update

wotan/flatten.py

@@ -36,7 +36,8 @@ def flatten(time,
  robust=False, 
  max_splines=constants.PSPLINES_MAX_SPLINES,
  return_nsplines=False,
- mask=None
+ mask=None,
+ verbose=False
  ):
  """
  ``flatten`` removes low frequency trends in time-series data.
@@ -286,8 +287,11 @@ def flatten(time,
  elif method == 'rspline':
  trend_segment = iter_spline(time_view, flux_view, mask_view, window_length)
  elif method == 'pspline':
- print('Segment', i + 1, 'of', len(gaps_indexes) - 1)
- trend_segment, nsplines_segment = pspline(time_view, flux_view, edge_cutoff, max_splines, return_nsplines)
+ if verbose:
+ print('Segment', i + 1, 'of', len(gaps_indexes) - 1)
+ trend_segment, nsplines_segment = pspline(
+ time_view, flux_view, edge_cutoff, max_splines, return_nsplines, verbose
+ )
  nsplines = append(nsplines, nsplines_segment)
  elif method in "ridge lasso elasticnet":
  trend_segment = regression(time_view, flux_view, method, window_length, cval)

wotan/pspline.py

@@ -4,7 +4,7 @@
 from wotan.helpers import cleaned_array
 
 
-def pspline(time, flux, edge_cutoff, max_splines, return_nsplines):
+def pspline(time, flux, edge_cutoff, max_splines, return_nsplines, verbose):
  try:
  from pygam import LinearGAM, s
  except:
@@ -25,11 +25,12 @@ def pspline(time, flux, edge_cutoff, max_splines, return_nsplines):
  stdev = np.std(detrended_flux)
  mask_outliers = np.ma.where(
  np.abs(1-detrended_flux) > constants.PSPLINES_STDEV_CUT*np.std(detrended_flux))
- print('Iteration:', i + 1, 'Rejected outliers:', len(mask_outliers[0]))
- # Check convergence
- if len(mask_outliers[0]) == 0:
- print('Converged.')
- break
+ if verbose:
+ print('Iteration:', i + 1, 'Rejected outliers:', len(mask_outliers[0]))
+ # Check convergence
+ if len(mask_outliers[0]) == 0:
+ print('Converged.')
+ break
 
  # Final iteration, applied to unclipped time series (interpolated over clipped values)
  mask_outliers = np.ma.where(np.abs(1-detrended_flux) < constants.PSPLINES_STDEV_CUT*stdev)

wotan/tests.py

@@ -431,19 +431,20 @@ def main():
  numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc2), 1948.9000170463796, decimal=1)
 
 
-
+ print('Detrending 35 (pspline full features)')
  flatten_lc, trend_lc, nsplines = flatten(
  time,
  flux,
  method='pspline',
  max_splines=100,
  edge_cutoff=0.5,
  return_trend=True,
- return_nsplines=True
+ return_nsplines=True,
+ verbose=True
  )
 
- print('lightcurve was split into', len(nsplines), 'segments')
- print('chosen number of splines', nsplines)
+ #print('lightcurve was split into', len(nsplines), 'segments')
+ #print('chosen number of splines', nsplines)
  """
  import matplotlib.pyplot as plt
  plt.scatter(time, flux, s=3, color='black')

wotan/version.py

@@ -1 +1 @@
-WOTAN_VERSIONING = "1.6"
+WOTAN_VERSIONING = "1.7"