User Registration Dates for 2025

Fired up the autism engine and wrote a script that interpolates between the starting magenta and the ending red, while passing through Kiwi Green at 7 years (ie, 2018). The codes are:
2025 - ff5cff
2024 - b25dfa
2023 - 6c5fed
2022 - 618edc
2021 - 62b6c9
2020 - 61b7a0
2019 - 5fab74
2018 - 6ca65e (!)
2017 - 86a555
2016 - a2a33d
2015 - a17722
2014 - 9f3eob
2013 - 9e0000
1735666386919.png

It can also be adjusted so that 2023-24 are more pink (like they are now) by fiddling with curves a bit.
DISCLAIMER: This won't work without the curve setup I currently have in the engine, but eh. This can also get exact colors for account age in days instead of years, but that's probably not necessary. Not gonna post the project file to avoid accidentally doxxing myself.
1735667674961.png

Code: 
extends Node2D

@onready var color_rect = $ColorRect
@onready var slider = $HSlider # Step size 365 to get individual years
@onready var label = $Label
@onready var year_rects = $YearRects

@export var H_curve: Curve
@export var S_curve: Curve
@export var V_curve: Curve

# Account must be at least 4380 days old to land in 2013 (as of 12/31/2024)
# Start color: FF5CFF
# Must pass through: 6BA65E from_hsv(0.30278, 0.43, 0.65)
#  Will occur at Year 7 (0.58333)
# End color: 9E0000
func days_to_col(days: int) -> Color:
    # TODO: Get days since 12/31/2013 and replace 4380 with that
    days = clampi(days, 0, 4380)
    var day_weight: float = float(days) / 4380.0
    print("Day weight: ", day_weight)
    var h: float # start: 0.833 end: 0.0
    h = H_curve.sample(day_weight)
 
    var s: float # start: 0.64 end: 1.0
    s = S_curve.sample(day_weight)
 
    var v: float # start: 1.0 end: 0.62
    v = V_curve.sample(day_weight)
 
    return Color.from_hsv(h, s, v)

func _ready() -> void:
    update_everything()
    color_years()

func update_everything() -> void:
    color_rect.color = days_to_col(slider.value)
    label.text = ("Days since joined: " + str(slider.value) + "
    Output color: #" + color_rect.color.to_html(false))

func _on_h_slider_value_changed(value):
    update_everything()

func color_years() -> void:
    var rects: Array[Node] = year_rects.get_children()
    for i:int in 13:
        rects[i].color = days_to_col(i * 365)
 
Last edited:
  • Like
  • Winner
  • Semper Fidelis
Reactions: h2_jGX, UERISIMILITUDO, Kaktus Kompot and 31 others
Like @my penis is on fire I also fired up the autism engine and wrote a Python program to generate a list of colors for any year range with adjustable brightness:
EDIT: Code updated to use HLS colorspace.
Python: 
import colorsys
import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

# Generate a rainbow color ramp from Oldhead red to Newfriend pink, starting in 2012.
#   end_year: Final year of color ramp.
#   start_year: First year to begin 'ramping.' The start_year and all years before start_year will be Oldhead red.
#   kiwi_lightness: Reference lightness of Kiwi green. Increase to make color ramp brighter.
def create_kiwi_color_ramp_hls(end_year, start_year=2013, kiwi_lightness=0.51):
    # Use HLS colorspace to create color ramp. Use generic points to define the curves.
    # Saturation and lightness should stay in the neighborhood of Kiwi green.
    # Kiwi green: (H=109°, L=51%, S=29%)
    # Oldhead red: (H=0°, L=31%, S=100%)
    # Newfriend pink: (H=300°, L=50%, S=100%)

    # Saturation needs to start at 100%, go down to 29%, and back up to 100%. Use a quadratic function.
    saturation_curve_x = np.linspace(0, 1, 10)
    saturation_curve_y = [1.0, 0.65, 0.43, 0.32, 0.29, 0.29, 0.32, 0.43, 0.65, 1.0]
    saturation_f = interpolate.interp1d(saturation_curve_x, saturation_curve_y, kind='quadratic')
    
    # Lightness needs to start at 31%, quickly scale to 'kiwi_lightness', then to 50%. Use a cubic function.
    lightness_curve_x = np.linspace(0, 1, 12)
    lightness_curve_y_a = np.linspace(0.31/kiwi_lightness, 1.0, 4)
    lightness_curve_y_b = np.linspace(1.0, 1.0, 7)
    lightness_curve_y_c = np.linspace(1.03, 0.5/kiwi_lightness, 3)
    lightness_curve_y = np.concatenate((lightness_curve_y_a[0:3], lightness_curve_y_b, lightness_curve_y_c[1:3]))
    lightness_curve_y = [min(x * kiwi_lightness, 1.0) for x in lightness_curve_y]
    lightness_f = interpolate.interp1d(lightness_curve_x, lightness_curve_y, kind='cubic')
    
    # Plot curves
    xnew = np.linspace(0, 1, 200)
    saturation_new = saturation_f(xnew)
    lightness_new = lightness_f(xnew)
    fig = plt.figure(figsize=(10, 10))
    ax = plt.axes()
    fig.patch.set_facecolor('white')
    ax.set_title("HLS curves\nLightness = %.2f" % kiwi_lightness)
    ax.scatter(saturation_curve_x, saturation_curve_y, color='red', label='saturation')
    ax.plot(xnew, saturation_new, color='red')
    ax.scatter(lightness_curve_x, lightness_curve_y, color='blue', label='lightness')
    ax.plot(xnew, lightness_new, color='blue')
    ax.legend()
    plt.show()
    plt.close()
    
    # generate HLS ramps
    num_years = end_year - start_year + 1
    num_preceding_years = max(0, start_year - 2012)
    num_years_total = num_years + num_preceding_years
    hues = np.linspace(0, (5.0/6.0), num=num_years)
    saturations = saturation_f(np.linspace(0, 1, num=num_years))
    lightnesses = lightness_f(np.linspace(0, 1, num=num_years))
    hlss = zip(hues, lightnesses, saturations)
    rgbs = [colorsys.hls_to_rgb(x[0], x[1], x[2]) for x in hlss]
    # add preceding year(s)
    preceding_rgbs = [rgbs[0]]*num_preceding_years
    rgbs = preceding_rgbs + rgbs
    
    # plot on each background
    fig, axs = plt.subplots(2, figsize=(15, 6))
    fig.patch.set_facecolor('white')
    fig.suptitle("Color ramp for years 2012–%d\nLightness = %.2f" % (end_year, kiwi_lightness))
    axs[0].set_facecolor('#42464d')
    axs[1].set_facecolor('white')
    for ax in axs:
        ax.scatter(np.arange(2012, end_year+1), [0]*num_years_total, color=rgbs, s=600)
        ax.xaxis.set_major_locator(mticker.MultipleLocator(1.0))
        ax.set_yticks([])
    plt.show()
    plt.close()

    # return as list of rgbs in range 0-255
    rgbs2 = []
    for r, g, b in rgbs:
        rgbs2.append((min(int(r * 256), 255), min(int(g * 256), 255), min(int(b * 256), 255)))
    return rgbs2
    
# examples
create_kiwi_color_ramp_hls(2024)
create_kiwi_color_ramp_hls(2028, kiwi_lightness=0.6)

Example outputs:
End year 2024, lightness=0.51 (default)
curves_2024_l0.51.pngrange_2024_l0.51.png

End year 2028, lightness=0.60
curves_2028_l0.60.pngrange_2028_l0.60.png

You can mess with the curves to change how brightness and saturation ramp if you want.

I posted the version below first, which uses HSV colorspace.
Python: 
import colorsys
import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

# Generate a rainbow color ramp from Oldhead red to Newfriend pink, starting in 2012.
#   end_year: Final year of color ramp.
#   start_year: First year to begin "ramping." The start_year and all years before start_year will be Oldhead red.
#   kiwi_brightness: Reference brightness of Kiwi green. Increase to make color ramp brighter.
def create_kiwi_color_ramp(end_year, start_year=2013, kiwi_brightness=0.65):
    # Use HSV colorspace to create color ramp. Use generic points to define the curves.
    # Saturation and brightness (value) should stay in the neighborhood of Kiwi green.
    # Kiwi green: (H=109°, S=43%, V=65%)
    # Oldhead red: (H=0°, S=100%, V=62%)
    # Newfriend pink: (H=300°, S=100%, V=100%)

    # Saturation needs to start at 100%, go down to 43%, and back up to 100%. Use a quadratic function.
    saturation_curve_x = np.linspace(0, 1, 10)
    saturation_curve_y = [1.0, 0.72, 0.55, 0.46, 0.43, 0.43, 0.46, 0.55, 0.72, 1.0]
    saturation_f = interpolate.interp1d(saturation_curve_x, saturation_curve_y, kind='quadratic')
  
    # Brightness needs to start at 62%, quickly scale to 'kiwi_brightness', then to 100%. Use a cubic function.
    brightness_curve_x = np.linspace(0, 1, 12)
    brightness_curve_y_a = np.linspace(0.62/kiwi_brightness, 0.97, 4)
    brightness_curve_y_b = np.linspace(0.97, 1.03, 7)
    brightness_curve_y_c = np.linspace(1.03, 1.0/kiwi_brightness, 3)
    brightness_curve_y = np.concatenate((brightness_curve_y_a[0:3], brightness_curve_y_b, brightness_curve_y_c[1:3]))
    brightness_curve_y = [min(x * kiwi_brightness, 1.0) for x in brightness_curve_y]
    brightness_f = interpolate.interp1d(brightness_curve_x, brightness_curve_y, kind='cubic')
  
    # Plot curves
    xnew = np.linspace(0, 1, 200)
    saturation_new = saturation_f(xnew)
    brightness_new = brightness_f(xnew)
    fig = plt.figure(figsize=(10, 10))
    ax = plt.axes()
    fig.patch.set_facecolor('white')
    ax.set_title("HSV curves\nBrightness = %.2f" % kiwi_brightness)
    ax.scatter(saturation_curve_x, saturation_curve_y, color='red', label='saturation')
    ax.plot(xnew, saturation_new, color='red')
    ax.scatter(brightness_curve_x, brightness_curve_y, color='blue', label='brightness')
    ax.plot(xnew, brightness_new, color='blue')
    ax.legend()
    plt.show()
    plt.close()
  
    # Generate HSV ramps
    num_years = end_year - start_year + 1
    num_preceding_years = max(0, start_year - 2012)
    num_years_total = num_years + num_preceding_years
    hues = np.linspace(0, (5.0/6.0), num=num_years)
    saturations = saturation_f(np.linspace(0, 1, num=num_years))
    values = brightness_f(np.linspace(0, 1, num=num_years))
    hsvs = zip(hues, saturations, values)
    rgbs = [colorsys.hsv_to_rgb(x[0], x[1], x[2]) for x in hsvs]
    # add preceding year(s)
    preceding_rgbs = [rgbs[0]]*num_preceding_years
    rgbs = preceding_rgbs + rgbs
  
    # Plot on each background
    fig, axs = plt.subplots(2, figsize=(15, 6))
    fig.patch.set_facecolor('white')
    fig.suptitle("Color ramp for years 2012–%d\nBrightness = %.2f" % (end_year, kiwi_brightness))
    axs[0].set_facecolor('#42464d')
    axs[1].set_facecolor('white')
    for ax in axs:
        ax.scatter(np.arange(2012, end_year+1), [0]*num_years_total, color=rgbs, s=600)
        ax.xaxis.set_major_locator(mticker.MultipleLocator(1.0))
        ax.set_yticks([])
    plt.show()
    plt.close()

    # Return as list of rgbs in range 0-255
    rgbs2 = []
    for r, g, b in rgbs:
        rgbs2.append((min(int(r * 256), 255), min(int(g * 256), 255), min(int(b * 256), 255)))
    return rgbs2

# examples
create_kiwi_color_ramp(2024)
create_kiwi_color_ramp(2028, kiwi_brightness=0.75)

Example outputs:
End year 2024, brightness=0.65 (default)
curves_2024_b0.65.pngrange_2024_b0.65.png
(158, 0, 0),
(158, 0, 0),
(159, 93, 37),
(160, 151, 64),
(132, 161, 80),
(103, 163, 90),
(94, 164, 113),
(95, 166, 146),
(92, 154, 168 ),
(84, 115, 169),
(77, 68, 171),
(139, 50, 213),
(255, 0, 255)

End year 2028, brightness=0.75
curves_2028_b0.75.pngrange_2028_b0.75.png
(158, 0, 0),
(158, 0, 0),
(165, 75, 30),
(171, 133, 55),
(179, 179, 75),
(154, 185, 91),
(130, 187, 101),
(106, 188, 106),
(108, 190, 136),
(109, 191, 164),
(108, 192, 192),
(105, 165, 194),
(96, 129, 195),
(82, 82, 195),
(115, 67, 210),
(171, 42, 236),
(255, 0, 255)

I think the colors look better using HLS. Using HSV made the colors kind of dull, especially the blues which are bordering on gray.
 
Last edited:
  • Winner
  • Like
  • Semper Fidelis
Reactions: UERISIMILITUDO, Da Capo, RL Steinbeck and 20 others
Here's a setup that favors magenta more for new users, and uses @We Are The Witches's suggestion to have e54c4c be the 2013 red. E: Now also has dark colors
point9.PNG

LIGHT:
2025 - ff5cff
2024 - dc5dfa
2023 - a75fed
2022 - 6165dc
2021 - 629dc9
2020 - 61b7ae
2019 - 5fab7a
2018 - 6ca65e
2017 - 8eac5e
2016 - bbbc5c
2015 - cfa858
2014 - df8251
2013 - e64c4c

DARK:
2025 - e553e6
2024 - c654e1
2023 - 9756d6
2022 - 585bc6
2021 - 588db5
2020 - 57a59c
2019 - 569a6d
2018 - 619555
2017 - 809b55
2016 - a8a953
2015 - ba974f
2014 - c97549
2013 - cf4444
 
Last edited:
  • Like
  • DRINK!
Reactions: h2_jGX, JudyTester, tetraterezi and 7 others
Whoa, I didn't know these changed. Is this something that happens every year and I just didn't notice until now?
 
my penis is on fire said:
Could you set it so the lighter joindate colors show up in dark mode, and vice versa? Just re-read the OP and noticed that's how it was before. Thanks by the way
Click to expand...
I agree with this, in fact the red color I suggested was in regards to the Dark-theme (the default, which most people use), and I think that the others work just as well in Dark-mode.

So these ones.
Which I also think that work well with the light theme, you don't even need more, these could be for both, in my opinion anyways.
2025 - ff5cff
2024 - dc5dfa
2023 - a75fed
2022 - 6165dc
2021 - 629dc9
2020 - 61b7ae
2019 - 5fab7a
2018 - 6ca65e
2017 - 8eac5e
2016 - bbbc5c
2015 - cfa858
2014 - df8251
2013 - e64c4c
 
  • Like
  • Agree
Reactions: .iota., invisibult plus+, Suikafag and 3 others
So am I pale blue now?

Oh I am.
 
My vote is for banning all 2024 reg date users and reusing fuchsia (or whatever that terrible color is) for new 2025 users. Don't want any fags getting a big head.

Edit: I will accept badass olive green instead of a purge.
 
I'm glad I'm not pink anymore but it would be cool if there was a special badge for me and all my niggas who have 2022 registration dates but lost our old accounts in the password reset purge.
 
You must log in or register to reply here.
Back