"there is an automatic procedure to find the curvature for an HRIXS operation point."
"there is an automatic procedure to find the curvature for an HRIXS operation point.\n",
"The method `find_curvature` takes a run, integrates the images and fits the curvature parameters to it. During the fit, it will use the current curvature as starting values. It outputs the new curvature parameters, together with a plot of the curvature overlayed on top of the data, where the starting values are shown in blue, while the fitted curve is in orange.\n"
]
]
},
},
{
{
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@@ -1660,7 +1661,7 @@
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],
],
"source": [
"source": [
"h.Y_RANGE = slice(700, 900) # tell the algorithm where the data actually is\n",
"h.Y_RANGE = slice(700, 900) # tell the algorithm where the data actually is\n",
"h.find_curvature(1, 3485) # use run 272 of proposal 2769 to fit the curvature"
"h.find_curvature(1, 3485) # use run 1 of proposal 3485 to fit the curvature"
HRIXS spectra are, at the base, one dimensional spectra. They are recorded on a 2 D camera. The complication is that due to abberations, the spectra do not show up as straight lines on the camera, but are curved. These abberations need to be corrected for data analyis.
HRIXS spectra are, at the base, one dimensional spectra. They are recorded on a 2 D camera. The complication is that due to abberations, the spectra do not show up as straight lines on the camera, but are curved. These abberations need to be corrected for data analyis.
now we can read the data. There are two different ways to analyze the data: for low intensities, we can do single photon counting, for high intensities as simple integration is better. No matter which method, all data is read into an xarray DataSet, and further treated therein.
now we can read the data. There are two different ways to analyze the data: for low intensities, we can do single photon counting, for high intensities as simple integration is better. No matter which method, all data is read into an xarray DataSet, and further treated therein.
### Centroiding
### Centroiding
The centroiding algorithm then finds single photons, creates as spectrum and adds that to the DataSet. The parameters for the centroiding only need to be set once.
The centroiding algorithm then finds single photons, creates as spectrum and adds that to the DataSet. The parameters for the centroiding only need to be set once.
we make full use of xarray to select for parameters we scan over, in this example a pump-probe dela. "hRIXS_index" is an index into the table of delays, as it is an integer it makes it easier to group the data. Ideally, this index should also be used for all other types of scans, like energy scans, this is why the nameOne may then reduce the data, summing all corresponding images.
we make full use of xarray to select for parameters we scan over, in this example a pump-probe dela. "hRIXS_index" is an index into the table of delays, as it is an integer it makes it easier to group the data. Ideally, this index should also be used for all other types of scans, like energy scans, this is why the nameOne may then reduce the data, summing all corresponding images.
Instead of reducing all data at once, one can also just look at one delay. The first axis (the 0 in the example) is the index in the delay scan table, so the 0 means: take the data for the 0th delay in the list of delays.
Instead of reducing all data at once, one can also just look at one delay. The first axis (the 0 in the example) is the index in the delay scan table, so the 0 means: take the data for the 0th delay in the list of delays.
there is an automatic procedure to find the curvature for an HRIXS operation point.
there is an automatic procedure to find the curvature for an HRIXS operation point.
The method `find_curvature` takes a run, integrates the images and fits the curvature parameters to it. During the fit, it will use the current curvature as starting values. It outputs the new curvature parameters, together with a plot of the curvature overlayed on top of the data, where the starting values are shown in blue, while the fitted curve is in orange.
