stitching
As reflectometry measurements typically consist of multiple scans at different attenutation, we must stitch these together.
Module Contents
Functions
Concatenate each of the datasets together. |
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Rebin the data on a linear or logarithmic q-scale. |
API
- stitching.concatenate(scan_list: List[islatu.scan.Scan])
Concatenate each of the datasets together.
- Args:
- scans:
List of reflectometry scans.
- Returns:
tuple: Containing:q-values.
Reflected intensities.
– Errors on reflected intensities.
- stitching.rebin(q_vectors, reflected_intensity, new_q=None, rebin_as='linear', number_of_q_vectors=5000)
Rebin the data on a linear or logarithmic q-scale.
- Args:
- q_vectors:
q - the current q vectors.
- reflected_intensity (
tuple): (I, I_e) - The current reflected intensities, and their errors.
- new_q (
array_like): Array of potential q-values. Defaults to
None. If this argument is not specified, then the new q, R values are binned according to rebin_as and number_of_q_vectors.- rebin_as (py:attr:str):
String specifying how the data should be rebinned. Options are “linear” and “log”. This is only used if the new_q are unspecified.
- number_of_q_vectors (
int, optional): The max number of q-vectors to be using initially in the rebinning of the data. Defaults to
400.
- Returns:
tuple: Containing:q: rebinned q-values.
intensity: rebinned intensities.
intensity_e: rebinned intensity errors.