File:Inserter Efficiency Graph (Capacity Bonus 2).png: Difference between revisions

Graph showing the efficiency of Inserters at capacity bonus 2. Worth observing is how Stack Inserters and Green Inserters are identically efficient at this stage.

Code to recreate if needed

import matplotlib.pyplot as plt import numpy as np

burner_speed = 60 / 76 yellow_speed = 60 / 70 red_speed = 60 / 50 blue_speed = 60 / 24

yellow_drain = 0.4 blue_drain = 0.5 green_drain = 1

burner_energy = 66.9 yellow_energy = 6.65 red_energy = 7 blue_energy = 8.12 green_energy = 23.2 stack_energy = 46.4

stack_size = 2 green_stack = 4 stack_stack = 8

x = np.linspace(0, blue_speed * stack_stack, 300)

yellow_count = np.trunc(x / (yellow_speed * stack_size)) + 1 red_count = np.trunc(x / (red_speed * stack_size)) + 1 blue_count = np.trunc(x / (blue_speed * stack_size)) + 1 green_count = np.trunc(x / (blue_speed * green_stack)) + 1

dynamic_stack = x / stack_size dynamic_green = x / green_stack dynamic_stacker = x / stack_stack

burner = burner_energy * dynamic_stack yellow = (yellow_energy * dynamic_stack) + (yellow_drain * yellow_count) red = (red_energy * dynamic_stack) + (yellow_drain * red_count) blue = (blue_energy * dynamic_stack) + (blue_drain * blue_count) green = (green_energy * dynamic_green) + (green_drain * green_count) stack = (stack_energy * dynamic_stacker) + green_drain

fig = plt.figure(figsize = (20, 15)) ax = plt.axes()

ax.plot(x, burner, label = "Burner", color = "black") ax.plot(x, yellow, label = "Yellow", color = "orange") ax.plot(x, red, label = "Red", color = "red") ax.plot(x, blue, label = "Blue", color = "blue") ax.plot(x, green, label = "Green", color = "green") ax.plot(x, stack, label = "Stack", color = "purple")

xlim = blue_speed * stack_stack ylim = (stack_energy / stack_stack) * xlim

ax.set_ylim(0, ylim) ax.set_xlim(0, xlim)

xtick = 0.5 ytick = 2

ax.set_xticks(np.arange(0, xlim + xtick, xtick)) ax.set_yticks(np.arange(0, ylim + ytick, ytick))

ax.set_title("Inserter Efficiency (Capacity Bonus 2)") ax.set_xlabel("Items / second", fontweight = "bold") ax.set_ylabel("Power Cost (kW)", fontweight = "bold")

ax.grid() ax.legend(loc = "best")

plt.show()