IPKM/nmreval
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set new y_err with value zero #215

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dominik merged 1 commits from issue-213-moving-mean into master 2024-01-15 18:41:38 +00:00
Conversation 0 Commits 1 Files Changed 2 +39 -16
2 changed files with 39 additions and 16 deletions
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4
.gitea/workflows/build-appimage.yaml
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@ -29,10 +29,10 @@ jobs:
env: env:
GPG_KEYGRIP: ${{ vars.GPG_KEYGRIP }} GPG_KEYGRIP: ${{ vars.GPG_KEYGRIP }}
GPG_PASSPHRASE: ${{ secrets.GPG_PASSPHRASE }} 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 - name: Upload AppImage
run: ./tools/upload_gitea.sh run: ./tools/upload_gitea.sh
env: 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_TOKEN: ${{ secrets.UPLOAD_TOKEN }}
UPLOAD_USER: ${{ vars.UPLOAD_USER }} UPLOAD_USER: ${{ vars.UPLOAD_USER }}

51
src/nmreval/math/smooth.py
View File

@ -3,14 +3,21 @@ import numpy.polynomial.polynomial as poly
from scipy import signal as signal from scipy import signal as signal
__all__ = ['smooth', 'loess', 'savgol', __all__ = [
'running_max', 'running_min', 'smooth',
'running_var', 'running_std', 'loess',
'running_median', 'running_mean', 'savgol',
'running_sum'] '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 !!! # ULTRA LANGSAM !!!
it = max(it, 0) it = max(it, 0)
@ -81,19 +88,19 @@ def savgol(x, y, window_size: int, deg: int = 2, mode: str = 'mirror'):
return new_y 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) 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) 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) 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) 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) return np.lib.stride_tricks.as_strided(arr, shapes, strides)
_funcs = {'loess': loess, 'savgol': savgol, 'mean': running_mean, 'median': running_median, _funcs = {
'std': running_std, 'var': running_var, 'max': running_max, 'min': running_min, 'sum': running_sum} '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: try:
func = _funcs[mode] func = _funcs[mode]
except KeyError: except KeyError:
@ -162,6 +185,6 @@ def smooth(data, window_size, mode='mean', logx=False, logy=False, **kwargs):
new_y = 10**new_y new_y = 10**new_y
new_data = data.copy() 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 return new_data