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cliques_MCR_vs_sigma_life.py
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cliques_MCR_vs_sigma_life.py
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# Written 1/8/14 by dh4gan
# Generates a collection of civilisations with some distribution in space and arrival time
# Then calculates causally connected groups
# Repeats this to allow MCR analysis
# Repeats MCR analysis over various values of the standard deviation of civilisation lifetime
import galaxy as gal
import params
import numpy as np
import matplotlib.pyplot as plt
from sys import argv
# Read in input parameters from file
if len(argv)==1:
paramfile = raw_input("Enter the parameter file: ")
elif len(argv)==2:
paramfile = argv[1]
nruns,nciv, rinner,router,rscale, zmin,zmax, mu_life,sigma_life,mu_t,sigma_t = params.read_parameters_mcr(paramfile)
iseed = -47
# Override definition of sigma_t, and specify the nciv values to be explored
sigma_life_values = [0.1, 0.5, 1, 5, 10]
# Set up MCR output file
outputfile = 'output.MCR'
print 'Outputting to file ', outputfile
f_obj = open(outputfile,'w')
line = 'Nciv meanLife sdLife meanArrive sdArrive '
line += 'meanNgroup sdNgroup meanGroupSize sdGroupSize meanGroupArrive sdGroupArrive \n'
f_obj.write(line)
for sigma_life in sigma_life_values:
# Now loop over runs
mcr_ngroups = np.empty(0)
mcr_groupcounts = np.empty(0)
mcr_groupsizes = np.empty(0)
mcr_grouparrive = np.empty(0)
for irun in range(nruns):
print 'Beginning run ', irun+1
# Generate a galaxy of civilisations
myGalaxy = gal.galaxy(nciv,iseed)
# Spatial distribution of civilisations
#myGalaxy.generate_uniform_GHZ(rinner, router, zmin, zmax)
myGalaxy.generate_exponential_GHZ(rinner, router,rscale, zmin, zmax)
# Distribution of civilisation arrival times
#myGalaxy.generate_uniform_arrival_time(tmin, tmax)
myGalaxy.generate_gaussian_arrival_time(mu_t,sigma_t)
# Distribution of civilisation lifetimes
myGalaxy.generate_fixed_lifetimes(mu_life)
#myGalaxy.generate_gaussian_lifetimes(mu_life, sigma_life)
# Arrange civilisations by arrival time and construct groups
myGalaxy.sort_by_arrival_time()
myGalaxy.check_for_groups()
myGalaxy.count_groups()
myGalaxy.get_group_sizes()
print "There are ", myGalaxy.ngroups, " groups, with latest group established by civilisation ", myGalaxy.groupmax
# Plot this galaxy and its groups
#myGalaxy.arrival_time_histogram()
#myGalaxy.plot_spatial2D()
# Write data to outputfile
#outputfile = 'output.'+str(irun)
#myGalaxy.output_group_statistics(outputfile)
print 'Run ',irun+1, ' complete'
# Store the data in an array?
mcr_ngroups = np.append(mcr_ngroups,np.asarray(myGalaxy.ngroups))
mcr_groupcounts = np.append(mcr_groupcounts,np.asarray(myGalaxy.groupcount))
mcr_groupsizes = np.append(mcr_groupsizes,np.asarray(myGalaxy.groupsizes))
mcr_grouparrive = np.append(mcr_grouparrive,np.asarray(myGalaxy.grouparrive))
print 'MCR complete - now producing means and standard deviations'
# Groups with a single member have zero size - remove them
mcr_groupsizes = mcr_groupsizes[np.nonzero(mcr_groupsizes)]
# Calculate means and standard deviations
mean_ngroups = np.mean(mcr_ngroups)
sd_ngroups = np.std(mcr_ngroups)
mean_groupsize = np.mean(mcr_groupsizes)
sd_groupsize = np.std(mcr_groupsizes)
mean_grouparrive = np.mean(mcr_grouparrive)
sd_grouparrive = np.std(mcr_grouparrive)
fig1 = plt.figure(1)
ax = fig1.add_subplot(111)
ax.set_ylabel('Relative Frequency')
ax.set_xlabel('Group Population')
ax.hist(mcr_groupcounts, bins=100, normed=True)
plt.show()
fig1 = plt.figure(1)
ax = fig1.add_subplot(111)
ax.set_ylabel('Relative Frequency')
ax.set_xlabel('Group Size (kpc)')
ax.hist(mcr_groupsizes, bins=100)
plt.show()
fig1 = plt.figure(1)
ax = fig1.add_subplot(111)
ax.set_ylabel('Relative Frequency')
ax.set_xlabel('Group Arrival Time (Myr)')
ax.hist(mcr_grouparrive, bins=100)
plt.show()
# Write MCR data to output file
print "Writing data for sigma_life ", sigma_life, "to file ",outputfile
line = str(nciv)+'\t'+str(mu_life)+'\t'+str(sigma_life)+'\t'+str(mu_t)+'\t'+str(sigma_t)+'\t'
line += str(mean_ngroups)+'\t'+str(sd_ngroups)+'\t'+str(mean_groupsize)+'\t'+str(sd_groupsize)+'\t'+str(mean_grouparrive)+'\t'+str(sd_grouparrive)+'\n'
f_obj.write(line)
print "MCR complete for all values of sigma_life"
f_obj.close()