147 lines
5.2 KiB
Python
147 lines
5.2 KiB
Python
import numpy as np
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import math
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import matplotlib.pyplot as plt
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from wavelength import wavelength_to_ansi
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def gaussian_sensitivity(peak1, peak2, sigma, wavelengths, depth=2):
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if depth <= 0: return 0
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return (np.exp(-((wavelengths - peak1) ** 2) / (2 * sigma ** 2))
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+ .25*np.exp(-((wavelengths - peak2) ** 2) / (2 * sigma ** 2)))
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def section_break():
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input("\n" + "="*80 + "\n")
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wavelengths = np.linspace(300, 700, 500)
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# Estimated sigma values for human and bird cones
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sigma_human = 40
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sigma_starling = 35
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# Human cone sensitivities
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human_peaks = {(437, 315): 'blue', (533,345): 'green', (564,355): 'red'}
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# Starling cone sensitivities (including UV)
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starling_peaks = {(362,250): 'violet', (449,320): 'cyan', (504,340): 'green', (563,360): 'red'}
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ANSI_COLORS = {
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"blue": "\033[34m",
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"red": "\033[31m",
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"green": "\033[32m",
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"cyan": "\033[36m",
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"violet": "\033[38;5;177m",
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"purple": "\033[38;5;177m",
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}
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'''
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plt.figure(figsize=(10, 6))
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# Plot human sensitivities
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plt.subplot(2, 1, 1)
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for (peak1, peak2), color in human_peaks.items():
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plt.plot(wavelengths, gaussian_sensitivity(peak1, peak2, sigma_human, wavelengths), color=color, label=f'λ_max = {peak1} nm')
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plt.title("Human Retinal Cone Sensitivity")
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plt.ylabel("Relative Absorption")
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plt.legend()
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# Plot starling sensitivities
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plt.subplot(2, 1, 2)
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for (peak1,peak2), color in starling_peaks.items():
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plt.plot(wavelengths, gaussian_sensitivity(peak1, peak2, sigma_starling, wavelengths), color=color, label=f'λ_max = {peak1} nm')
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plt.title("European Starling Retinal Cone Sensitivity")
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plt.xlabel("Wavelength (nm)")
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plt.ylabel("Relative Absorption")
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plt.legend()
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plt.tight_layout()
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plt.savefig("retinal_cones.png")
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'''
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def bar(progress : float, width : int):
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progress = min(1, max(0, progress))
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whole_width = math.floor(progress * width)
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remainder_width = (progress * width) % 1
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part_width = math.floor(remainder_width * 8)
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part_char = [" ", "▏", "▎", "▍", "▌", "▋", "▊", "▉"][part_width]
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if (width - whole_width - 1) < 0:
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part_char = ""
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line = "\033[48;5;237m" + "█" * whole_width + part_char + " " * (width - whole_width - 1) + "\033[40m"
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return line
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def human_activations(color_name, wavelengths, verbose=False):
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raw_activations = {}
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for (peak1,peak2), color in human_peaks.items():
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raw_activations[color] = gaussian_sensitivity(peak1, peak2, sigma_human, wavelengths)
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if verbose:
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print(f"\033[1mHumans\033[m see {color_name}\033[m as:")
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activations = []
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for color,levels in raw_activations.items():
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k = sum(levels)/sum(sum(l) for l in raw_activations.values())
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activations.append(k)
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if verbose:
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print(f"{ANSI_COLORS[color]}{color}: \033[10G {k:%} \033[30G{bar(k, 30)}\033[m")
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if verbose:
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print("")
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vec = np.array(activations)
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return vec / np.linalg.norm(vec)
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colored_purple = "".join(f"\033[38;2;255;78;0m{c}" if i % 2 == 0 else f"\033[38;2;0;146;255m{c}" for i,c in enumerate("purple")) + "\033[m"
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colored_purple += " (\033[38;2;255;78;0m630nm\033[m & \033[38;2;0;146;255m465nm\033[m)"
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human_activations("\033[38;5;177mviolet (384nm)\033[m", np.array([384]), verbose=True)
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human_activations(colored_purple, np.array([630, 465]), verbose=True)
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print("\nThey look the same to us!")
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section_break()
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def starling_activations(color_name, wavelengths, verbose=False):
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raw_activations = {}
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for (peak1,peak2), color in starling_peaks.items():
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raw_activations[color] = gaussian_sensitivity(peak1, peak2, sigma_starling, wavelengths)
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if verbose:
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print(f"\033[1mStarlings\033[m see {color_name} as:")
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activations = []
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for color,levels in raw_activations.items():
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k = sum(levels)/sum(sum(l) for l in raw_activations.values())
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activations.append(k)
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if verbose:
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print(f"{ANSI_COLORS[color]}{color}: \033[10G {k:%} \033[30G{bar(k, 30)}\033[m")
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if verbose:
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print("")
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vec = np.array(activations)
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return vec / np.linalg.norm(vec)
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starling_activations("\033[38;5;177mviolet (384nm)\033[m", np.array([384]), verbose=True)
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starling_purple = starling_activations(colored_purple, np.array([630, 465]), verbose=True)
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print("\nThey look completely differe to a starling!")
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section_break()
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print("...But what pure wavelength looks like purple?")
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closest_wavelength = None
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best_dist = None
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for wavelength in range(100,700,1):
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activations = starling_activations("???", np.array([wavelength]), verbose=False)
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# dist = np.linalg.norm(starling_purple - activations)
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dist = np.dot(starling_purple, activations)
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if closest_wavelength is None or dist > best_dist:
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closest_wavelength, best_dist = wavelength, dist
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#print(f"\033[48;2;{r};{g};{b}m Scanning: {closest_wavelength}nm... \033[m dist = {dist}", end="\033[K\n", flush=True)
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section_break()
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color = wavelength_to_ansi(closest_wavelength)
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print(f"{color}\033[30m WINNER: {closest_wavelength}nm! (blue) \033[m\n\n")
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starling_activations(f"{color}blue ({closest_wavelength}nm)\033[m", np.array([closest_wavelength]), verbose=True)
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print(f"Which looks more like {colored_purple} than any other pure wavelength!")
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# import subprocess
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# subprocess.run(["sxiv", "retinal_cones.png"])
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# plt.show()
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