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gen_paper_figs.py
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gen_paper_figs.py
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import numpy as np
import matplotlib.pyplot as plt
import cPickle as pickle
import math
def gen_fig_1():
length_key=["1","10","100","1000","10000","100000"]
x_values=[0,1,2,3,4,5]
data_holder_mean=[0 for i in xrange(len(length_key)*3)]
data_holder_se=[0 for i in xrange(len(length_key)*3)]
a=open("/users/thomas/documents/research/cse845f/chow_replication/replication_plot_data/cluster_species_mean_100.data","rb")
high_inflow_mean=pickle.load(a)
a.close()
b=open("/users/thomas/documents/research/cse845f/chow_replication/replication_plot_data/cluster_species_mean_1.data","rb")
low_inflow_mean=pickle.load(b)
b.close()
c=open("/users/thomas/documents/research/cse845f/chow_replication/replication_plot_data/cluster_species_mean_10.data","rb")
med_inflow_mean=pickle.load(c)
c.close()
index=0
for h in ["sync","lowhigh","stag"]:
for i in length_key:
for k in ["mean","se"]:
f=open("/users/thomas/documents/research/cse845f/fluc_env/plot_data/cluster_species_"+str(k)+"_"+str(h)+"_"+str(i)+".data","rb")
if k=="mean":
data_holder_mean[index]=pickle.load(f)
elif k=="se":
data_holder_se[index]=pickle.load(f)
f.close()
index+=1
fig1,(s1)=plt.subplots(1,1,figsize=(9,6))
s1.errorbar(x_values,data_holder_mean[0:6],yerr=[i*1.96 for i in data_holder_se[0:6]],fmt="ko")
s1.errorbar(x_values,data_holder_mean[6:12],yerr=[i*1.96 for i in data_holder_se[6:12]],fmt="ko",fillstyle="none")
s1.errorbar(x_values,data_holder_mean[12:18],yerr=[i*1.96 for i in data_holder_se[12:18]],fmt="ks")
s1.plot([i for i in range(-1,7)],[high_inflow_mean for i in range(-1,7)],"r-")
s1.plot([i for i in range(-1,7)],[med_inflow_mean for i in range(-1,7)],"b-")
s1.plot([i for i in range(-1,7)],[low_inflow_mean for i in range(-1,7)],"g-")
s1.plot(x_values,data_holder_mean[0:6],"k-")
s1.plot(x_values,data_holder_mean[6:12],"k--")
s1.plot(x_values,data_holder_mean[12:18],"k:")
s1.axis([-0.5,5.5,0,5])
s1.set_title("Final Species Richness (n=30; 95% C.I.)")
s1.set_ylabel("Number of Species")
s1.set_xlabel("Log10 Fluctuation Length")
s1.legend(("Synchronous: start high","Synchronous: start low","Staggered","100 Inflow","10 Inflow","1 Inflow"),loc=1)
plt.savefig("paper_figs/fig_1_species_richness.png")
return 0
def gen_fig_2():
length_key=["1","10","100","1000","10000"]
x_values=[0,1,2,3,4]
data_holder_mean=[[] for i in xrange(len(length_key)*2)]
data_holder_se=[[] for i in xrange(len(length_key)*2)]
index=0
for fluc_type in ["sync","stag"]:
for i in length_key:
for k in ["mean","se"]:
f=open("/users/thomas/documents/research/cse845f/fluc_env/plot_data/total_task_counts_"+str(k)+"_"+str(fluc_type)+"_"+str(i)+".data","rb")
if k=="mean":
data_holder_mean[index]=pickle.load(f)
elif k=="se":
data_holder_se[index]=pickle.load(f)
f.close()
index+=1
fig1,(s1,s2)=plt.subplots(1,2,figsize=(18,6))
s1.plot([j for j in range(1001)],data_holder_mean[0][7000:],"y-")
s1.plot([j for j in range(1001)],data_holder_mean[1][7000:],"b-")
s1.plot([j for j in range(1001)],data_holder_mean[2][7000:],"g-")
s1.plot([j for j in range(1001)],data_holder_mean[3][7000:],"r-")
s1.plot([j for j in range(1001)],data_holder_mean[4][7000:],"k-")
s1.set_title("Synchronous Fluctuations; Last 50k updates")
s1.set_ylabel("Total Tasks Performed")
s1.set_xlabel("'Time'")
s1.legend(("1","10","100","1000","10000"))
s1.axis([0,1000,0,20000])
s2.plot([j for j in range(1001)],data_holder_mean[5][7000:],"y-")
s2.plot([j for j in range(1001)],data_holder_mean[6][7000:],"b-")
s2.plot([j for j in range(1001)],data_holder_mean[7][7000:],"g-")
s2.plot([j for j in range(1001)],data_holder_mean[8][7000:],"r-")
s2.plot([j for j in range(1001)],data_holder_mean[9][7000:],"k-")
s2.set_title("Staggered Fluctuations; Last 50k updates")
s2.set_ylabel("Total Tasks Performed")
s2.set_xlabel("'Time'")
s2.legend(("1","10","100","1000","10000"))
s2.axis([0,1000,0,20000])
plt.savefig("paper_figs/fig_2_total_task_performance.png")
return 0
def gen_fig_3():
length_key=["1","10","100","1000","10000"]
x_values=[0,1,2,3,4]
data_holder_mean=[[] for i in xrange(len(length_key)*2)]
data_holder_se=[[] for i in xrange(len(length_key)*2)]
index=0
for fluc_type in ["sync","stag"]:
for i in length_key:
for k in ["mean","se"]:
f=open("/users/thomas/documents/research/cse845f/fluc_env/plot_data/total_resource_counts_"+str(k)+"_"+str(fluc_type)+"_"+str(i)+".data","rb")
if k=="mean":
data_holder_mean[index]=pickle.load(f)
elif k=="se":
data_holder_se[index]=pickle.load(f)
f.close()
index+=1
fig1,(s1,s2)=plt.subplots(1,2,figsize=(18,6))
s1.plot([j for j in range(1001)],data_holder_mean[0][7000:],"y-")
s1.plot([j for j in range(1001)],data_holder_mean[1][7000:],"b-")
s1.plot([j for j in range(1001)],data_holder_mean[2][7000:],"g-")
s1.plot([j for j in range(1001)],data_holder_mean[3][7000:],"r-")
s1.plot([j for j in range(1001)],data_holder_mean[4][7000:],"k-")
s1.set_title("Synchronous Fluctuations; Last 50k updates")
s1.set_ylabel("Total Resource Levels")
s1.set_xlabel("'Time'")
s1.legend(("1","10","100","1000","10000"))
s1.axis([0,1000,0,30000])
s2.plot([j for j in range(1001)],data_holder_mean[5][7000:],"y-")
s2.plot([j for j in range(1001)],data_holder_mean[6][7000:],"b-")
s2.plot([j for j in range(1001)],data_holder_mean[7][7000:],"g-")
s2.plot([j for j in range(1001)],data_holder_mean[8][7000:],"r-")
s2.plot([j for j in range(1001)],data_holder_mean[9][7000:],"k-")
s2.set_title("Staggered Fluctuations; Last 50k updates")
s2.set_ylabel("Total Resource Levels")
s2.set_xlabel("'Time'")
s2.legend(("1","10","100","1000","10000"))
s2.axis([0,1000,0,30000])
plt.savefig("paper_figs/fig_3_total_resource_levels.png")
return 0
def gen_fig_7():
length_key=["1","10","100","1000","10000","100000"]
x_values=[0,1,2,3,4,5]
generation_mean=[[] for i in xrange(3*len(length_key))]
generation_se=[[] for i in xrange(3*len(length_key))]
chow_gen_mean=[[] for i in range(7)]
chow_gen_se=[[] for i in range(7)]
index=0
for j in ["sync","lowhigh","stag"]:
for i in length_key:
for k in ["means","ses"]:
f=open("/users/thomas/documents/research/cse845f/fluc_env/plot_data/generation_"+str(k)+"_"+str(j)+"_"+str(i)+".data","rb")
if k=="means":
generation_mean[index]=pickle.load(f)
elif k=="ses":
generation_se[index]=pickle.load(f)
f.close()
index+=1
index=0
for j in ["01","1","10","100","1000","10000","100000"]:
for k in ["means","ses"]:
f=open("/users/thomas/documents/research/cse845f/chow_replication/replication_plot_data/generation_"+str(k)+"_"+str(j)+".data","rb")
if k=="means":
chow_gen_mean[index]=pickle.load(f)
elif k=="ses":
chow_gen_se[index]=pickle.load(f)
f.close()
index+=1
fig1,(s1)=plt.subplots(1,1,figsize=(9,6))
s1.errorbar([0,1,2,3,4,5],[generation_mean[i][8000] for i in range(6)],[1.96*generation_se[i][8000] for i in range(6)],fmt="ko")
s1.errorbar([0,1,2,3,4,5],[generation_mean[i][8000] for i in range(6,12)],[1.96*generation_se[i][8000] for i in range(6,12)],fmt="ko",fillstyle="none")
s1.errorbar([0,1,2,3,4,5],[generation_mean[i][8000] for i in range(12,18)],[1.96*generation_se[i][8000] for i in range(12,18)],fmt="ks")
s1.axis([-1,6,0,70000])
s1.plot([-1,0,1,2,3,4,5,6,],[chow_gen_mean[3][8000] for i in range(8)],"r-")
s1.plot([-1,0,1,2,3,4,5,6,],[chow_gen_mean[1][8000] for i in range(8)],"g-")
s1.legend(("Sync: start high","Sync: start low","Staggered","Inflow=100","Inflow=1"),loc=2)
s1.set_ylabel("Avg. Generation")
s1.set_xlabel("Log10 Fluctuation Length")
s1.set_title("Final Avg. Generation")
plt.savefig("paper_figs/fig_7_total_generations.png")
def gen_fig_8():
length_key=["1","10","100","1000","10000","100000"]
x_values=[0,1,2,3,4,5]
generation_mean=[[] for i in xrange(3*len(length_key))]
generation_se=[[] for i in xrange(3*len(length_key))]
chow_gen_mean=[[] for i in range(7)]
chow_gen_se=[[] for i in range(7)]
index=0
for j in ["sync","lowhigh","stag"]:
for i in length_key:
for k in ["means","ses"]:
f=open("/users/thomas/documents/research/cse845f/fluc_env/plot_data/generation_"+str(k)+"_"+str(j)+"_"+str(i)+".data","rb")
if k=="means":
generation_mean[index]=pickle.load(f)
elif k=="ses":
generation_se[index]=pickle.load(f)
f.close()
index+=1
index=0
for j in ["01","1","10","100","1000","10000","100000"]:
for k in ["means","ses"]:
f=open("/users/thomas/documents/research/cse845f/chow_replication/replication_plot_data/generation_"+str(k)+"_"+str(j)+".data","rb")
if k=="means":
chow_gen_mean[index]=pickle.load(f)
elif k=="ses":
chow_gen_se[index]=pickle.load(f)
f.close()
index+=1
fig1,(s1)=plt.subplots(1,1,figsize=(9,6))
s1.plot([0,1,2,3,4,5],[math.log10(generation_mean[i][8000]/(400000.0/10**i)) for i in range(6)],"ko")
s1.plot([0,1,2,3,4,5],[math.log10(generation_mean[i][8000]/(400000.0/10**(i%6))) for i in range(6,12)],"ko",fillstyle="none")
s1.plot([0,1,2,3,4,5],[math.log10(generation_mean[i][8000]/(400000.0/10**(i%12))) for i in range(12,18)],"ks")
s1.axis([-1,6,-2,4.5])
s1.legend(("Sync: start high","Sync: start low","Staggered","Inflow=100","Inflow=1"),loc=2)
s1.set_ylabel("Log10 Generations per fluctuation")
s1.set_xlabel("Log10 Fluctuation Length")
s1.set_title("Generations per fluctuation")
plt.savefig("paper_figs/fig_8_generations_per_fluc.png")
gen_fig_1()
gen_fig_2()
gen_fig_3()
gen_fig_7()
gen_fig_8()