How do I code, in Processing, a fixed number of lines that are semi-randomly spaced apart and still fit into a fixed-size image?

If you want that there were no overlaps, you can try to implement this algorithm:

Another similar algorithm: if you need N buildngs inside the width W, you make N*2-1 buildings that have a total width W and remove every second building. Drawback of this algorithm: the spaces and buildings will have about the same widths.

Even easier implementation with random.sample() function:

import random

num_of_buildings = 5
total_width      = 100
max_height       = 50
min_height       = 10

x_coords = sorted(random.sample(range(0, total_width), num_of_buildings * 2))
print('x_coords:', x_coords)

heights = random.sample(range(min_height, max_height), num_of_buildings)
print('heights:', heights)

buildings = list(zip(x_coords[0::2], x_coords[1::2], heights))
print('buildings:', buildings)

for b in buildings:
    x = b[0]
    w = b[1] - b[0]
    h = b[2]
    print (f'Make the building: x = {x :2}, width = {w :2}, height = {h :2}')

Output:

x_coords: [2, 7, 9, 13, 21, 46, 47, 62, 79, 83]

heights: [35, 14, 28, 20, 12]

buildings: [(2, 7, 35), (9, 13, 14), (21, 46, 28), (47, 62, 20), (79, 83, 12)]

Make the building: x =  2, width =  5, height = 35
Make the building: x =  9, width =  4, height = 14
Make the building: x = 21, width = 25, height = 28
Make the building: x = 47, width = 15, height = 20
Make the building: x = 79, width =  4, height = 12

One of drawbacks of this implementation is you rare will get a building at the very edge of your area. There will be almost always a gap an the begin and at the end. It can be fixed roughly: just make for the first building x = 0 and for the last building width = total_width - x. Not exactly pretty solution, though.

https://docs.python.org/3/library/random.html

Full variant:

import random
from PIL import Image, ImageDraw

num_of_buildings = 30
total_width      = 800
max_height       = 400
min_height       = 100

im   = Image.new('RGB', (total_width, max_height), (255, 255, 255))
draw = ImageDraw.Draw(im)

x_coords  = sorted(random.sample(range(0, total_width), num_of_buildings * 2))
heights   = random.sample(range(min_height, max_height), num_of_buildings)
buildings = zip(x_coords[0::2], x_coords[1::2], heights)

for b in buildings:
    x1    = b[0]
    x2    = b[1]
    h     = max_height - b[2]
    color = (random.randint(128, 230), random.randint(128, 230), random.randint(128, 230))
    draw.rectangle((x1, max_height, x2, h), fill=color)

im.save('image.jpg')

Output:

It's obviously desperate for max and min width limits for buildings.

Ok, here is another approach based on Multinomial Distribution.

It is handy to use here because sum of all blocks (block is building plus left and right margins) is automatically, by definition, equal to view size.

So first we sample all blocks, second inside block we sample margins and get building position

Code, Python 3.8 Win 10 x64

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw

rng = np.random.default_rng()

building_start = 25
building_end   = 975
nof_buildings  = 50

p = nof_buildings*[1.0/nof_buildings] # probabilities

q = None
while True:
    q = rng.multinomial(building_end-building_start, p)

    if np.any(q < 7): # minimum width condition, continue sampling
        continue

    break

print(q) # sampled blocks array
print(np.sum(q)) # be sure sum is equal to 950

# making positions/separators
min_height       = 100
max_height       = 400

buildings = list()
left = building_start
for k in range(0, nof_buildings):
    # position a block
    left +=  (0 if k == 0 else q[k])
    rght  = left + (0 if k == nof_buildings-1 else q[k]-1)

    # add margins
    left += rng.choice([1, 2, 3], p=[0.4, 0.35, 0.25])
    rght -= rng.choice([1, 2, 3], p=[0.4, 0.35, 0.25])

    height = rng.integers(min_height, max_height)

    buildings.append((left, rght, height)) # here is single building

im   = Image.new('RGB', (1024, 512), (255, 255, 255))
draw = ImageDraw.Draw(im)

for bld in buildings: # plot all buildings
    l    = bld[0]
    r    = bld[1]
    h    = max_height - bld[2]
    color = (rng.integers(128, 230),
             rng.integers(128, 230),
             rng.integers(128, 230))
    draw.rectangle((l, max_height, r, h), fill=color)

im.save('buildings.jpg')

And at the end you'll get something like