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Tunnels Can Lead to A Place
What Tales will be sought?
Converging Entities
Slowly Drifting
Making Experiences through HydraVideoSynth & P5
The prompt for Genuary2025 Day 21 is “CollisionDetection“, Day 28 is “ InfiniteScroll “ & Day 30 is “ AbstractMap ”
ScrollMaps uses Python, Hydra & SonicPi to bring the three prompts together: Heat Maps & Radial Maps in Python, Abstraction & scrolling with Hydra and Collision Detection used for the SonicPi composition.
Maps Scrolling
Paths Unrolling
Abstraction Evading
Infinite Scrolls Shifting
Lifting
To the next point where the shade is in
#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())
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')
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()
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()
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