) == len(colors)
):
# a list of colors was passed, one for each value in vals
# convert to ndarray
colors = np.array(colors)
return colors
# Shape of array did not match n_vals. Error due to size mismatch:
raise ValueError(
"Incorrect color input. Colors must be specified either as "
"list of colors recognized by matplotlib with length equal to that of `vals` "
f"(here: {n_vals}), or as a single matplotlib color."
)
def assign_colors_for_list(
num_entries: int,
colors: COLOR | list[COLOR] | np.ndarray | None = None,
) -> list[list[float]] | np.ndarray:
"""
Create a list of colors for a list of items.
Can also check a user-provided list of colors and use this if
everything is ok.
Parameters
----------
num_entries:
number of results in list
colors:
list of colors, or single color
Returns
-------
colors:
List of RGBA, one for each element in 'vals'.
"""
# if the user did not specify any colors:
if colors is None:
# default colors will be used, one for each entry in the result list.
# Colors are created from assign_colors, which needs a dummy list
# doubled for clustering
dummy_clusters = np.array(list(range(num_entries)) * 2)
# we don't want alpha levels for all plotting routines in this case...
colors = assign_colors(
dummy_clusters, balance_alpha=False, highlight_global=False
)
# dummy cluster had twice as many entries as really there. Reduce.
real_indices = np.arange(0, colors.shape[0], 2)
return colors[real_indices]
# Pass the colors through assign_colors to check correct format of RGBA
return assign_colors(
vals=np.array(list(range(num_entries))),
colors=colors,
balance_alpha=False,
highlight_global=False,
)