refactor transformers

neuroglia/calcium.py

@@ -4,7 +4,7 @@
 from scipy.signal import medfilt, savgol_filter
 
 
-class MedianFilterDetrend(BaseEstimator, TransformerMixin):
+class MedianFilterDetrender(BaseEstimator, TransformerMixin):
  """
  Median filter detrending
  """
@@ -15,30 +15,30 @@ def __init__(self,
  self.window = window
  self.peak_std_threshold = peak_std_threshold
 
- def robust_std(self, x):
+ def _robust_std(self, x):
  '''
  Robust estimate of std
  '''
  MAD = np.median(np.abs(x - np.median(x)))
  return 1.4826*MAD
 
  def fit(self, X, y=None):
- self.fit_params = {}
  return self
 
  def transform(self,X):
+ self.fit_params = {}
  X_new = X.copy()
  for col in X.columns:
  tmp_data = X[col].values.astype(np.double)
  mf = medfilt(tmp_data, self.window)
- mf = np.minimum(mf, self.peak_std_threshold * self.robust_std(mf))
+ mf = np.minimum(mf, self.peak_std_threshold * self._robust_std(mf))
  self.fit_params[col] = dict(mf=mf)
  X_new[col] = tmp_data - mf
 
  return X_new
 
 
-class SavGolFilterDetrend(BaseEstimator, TransformerMixin):
+class SavGolFilterDetrender(BaseEstimator, TransformerMixin):
  """
  Savitzky-Golay filter detrending
  """
@@ -50,10 +50,10 @@ def __init__(self,
  self.order = order
 
  def fit(self, X, y=None):
- self.fit_params = {}
  return self
 
  def transform(self,X):
+ self.fit_params = {}
  X_new = X.copy()
  for col in X.columns:
  tmp_data = X[col].values.astype(np.double)
@@ -64,19 +64,16 @@ def transform(self,X):
  return X_new
 
 
-class EventRescale(BaseEstimator, TransformerMixin):
+class EventRescaler(BaseEstimator, TransformerMixin):
  """
- Savitzky-Golay filter detrending
+ rescale events
  """
- def __init__(self,
- log_transform=True,
- scale=5):
+ def __init__(self,log_transform=True,scale=5):
 
  self.log_transform = log_transform
  self.scale = scale
 
  def fit(self, X, y=None):
- self.fit_params = {}
  return self
 
  def transform(self,X):
@@ -91,56 +88,51 @@ def transform(self,X):
  return X_new
 
 
-class OASISInferer(BaseEstimator, TransformerMixin):
+
+def oasis_kwargs(penalty,indicator):
+
+ kwargs = {}
+
+ if penalty=='l0':
+ kwargs.update(penalty=0)
+ elif penalty=='l1':
+ kwargs.update(penalty=1)
+ # elif penalty=='l2':
+ # kwargs.update(penalty=2)
+
+ if indicator.lower()=='gcamp6f':
+ kwargs.update(g=(None,))
+ elif indicator.lower()=='gcamp6s':
+ kwargs.update(g=(None,None))
+
+ return kwargs
+
+
+class CalciumDeconvolver(BaseEstimator, TransformerMixin):
  """docstring for OASISInferer."""
- def __init__(self,
- output='spikes',
- g=(None,),
- sn=None,
- b=None,
- b_nonneg=True,
- optimize_g=0,
- penalty=0,
- **kwargs
- ):
- super(OASISInferer, self).__init__()
-
- self.output = output
- self.g = g
- self.sn = sn
- self.b = b
- self.b_nonneg = b_nonneg
- self.optimize_g = optimize_g
+ def __init__(self,penalty='l0',indicator='GCaMP6f'):
  self.penalty = penalty
- self.kwargs = kwargs
+ self.indicator = indicator
 
  def fit(self, X, y=None):
- self.fit_params = {}
  return self
 
  def transform(self,X):
 
+ kwargs = oasis_kwargs(
+ self.penalty,
+ self.indicator,
+ )
+
  X_new = X.copy()
 
+ self.fit_params = {}
  for col in X.columns:
- c, s, b, g, lam = deconvolve(
+ denoised, spikes, b, g, lam = deconvolve(
  X[col].values.astype(np.double),
- g = self.g,
- sn = self.sn,
- b = self.b,
- b_nonneg = self.b_nonneg,
- optimize_g = self.optimize_g,
- penalty = self.penalty,
- **self.kwargs
- )
+ **kwargs)
  self.fit_params[col] = dict(b=b,g=g,lam=lam,)
-
- if self.output=='denoised':
- X_new[col] = c
- elif self.output=='spikes':
- X_new[col] = np.maximum(0, s)
- else:
- raise NotImplementedError
+ X_new[col] = spikes
 
  return X_new