Update proc_autophase.py

nmrglue/process/proc_autophase.py

@@ -14,7 +14,7 @@
 from .proc_base import ps
 
 
-def autops(data, fn, p0=0.0, p1=0.0):
+def autops(data, fn, p0=0.0, p1=0.0, **kwargs):
  """
  Automatic linear phase correction
 
@@ -29,12 +29,17 @@ def autops(data, fn, p0=0.0, p1=0.0):
  Initial zero order phase in degrees.
  p1 : float
  Initial first order phase in degrees.
+ kwargs : keyword arguments
+ gamma : constant for penalty term for 'acme' to penalize negative peaks. 
+ order : order of derivative used in 'acme'
 
  Returns
  -------
  ndata : ndarray
  Phased NMR data.
-
+ opt : tuple
+ optimized 0th and 1st order phases
+
  """
  if not callable(fn):
  fn = {
@@ -43,62 +48,57 @@ def autops(data, fn, p0=0.0, p1=0.0):
  }[fn]
 
  opt = [p0, p1]
- opt = scipy.optimize.fmin(fn, x0=opt, args=(data, ))
+ opt = scipy.optimize.fmin(fn, x0=opt, args=(data, kwargs))
 
  phasedspc = ps(data, p0=opt[0], p1=opt[1])
 
- return phasedspc
-
+ return tuple(opt), phasedspc
+ 
 
-def _ps_acme_score(ph, data):
+def _ps_acme_score(ph, data, kwargs):
  """
  Phase correction using ACME algorithm by Chen Li et al.
  Journal of Magnetic Resonance 158 (2002) 164-168
 
  Parameters
  ----------
- pd : tuple
+ ph : tuple
  Current p0 and p1 values
  data : ndarray
- Array of NMR data.
+ Array of NMR data
+ kwargs : dictionary
+ gamma : constant for penalty term for 'acme'
+ order : order of derivative used in 'acme'
 
  Returns
  -------
  score : float
  Value of the objective function (phase score)
 
  """
- stepsize = 1
-
  phc0, phc1 = ph
+ s = ps(data, p0=phc0, p1=phc1)
+ data = np.real(s)
 
- 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)
+ # Calculation of derivatives
+ ds = np.diff(data,kwargs['order'])
+ dsn = np.abs(ds)
+ p1 = dsn / np.nansum(dsn)
 
  # Calculation of entropy
- p1[p1 == 0] = 1
-
  h1 = -p1 * np.log(p1)
- h1s = np.sum(h1)
+ h1s = np.nansum(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)
-
- p = 1000 * pfun
-
- return h1s + p
+ fr = data
+ fr[fr >= 0] = 0
+ fr[fr < 0] = 1
+ pr = kwargs['gamma'] * np.nansum(fr * data**2)
+
+ return h1s + pr
 
 
-def _ps_peak_minima_score(ph, data):
+def _ps_peak_minima_score(ph, data, kwargs):
  """
  Phase correction using simple minima-minimisation around highest peak
 
@@ -108,10 +108,11 @@ def _ps_peak_minima_score(ph, data):
 
  Parameters
  ----------
- pd : tuple
+ ph : tuple
  Current p0 and p1 values
  data : ndarray
  Array of NMR data.
+ kwargs : to keep compatibility with autops
 
  Returns
  -------