def IndividualTraitStats(species_list, trait_matrix, trait_names):
'''Get variance and kurtosis for each trait. returns dict with
keys trait_names and data a tuple of range, var, kurtosis (G2),
nndist_mean, nndist_var
'''
result = {}
for trait in trait_names :
temp_matrix = {}
vals = []
for spec in species_list :
val = trait_matrix[spec][trait_names.index(trait)]
vals.append(val)
temp_matrix[spec] = [val]
distances = NearestNeighborDistances(species_list, temp_matrix)
range = max(vals) - min(vals)
result[trait] = (range, stats.var(vals), G2(vals),
stats.mean(distances), stats.var(distances))
return result
def stddev(*arg):
def mean():
return sum(arg)/len(arg)
def var(m):
total = 0
for i in arg:
total += (i-m)**2
return total/(len(arg)-1)
v = var(mean()) # 호출
return math.sqrt(v)
def stddev(*arg):
def var(arg):
def avg(arg):
return sum(arg)/len(arg)
avg = avg(arg)
d = 0
for i in arg:
d += (i - avg)**2
return d/(len(arg)-1)
return math.sqrt(var(arg))
def _std(self, data):
"""
Computes the standard deviation
"""
var = stats.var(data)
if var > 0.0:
sd = math.sqrt(var)
else:
sd = 0.0
return sd
def _std(self, data):
"""
Computes the standard deviation
"""
var = stats.var(data)
if var>0.0:
sd = math.sqrt(var)
else:
sd = 0.0
return sd
def calc(x, conf): size = len(x) sum = stats.sum(x) av = stats.average(sum, size) gm = stats.gmean(x) v = stats.var(sum, stats.sqsum(x), size) med = stats.median(x) if v != 'error': sd = stats.stdv1(v) c = stats.conf(float(conf), sd, size) else: sd = 'error' c = 'none' return av, gm, v, sd, c, med
def PrintResultRow(rowname, species_set, trait_matrix, trait_names,
options, include_indiv=0) :
"Print one row of output"
print "%s\t%d\t%f" % (rowname, len(species_set), HullVolume(species_set, trait_matrix)),
#individual trait stats
if include_indiv :
trait_stats = IndividualTraitStats(species_set, trait_matrix, trait_names)
for trait in trait_names :
print "\t%f\t%f\t%f\t%f\t%f" % trait_stats[trait],
#Nearest-neighbor distances
if options.do_dist :
distances = NearestNeighborDistances(species_set, trait_matrix)
print "\t%f\t%f" % (stats.mean(distances), stats.var(distances)),
if options.do_aussie :
distances = AussieDistances(species_set, trait_matrix,EuclideanDistanceSquare)
print "\t%f" % sum(distances),
# finish row
print '\n',
def bartlett(*args):
"""Perform Bartlett test with the null hypothesis that all input samples
have equal variances.
Inputs are sample vectors: bartlett(x,y,z,...)
Outputs: (T, pval)
T -- the Test statistic
pval -- significance level if null is rejected with this value of T
(prob. that null is true but rejected with this p-value.)
Sensitive to departures from normality. The Levene test is
an alternative that is less sensitive to departures from
normality.
References:
http://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm
Snedecor, George W. and Cochran, William G. (1989), Statistical
Methods, Eighth Edition, Iowa State University Press.
"""
k = len(args)
if k < 2:
raise ValueError, "Must enter at least two input sample vectors."
Ni = zeros(k)
ssq = zeros(k,'d')
for j in range(k):
Ni[j] = len(args[j])
ssq[j] = stats.var(args[j])
Ntot = sum(Ni,axis=0)
spsq = sum((Ni-1)*ssq,axis=0)/(1.0*(Ntot-k))
numer = (Ntot*1.0-k)*log(spsq) - sum((Ni-1.0)*log(ssq),axis=0)
denom = 1.0 + (1.0/(3*(k-1)))*((sum(1.0/(Ni-1.0),axis=0))-1.0/(Ntot-k))
T = numer / denom
pval = distributions.chi2.sf(T,k-1) # 1 - cdf
return T, pval
def bartlett(*args):
"""Perform Bartlett test with the null hypothesis that all input samples
have equal variances.
Inputs are sample vectors: bartlett(x,y,z,...)
Outputs: (T, pval)
T -- the Test statistic
pval -- significance level if null is rejected with this value of T
(prob. that null is true but rejected with this p-value.)
Sensitive to departures from normality. The Levene test is
an alternative that is less sensitive to departures from
normality.
References:
http://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm
Snedecor, George W. and Cochran, William G. (1989), Statistical
Methods, Eighth Edition, Iowa State University Press.
"""
k = len(args)
if k < 2:
raise ValueError, "Must enter at least two input sample vectors."
Ni = zeros(k)
ssq = zeros(k,'d')
for j in range(k):
Ni[j] = len(args[j])
ssq[j] = stats.var(args[j])
Ntot = sum(Ni,axis=0)
spsq = sum((Ni-1)*ssq,axis=0)/(1.0*(Ntot-k))
numer = (Ntot*1.0-k)*log(spsq) - sum((Ni-1.0)*log(ssq),axis=0)
denom = 1.0 + (1.0/(3*(k-1)))*((sum(1.0/(Ni-1.0),axis=0))-1.0/(Ntot-k))
T = numer / denom
pval = distributions.chi2.sf(T,k-1) # 1 - cdf
return T, pval
def oneway(*args,**kwds):
"""Test for equal means in two or more samples from the
normal distribution.
If the keyword parameter <equal_var> is true then the variances
are assumed to be equal, otherwise they are not assumed to
be equal (default).
Return test statistic and the p-value giving the probability
of error if the null hypothesis (equal means) is rejected at this value.
"""
k = len(args)
if k < 2:
raise ValueError, "Must enter at least two input sample vectors."
if 'equal_var' in kwds.keys():
if kwds['equal_var']: evar = 1
else: evar = 0
else:
evar = 0
Ni = array([len(args[i]) for i in range(k)])
Mi = array([stats.mean(args[i]) for i in range(k)])
Vi = array([stats.var(args[i]) for i in range(k)])
Wi = Ni / Vi
swi = sum(Wi,axis=0)
N = sum(Ni,axis=0)
my = sum(Mi*Ni,axis=0)*1.0/N
tmp = sum((1-Wi/swi)**2 / (Ni-1.0),axis=0)/(k*k-1.0)
if evar:
F = ((sum(Ni*(Mi-my)**2,axis=0) / (k-1.0)) / (sum((Ni-1.0)*Vi,axis=0) / (N-k)))
pval = distributions.f.sf(F,k-1,N-k) # 1-cdf
else:
m = sum(Wi*Mi,axis=0)*1.0/swi
F = sum(Wi*(Mi-m)**2,axis=0) / ((k-1.0)*(1+2*(k-2)*tmp))
pval = distributions.f.sf(F,k-1.0,1.0/(3*tmp))
return F, pval
def oneway(*args,**kwds):
"""Test for equal means in two or more samples from the
normal distribution.
If the keyword parameter <equal_var> is true then the variances
are assumed to be equal, otherwise they are not assumed to
be equal (default).
Return test statistic and the p-value giving the probability
of error if the null hypothesis (equal means) is rejected at this value.
"""
k = len(args)
if k < 2:
raise ValueError, "Must enter at least two input sample vectors."
if 'equal_var' in kwds.keys():
if kwds['equal_var']: evar = 1
else: evar = 0
else:
evar = 0
Ni = array([len(args[i]) for i in range(k)])
Mi = array([stats.mean(args[i]) for i in range(k)])
Vi = array([stats.var(args[i]) for i in range(k)])
Wi = Ni / Vi
swi = sum(Wi,axis=0)
N = sum(Ni,axis=0)
my = sum(Mi*Ni,axis=0)*1.0/N
tmp = sum((1-Wi/swi)**2 / (Ni-1.0),axis=0)/(k*k-1.0)
if evar:
F = ((sum(Ni*(Mi-my)**2,axis=0) / (k-1.0)) / (sum((Ni-1.0)*Vi,axis=0) / (N-k)))
pval = distributions.f.sf(F,k-1,N-k) # 1-cdf
else:
m = sum(Wi*Mi,axis=0)*1.0/swi
F = sum(Wi*(Mi-m)**2,axis=0) / ((k-1.0)*(1+2*(k-2)*tmp))
pval = distributions.f.sf(F,k-1.0,1.0/(3*tmp))
return F, pval
def fligner(*args,**kwds):
"""Perform Levene test with the null hypothesis that all input samples
have equal variances.
Inputs are sample vectors: bartlett(x,y,z,...)
One keyword input, center, can be used with values
center = 'mean', center='median' (default), center='trimmed'
Outputs: (Xsq, pval)
Xsq -- the Test statistic
pval -- significance level if null is rejected with this value of X
(prob. that null is true but rejected with this p-value.)
References:
http://www.stat.psu.edu/~bgl/center/tr/TR993.ps
Fligner, M.A. and Killeen, T.J. (1976). Distribution-free two-sample
tests for scale. 'Journal of the American Statistical Association.'
71(353), 210-213.
"""
k = len(args)
if k < 2:
raise ValueError, "Must enter at least two input sample vectors."
if 'center' in kwds.keys():
center = kwds['center']
else:
center = 'median'
if not center in ['mean','median','trimmed']:
raise ValueError, "Keyword argument <center> must be 'mean', 'median'"\
+ "or 'trimmed'."
if center == 'median':
func = stats.median
elif center == 'mean':
func = stats.mean
else:
func = stats.trim_mean
Ni = asarray([len(args[j]) for j in range(k)])
Yci = asarray([func(args[j]) for j in range(k)])
Ntot = sum(Ni,axis=0)
# compute Zij's
Zij = [abs(asarray(args[i])-Yci[i]) for i in range(k)]
allZij = []
g = [0]
for i in range(k):
allZij.extend(list(Zij[i]))
g.append(len(allZij))
a = distributions.norm.ppf(stats.rankdata(allZij)/(2*(Ntot+1.0)) + 0.5)
# compute Aibar
Aibar = _apply_func(a,g,sum) / Ni
anbar = stats.mean(a)
varsq = stats.var(a)
Xsq = sum(Ni*(asarray(Aibar)-anbar)**2.0,axis=0)/varsq
pval = distributions.chi2.sf(Xsq,k-1) # 1 - cdf
return Xsq, pval
print stats.relfreq(af) print '\nVARIATION' print 'obrientransform:' l = range(1,21) a = N.array(l) ll = [l]*5 aa = N.array(ll) print stats.obrientransform(l,l,l,l,l) print stats.obrientransform(a,a,a,a,a) print 'samplevar:',stats.samplevar(l),stats.samplevar(a) print 'samplestdev:',stats.samplestdev(l),stats.samplestdev(a) print 'var:',stats.var(l),stats.var(a) print 'stdev:',stats.stdev(l),stats.stdev(a) print 'sterr:',stats.sterr(l),stats.sterr(a) print 'sem:',stats.sem(l),stats.sem(a) print 'z:',stats.z(l,4),stats.z(a,4) print 'zs:' print stats.zs(l) print stats.zs(a) print '\nTRIMMING' print 'trimboth:' print stats.trimboth(l,.2) print stats.trimboth(lf,.2) print stats.trimboth(a,.2) print stats.trimboth(af,.2) print 'trim1:'
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)
print 'scoreatpercentile:', stats.scoreatpercentile(
l, 40), stats.scoreatpercentile(lf, 40), stats.scoreatpercentile(
def PrintNetwork(self, *args):
'''Prints the network to terminal.
Args:
Returns:
Raises:'''
self.__ForwardPass() #recalculate project dates
header = [heading for heading in args]
data = [header]
for i in self.IDs:
try:
row = []
for heading in args:
if heading == 'ID':
row.append(str(self[i].GetID()))#, str(self[i].GetStart(asobject=True))]
if heading == 'Start':
row.append(str(self[i].GetStart(asobject=True)))
if heading == 'End':
row.append(str(self[i].GetEnd(asobject=True)))
if heading == 'Duration':
row.append(str(self[i].GetDuration()))
if heading == 'Slack':
row.append(str(self[i].GetSlack()))
if heading == 'DurationRangeML':
row.append(str(self[i].GetDurationRangeML()))
if heading == 'DurationRangeMax':
row.append(str(self[i].GetDurationRangeMax()))
if heading == 'DurationRangeMin':
row.append(str(self[i].GetDurationRangeMin()))
if heading == 'Name':
row.append(str(self[i].GetName()))
if heading == 'Predecesors':
row.append(str(self[i].GetPredecesors()))
if heading == 'Succsesors':
row.append(str(self[i].GetSuccsesors()))
if heading == 'Slack':
row.append(str(self[i].GetSlack()))
data.append(row)
except KeyError:
continue
print MakeTable(data)
print "\n\n"
print "OTHER INFORMATION:"
print "------------------"
print str('Deterministic Duration:').ljust(15), (self.GetNetworkEnd()-self.GetNetworkStart()).days
print str('Deterministic Finish:').ljust(15), self.GetNetworkEnd()
print str('Critical Path:').ljust(15), self.GetCriticalPath()
print "\n"
print "SIMULATION RESULTS:"
print "-------------------"
end_id = self.GetNetworkEnd(return_ID=True)
start = self.GetNetworkStart(asobject=True)
mean = self.GetSimulationMean(end_id)
print str('E(x):').ljust(15), self.GetSimulationMean(end_id), start + datetime.timedelta(mean)
print str('P10:').ljust(15), self.GetSimulationPercentile(end_id, 0.1), self.GetSimulationPercentile(end_id, 0.1, date=True)
print str('P50:').ljust(15), self.GetSimulationPercentile(end_id, 0.5), self.GetSimulationPercentile(end_id, 0.5, date=True)
print str('P90:').ljust(15), self.GetSimulationPercentile(end_id, 0.9), self.GetSimulationPercentile(end_id, 0.9, date=True)
print str('Var:').ljust(15), stats.var(self.GetSimulationVariates(ID = end_id))
print(stats.relfreq(l))
print(stats.relfreq(lf))
print(stats.relfreq(l))
print(stats.relfreq(lf))
print('\nVARIATION')
print('obrientransform:')
l = [float(f) for f in list(range(1,21))]
ll = [l]*5
print(stats.obrientransform(l,l,l,l,l))
print('samplevar:',stats.samplevar(l),stats.samplevar(l))
print('samplestdev:',stats.samplestdev(l),stats.samplestdev(l))
print('var:',stats.var(l),stats.var(l))
print('stdev:',stats.stdev(l),stats.stdev(l))
print('sterr:',stats.sterr(l),stats.sterr(l))
print('sem:',stats.sem(l),stats.sem(l))
print('z:',stats.z(l,4),stats.z(l,4))
print('zs:')
print(stats.zs(l))
print(stats.zs(l))
print('\nTRIMMING')
print('trimboth:')
print(stats.trimboth(l,.2))
print(stats.trimboth(lf,.2))
print(stats.trimboth(l,.2))
print(stats.trimboth(lf,.2))
print('trim1:')
fileNames = sys.stdin.readlines()
for fileName in fileNames:
fileName = fileName.strip()
fileBuf = open(fileName)
lines = fileBuf.readlines()
data = []
for l in lines:
try:
data.append(float(l))
except ValueError:
pass
if len(data) > 0:
mean = stats.mean(data)
var = math.sqrt(stats.var(data))
print "file name :", fileName
print "mean :", mean
print "variance :", var
if fileName.find("logBallDist") != -1:
ballDistVars[0].append((mean, var))
elif fileName.find("logBallHead") != -1:
ballHeadVars[0].append((mean, var))
elif fileName.find("logBeaconDist") != -1:
beaconDistVars[0].append((mean, var))
elif fileName.find("logBeaconHead") != -1:
beaconHeadVars[0].append((mean, var))
elif fileName.find("logGoalDist") != -1:
goalDistVars[0].append((mean, var))
def fligner(*args,**kwds):
"""Perform Levene test with the null hypothesis that all input samples
have equal variances.
Inputs are sample vectors: bartlett(x,y,z,...)
One keyword input, center, can be used with values
center = 'mean', center='median' (default), center='trimmed'
Outputs: (Xsq, pval)
Xsq -- the Test statistic
pval -- significance level if null is rejected with this value of X
(prob. that null is true but rejected with this p-value.)
References:
http://www.stat.psu.edu/~bgl/center/tr/TR993.ps
Fligner, M.A. and Killeen, T.J. (1976). Distribution-free two-sample
tests for scale. 'Journal of the American Statistical Association.'
71(353), 210-213.
"""
k = len(args)
if k < 2:
raise ValueError, "Must enter at least two input sample vectors."
if 'center' in kwds.keys():
center = kwds['center']
else:
center = 'median'
if not center in ['mean','median','trimmed']:
raise ValueError, "Keyword argument <center> must be 'mean', 'median'"\
+ "or 'trimmed'."
if center == 'median':
func = stats.median
elif center == 'mean':
func = stats.mean
else:
func = stats.trim_mean
Ni = asarray([len(args[j]) for j in range(k)])
Yci = asarray([func(args[j]) for j in range(k)])
Ntot = sum(Ni,axis=0)
# compute Zij's
Zij = [abs(asarray(args[i])-Yci[i]) for i in range(k)]
allZij = []
g = [0]
for i in range(k):
allZij.extend(list(Zij[i]))
g.append(len(allZij))
a = distributions.norm.ppf(stats.rankdata(allZij)/(2*(Ntot+1.0)) + 0.5)
# compute Aibar
Aibar = _apply_func(a,g,sum) / Ni
anbar = stats.mean(a)
varsq = stats.var(a)
Xsq = sum(Ni*(asarray(Aibar)-anbar)**2.0,axis=0)/varsq
pval = distributions.chi2.sf(Xsq,k-1) # 1 - cdf
return Xsq, pval
sys.path # stddev.py (module 이름은 py 파일 이름명) import stddev stddev.stddev(2,3,1,7) # 모듈이름.함수이름 from stddev import * # 함수 전부다는 * 특정함수는 작성 round(stddev(2,3,1,7), 6) dir() # 내가 만든 것들 조회 더 편하게 하려면 환경변수에서 잡아주면됨 PYTHONPATH C:\python # 숙제 평균함수 만들어라 분산함수 만들어라 stats 모듈을 만들어라 (즉, stats.py 안에 함수 다 넣어버리자) import sys sys.path from stats import mean,var,stddev mean(1,2,3,4) var(1,2,3,4) stddev(1,2,3,4) dir()
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)
t_list.append(num_bytes)
datetime_to_bytelist[outputtime] = t_list
else :
datetime_to_bytelist[outputtime] = [num_bytes]
num_packets = 0
num_bytes = 0
tmpdeltamultiple = tmpdeltamultiple + 1
timestamps = datetime_to_packetlist.keys()
timestamps.sort()
tmp_total = 0
tmp_size = 0
for time_it in timestamps:
if len(datetime_to_packetlist[time_it]) > 1:
mtmp = stats.mean(datetime_to_packetlist[time_it])
vtmp = stats.var(datetime_to_packetlist[time_it])
tmp_size = len(datetime_to_packetlist[time_it])
tmp_total = tmp_total + tmp_size*mtmp
packetstatsout.write( "%s %f %f %i %i\n" % \
(time_it,mtmp,vtmp,tmp_total,tmp_size) )
else :
mtmp = stats.mean(datetime_to_packetlist[time_it])
tmp_size = len(datetime_to_packetlist[time_it])
tmp_total = tmp_total + tmp_size*mtmp
packetstatsout.write( "%s %f -1 %i %i\n" % \
(time_it,mtmp,tmp_total,tmp_size) )
#datetime_to_packetlist[time_it]
timestamps = datetime_to_bytelist.keys()
timestamps.sort()
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))
print('scoreatpercentile:',stats.scoreatpercentile(l,40),stats.scoreatpercentile(lf,40),stats.scoreatpercentile(a,40),stats.scoreatpercentile(af,40))
print('percentileofscore:',stats.percentileofscore(l,12),stats.percentileofscore(lf,12),stats.percentileofscore(a,12),stats.percentileofscore(af,12))
print('histogram:',stats.histogram(l),stats.histogram(a))
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)
