diff --git a/doc/changelog.rst b/doc/changelog.rst index 7e249afd5933b8c29969b14260030ac6c6510a56..930cba697db30a39dad749af887227248f887103 100644 --- a/doc/changelog.rst +++ b/doc/changelog.rst @@ -7,6 +7,7 @@ unreleased - **Bug fixes** - fix :issue:`61` regarding sign of XAS in some cases :mr:`207` + - Use xarray.values instead of .to_numpy() for backward-compatibility :mr:`214` - **Improvements** diff --git a/src/toolbox_scs/detectors/hrixs.py b/src/toolbox_scs/detectors/hrixs.py index 6282d85f15c4bd1d9721fc1e1793a48d12a3f825..82966529dfeeef6af260ceba40692890667ce512 100644 --- a/src/toolbox_scs/detectors/hrixs.py +++ b/src/toolbox_scs/detectors/hrixs.py @@ -331,7 +331,7 @@ class hRIXS: data = self.from_run(runNB, proposal) image = data['hRIXS_det'].sum(dim='trainId') \ - .to_numpy()[self.X_RANGE, self.Y_RANGE].T + .values[self.X_RANGE, self.Y_RANGE].T if args is None: spec = (image - image[:10, :].mean()).mean(axis=1) mean = np.average(np.arange(len(spec)), weights=spec) @@ -347,7 +347,7 @@ class hRIXS: ret = np.zeros((len(data["hRIXS_det"]), bins)) for image, r in zip(data["hRIXS_det"], ret): c = centroid( - image.to_numpy()[self.X_RANGE, self.Y_RANGE].T, + image.values[self.X_RANGE, self.Y_RANGE].T, threshold=self.THRESHOLD, std_threshold=self.STD_THRESHOLD, curvature=(self.CURVE_A, self.CURVE_B)) @@ -370,7 +370,7 @@ class hRIXS: for image, r in zip(data["hRIXS_det"], ret): if self.USE_DARK: image = image - self.dark_image - r[:] = integrate(image.to_numpy()[self.X_RANGE, self.Y_RANGE].T, factor=1, + r[:] = integrate(image.values[self.X_RANGE, self.Y_RANGE].T, factor=1, range=(10, bins - 10), curvature=(self.CURVE_A, self.CURVE_B)) data = data.assign_coords( diff --git a/src/toolbox_scs/routines/boz.py b/src/toolbox_scs/routines/boz.py index 3ba0b73d9fe99d8211b5fe7c4973309fa42e5bd1..25ac0a2e9d524ec78d9ae959811ffc062f83e691 100644 --- a/src/toolbox_scs/routines/boz.py +++ b/src/toolbox_scs/routines/boz.py @@ -1569,7 +1569,7 @@ def load_dssc_module(proposalNB, runNB, moduleNB=15, ppt = run[source, key][subset].data_counts() # ignore train with no pulses, can happen in burst mode acquisition ppt = ppt[ppt > 0] - tid = ppt.index.to_numpy() + tid = ppt.index.values ppt = np.unique(ppt) assert ppt.shape[0] == 1, "number of pulses changed during the run"