def main(run_dir, cluster_file, genes=None):
if genes is None:
genes = [4,5,7,11]
funcs = [get_gene_fn(i) for i in genes]
labels = [genome.get_label(i, abbr=True) for i in genes]
plot(cluster_file, funcs, labels,
plot_title="Representative Gene Values", run_dir=run_dir)
def main(run_dir, cluster_file, genes=None):
if genes is None:
genes = [4, 5, 7, 11]
funcs = [get_gene_fn(i) for i in genes]
labels = [genome.get_label(i, abbr=True) for i in genes]
plot(cluster_file,
funcs,
labels,
plot_title="Representative Gene Values",
run_dir=run_dir)
root = 'plots/'
root += os.path.basename(cluster)
root = root.rsplit('.', 1)[0]
outfile = "%s_%s.png" % (root, options.mode)
if options.mode == 'complexity':
plot(cluster,
outfile,
filter=lambda a: a.complexity,
func=lambda a: a.complexity,
cmap=options.cmap)
elif options.mode == 'fitness':
plot(cluster,
outfile,
filter=lambda a: a.fitness,
func=lambda a: a.fitness,
cmap=options.cmap)
elif options.gene is not None:
if not options.outfile:
outfile = "%s_%s%d_%s.png" % (root, options.mode, options.gene,
genome.get_label(options.gene))
plot(cluster,
outfile,
func=lambda a: a.genome[options.gene],
plot_title=genome.get_label(options.gene),
cmap=options.cmap)
else:
parser.print_help()
print "\n*** ERROR: Please specify an action! ***"
def plot(genes,
filename="plot.png",
plot_title='',
cmap='Paired',
draw_legend=False):
# get population
p = get_population()
# initialize population count
pops = [[] for i in xrange(30000)]
for a in p:
for step in range(a.birth, a.death):
pops[step].append(a)
# initialize plot variables
cmap = pylab.cm.jet
lines = []
# build plot for each gene
for i, gene in enumerate(genes):
# initialize line variables
data = []
err_up = []
err_down = []
# calculate points for each time step
for step in xrange(30000):
datum = [agent.genome[gene] for agent in pops[step]]
mean = sum(datum) / len(pops[step])
stderr = np.std(datum) / sqrt(len(pops[step]))
# calculate 1 std dev error bars
err_up.append(min(mean + np.std(datum), 255))
err_down.append(max(mean - np.std(datum), 0))
data.append(mean)
# TODO: Implement stderr/stddev toggle
"""
# calculate 1 std err error bars
err_up.append(min(mean+stderr, 255))
err_down.append(max(mean-stderr,0))
"""
color = cmap(float(i) / len(genes))
# use error bars
fill = pylab.fill_between(range(0, 30000, 5),
err_up[::5],
err_down[::5],
color=color,
alpha=0.35,
linewidth=0,
edgecolor=None,
facecolor=color)
# plot line
line = pylab.plot(range(30000), data, color=color)
# append to list of lines for use with data legend
lines.append(line)
# plot all the things
pylab.title("Size and Internal Neural Group Count (INGC)", weight='black')
pylab.ylim(0, 255)
pylab.xlim(0, 30000)
pylab.ylabel("Raw Gene Value", weight='bold')
pylab.xlabel("Time", weight='bold')
pylab.legend(lines, [get_label(gene) for gene in genes],
loc='center right')
pylab.figtext(0, .948, '(d)', size=6, weight='black')
pylab.savefig("plots/test.pdf", dpi=300)
def plot(genes, filename="plot.png", plot_title='', cmap='Paired',
draw_legend=False):
# get population
p = get_population()
# initialize population count
pops = [[] for i in xrange(30000)]
for a in p:
for step in range(a.birth, a.death):
pops[step].append(a)
# initialize plot variables
cmap = pylab.cm.jet
lines=[]
# build plot for each gene
for i, gene in enumerate(genes):
# initialize line variables
data = []
err_up = []
err_down = []
# calculate points for each time step
for step in xrange(30000):
datum = [agent.genome[gene] for agent in pops[step]]
mean = sum(datum) / len(pops[step])
stderr = np.std(datum) / sqrt(len(pops[step]))
# calculate 1 std dev error bars
err_up.append(min(mean+np.std(datum), 255))
err_down.append(max(mean-np.std(datum),0))
data.append(mean)
# TODO: Implement stderr/stddev toggle
"""
# calculate 1 std err error bars
err_up.append(min(mean+stderr, 255))
err_down.append(max(mean-stderr,0))
"""
color = cmap(float(i)/len(genes))
# use error bars
fill = pylab.fill_between(range(0,30000,5), err_up[::5], err_down[::5],
color=color, alpha=0.35, linewidth=0,
edgecolor=None, facecolor=color)
# plot line
line = pylab.plot(range(30000), data, color=color)
# append to list of lines for use with data legend
lines.append(line)
# plot all the things
pylab.title("Size and Internal Neural Group Count (INGC)", weight='black')
pylab.ylim(0, 255)
pylab.xlim(0, 30000)
pylab.ylabel("Raw Gene Value", weight='bold')
pylab.xlabel("Time", weight='bold')
pylab.legend(lines, [get_label(gene) for gene in genes], loc='center right')
pylab.figtext(0,.948, '(d)', size=6, weight='black')
pylab.savefig("plots/test.pdf", dpi=300)
def calc_entropy(gene):
e = 1 - gene_entropy(gene, N)
return (gene, e, get_label(gene))
],
fontsize=8)
legend([b[0] for b in bars], labels, loc='upper left', prop=dict(size=6))
savefig(filename, dpi=200)
if __name__ == '__main__':
import sys
from optparse import OptionParser
usage = "usage: %prog [options] cluster_file"
parser = OptionParser(usage)
options, args = parser.parse_args()
if args:
cluster = args[-1]
plot(cluster,
funcs=(lambda x: Agent(x).genome[4] / 255.0,
lambda x: Agent(x).genome[5] / 255.0,
lambda x: Agent(x).genome[7] / 255.0,
lambda x: Agent(x).genome[11] / 255.0,
lambda x: Agent(x).complexity),
labels=[
genome.get_label(4),
genome.get_label(5),
genome.get_label(7),
genome.get_label(11), 'complexity'
])
def calc_entropy(gene):
e = 1- gene_entropy(gene, N)
return (gene, e, get_label(gene))
for i, clust in enumerate(clusters)],
fontsize=8)
legend([b[0] for b in bars], labels, loc='upper left', prop=dict(size=6))
savefig(filename, dpi=200)
if __name__ == '__main__':
import sys
from optparse import OptionParser
usage = "usage: %prog [options] cluster_file"
parser = OptionParser(usage)
options, args = parser.parse_args()
if args:
cluster = args[-1]
plot(cluster, funcs=(lambda x: Agent(x).genome[4] / 255.0,
lambda x: Agent(x).genome[5] / 255.0,
lambda x: Agent(x).genome[7] / 255.0,
lambda x: Agent(x).genome[11] / 255.0,
lambda x: Agent(x).complexity),
labels=[genome.get_label(4),
genome.get_label(5),
genome.get_label(7),
genome.get_label(11),
'complexity'
])
else:
#autoname rendering
if not os.path.isdir('plots'):
os.mkdir('plots')
root = 'plots/'
root += os.path.basename(cluster)
root = root.rsplit('.', 1)[0]
outfile = "%s_%s.png" % (root, options.mode)
if options.mode == 'complexity':
plot(cluster, outfile, filter=lambda a: a.complexity,
func=lambda a: a.complexity, cmap=options.cmap)
elif options.mode == 'fitness':
plot(cluster, outfile, filter=lambda a: a.fitness,
func=lambda a: a.fitness, cmap=options.cmap)
elif options.gene is not None:
if not options.outfile:
outfile = "%s_%s%d_%s.png" % (root, options.mode, options.gene,
genome.get_label(options.gene))
plot(cluster, outfile,
func=lambda a: a.genome[options.gene],
plot_title=genome.get_label(options.gene), cmap=options.cmap)
else:
parser.print_help()
print "\n*** ERROR: Please specify an action! ***"
