Suggest edit — minimum user base requirements for coveather

Title

Name

Note

---
title: "minimum user base requirements for coveather"
source: https://www.jemoka.com/posts/kbhminimum_user_base_requirements_for_coveather/
---

How many disturbance users can coveather take without crashing? Let&rsquo;s find out.
Code Util function to mapreduce a list:
def multiplyList(l) : # Multiply elements one by one result = 1 for x in l: result = result * x return result We first set a user count:
N = var(&#34;N&#34;) # Pool size val_percent = var(&#34;val_percent&#34;) # Pools val_pool = N*val_percent user_pool = N*(1-val_percent) # Disturbance disturbance_percent = var(&#34;disturbance_percent&#34;) # Validation Pools + Disburbance val_disturbance_pool = disturbance_percent*val_pool val_normal_pool = (1-disturbance_percent)*val_pool # Chance of three or more disturbance attestors # which is equal to one minus chance of zero, one, or two disturbance attesors no_disturbance_attestor = (val_normal_pool/val_pool)*((val_normal_pool-1)/(val_pool-1))*((val_normal_pool-2)/(val_pool-2))*((val_normal_pool-3)/(val_pool-3)) one_disturbance = [] for disturbance_point in range(0,4): res = [] res.append((val_disturbance_pool)/(val_pool-disturbance_point)) for pre_disturbance in range(0,disturbance_point): res.append((val_normal_pool-pre_disturbance)/(val_pool-pre_disturbance)) for post_disturbance in range(disturbance_point+1,4): res.append((val_normal_pool-post_disturbance)/(val_pool-post_disturbance)) one_disturbance.append(multiplyList(res)) one_disturbance_attestor = sum(one_disturbance) two_disturbance = [] for disturbance_point_i in range(0,4): for disturbance_point_j in range(disturbance_point_i+1,4): res = [] res.append((val_disturbance_pool)/(val_pool-disturbance_point_i)) res.append((val_disturbance_pool-1)/(val_pool-disturbance_point_j)) for pre_i_disturbance in range(0,disturbance_point_i): res.append((val_normal_pool-pre_disturbance)/(val_pool-pre_disturbance)) for sandwich in range(disturbance_point_i+1,disturbance_point_j): res.append((val_normal_pool-post_disturbance)/(val_pool-sandwich)) for post_j_disturbance in range(disturbance_point_j+1,4): res.append((val_normal_pool-post_disturbance)/(val_pool-post_j_disturbance)) two_disturbance.append(multiplyList(res)) two_disturbance_attestor = sum(two_disturbance) distubrance_chance(N, val_percent, disturbance_percent) = expand(1-(no_disturbance_attestor+one_disturbance_attestor+two_disturbance_attestor)) # no_disturbance_attestor (N*(disturbance_percent - 1)*val_percent + 3)*(N*(disturbance_percent - 1)*val_percent + 2)*(N*(disturbance_percent - 1)*val_percent + 1)*(disturbance_percent - 1)/((N*val_percent - 1)*(N*val_percent - 2)*(N*val_percent - 3)) z = var(&#34;z&#34;) val_dist(val_percent, disturbance_percent) = distubrance_chance(100, val_percent, disturbance_percent) implicit_plot3d(val_dist-z, (val_percent,0.1,1), (disturbance_percent, 0,1), (z, 0,1) ,frame=True,axes_labels=[&#39;Validation&#39;,&#39;Disturbance&#39;, &#39;Chance&#39;],axes=False, color=(val_dist,colormaps.Blues)) Launched html viewer for Graphics3d Object z = var(&#34;z&#34;) n_dist(N, disturbance_percent) = distubrance_chance(N, 0.1, disturbance_percent) show(implicit_plot3d(n_dist-z, (N,100,100000), (disturbance_percent, 0,1), (z, 0,1) ,frame=True,axes_labels=[&#39;N&#39;,&#39;Disturbance&#39;, &#39;Chance&#39;],axes=False, color=(n_dist,colormaps.Blues)), aspect_ratio=[1,100000,100000], plot_points=100) Launched html viewer for Graphics3d Object n_dir(N) = distubrance_chance(N, 0.1, 0.1) # plot(n_dir, (N,100,100000),axes_labels=[&#39;N&#39;, &#39;Disturbance&#39;], thickness=1) # solve(distubrance_chance(100, N, 0.1)==0.01, N, to_poly_solve=True) # implicit_plot(distubrance_chance(100, N, 0.1)==0.01, (N, 0,1), (z, 0, # solve(distubrance_chance(N, val_perc, 0.1)==0.01, val_perc, to_poly_solve=True) # implicit_plot(solve(distubrance_chance(N, val_perc, 0.1)==0.01, val_perc)[0]) # val_perc = var(&#34;var_perc&#34;) show(implicit_plot(distubrance_chance(N, val_perc, 0.1)==0.01, (N, 15, 1000), (val_perc, 0,1), plot_points=300,axes_labels=[&#39;N&#39;,&#39;Val Ratio&#39;],axes=False), aspect_ratio=800) # solve(distubrance_chance(800, val_perc, 0.1)==0.01, val_perc, to_poly_solve=True) &lt;/Users/houliu/.sage/temp/baboon.jemoka.com/64368/tmp_9bdcu2si.pn&gt;