def run():
"""
Retrieve the records of those simulations that finished in the past
24 hours and draw their traces into one matplotlib figure as a PDF and
PNG file.
"""
(project_path, path_to_src) = util.get_project_and_src_paths()
path_to_sims = os.path.join(project_path, 'sims')
db, simdir = simdb.build_database(path_to_sims) # Build the database
sub_dict = dict(dictdb.filter_dict(db, lambda k, v, p: \
k == 'end_time' and \
len(p) >= 2 and p[-2] == 'sim_info' and \
(datetime.datetime.now() - v).days <= 1))
# Initialize a figure
fig = pyplot.figure()
ax = fig.add_subplot(111)
for (k, v) in sub_dict.iteritems():
# k is the name of the simulation folder in the simdir directory.
simpath = os.path.join(simdir, k)
# Unpickle the vec.p files in the folder
with open(os.path.join(simpath, 'v_vec.p')) as vec_file:
v_vec = pickle.load(vec_file)
with open(os.path.join(simpath, 't_vec.p')) as vec_file:
t_vec = pickle.load(vec_file)
# Draw the results to the figure
ax.plot(t_vec, v_vec)
# Save as a pdf file and a png file.
outdir = path_to_sims = os.path.join(project_path, 'analysis')
fig.savefig(os.path.join(outdir, 'fig1.pdf'))
fig.savefig(os.path.join(outdir, 'fig1.png'))
import os, datetime
from neuronpy.util import dictdb
import simdb
import main
main.run()
main.run(["sim_var['asyn_gmax']=2"])
main.run(["sim_var['asyn_gmax']=2", "sim_var['asyn_onset']=30"])
main.run(["sim_var['asyn_onset']=30"])
import fig1
fig1.run()
db, simdir = simdb.build_database() # Build the database
sub_sims = dict(dictdb.filter_dict(db, lambda k, v, p: \
k == 'end_time' and \
len(p) >= 2 and p[-2] == 'sim_info' and \
(datetime.datetime.now() - v).days <= 1))
for some_dir in sub_sims.iterkeys():
filepath = os.path.join(simdir, some_dir)
exec_str = 'rm -r -f ' + filepath
os.system(exec_str)
