def main(hdf, name):
# Create a csv file to tabulate into
f = open('{0}.csv'.format(name), 'w')
csvw = csv.writer(f)
# Make a header for the table
header = ["x", "count", "cond"]
csvw.writerow(header)
conds = set(["acc", "value", "rpe", "p", "rand", "box"])
# Find a dmcol from models,
# pick the first model with
# that dm cond. Conds across
# models are the same.
locations = {}
models = get_model_names(hdf)
for model in models:
# Get model meta data and compare it too conds
meta = get_model_meta(hdf, model)
for ii, dmname in enumerate(meta["dm"]):
if dmname in conds:
locations[dmname] = (model, ii)
conds.remove(dmname)
## conds loses elements!
# If conds is empty, stop
if not conds:
print("All conds found. {0}".format(model))
break
# Get each dm's data and pick a col using pos;
# pos matches cond.
dmdata = {}
for cond, loci in locations.items():
print("Getting {0}".format(cond))
model, pos = loci
dm = np.array(read_hdf(hdf, '/' + model + '/dm'))
dmdata[cond] = dm[:,:,pos].flatten()
# Create a histogram then write it out.
for cond, data in dmdata.items():
print("Histogramming {0}".format(cond))
# Instantiate a RHist instance and
# use it to make a histogram
hist = RHist(name=cond, decimals=2)
[hist.add(x) for x in data]
# Tell that textfile a tale.
[csvw.writerow([k, v, cond]) for k, v in hist.h.items()]
f.close()
