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cliques_MCR_vs_mu_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 mean 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
mu_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 mu_life in mu_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 N_civ ", nciv, "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 nciv"
f_obj.close()