update readme

README.md

@@ -11,222 +11,9 @@ short_description: ShimNet Spectra Correction
 
 # ShimNet
 ShimNet is a data-driven AI solution to improve high-resolution nuclear magnetic resonance (NMR) spectra
-distorted by the inhomogeneous magnetic field (less than optimal shimming). To use it, the experimental training data has to be collected (see **Data collection** below).
-Example data can also be downloaded (see below). 
 
-Paper: [ShimNet: A neural network for post-acquisition improvement of NMR spectra distorted by magnetic-field inhomogeneity](https://chemrxiv.org/engage/chemrxiv/article-details/67ef866)
+Paper: [ShimNet: A neural network for post-acquisition improvement of NMR spectra distorted by magnetic-field inhomogeneity](https://doi.org/10.1021/acs.jpcb.5c02632)
 
-## Installation
+Full documentation may be found in the original repository: https://github.com/center4ml/shimnet
 
-Python 3.9+ (3.10+ for GUI)
-
-GPU version (for training and inference)
-```
-pip install -r requirements-gpu.txt
-```
-
-CPU version (for inference, not recommended for training)
-```
-pip install -r requirements-cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu
-```
-
-## Usage
-To correct spectra presented in the paper:
-1. download weights (model parameters):
-```
-python download_files.py
-```
-or directly from [Google Drive 700MHz](https://drive.google.com/uc?export=download&id=17fTNWl7YW6mPbbZWga0EfdoF_6S8fCke) and [Google Drive 600MHz](https://drive.google.com/uc?export=download&id=1_VxOpFGJcFsOa5DHOW2GJbP8RvHCmC1N) and place it in `weights` directory
-
-
-2. : run correction (e.g. `Azarone_20ul_700MHz.csv`):
-```
-python predict.py sample_data/Azarone_20ul_700MHz.csv -o output --config configs/shimnet_700.yaml --weights weights/shimnet_700MHz.pt
-```
-The output will be `output/Azarone_20ul_700MHz_processed.csv` file
-
-Multiple files may be processed using "*" syntax:
-```
-python predict.py sample_data/*700MHz.csv -o output --config configs/shimnet_700.yaml --weights weights/shimnet_700MH
-z.pt
-```
-
-For 600 MHz data use `--config configs/shimnet_600.yaml` and  `--weights weights/shimnet_600MHz.pt`, e.g.:
-
-```
-python predict.py sample_data/CresolRed_after_styrene_600MHz.csv -o output --config configs/shimnet_600.yaml --weights weights/shimnet_600MHz.pt
-```
-
-### input format
-
-The spectrum file for reconstruction should be in the format of two columns separated by a space and without the sign at the end of the line at the end of the file(example below):
-```csv
--1.97134	0.0167137	
--1.97085	-0.00778748	
--1.97036	-0.0109595	
--1.96988	0.00825978	
--1.96939	0.0133886	
-```
-
-## Train on your data
-
-For the model to function properly, it should be trained on calibration data from the spectrometer used for the measurements. To train a model on data from your spectrometer, please follow the instructions below.
-
-### Training data collection
-
-Below we describe the training data collection for Agilent/Varian spectrometers. For machines of other vendors similar procedure can be implemented.
-To collect ShimNet training data use Python script (sweep_shims_lineshape_Z1Z2.py) from the calibration_loop folder to drive the spectrometer:
- 1. Install TReNDS package ( trends.spektrino.com )
- 2. Open VnmrJ and type: 'listenon'
- 3. Put the lineshape sample (1% CHCl3 in deuterated acetone), set standard PROTON parameters, and set nt=1 (do not modify sw and at!)
- 4. Shim the sample and collect the data. Save the optimally shimmed dataset
- 5. Edit the sweep_shims_lineshape_Z1Z2.py script
- 6. Put optimum z1 and z2 shim values as optiz1 and optiz2 below
- 7. Define the calibration range as range_z1 and range_z2 (default is ok)
- 8. Start the python script:
-   ```
-     python3 ./sweep_shims_lineshape_Z1Z2.py
-   ```
-   The spectrometer will start collecting spectra
-
-### SCRF extraction
-Shim Coil Response Functions (SCRF) should be extracted from the spectra with `extract_scrf_from_fids.py` script.
-```
-python extract_scrf_from_fids.py
-```
-
-The script uses hardcoded paths to the NMR signals (fid-s) in Agilent/Varian format: a directory with optimal measurement (`opti_fid_path` available) and a directory with calibration loop measurements (`data_dir`):
-```python
-# input
-data_dir = "../../sample_run/loop"
-opti_fid_path = "../../sample_run/opti.fid"
-
-```
-
-The output files are also hardcoded:
-```python
-# output
-spectra_file = "../../sample_run/total.npy"
-spectra_file_names = "../../sample_run/total.csv"
-opi_spectrum_file = "../../sample_run/opti.npy"
-responses_file = "../../sample_run/scrf_61.pt"
-```
-where only the `responses_file` is used in ShimNet training.
-
-If the measurements are stored in a format other than Varian, you may need to change this line:
-```python
-dic, data = ng.varian.read(varian_fid_path)
-```
-(see nmrglue package documentation for details)
-
-### Training
-
-1. Download multiplets database:
-    ```
-    python download_files.py --multiplets
-    ```
-2. Configure run:
-  - create a run directory, e.g. `runs/my_lab_spectrometer_2025`
-  - create a configuration file:
-    1. copy `configs/shimnet_template.py` to the run directory and rename it to `config.yaml`
-       ```bash
-       cp configs/shimnet_template.py runs/my_lab_spectrometer_2025/config.yaml
-       ```
-    2. edit the SCRF in path in the config file:
-       ```yaml
-         response_functions_files:
-         - path/to/srcf_file
-       ```
-       e.g.
-       ```yaml
-         response_functions_files:
-         - ../../sample_run/scrf_61.pt
-       ```
-    3. adjust spectrometer frequency step `frq_step` to match your data (spectrometer range in Hz divided by number of points in spectrum):
-        ```yaml
-        frq_step: 0.34059797
-        ```
-    4. adjust spectromer frequency in the metadata
-        ```yaml
-        metadata: # additional metadata, not used in the training process
-          spectrometer_frequency: 700.0 # MHz
-        ```
-3. Run training:
-    ```
-    python train.py runs/my_lab_spectrometer_2025
-    ```
-    Training results will appear in `runs/my_lab_spectrometer_2025` directory.
-    Model parameters are stored in `runs/my_lab_spectrometer_2025/model.pt` file
-4. Use trained model:
-
-    use `--config runs/my_lab_spectrometer_2025/config.yaml` and  `--weights runs/my_lab_spectrometer_2025/model.pt` flags, e.g.
-    ```
-    python predict.py my_sample1.csv -o my_output --config runs/my_lab_spectrometer_2025/config.yaml --weights runs/my_lab_spectrometer_2025/model.pt
-    ```
-
-## Repeat training on our data
-
-If you want to train the network using the calibration data from our paper, follow the procedure below.
-
-1. Download multiplets database and our SCRF files:
-    ```
-    python download_files.py --multiplets --SCRF --no-weights
-    ```
-    or directly download from Google Drive and store in `data/` directory: [Response Functions 600MHz](https://drive.google.com/file/d/1J-DsPtaITXU3TFrbxaZPH800U1uIiwje/view?usp=sharing), [Response Functions 700MHz](https://drive.google.com/file/d/113al7A__yYALx_2hkESuzFIDU3feVtNY/view?usp=sharing), [Multiplets data](https://drive.google.com/file/d/1QGvV-Au50ZxaP1vFsmR_auI299Dw-Wrt/view?usp=sharing)
-
-2. Configure run
-    - For 600MHz spectrometer:
-      ```bash
-      mkdir -p runs/repeat_paper_training_600MHz
-      cp configs/shimnet_600.yaml runs/repeat_paper_training_600MHz/config.yaml
-      ```
-    - For 700 MHz spectrometer:
-      ```bash
-      mkdir -p runs/repeat_paper_training_700MHz
-      cp configs/shimnet_700.yaml runs/repeat_paper_training_700MHz/config.yaml
-      ```
-3. Run training:
-    ```
-    python train.py runs/repeat_paper_training_600MHz
-    ```
-    or
-    ```
-    python train.py runs/repeat_paper_training_700MHz
-    ```
-    Training results will appear in `runs/repeat_paper_training_600MHz` or `runs/repeat_paper_training_700MHz` directory.
-
-## GUI
-
-### Installation
-
-To use the ShimNet GUI, ensure you have Python 3.10 installed (not tested with Python 3.11+). After installing the ShimNet requirements (CPU/GPU), install the additional dependencies for the GUI:
-
-```bash
-pip install -r requirements-gui.txt
-```
-
-### Launching the GUI
-
-The ShimNet GUI is built using Gradio. To start the application, run:
-
-```bash
-python predict-gui.py
-```
-
-Once the application starts, open your browser and navigate to:
-
-```
-http://127.0.0.1:7860
-```
-
-to access the GUI locally.
-
-### Sharing the GUI
-
-To make the GUI accessible over the internet, use the `--share` flag:
-
-```bash
-python predict-gui.py --share
-```
-
-A public web address will be displayed in the terminal, which you can use to access the GUI remotely or share with others.
+Sample input data: https://huggingface.co/spaces/NMR-CeNT-UW/ShimNet/tree/main/sample_data