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docs/html/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: a16491b51de936a20417e9b4dd14b59f
+tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/html/_sources/cite.rst.txt

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+Cite:
+======
+
+(Under construction) Please cite the following papers if you are using the ``TelescopeML`` package or any of its concepts, modules, or
+functions in your work:
+
+1. Will be updated after publishing the package!
+
+2. Will be updated after publishing the first case study!

docs/html/_sources/code.rst.txt

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+The Modules
+============
+
+
+TelescopeML\.DataMaster Module
+--------------------------------
+
+.. automodule:: TelescopeML.DataMaster
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+TelescopeML\.DeepTrainer Module
+--------------------------------
+
+.. automodule:: TelescopeML.DeepTrainer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+TelescopeML\.Predictor Module
+------------------------------------
+
+.. automodule:: TelescopeML.Predictor
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+TelescopeML\.StatVisAnalyzer Module
+------------------------------------
+
+.. automodule:: TelescopeML.StatVisAnalyzer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+

docs/html/_sources/index.rst.txt

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+.. TelescopeML documentation master file, created by
+   sphinx-quickstart on Tue Dec 27 15:39:09 2022.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+TelescopeML
+==============
+
+
+``TelescopeML`` is a Python package comprising a series of modules, each equipped with specialized machine learning and
+statistical capabilities for conducting Convolutional Neural Networks (CNN) or Machine Learning (ML) training on
+datasets captured from the atmospheres of extrasolar planets and brown dwarfs. The tasks executed by the ``TelescopeML``
+modules are outlined below:
+
+    - *StatVisAnalyzer*: Explore and process the synthetic datasets (or the training examples) and perform statistical analysis.
+    - *DeepBuilder*: Specify training and target features, normalize/scale datasets, and construct a CNN model.
+    - *DeepTrainer*: Create an ML model, train the model with the training examples, and utilize hyperparameters.
+    - *Predictor*: Train the module using specified hyperparameters.
+
+or simply...
+
+    - Load the pre-trained CNN models based on the latest synthetic datasets
+    - Predict the stellar/(exo-)planetary parameters
+    - Report the statistical analysis
+
+.. image:: figures/TelescopeML_modules.png
+  :width: 1100
+
+
+
+
+
+======================
+
+.. toctree::
+   :maxdepth: 2
+   :hidden:
+
+   Installation <installation>
+   Tutorials <tutorials>
+   The Code <code>
+   KnowledgeBase <knowledgebase> 
+   Github <https://github.com/ehsangharibnezhad>
+   Publications <publications>
+   What to Cite <cite>
+
+

docs/html/_sources/installation.rst.txt

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+Installation
+=============
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+
+
+
+.. note::
+	`TelescopeML` requires python >= 3.8.
+
+
+Step 1: Create your directory structure
+----------------------------------------
+Let’s start by creating the folder structure. Here is the folder structure of
+an example project, named TelescopeML_project with two sub-directories: *reference_data* and
+*notebooks*.
+
+
+| TelescopeML_project
+| ├── reference_data
+| │   ├── training_datasets
+| │   ├── observational_datasets
+| │   ├── figures
+| │   └── trained_ML_models
+| └── notebooks
+|
+
+.. note::
+    You need to be inside the `TelescopeML_project` directory if you want to follow method 2
+    (i.e., `Install with Git`) and clone the github repository.
+
+
+Step 2: Download Input Data and Trained ML Models
+--------------------------------------------------
+First things first, after creating the home directory, you need  to download the following datasets, 1--4,
+as well as the trained CNN models to fully utilize the code and apply it to your datasets.
+
+
+    1.  for **training_datasets/** directory: `Link to the pre-trained ML models to deploy <https://zenodo.org/records/2459971/files/EGNMRL__H2O__1000K__1E+00bar__H2He.XS.bz2?download=1>`_
+    2.  for **observational_datasets/** directory:  `Link the synthetic Brown-dwarf training datasets <https://stackoverflow.com/>`_
+    3.  for **trained_ML_models/** directory:  `Link to the observational telescope spectra for few brown dwarfs <https://stackoverflow.com/>`_
+    4.  for **notebooks/** directory: `Download the Jupyter notebook tutorials to deploy the ML models <https://stackoverflow.com/>`_
+
+
+
+Step 3: Install the Package
+----------------------------
+
+Method 1: Install via Pip (Straightforward)
++++++++++++++++++++++++++++++++++++++++++++
+The easiest way to install the most stable version is with *pip*, the Python package manager,
+but do not forget that you still need to create a virtual environment using the `Anaconda distribution <https://www.anaconda.com/download/>`_
+and then install ``TelescopeML`` there by the following steps:
+
+1. Create a conda virtual environment using `python>=3.8`:
+
+.. code-block:: bash
+
+    conda create --name  TelescopeML  python=3.9
+
+2. Activate the new environment:
+
+.. code-block:: bash
+
+    conda activate TelescopeML
+
+3. Now install `TelescopeML` via pip:
+
+.. code-block:: bash
+
+    pip install TelescopeML
+
+
+
+Method 2: Install with Git (Recommended)
++++++++++++++++++++++++++++++++++++++++++++
+If you want to access the latest features or modify the code and contribute, we suggest that you clone the source code
+from GitHub by following steps:
+
+1. Clone the repo and Create `Conda` environment named *TelescopeML*:
+
+.. code-block:: bash
+
+    git clone https://github.com/ehsangharibnezhad/TelescopeML.git
+    cd TelescopeML
+    conda env create -f environment.yml
+
+
+
+2. Activate the new environment:
+
+.. code-block:: bash
+
+    conda activate TelescopeML
+
+3. Install the library via the `setup.py` file:
+
+.. code-block:: bash
+
+    python3 setup.py develop
+
+
+Now, you should have the package installed alongside the trained models and telescope datasets.
+
+Step 4: Set input file environment variables
+---------------------------------------------
+
+
+For Mac OS
+++++++++++++++++++++++++++++++++++++++++++++
+
+follow the following steps to set the link to the input data:
+
+1. Check your default shell in your terminal:
+
+.. code-block:: bash
+
+    echo $SHELL
+
+This command will display the path to your default shell, typically something
+like `/bin/bash` or `/bin/zsh`, or `/bin/sh`.
+
+2. Set the environment variables :
+
+    * If your shell is `/bin/zsh`:
+
+    .. code-block:: bash
+
+        echo 'export $TelescopeML_reference_data="/PATH_TO_YOUR_TelescopeML_project/" ' >>~/.zshrc
+        source ~/.zshrc
+        echo $TelescopeML_reference_data
+
+
+    * if your shell is `/bin/bash`:
+
+    .. code-block:: bash
+
+        echo 'export $TelescopeML_reference_data="/PATH_TO_YOUR_TelescopeML_project/"' >>~/.bash_profile
+        source ~/.bash_profile
+        echo $TelescopeML_reference_data
+
+    * if your sell is `/bin/sh`:
+
+    .. code-block:: bash
+
+        echo 'export $TelescopeML_reference_data="/PATH_TO_YOUR_TelescopeML_project/"' >>~/.profile
+        source ~/.profile
+        echo $TelescopeML_reference_data
+
+
+.. note::
+    - Replace `PATH_TO_YOUR_TelescopeML_project` with the actual path to your TelescopeML directory.
+    - *echo* command is used to check that your variable has been defined properly.
+
+
+For Linux
+++++++++++
+In Linux, the choice between `~/.bashrc` and `~/.bash_profile` depends on your specific use case and how
+you want environment variables to be set, but `~/.bashrc` is a common and practical choice for
+modern Linux system.
+
+.. code-block:: bash
+
+    echo 'export $TelescopeML_reference_data="/PATH_TO_YOUR_TelescopeML_project/" ' >>~/.bashrc
+    source ~/.bashrc
+    echo $TelescopeML_reference_data
+
+
+Replace `PATH_TO_YOUR_TelescopeML_project` with the actual path to your TelescopeML directory.
+
+For Windows
+++++++++++++
+
+For Windows users, we recommend installing Windows Subsystem for Linux (WSL) before proceeding.
+WSL is a compatibility layer for Windows that allows you to run a Linux distribution alongside
+your Windows installation.
+
+
+

docs/html/_sources/knowledgebase.rst.txt

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+KnowledgeBase
+===============
+

docs/html/_sources/publications.rst.txt

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+Publications:
+==============

docs/html/_sources/tutorials.rst.txt

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+Tutorials
+==========
+
+
+Exploring the Datasets
+-----------------------
+.. toctree::
+   :maxdepth: 1
+
+    Brown Dwarf Synthetic Dataset  <tutorials/1__BrownDwarf_Data_Exploration.ipynb>
+
+
+
+Predict Atmospheric Parameters
+-------------------------------
+.. toctree::
+   :maxdepth: 2
+
+   Predict Brown Dwarf Atmospheric Parameters  <tutorials/2__Predict_BrownDwarf_Parameters.ipynb>
+
+
+
+
+Data Processing
+----------------
+.. toctree::
+   :maxdepth: 2
+
+   Data Processing  <tutorials/3__Data_Processing.ipynb>
+
+
+
+Train a Regression CNNs Model
+------------------------------
+.. toctree::
+   :maxdepth: 2
+
+   Train a Regression CNNs Model  <tutorials/4__Train_CNN_Model.ipynb>
+
+
+
+