calculations and some plots

Author: ccfelius

histogram.py

@@ -3,6 +3,7 @@
 import math
 import numpy as np
 from scipy import stats
+import seaborn as sns
 
 data = pd.read_csv("data/500-4.txt", sep="\t")
 
@@ -24,7 +25,7 @@
 print("\n START MEAN, STDEV, CONF INT")
 
 data = pd.read_csv(f"data/500-2.txt", sep="\t")
-example = data[data["SIM_TIME"] == 25]
+example = data[data["SIM_TIME"] == 150]
 example1 = data[data["SIM_TIME"] == 500]
 ex = example[example['RHO'] == 0.1]['AVG_WAIT']
 ex2 = example1[example1['RHO'] == 0.1]['AVG_WAIT']
@@ -40,16 +41,20 @@
 
 fig = plt.figure(facecolor='w')
 ax = fig.add_subplot(111, facecolor='whitesmoke', axisbelow=True)
-ax.hist(ex_9, bins = 100, alpha=0.5, color = 'cornflowerblue')
-ax.hist(ex2_9, bins = 100, alpha = 0.6, color='springgreen')
-ax.set_xlabel(r'$Mean waiting time / time unit$', fontsize=12)
+ax.hist(ex_9, bins = 100, alpha=0.8, color = 'cornflowerblue', label="Simtime=150")
+ax.hist(ex2_9, bins = 100, alpha = 0.5, color='springgreen', label="Simtime=500")
+# sns.displot(ex_9,)
+# sns.displot(ex2_9)
+ax.set_xlabel('Mean waiting time / time unit', fontsize=12)
 ax.set_ylabel('Density', fontsize=12)
 ax.set_title('Distribution mean waiting time', fontsize = 14)
 ax.yaxis.set_tick_params(length=0)
 ax.xaxis.set_tick_params(length=0)
 ax.grid(b=True, which='major', c='w', lw=2, ls='-')
+legend = ax.legend()
+legend.get_frame().set_alpha(0.5)
 for spine in ('top', 'right', 'bottom', 'left'):
     ax.spines[spine].set_visible(False)
 
-plt.savefig("plots/histogram-25-500.png", dpi=300)
+plt.savefig("plots/histogram-150-500-01.png", dpi=300)
 plt.show()

probabilities.py

@@ -66,4 +66,28 @@ def expquel(c, rho):
 
 # print(f'Probability that a job has to wait: {pwait(c, rho):.2f}')
 # print(f'Expected waiting time E(W): {expw(mu, c, rho):.2f} time units')
-# print(f'Expected queue length E(Lq): {expquel(c, rho):.2f} customers')
+# print(f'Expected queue length E(Lq): {expquel(c, rho):.2f} customers')
+# print dataframe with data
+# print(data)
+# run_data.loc[round(SIM_TIME/5)] = [rho, SIM_TIME, data["AVG_WAITING"].mean()]
+# print(f'Expected waiting time E(W): {expw(MU, SERVERS, RHO):.3f} time units')
+# print(f'AVG waiting: {data["AVG_WAITING"].mean():.3f} time units')
+# print(conf_int(data["AVG_WAITING"].mean(), data["AVG_WAITING"].var(), SIMULATIONS, p=0.95))
+# print()
+# print(f'AVG arriving: {data["AVG_ARRIVING"].mean():.3f} per time unit')
+# print(conf_int(data["AVG_ARRIVING"].mean(), data["AVG_ARRIVING"].var(), SIMULATIONS, p=0.95))
+# print()
+# print(f'AVG leaving: {data["AVG_LEAVING"].mean():.3f} per time unit')
+# print(conf_int(data["AVG_LEAVING"].mean(), data["AVG_LEAVING"].var(), SIMULATIONS, p=0.95))
+# print(f'Simulation {s+1}')
+# print(f'Average waiting time: {avg_waiting:.3f} time units')
+# print(f'Avg customers arriving per time unit: {avg_arrivals:.3f} time units')
+# print(f'Avg customers leaving per time unit: {avg_leavers:.3f} time units\n')
+
+# print("EXPECTED VALUES AND PROBABILITIES")
+#
+# print(f'Rho: {RHO}\nMu: {MU}\nLambda: {LAMBDA}\nExpected interarrival time: {1 / LAMBDA:.2f} time units')
+# print(f'Expected processing time per server: {1 / MU:.2f} time units\n')
+# print(f'Probability that a job has to wait: {pwait(SERVERS, RHO):.2f}')
+# print(f'Expected waiting time E(W): {expw(MU, SERVERS, RHO):.2f} time units')
+# print(f'Expected queue length E(Lq): {expquel(SERVERS, RHO):.2f} customers\n')