def test_cumsum(self):
"Testing cumsum"
data = [ self.L, self.LF, self.A, self.AF ]
results = [1, 3, 6, 10, 15, 21, 28, 36, 45, 55, 66, 78, 91, 105, 120, 136, 153, 171, 190, 210]
i = 0
for d in data:
self.assertEqual( stats.cumsum( d )[i], results[i])
i += 1
def test_cumsum(self):
"Testing cumsum"
data = [ self.L, self.LF, self.A, self.AF ]
results = [1, 3, 6, 10, 15, 21, 28, 36, 45, 55, 66, 78, 91, 105, 120, 136, 153, 171, 190, 210]
i = 0
for d in data:
self.assertEqual( stats.cumsum( d )[i], results[i])
i += 1
def legacy(): patts = stats.ltrimboth(patts, 0.1) # statlib. #patts = statlib.stats.ltrimboth(patts, 0.1) # statlib. print 'len(patts)', len(patts) #stats.writecc([patts,patts],'test.txt') alist = range(1,6) print 'sum(alist)', sum(alist) print 'cumsum(alist)', stats.cumsum(alist) print 'geometricmean(alist)', stats.geometricmean(alist)
def testFiltre():
fTilObj = FiltreTil('lf')
fTilObj.getPatternsDictFor(5)
fTilObj.analyze()
patts = fTilObj.getPatternsFor(5)
print 'len(patts)', len(patts)
patts = stats.ltrimboth(patts, 0.1) # statlib.
#patts = statlib.stats.ltrimboth(patts, 0.1) # statlib.
print 'len(patts)', len(patts)
#stats.writecc([patts,patts],'test.txt')
alist = range(1,6)
print 'sum(alist)', sum(alist)
print 'cumsum(alist)', stats.cumsum(alist)
print 'geometricmean(alist)', stats.geometricmean(alist)
def evaluate( self, *args, **params):
# extracting data from the result
from statlib.stats import cumsum
ydata, passPos = homogenize( *args[0], returnPos= True)
if args[0] != None:
xticLabel= numpy.array( args[1])[passPos]
else:
xticLabel= None
plots= list()
for ydat in ydata:
plots.append( pltobj( None, xlabel= 'variable', ylabel= __(u'value'), title= __(self.name)))
plt= plots[-1]
ax1= plt
ax1.hold( True)
bars= list()
colour= generateColors()
ydat= numpy.array( ydat)
xdat= numpy.arange( 1, len( ydat)+1)
bars.append( ax1.bar( xdat, ydat, color= colour.next())) #align = 'center',
width= bars[-1][0]._width/2.0
# plot the line
suma= lambda x,y: x+y
ydat= cumsum( ydat)
self._maxYValue= ydat[-1]
ax1.plot( xdat+width, ydat,'bo-',linewidth=3.0)
ax1.hold( False)
# add the percent axis
ax2 = ax1.twinx()
ax2.set_ylim( numpy.array( [ax1.get_ylim()[0]/float(self._maxYValue), 1])*100*numpy.array( [1.0, 1.05]))
ax1.set_xticks( xdat + width)
ax1.set_xticklabels( xticLabel)
ax1.set_xlim( min( xdat)-0.5, max( xdat)+width*2+0.5)
ax1.set_ylim( numpy.array( [ax1.get_ylim()[0], self._maxYValue])*numpy.array( [1, 1.05]))
legend= plt.legend( [bar[0] for bar in bars], self.colNameSelect )
legend.draggable( True)
plt.updateControls()
plt.canvas.draw()
return plots
ll = [l] * 5
aa = N.array(ll)
m = list(range(4, 24))
m[10] = 34
b = N.array(m)
print('\n\nF_oneway:')
print(stats.F_oneway(l, m))
print(stats.F_oneway(a, b))
#print 'F_value:',stats.F_value(l),stats.F_value(a)
print('\nSUPPORT')
print('sum:', stats.sum(l), stats.sum(lf), stats.sum(a), stats.sum(af))
print('cumsum:')
print(stats.cumsum(l))
print(stats.cumsum(lf))
print(stats.cumsum(a))
print(stats.cumsum(af))
print('ss:', stats.ss(l), stats.ss(lf), stats.ss(a), stats.ss(af))
print('summult:', stats.summult(l, m), stats.summult(lf, m),
stats.summult(a, b), stats.summult(af, b))
print('sumsquared:', stats.square_of_sums(l), stats.square_of_sums(lf),
stats.square_of_sums(a), stats.square_of_sums(af))
print('sumdiffsquared:', stats.sumdiffsquared(l, m),
stats.sumdiffsquared(lf, m), stats.sumdiffsquared(a, b),
stats.sumdiffsquared(af, b))
print('shellsort:')
print(stats.shellsort(m))
print(stats.shellsort(b))
print('rankdata:')
a = N.array(l) ll = [l]*5 aa = N.array(ll) m = range(4,24) m[10] = 34 b = N.array(m) print '\n\nF_oneway:' print stats.F_oneway(l,m) print stats.F_oneway(a,b) #print 'F_value:',stats.F_value(l),stats.F_value(a) print '\nSUPPORT' print 'sum:',stats.sum(l),stats.sum(lf),stats.sum(a),stats.sum(af) print 'cumsum:' print stats.cumsum(l) print stats.cumsum(lf) print stats.cumsum(a) print stats.cumsum(af) print 'ss:',stats.ss(l),stats.ss(lf),stats.ss(a),stats.ss(af) print 'summult:',stats.summult(l,m),stats.summult(lf,m),stats.summult(a,b),stats.summult(af,b) print 'sumsquared:',stats.square_of_sums(l),stats.square_of_sums(lf),stats.square_of_sums(a),stats.square_of_sums(af) print 'sumdiffsquared:',stats.sumdiffsquared(l,m),stats.sumdiffsquared(lf,m),stats.sumdiffsquared(a,b),stats.sumdiffsquared(af,b) print 'shellsort:' print stats.shellsort(m) print stats.shellsort(b) print 'rankdata:' print stats.rankdata(m) print stats.rankdata(b)
ll = [l] * 5
aa = N.array(ll)
m = range(4, 24)
m[10] = 34
b = N.array(m)
print '\n\nF_oneway:'
print stats.F_oneway(l, m)
print stats.F_oneway(a, b)
#print 'F_value:',stats.F_value(l),stats.F_value(a)
print '\nSUPPORT'
print 'sum:', stats.sum(l), stats.sum(lf), stats.sum(a), stats.sum(af)
print 'cumsum:'
print stats.cumsum(l)
print stats.cumsum(lf)
print stats.cumsum(a)
print stats.cumsum(af)
print 'ss:', stats.ss(l), stats.ss(lf), stats.ss(a), stats.ss(af)
print 'summult:', stats.summult(l, m), stats.summult(lf, m), stats.summult(
a, b), stats.summult(af, b)
print 'sumsquared:', stats.square_of_sums(l), stats.square_of_sums(
lf), stats.square_of_sums(a), stats.square_of_sums(af)
print 'sumdiffsquared:', stats.sumdiffsquared(l, m), stats.sumdiffsquared(
lf, m), stats.sumdiffsquared(a, b), stats.sumdiffsquared(af, b)
print 'shellsort:'
print stats.shellsort(m)
print stats.shellsort(b)
print 'rankdata:'
print stats.rankdata(m)
