def get_colors(n: int, labels: Union[dict, np.ndarray, None], scores: Union[dict, np.ndarray, None], color: str) \
-> np.ndarray:
"""Return the colors using either labels or scores or default color."""
colors = np.array(n * [color]).astype('U64')
if labels is not None:
colors_label = STANDARD_COLORS.copy()
if isinstance(labels, dict):
index = np.array(list(labels.keys()))
values = np.array(list(labels.values())).astype(int)
labels = -np.ones(n, dtype=int)
labels[index] = values
index = labels >= 0
colors[index] = colors_label[labels[index] % len(colors_label)]
elif scores is not None:
colors_score = COOLWARM_RGB.copy()
n_colors = colors_score.shape[0]
colors_score_svg = np.array(['rgb' + str(tuple(colors_score[i])) for i in range(n_colors)])
if isinstance(scores, dict):
index = np.array(list(scores.keys()))
values = np.array(list(scores.values()))
scores = (min_max_scaling(values) * (n_colors - 1)).astype(int)
colors[index] = colors_score_svg[scores]
else:
scores = (min_max_scaling(scores) * (n_colors - 1)).astype(int)
colors = colors_score_svg[scores]
return colors
def get_node_colors(n: int,
labels: Optional[Iterable],
scores: Optional[Iterable],
membership: Optional[sparse.csr_matrix],
node_color: str,
label_colors: Optional[Iterable],
score_min: Optional[float] = None,
score_max: Optional[float] = None) -> np.ndarray:
"""Return the colors of the nodes using either labels or scores or default color."""
node_colors = np.array(n * [node_color]).astype('U64')
if labels is not None:
if isinstance(labels, dict):
keys = np.array(list(labels.keys()))
values = np.array(list(labels.values())).astype(int)
labels = -np.ones(n, dtype=int)
labels[keys] = values
elif isinstance(labels, list):
if len(labels) != n:
raise ValueError(
"The number of labels must be equal to the corresponding number of nodes."
)
else:
labels = np.array(labels)
index = labels >= 0
label_colors = get_label_colors(label_colors)
node_colors[index] = label_colors[labels[index] % len(label_colors)]
elif scores is not None:
colors_score = COOLWARM_RGB.copy()
n_colors = colors_score.shape[0]
colors_score_svg = np.array(
['rgb' + str(tuple(colors_score[i])) for i in range(n_colors)])
if isinstance(scores, dict):
keys = np.array(list(scores.keys()))
values = np.array(list(scores.values()))
scores = (min_max_scaling(values, score_min, score_max) *
(n_colors - 1)).astype(int)
node_colors[keys] = colors_score_svg[scores]
else:
if isinstance(scores, list):
if len(scores) != n:
raise ValueError(
"The number of scores must be equal to the corresponding number of nodes."
)
else:
scores = np.array(scores)
scores = (min_max_scaling(scores, score_min, score_max) *
(n_colors - 1)).astype(int)
node_colors = colors_score_svg[scores]
elif membership is not None:
if isinstance(label_colors, dict):
raise TypeError(
"Label colors must be a list or an array when using a membership."
)
label_colors = get_label_colors(label_colors)
node_colors = label_colors
return node_colors