Update proc_autophase.py

nmrglue/process/proc_autophase.py

@@ -14,46 +14,46 @@
 from .proc_base import ps
 
 
-def autops(data, fn, p0=0.0, p1=0.0):
+def psacme(data, p0=0.0, p1=0.0, gamma=5.0e-3):
  """
- Automatic linear phase correction
+ Phase correction using ACME algorithm by Chen Li et al.
+ Journal of Magnetic Resonance 158 (2002) 164-168
 
  Parameters
  ----------
+ ph : tuple
+ Current p0 and p1 values
  data : ndarray
  Array of NMR data.
- fn : str or function
- Algorithm to use for phase scoring. Built in functions can be
- specified by one of the following strings: "acme", "peak_minima"
- p0 : float
- Initial zero order phase in degrees.
- p1 : float
- Initial first order phase in degrees.
+ gamma : float
+ scale factor set to balance the penalty and entropy
+ optreturn : boolean
+ option to return the optimum p0 and p1
 
  Returns
  -------
- ndata : ndarray
- Phased NMR data.
+ score : float
+ Value of the objective function (phase score)
+ opt : tuple
+ phases returned by algoritm in a tuple
 
  """
- if not callable(fn):
- fn = {
- 'peak_minima': _ps_peak_minima_score,
- 'acme': _ps_acme_score,
- }[fn]
 
  opt = [p0, p1]
- opt = scipy.optimize.fmin(fn, x0=opt, args=(data, ))
+ opt = scipy.optimize.fmin(_ps_acme_score, x0=opt, args=(data, gamma))
 
  phasedspc = ps(data, p0=opt[0], p1=opt[1])
 
- return phasedspc
+ return tuple(opt), phasedspc
 
 
-def _ps_acme_score(ph, data):
+def pspmin(data, p0=0.0, p1=0.0):
  """
- Phase correction using ACME algorithm by Chen Li et al.
- Journal of Magnetic Resonance 158 (2002) 164-168
+ Phase correction using simple minima-minimisation around highest peak
+
+ This is a naive approach but is quick and often achieves reasonable
+ results. The optimisation is performed by finding the highest peak in the
+ spectra (e.g. TMSP) and then attempting to reduce minima surrounding it.
 
  Parameters
  ----------
@@ -66,60 +66,43 @@ def _ps_acme_score(ph, data):
  -------
  score : float
  Value of the objective function (phase score)
+ opt : tuple
+ phases returned by algoritm in a tuple
 
  """
- stepsize = 1
 
+ opt = [p0, p1]
+ opt = scipy.optimize.fmin(_ps_acme_score, x0=opt, args=(data,))
+
+ phasedspc = ps(data, p0=opt[0], p1=opt[1])
+
+ return tuple(opt), phasedspc
+
+
+def _ps_acme_score(ph, data, gamma):
  phc0, phc1 = ph
 
  s0 = ps(data, p0=phc0, p1=phc1)
  data = np.real(s0)
 
  # Calculation of first derivatives
- ds1 = np.abs((data[1:]-data[:-1]) / (stepsize*2))
- p1 = ds1 / np.sum(ds1)
+ ds1 = np.diff(data, 1)
+ p1 = np.abs(ds1) / np.abs(np.sum(ds1))
 
  # Calculation of entropy
- p1[p1 == 0] = 1
-
  h1 = -p1 * np.log(p1)
  h1s = np.sum(h1)
 
  # Calculation of penalty
- pfun = 0.0
- as_ = data - np.abs(data)
- sumas = np.sum(as_)
-
- if sumas < 0:
- pfun = pfun + np.sum((as_/2) ** 2)
+ fr = data
+ fr[fr >= 0] = 0
+ fr[fr < 0] = 1
+ pr = gamma * np.sum(fr * data**2)
 
- p = 1000 * pfun
-
- return h1s + p
+ return h1s + pr
 
 
 def _ps_peak_minima_score(ph, data):
- """
- Phase correction using simple minima-minimisation around highest peak
-
- This is a naive approach but is quick and often achieves reasonable
- results. The optimisation is performed by finding the highest peak in the
- spectra (e.g. TMSP) and then attempting to reduce minima surrounding it.
-
- Parameters
- ----------
- pd : tuple
- Current p0 and p1 values
- data : ndarray
- Array of NMR data.
-
- Returns
- -------
- score : float
- Value of the objective function (phase score)
-
- """
-
  phc0, phc1 = ph
 
  s0 = ps(data, p0=phc0, p1=phc1)