diff --git a/arcadia_pycolor/__init__.py b/arcadia_pycolor/__init__.py
index 43235eb..4da59b7 100644
--- a/arcadia_pycolor/__init__.py
+++ b/arcadia_pycolor/__init__.py
@@ -1,4 +1,4 @@
-from arcadia_pycolor import colors, gradients, mpl, palettes, plot, style_defaults
+from arcadia_pycolor import colors, cvd, gradients, mpl, palettes, plot, style_defaults
 
 from .colors import *
 from .gradient import *
@@ -6,6 +6,7 @@
 from .palette import *
 
 __all__ = [
+    "cvd",
     "gradients",
     "mpl",
     "palettes",
diff --git a/arcadia_pycolor/cvd.py b/arcadia_pycolor/cvd.py
new file mode 100644
index 0000000..ed8677f
--- /dev/null
+++ b/arcadia_pycolor/cvd.py
@@ -0,0 +1,147 @@
+from typing import Union
+
+import matplotlib as mpl
+import numpy as np
+from colorspacious import cspace_convert
+
+from arcadia_pycolor.gradient import Gradient
+from arcadia_pycolor.hexcode import HexCode
+from arcadia_pycolor.palette import Palette
+from arcadia_pycolor.plot import plot_gradient_lightness
+
+CVD_TYPES = {"d": "deuteranomaly", "p": "protanomaly", "t": "tritanomaly"}
+
+
+def _make_cvd_dict(cvd_type: str, severity: int = 100) -> dict:
+    """
+    Makes a dictionary for colorspacious to simulate color vision deficiency.
+
+    Args:
+        cvd_type (str): 'd' for deuteranomaly, 'p' for protanomaly, and 't' for tritanomaly.
+        severity (int): severity of the color vision deficiency, from 0 to 100.
+    """
+
+    if cvd_type not in CVD_TYPES:
+        raise ValueError(
+            "Choose 'd' for deuteranomaly, 'p' for protanomaly, and 't' for tritanomaly."
+        )
+
+    clipped_severity = np.clip(severity, 0, 100)
+
+    cvd_space = {"name": "sRGB1+CVD", "cvd_type": CVD_TYPES[cvd_type], "severity": clipped_severity}
+
+    return cvd_space
+
+
+def simulate_color(
+    colors: Union[HexCode, list[HexCode]], cvd_type: str = "d", severity: int = 100
+) -> Union[HexCode, list[HexCode]]:
+    """
+    Simulates color vision deficiency for a single HexCode or list of HexCodes.
+
+    Args:
+        colors (HexCode or list[HexCode]): colors to simulate color vision deficiency on.
+        cvd_type (str): 'd' for deuteranomaly, 'p' for protanomaly, and 't' for tritanomaly.
+        severity (int): severity of the color vision deficiency, from 0 to 100.
+    """
+    cvd_space = _make_cvd_dict(cvd_type=cvd_type, severity=severity)
+
+    if not isinstance(colors, list):
+        processed_colors = [colors]
+    else:
+        processed_colors = colors
+
+    returned_colors = []
+    for color in processed_colors:
+        rgb_color = color.to_rgb()
+        cvd_color_name = f"{color.name}_{cvd_type}"
+        cvd_rgb_color = np.clip(cspace_convert(rgb_color, cvd_space, "sRGB1") / 255, 0, 1)
+        cvd_hexcode = HexCode(name=cvd_color_name, hex_code=mpl.colors.to_hex(cvd_rgb_color))
+        returned_colors.append(cvd_hexcode)
+
+    if len(returned_colors) == 1:
+        return returned_colors[0]
+    else:
+        return returned_colors
+
+
+def display_all_color(color: HexCode, severity: int = 100) -> None:
+    """
+    Display all color vision deficiency types for a single HexCode.
+
+    Args:
+        color (HexCode): color to simulate color vision deficiency on.
+        severity (int): severity of the color vision deficiency, from 0 to 100.
+    """
+    cvd_colors = [color] + [simulate_color(color, cvd_type, severity) for cvd_type in CVD_TYPES]
+    for cvd_color in cvd_colors:
+        print(cvd_color.swatch())
+
+
+def simulate_palette(palette: Palette, cvd_type: str = "d", severity: int = 100) -> Palette:
+    """
+    Simulates color vision deficiency on a Palette.
+
+    Args:
+        palette (Palette): Palette object on which to simulate color vision deficiency.
+        cvd_type (str): 'd' for deuteranomaly, 'p' for protanomaly, and 't' for tritanomaly.
+        severity (int): severity of the color vision deficiency, from 0 to 100.
+    """
+    cvd_hex_colors = simulate_color(palette.colors, cvd_type=cvd_type, severity=severity)
+    cvd_palette = Palette(f"{palette.name}_{cvd_type}", cvd_hex_colors)
+
+    return cvd_palette
+
+
+def display_all_palette(palette: Palette, severity: int = 100) -> None:
+    """
+    Display all color vision deficiency types for a Palette.
+
+    Args:
+        palette (Palette): Palette object on which to simulate color vision deficiency.
+        severity (int): severity of the color vision deficiency, from 0 to 100.
+    """
+    cvd_palettes = [palette] + [
+        simulate_palette(palette, cvd_type, severity) for cvd_type in CVD_TYPES
+    ]
+    for palette in cvd_palettes:
+        print(palette.name)
+        print(palette.swatch())
+
+
+def simulate_gradient(gradient: Gradient, cvd_type="d", severity: int = 100) -> Gradient:
+    """
+    Simulates color vision deficiency on a Gradient.
+
+    Args:
+        gradient (Gradient): the Gradient object to display
+        cvd_type (str): 'd' for deuteranomaly, 'p' for protanomaly, and 't' for tritanomaly.
+        severity (int): severity of the color vision deficiency, from 0 to 100.
+    """
+    cvd_hex_colors = simulate_color(gradient.colors, cvd_type=cvd_type, severity=severity)
+    cvd_gradient = Gradient(f"{gradient.name}_{cvd_type}", cvd_hex_colors, gradient.values)
+
+    return cvd_gradient
+
+
+def display_all_gradient(gradient: Gradient, severity: int = 100) -> None:
+    """
+    Display all color vision deficiency types for a Gradient.
+
+    Args:
+        gradient (Gradient): the Gradient object to display
+        severity (int): severity of the color vision deficiency, from 0 to 100.
+    """
+    cvd_gradients = [gradient] + [
+        simulate_gradient(gradient, cvd_type, severity) for cvd_type in CVD_TYPES
+    ]
+    for grad in cvd_gradients:
+        print(grad.name)
+        print(grad.swatch())
+
+
+def display_all_gradient_lightness(gradient: Gradient, severity: int = 100, **kwargs):
+    plot_gradient_lightness(
+        [gradient] + [simulate_gradient(gradient, cvd_type, severity) for cvd_type in CVD_TYPES],
+        **kwargs,
+    )
diff --git a/arcadia_pycolor/gradient.py b/arcadia_pycolor/gradient.py
index 8b2b894..a86061a 100644
--- a/arcadia_pycolor/gradient.py
+++ b/arcadia_pycolor/gradient.py
@@ -3,7 +3,12 @@
 from arcadia_pycolor.display import colorize
 from arcadia_pycolor.hexcode import HexCode
 from arcadia_pycolor.palette import Palette
-from arcadia_pycolor.utils import distribute_values
+from arcadia_pycolor.utils import (
+    distribute_values,
+    interpolate_x_values,
+    is_monotonic,
+    rescale_and_concatenate_values,
+)
 
 
 class Gradient(Palette):
@@ -58,6 +63,64 @@ def swatch(self, steps=21):
 
         return "".join(swatches)
 
+    def reverse(self):
+        return Gradient(
+            name=f"{self.name}_r",
+            colors=self.colors[::-1],
+            values=[1 - value for value in self.values[::-1]],
+        )
+
+    def resample_as_palette(self, steps=5):
+        """
+        Resamples the gradient, returning a Palette with the specified number of steps.
+        """
+        gradient = self.to_mpl_cmap()
+        values = distribute_values(steps)
+        colors = [
+            HexCode(name=f"{self.name}_{i}", hex_code=mcolors.to_hex(gradient(value)))
+            for i, value in enumerate(values)
+        ]
+
+        return Palette(
+            name=f"{self.name}_resampled_{steps}",
+            colors=colors,
+        )
+
+    def interpolate_lightness(self):
+        """
+        Interpolates the gradient to new values based on lightness.
+        """
+
+        if len(self.colors) < 3:
+            raise ValueError("Interpolation requires at least three colors.")
+        if not is_monotonic(self.values):
+            raise ValueError("Lightness must be monotonically increasing or decreasing.")
+
+        lightness_values = [color.to_cam02ucs()[0] for color in self.colors]
+        new_values = interpolate_x_values(lightness_values)
+
+        return Gradient(
+            name=f"{self.name}_interpolated",
+            colors=self.colors,
+            values=new_values,
+        )
+
+    def __add__(self, other: "Gradient"):
+        new_colors = []
+        new_values = []
+
+        # If the first gradient ends with the same color as the start of the second gradient,
+        # drop the repeated color.
+        offset = 1 if self.colors[-1] == other.colors[0] else 0
+        new_colors = self.colors + other.colors[offset:]
+        new_values = rescale_and_concatenate_values(self.values, other.values[offset:])
+
+        return Gradient(
+            name=f"{self.name}_{other.name}",
+            colors=new_colors,
+            values=new_values,
+        )
+
     def __repr__(self):
         longest_name_length = self._get_longest_name_length()
 
diff --git a/arcadia_pycolor/gradients.py b/arcadia_pycolor/gradients.py
index c937141..9d0e448 100644
--- a/arcadia_pycolor/gradients.py
+++ b/arcadia_pycolor/gradients.py
@@ -134,3 +134,5 @@
     [colors.depths, colors.seaweed, colors.paper, colors.tangerine, colors.umber, colors.soil],
     [0.0, 0.21, 0.5, 0.6, 0.81, 1.0],
 )
+
+_all_gradients = [obj for obj in globals().values() if isinstance(obj, Gradient)]
diff --git a/arcadia_pycolor/hexcode.py b/arcadia_pycolor/hexcode.py
index 4ea8d9b..f38e99f 100644
--- a/arcadia_pycolor/hexcode.py
+++ b/arcadia_pycolor/hexcode.py
@@ -1,6 +1,7 @@
 import re
 
 import matplotlib.colors as mcolors
+from colorspacious import cspace_converter
 
 from arcadia_pycolor.display import colorize
 
@@ -37,6 +38,18 @@ def to_rgb(self):
         """Returns a tuple of RGB values for the color."""
         return [int(c * 255) for c in mcolors.to_rgb(self.hex_code)]
 
+    def to_cam02ucs(self):
+        """Returns a tuple of CAM02-UCS values for the color, where
+        the first value is the lightness (J) and the second and third values
+        are the chromaticity coordinates (a: redness-to-greenness, b: blueness-to-yellowness)."""
+        # Convert RGB255 to RGB1
+        rgb = [i / 255 for i in self.to_rgb()]
+
+        # Convert RGB1 to CAM02-UCS
+        cam02ucs = cspace_converter("sRGB1", "CAM02-UCS")(rgb)
+
+        return cam02ucs
+
     def swatch(self, width: int = 2, min_name_width: int = None):
         """
         Returns a color swatch with the specified width and color name.
diff --git a/arcadia_pycolor/mpl.py b/arcadia_pycolor/mpl.py
index 12ce1af..5f008ba 100644
--- a/arcadia_pycolor/mpl.py
+++ b/arcadia_pycolor/mpl.py
@@ -9,6 +9,7 @@
 import arcadia_pycolor.colors as colors
 import arcadia_pycolor.gradients
 import arcadia_pycolor.palettes
+from arcadia_pycolor.gradient import Gradient
 from arcadia_pycolor.palette import Palette
 from arcadia_pycolor.style_defaults import (
     ARCADIA_RC_PARAMS,
@@ -328,8 +329,12 @@ def load_colormaps():
     )
     for object in cmaps:
         if isinstance(object, Palette):
-            if (gradient_name := f"apc:{object.name}") not in colormaps:
-                plt.register_cmap(name=gradient_name, cmap=object.to_mpl_cmap())
+            if (colormap_name := f"apc:{object.name}") not in colormaps:
+                plt.register_cmap(name=colormap_name, cmap=object.to_mpl_cmap())
+        # Register the reversed version of the gradient as well.
+        if isinstance(object, Gradient):
+            if (colormap_name := f"apc:{object.name}_r") not in colormaps:
+                plt.register_cmap(name=colormap_name, cmap=object.reverse().to_mpl_cmap())
 
 
 def load_styles():
diff --git a/arcadia_pycolor/palette.py b/arcadia_pycolor/palette.py
index 60d3505..34e73dd 100644
--- a/arcadia_pycolor/palette.py
+++ b/arcadia_pycolor/palette.py
@@ -1,5 +1,6 @@
 import matplotlib.colors as mcolors
 
+from arcadia_pycolor.display import colorize
 from arcadia_pycolor.hexcode import HexCode
 
 
@@ -24,11 +25,23 @@ def from_dict(cls, name: str, colors: dict[str, str]):
         hex_codes = [HexCode(name, hex_code) for name, hex_code in colors.items()]
         return cls(name, hex_codes)
 
+    def swatch(self):
+        swatches = [colorize("  ", bg_color=color) for color in self.colors]
+
+        return "".join(swatches)
+
+    def reverse(self):
+        return Palette(
+            name=f"{self.name}_r",
+            colors=self.colors[::-1],
+        )
+
     def __repr__(self):
         longest_name_length = self._get_longest_name_length()
 
         return "\n".join(
-            [color.swatch(min_name_width=longest_name_length) for color in self.colors]
+            [self.swatch()]
+            + [color.swatch(min_name_width=longest_name_length) for color in self.colors]
         )
 
     def __add__(self, other: "Palette"):
diff --git a/arcadia_pycolor/palettes.py b/arcadia_pycolor/palettes.py
index 711a4ac..6f8ff97 100644
--- a/arcadia_pycolor/palettes.py
+++ b/arcadia_pycolor/palettes.py
@@ -139,3 +139,5 @@
     core + neutral + accent + light_accent + accent_expanded + light_accent_expanded + other + named
 )
 all.name = "All"
+
+_all_palettes = [obj for obj in globals().values() if isinstance(obj, Palette)]
diff --git a/arcadia_pycolor/plot.py b/arcadia_pycolor/plot.py
index f778944..56cbd89 100644
--- a/arcadia_pycolor/plot.py
+++ b/arcadia_pycolor/plot.py
@@ -6,6 +6,8 @@
 from colorspacious import cspace_converter
 
 from arcadia_pycolor.gradient import Gradient
+from arcadia_pycolor.gradients import _all_gradients
+from arcadia_pycolor.palettes import _all_palettes
 
 
 def plot_gradient_lightness(
@@ -107,3 +109,15 @@ def plot_gradient_lightness(
         return fig
 
     plt.show()
+
+
+def display_all_gradients():
+    for gradient in _all_gradients:
+        print(gradient.name)
+        print(gradient.swatch())
+
+
+def display_all_palettes():
+    for palette in _all_palettes:
+        print(palette.name)
+        print(palette.swatch())
diff --git a/arcadia_pycolor/tests/test_palette.py b/arcadia_pycolor/tests/test_palette.py
index cc0a21b..7d198e3 100644
--- a/arcadia_pycolor/tests/test_palette.py
+++ b/arcadia_pycolor/tests/test_palette.py
@@ -31,7 +31,7 @@ def test_palette_from_dict():
 
 
 def test_palette_repr():
-    expected_swatch = "\x1b[48;2;255;255;255m  \x1b[0m\x1b[38;2;255;255;255m white #FFFFFF\x1b[0m\n\x1b[48;2;0;0;0m  \x1b[0m\x1b[38;2;0;0;0m black #000000\x1b[0m"  # noqa E501
+    expected_swatch = "\x1b[48;2;255;255;255m  \x1b[0m\x1b[48;2;0;0;0m  \x1b[0m\n\x1b[48;2;255;255;255m  \x1b[0m\x1b[38;2;255;255;255m white #FFFFFF\x1b[0m\n\x1b[48;2;0;0;0m  \x1b[0m\x1b[38;2;0;0;0m black #000000\x1b[0m"  # noqa E501
     assert (
         Palette(
             "my_palette",
diff --git a/arcadia_pycolor/utils.py b/arcadia_pycolor/utils.py
index 5fcc75d..bc10829 100644
--- a/arcadia_pycolor/utils.py
+++ b/arcadia_pycolor/utils.py
@@ -1,7 +1,54 @@
+from typing import Iterable, Union
+
 import numpy as np
 
 
-def distribute_values(num_points: int, min_val: float = 0.0, max_val: float = 1.0):
+def distribute_values(num_points: int, min_val: float = 0.0, max_val: float = 1.0) -> list[float]:
     if num_points <= 1:
         return [(max_val - min_val) / 2] * num_points
     return np.linspace(min_val, max_val, num_points).tolist()
+
+
+def interpolate_x_values(y_values: list[float], round_digits=3) -> list[float]:
+    """Takes a list of y-values and returns a list of x-values that are
+    linearly interpolated between 0 and 1; the first and last y-values
+    correspond to x-values of 0 and 1, respectively."""
+    # Retrieve first and last values.
+    x0 = 0
+    y0 = y_values[0]
+
+    x1 = 1
+    y1 = y_values[-1]
+
+    # Calculate the slope (m) of the line through the two points.
+    m = (y1 - y0) / (x1 - x0)
+
+    # y0 is the y-intercept of the line.
+    # Find x-values corresponding to each y-value.
+    x_values = [np.round((y - y0) / m, round_digits) for y in y_values]
+
+    return x_values
+
+
+def is_non_decreasing(values: Union[Iterable[int], Iterable[float]]) -> bool:
+    """Determine if the numbers in `values` are in strictly non-decreasing order.
+    Copied from https://stackoverflow.com/questions/4983258.
+    """
+    return all(x <= y for x, y in zip(values, values[1:], strict=False))
+
+
+def is_non_increasing(values: Union[Iterable[int], Iterable[float]]) -> bool:
+    """Determine if the numbers in `values` are in strictly non-increasing order.
+    Copied from https://stackoverflow.com/questions/4983258.
+    """
+    return all(x >= y for x, y in zip(values, values[1:], strict=False))
+
+
+def is_monotonic(values: Union[Iterable[int], Iterable[float]]) -> bool:
+    return is_non_decreasing(values) or is_non_increasing(values)
+
+
+def rescale_and_concatenate_values(list1: list[float], list2: list[float]) -> list[float]:
+    rescaled_list1 = [0.5 * x for x in list1]
+    rescaled_list2 = [0.5 * x + 0.5 for x in list2]
+    return rescaled_list1 + rescaled_list2
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index 2615f14..1399f0c 100644
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diff --git a/examples/scatter_half_small.pdf b/examples/scatter_half_small.pdf
index de50ed0..5972167 100644
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diff --git a/examples/scatter_setup.pdf b/examples/scatter_setup.pdf
index a554755..8db7d53 100644
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diff --git a/examples/scatter_style_axis.pdf b/examples/scatter_style_axis.pdf
index 464074c..403b420 100644
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diff --git a/examples/scatter_threeq_small.pdf b/examples/scatter_threeq_small.pdf
index 1a9a2f6..7d0fc4e 100644
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diff --git a/examples/scatter_threeq_wide.pdf b/examples/scatter_threeq_wide.pdf
index 9408d07..d9d36fb 100644
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diff --git a/examples/violin_setup.pdf b/examples/violin_setup.pdf
index 9986759..62be785 100644
Binary files a/examples/violin_setup.pdf and b/examples/violin_setup.pdf differ
diff --git a/examples/violin_style_axis.pdf b/examples/violin_style_axis.pdf
index 1886267..0821bd2 100644
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diff --git a/usage_example.ipynb b/usage_example.ipynb
index 521fea8..223c03b 100644
--- a/usage_example.ipynb
+++ b/usage_example.ipynb
@@ -40,6 +40,7 @@
     {
      "data": {
       "text/plain": [
+       "\u001b[48;2;80;136;197m  \u001b[0m\u001b[48;2;242;131;96m  \u001b[0m\u001b[48;2;59;152;134m  \u001b[0m\u001b[48;2;247;184;70m  \u001b[0m\u001b[48;2;122;119;171m  \u001b[0m\u001b[48;2;248;152;174m  \u001b[0m\n",
        "\u001b[48;2;80;136;197m  \u001b[0m\u001b[38;2;80;136;197m aegean  #5088C5\u001b[0m\n",
        "\u001b[48;2;242;131;96m  \u001b[0m\u001b[38;2;242;131;96m amber   #F28360\u001b[0m\n",
        "\u001b[48;2;59;152;134m  \u001b[0m\u001b[38;2;59;152;134m seaweed #3B9886\u001b[0m\n",
@@ -122,6 +123,7 @@
     {
      "data": {
       "text/plain": [
+       "\u001b[48;2;80;136;197m  \u001b[0m\u001b[48;2;242;131;96m  \u001b[0m\u001b[48;2;59;152;134m  \u001b[0m\u001b[48;2;247;184;70m  \u001b[0m\n",
        "\u001b[48;2;80;136;197m  \u001b[0m\u001b[38;2;80;136;197m aegean  #5088C5\u001b[0m\n",
        "\u001b[48;2;242;131;96m  \u001b[0m\u001b[38;2;242;131;96m amber   #F28360\u001b[0m\n",
        "\u001b[48;2;59;152;134m  \u001b[0m\u001b[38;2;59;152;134m seaweed #3B9886\u001b[0m\n",
@@ -148,6 +150,7 @@
     {
      "data": {
       "text/plain": [
+       "\u001b[48;2;255;0;0m  \u001b[0m\u001b[48;2;0;255;0m  \u001b[0m\u001b[48;2;0;0;255m  \u001b[0m\n",
        "\u001b[48;2;255;0;0m  \u001b[0m\u001b[38;2;255;0;0m red   #FF0000\u001b[0m\n",
        "\u001b[48;2;0;255;0m  \u001b[0m\u001b[38;2;0;255;0m green #00FF00\u001b[0m\n",
        "\u001b[48;2;0;0;255m  \u001b[0m\u001b[38;2;0;0;255m blue  #0000FF\u001b[0m"
@@ -488,7 +491,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/dennis/Code/arcadia-pycolor/arcadia_pycolor/mpl.py:97: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/Users/dennis/Code/arcadia-pycolor/arcadia_pycolor/mpl.py:98: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  ax.set_xticklabels(xticklabels)\n"
      ]
     },
@@ -608,9 +611,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/dennis/Code/arcadia-pycolor/arcadia_pycolor/mpl.py:97: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/Users/dennis/Code/arcadia-pycolor/arcadia_pycolor/mpl.py:98: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  ax.set_xticklabels(xticklabels)\n",
-      "/Users/dennis/Code/arcadia-pycolor/arcadia_pycolor/mpl.py:105: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/Users/dennis/Code/arcadia-pycolor/arcadia_pycolor/mpl.py:106: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  ax.set_yticklabels(yticklabels)\n"
      ]
     },
@@ -789,6 +792,328 @@
     "apc.mpl.save_figure(fname=\"examples/scatter_half_small.pdf\")\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[48;2;52;30;96m \u001b[0m\u001b[48;2;60;38;107m \u001b[0m\u001b[48;2;68;46;118m \u001b[0m\u001b[48;2;75;54;129m \u001b[0m\u001b[48;2;83;62;140m \u001b[0m\u001b[48;2;90;70;151m \u001b[0m\u001b[48;2;88;84;160m \u001b[0m\u001b[48;2;86;96;169m \u001b[0m\u001b[48;2;84;108;177m \u001b[0m\u001b[48;2;82;120;186m \u001b[0m\u001b[48;2;81;132;194m \u001b[0m\u001b[48;2;89;145;187m \u001b[0m\u001b[48;2;101;156;174m \u001b[0m\u001b[48;2;113;168;161m \u001b[0m\u001b[48;2;125;180;148m \u001b[0m\u001b[48;2;137;191;135m \u001b[0m\u001b[48;2;150;204;121m \u001b[0m\u001b[48;2;170;214;98m \u001b[0m\u001b[48;2;191;224;74m \u001b[0m\u001b[48;2;212;235;49m \u001b[0m\u001b[48;2;234;245;25m \u001b[0m\n",
+      "\u001b[48;2;52;30;96m  \u001b[0m\u001b[38;2;52;30;96m concord #341E60\u001b[0m 0\n",
+      "\u001b[48;2;90;69;150m  \u001b[0m\u001b[38;2;90;69;150m grape   #5A4596\u001b[0m 0.23\n",
+      "\u001b[48;2;80;136;197m  \u001b[0m\u001b[38;2;80;136;197m aegean  #5088C5\u001b[0m 0.49\n",
+      "\u001b[48;2;151;205;120m  \u001b[0m\u001b[38;2;151;205;120m lime    #97CD78\u001b[0m 0.77\n",
+      "\u001b[48;2;255;255;0m  \u001b[0m\u001b[38;2;255;255;0m yellow  #FFFF00\u001b[0m 1\n",
+      "\u001b[48;2;255;255;0m \u001b[0m\u001b[48;2;234;245;25m \u001b[0m\u001b[48;2;212;235;49m \u001b[0m\u001b[48;2;191;224;74m \u001b[0m\u001b[48;2;170;214;98m \u001b[0m\u001b[48;2;150;204;121m \u001b[0m\u001b[48;2;137;191;135m \u001b[0m\u001b[48;2;125;180;148m \u001b[0m\u001b[48;2;113;168;161m \u001b[0m\u001b[48;2;101;156;174m \u001b[0m\u001b[48;2;89;145;187m \u001b[0m\u001b[48;2;81;132;194m \u001b[0m\u001b[48;2;82;120;186m \u001b[0m\u001b[48;2;84;108;177m \u001b[0m\u001b[48;2;86;96;169m \u001b[0m\u001b[48;2;88;84;160m \u001b[0m\u001b[48;2;90;70;151m \u001b[0m\u001b[48;2;83;62;140m \u001b[0m\u001b[48;2;75;54;129m \u001b[0m\u001b[48;2;68;46;118m \u001b[0m\u001b[48;2;60;38;107m \u001b[0m\n",
+      "\u001b[48;2;255;255;0m  \u001b[0m\u001b[38;2;255;255;0m yellow  #FFFF00\u001b[0m 0\n",
+      "\u001b[48;2;151;205;120m  \u001b[0m\u001b[38;2;151;205;120m lime    #97CD78\u001b[0m 0.22999999999999998\n",
+      "\u001b[48;2;80;136;197m  \u001b[0m\u001b[38;2;80;136;197m aegean  #5088C5\u001b[0m 0.51\n",
+      "\u001b[48;2;90;69;150m  \u001b[0m\u001b[38;2;90;69;150m grape   #5A4596\u001b[0m 0.77\n",
+      "\u001b[48;2;52;30;96m  \u001b[0m\u001b[38;2;52;30;96m concord #341E60\u001b[0m 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(apc.gradients.viridis)\n",
+    "print(apc.gradients.viridis.reverse())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[48;2;52;30;96m  \u001b[0m\u001b[48;2;85;64;143m  \u001b[0m\u001b[48;2;83;113;181m  \u001b[0m\u001b[48;2;108;163;167m  \u001b[0m\u001b[48;2;165;212;104m  \u001b[0m\u001b[48;2;255;255;0m  \u001b[0m\n",
+       "\u001b[48;2;52;30;96m  \u001b[0m\u001b[38;2;52;30;96m viridis_0 #341e60\u001b[0m\n",
+       "\u001b[48;2;85;64;143m  \u001b[0m\u001b[38;2;85;64;143m viridis_1 #55408f\u001b[0m\n",
+       "\u001b[48;2;83;113;181m  \u001b[0m\u001b[38;2;83;113;181m viridis_2 #5371b5\u001b[0m\n",
+       "\u001b[48;2;108;163;167m  \u001b[0m\u001b[38;2;108;163;167m viridis_3 #6ca3a7\u001b[0m\n",
+       "\u001b[48;2;165;212;104m  \u001b[0m\u001b[38;2;165;212;104m viridis_4 #a5d468\u001b[0m\n",
+       "\u001b[48;2;255;255;0m  \u001b[0m\u001b[38;2;255;255;0m viridis_5 #ffff00\u001b[0m"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "apc.gradients.viridis.resample_as_palette(6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[48;2;80;136;197m  \u001b[0m\u001b[38;2;80;136;197m aegean #5088C5\u001b[0m\n",
+      "\u001b[48;2;94;128;196m  \u001b[0m\u001b[38;2;94;128;196m aegean_d #5e80c4\u001b[0m\n",
+      "\u001b[48;2;110;139;200m  \u001b[0m\u001b[38;2;110;139;200m aegean_p #6e8bc8\u001b[0m\n",
+      "\u001b[48;2;0;150;158m  \u001b[0m\u001b[38;2;0;150;158m aegean_t #00969e\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "apc.cvd.display_all_color(apc.aegean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[48;2;0;150;158m  \u001b[0m\u001b[48;2;255;110;123m  \u001b[0m\u001b[48;2;255;166;161m  \u001b[0m\u001b[48;2;151;199;185m  \u001b[0m\u001b[48;2;110;128;138m  \u001b[0m\u001b[48;2;255;144;160m  \u001b[0m\u001b[48;2;0;154;147m  \u001b[0m\u001b[48;2;218;55;83m  \u001b[0m\u001b[48;2;46;192;196m  \u001b[0m\u001b[48;2;190;174;174m  \u001b[0m\u001b[48;2;129;157;159m  \u001b[0m\u001b[48;2;255;171;172m  \u001b[0m\n",
+       "\u001b[48;2;0;150;158m  \u001b[0m\u001b[38;2;0;150;158m aegean_t     #00969e\u001b[0m\n",
+       "\u001b[48;2;255;110;123m  \u001b[0m\u001b[38;2;255;110;123m amber_t      #ff6e7b\u001b[0m\n",
+       "\u001b[48;2;255;166;161m  \u001b[0m\u001b[38;2;255;166;161m canary_t     #ffa6a1\u001b[0m\n",
+       "\u001b[48;2;151;199;185m  \u001b[0m\u001b[38;2;151;199;185m lime_t       #97c7b9\u001b[0m\n",
+       "\u001b[48;2;110;128;138m  \u001b[0m\u001b[38;2;110;128;138m aster_t      #6e808a\u001b[0m\n",
+       "\u001b[48;2;255;144;160m  \u001b[0m\u001b[38;2;255;144;160m rose_t       #ff90a0\u001b[0m\n",
+       "\u001b[48;2;0;154;147m  \u001b[0m\u001b[38;2;0;154;147m seaweed_t    #009a93\u001b[0m\n",
+       "\u001b[48;2;218;55;83m  \u001b[0m\u001b[38;2;218;55;83m dragon_t     #da3753\u001b[0m\n",
+       "\u001b[48;2;46;192;196m  \u001b[0m\u001b[38;2;46;192;196m vitalblue_t  #2ec0c4\u001b[0m\n",
+       "\u001b[48;2;190;174;174m  \u001b[0m\u001b[38;2;190;174;174m chateau_t    #beaeae\u001b[0m\n",
+       "\u001b[48;2;129;157;159m  \u001b[0m\u001b[38;2;129;157;159m marineblue_t #819d9f\u001b[0m\n",
+       "\u001b[48;2;255;171;172m  \u001b[0m\u001b[38;2;255;171;172m tangerine_t  #ffabac\u001b[0m"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "apc.cvd.simulate_palette(apc.palettes.accent_ordered, \"t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AccentOrdered\n",
+      "\u001b[48;2;80;136;197m  \u001b[0m\u001b[48;2;242;131;96m  \u001b[0m\u001b[48;2;247;184;70m  \u001b[0m\u001b[48;2;151;205;120m  \u001b[0m\u001b[48;2;122;119;171m  \u001b[0m\u001b[48;2;248;152;174m  \u001b[0m\u001b[48;2;59;152;134m  \u001b[0m\u001b[48;2;200;81;82m  \u001b[0m\u001b[48;2;115;181;227m  \u001b[0m\u001b[48;2;186;176;168m  \u001b[0m\u001b[48;2;138;153;173m  \u001b[0m\u001b[48;2;255;185;132m  \u001b[0m\n",
+      "AccentOrdered_d\n",
+      "\u001b[48;2;94;128;196m  \u001b[0m\u001b[48;2;187;172;94m  \u001b[0m\u001b[48;2;223;202;75m  \u001b[0m\u001b[48;2;203;189;125m  \u001b[0m\u001b[48;2;103;123;170m  \u001b[0m\u001b[48;2;191;187;172m  \u001b[0m\u001b[48;2;133;134;135m  \u001b[0m\u001b[48;2;142;131;79m  \u001b[0m\u001b[48;2;145;169;226m  \u001b[0m\u001b[48;2;181;179;168m  \u001b[0m\u001b[48;2;142;150;173m  \u001b[0m\u001b[48;2;222;206;132m  \u001b[0m\n",
+      "AccentOrdered_p\n",
+      "\u001b[48;2;110;139;200m  \u001b[0m\u001b[48;2;160;148;93m  \u001b[0m\u001b[48;2;208;186;52m  \u001b[0m\u001b[48;2;211;193;113m  \u001b[0m\u001b[48;2;104;126;173m  \u001b[0m\u001b[48;2;167;169;175m  \u001b[0m\u001b[48;2;147;144;133m  \u001b[0m\u001b[48;2;112;106;81m  \u001b[0m\u001b[48;2;161;181;229m  \u001b[0m\u001b[48;2;179;176;168m  \u001b[0m\u001b[48;2;146;154;174m  \u001b[0m\u001b[48;2;206;191;128m  \u001b[0m\n",
+      "AccentOrdered_t\n",
+      "\u001b[48;2;0;150;158m  \u001b[0m\u001b[48;2;255;110;123m  \u001b[0m\u001b[48;2;255;166;161m  \u001b[0m\u001b[48;2;151;199;185m  \u001b[0m\u001b[48;2;110;128;138m  \u001b[0m\u001b[48;2;255;144;160m  \u001b[0m\u001b[48;2;0;154;147m  \u001b[0m\u001b[48;2;218;55;83m  \u001b[0m\u001b[48;2;46;192;196m  \u001b[0m\u001b[48;2;190;174;174m  \u001b[0m\u001b[48;2;129;157;159m  \u001b[0m\u001b[48;2;255;171;172m  \u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "apc.cvd.display_all_palette(apc.palettes.accent_ordered)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "viridis\n",
+      "\u001b[48;2;52;30;96m \u001b[0m\u001b[48;2;60;38;107m \u001b[0m\u001b[48;2;68;46;118m \u001b[0m\u001b[48;2;75;54;129m \u001b[0m\u001b[48;2;83;62;140m \u001b[0m\u001b[48;2;90;70;151m \u001b[0m\u001b[48;2;88;84;160m \u001b[0m\u001b[48;2;86;96;169m \u001b[0m\u001b[48;2;84;108;177m \u001b[0m\u001b[48;2;82;120;186m \u001b[0m\u001b[48;2;81;132;194m \u001b[0m\u001b[48;2;89;145;187m \u001b[0m\u001b[48;2;101;156;174m \u001b[0m\u001b[48;2;113;168;161m \u001b[0m\u001b[48;2;125;180;148m \u001b[0m\u001b[48;2;137;191;135m \u001b[0m\u001b[48;2;150;204;121m \u001b[0m\u001b[48;2;170;214;98m \u001b[0m\u001b[48;2;191;224;74m \u001b[0m\u001b[48;2;212;235;49m \u001b[0m\u001b[48;2;234;245;25m \u001b[0m\n",
+      "viridis_d\n",
+      "\u001b[48;2;0;44;95m \u001b[0m\u001b[48;2;5;52;106m \u001b[0m\u001b[48;2;10;60;117m \u001b[0m\u001b[48;2;15;67;128m \u001b[0m\u001b[48;2;20;75;138m \u001b[0m\u001b[48;2;26;83;149m \u001b[0m\u001b[48;2;40;92;158m \u001b[0m\u001b[48;2;52;100;167m \u001b[0m\u001b[48;2;65;109;176m \u001b[0m\u001b[48;2;77;117;184m \u001b[0m\u001b[48;2;90;125;193m \u001b[0m\u001b[48;2;108;136;187m \u001b[0m\u001b[48;2;126;146;175m \u001b[0m\u001b[48;2;144;156;163m \u001b[0m\u001b[48;2;163;166;151m \u001b[0m\u001b[48;2;181;177;139m \u001b[0m\u001b[48;2;201;188;126m \u001b[0m\u001b[48;2;212;200;113m \u001b[0m\u001b[48;2;223;213;100m \u001b[0m\u001b[48;2;234;225;87m \u001b[0m\u001b[48;2;244;238;73m \u001b[0m\n",
+      "viridis_p\n",
+      "\u001b[48;2;0;46;98m \u001b[0m\u001b[48;2;3;54;109m \u001b[0m\u001b[48;2;6;62;121m \u001b[0m\u001b[48;2;9;70;132m \u001b[0m\u001b[48;2;11;78;143m \u001b[0m\u001b[48;2;16;86;154m \u001b[0m\u001b[48;2;35;97;163m \u001b[0m\u001b[48;2;52;106;172m \u001b[0m\u001b[48;2;70;116;180m \u001b[0m\u001b[48;2;87;126;189m \u001b[0m\u001b[48;2;104;136;197m \u001b[0m\u001b[48;2;123;146;189m \u001b[0m\u001b[48;2;140;155;174m \u001b[0m\u001b[48;2;157;164;160m \u001b[0m\u001b[48;2;174;173;145m \u001b[0m\u001b[48;2;191;182;130m \u001b[0m\u001b[48;2;209;192;115m \u001b[0m\u001b[48;2;219;202;92m \u001b[0m\u001b[48;2;228;213;69m \u001b[0m\u001b[48;2;237;223;46m \u001b[0m\u001b[48;2;246;234;23m \u001b[0m\n",
+      "viridis_t\n",
+      "\u001b[48;2;38;48;62m \u001b[0m\u001b[48;2;45;56;71m \u001b[0m\u001b[48;2;52;64;79m \u001b[0m\u001b[48;2;59;72;88m \u001b[0m\u001b[48;2;67;80;96m \u001b[0m\u001b[48;2;72;88;105m \u001b[0m\u001b[48;2;57;101;116m \u001b[0m\u001b[48;2;44;112;125m \u001b[0m\u001b[48;2;31;123;135m \u001b[0m\u001b[48;2;18;135;145m \u001b[0m\u001b[48;2;4;146;155m \u001b[0m\u001b[48;2;19;156;161m \u001b[0m\u001b[48;2;45;164;166m \u001b[0m\u001b[48;2;70;173;170m \u001b[0m\u001b[48;2;95;181;175m \u001b[0m\u001b[48;2;121;189;180m \u001b[0m\u001b[48;2;148;198;184m \u001b[0m\u001b[48;2;170;206;191m \u001b[0m\u001b[48;2;191;214;198m \u001b[0m\u001b[48;2;212;223;205m \u001b[0m\u001b[48;2;234;231;212m \u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "apc.cvd.display_all_gradient(apc.gradients.viridis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "apc.cvd.display_all_gradient_lightness(apc.gradients.viridis, figsize=(10, 5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Core\n",
+      "\u001b[48;2;235;237;232m  \u001b[0m\u001b[48;2;237;224;214m  \u001b[0m\u001b[48;2;248;244;241m  \u001b[0m\u001b[48;2;249;252;240m  \u001b[0m\u001b[48;2;255;185;132m  \u001b[0m\u001b[48;2;9;9;10m  \u001b[0m\u001b[48;2;72;75;80m  \u001b[0m\u001b[48;2;138;153;173m  \u001b[0m\u001b[48;2;89;111;116m  \u001b[0m\n",
+      "Neutral\n",
+      "\u001b[48;2;244;251;255m  \u001b[0m\u001b[48;2;247;249;253m  \u001b[0m\u001b[48;2;247;251;239m  \u001b[0m\u001b[48;2;255;253;247m  \u001b[0m\u001b[48;2;255;251;248m  \u001b[0m\u001b[48;2;143;136;133m  \u001b[0m\u001b[48;2;67;65;63m  \u001b[0m\u001b[48;2;41;41;40m  \u001b[0m\n",
+      "Accent\n",
+      "\u001b[48;2;80;136;197m  \u001b[0m\u001b[48;2;242;131;96m  \u001b[0m\u001b[48;2;59;152;134m  \u001b[0m\u001b[48;2;247;184;70m  \u001b[0m\u001b[48;2;122;119;171m  \u001b[0m\u001b[48;2;248;152;174m  \u001b[0m\n",
+      "LightAccent\n",
+      "\u001b[48;2;198;231;244m  \u001b[0m\u001b[48;2;248;197;193m  \u001b[0m\u001b[48;2;181;190;164m  \u001b[0m\u001b[48;2;245;228;190m  \u001b[0m\u001b[48;2;220;191;252m  \u001b[0m\u001b[48;2;245;203;228m  \u001b[0m\n",
+      "AccentExpanded\n",
+      "\u001b[48;2;151;205;120m  \u001b[0m\u001b[48;2;115;181;227m  \u001b[0m\u001b[48;2;255;185;132m  \u001b[0m\u001b[48;2;186;176;168m  \u001b[0m\u001b[48;2;138;153;173m  \u001b[0m\u001b[48;2;200;81;82m  \u001b[0m\n",
+      "LightAccentExpanded\n",
+      "\u001b[48;2;209;234;223m  \u001b[0m\u001b[48;2;186;190;224m  \u001b[0m\u001b[48;2;241;232;218m  \u001b[0m\u001b[48;2;218;211;199m  \u001b[0m\u001b[48;2;218;144;133m  \u001b[0m\u001b[48;2;182;200;212m  \u001b[0m\n",
+      "AccentOrdered\n",
+      "\u001b[48;2;80;136;197m  \u001b[0m\u001b[48;2;242;131;96m  \u001b[0m\u001b[48;2;247;184;70m  \u001b[0m\u001b[48;2;151;205;120m  \u001b[0m\u001b[48;2;122;119;171m  \u001b[0m\u001b[48;2;248;152;174m  \u001b[0m\u001b[48;2;59;152;134m  \u001b[0m\u001b[48;2;200;81;82m  \u001b[0m\u001b[48;2;115;181;227m  \u001b[0m\u001b[48;2;186;176;168m  \u001b[0m\u001b[48;2;138;153;173m  \u001b[0m\u001b[48;2;255;185;132m  \u001b[0m\n",
+      "LightOrdered\n",
+      "\u001b[48;2;198;231;244m  \u001b[0m\u001b[48;2;248;197;193m  \u001b[0m\u001b[48;2;245;228;190m  \u001b[0m\u001b[48;2;181;190;164m  \u001b[0m\u001b[48;2;220;191;252m  \u001b[0m\u001b[48;2;182;200;212m  \u001b[0m\u001b[48;2;218;211;199m  \u001b[0m\u001b[48;2;218;144;133m  \u001b[0m\u001b[48;2;245;203;228m  \u001b[0m\u001b[48;2;209;234;223m  \u001b[0m\u001b[48;2;186;190;224m  \u001b[0m\u001b[48;2;241;232;218m  \u001b[0m\n",
+      "AccentAllOrdered\n",
+      "\u001b[48;2;80;136;197m  \u001b[0m\u001b[48;2;242;131;96m  \u001b[0m\u001b[48;2;247;184;70m  \u001b[0m\u001b[48;2;151;205;120m  \u001b[0m\u001b[48;2;122;119;171m  \u001b[0m\u001b[48;2;248;152;174m  \u001b[0m\u001b[48;2;59;152;134m  \u001b[0m\u001b[48;2;200;81;82m  \u001b[0m\u001b[48;2;115;181;227m  \u001b[0m\u001b[48;2;186;176;168m  \u001b[0m\u001b[48;2;138;153;173m  \u001b[0m\u001b[48;2;255;185;132m  \u001b[0m\u001b[48;2;198;231;244m  \u001b[0m\u001b[48;2;248;197;193m  \u001b[0m\u001b[48;2;245;228;190m  \u001b[0m\u001b[48;2;181;190;164m  \u001b[0m\u001b[48;2;220;191;252m  \u001b[0m\u001b[48;2;182;200;212m  \u001b[0m\u001b[48;2;218;211;199m  \u001b[0m\u001b[48;2;218;144;133m  \u001b[0m\u001b[48;2;245;203;228m  \u001b[0m\u001b[48;2;209;234;223m  \u001b[0m\u001b[48;2;186;190;224m  \u001b[0m\u001b[48;2;241;232;218m  \u001b[0m\n",
+      "Other\n",
+      "\u001b[48;2;52;30;96m  \u001b[0m\u001b[48;2;90;69;150m  \u001b[0m\u001b[48;2;232;116;133m  \u001b[0m\u001b[48;2;234;234;234m  \u001b[0m\u001b[48;2;252;252;252m  \u001b[0m\u001b[48;2;82;24;10m  \u001b[0m\u001b[48;2;77;44;3m  \u001b[0m\u001b[48;2;63;45;92m  \u001b[0m\u001b[48;2;71;17;34m  \u001b[0m\u001b[48;2;9;71;62m  \u001b[0m\u001b[48;2;69;143;153m  \u001b[0m\u001b[48;2;30;72;18m  \u001b[0m\u001b[48;2;199;73;112m  \u001b[0m\u001b[48;2;77;37;0m  \u001b[0m\u001b[48;2;168;94;40m  \u001b[0m\u001b[48;2;253;248;242m  \u001b[0m\n",
+      "Named\n",
+      "\u001b[48;2;255;255;255m  \u001b[0m\u001b[48;2;255;0;0m  \u001b[0m\u001b[48;2;0;255;0m  \u001b[0m\u001b[48;2;0;0;255m  \u001b[0m\u001b[48;2;0;255;255m  \u001b[0m\u001b[48;2;255;0;255m  \u001b[0m\u001b[48;2;255;255;0m  \u001b[0m\u001b[48;2;0;0;0m  \u001b[0m\n",
+      "All\n",
+      "\u001b[48;2;235;237;232m  \u001b[0m\u001b[48;2;237;224;214m  \u001b[0m\u001b[48;2;248;244;241m  \u001b[0m\u001b[48;2;249;252;240m  \u001b[0m\u001b[48;2;255;185;132m  \u001b[0m\u001b[48;2;9;9;10m  \u001b[0m\u001b[48;2;72;75;80m  \u001b[0m\u001b[48;2;138;153;173m  \u001b[0m\u001b[48;2;89;111;116m  \u001b[0m\u001b[48;2;244;251;255m  \u001b[0m\u001b[48;2;247;249;253m  \u001b[0m\u001b[48;2;247;251;239m  \u001b[0m\u001b[48;2;255;253;247m  \u001b[0m\u001b[48;2;255;251;248m  \u001b[0m\u001b[48;2;143;136;133m  \u001b[0m\u001b[48;2;67;65;63m  \u001b[0m\u001b[48;2;41;41;40m  \u001b[0m\u001b[48;2;80;136;197m  \u001b[0m\u001b[48;2;242;131;96m  \u001b[0m\u001b[48;2;59;152;134m  \u001b[0m\u001b[48;2;247;184;70m  \u001b[0m\u001b[48;2;122;119;171m  \u001b[0m\u001b[48;2;248;152;174m  \u001b[0m\u001b[48;2;198;231;244m  \u001b[0m\u001b[48;2;248;197;193m  \u001b[0m\u001b[48;2;181;190;164m  \u001b[0m\u001b[48;2;245;228;190m  \u001b[0m\u001b[48;2;220;191;252m  \u001b[0m\u001b[48;2;245;203;228m  \u001b[0m\u001b[48;2;151;205;120m  \u001b[0m\u001b[48;2;115;181;227m  \u001b[0m\u001b[48;2;255;185;132m  \u001b[0m\u001b[48;2;186;176;168m  \u001b[0m\u001b[48;2;138;153;173m  \u001b[0m\u001b[48;2;200;81;82m  \u001b[0m\u001b[48;2;209;234;223m  \u001b[0m\u001b[48;2;186;190;224m  \u001b[0m\u001b[48;2;241;232;218m  \u001b[0m\u001b[48;2;218;211;199m  \u001b[0m\u001b[48;2;218;144;133m  \u001b[0m\u001b[48;2;182;200;212m  \u001b[0m\u001b[48;2;52;30;96m  \u001b[0m\u001b[48;2;90;69;150m  \u001b[0m\u001b[48;2;232;116;133m  \u001b[0m\u001b[48;2;234;234;234m  \u001b[0m\u001b[48;2;252;252;252m  \u001b[0m\u001b[48;2;82;24;10m  \u001b[0m\u001b[48;2;77;44;3m  \u001b[0m\u001b[48;2;63;45;92m  \u001b[0m\u001b[48;2;71;17;34m  \u001b[0m\u001b[48;2;9;71;62m  \u001b[0m\u001b[48;2;69;143;153m  \u001b[0m\u001b[48;2;30;72;18m  \u001b[0m\u001b[48;2;199;73;112m  \u001b[0m\u001b[48;2;77;37;0m  \u001b[0m\u001b[48;2;168;94;40m  \u001b[0m\u001b[48;2;253;248;242m  \u001b[0m\u001b[48;2;255;255;255m  \u001b[0m\u001b[48;2;255;0;0m  \u001b[0m\u001b[48;2;0;255;0m  \u001b[0m\u001b[48;2;0;0;255m  \u001b[0m\u001b[48;2;0;255;255m  \u001b[0m\u001b[48;2;255;0;255m  \u001b[0m\u001b[48;2;255;255;0m  \u001b[0m\u001b[48;2;0;0;0m  \u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "apc.plot.display_all_palettes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "viridis\n",
+      "\u001b[48;2;52;30;96m \u001b[0m\u001b[48;2;60;38;107m \u001b[0m\u001b[48;2;68;46;118m \u001b[0m\u001b[48;2;75;54;129m \u001b[0m\u001b[48;2;83;62;140m \u001b[0m\u001b[48;2;90;70;151m \u001b[0m\u001b[48;2;88;84;160m \u001b[0m\u001b[48;2;86;96;169m \u001b[0m\u001b[48;2;84;108;177m \u001b[0m\u001b[48;2;82;120;186m \u001b[0m\u001b[48;2;81;132;194m \u001b[0m\u001b[48;2;89;145;187m \u001b[0m\u001b[48;2;101;156;174m \u001b[0m\u001b[48;2;113;168;161m \u001b[0m\u001b[48;2;125;180;148m \u001b[0m\u001b[48;2;137;191;135m \u001b[0m\u001b[48;2;150;204;121m \u001b[0m\u001b[48;2;170;214;98m \u001b[0m\u001b[48;2;191;224;74m \u001b[0m\u001b[48;2;212;235;49m \u001b[0m\u001b[48;2;234;245;25m \u001b[0m\n",
+      "magma\n",
+      "\u001b[48;2;9;9;10m \u001b[0m\u001b[48;2;19;16;27m \u001b[0m\u001b[48;2;29;24;45m \u001b[0m\u001b[48;2;39;31;62m \u001b[0m\u001b[48;2;49;39;79m \u001b[0m\u001b[48;2;59;46;97m \u001b[0m\u001b[48;2;70;54;115m \u001b[0m\u001b[48;2;80;62;133m \u001b[0m\u001b[48;2;90;69;150m \u001b[0m\u001b[48;2;110;76;148m \u001b[0m\u001b[48;2;129;82;145m \u001b[0m\u001b[48;2;151;89;143m \u001b[0m\u001b[48;2;170;96;140m \u001b[0m\u001b[48;2;190;102;138m \u001b[0m\u001b[48;2;210;109;136m \u001b[0m\u001b[48;2;229;115;133m \u001b[0m\u001b[48;2;238;133;133m \u001b[0m\u001b[48;2;244;151;132m \u001b[0m\u001b[48;2;250;169;132m \u001b[0m\u001b[48;2;254;188;135m \u001b[0m\u001b[48;2;250;208;163m \u001b[0m\n",
+      "cividis\n",
+      "\u001b[48;2;41;41;40m \u001b[0m\u001b[48;2;47;49;49m \u001b[0m\u001b[48;2;53;58;58m \u001b[0m\u001b[48;2;58;66;68m \u001b[0m\u001b[48;2;64;75;77m \u001b[0m\u001b[48;2;70;83;86m \u001b[0m\u001b[48;2;76;92;96m \u001b[0m\u001b[48;2;82;101;105m \u001b[0m\u001b[48;2;88;109;114m \u001b[0m\u001b[48;2;102;117;112m \u001b[0m\u001b[48;2;118;124;108m \u001b[0m\u001b[48;2;136;133;102m \u001b[0m\u001b[48;2;152;140;98m \u001b[0m\u001b[48;2;168;147;93m \u001b[0m\u001b[48;2;184;155;88m \u001b[0m\u001b[48;2;200;162;84m \u001b[0m\u001b[48;2;218;170;79m \u001b[0m\u001b[48;2;234;178;74m \u001b[0m\u001b[48;2;247;187;79m \u001b[0m\u001b[48;2;245;202;125m \u001b[0m\u001b[48;2;243;217;172m \u001b[0m\n",
+      "reds\n",
+      "\u001b[48;2;82;24;10m \u001b[0m\u001b[48;2;95;30;18m \u001b[0m\u001b[48;2;108;36;26m \u001b[0m\u001b[48;2;121;43;34m \u001b[0m\u001b[48;2;134;49;42m \u001b[0m\u001b[48;2;147;55;49m \u001b[0m\u001b[48;2;161;62;58m \u001b[0m\u001b[48;2;173;68;66m \u001b[0m\u001b[48;2;186;74;74m \u001b[0m\u001b[48;2;199;81;82m \u001b[0m\u001b[48;2;209;92;85m \u001b[0m\u001b[48;2;219;104;88m \u001b[0m\u001b[48;2;229;115;92m \u001b[0m\u001b[48;2;238;126;95m \u001b[0m\u001b[48;2;243;140;108m \u001b[0m\u001b[48;2;244;156;128m \u001b[0m\u001b[48;2;245;173;150m \u001b[0m\u001b[48;2;247;189;170m \u001b[0m\u001b[48;2;248;205;191m \u001b[0m\u001b[48;2;249;220;211m \u001b[0m\u001b[48;2;251;236;232m \u001b[0m\n",
+      "oranges\n",
+      "\u001b[48;2;77;37;0m \u001b[0m\u001b[48;2;88;44;5m \u001b[0m\u001b[48;2;100;51;10m \u001b[0m\u001b[48;2;111;58;15m \u001b[0m\u001b[48;2;122;65;20m \u001b[0m\u001b[48;2;133;72;25m \u001b[0m\u001b[48;2;146;80;30m \u001b[0m\u001b[48;2;157;87;35m \u001b[0m\u001b[48;2;168;94;40m \u001b[0m\u001b[48;2;178;104;50m \u001b[0m\u001b[48;2;188;114;61m \u001b[0m\u001b[48;2;198;126;72m \u001b[0m\u001b[48;2;208;136;82m \u001b[0m\u001b[48;2;218;146;92m \u001b[0m\u001b[48;2;227;156;103m \u001b[0m\u001b[48;2;237;166;113m \u001b[0m\u001b[48;2;248;177;124m \u001b[0m\u001b[48;2;255;189;139m \u001b[0m\u001b[48;2;254;205;167m \u001b[0m\u001b[48;2;253;220;196m \u001b[0m\u001b[48;2;253;236;224m \u001b[0m\n",
+      "yellows\n",
+      "\u001b[48;2;77;44;3m \u001b[0m\u001b[48;2;88;53;7m \u001b[0m\u001b[48;2;98;61;11m \u001b[0m\u001b[48;2;109;70;15m \u001b[0m\u001b[48;2;119;79;20m \u001b[0m\u001b[48;2;130;87;24m \u001b[0m\u001b[48;2;141;97;28m \u001b[0m\u001b[48;2;152;105;32m \u001b[0m\u001b[48;2;162;114;37m \u001b[0m\u001b[48;2;173;123;41m \u001b[0m\u001b[48;2;183;131;45m \u001b[0m\u001b[48;2;195;141;49m \u001b[0m\u001b[48;2;205;149;53m \u001b[0m\u001b[48;2;216;158;58m \u001b[0m\u001b[48;2;226;167;62m \u001b[0m\u001b[48;2;237;175;66m \u001b[0m\u001b[48;2;247;185;74m \u001b[0m\u001b[48;2;246;200;114m \u001b[0m\u001b[48;2;246;215;155m \u001b[0m\u001b[48;2;245;229;194m \u001b[0m\u001b[48;2;249;241;223m \u001b[0m\n",
+      "greens\n",
+      "\u001b[48;2;30;72;18m \u001b[0m\u001b[48;2;38;81;25m \u001b[0m\u001b[48;2;46;90;32m \u001b[0m\u001b[48;2;54;99;39m \u001b[0m\u001b[48;2;63;108;45m \u001b[0m\u001b[48;2;71;117;52m \u001b[0m\u001b[48;2;79;126;60m \u001b[0m\u001b[48;2;88;135;67m \u001b[0m\u001b[48;2;96;144;73m \u001b[0m\u001b[48;2;104;153;80m \u001b[0m\u001b[48;2;112;162;87m \u001b[0m\u001b[48;2;121;172;95m \u001b[0m\u001b[48;2;129;181;101m \u001b[0m\u001b[48;2;137;190;108m \u001b[0m\u001b[48;2;145;199;115m \u001b[0m\u001b[48;2;156;207;126m \u001b[0m\u001b[48;2;173;215;148m \u001b[0m\u001b[48;2;189;223;169m \u001b[0m\u001b[48;2;204;230;190m \u001b[0m\u001b[48;2;220;237;211m \u001b[0m\u001b[48;2;236;245;231m \u001b[0m\n",
+      "teals\n",
+      "\u001b[48;2;9;71;62m \u001b[0m\u001b[48;2;15;80;70m \u001b[0m\u001b[48;2;20;89;78m \u001b[0m\u001b[48;2;26;98;86m \u001b[0m\u001b[48;2;31;107;94m \u001b[0m\u001b[48;2;37;116;102m \u001b[0m\u001b[48;2;43;126;111m \u001b[0m\u001b[48;2;49;135;119m \u001b[0m\u001b[48;2;54;144;127m \u001b[0m\u001b[48;2;61;153;136m \u001b[0m\u001b[48;2;77;161;145m \u001b[0m\u001b[48;2;94;170;155m \u001b[0m\u001b[48;2;110;178;165m \u001b[0m\u001b[48;2;125;186;175m \u001b[0m\u001b[48;2;141;195;184m \u001b[0m\u001b[48;2;157;203;194m \u001b[0m\u001b[48;2;174;211;204m \u001b[0m\u001b[48;2;189;220;214m \u001b[0m\u001b[48;2;205;228;223m \u001b[0m\u001b[48;2;221;236;233m \u001b[0m\u001b[48;2;236;244;242m \u001b[0m\n",
+      "blues\n",
+      "\u001b[48;2;52;30;96m \u001b[0m\u001b[48;2;55;41;106m \u001b[0m\u001b[48;2;58;51;116m \u001b[0m\u001b[48;2;60;62;126m \u001b[0m\u001b[48;2;63;72;136m \u001b[0m\u001b[48;2;66;83;147m \u001b[0m\u001b[48;2;69;95;158m \u001b[0m\u001b[48;2;72;105;168m \u001b[0m\u001b[48;2;75;116;178m \u001b[0m\u001b[48;2;77;126;188m \u001b[0m\u001b[48;2;81;137;198m \u001b[0m\u001b[48;2;90;149;206m \u001b[0m\u001b[48;2;99;160;213m \u001b[0m\u001b[48;2;108;171;221m \u001b[0m\u001b[48;2;118;182;227m \u001b[0m\u001b[48;2;137;192;231m \u001b[0m\u001b[48;2;157;203;235m \u001b[0m\u001b[48;2;176;213;238m \u001b[0m\u001b[48;2;195;223;242m \u001b[0m\u001b[48;2;214;232;245m \u001b[0m\u001b[48;2;233;242;249m \u001b[0m\n",
+      "purples\n",
+      "\u001b[48;2;63;45;92m \u001b[0m\u001b[48;2;70;54;101m \u001b[0m\u001b[48;2;77;62;111m \u001b[0m\u001b[48;2;84;71;120m \u001b[0m\u001b[48;2;91;80;129m \u001b[0m\u001b[48;2;98;89;138m \u001b[0m\u001b[48;2;105;98;149m \u001b[0m\u001b[48;2;112;107;158m \u001b[0m\u001b[48;2;119;115;167m \u001b[0m\u001b[48;2;127;125;175m \u001b[0m\u001b[48;2;136;135;183m \u001b[0m\u001b[48;2;146;145;191m \u001b[0m\u001b[48;2;154;155;198m \u001b[0m\u001b[48;2;163;165;205m \u001b[0m\u001b[48;2;172;175;213m \u001b[0m\u001b[48;2;181;185;220m \u001b[0m\u001b[48;2;192;196;227m \u001b[0m\u001b[48;2;204;207;232m \u001b[0m\u001b[48;2;216;218;237m \u001b[0m\u001b[48;2;228;230;242m \u001b[0m\u001b[48;2;240;241;247m \u001b[0m\n",
+      "magentas\n",
+      "\u001b[48;2;71;17;34m \u001b[0m\u001b[48;2;85;23;42m \u001b[0m\u001b[48;2;98;29;51m \u001b[0m\u001b[48;2;112;35;59m \u001b[0m\u001b[48;2;126;41;67m \u001b[0m\u001b[48;2;139;47;76m \u001b[0m\u001b[48;2;154;53;85m \u001b[0m\u001b[48;2;168;59;93m \u001b[0m\u001b[48;2;182;65;101m \u001b[0m\u001b[48;2;195;71;110m \u001b[0m\u001b[48;2;205;82;119m \u001b[0m\u001b[48;2;213;96;130m \u001b[0m\u001b[48;2;221;109;140m \u001b[0m\u001b[48;2;229;122;150m \u001b[0m\u001b[48;2;237;135;160m \u001b[0m\u001b[48;2;245;148;171m \u001b[0m\u001b[48;2;249;165;184m \u001b[0m\u001b[48;2;249;182;198m \u001b[0m\u001b[48;2;250;200;211m \u001b[0m\u001b[48;2;251;217;225m \u001b[0m\u001b[48;2;251;235;238m \u001b[0m\n",
+      "aegean_amber\n",
+      "\u001b[48;2;80;136;197m \u001b[0m\u001b[48;2;96;147;202m \u001b[0m\u001b[48;2;112;158;207m \u001b[0m\u001b[48;2;129;169;213m \u001b[0m\u001b[48;2;145;180;218m \u001b[0m\u001b[48;2;161;191;223m \u001b[0m\u001b[48;2;178;202;228m \u001b[0m\u001b[48;2;195;213;234m \u001b[0m\u001b[48;2;211;224;239m \u001b[0m\u001b[48;2;227;235;244m \u001b[0m\u001b[48;2;243;246;249m \u001b[0m\u001b[48;2;251;246;244m \u001b[0m\u001b[48;2;251;234;229m \u001b[0m\u001b[48;2;250;223;215m \u001b[0m\u001b[48;2;249;212;200m \u001b[0m\u001b[48;2;248;200;185m \u001b[0m\u001b[48;2;247;188;169m \u001b[0m\u001b[48;2;246;177;155m \u001b[0m\u001b[48;2;245;165;140m \u001b[0m\u001b[48;2;244;154;125m \u001b[0m\u001b[48;2;243;142;111m \u001b[0m\n",
+      "aster_canary\n",
+      "\u001b[48;2;122;119;171m \u001b[0m\u001b[48;2;134;132;179m \u001b[0m\u001b[48;2;146;144;186m \u001b[0m\u001b[48;2;159;157;194m \u001b[0m\u001b[48;2;171;169;201m \u001b[0m\u001b[48;2;183;182;209m \u001b[0m\u001b[48;2;196;195;217m \u001b[0m\u001b[48;2;209;208;225m \u001b[0m\u001b[48;2;221;220;233m \u001b[0m\u001b[48;2;233;233;240m \u001b[0m\u001b[48;2;245;245;248m \u001b[0m\u001b[48;2;252;249;243m \u001b[0m\u001b[48;2;251;242;226m \u001b[0m\u001b[48;2;251;236;208m \u001b[0m\u001b[48;2;250;229;191m \u001b[0m\u001b[48;2;250;223;174m \u001b[0m\u001b[48;2;249;216;156m \u001b[0m\u001b[48;2;249;210;139m \u001b[0m\u001b[48;2;248;203;121m \u001b[0m\u001b[48;2;248;197;104m \u001b[0m\u001b[48;2;247;190;87m \u001b[0m\n",
+      "lime_rose\n",
+      "\u001b[48;2;151;205;120m \u001b[0m\u001b[48;2;161;209;132m \u001b[0m\u001b[48;2;170;214;145m \u001b[0m\u001b[48;2;180;218;157m \u001b[0m\u001b[48;2;189;223;170m \u001b[0m\u001b[48;2;199;227;182m \u001b[0m\u001b[48;2;209;232;196m \u001b[0m\u001b[48;2;218;236;208m \u001b[0m\u001b[48;2;228;241;220m \u001b[0m\u001b[48;2;237;245;233m \u001b[0m\u001b[48;2;247;250;245m \u001b[0m\u001b[48;2;252;247;248m \u001b[0m\u001b[48;2;251;237;241m \u001b[0m\u001b[48;2;251;228;233m \u001b[0m\u001b[48;2;251;219;226m \u001b[0m\u001b[48;2;250;209;219m \u001b[0m\u001b[48;2;250;199;211m \u001b[0m\u001b[48;2;250;190;203m \u001b[0m\u001b[48;2;249;180;196m \u001b[0m\u001b[48;2;249;171;189m \u001b[0m\u001b[48;2;248;161;181m \u001b[0m\n",
+      "seaweed_tangerine\n",
+      "\u001b[48;2;59;152;134m \u001b[0m\u001b[48;2;77;161;145m \u001b[0m\u001b[48;2;95;171;156m \u001b[0m\u001b[48;2;113;180;167m \u001b[0m\u001b[48;2;132;190;178m \u001b[0m\u001b[48;2;150;199;190m \u001b[0m\u001b[48;2;170;209;202m \u001b[0m\u001b[48;2;188;219;213m \u001b[0m\u001b[48;2;206;228;224m \u001b[0m\u001b[48;2;224;237;235m \u001b[0m\u001b[48;2;242;247;246m \u001b[0m\u001b[48;2;252;249;246m \u001b[0m\u001b[48;2;252;242;235m \u001b[0m\u001b[48;2;253;236;223m \u001b[0m\u001b[48;2;253;230;212m \u001b[0m\u001b[48;2;253;223;201m \u001b[0m\u001b[48;2;254;217;188m \u001b[0m\u001b[48;2;254;210;177m \u001b[0m\u001b[48;2;254;204;166m \u001b[0m\u001b[48;2;254;198;155m \u001b[0m\u001b[48;2;255;191;143m \u001b[0m\n",
+      "poppies\n",
+      "\u001b[48;2;52;30;96m \u001b[0m\u001b[48;2;58;51;116m \u001b[0m\u001b[48;2;63;72;136m \u001b[0m\u001b[48;2;69;94;157m \u001b[0m\u001b[48;2;74;115;177m \u001b[0m\u001b[48;2;80;136;197m \u001b[0m\u001b[48;2;99;160;213m \u001b[0m\u001b[48;2;118;182;227m \u001b[0m\u001b[48;2;156;202;234m \u001b[0m\u001b[48;2;194;222;241m \u001b[0m\u001b[48;2;231;241;248m \u001b[0m\u001b[48;2;251;235;230m \u001b[0m\u001b[48;2;248;203;189m \u001b[0m\u001b[48;2;245;172;148m \u001b[0m\u001b[48;2;243;140;108m \u001b[0m\u001b[48;2;229;115;92m \u001b[0m\u001b[48;2;208;91;85m \u001b[0m\u001b[48;2;185;74;73m \u001b[0m\u001b[48;2;159;61;57m \u001b[0m\u001b[48;2;134;49;42m \u001b[0m\u001b[48;2;108;36;26m \u001b[0m\n",
+      "pansies\n",
+      "\u001b[48;2;63;45;92m \u001b[0m\u001b[48;2;77;62;111m \u001b[0m\u001b[48;2;91;80;129m \u001b[0m\u001b[48;2;105;97;148m \u001b[0m\u001b[48;2;119;115;166m \u001b[0m\u001b[48;2;135;134;182m \u001b[0m\u001b[48;2;154;155;198m \u001b[0m\u001b[48;2;172;175;213m \u001b[0m\u001b[48;2;191;195;226m \u001b[0m\u001b[48;2;215;217;236m \u001b[0m\u001b[48;2;239;240;247m \u001b[0m\u001b[48;2;248;240;220m \u001b[0m\u001b[48;2;246;214;151m \u001b[0m\u001b[48;2;247;184;71m \u001b[0m\u001b[48;2;226;167;62m \u001b[0m\u001b[48;2;205;149;53m \u001b[0m\u001b[48;2;182;131;44m \u001b[0m\u001b[48;2;161;113;36m \u001b[0m\u001b[48;2;140;96;28m \u001b[0m\u001b[48;2;119;79;20m \u001b[0m\u001b[48;2;98;61;11m \u001b[0m\n",
+      "dahlias\n",
+      "\u001b[48;2;30;72;18m \u001b[0m\u001b[48;2;46;90;32m \u001b[0m\u001b[48;2;63;108;45m \u001b[0m\u001b[48;2;79;126;59m \u001b[0m\u001b[48;2;95;144;73m \u001b[0m\u001b[48;2;111;161;87m \u001b[0m\u001b[48;2;129;181;101m \u001b[0m\u001b[48;2;145;199;115m \u001b[0m\u001b[48;2;171;215;147m \u001b[0m\u001b[48;2;203;229;188m \u001b[0m\u001b[48;2;235;244;230m \u001b[0m\u001b[48;2;251;233;237m \u001b[0m\u001b[48;2;250;198;210m \u001b[0m\u001b[48;2;248;163;183m \u001b[0m\u001b[48;2;237;135;160m \u001b[0m\u001b[48;2;221;109;140m \u001b[0m\u001b[48;2;204;81;119m \u001b[0m\u001b[48;2;181;65;101m \u001b[0m\u001b[48;2;153;53;84m \u001b[0m\u001b[48;2;126;41;67m \u001b[0m\u001b[48;2;98;29;51m \u001b[0m\n",
+      "lillies\n",
+      "\u001b[48;2;9;71;62m \u001b[0m\u001b[48;2;20;89;78m \u001b[0m\u001b[48;2;31;107;94m \u001b[0m\u001b[48;2;43;125;110m \u001b[0m\u001b[48;2;54;144;127m \u001b[0m\u001b[48;2;76;161;144m \u001b[0m\u001b[48;2;110;178;165m \u001b[0m\u001b[48;2;141;195;184m \u001b[0m\u001b[48;2;172;211;203m \u001b[0m\u001b[48;2;204;227;222m \u001b[0m\u001b[48;2;235;243;242m \u001b[0m\u001b[48;2;253;235;221m \u001b[0m\u001b[48;2;254;203;165m \u001b[0m\u001b[48;2;247;177;123m \u001b[0m\u001b[48;2;227;156;103m \u001b[0m\u001b[48;2;208;136;82m \u001b[0m\u001b[48;2;187;114;60m \u001b[0m\u001b[48;2;167;93;40m \u001b[0m\u001b[48;2;145;79;30m \u001b[0m\u001b[48;2;122;65;20m \u001b[0m\u001b[48;2;100;51;10m \u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "apc.plot.display_all_gradients()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "grad = apc.Gradient(\n",
+    "    \"test_gradient\",\n",
+    "    [apc.pitch, apc.grape, apc.aegean, apc.vitalblue, apc.bluesky, apc.paper],\n",
+    ")\n",
+    "interpolated_grad = grad.interpolate_lightness()\n",
+    "\n",
+    "apc.plot.plot_gradient_lightness([grad, interpolated_grad], figsize=(10, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[48;2;82;24;10m \u001b[0m\u001b[48;2;108;36;26m \u001b[0m\u001b[48;2;134;49;42m \u001b[0m\u001b[48;2;159;61;57m \u001b[0m\u001b[48;2;185;74;73m \u001b[0m\u001b[48;2;208;91;85m \u001b[0m\u001b[48;2;229;115;92m \u001b[0m\u001b[48;2;243;140;108m \u001b[0m\u001b[48;2;245;172;148m \u001b[0m\u001b[48;2;248;203;189m \u001b[0m\u001b[48;2;251;235;230m \u001b[0m\u001b[48;2;231;241;248m \u001b[0m\u001b[48;2;194;222;241m \u001b[0m\u001b[48;2;156;202;234m \u001b[0m\u001b[48;2;118;182;227m \u001b[0m\u001b[48;2;99;160;213m \u001b[0m\u001b[48;2;80;136;197m \u001b[0m\u001b[48;2;74;115;177m \u001b[0m\u001b[48;2;69;94;157m \u001b[0m\u001b[48;2;63;72;136m \u001b[0m\u001b[48;2;58;51;116m \u001b[0m\n",
+       "\u001b[48;2;82;24;10m  \u001b[0m\u001b[38;2;82;24;10m redwood   #52180A\u001b[0m 0.0\n",
+       "\u001b[48;2;200;81;82m  \u001b[0m\u001b[38;2;200;81;82m dragon    #C85152\u001b[0m 0.215\n",
+       "\u001b[48;2;242;131;96m  \u001b[0m\u001b[38;2;242;131;96m amber     #F28360\u001b[0m 0.32\n",
+       "\u001b[48;2;252;252;252m  \u001b[0m\u001b[38;2;252;252;252m paper     #FCFCFC\u001b[0m 0.5\n",
+       "\u001b[48;2;115;181;227m  \u001b[0m\u001b[38;2;115;181;227m vitalblue #73B5E3\u001b[0m 0.6699999999999999\n",
+       "\u001b[48;2;80;136;197m  \u001b[0m\u001b[38;2;80;136;197m aegean    #5088C5\u001b[0m 0.765\n",
+       "\u001b[48;2;52;30;96m  \u001b[0m\u001b[38;2;52;30;96m concord   #341E60\u001b[0m 1.0"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "apc.gradients.reds + apc.gradients.blues.reverse()"
+   ]
   }
  ],
  "metadata": {