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https://github.com/radi0sus/cal-mb
Easily calibrate Mößbauer (MB) spectra
https://github.com/radi0sus/cal-mb
calibration fitting least-squares-optimization levenberg-marquardt lmfit lorentz mossbauer mossbauer-spectroscopy nonlinear-optimization python spectroscopy spectrum
Last synced: 17 days ago
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Easily calibrate Mößbauer (MB) spectra
- Host: GitHub
- URL: https://github.com/radi0sus/cal-mb
- Owner: radi0sus
- License: bsd-3-clause
- Created: 2023-12-17T15:34:18.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-05T09:57:19.000Z (12 months ago)
- Last Synced: 2024-01-27T22:09:47.303Z (11 months ago)
- Topics: calibration, fitting, least-squares-optimization, levenberg-marquardt, lmfit, lorentz, mossbauer, mossbauer-spectroscopy, nonlinear-optimization, python, spectroscopy, spectrum
- Language: Python
- Homepage:
- Size: 2.64 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Cal-MB
A Python 3 script for easy calibration of ⁵⁷Fe-Mößbauer (MB) spectra from a ⁵⁷Fe sample.
## External modules
`lmfit`
`numpy`
`scipy`
`matplotlib`
## Quick start
Start the script with:
```console
python3 cal-mb.py 57Fe_calib_raw_data.ws5
```
calculates the folding point `FP`, `v0` (channel where the velocity is zero),
`vmax` (maximum velocity), and the velocity / channel `f`.
The file should contain intensities or counts from a multi-channel analyzer. The popular WissEl
format has the extension `.ws5`. In principle any raw data (not only WissEl) can be processed. Pay
attention to the start channel number and the folding direction.
Terminal output:
```
======================================
Results for 57Fe_calib_raw_data.ws5 : ⇦ name of the file that contains the ⁵⁷Fe-MB-spectrum
File modified on 24.08.2023 13:33:53 ⇦ modification date
--------------------------------------
FP (channel) = 256.7889±0.0588 ⇦ folding point
v₀ (channel) = 125.6873±0.0620 ⇦ channel where velocity is zero
vmax /mm·s⁻¹ = -4.6926±0.0111 ⇦ maximum velocity
f /mm·s⁻¹/c = 0.0368±0.0001 ⇦ velocity / channel
======================================
Statistics (folded data with weights):
--------------------------------------
data points : 256 ⇦ number of data points
variables : 13 ⇦ number of variables
mean σ data : 152.68 ⇦ weights for χ² and red. χ²
χ² : 537.49 ⇦ Chi square(d)
red. χ² : 2.21 ⇦ reduced Chi square(d)
R² : 0.9736 ⇦ R square(d)
======================================
```
You can use the first three parameters to fit MB spectra with [fit-mb](https://github.com/radi0sus/fit-mb).
Start the script with:
```console
python3 cal-mb.py 57Fe_calib_raw_data.ws5 -s
```
calculates the same values as described above. In addition a `matplotlib` window is shown that
summarizes the fit results.
The results will not be saved. To keep the output you have to start the script with:
```console
python3 cal-mb.py 57Fe_calib_raw_data.ws5 > calib_from_today.txt
```
To keep the figure, you have to click the floppy symbol (similar to 💾) in the `matplotlib` window.
## Remarks
- The script is benchmarked against the `mcal` program from Dr. Eckhard Bill.
Within the given restrictions, the results match quite well.
- Raw spectra (WissEl .ws5 for example) are expected to start at channel 1 and be folded to the right.
- With the `-fl` option the raw spectrum can be folded to the left.
- `FP` is the mean of the centers of the individual Lorentz functions (4, 8 or 12) of the raw spectrum.
- `v0` is the mean of the centers of the individual Lorentz functions (2, 4 or 6) of the folded spectrum.
- `f` is the mean of `f` calculated with $\Delta E_Q$ from ⁵⁷Fe divided by the difference of the centers of
the single Lorentz functions from the outermost to the innermost pair (see also comment in the script).
- `vmax` is `f * 127.5` in case of 256 channels.
- In case of unfolded data, the error can be estimated from the differences in the intensities of the left-hand side and
right-hand side sub-spectra. The weighting for χ² and red. χ² is 1 / (mean standard deviation).
The mean standard deviation is the square root of the mean variance of two times the intensities of the left-hand side
and right-hand side data pairs which are supposed to be equal. χ² should be close to the number of data points and red. χ²
should close to 1 in case of a good fit.
Please note that the calibration parameters are mainly derived from channel or velocity data (x-values), while only errors
from intensity data or counts (y-values) are taken into account for the weigths of χ² and red. χ².
- R² is calculated by 1 - variance(residual * mean standard deviation) / variance(intensities or counts),
because R² is calculated wrongly by `lmfit` in case of weights.
- All other values and errors are calculated with `lmfit`.
- The script has not been tested with raw data from 1024 channel multi-channel analyzers.
## Example
