from collections import OrderedDict
from typing import *
import numpy as np
import cmap as cmap_lib
from pygfx import Texture, Color
cmap_catalog = cmap_lib.Catalog()
COLORMAPS = sorted([
"viridis",
"plasma",
"inferno",
"magma",
"cividis",
"Greys",
"Purples",
"Blues",
"Greens",
"Oranges",
"Reds",
"tol:YlOrBr",
"YlOrRd",
"OrRd",
"PuRd",
"RdPu",
"BuPu",
"GnBu",
"PuBu",
"YlGnBu",
"PuBuGn",
"BuGn",
"YlGn",
"binary",
"gist_yarg",
"gist_gray",
"gray",
"bone",
"pink",
"spring",
"summer",
"autumn",
"winter",
"cool",
"Wistia",
"hot",
"afmhot",
"gist_heat",
"copper",
"PiYG",
"tol:PRGn",
"BrBG",
"PuOr",
"RdGy",
"vispy:RdBu",
"RdYlBu",
"RdYlGn",
"Spectral",
"coolwarm",
"bwr",
"seismic",
"berlin",
"vanimo",
"twilight",
"twilight_shifted",
"hsv",
"Pastel1",
"Pastel2",
"Paired",
"Accent",
"Dark2",
"Set1",
"Set2",
"Set3",
"tab10",
"tab20",
"tab20b",
"tab20c",
"flag",
"prism",
"ocean",
"gist_earth",
"terrain",
"gist_stern",
"gnuplot",
"gnuplot2",
"CMRmap",
"cubehelix",
"brg",
"gist_rainbow",
"yorick:rainbow",
"jet",
"turbo",
"nipy_spectral",
"gist_ncar",
])
SEQUENTIAL_CMAPS = list()
QUALITATIVE_CMAPS = list()
CYCLIC_CMAPS = list()
DIVERGING_CMAPS = list()
MISC_CMAPS = list()
for name in COLORMAPS:
_colormap = cmap_lib.Colormap(name)
match _colormap.category:
case "sequential":
if _colormap.interpolation == "nearest":
continue
SEQUENTIAL_CMAPS.append(name)
case "cyclic":
if _colormap.interpolation == "nearest":
continue
CYCLIC_CMAPS.append(name)
case "diverging":
if _colormap.interpolation == "nearest":
continue
DIVERGING_CMAPS.append(name)
case "qualitative":
QUALITATIVE_CMAPS.append(name)
case "miscellaneous":
if _colormap.interpolation == "nearest":
continue
MISC_CMAPS.append(name)
COLORMAP_NAMES = {
"sequential": sorted(SEQUENTIAL_CMAPS),
"cyclic": sorted(CYCLIC_CMAPS),
"diverging": sorted(DIVERGING_CMAPS),
"qualitative": sorted(QUALITATIVE_CMAPS),
"miscellaneous": sorted(MISC_CMAPS),
}
[docs]
def get_cmap(name: str, alpha: float = 1.0, gamma: float = 1.0) -> np.ndarray:
"""
Get a colormap as numpy array
Parameters
----------
name: str
name of colormap
alpha: float
alpha, 0.0 - 1.0
gamma: float
gamma, 0.0 - 1.0
Returns
-------
np.ndarray
[n_colors, 4], i.e. [n_colors, RGBA]
"""
cmap = cmap_lib.Colormap(name).lut(256, gamma=gamma)
cmap[:, -1] = alpha
return cmap.astype(np.float32)
[docs]
def make_colors(n_colors: int, cmap: str, alpha: float = 1.0) -> np.ndarray:
"""
Get colors from a colormap. The returned colors are uniformly spaced, except
for qualitative colormaps where they are returned subsequently.
Parameters
----------
n_colors: int
number of colors to get
cmap: str
name of colormap
alpha: float, default 1.0
alpha value
Returns
-------
np.ndarray
shape is [n_colors, 4], where the last dimension is RGBA
"""
cm = cmap_lib.Colormap(cmap)
# can also use cm.category == "qualitative", but checking for non-interpolated
# colormaps is a bit more general. (and not all "custom" colormaps will be
# assigned a category)
if cm.interpolation == "nearest":
max_colors = len(cm.color_stops)
if n_colors > max_colors:
raise ValueError(
f"You have requested <{n_colors}> colors but only <{max_colors}> exist for the "
f"chosen cmap: <{cmap}>"
)
return np.asarray(cm.color_stops, dtype=np.float32)[:n_colors, 1:]
cm_ixs = np.linspace(0, 255, n_colors, dtype=int)
return cm(cm_ixs).astype(np.float32)
def get_cmap_texture(name: str, alpha: float = 1.0) -> Texture:
return cmap_lib.Colormap(name).to_pygfx()
[docs]
def make_colors_dict(labels: Sequence, cmap: str, **kwargs) -> OrderedDict:
"""
Get a dict for mapping labels onto colors.
Parameters
----------
labels: Sequence[Any]
labels for creating a colormap. Order is maintained if it is a list of unique elements.
cmap: str
name of colormap
**kwargs
passed to make_colors()
Returns
-------
OrderedDict
keys are labels, values are colors
Examples
--------
.. code-block:: python
from fastplotlib.utils import get_colors_dict
labels = ["l1", "l2", "l3"]
labels_cmap = get_colors_dict(labels, cmap="tab10")
# illustration of what the `labels_cmap` dict would look like:
# keep in mind that the tab10 cmap was chosen here
{
"l1": <RGBA array for the blue 'tab10' color>,
"l2": <RGBA array for the orange 'tab10' color>,
"l3": <RGBA array for the green 'tab10' color>,
}
# another example with a non-qualitative cmap
labels_cmap_seismic = get_colors_dict(labels, cmap="bwr")
{
"l1": <RGBA array for the blue 'bwr' color>,
"l2": <RGBA array for the white 'bwr' color>,
"l3": <RGBA array for the red 'bwr' color>,
}
"""
if not len(set(labels)) == len(labels):
labels = list(set(labels))
else:
labels = list(labels)
colors = make_colors(len(labels), cmap, **kwargs)
return OrderedDict(zip(labels, colors))
[docs]
def quick_min_max(data: np.ndarray) -> tuple[float, float]:
"""
Adapted from pyqtgraph.ImageView.
Estimate the min/max values of *data* by subsampling.
Parameters
----------
data: np.ndarray or array-like with `min` and `max` attributes
Returns
-------
(float, float)
(min, max)
"""
if hasattr(data, "min") and hasattr(data, "max"):
# if value is pre-computed
if isinstance(data.min, (float, int, np.number)) and isinstance(
data.max, (float, int, np.number)
):
return data.min, data.max
while data.size > 1e6:
ax = np.argmax(data.shape)
sl = [slice(None)] * data.ndim
sl[ax] = slice(None, None, 2)
data = data[tuple(sl)]
return float(np.nanmin(data)), float(np.nanmax(data))
[docs]
def make_pygfx_colors(colors, n_colors):
"""
Parse and make colors array using pyfx.Color
Parameters
----------
colors: str, list, tuple, or np.ndarray
pygfx parseable color
n_colors: int
number of repeats of the color
Returns
-------
np.ndarray
shape is [n_colors, 4], i.e. [n_colors, RGBA]
"""
c = Color(colors)
colors_array = np.repeat(np.array([c]), n_colors, axis=0)
return colors_array
[docs]
def normalize_min_max(a):
"""normalize an array between 0 - 1"""
if np.unique(a).size == 1:
return np.zeros(a.size)
return (a - np.min(a)) / (np.max(a - np.min(a)))
[docs]
def parse_cmap_values(
n_colors: int,
cmap_name: str,
transform: np.ndarray | list[int | float] = None,
) -> np.ndarray:
"""
Parameters
----------
n_colors: int
number of graphics in collection
cmap_name: str
colormap name
transform: np.ndarray | List[int | float], optional
cmap transform
Returns
-------
"""
if transform is None:
colors = make_colors(n_colors, cmap_name)
return colors
else:
if not isinstance(transform, np.ndarray):
transform = np.array(transform)
# use the of the cmap_transform to set the color of the corresponding data
# each individual data[i] has its color based on the transform values
if len(transform) != n_colors:
raise ValueError(
f"len(cmap_values) != len(data): {len(transform)} != {n_colors}"
)
colormap = get_cmap(cmap_name)
n_colors = colormap.shape[0] - 1
# can also use cm.category == "qualitative"
if cmap_lib.Colormap(cmap_name).interpolation == "nearest":
# check that cmap_values are <int> and within the number of colors `n_colors`
# do not scale, use directly
if not np.issubdtype(transform.dtype, np.integer):
raise TypeError(
f"<int> `cmap_transform` values should be used with qualitative colormaps, "
f"the dtype you have passed is {transform.dtype}"
)
if max(transform) > n_colors:
raise IndexError(
f"You have chosen the qualitative colormap <'{cmap_name}'> which only has "
f"<{n_colors}> colors, which is lower than the max value of your `cmap_transform`."
f"Choose a cmap with more colors, or a non-quantitative colormap."
)
norm_cmap_values = transform
else:
# scale between 0 - n_colors so we can just index the colormap as a LUT
norm_cmap_values = (normalize_min_max(transform) * n_colors).astype(int)
# use colormap as LUT to map the cmap_values to the colormap index
colors = np.vstack([colormap[val] for val in norm_cmap_values])
return colors
