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)