updated README

Author: Michael Fuest

MANIFEST.in

@@ -3,123 +3,61 @@
 <i>An open source project from Data to AI Lab at MIT.</i>
 </p>
 
-[![PyPI Shield](https://img.shields.io/pypi/v/Cents.svg)](https://pypi.python.org/pypi/cents)
-[![Downloads](https://pepy.tech/badge/cents)](https://pepy.tech/project/cents)
+[![PyPI Shield](https://img.shields.io/pypi/v/Cents.svg)](https://pypi.python.org/pypi/cents-ml)
+[![Downloads](https://pepy.tech/badge/cents-ml)](https://pepy.tech/project/cents-ml)
 [![GitHub Actions Build Status](https://github.com/DAI-Lab/Cents/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/DAI-Lab/Cents/actions)
 
 
 # Cents
 
 A library for generative modeling and evaluation of synthetic household-level electricity load timeseries. This package is still under active development.
 
-- Documentation: (tbd)
+- [Documentation](https://dtail.gitbook.io/cents)
 
 # Overview
 
-Cents is a library built for generating *synthetic household-level electric load and generation timeseries*. Cents supports several generative time series models that can be used to train a time series data generator from scratch on a user-defined dataset. Additionally, Cents provides functionality for loading pre-trained model checkpoints that can be used to generate data instantly. Trained models can be evaluated using a series of metrics and visualizations also implemented here.
+Cents is a library built for generating *contextual time series data*. Cents supports several generative time series model architectures that can be used to train a time series data generator from scratch on a user-defined dataset. Additionally, Cents provides functionality for loading pre-trained model checkpoints that can be used to generate data instantly.
 
-These currently supported models are:
+Cents was used to train the [Watts](https://huggingface.co/michaelfuest/watts) model series.
 
-- [Diffusion-TS](https://github.com/Y-debug-sys/Diffusion-TS/tree/main)
-- [ACGAN](https://arxiv.org/abs/1610.09585)
-
-Feel free to look at our [tutorial notebooks]() to get started.
+Feel free to look at our [tutorial notebooks](https://github.com/DAI-Lab/Cents/tree/main/tutorials) to get started.
 
 # Install
 
 ## Requirements
 
 **Cents** has been developed and tested on [Python 3.9]((https://www.python.org/downloads/)), [Python 3.10]((https://www.python.org/downloads/)) and [Python 3.11]((https://www.python.org/downloads/)).
 
-Also, although it is not strictly required, the usage of a [virtualenv](https://virtualenv.pypa.io/en/latest/)
-is highly recommended in order to avoid interfering with other software installed in the system
-in which **Cents** is run.
+We recommend using [Poetry](https://python-poetry.org/docs/) for dependency management. Make sure you have poetry installed before following these setup instructions.
 
-These are the minimum commands needed to create a virtualenv using python3.8 for **Cents**:
+Poetry will automatically create a virtual environment and install all dependencies:
 
 ```bash
-pip install virtualenv
-virtualenv -p $(which python3.9) cents-venv
+poetry install
 ```
 
-Afterwards, you have to execute this command to activate the virtualenv:
+Once installed, activate the virtual environment:
 
 ```bash
-source cents-venv/bin/activate
+poetry shell
 ```
 
-Remember to execute it every time you start a new console to work on **Cents**!
+This gives you a clean, reproducible setup for development.
 
 ## Install from PyPI
 
-After creating the virtualenv and activating it, we recommend using
+If you are only interested in using Cents functionality, we recommend using
 [pip](https://pip.pypa.io/en/stable/) in order to install **Cents**:
 
 ```bash
-pip install cents
+pip install cents-ml
 ```
 
 This will pull and install the latest stable release from [PyPI](https://pypi.org/).
--->
-
-<!-- ## Install from source
-
-With your virtualenv activated, you can clone the repository and install it from
-source by running `make install` on the `stable` branch:
-
-```bash
-git clone git@github.com:michael-fuest/Cents.git
-cd Cents
-git checkout stable
-make install
-``` -->
-
-<!-- ## Install for Development
-
-If you want to contribute to the project, a few more steps are required to make the project ready
-for development.
-
-Please head to the [Contributing Guide](https://michael-fuest.github.io/Cents/contributing.html#get-started)
-for more details about this process. -->
-
-<!-- # Quickstart
-
-In this short tutorial we will guide you through a series of steps that will help you
-getting started with **Cents**.
-
-## Generating Data
-
-To get started, define a DataGenerator and specify the name of the model you would like to use.
-
-```python
-generator = DataGenerator(model_name="diffusion_ts")
-```
-
-We provide pre-trained model checkpoints that were trained on the [PecanStreet Dataport](https://www.pecanstreet.org/dataport/) dataset. You can use these checkpoints to load a trained model. The first step is to assign the `DataGenerator` a `TimeSeriesDataset`instance. We are using the `PecanStreetDataset` class here, which is an extension of `TimeSeriesDataset`.
-
-```python
-dataset = PecanStreetDataset()
-generator.set_dataset(dataset)
-```
-
-Once a dataset has been assigned, we can load a pre-trained model for that dataset as follows:
-
-```python
-generator.load_model()
-```
-
-These pre-trained models are conditional models, meaning they require a set of conditioning variables to generate synthetic time series data. If you want to generate data for a random set of conditioning variables, you can do so as follows:
-
-```python
-conditioning_variables = generator.sample_random_conditioning_variables()
-synthetic_data = generator.generate(conditioning_variables)
-```
-
-For a more in-depth tutorial, please refer to the tutorial notebooks in the `tutorials` directory. -->
 
 ## Datasets
 
-If you want to reproduce our models from scratch, you will need to download the [PecanStreet DataPort dataset](https://www.pecanstreet.org/dataport/) and place it under the path specified in `pecanstreet.yaml`. Specifically you will require the following files:
+If you want to reproduce the pretrained Watts model series from scratch, you will need to download the [PecanStreet DataPort dataset](https://www.pecanstreet.org/dataport/) and place it in an appropriate location specified in `cents/config/dataset/pecanstreet.yaml`. Specifically you will require the following files:
 
 - 15minute_data_austin.csv
 - 15minute_data_california.csv