Commit 7045c58c authored by Saman Sarraf's avatar Saman Sarraf
Browse files

added no display functionality to suppress the figures

parent 85431f30

CommunityDetectionMethods.py

+2 −2
Original line number Diff line number Diff line
@@ -533,7 +533,7 @@ class CommunityDetection: 
            print(comments + " Silhouette Score is : " + str(s_s))

        return s_s



    def modality_optimizer(self, modality_list):

    def modality_optimizer(self, modality_list, display_figures=True):

        self.comment = self.comment

        possible_combinations = comb_mod(modality_list)

        self.data_opt = []
@@ -567,7 +567,7 @@ class CommunityDetection: 
            preprocessed_data_opt = pd.concat([preprocessed_data_opt, self.data_struct_norm[mod_label]], axis=1)



        snf_groups_opt_temporary = self.similarity_network_fusion(self.number_cluster, prep_data=self.data_opt,

                                                                  display=True, print_flag=False,

                                                                  display=display_figures, print_flag=False,

                                                                  plot_pref="Optimized_")

        snf_groups_opt = []

        for qq in tuple_helper_:

README.md

+68 −29
Original line number Diff line number Diff line
@@ -72,26 +72,29 @@ MINT is designed for scalable and interpretable community detection, with applic 
  python main.py --json ../SNF.json

  ```

### Command Line Arguments for MINT



| Short Argument | Long Argument     | Description                                                                                                          |

|----------------|----------------|-------------|

| `-j`  | `--json`         | JSON Configuration from JSON file |

|----------------|-------------------|----------------------------------------------------------------------------------------------------------------------|

| `-j`           | `--json`          | JSON configuration file                                                                                              |

| `-nm`          | `--numbermodal`   | Number of modalities                                                                                                 |

| `-ns`          | `--numbersubj`    | Number of subjects (consistent across modalities)                                                                    |

| `-f`  | `--filetype`     | Type of files (either csv or excel) |

| `-f`           | `--filetype`      | Type of files (`csv` or `excel`)                                                                                     |

| `-cp`          | `--csvpath`       | List of CSV files (one per modality), comma-separated                                                                |

| `-ep` | `--excelpath`    | List of Excel files (one per modality) OR one Excel file with data in multiple sheets |

| `-ep`          | `--excelpath`     | List of Excel files (one per modality) OR one Excel file with multiple sheets                                        |

| `-sn`          | `--sheetname`     | List of sheet names (comma-separated, one sheet per modality)                                                        |

| `-cl`          | `--colnames`      | List of columns to include (optional, defaults to all columns)                                                       |

| `-nr`          | `--norm`          | List of modalities (by index) requiring normalization                                                                |

| `-nt` | `--normtype`     | Normalization method (either minmax or max) |

| `-nt`          | `--normtype`      | Normalization method (`minmax` or `max`)                                                                             |

| `-m`           | `--method`        | Community detection method: `snf` (Similarity Network Fusion) or `gl` (Generalized Louvain)                          |

| `-sf`          | `--snfpath`       | Path to store SNF results (CSV)                                                                                      |

| `-gp`          | `--glpath`        | Path to store GenLouvain results (CSV)                                                                               |

| `-nc`          | `--numbercluster` | Number of clusters for SNF                                                                                           |

| `-v`           | `--verbose`       | Display figures (True/False)                                                                                         |

| `-co`          | `--featcoef`      | Coefficient for top feature selection                                                                                |

| `-cv` | `--crossvaltype` | Cross Validation Method (default = 4)<br> 1 = all subjects<br>2 = centroid<br>3 = centroid replacement<br>4 = k-fold |

| `-cv`          | `--crossvaltype`  | Cross validation method (default = 4).<br>1 = all subjects<br>2 = centroid<br>3 = centroid replacement<br>4 = k-fold |

| `-lf`          | `--listfeat`      | List of user-defined features (overrides feature ranking algorithms)                                                 |

| `-nd`          | `--no-display`    | Suppress figure display while still generating and saving figures                                                    |





NB: The table above helps you understand the features available in MINT; however, you can also use the JSON templates provided with the toolbox and run the analysis using the JSON option, instead of specifying all arguments directly on the command line.
@@ -132,7 +135,8 @@ The following is an example of a JSON configuration file with parameters set. To 
    "OUTLIER": false,

    "OUTLIERPARAMS": [3, "median"],

    "QUALITYCONTORL":true,

    "OPTIMIZATION":"all"}

    "OPTIMIZATION":"all",

    "NO_DISPLAY": false}

  ```



### Input Data Format
@@ -155,19 +159,54 @@ Assuming all three steps including SNF/GL, cross validation and optimization are 
  <img src="examples/SNF_image.png" width="45%" />

</p>



### Display Control



MINT supports running analyses in **headless mode**, where figures are generated and saved but not displayed on screen.



This is useful when running analyses:



- on remote servers

- on HPC clusters

- in automated pipelines

- during large batch experiments



Figure display can be disabled in two ways.



**Command line**



```bash

python main.py --json config.json --no-display

```



or



```bash

python main.py -j config.json -nd

```



**JSON configuration**



```json

{

  "NO_DISPLAY": true

}

```



When `NO_DISPLAY` is enabled, MINT switches Matplotlib to a non-interactive backend so figures are generated and saved but no GUI windows appear.

## Citation



If you use MINT in your research, please cite:



```bibtex

@article{yourpaper2024,

  author = {Your Name et al.},

  title = {MINT: A Multimodal Community Detection Toolbox},

  journal = {Journal Name},

  year = {2024},

  volume = {X},

  pages = {XX-XX},

  doi = {DOI_LINK}

@article{sarraf2025multilayer,

  title={Multilayer integration of networks toolbox (MINT)},

  author={Sarraf, Saman and Avelar-Pereira, B{\'a}rbara and Hosseini, SM Hadi and Alzheimer’s Disease Neuroimaging Initiative},

  journal={Communications Biology},

  volume={8},

  number={1},

  pages={894},

  year={2025},

  publisher={Nature Publishing Group UK London}

}

```

## Contact

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jsonprep.py

+2 −1
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@@ -27,7 +27,8 @@ def json_empty(json_name): 
                 'NORMROW': False,

                 'OUTLIER': False,

                 'OUTLIERPARAMS': [3, 'median'],

                 'QC': True}

                 'QC': True,

                 'NO_DISPLAY': False}



    json_object = json.dumps(json_file, indent=4)