bfx@modelling_polyFit

saqc/funcs/data_modelling.py

@@ -8,10 +8,10 @@ from saqc.lib.ts_operators import polyRoller, polyRollerNoMissing, polyRoller_nu
     validationAgg
 
 
-
 @register
 def modelling_polyFit(data, field, flagger, winsz, polydeg, numba='auto', eval_flags=True, min_periods=0, **kwargs):
-    to_fit = data[field].copy()
+    data = data.copy()
+    to_fit = data[field]
     flags = flagger.getFlags(field)
     if numba == 'auto':
         if to_fit.shape[0] < 200000:
@@ -33,10 +33,10 @@ def modelling_polyFit(data, field, flagger, winsz, polydeg, numba='auto', eval_f
         if numba:
             residues = to_fit.rolling(winsz, center=True).apply(polyRoller_numba, args=(miss_marker, val_range,
                                                                                     center_index, polydeg),
-                                                    engine='numba', engine_kwargs={'no_python': True})
+                                                    raw=True, engine='numba', engine_kwargs={'no_python': True})
         else:
             residues = to_fit.rolling(winsz, center=True).apply(polyRoller,
-                                                            args=(miss_marker, val_range, center_index, polydeg))
+                                                            args=(miss_marker, val_range, center_index, polydeg), raw=True)
         residues = residues - to_fit
         residues[na_mask] = np.nan
     else:
@@ -44,10 +44,10 @@ def modelling_polyFit(data, field, flagger, winsz, polydeg, numba='auto', eval_f
         if numba:
             residues = to_fit.rolling(winsz, center=True).apply(polyRollerNoMissing_numba, args=(val_range,
                                                                                         center_index, polydeg),
-                                                                engine='numba', engine_kwargs={'no_python': True})
+                                                                engine='numba', engine_kwargs={'no_python': True}, raw=True)
         else:
             residues = to_fit.rolling(winsz, center=True).apply(polyRollerNoMissing,
-                                                                args=(val_range, center_index, polydeg))
+                                                                args=(val_range, center_index, polydeg), raw=True)
 
     data[field] = residues
     if eval_flags:

saqc/lib/ts_operators.py

@@ -367,6 +367,7 @@ def _fit_x(a, b):
 
 @nb.jit
 def fit_poly(x, y, deg):
+    # a numba compatible polynomial fit function
     a = _coeff_mat(x, deg)
     p = _fit_x(a, y)
     # Reverse order so p[0] is coefficient of highest order
@@ -375,6 +376,7 @@ def fit_poly(x, y, deg):
 
 @nb.jit
 def eval_polynomial(P, x):
+    # a numba compatible polynomial evaluator
     result = 0
     for coeff in P:
         result = x * result + coeff
@@ -382,6 +384,7 @@ def eval_polynomial(P, x):
 
 
 def polyRoller_numba(in_slice, miss_marker, val_range, center_index, poly_deg):
+    # numba compatible function to roll with when modelling data with polynomial model
     miss_mask = (in_slice == miss_marker)
     x_data = val_range[~miss_mask]
     y_data = in_slice[~miss_mask]
@@ -390,11 +393,14 @@ def polyRoller_numba(in_slice, miss_marker, val_range, center_index, poly_deg):
 
 
 def polyRollerNoMissing_numba(in_slice, val_range, center_index, poly_deg):
+    # numba compatible function to roll with when modelling data with polynomial model -
+    # it is assumed, that in slice is an equidistant sample
     fitted = fit_poly(val_range, in_slice, deg=poly_deg)
     return eval_polynomial(fitted, center_index)
 
 
 def polyRoller(in_slice, miss_marker, val_range, center_index, poly_deg):
+    # function to roll with when modelling data with polynomial model
     miss_mask = (in_slice == miss_marker)
     x_data = val_range[~miss_mask]
     y_data = in_slice[~miss_mask]
@@ -403,5 +409,7 @@ def polyRoller(in_slice, miss_marker, val_range, center_index, poly_deg):
 
 
 def polyRollerNoMissing(in_slice, val_range, center_index, poly_deg):
+    # function to roll with when modelling data with polynomial model
+    # it is assumed, that in slice is an equidistant sample
     fitted = poly.polyfit(x=val_range, y=in_slice, deg=poly_deg)
     return poly.polyval(center_index, fitted)
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