def general_results(model, results):
global nfailed, nsucceeded
results["size"] = model.size
results["load"] = model.load
counts["GENERAL"] += 2
# grid performance
results["resource_util"] = stats.mean_resource_util(model)
results["server_util"] = stats.mean_server_util(model)
results["queue_time"] = stats.mean_queue_time(model)
results["prop_failed"] = njobs.prop_failed
results["succeed_sizes_mean"] = succeeded_tracker.sizes.mean()
results["succeed_sizes_skew"] = stats.skew(succeeded_tracker.sizes)
results["succeed_times_mean"] = succeeded_tracker.times.mean()
results["succeed_times_skew"] = stats.skew(succeeded_tracker.times)
results["failed_sizes_mean"] = failed_tracker.sizes.mean() if njobs.failed else 0
results["failed_sizes_skew"] = stats.skew(failed_tracker.sizes) if njobs.failed else 0
results["failed_times_mean"] = failed_tracker.times.mean() if njobs.failed else 0
results["failed_times_skew"] = stats.skew(failed_tracker.times) if njobs.failed else 0
counts["GRID"] += 12
return results
def calculate_stats_num(self, name, per = [5,25,50,75,95]):
# get columnt instance
Col = self.get_column(name)
# type validation
assert Col.type == 'numerical', 'only possible numerical columns.'
# get data
data = self.get_data(name)
# initialize
dstats = dict()
# calculate statistics
dstats['mean'] = stats.mean(data)
dstats['median'] = stats.median(data)
dstats['std'] = stats.std(data)
dstats['min'] = stats.min(data)
dstats['max'] = stats.max(data)
dstats['skew'] = stats.skew(data)
dstats['kurtosis'] = stats.kurtosis(data)
for ip in per:
dstats['per%s'%ip] = stats.percentile(data, ip)
# return
Col.stats = dstats
a = N.array(l)
af = N.array(lf)
ll = [l]*5
aa = N.array(ll)
print('\nCENTRAL TENDENCY')
print('geometricmean:',stats.geometricmean(l), stats.geometricmean(lf), stats.geometricmean(a), stats.geometricmean(af))
print('harmonicmean:',stats.harmonicmean(l), stats.harmonicmean(lf), stats.harmonicmean(a), stats.harmonicmean(af))
print('mean:',stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af))
print('median:',stats.median(l),stats.median(lf),stats.median(a),stats.median(af))
print('medianscore:',stats.medianscore(l),stats.medianscore(lf),stats.medianscore(a),stats.medianscore(af))
print('mode:',stats.mode(l),stats.mode(a))
print('\nMOMENTS')
print('moment:',stats.moment(l),stats.moment(lf),stats.moment(a),stats.moment(af))
print('variation:',stats.variation(l),stats.variation(a),stats.variation(lf),stats.variation(af))
print('skew:',stats.skew(l),stats.skew(lf),stats.skew(a),stats.skew(af))
print('kurtosis:',stats.kurtosis(l),stats.kurtosis(lf),stats.kurtosis(a),stats.kurtosis(af))
print('mean:',stats.mean(a),stats.mean(af))
print('var:',stats.var(a),stats.var(af))
print('stdev:',stats.stdev(a),stats.stdev(af))
print('sem:',stats.sem(a),stats.sem(af))
print('describe:')
print(stats.describe(l))
print(stats.describe(lf))
print(stats.describe(a))
print(stats.describe(af))
print('\nFREQUENCY')
print('freqtable:')
print('itemfreq:')
print(stats.itemfreq(l))
print(stats.itemfreq(a))
af = N.array(lf) ll = [l]*5 aa = N.array(ll) print '\nCENTRAL TENDENCY' print 'geometricmean:',stats.geometricmean(l), stats.geometricmean(lf), stats.geometricmean(a), stats.geometricmean(af) print 'harmonicmean:',stats.harmonicmean(l), stats.harmonicmean(lf), stats.harmonicmean(a), stats.harmonicmean(af) print 'mean:',stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af) print 'median:',stats.median(l),stats.median(lf),stats.median(a),stats.median(af) print 'medianscore:',stats.medianscore(l),stats.medianscore(lf),stats.medianscore(a),stats.medianscore(af) print 'mode:',stats.mode(l),stats.mode(a) print '\nMOMENTS' print 'moment:',stats.moment(l),stats.moment(lf),stats.moment(a),stats.moment(af) print 'variation:',stats.variation(l),stats.variation(a),stats.variation(lf),stats.variation(af) print 'skew:',stats.skew(l),stats.skew(lf),stats.skew(a),stats.skew(af) print 'kurtosis:',stats.kurtosis(l),stats.kurtosis(lf),stats.kurtosis(a),stats.kurtosis(af) print 'tmean:',stats.tmean(a,(5,17)),stats.tmean(af,(5,17)) print 'tvar:',stats.tvar(a,(5,17)),stats.tvar(af,(5,17)) print 'tstdev:',stats.tstdev(a,(5,17)),stats.tstdev(af,(5,17)) print 'tsem:',stats.tsem(a,(5,17)),stats.tsem(af,(5,17)) print 'describe:' print stats.describe(l) print stats.describe(lf) print stats.describe(a) print stats.describe(af) print '\nFREQUENCY' print 'freqtable:' print 'itemfreq:' print stats.itemfreq(l)
lf), stats.geometricmean(a), stats.geometricmean(af)
print 'harmonicmean:', stats.harmonicmean(l), stats.harmonicmean(
lf), stats.harmonicmean(a), stats.harmonicmean(af)
print 'mean:', stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af)
print 'median:', stats.median(l), stats.median(lf), stats.median(
a), stats.median(af)
print 'medianscore:', stats.medianscore(l), stats.medianscore(
lf), stats.medianscore(a), stats.medianscore(af)
print 'mode:', stats.mode(l), stats.mode(a)
print '\nMOMENTS'
print 'moment:', stats.moment(l), stats.moment(lf), stats.moment(
a), stats.moment(af)
print 'variation:', stats.variation(l), stats.variation(a), stats.variation(
lf), stats.variation(af)
print 'skew:', stats.skew(l), stats.skew(lf), stats.skew(a), stats.skew(af)
print 'kurtosis:', stats.kurtosis(l), stats.kurtosis(lf), stats.kurtosis(
a), stats.kurtosis(af)
print 'mean:', stats.mean(a), stats.mean(af)
print 'var:', stats.var(a), stats.var(af)
print 'stdev:', stats.stdev(a), stats.stdev(af)
print 'sem:', stats.sem(a), stats.sem(af)
print 'describe:'
print stats.describe(l)
print stats.describe(lf)
print stats.describe(a)
print stats.describe(af)
print '\nFREQUENCY'
print 'freqtable:'
print 'itemfreq:'
def _fill_moment_results(self):
"""
Fills the result arrays used for storing the calculated moments
common format: time, mean, mode, var, skewness, kurtosis,
95% confidence lower limit, 95% upper limit
"""
toprocess = [('stock_tom', self.c_stock, 2),
('stock_woody', self.c_stock, 3),
('stock_non_woody', self.c_stock, 4),
('stock_acid', self.c_stock, 5),
('stock_water', self.c_stock, 6),
('stock_ethanol', self.c_stock, 7),
('stock_non_soluble', self.c_stock, 8),
('stock_humus', self.c_stock, 9),
('change_tom', self.c_change, 2),
('change_woody', self.c_change, 3),
('change_non_woody', self.c_change, 4),
('change_acid', self.c_change, 5),
('change_water', self.c_change, 6),
('change_ethanol', self.c_change, 7),
('change_non_soluble', self.c_change, 8),
('change_humus', self.c_change, 9),
('co2', self.co2_yield, 2)]
for (resto, dataarr, dataind) in toprocess:
# filter time steps
ts = numpy.unique(dataarr[:,1])
# extract data for the timestep
for timestep in ts:
ind = numpy.where(dataarr[:,1]==timestep)
mean = stats.mean(dataarr[ind[0], dataind])
mode_res = stats.mode(dataarr[ind[0], dataind])
mode = mode_res[0]
var = stats.var(dataarr[ind[0], dataind])
skew = stats.skew(dataarr[ind[0], dataind])
kurtosis = stats.kurtosis(dataarr[ind[0], dataind])
if var>0.0:
sd2 = 2 * math.sqrt(var)
else:
sd2 = var
res = [[timestep, mean, mode[0], var, skew, kurtosis,
mean - sd2, mean + sd2]]
if resto=='stock_tom':
self.md.stock_tom = numpy.append(self.md.stock_tom,
res, axis=0)
elif resto=='stock_woody':
self.md.stock_woody = numpy.append(self.md.stock_woody,
res, axis=0)
elif resto=='stock_non_woody':
self.md.stock_non_woody = numpy.append(\
self.md.stock_non_woody, res, axis=0)
elif resto=='stock_acid':
self.md.stock_acid = numpy.append(self.md.stock_acid,
res, axis=0)
elif resto=='stock_water':
self.md.stock_water = numpy.append(self.md.stock_water,
res, axis=0)
elif resto=='stock_ethanol':
self.md.stock_ethanol = numpy.append(self.md.stock_ethanol,
res, axis=0)
elif resto=='stock_non_soluble':
self.md.stock_non_soluble= numpy.append(
self.md.stock_non_soluble, res, axis=0)
elif resto=='stock_humus':
self.md.stock_humus = numpy.append(self.md.stock_humus,
res, axis=0)
elif resto=='change_tom':
self.md.change_tom = numpy.append(self.md.change_tom,
res, axis=0)
elif resto=='change_woody':
self.md.change_woody = numpy.append(self.md.change_woody,
res, axis=0)
elif resto=='change_non_woody':
self.md.change_non_woody = numpy.append(\
self.md.change_non_woody, res, axis=0)
elif resto=='change_acid':
self.md.change_acid = numpy.append(self.md.change_acid,
res, axis=0)
elif resto=='change_water':
self.md.change_water = numpy.append(self.md.change_water,
res, axis=0)
elif resto=='change_ethanol':
self.md.change_ethanol = numpy.append(
self.md.change_ethanol, res, axis=0)
elif resto=='change_non_soluble':
self.md.change_non_soluble=numpy.append(
self.md.change_non_soluble, res, axis=0)
elif resto=='change_humus':
self.md.change_humus = numpy.append(self.md.change_humus,
res, axis=0)
elif resto=='co2':
self.md.co2 = numpy.append(self.md.co2, res, axis=0)
def _fill_moment_results(self):
"""
Fills the result arrays used for storing the calculated moments
common format: time, mean, mode, var, skewness, kurtosis,
95% confidence lower limit, 95% upper limit
"""
toprocess = [('stock_tom', self.c_stock, 2),
('stock_woody', self.c_stock, 3),
('stock_non_woody', self.c_stock, 4),
('stock_acid', self.c_stock, 5),
('stock_water', self.c_stock, 6),
('stock_ethanol', self.c_stock, 7),
('stock_non_soluble', self.c_stock, 8),
('stock_humus', self.c_stock, 9),
('change_tom', self.c_change, 2),
('change_woody', self.c_change, 3),
('change_non_woody', self.c_change, 4),
('change_acid', self.c_change, 5),
('change_water', self.c_change, 6),
('change_ethanol', self.c_change, 7),
('change_non_soluble', self.c_change, 8),
('change_humus', self.c_change, 9),
('co2', self.co2_yield, 2)]
for (resto, dataarr, dataind) in toprocess:
# filter time steps
ts = numpy.unique(dataarr[:, 1])
# extract data for the timestep
for timestep in ts:
ind = numpy.where(dataarr[:, 1] == timestep)
mean = stats.mean(dataarr[ind[0], dataind])
mode_res = stats.mode(dataarr[ind[0], dataind])
mode = mode_res[0]
var = stats.var(dataarr[ind[0], dataind])
skew = stats.skew(dataarr[ind[0], dataind])
kurtosis = stats.kurtosis(dataarr[ind[0], dataind])
if var > 0.0:
sd2 = 2 * math.sqrt(var)
else:
sd2 = var
res = [[
timestep, mean, mode[0], var, skew, kurtosis, mean - sd2,
mean + sd2
]]
if resto == 'stock_tom':
self.md.stock_tom = numpy.append(self.md.stock_tom,
res,
axis=0)
elif resto == 'stock_woody':
self.md.stock_woody = numpy.append(self.md.stock_woody,
res,
axis=0)
elif resto == 'stock_non_woody':
self.md.stock_non_woody = numpy.append(\
self.md.stock_non_woody, res, axis=0)
elif resto == 'stock_acid':
self.md.stock_acid = numpy.append(self.md.stock_acid,
res,
axis=0)
elif resto == 'stock_water':
self.md.stock_water = numpy.append(self.md.stock_water,
res,
axis=0)
elif resto == 'stock_ethanol':
self.md.stock_ethanol = numpy.append(self.md.stock_ethanol,
res,
axis=0)
elif resto == 'stock_non_soluble':
self.md.stock_non_soluble = numpy.append(
self.md.stock_non_soluble, res, axis=0)
elif resto == 'stock_humus':
self.md.stock_humus = numpy.append(self.md.stock_humus,
res,
axis=0)
elif resto == 'change_tom':
self.md.change_tom = numpy.append(self.md.change_tom,
res,
axis=0)
elif resto == 'change_woody':
self.md.change_woody = numpy.append(self.md.change_woody,
res,
axis=0)
elif resto == 'change_non_woody':
self.md.change_non_woody = numpy.append(\
self.md.change_non_woody, res, axis=0)
elif resto == 'change_acid':
self.md.change_acid = numpy.append(self.md.change_acid,
res,
axis=0)
elif resto == 'change_water':
self.md.change_water = numpy.append(self.md.change_water,
res,
axis=0)
elif resto == 'change_ethanol':
self.md.change_ethanol = numpy.append(
self.md.change_ethanol, res, axis=0)
elif resto == 'change_non_soluble':
self.md.change_non_soluble = numpy.append(
self.md.change_non_soluble, res, axis=0)
elif resto == 'change_humus':
self.md.change_humus = numpy.append(self.md.change_humus,
res,
axis=0)
elif resto == 'co2':
self.md.co2 = numpy.append(self.md.co2, res, axis=0)
