Merge branch 'output' of PSSAI_Team/JobShopScheduling into devel
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commit
dd18b64203
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@ -1,2 +1,3 @@
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mypy
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Arpeggio
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matplotlib
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@ -38,7 +38,8 @@ def accept(solution):
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Maybe skip this during the first step to generate a more
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random solution.
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"""
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return tighten(solution)
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#return tighten(solution)
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return solution
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def tighten(solution):
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30
src/Output/output.py
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30
src/Output/output.py
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import matplotlib.pyplot as plt
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from matplotlib import colors
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from matplotlib import patches
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import numpy as np
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from SchedulingAlgorithms.simanneal import rate
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import random
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def create_plot(problem, solution):
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end = rate(solution)
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with plt.xkcd():
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fig,ax = plt.subplots()
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colorlist = list(colors.XKCD_COLORS.values())
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random.shuffle(colorlist)
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for m in range(0, problem.machines):
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mach_ops = [ x for x in solution if problem.problem_data[x[1][0]][x[1][1]][1] == m ]
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xranges = [ (x[0], problem.problem_data[x[1][0]][x[1][1]][0]) for x in mach_ops ]
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ax.broken_barh(xranges, ((problem.machines - m)*10, 9),
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facecolors=[colorlist[x[1][0]] for x in mach_ops])
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ax.set_ylim(5, 5 + (problem.machines+1)*10)
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ax.set_xlim(0, 1.25 * end)
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ax.set_yticks([15 + m * 10 for m in range(0, problem.machines)])
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ax.set_yticklabels([ problem.machines - 1 - m for m in range(0, problem.machines)] )
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handlecolors = colorlist[0:problem.jobs]
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handles = [ patches.Patch(color = handlecolors[j], label = "Job "+str(j)) for j in range(0,problem.jobs) ]
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labels = ["Job "+str(j) for j in range(0,problem.jobs)]
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ax.legend(handles, labels)
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plt.show()
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@ -1,7 +1,10 @@
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INSTANCES = [(5, 5)]
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TASKS = [[(1, 21), (0, 53), (4, 95), (3, 55), (2, 35)],
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[(0, 21), (3, 52), (4, 16), (2, 26), (1, 71)],
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[(3, 39), (4, 98), (1, 42), (2, 31), (0, 12)],
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[(1, 77), (0, 55), (4, 79), (2, 66), (3, 77)],
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[(0, 83), (3, 34), (2, 64), (1, 19), (4, 37)]]
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#import Parser.js1_style as p
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import Parser.js2_style as p
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from SchedulingAlgorithms import simanneal as sim
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from Output import output as o
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#problem = p.parse_file("../inputdata/jobshop1.txt")[0]
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problem = p.parse_file("../inputdata/sample")
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sim.init(problem)
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solution = sim.anneal()
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o.create_plot(problem, solution)
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