def transformdata_1d(x, f, plotflag):
transform_id = np.mod(plotflag // 10, 10)
transform = [lambda f, x: f,
lambda f, x: 1 - f,
lambda f, x: cumtrapz(f, x),
lambda f, x: 1 - cumtrapz(f, x),
lambda f, x: np.log(f),
lambda f, x: np.log1p(-f),
lambda f, x: np.log(cumtrapz(f, x)),
lambda f, x: np.log1p(-cumtrapz(f, x)),
lambda f, x: 10*np.log10(f)
][transform_id]
return transform(f, x)
def tau_integ(chi_in, height):
''' Integrates the opacity to get the optical depth tau '''
chi = N.transpose(chi_in)
tau = N.zeros(chi.shape)
try:
# if there is a masked array, sum only over used depth points
mm = N.invert(chi[0].mask)
zcut = N.where(mm == 1)[0][0]
tau[:,zcut+1:] = cumtrapz(chi[:,mm], x=-height[mm])
except AttributeError:
tau[:,1:] = cumtrapz(chi, x=-height)
return tau
def transformdata(x, f, plotflag):
transFlag = np.mod(plotflag // 10, 10)
if transFlag == 0:
data = f
elif transFlag == 1:
data = 1 - f
elif transFlag == 2:
data = cumtrapz(f, x)
elif transFlag == 3:
data = 1 - cumtrapz(f, x)
if transFlag in (4, 5):
if transFlag == 4:
data = -np.log1p(-cumtrapz(f, x))
else:
if any(f < 0):
raise ValueError('Invalid plotflag: Data or dataCI is negative, but must be positive')
data = 10 * np.log10(f)
return data
def to_cdf(self):
if isinstance(self.args, (list, tuple)): # Multidimensional data
raise NotImplementedError('integration for ndim>1 not implemented')
cdf = np.hstack((0, cumtrapz(self.data, self.args)))
return PlotData(cdf, np.copy(self.args), xlab='x', ylab='F(x)')
