Commit 106c36a2 authored by William Clarke's avatar William Clarke
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Update readme.

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Code associated with `Uncertainty in denoising of MRSI using low rank methods,
WT Clarke & M Chiew, 2021`
## Data & Figure generation
All output figures are found in the figures directory. The generation process is documented for each figure or set of related figures in turn. This assume the below requirements are installed.
### Bias and rank selection in simulated data (figures 2 & 3)
Data and figures are generated by running mp_performance_noisy_data.ipynb
### Uncertainty in denoising of a uniform single-peak simulation (figures 4-6)
Data and figures are generated by running single_peak_st_lp_lora.ipynb
### Evaluation of denoising methods in simulated 1H-MRSI (figures 1, 7 & 8)
Data is generated by running:
1. python gen_avg_fit_noiseless.py
2. python create_all_noisy.py
3. python synthetic_denoise_and_fit_1.py
4. python synthetic_denoise_and_fit_2.py
5. python noiseless_fit.py
Figures are then generated by running synthetic_1_figures.ipynb and synthetic_2_figures.ipynb
### Reproducibility of 1H-MRSI (figure 9)
Data is generated by running:
1. python invivo_denoise_and_fit.py
2. python invivo_bs_fits.py
Figures are then generated in invivo_figures.ipynb
## Requirements
Required python packages can be installed using Miniconda. To get Miniconda follow the instructions on the [Miniconda website](https://docs.conda.io/en/latest/miniconda.html). You can then create a suitable environment using the following commands:
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