islatu.stitching¶
As reflectometry measurements typically consist of multiple scans at different attenutation, we must stitch these together.
- islatu.stitching.concatenate(scan_list: List[Scan])[source]¶
Concatenate each of the datasets together.
- Parameters
scans – List of reflectometry scans.
- Returns
- Containing:
q-values.
Reflected intensities.
– Errors on reflected intensities.
- Return type
tuple
- islatu.stitching.rebin(q_vectors, reflected_intensity, new_q=None, rebin_as='linear', number_of_q_vectors=5000)[source]¶
Rebin the data on a linear or logarithmic q-scale.
- Parameters
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 toNone
. 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 to400
.
- Returns
- Containing:
q: rebinned q-values.
intensity: rebinned intensities.
intensity_e: rebinned intensity errors.
- Return type
tuple