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Merge pull request #5 from nanxstats/colormap
Add custom color scale that supports arbitrary number of topics
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,9 @@ | ||
| # Colors | ||
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| ::: tinytopics.colors | ||
| options: | ||
| members: | ||
| - pal_tinytopics | ||
| - scale_color_tinytopics | ||
| show_root_heading: true | ||
| show_source: false |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,92 @@ | ||
| import numpy as np | ||
| from matplotlib import colors | ||
| from skimage import color | ||
| from scipy.interpolate import make_interp_spline | ||
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| def pal_tinytopics(format="hex"): | ||
| """ | ||
| The tinytopics 10 color palette. | ||
| A rearranged version of the original Observable 10 palette. | ||
| Reordered to align with the color ordering of the D3 Category 10 palette, | ||
| also known as `matplotlib.cm.tab10`. | ||
| The rearrangement aims to improve perceptual familiarity and color harmony, | ||
| especially when used in a context where color interpolation is needed. | ||
| Args: | ||
| format (str, optional): | ||
| Returned color format. Options are: | ||
| `hex`: Hex strings (default). | ||
| `rgb`: Array of RGB values. | ||
| `lab`: Array of CIELAB values. | ||
| Returns: | ||
| (list or np.ndarray): | ||
| - If `format='hex'`, returns a list of hex color strings. | ||
| - If `format='rgb'`, returns an Nx3 numpy array of RGB values. | ||
| - If `format='lab'`, returns an Nx3 numpy array of CIELAB values. | ||
| """ | ||
| tinytopics_10_colors_hex = [ | ||
| "#4269D0", # Blue | ||
| "#EFB118", # Orange | ||
| "#3CA951", # Green | ||
| "#FF725C", # Red | ||
| "#A463F2", # Purple | ||
| "#9C6B4E", # Brown | ||
| "#FF8AB7", # Pink | ||
| "#9498A0", # Gray | ||
| "#6CC5B0", # Cyan | ||
| "#97BBF5", # Light Blue | ||
| ] | ||
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| if format == "hex": | ||
| return tinytopics_10_colors_hex | ||
| elif format == "rgb": | ||
| # Convert hex to RGB | ||
| return np.array([colors.to_rgb(color) for color in tinytopics_10_colors_hex]) | ||
| elif format == "lab": | ||
| # Convert hex to RGB, then to CIELAB | ||
| rgb_colors = np.array( | ||
| [colors.to_rgb(color) for color in tinytopics_10_colors_hex] | ||
| ) | ||
| return color.rgb2lab(rgb_colors.reshape(1, -1, 3)).reshape(-1, 3) | ||
| else: | ||
| raise ValueError("Format must be 'hex', 'rgb', or 'lab'.") | ||
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| def scale_color_tinytopics(n): | ||
| """ | ||
| A tinytopics 10 color scale. If > 10 colors are required, will generate | ||
| an interpolated color palette based on the 10-color palette in the CIELAB | ||
| color space using B-splines. | ||
| Args: | ||
| n (int): The number of colors needed. | ||
| Returns: | ||
| (matplotlib.colors.ListedColormap): A colormap with n colors, | ||
| possibly interpolated from the 10 colors. | ||
| """ | ||
| base_rgb_colors = pal_tinytopics(format="rgb") | ||
| base_lab_colors = pal_tinytopics(format="lab") | ||
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| # If interpolation is NOT needed, return the first n colors directly | ||
| if n <= len(base_rgb_colors): | ||
| return colors.ListedColormap(base_rgb_colors[:n]) | ||
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| # If interpolation is needed, interpolate in the CIELAB space | ||
| # for perceptually uniform colors | ||
| additional_colors_needed = n - 10 | ||
| # Original positions of the 10 base colors | ||
| x = np.linspace(0, 1, len(base_lab_colors)) | ||
| # B-spline interpolator in the CIELAB space | ||
| bspline = make_interp_spline(x, base_lab_colors, k=3) | ||
| # Interpolated positions for new colors | ||
| x_new = np.linspace(0, 1, additional_colors_needed + 10) | ||
| interpolated_lab = bspline(x_new) | ||
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| # Convert interpolated LAB colors back to RGB | ||
| interpolated_rgb = color.lab2rgb(interpolated_lab.reshape(1, -1, 3)).reshape(-1, 3) | ||
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| return colors.ListedColormap(interpolated_rgb) |
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