interarrival times in exponential distributions
Arrivals occur following a Poisson distribution with a rate parameter of 84 arrivals per hour. Find: the probability that the time to arrival of the next customer is less than one minute.
When calculating the interarrival rate, would I have to convert it into minutes as the question is asking for the probability that is is less than an minute but the rate parameter has been given in terms of hours.
If this is the case, would the interarrival rate be 1/84 per hour; then converting into minutes would make it 0.714 minutes
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I am attempting to replicate realistic vegetation placement on a 2d grid. To accomplish this I am using poisson disc sampling(Bridson algorithm) vegetation placement and perlin noise to determine density of vegetation per area.
When I exclude perlin noise and keep a constant minimum distance, I achieve desirable results. However, when I vary the minimum distance via perlin noise, the results do not make sense.
What am I doing incorrectly?
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But I cannot seem to grasp my error.
main.py
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Poisson.py
from random import random from math import cos, sin, floor, sqrt, pi, ceil def euclidean_distance(a, b): dx = a[0]  b[0] dy = a[1]  b[1] return sqrt(dx * dx + dy * dy) def poisson_disc_samples(width, height, r_max, r_min, k=3, r_array=[], distance=euclidean_distance, random=random): tau = 2 * pi cellsize = r_max / sqrt(2) grid_width = int(ceil(width / cellsize)) grid_height = int(ceil(height / cellsize)) grid = [None] * (grid_width * grid_height) def grid_coords(p): return int(floor(p[0] / cellsize)), int(floor(p[1] / cellsize)) def fits(p, gx, gy, r): yrange = list(range(max(gy  2, 0), min(gy + 3, grid_height))) for x in range(max(gx  2, 0), min(gx + 3, grid_width)): for y in yrange: g = grid[x + y * grid_width] if g is None: continue r = r_array[int(floor(g[0]))][int(floor(g[1]))] if distance(p, g) <= r: # too close return False return True p = width * random(), height * random() queue = [p] grid_x, grid_y = grid_coords(p) grid[grid_x + grid_y * grid_width] = p z_max = width * height * 8 z = 0 while queue: qindex = int(random() * len(queue)) # select random point from queue qx, qy = queue.pop(qindex) r = r_array[int(floor(qx))][int(floor(qy))] # print('min_dist:', r) z += 1 if z > z_max: print('max iteration exceeded') break for _ in range(k): alpha = tau * random() d = r * sqrt(3 * random() + 1) px = qx + d * cos(alpha) py = qy + d * sin(alpha) if not (0 <= px < width and 0 <= py < height): continue p = (px, py) grid_x, grid_y = grid_coords(p) if not fits(p, grid_x, grid_y, r): continue queue.append(p) grid[grid_x + grid_y * grid_width] = p return [p for p in grid if p is not None]
I expected results like , where I can almost visualize the perlin noise map. Btw this is from the 1st link up above.
But I get outputs like . The gray scale map is the associated generated perlin noise.
I am aware there are more efficient ways of doing things. I plan to stick to Python however.
EDIT: I apologize, I cannot get the images to show, there are on imgur. The links from the images do work however.