def LogicleOp(data, indices, inputs):
m = inputs['M'] * log(10) # convert from log10 to log
for i in indices:
d = data[:, i]
r = quantile(d[d<0], inputs['r'])
data[:, i] = logicle(data[:, i], inputs['T'], m, r)
xx = exp(arange(0, ub, ub/intervals))-1+min(x)
yy = biex0(xx, a, b, c, d, f)
t = interpolate.splrep(xx, yy, k=order)
return interpolate.splev(x, t)
if __name__ == '__main__':
import pylab
d1 = normal(0, 50, (50000))
d2 = lognormal(8, 1, (50000))
d3 = concatenate([d1, d2])
T = 262144
d = 4
m = d*log(10)
r = quantile(d3[d3<0], 0.05)
w = (m-log(T/abs(r)))/2
pylab.clf()
pylab.figtext(0.5, 0.94, 'Logicle transform with r=%.2f, d=%d and T=%d\nData is normal(0, 50, 50000) + lognormal(8, 1, 50000)' % (r, d, T),
va='center', ha='center', fontsize=12)
pylab.subplot(3,1,1)
x = arange(0, m, 0.1)
pylab.plot(x, S(x, 0, T, m, w))
locs, labs = pylab.xticks()
pylab.xticks([])
pylab.yticks([])
pylab.ylabel('Inverse logicle')
pylab.subplot(3,1,2)