GlitchyScrollMaps

The prompt for Genuary2025 Day 29 is “ Grid Based“, Day 31 : “Pixel sort”

GlitchyScrollMaps uses Hydra to remix ScrollMaps: https://blog.illestpreacha.com/genuary2025scrollmaps

It takes inspiration from pixel sort algorithms to add glitchyness through Hydra ( Orignally Coded in Python, Hydra & SonicPi)

Maps Scrolling
Paths Unrolling
Abstraction Evading
Infinite Scrolls Shifting
Lifting
To the next point where the shade is in

Video

Glitchy Code:

s0.initScreen()

src(s0).
modulate(s0).
add(src(s0).colorama()).
modulateRotate(o0,0.0001)
.modulate(o0,-0.3)
.repeat(()=> Math.sin(time)/5 + 0.1,()=> Math.cos(time)/5  + 0.2)
.diff(src(s0).scale(0.25))
.add(src(s0).repeat(2,2)).

out(o0)

speed = 0.15

Previous Code :

Python

HeatMaps

#battleship is a 10 x 10 board game, for the purpose of this, we will be arranging various new classes of ships

import numpy as np
import seaborn as sns
import matplotlib.pylab as plt

def battle():
    #using the 0 & 1 to represent a ship versus the ocean and having a weighted value of 0.8 for ocean, 0.18 for boats and 0.02 for mines
    boats = [0,1,2,3,4,5,6,7,8,9,10,11]
    prob = [0.025,0.025,0.05,0.05,0.05,0.05,0.1,0.1,0.1,0.1,0.2,0.15]

    #by adding the (10,10) instead of 5, able to make an array with the weighted values
    shipyard2 = np.random.choice(boats, (16,16), p=prob)
    return shipyard2



def plotting(x):
    letters = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P'] #set for the columns
    uniform_data = x
    ax = sns.heatmap(uniform_data, linewidth=0.5,cmap="Reds") #changing it to blue for ocean representation
    ax.set_xticklabels(letters) #setting the ticks of the x axis to letters
    plt.show()
    return x

#plotting(battle())

Plot

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

def plotting(num,var_mean1,var_mean2,rate,styling):

    sns.set_theme(style="dark")

    # Simulate data from a bivariate Gaussian
    n = num
    mean = [0, var_mean2]
    cov = [(2, var_mean1), (var_mean1, .2)]
    rng = np.random.RandomState(rate)
    x, y = rng.multivariate_normal(mean, cov, n).T

    # Draw a combo histogram and scatterplot with density contours
    f, ax = plt.subplots(figsize=(6, 6))
    sns.scatterplot(x=x, y=y, s=5, color=".15")
    sns.histplot(x=x, y=y, bins=50, pthresh=.1, cmap= styling)
    sns.kdeplot(x=x, y=y, levels=5, color="w", linewidths=1)

plotting(5000,0.4,1,5,'Set3')
plotting(20000,0.8,0,10,'mako')
plotting(250,1.2,0.5,3,'cool')

Hydra

Abstacting the Map

s1.initScreen()

src(s1).scale(2).repeat(1,2).modulatePixelate(src(s1),0.31).modulateScrollX(src(s1)).modulateScrollY(src(s1).repeat(3,3).colorama()).out()

Quad Code

s1.initScreen()
s2.initScreen()
s3.initScreen()
s0.initScreen()

src(s1).modulateRotate(s1).out(o0)
src(s2).modulatePixelate(s2).out(o2)
src(s3).ModulateScrollX(s3).out(o1)
src(s0).out(o3)

render()

SonicPi - Detection

def Chosen(x,timer)
  #takes the arrays and uses a random seed to reshuffle the order and returns back

  use_random_seed Time.now.to_i / timer
  y = x.shuffle.shuffle
  y = y * 3
  z = y.shuffle

  return z
end

def Collisions(a, b)

  #checks to see which numbers are the same position between the two arrays
  #treats this as the collision detection

  c = []
  count = 0

  #this will do a counter to see which numbers are in the same position
  #and for those that are in the same position it will return those that "collide"

  while count < a.length - 1
    if a[count] == b[count]
      c.append(a[count])
    end
    count = count + 1
  end

  puts c
  return c

end

#detection comes with the chipbass sound

live_loop :detection2 do
  use_random_seed Time.now.to_i / 3

  #with this loop counter this would play the next tick, in every collision iteration
  x = Collisions(Chosen([30,4,5,12,6,19,7,8,9,12,17,25],7),Chosen([30,4,5,12,6,19,7,8,9,12,17,25],9))

  with_fx [:flanger,:ixi_techno].choose do
    use_synth [:chipbass,:piano,:prophet ].choose
    play x.tick * 5
  end


  sleep [0.25,0.5,0.75,1].choose
end


live_loop :detection1 do
  use_random_seed Time.now.to_i / 3

  #with this loop counter this would play the next tick, in every collision iteration
  x = Collisions(Chosen([30,4,5,12,6,19,7,8,9,12,17,25],3),Chosen([30,4,5,12,6,19,7,8,9,12,17,25],2))

  with_fx [:flanger,:ixi_techno].choose do
    use_synth [:chipbass,:piano,:prophet ].choose
    play x.tick 
  end


  sleep [0.25,0.5,0.75,1].choose
end