set new y_err with value zero (#215)

closes #215

.gitea/workflows/build-appimage.yaml

@@ -29,10 +29,10 @@ jobs:
         env:
           GPG_KEYGRIP: ${{ vars.GPG_KEYGRIP }}
           GPG_PASSPHRASE: ${{ secrets.GPG_PASSPHRASE }}
-          GO_PIPELINE_LABEL: ${{ env.YEAR }}.${{ gitea.run_number }}_${{ env.SHA_SHORT }}_bookworm
+          GO_PIPELINE_LABEL: ${{ env.YEAR }}.${{ gitea.run_number }}_${{ env.SHA_SHORT }}
       - name: Upload AppImage
         run: ./tools/upload_gitea.sh
         env:
-          GO_PIPELINE_LABEL: ${{ env.YEAR }}.${{ gitea.run_number }}_${{ env.SHA_SHORT }}_bookworm
+          GO_PIPELINE_LABEL: ${{ env.YEAR }}.${{ gitea.run_number }}_${{ env.SHA_SHORT }}
           UPLOAD_TOKEN: ${{ secrets.UPLOAD_TOKEN }}
           UPLOAD_USER: ${{ vars.UPLOAD_USER }}

src/nmreval/math/smooth.py

@@ -3,14 +3,21 @@ import numpy.polynomial.polynomial as poly
 from scipy import signal as signal
 
 
-__all__ = ['smooth', 'loess', 'savgol',
-           'running_max', 'running_min',
-           'running_var', 'running_std',
-           'running_median', 'running_mean',
-           'running_sum']
+__all__ = [
+    'smooth',
+    'loess',
+    'savgol',
+    'running_max',
+    'running_min',
+    'running_var',
+    'running_std',
+    'running_median',
+    'running_mean',
+    'running_sum',
+]
 
 
-def loess(x, y, window_size, it=0, deg=2):
+def loess(x, y, window_size: int, it: int = 0, deg: int = 2):
     # ULTRA LANGSAM !!!
     it = max(it, 0)
 
@@ -81,19 +88,19 @@ def savgol(x, y, window_size: int, deg: int = 2, mode: str = 'mirror'):
     return new_y
 
 
-def running_mean(x, y, window_size):
+def running_mean(x, y, window_size: int):
     return _running_func(np.nanmean, x, y, window_size)
 
 
-def running_median(x, y, window_size):
+def running_median(x, y, window_size: int):
     return _running_func(np.nanmedian, x, y, window_size)
 
 
-def running_std(x, y, window_size):
+def running_std(x, y, window_size: int):
     return _running_func(np.nanstd, x, y, window_size)
 
 
-def running_var(x, y, window_size):
+def running_var(x, y, window_size: int):
     return _running_func(np.nanvar, x, y, window_size)
 
 
@@ -132,11 +139,27 @@ def _moving_window(arr, nn):
     return np.lib.stride_tricks.as_strided(arr, shapes, strides)
 
 
-_funcs = {'loess': loess, 'savgol': savgol, 'mean': running_mean, 'median': running_median,
-          'std': running_std, 'var': running_var, 'max': running_max, 'min': running_min, 'sum': running_sum}
+_funcs = {
+    'loess': loess,
+    'savgol': savgol,
+    'mean': running_mean,
+    'median': running_median,
+    'std': running_std,
+    'var': running_var,
+    'max': running_max,
+    'min': running_min,
+    'sum': running_sum,
+}
 
 
-def smooth(data, window_size, mode='mean', logx=False, logy=False, **kwargs):
+def smooth(
+        data: 'Data',
+        window_size: int,
+        mode: str = 'mean',
+        logx: bool = False,
+        logy: bool = False,
+        **kwargs
+):
     try:
         func = _funcs[mode]
     except KeyError:
@@ -162,6 +185,6 @@ def smooth(data, window_size, mode='mean', logx=False, logy=False, **kwargs):
         new_y = 10**new_y
 
     new_data = data.copy()
-    new_data.set_data(x=new_x, y=new_y, y_err=None)
+    new_data.set_data(x=new_x, y=new_y, y_err=np.zeros_like(new_y))
 
     return new_data