Tweaks and fixes

purple/purple.py

@@ -1,5 +1,7 @@
 import numpy as np
+import math
 import matplotlib.pyplot as plt
+from wavelength import wavelength_to_ansi
 
 def gaussian_sensitivity(peak1, peak2, sigma, wavelengths, depth=2):
     if depth <= 0: return 0
@@ -19,12 +21,13 @@ sigma_starling = 35
 human_peaks = {(437, 315): 'blue', (533,345): 'green', (564,355): 'red'}
 
 # Starling cone sensitivities (including UV)
-starling_peaks = {(362,250): 'violet', (449,320): 'blue', (504,340): 'green', (563,360): 'red'}
+starling_peaks = {(362,250): 'violet', (449,320): 'cyan', (504,340): 'green', (563,360): 'red'}
 
 ANSI_COLORS = {
     "blue": "\033[34m",
     "red": "\033[31m",
     "green": "\033[32m",
+    "cyan": "\033[36m",
     "violet": "\033[38;5;177m",
     "purple": "\033[38;5;177m",
 }
@@ -53,27 +56,40 @@ plt.tight_layout()
 plt.savefig("retinal_cones.png") 
 '''
 
+def bar(progress : float, width : int):
+    progress = min(1, max(0, progress))
+    whole_width = math.floor(progress * width)
+    remainder_width = (progress * width) % 1
+    part_width = math.floor(remainder_width * 8)
+    part_char = [" ", "▏", "▎", "▍", "▌", "▋", "▊", "▉"][part_width]
+    if (width - whole_width - 1) < 0:
+        part_char = ""
+    line = "\033[48;5;237m" + "█" * whole_width + part_char + " " * (width - whole_width - 1) + "\033[40m"
+    return line
+
 def human_activations(color_name, wavelengths, verbose=False):
     raw_activations = {}
     for (peak1,peak2), color in human_peaks.items():
         raw_activations[color] = gaussian_sensitivity(peak1, peak2, sigma_human, wavelengths)
 
     if verbose:
-        print(f"\033[1mHumans\033[m see {ANSI_COLORS[color_name]}{color_name}\033[m as:")
+        print(f"\033[1mHumans\033[m see {color_name}\033[m as:")
 
     activations = []
     for color,levels in raw_activations.items():
         k = sum(levels)/sum(sum(l) for l in raw_activations.values())
         activations.append(k)
         if verbose:
-            print(f"{ANSI_COLORS[color]}{color}: \033[10G {k:%}\033[m")
+            print(f"{ANSI_COLORS[color]}{color}: \033[10G {k:%} \033[30G{bar(k, 30)}\033[m")
     if verbose:
         print("")
     vec = np.array(activations)
     return vec / np.linalg.norm(vec)
-    
+
-human_activations("violet", np.array([380]), verbose=True)
+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"
-human_activations("purple", np.array([623, 462]), verbose=True)
+colored_purple += " (\033[38;2;255;78;0m630nm\033[m & \033[38;2;0;146;255m465nm\033[m)"
+human_activations("\033[38;5;177mviolet (384nm)\033[m", np.array([384]), verbose=True)
+human_activations(colored_purple, np.array([630, 465]), verbose=True)
 print("\nThey look the same to us!")
 
 section_break()
@@ -84,21 +100,21 @@ def starling_activations(color_name, wavelengths, verbose=False):
         raw_activations[color] = gaussian_sensitivity(peak1, peak2, sigma_starling, wavelengths)
 
     if verbose:
-        print(f"\033[1mStarlings\033[m see {ANSI_COLORS[color_name]}{color_name}\033[m as:")
+        print(f"\033[1mStarlings\033[m see {color_name} as:")
 
     activations = []
     for color,levels in raw_activations.items():
         k = sum(levels)/sum(sum(l) for l in raw_activations.values())
         activations.append(k)
         if verbose:
-            print(f"{ANSI_COLORS[color]}{color}: \033[10G {k:%}\033[m")
+            print(f"{ANSI_COLORS[color]}{color}: \033[10G {k:%} \033[30G{bar(k, 30)}\033[m")
     if verbose:
         print("")
     vec = np.array(activations)
     return vec / np.linalg.norm(vec)
 
-starling_activations("violet", np.array([380]), verbose=True)
+starling_activations("\033[38;5;177mviolet (384nm)\033[m", np.array([384]), verbose=True)
-starling_purple = starling_activations("purple", np.array([623, 455]), verbose=True)
+starling_purple = starling_activations(colored_purple, np.array([630, 465]), verbose=True)
 print("\nThey look completely differe to a starling!")
 
 
@@ -106,68 +122,23 @@ section_break()
 
 print("...But what pure wavelength looks like purple?")
 
-def wavelength_to_rgb(wavelength, gamma=0.8):
-    '''
-    This converts a given wavelength of light to an 
-    approximate RGB color value. The wavelength must be given
-    in nanometers in the range from 380 nm through 750 nm
-    (789 THz through 400 THz).
-    Based on code by Dan Bruton
-    http://www.physics.sfasu.edu/astro/color/spectra.html
-    '''
-    wavelength = float(wavelength)
-    if wavelength >= 380 and wavelength <= 440:
-        attenuation = 0.3 + 0.7 * (wavelength - 380) / (440 - 380)
-        R = ((-(wavelength - 440) / (440 - 380)) * attenuation) ** gamma
-        G = 0.0
-        B = (1.0 * attenuation) ** gamma
-    elif wavelength >= 440 and wavelength <= 490:
-        R = 0.0
-        G = ((wavelength - 440) / (490 - 440)) ** gamma
-        B = 1.0
-    elif wavelength >= 490 and wavelength <= 510:
-        R = 0.0
-        G = 1.0
-        B = (-(wavelength - 510) / (510 - 490)) ** gamma
-    elif wavelength >= 510 and wavelength <= 580:
-        R = ((wavelength - 510) / (580 - 510)) ** gamma
-        G = 1.0
-        B = 0.0
-    elif wavelength >= 580 and wavelength <= 645:
-        R = 1.0
-        G = (-(wavelength - 645) / (645 - 580)) ** gamma
-        B = 0.0
-    elif wavelength >= 645 and wavelength <= 750:
-        attenuation = 0.3 + 0.7 * (750 - wavelength) / (750 - 645)
-        R = (1.0 * attenuation) ** gamma
-        G = 0.0
-        B = 0.0
-    else:
-        R = 0.0
-        G = 0.0
-        B = 0.0
-    R *= 255
-    G *= 255
-    B *= 255
-    return (int(R), int(G), int(B))
-
-print("")
 closest_wavelength = None
 best_dist = None
-import time
+for wavelength in range(100,700,1):
-for wavelength in range(100,700,2):
     activations = starling_activations("???", np.array([wavelength]), verbose=False)
     # dist = np.linalg.norm(starling_purple - activations)
     dist = np.dot(starling_purple, activations)
     if closest_wavelength is None or dist > best_dist:
         closest_wavelength, best_dist = wavelength, dist
-    r,g,b = wavelength_to_rgb(wavelength)
     #print(f"\033[48;2;{r};{g};{b}m  Scanning: {closest_wavelength}nm...  \033[m dist = {dist}", end="\033[K\n", flush=True)
 
 section_break()
 
-r,g,b = wavelength_to_rgb(closest_wavelength)
+color = wavelength_to_ansi(closest_wavelength)
-print(f"\033[48;2;{r};{g};{b}m  WINNER: {closest_wavelength}nm! (blue)  \033[m\n\n")
+print(f"{color}\033[30m  WINNER: {closest_wavelength}nm! (blue)  \033[m\n\n")
+
+starling_activations(f"{color}blue ({closest_wavelength}nm)\033[m", np.array([closest_wavelength]), verbose=True)
+print(f"Which looks more like {colored_purple} than any other pure wavelength!")
 
 
 # import subprocess