def table4simav(dirpath, title):
"""Return None. Provides a summary table for one simulation.
"""
table = [title]
table_data = dict() #map eps -> y2pp (a dict)
years = range(249)
for eps in range(11):
sheetname = 'eps{0:02d}simav'.format(eps)
fname = os.path.join(dirpath , sheetname + '.csv')
fname = os.path.normpath(fname)
y2pp = dict() #map year -> data for this eps
table_data[eps] = y2pp
with open(fname, 'r') as fin:
next(fin) #discard header line
for rownum, line in enumerate(fin):
year = rownum
if year in years:
line = line.split(',')
male_partner_dist = np.array([map(float, line[8:20])])
female_partner_dist = np.array([map(float, line[20:32])])
all_tab = DegreeTab(male_partner_dist + female_partner_dist)
#get point prevalence (ok to compute from averaged data)
concurrency_point_prevalence = all_tab.point_prevalence()
total_inf = float(line[0]) + float(line[1])
pp = total_inf / 200 #200=20000/100 -> point prevalance as pct
y2pp[year] = pp, round(100*concurrency_point_prevalence[0])
header = 'year '
for eps in range(11):
header += '{0:10}'.format('eps{0:02d}'.format(eps) )
table.append(header)
for year in years:
data = '{0:4d}'.format(year)
for eps in range(11):
data += '{0:6.2f}({1:2.0f})'.format(*table_data[eps][year])
table.append(data)
return '\n'.join(table)
def simav2csv(dirpath, fpattern, outname, force=False):
"""Compute average of the replicates
(where the replicates are in a given `dirpath` and share `fpattern`).
The results files are assumed to have a single header line.
Past computation of simav used if possible,
unless `force` is True.
"""
assert outname.endswith('csv')
#full path to results files
patternpath = os.path.join(dirpath, fpattern)
outpath = os.path.join(dirpath, outname)
#get all completed replicates of this simulation
fnames = glob.glob(patternpath)
n_sims = len(fnames)
if n_sims == 0:
print 'WARN: no data for ', patternpath
return None
elif n_sims < 100:
print 'WARN: incomplete data for ', patternpath
print 'Only {0:d} simulations run. (Computing average anyway!)'.format(n_sims)
#check if simav needs updating
ftimes = [os.path.getmtime(fname) for fname in fnames]
use_existing = False
try:
csvtime = os.path.getmtime(outpath)
if any((csvtime < ftime) for ftime in ftimes):
print 'Updating ' + outpath
else:
use_existing = not force
except os.error:
print 'Creating ' + outpath
#compute average across simulations
if use_existing:
print 'reuse existing simav'
simav = np.loadtxt(outpath, delimiter=',', skiprows=1)
point_prevalence = DegreeTab(simav[:,8:20] + simav[:,20:32]).point_prevalence()
kappa3 = None #must compute within loop (see below)
with open(outpath, 'r') as temp:
csv_header = next(temp).strip()
headers = tuple(hdr for hdr in csv_header.split(','))
else: # compute the average of the replicates
asum = 0
kappa3 = 0 #must be computed in the loop!
point_prevalence = 0 #compute in the loop *if* population changes
#get the header from the first file (assumed to be identical across results files)
with open(fnames[0], 'r') as temp:
csv_header = next(temp).strip()
headers = tuple(hdr for hdr in csv_header.split(','))
for fname in fnames: #get the individual replicates one at a time
anew = np.loadtxt(fname, delimiter=',', skiprows=1)
asum += anew
male_partner_dist = anew[:,8:20]
female_partner_dist = anew[:,20:32]
all_tab = DegreeTab(male_partner_dist + female_partner_dist)
assert (all_tab._data==(anew[:,8:20]+anew[:,20:32])).all()
#get kappa3 for every period: array shape (t,)
kappa3 += all_tab.kappa3
#get point prevalence for every period: array shape (t,)
point_prevalence += all_tab.point_prevalence()
assert asum.shape in [(250, 2+6+4*12), (250, 2+6+4*12+2)]
pptest = DegreeTab(asum[:,8:20]+asum[:,20:32]).point_prevalence()
simav = asum / float(n_sims) #deflate sum to average
"""
Write `a` to dirpath, where `a` is simulation average.
(Average of 100 replications of simulation for this `eps`.)
"""
#this data set is not huge, so make and write a string representing all of it
output = [csv_header]
output.extend((','.join(str(d) for d in row) for row in simav))
print 'writing ' + outpath
with open(outpath, 'w') as fout:
fout.write('\n'.join(output))
#create other data used for graphs
kappa3 /= float(n_sims) #deflate sum to average
point_prevalence /= float(n_sims) #deflate sum to average
assert np.allclose(point_prevalence, pptest)
return dict(headers=headers, simav=simav, point_prevalence=point_prevalence, kappa3=kappa3)