def _logicle(y, T=262144, m=4.5, r=None, w=0.5, a=0):
y = array(y, dtype='double')
if w is None: # we need an r then...
if r == 0:
w = 1 # don't like this but it works... FIX!
else:
w = (m - log10(T / abs(r))) / 2.0
clogicle.logicle_scale(T, w, m, a, y)
return y
def _logicle(y, T=262144, m=4.5, r=None, w=0.5, a=0):
y = array(y, dtype='double')
if w is None: # we need an r then...
if r == 0:
w = 1 # don't like this but it works... FIX!
else:
w = (m - log10(T / abs(r))) / 2.0
clogicle.logicle_scale(T, w, m, a, y)
return y
def _logicle(y, T, m, r, w):
y = array(y, dtype='double')
if w is None:
if r == 0:
w = 1 # don't like this but it works... FIX!
else:
w = (m - log10(T / abs(r))) / 2.0
clogicle.logicle_scale(T, w, m, 0, y)
return y
d3 = concatenate([d1, d2])
T = 262144
d = 4
#m = d*log(10)
m = 4.5
print m
r = quantile(d3[d3 < 0], 0.05)
w = (m - log(T / abs(r))) / 2
w = (m - numpy.log10(T / numpy.abs(r))) / 2.0
#if (w<0):
#w = .5
print w
print _logicle([0, T], T, m, r)
n = array([0, T], dtype='double')
clogicle.logicle_scale(T, w, 4.5, 0, n)
print n
lmin, lmax = _logicle([0, T], T, m, r)
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(4, 1, 1)
x = arange(0, m, 0.1)
pylab.plot(x, S(x, 0, T, m, w))
d3 = concatenate([d1, d2])
T = 262144
d = 4
#m = d*log(10)
m = 4.5
print m
r = quantile(d3[d3 < 0], 0.05)
w = (m - log(T / abs(r))) / 2
w = (m - numpy.log10(T / numpy.abs(r))) / 2.0
#if (w<0):
#w = .5
print w
print _logicle([0, T], T, m, r)
n = array([0, T], dtype='double')
clogicle.logicle_scale(T, w, 4.5, 0, n)
print n
lmin, lmax = _logicle([0, T], T, m, r)
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(4, 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(4, 1, 2)
