From d7b247dc7b68c214c7dcf3855a90cd36a616c98a Mon Sep 17 00:00:00 2001
From: Laurent Mercadier <laurent.mercadier@xfel.eu>
Date: Thu, 27 Feb 2025 09:47:48 +0100
Subject: [PATCH] Update XAS function and remove printouts
---
src/toolbox_scs/detectors/viking.py | 37 +++++++++++++++--------------
1 file changed, 19 insertions(+), 18 deletions(-)
diff --git a/src/toolbox_scs/detectors/viking.py b/src/toolbox_scs/detectors/viking.py
index ff96db9..9b7124c 100644
--- a/src/toolbox_scs/detectors/viking.py
+++ b/src/toolbox_scs/detectors/viking.py
@@ -290,7 +290,6 @@ class Viking:
"""
if key not in data:
return
- print('hello')
x = data.newt_x
spectra = data[key]
mask = xr.ones_like(x, dtype=bool)
@@ -302,7 +301,6 @@ class Viking:
for xrange in ranges:
mask = mask & ((x < xrange[0]) | (x > xrange[1]))
x_bl = x.where(mask, drop=True).astype(int)
- print(x_bl)
bl = spectra.sel(newt_x=x_bl)
fit = np.polyfit(x_bl, bl.T, self.BL_POLY_DEG)
if len(spectra.shape) == 1:
@@ -312,8 +310,9 @@ class Viking:
final_bl[t] = np.polyval(fit[:, t], x)
data[key+'_nobl'] = spectra - final_bl
return data
-
- def xas(self, data, data_ref, thickness=1, plot=False,
+
+
+ def xas(self, sam, ref, thickness=1, dim='newt_x', plot=False,
plot_errors=True, xas_ylim=(-1, 3)):
"""
Given two independent datasets (one with sample and one reference),
@@ -323,13 +322,15 @@ class Viking:
Parameters
----------
- data: xarray Dataset
- the dataset containing the spectra with sample
- data_ref: xarray Dataset
- the dataset containing the spectra without sample
+ sam: xarray DataArray
+ the data array containing the spectra with sample
+ ref: xarray DataArray
+ the data array containing the spectra without sample
thickness: float
the thickness used for the calculation of the absorption
coefficient
+ dim: string
+ the name of the dimension along the dispersion axis
plot: bool
If True, plot the resulting average spectra.
plot_errors: bool
@@ -344,15 +345,14 @@ class Viking:
I0, It, absorptionCoef and their associated errors.
"""
- key = 'spectrum_nobl' if 'spectrum_nobl' in data else 'spectrum'
- if data['newt_x'].equals(data_ref['newt_x']) is False:
+ if sam[dim].equals(ref[dim]) is False:
return
- spectrum = data[key].mean(dim='trainId')
- std = data[key].std(dim='trainId')
- std_err = std / np.sqrt(data.sizes['trainId'])
- spectrum_ref = data_ref[key].mean(dim='trainId')
- std_ref = data_ref[key].std(dim='trainId')
- std_err_ref = std_ref / np.sqrt(data_ref.sizes['trainId'])
+ spectrum = sam.mean(dim='trainId')
+ std = sam.std(dim='trainId')
+ std_err = std / np.sqrt(sam.sizes['trainId'])
+ spectrum_ref = ref.mean(dim='trainId')
+ std_ref = ref.std(dim='trainId')
+ std_err_ref = std_ref / np.sqrt(ref.sizes['trainId'])
ds = xr.Dataset()
ds['It'] = spectrum
@@ -372,13 +372,14 @@ class Viking:
np.abs(absorption))
ds['absorptionCoef_stderr'] = absorption_stderr / (thickness *
np.abs(absorption))
- ds.attrs['n_It'] = data[key].sizes['trainId']
- ds.attrs['n_I0'] = data_ref[key].sizes['trainId']
+ ds.attrs['n_It'] = sam.sizes['trainId']
+ ds.attrs['n_I0'] = ref.sizes['trainId']
if plot:
plot_viking_xas(ds, plot_errors, xas_ylim)
return ds
+
def calibrate(self, runList, plot=True):
"""
This routine determines the calibration coefficients to translate the
--
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