def load_config():
config_parser = ConfigParser.RawConfigParser()
config_parser.read(options['load'])
if not config_parser.has_section('Result'):
raise IOError("Invalid configuration file.")
moments = config.readMatrix(config_parser, 'Result', 'moments', float)
density = config.readMatrix(config_parser, 'Result', 'density', float)
mpost = config.readVector(config_parser, 'Result', 'mpost', float)
if config_parser.has_option('Result', 'bprob'):
bprob = config.readVector(config_parser, 'Result', 'bprob', float)
else:
bprob = []
result = {
'moments' : moments,
'density' : density,
'bprob' : bprob,
'mpost' : mpost }
return result
def parseConfig(config_file):
config_parser = ConfigParser.RawConfigParser()
config_parser.read(config_file)
data = { 'K' : 2,
'L' : 0,
'gt' : None,
'alpha' : None,
'beta' : None,
'gamma' : None }
if config_parser.sections() == []:
raise IOError("Invalid configuration file.")
if config_parser.has_section('Ground Truth'):
config.readVisualization(config_parser, 'Ground Truth', os.path.dirname(config_file), options)
config.readFilter(config_parser, 'Ground Truth', os.path.dirname(config_file), options)
config.readAlgorithm(config_parser, 'Ground Truth', os.path.dirname(config_file), options)
config.readMgsSamples(config_parser, 'Ground Truth', os.path.dirname(config_file), options)
data['gt'] = config.readVector(config_parser, 'Ground Truth', 'gt', float)
data['L'] = len(data['gt'])
data['alpha'], data['beta'], data['gamma'] = \
config.getParameters(config_parser, 'Ground Truth', os.path.dirname(config_file), data['K'], data['L'])
config.readStrategy(config_parser, 'Ground Truth', options)
result = loadResult()
if not result['states']:
result['states'] = config.readSeeds(config_parser, 'Ground Truth', 'seeds')
result = sample(result, data)
if config_parser.has_section('Experiment'):
config.readVisualization(config_parser, 'Experiment', os.path.dirname(config_file), options)
config.readFilter(config_parser, 'Experiment', os.path.dirname(config_file), options)
config.readAlgorithm(config_parser, 'Experiment', os.path.dirname(config_file), options)
config.readMgsSamples(config_parser, 'Experiment', os.path.dirname(config_file), options)
data['L'] = int(config_parser.get('Experiment', 'bins'))
data['alpha'], data['beta'], data['gamma'] = \
config.getParameters(config_parser, 'Experiment', os.path.dirname(config_file), data['K'], data['L'])
config.readStrategy(config_parser, 'Experiment', options)
result = loadResult()
result = sample(result, data)
if options['video']:
save_video()
if options['save']:
saveResult(result)
else:
if options['savefig']:
importMatplotlib('Agg')
from matplotlib.pyplot import savefig
vis.plotSampling(result, options, data)
# savefig(options['savefig'], bbox_inches='tight', pad_inches=0)
savefig(options['savefig'])
else:
importMatplotlib()
from matplotlib.pyplot import show
vis.plotSampling(result, options, data)
show()
def parseConfig(config_file):
config_parser = ConfigParser.RawConfigParser()
config_parser.read(config_file)
if config_parser.sections() == []:
raise IOError("Invalid configuration file.")
if config_parser.has_section('Counts'):
config.readVisualization(config_parser, 'Counts', os.path.dirname(config_file), options)
config.readAlgorithm(config_parser, 'Counts', os.path.dirname(config_file), options)
config.readMgsSamples(config_parser, 'Counts', os.path.dirname(config_file), options)
counts = config.readCounts(config_parser, 'Counts')
K, L = len(counts), len(counts[0])
alpha, beta, gamma = config.getParameters(config_parser, 'Counts', os.path.dirname(config_file), K, L)
result = call_posterior(counts, alpha, beta, gamma)
if options['save']:
saveResult(result)
else:
if options['savefig']:
importMatplotlib('Agg')
from matplotlib.pyplot import savefig
vis.plotBinning(result, options)
savefig(options['savefig'], bbox_inches='tight', pad_inches=0)
else:
importMatplotlib()
from matplotlib.pyplot import show
vis.plotBinning(result, options)
show()
if config_parser.has_section('Trials'):
config.readVisualization(config_parser, 'Trials', os.path.dirname(config_file), options)
config.readAlgorithm(config_parser, 'Trials', os.path.dirname(config_file), options)
config.readMgsSamples(config_parser, 'Trials', os.path.dirname(config_file), options)
binsize = config_parser.getint('Trials', 'binsize')
timings = config.readMatrix(config_parser, 'Trials', 'timings', int)
srange = None
if config_parser.has_option('Trials', 'range'):
srange = config.readVector(config_parser, 'Trials', 'range', int)
x, counts = timingsToCounts(timings, binsize, srange)
K, L = len(counts), len(counts[0])
alpha, beta, gamma = config.getParameters(config_parser, 'Trials', os.path.dirname(config_file), K, L)
result = call_posterior(counts, alpha, beta, gamma)
if options['save']:
saveResult(result)
else:
if options['savefig']:
importMatplotlib('Agg')
from matplotlib.pyplot import savefig
vis.plotBinningSpikes(x, timings, result, options)
savefig(options['savefig'], bbox_inches='tight', pad_inches=0)
else:
importMatplotlib()
from matplotlib.pyplot import show
vis.plotBinningSpikes(x, timings, result, options)
show()
def loadResult():
if options['load']:
config_parser = ConfigParser.RawConfigParser()
config_parser.read(options['load'])
if not config_parser.has_section('Sampling Result'):
raise IOError("Invalid configuration file.")
distances = []
bprob = []
counts = config.readMatrix(config_parser, 'Sampling Result', 'counts', int)
moments = config.readMatrix(config_parser, 'Sampling Result', 'moments', float)
density = config.readMatrix(config_parser, 'Sampling Result', 'density', float)
samples = config.readVector(config_parser, 'Sampling Result', 'samples', int)
mpost = config.readVector(config_parser, 'Sampling Result', 'mpost', float)
states = config.readStates(config_parser, 'Sampling Result', 'states')
if config_parser.has_option('Sampling Result', 'distances'):
distances = config.readVector(config_parser, 'Sampling Result', 'distances', float)
if config_parser.has_option('Sampling Result', 'bprob'):
bprob = config.readVector(config_parser, 'Sampling Result', 'bprob', float)
result = {
'distances' : distances,
'moments' : moments,
'density' : density,
'bprob' : bprob,
'mpost' : mpost,
'counts' : counts,
'samples' : samples,
'states' : states }
else:
result = {
'distances' : [],
'moments' : [],
'density' : [],
'bprob' : [],
'mpost' : [],
'counts' : [],
'samples' : [],
'states' : [] }
return result