import vdj.analysis
import timeseries
option_parser = optparse.OptionParser()
option_parser.add_option('-r','--threshold',type='float')
option_parser.add_option('-o','--outputbasename')
option_parser.add_option('-q','--quantify')
option_parser.add_option('-n','--normalize',action='store_true')
(options,args) = option_parser.parse_args()
if len(args) == 1:
inhandle = open(args[0],'r')
else:
raise ValueError, "Must give a single argument that is a timeseries data file"
data = timeseries.load_timeseries(inhandle)
labels = data['labels']
times = data['times']
timeseriesmatrix = data['matrix']
try:
sums = data['sums']
except KeyError:
sums = timeseriesmatrix.sum(axis=0)
# normalize if desired
if options.normalize:
timeseriesmatrix = np.float_(timeseriesmatrix) / np.asarray(sums)
# define which time series to plot
if options.threshold:
option_parser = optparse.OptionParser()
# option_parser.add_option('-x','--xxx',dest='xxxx',type='int')
(options, args) = option_parser.parse_args()
if len(args) == 2:
inhandle = open(args[0], 'r')
outhandle = open(args[1], 'w')
elif len(args) == 1:
inhandle = open(args[0], 'r')
outhandle = sys.stdout
elif len(args) == 0:
inhandle = sys.stdin
outhandle = sys.stdout
data = timeseries.load_timeseries(inhandle)
# eliminate numpy-ness of objects before JSON output
np_matrix = data['matrix']
py_matrix = []
for row in np_matrix:
py_matrix.append(list(row))
data['matrix'] = py_matrix
data['labels'] = list(data['labels'])
for label in data.keys():
if label == 'labels' or label == 'matrix':
continue
data[label] = list(data[label])
json.dump(data, outhandle)