delete noise folder

docs/.ipynb_checkpoints/mkdocs-checkpoint.yml

@@ -1,10 +1,15 @@
-site_name: TEMPS
-
+site_name: TEMPS Documentation
 theme:
-  name: "material"
-
-plugins:
-  - mkdocstrings
+  name: material
 
-nav: 
+# Define the navigation structure for the docs
+nav:
   - Home: index.md
+  
+# Define the URL where the site will be deployed
+site_url: https://github.com/lauracabayol/TEMPS
+
+markdown_extensions:
+  - codehilite
+  - toc
+  - admonition

docs/docs/.ipynb_checkpoints/index-checkpoint.md

@@ -1,72 +0,0 @@
-# TEMPS documentation!
-Welcome to the documentation for TEMPS! 
-This repository contains a neural network to predict photometric redshifts. The neural network incorporates domain adaptation, a methodology to mitigate the impact of sample bias in the spectroscopic training samples. 
-
-The training and validation data are not available in the repository, but the model can be trained with new data. The model is also deployed and available [here](https://huggingface.co/spaces/lauracabayol/TEMPS). The model in production enables making predictions for new galaxies with the pretrained models. 
-
-Details on the data and the pa
-
-
-## Table of Contents
-
-- [Prerequisites](##Prerequisites)
-- [Installation](##installation)
-- [Usage](##usage)
-- [Deployed model](##Accessing-the-LSTM-depolyed-model)
-- [License](##license)
-
-## Prerequisites
-
-Before proceeding, ensure that the following software is installed on your system:
-
-- Python 3.10
-- [pip](https://pip.pypa.io/en/stable/installation/)
-- [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
-
-You will also need to clone the repository to your local environment by executing the following commands:
-
-```bash
-git clone https://github.com/lauracabayol/TEMPS
-cd TEMPS
-```
-## Installation
-
-We recommend using a conda environment with Python 3.10 by executing the following commands:
-```
-conda create -n temps -c conda-forge python=3.10
-conda activate temps
-```
-Once your environment is ready, proceed with the installation of the package:
-
-```
-pip install -e .
-```
-This will already install the dependencies. 
-
-
-## Deployed model
-Alternatively, one can access the deployed models at [HuggigFace](https://huggingface.co/spaces/lauracabayol/TEMPS). This enbles making predictions from a file with photometric measurements. The format should be a csv file with the following band photometries in this order: G,R,I,Z,Y,H,J. 
-
-
-## Notebooks
-The repository contains notebooks to reproduce the figures in the [Paper](paper)
-The notebooks are loaded on GitHub as .py files. To convert them to .ipynb use <jupytext>
-
-```bash
-jupytext --to ipynb notebooks/*.py
-```
-
-## Training the Model
-The model can be trained using the train.py script at the repo main directory. 
-
-```
-python train.py --config-file data/config.yml
-```
-One only needs to modify the config file to point at the input files. Make sure to also specify the photometric bands naming, and the spectroscopic and photometric redshift columns. 
-Input catalogs must be in .fits or .csv formats and these should already contain clean photometry. 
-
-If extinction_corr is set to True, one must specify the column namings of the E_VB corrections in the config file. 
-
-## License
-
-This project is licensed under the MIT License. You are free to use, modify, and distribute this project as long as you adhere to the license terms.