<div style="text-align: center; padding: 20px;">
<img src="intrasom/images/Logo_fundo_branco.svg" style="max-width: 600px;">
</div>
# IntraSOM
-----
<p style="text-align: justify; text-indent: 1.5cm;">
IntraSOM is a fully Python-based implementation of self-organizing maps (SOM) developed by the Integrated Technology for Rock and Fluid Analysis (InTRA) research center (https://www.usp.br/intra/). IntraSOM is built using Object-Oriented Programming and includes support for hexagonal grids, toroidal topologies, and a wide range of visualization tools to enhance the analysis, exploration, and classification of complex datasets. Furthermore, IntraSOM includes features for handling missing data during training and efficient clustering algorithms. This library aims to make Self-Organizing techniques more accessible to researchers and professionals in various fields by providing a comprehensive Python implementation of SOM and a framework for easily expanding and implementing other SOM-based algorithms.
</p>
-----
## Framework
<div style="text-align: center; padding: 20px;">
<img src="intrasom/images/framework.png" style="max-width: 850px;">
</div>
-----
## An visualization library
<div style="flex: 1; text-align: center; padding: 10px;">
<h3>U-Matrix</h3>
<img src="intrasom/images/umatrix.jpg" style="max-width: 800px;">
</div>
<div style="flex: 1; text-align: center; padding: 10px;">
<h3>U-Matrix with Samples Label</h3>
<img src="intrasom/images/umatrix_labels.jpg" style="max-width: 800px;">
</div>
<div style="flex: 1; text-align: center; padding: 10px;">
<h3>U-Matrix with Watermark Neuron Template</h3>
<img src="intrasom/images/umatrix_template.jpg" style="max-width: 800px;">
</div>
<div style="flex: 1; text-align: center; padding: 10px;">
<h3>Component Plots</h3>
<img src="intrasom/images/component_plots.jpg" style="max-width: 800px;">
</div>
<div style="flex: 1; text-align: center; padding: 10px;">
<h3>Clustering</h3>
<img src="intrasom/images/cluster1.jpg" style="max-width: 800px;">
</div>
<div style="flex: 1; text-align: center; padding: 10px;">
<h3>Clustering with Merged Visualization</h3>
<img src="intrasom/images/cluster2.jpg" style="max-width: 800px;">
</div>
<div style="flex: 1; text-align: center; padding: 10px;">
<h3>Clustering with Neuron Template</h3>
<img src="intrasom/images/cluster3.jpg" style="max-width: 800px;">
</div>
-----
## Structure
The structure of this library is based on the structure of the SOMPY library by Moosavi et al. (2014), with implementations of:
* Training projected toroidal topology
* Training on hexagonal lattice
* Training with missing data
* Data imputation
* Loading a previously performed training
* Module for evaluating semi-supervised training with ROC curve plotting
* Module for plotting and calculating the U-matrix and component maps of the training
* Saving training data
* Generation of Training Report
* Projection of new data onto a trained map
* Clustering module for trained neurons using k-means and visualization of the results
* Accelerated distance matrix calculation using matrix shifts
* Parquet format for input and output of data and training results
* Label plotting on the U-matrix
* Implementation of representative sample analysis and visualization on the U-matrix
-----
## Documentation and Examples
For documented examples of usage of the functions and features of this library, please refer to the Jupyter Notebook:
[IntraSOM: Documented Examples](exemplos/exemplos_doc.ipynb)<br>
**Note**: This notebook is not loaded via GitHub due to its size, but it can be accessed through any IDE that supports Jupyter Notebooks.
<div style="flex: 1; text-align: center; padding: 10px;">
<h3>Documented Examples in Jupyter Notebook</h3>
<img src="examples/images/jupyter_ex.jpg" style="max-width: 800px;">
</div>
### Open on Google Colab:
It is possible to access and visualize this notebook through Google Colab:<br>
[](https://githubtocolab.com/InTRA-USP/IntraSOM/blob/main/examples/examples_doc.ipynb)
To run the notebook inside your Google Drive and connect it to Google Colab, follow these steps:
* Upload the notebook and the data files to your Google Drive.
* Open Google Colab (https://colab.research.google.com/) in your web browser.
* Click on "File" in the menu and select "Open Notebook".
* In the "Notebook" tab, select the "Google Drive" option.
* Navigate to the location where you uploaded the notebook file in your Google Drive and select it.
* The notebook will open in Google Colab, and you will have access to your Google Drive files from within the notebook.
Here's an example of code you can use inside the notebook to access files in your Google Drive:
```
from google.colab import drive
# Mount Google Drive and Accept Connection
drive.mount('/content/drive')
# Access files in your Google Drive
file_path = '/content/drive/MyDrive/path/to/your/file.txt'
# Install Intrasom
!pip install intrasom
#Ignore versioning warnings
# Continue running the notebook
# Load dataframe
data = pd.read_excel(file_path+"data/Animais_missing.xlsx", index_col=0)
```
-----
## Access to Methods Docstrings
All functions in the IntraSOM library have documentation for input and output parameters in the form of Docstrings, which can be accessed using the Python *help(...)* built-in function.<br>
Example:
```
>>> help(som_test.train)
Help on method train in module intrasom.intrasom:
train(bootstrap=False, bootstrap_proportion=0.8, n_job=-1, save=True, summary=True, dtypes='parquet', shared_memory=False, train_rough_len=None, train_rough_radiusin=None, train_rough_radiusfin=None, train_finetune_len=None, train_finetune_radiusin=None, train_finetune_radiusfin=None, train_len_factor=1, maxtrainlen=1000, history_plot=False, previous_epoch=False) method of intrasom.intrasom.SOM instance
Class method for training the SOM object.
Args:
n_job: number of jobs to use and parallelize training.
shared_memory: flag to enable shared memory.
train_rough_len: number of iterations during rough training.
train_rough_radiusin: initial BMU fetching radius during
rough training.
train_rough_radiusfin: BMU search final radius during
rough training.
train_finetune_len: number of iterations during fine training.
train_finetune_radiusin: initial BMU scan radius during
fine training.
train_finetune_radiusfin: BMU search final radius during
fine training.
train_len_factor: factor that multiplies the values of the training
extension (rough, fine, etc)
maxtrainlen: maximum value of desired interactions.
Default: np.Inf (infinity).
Returns:
SOM object trained according to the chosen parameters.
```
-----
## Dependencies
The IntraSOM dependencies are:
| Library | Version |
| -------------- | --------- |
| matplotlib | 3.7.1 |
| scipy | 1.10.1 |
| joblib | 1.2.0 |
| scikit-learn | 1.2.2 |
| pandas | 2.0.1 |
| tqdm | 4.65.0 |
| plotly | 5.14.1 |
| scikit-image | 0.20.0 |
| pyarrow | 9.0.0 |
| openpyxl | 3.1.2 |
| geopandas | 0.13.0 |
| shapely | 2.0.1 |
| ipywidgets | 8.0.6 |
-----
## Instalation
### Progress Bar
For the progress bar to work in Jupyter Notebook or JupyterLab:
```
pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension
```
### Repository
```
# Clone repository
git clone https://github.com/InTRA-USP/IntraSOM.git
# Access directory where IntraSOM is placed
cd IntraSOM
# Install setup.py
pip install setup.py
```
### Pip
```
pip install intrasom
```
-----
## Citation
de Gouvêa, R. C. T., Gioria, R. dos S., Marques, G. R., & Carneiro, C. de C. (2023). IntraSOM: A comprehensive Python library for Self-Organizing Maps with hexagonal toroidal maps training and missing data handling. <i>Software Impacts</i>, <i>17</i>, 100570. https://doi.org/10.1016/j.simpa.2023.100570
### BibTeX
```
@article{DEGOUVEA2023100570,
title = {IntraSOM: A comprehensive Python library for Self-Organizing Maps with hexagonal toroidal maps training and missing data handling},
journal = {Software Impacts},
volume = {17},
pages = {100570},
year = {2023},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2023.100570},
url = {https://www.sciencedirect.com/science/article/pii/S2665963823001070},
author = {Rodrigo César Teixeira {de Gouvêa} and Rafael dos Santos Gioria and Gustavo Rodovalho Marques and Cleyton de Carvalho Carneiro},
keywords = {Self-Organizing Maps, Python, Missing data, Imputation, Visualization},
abstract = {IntraSOM is a new Python library that implements Self-Organizing Maps (SOM). It supports hexagonal lattices, toroidal topology, and provides visualization tools for analyzing complex data sets. The library handles missing data during training and offers efficient clustering algorithms. IntraSOM aims to make SOM more accessible to researchers and practitioners by providing a comprehensive Python implementation. It has an expandable framework and can be integrated with other Python algorithms and libraries. The IntraSOM library is available on GitHub at (https://github.com/InTRA-USP/IntraSOM).}
}
```
-----
## Code Ocean Reproducibility Badge
[](https://codeocean.com/capsule/5539170/tree/v1)
-----
## Main Authors
| [<img src="https://avatars.githubusercontent.com/InTRA-USP" width=115 alt="Avatar" style="border-radius: 50%;"><br><sub>InTRA</sub >](https://github.com/InTRA-USP) | [<img src="https://avatars.githubusercontent.com/rodiegeology" width=115 alt="Avatar" style="border-radius: 50%;"><br><sub>Rodrigo Gouvêa</sub>](https://github.com/rodiegeology) <br>[<sub>Lattes</sub>](http://lattes.cnpq.br/9798573275300072)| [<img src="https://avatars.githubusercontent.com/cleytoncarneiro" width=115 alt="Avatar" style="border-radius: 50%;"><br><sub>Cleyton Carneiro</sub>](https://github.com/cleytoncarneiro) <br>[<sub>Lattes</sub>](http://lattes.cnpq.br/2853220869923540)| [<img src="https://avatars.githubusercontent.com/rafaelgioria" width=115 alt="Avatar" style="border-radius: 50%;"><br><sub>Rafael Gioria</sub>](https://github.com/rafaelgioria) <br>[<sub>Lattes</sub>](http://lattes.cnpq.br/4272439944346432)| [<img src="https://avatars.githubusercontent.com/GustavoRodovalho" width=115 alt="Avatar" style="border-radius: 50%;"><br><sub>Gustavo Rodovalho</sub>](https://github.com/GustavoRodovalho) <br>[<sub>Lattes</sub>](http://lattes.cnpq.br/3543013173834786)|
| :---: | :---: | :---: | :---: | :---: |
-----
## Acknowledgments
|<img src="https://d1yjjnpx0p53s8.cloudfront.net/styles/logo-original-577x577/s3/0005/9717/brand.gif?itok=bGLAjIU-" width=115 alt="Avatar" style="border-radius: 50%;"><br>USP|<img src="https://th.bing.com/th/id/R.1f380c4ddec4cd14a8397b430fdace9c?rik=G0Ye4qCowg09xw&pid=ImgRaw&r=0" width=115 alt="Avatar" style="border-radius: 50%;"><br>PMI|<img src="https://th.bing.com/th/id/R.1f380c4ddec4cd14a8397b430fdace9c?rik=G0Ye4qCowg09xw&pid=ImgRaw&r=0" width=115 alt="Avatar" style="border-radius: 50%;"><br>PPGEMin|<img src="https://media-exp1.licdn.com/dms/image/C4D0BAQE8OHC6L0aI-g/company-logo_200_200/0/1519890295872?e=2159024400&v=beta&t=3zh0H0GYAmnZpwP1_-ffXhe_eICChYyROJLeIGbzJdA" width=115 alt="Avatar" style="border-radius: 50%;"><br>LCT - USP|<img src="https://www6.usp.br/wp-content/uploads/IGC.jpg" width=115 alt="Avatar" style="border-radius: 50%;"><br>IGCe - USP|<img src="https://th.bing.com/th/id/R.24bf4cc5fc734e64d444abf394f913ef?rik=SoT1E1XxLcF3tw&riu=http%3a%2f%2fwww.logotypes101.com%2flogos%2f673%2f9ABDABE96E2C6AC35701EF8382CFCA21%2flogoicmcnovo.png&ehk=6P1gCt3HrD2KHJpmp%2bfnVlJ2BzveWAvzR3SgJwM%2ftJI%3d&risl=&pid=ImgRaw&r=0" width=115 alt="Avatar" style="border-radius: 50%;"><br>ICMC - USP|<img src="https://th.bing.com/th/id/OIP.tavmpSpQNnFPbs1TuqDe6QAAAA?pid=ImgDet&rs=1" width=115 alt="Avatar" style="border-radius: 50%;"><br>CeMEAI - USP|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
<br>
Thanks to people who directly or indirectly contributed to the development of this library:
<br>
|||
| -------------- | --------- |
| Stephen Fraser | VectORE Pty Ltd|
|Michel J Friedel|University of Colorado/Univeristy of Hawaii|
|Carina Ulsen|PMI/InTRA/LCT - USP|
|Jean Ferrari|PMI/Intra - USP|
|Michele Kuroda|Cepetro - Unicamp|
|Guilherme Barreto|Universidade Federal do Ceará|
|Afonso Paiva Neto|ICMC - USP|
|Cibele Russo|ICMC - USP|
-----
## License
<a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.
<div style="text-align: left;">
<img src="intrasom/images/Foot_logo_fundo_branco.svg" style="max-width: 1800px;">
</div>
Raw data
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"author": "InTRA RDI Center (Universidade de S\u00e3o Paulo)",
"author_email": "intra@usp.br",
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"description": "\n<div style=\"text-align: center; padding: 20px;\">\n <img src=\"intrasom/images/Logo_fundo_branco.svg\" style=\"max-width: 600px;\">\n</div>\n\n# IntraSOM\n-----\n<p style=\"text-align: justify; text-indent: 1.5cm;\">\nIntraSOM is a fully Python-based implementation of self-organizing maps (SOM) developed by the Integrated Technology for Rock and Fluid Analysis (InTRA) research center (https://www.usp.br/intra/). IntraSOM is built using Object-Oriented Programming and includes support for hexagonal grids, toroidal topologies, and a wide range of visualization tools to enhance the analysis, exploration, and classification of complex datasets. Furthermore, IntraSOM includes features for handling missing data during training and efficient clustering algorithms. This library aims to make Self-Organizing techniques more accessible to researchers and professionals in various fields by providing a comprehensive Python implementation of SOM and a framework for easily expanding and implementing other SOM-based algorithms.\n</p>\n-----\n\n## Framework\n<div style=\"text-align: center; padding: 20px;\">\n <img src=\"intrasom/images/framework.png\" style=\"max-width: 850px;\">\n</div>\n\n-----\n## An visualization library\n<div style=\"flex: 1; text-align: center; padding: 10px;\">\n <h3>U-Matrix</h3>\n <img src=\"intrasom/images/umatrix.jpg\" style=\"max-width: 800px;\">\n</div>\n\n<div style=\"flex: 1; text-align: center; padding: 10px;\">\n <h3>U-Matrix with Samples Label</h3>\n <img src=\"intrasom/images/umatrix_labels.jpg\" style=\"max-width: 800px;\">\n</div>\n\n<div style=\"flex: 1; text-align: center; padding: 10px;\">\n <h3>U-Matrix with Watermark Neuron Template</h3>\n <img src=\"intrasom/images/umatrix_template.jpg\" style=\"max-width: 800px;\">\n</div>\n\n<div style=\"flex: 1; text-align: center; padding: 10px;\">\n <h3>Component Plots</h3>\n <img src=\"intrasom/images/component_plots.jpg\" style=\"max-width: 800px;\">\n</div>\n\n<div style=\"flex: 1; text-align: center; padding: 10px;\">\n <h3>Clustering</h3>\n <img src=\"intrasom/images/cluster1.jpg\" style=\"max-width: 800px;\">\n</div>\n\n<div style=\"flex: 1; text-align: center; padding: 10px;\">\n <h3>Clustering with Merged Visualization</h3>\n <img src=\"intrasom/images/cluster2.jpg\" style=\"max-width: 800px;\">\n</div>\n\n<div style=\"flex: 1; text-align: center; padding: 10px;\">\n <h3>Clustering with Neuron Template</h3>\n <img src=\"intrasom/images/cluster3.jpg\" style=\"max-width: 800px;\">\n</div>\n\n\n-----\n\n## Structure\n\nThe structure of this library is based on the structure of the SOMPY library by Moosavi et al. (2014), with implementations of:\n\n* Training projected toroidal topology\n* Training on hexagonal lattice\n* Training with missing data\n* Data imputation\n* Loading a previously performed training\n* Module for evaluating semi-supervised training with ROC curve plotting\n* Module for plotting and calculating the U-matrix and component maps of the training\n* Saving training data\n* Generation of Training Report\n* Projection of new data onto a trained map\n* Clustering module for trained neurons using k-means and visualization of the results\n* Accelerated distance matrix calculation using matrix shifts\n* Parquet format for input and output of data and training results\n* Label plotting on the U-matrix\n* Implementation of representative sample analysis and visualization on the U-matrix\n-----\n## Documentation and Examples\nFor documented examples of usage of the functions and features of this library, please refer to the Jupyter Notebook:\n[IntraSOM: Documented Examples](exemplos/exemplos_doc.ipynb)<br>\n**Note**: This notebook is not loaded via GitHub due to its size, but it can be accessed through any IDE that supports Jupyter Notebooks.\n\n<div style=\"flex: 1; text-align: center; padding: 10px;\">\n <h3>Documented Examples in Jupyter Notebook</h3>\n <img src=\"examples/images/jupyter_ex.jpg\" style=\"max-width: 800px;\">\n</div>\n\n### Open on Google Colab:\nIt is possible to access and visualize this notebook through Google Colab:<br>\n[](https://githubtocolab.com/InTRA-USP/IntraSOM/blob/main/examples/examples_doc.ipynb)\n\nTo run the notebook inside your Google Drive and connect it to Google Colab, follow these steps:\n\n* Upload the notebook and the data files to your Google Drive.\n* Open Google Colab (https://colab.research.google.com/) in your web browser.\n* Click on \"File\" in the menu and select \"Open Notebook\".\n* In the \"Notebook\" tab, select the \"Google Drive\" option.\n* Navigate to the location where you uploaded the notebook file in your Google Drive and select it.\n* The notebook will open in Google Colab, and you will have access to your Google Drive files from within the notebook.\n\nHere's an example of code you can use inside the notebook to access files in your Google Drive:\n```\nfrom google.colab import drive\n\n# Mount Google Drive and Accept Connection\ndrive.mount('/content/drive')\n\n# Access files in your Google Drive\nfile_path = '/content/drive/MyDrive/path/to/your/file.txt'\n\n# Install Intrasom\n!pip install intrasom\n\n#Ignore versioning warnings\n\n# Continue running the notebook\n# Load dataframe\ndata = pd.read_excel(file_path+\"data/Animais_missing.xlsx\", index_col=0)\n```\n\n-----\n\n## Access to Methods Docstrings\nAll functions in the IntraSOM library have documentation for input and output parameters in the form of Docstrings, which can be accessed using the Python *help(...)* built-in function.<br>\n\nExample:\n```\n>>> help(som_test.train)\nHelp on method train in module intrasom.intrasom:\n\ntrain(bootstrap=False, bootstrap_proportion=0.8, n_job=-1, save=True, summary=True, dtypes='parquet', shared_memory=False, train_rough_len=None, train_rough_radiusin=None, train_rough_radiusfin=None, train_finetune_len=None, train_finetune_radiusin=None, train_finetune_radiusfin=None, train_len_factor=1, maxtrainlen=1000, history_plot=False, previous_epoch=False) method of intrasom.intrasom.SOM instance\n Class method for training the SOM object.\n \n Args:\n n_job: number of jobs to use and parallelize training.\n \n shared_memory: flag to enable shared memory.\n \n train_rough_len: number of iterations during rough training.\n \n train_rough_radiusin: initial BMU fetching radius during\n rough training.\n \n train_rough_radiusfin: BMU search final radius during\n rough training.\n \n train_finetune_len: number of iterations during fine training.\n \n train_finetune_radiusin: initial BMU scan radius during\n fine training.\n \n train_finetune_radiusfin: BMU search final radius during\n fine training.\n \n train_len_factor: factor that multiplies the values \u200b\u200bof the training\n extension (rough, fine, etc)\n \n maxtrainlen: maximum value of desired interactions.\n Default: np.Inf (infinity).\n \n Returns:\n SOM object trained according to the chosen parameters.\n```\n\n-----\n## Dependencies\n\nThe IntraSOM dependencies are:\n| Library | Version |\n| -------------- | --------- |\n| matplotlib | 3.7.1 |\n| scipy | 1.10.1 |\n| joblib | 1.2.0 |\n| scikit-learn | 1.2.2 |\n| pandas | 2.0.1 |\n| tqdm | 4.65.0 |\n| plotly | 5.14.1 |\n| scikit-image | 0.20.0 |\n| pyarrow | 9.0.0 |\n| openpyxl | 3.1.2 |\n| geopandas | 0.13.0 |\n| shapely | 2.0.1 |\n| ipywidgets | 8.0.6 |\n\n\n-----\n\n## Instalation\n\n### Progress Bar\nFor the progress bar to work in Jupyter Notebook or JupyterLab:\n```\npip install ipywidgets\njupyter nbextension enable --py widgetsnbextension\n```\n\n### Repository\n```\n# Clone repository\ngit clone https://github.com/InTRA-USP/IntraSOM.git\n\n# Access directory where IntraSOM is placed\ncd IntraSOM\n\n# Install setup.py\npip install setup.py\n```\n### Pip\n```\npip install intrasom\n```\n-----\n\n## Citation\n\nde Gouv\u00eaa, R. C. T., Gioria, R. dos S., Marques, G. R., & Carneiro, C. de C. (2023). IntraSOM: A comprehensive Python library for Self-Organizing Maps with hexagonal toroidal maps training and missing data handling. <i>Software Impacts</i>, <i>17</i>, 100570. https://doi.org/10.1016/j.simpa.2023.100570\n\n### BibTeX\n```\n@article{DEGOUVEA2023100570,\ntitle = {IntraSOM: A comprehensive Python library for Self-Organizing Maps with hexagonal toroidal maps training and missing data handling},\njournal = {Software Impacts},\nvolume = {17},\npages = {100570},\nyear = {2023},\nissn = {2665-9638},\ndoi = {https://doi.org/10.1016/j.simpa.2023.100570},\nurl = {https://www.sciencedirect.com/science/article/pii/S2665963823001070},\nauthor = {Rodrigo C\u00e9sar Teixeira {de Gouv\u00eaa} and Rafael dos Santos Gioria and Gustavo Rodovalho Marques and Cleyton de Carvalho Carneiro},\nkeywords = {Self-Organizing Maps, Python, Missing data, Imputation, Visualization},\nabstract = {IntraSOM is a new Python library that implements Self-Organizing Maps (SOM). It supports hexagonal lattices, toroidal topology, and provides visualization tools for analyzing complex data sets. The library handles missing data during training and offers efficient clustering algorithms. IntraSOM aims to make SOM more accessible to researchers and practitioners by providing a comprehensive Python implementation. It has an expandable framework and can be integrated with other Python algorithms and libraries. The IntraSOM library is available on GitHub at (https://github.com/InTRA-USP/IntraSOM).}\n}\n```\n\n-----\n\n## Code Ocean Reproducibility Badge\n[](https://codeocean.com/capsule/5539170/tree/v1)\n\n-----\n\n## Main Authors\n| [<img src=\"https://avatars.githubusercontent.com/InTRA-USP\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br><sub>InTRA</sub >](https://github.com/InTRA-USP) | [<img src=\"https://avatars.githubusercontent.com/rodiegeology\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br><sub>Rodrigo Gouv\u00eaa</sub>](https://github.com/rodiegeology) <br>[<sub>Lattes</sub>](http://lattes.cnpq.br/9798573275300072)| [<img src=\"https://avatars.githubusercontent.com/cleytoncarneiro\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br><sub>Cleyton Carneiro</sub>](https://github.com/cleytoncarneiro) <br>[<sub>Lattes</sub>](http://lattes.cnpq.br/2853220869923540)| [<img src=\"https://avatars.githubusercontent.com/rafaelgioria\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br><sub>Rafael Gioria</sub>](https://github.com/rafaelgioria) <br>[<sub>Lattes</sub>](http://lattes.cnpq.br/4272439944346432)| [<img src=\"https://avatars.githubusercontent.com/GustavoRodovalho\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br><sub>Gustavo Rodovalho</sub>](https://github.com/GustavoRodovalho) <br>[<sub>Lattes</sub>](http://lattes.cnpq.br/3543013173834786)|\n| :---: | :---: | :---: | :---: | :---: |\n\n\n-----\n## Acknowledgments\n|<img src=\"https://d1yjjnpx0p53s8.cloudfront.net/styles/logo-original-577x577/s3/0005/9717/brand.gif?itok=bGLAjIU-\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br>USP|<img src=\"https://th.bing.com/th/id/R.1f380c4ddec4cd14a8397b430fdace9c?rik=G0Ye4qCowg09xw&pid=ImgRaw&r=0\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br>PMI|<img src=\"https://th.bing.com/th/id/R.1f380c4ddec4cd14a8397b430fdace9c?rik=G0Ye4qCowg09xw&pid=ImgRaw&r=0\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br>PPGEMin|<img src=\"https://media-exp1.licdn.com/dms/image/C4D0BAQE8OHC6L0aI-g/company-logo_200_200/0/1519890295872?e=2159024400&v=beta&t=3zh0H0GYAmnZpwP1_-ffXhe_eICChYyROJLeIGbzJdA\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br>LCT - USP|<img src=\"https://www6.usp.br/wp-content/uploads/IGC.jpg\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br>IGCe - USP|<img src=\"https://th.bing.com/th/id/R.24bf4cc5fc734e64d444abf394f913ef?rik=SoT1E1XxLcF3tw&riu=http%3a%2f%2fwww.logotypes101.com%2flogos%2f673%2f9ABDABE96E2C6AC35701EF8382CFCA21%2flogoicmcnovo.png&ehk=6P1gCt3HrD2KHJpmp%2bfnVlJ2BzveWAvzR3SgJwM%2ftJI%3d&risl=&pid=ImgRaw&r=0\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br>ICMC - USP|<img src=\"https://th.bing.com/th/id/OIP.tavmpSpQNnFPbs1TuqDe6QAAAA?pid=ImgDet&rs=1\" width=115 alt=\"Avatar\" style=\"border-radius: 50%;\"><br>CeMEAI - USP|\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n\n<br>\n\nThanks to people who directly or indirectly contributed to the development of this library:\n\n<br>\n\n|||\n| -------------- | --------- |\n| Stephen Fraser | VectORE Pty Ltd|\n|Michel J Friedel|University of Colorado/Univeristy of Hawaii|\n|Carina Ulsen|PMI/InTRA/LCT - USP|\n|Jean Ferrari|PMI/Intra - USP|\n|Michele Kuroda|Cepetro - Unicamp|\n|Guilherme Barreto|Universidade Federal do Cear\u00e1|\n|Afonso Paiva Neto|ICMC - USP|\n|Cibele Russo|ICMC - USP|\n\n-----\n\n## License\n\n<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc/4.0/\">Creative Commons Attribution-NonCommercial 4.0 International License</a>.\n\n<div style=\"text-align: left;\">\n <img src=\"intrasom/images/Foot_logo_fundo_branco.svg\" style=\"max-width: 1800px;\">\n</div>\n\n",
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