update readme with code minimally

README.md

@@ -27,20 +27,77 @@
 </div>
 
 
-
 ![tnns_network_with_layers](https://user-images.githubusercontent.com/8267869/234084036-f7d6585e-b7c2-4156-a825-cfa5b9658d71.png)
 
 `TopoModelX` (TMX) is a Python module for topological deep learning. It offers simple and efficient tools to implement topological neural networks for science and engineering.
 
-TMX's development follows the topological deep learning (TDL) blue print laid out in:
-- [Hajij et al. 2023. Topological Deep Learning: Going Beyond Graph Data](https://arxiv.org/abs/2206.00606).
+_**Note:** TMX is still under development._
 
-TMX can reproduce and extend the topological neural networks (TNNs) surveyed in:
-- [Papillon et al. 2023. Architectures of Topological Deep Learning: A Survey on Topological Neural Networks](https://arxiv.org/abs/2304.10031).
+## Quick Tour for New Users
 
-See [our graphical literature review](https://github.com/pyt-team/TopoModelX/blob/main/topomodelx.jpeg) with message-passing equations available at [https://github.com/awesome-tnns/awesome-tnns](https://github.com/awesome-tnns/awesome-tnns).
+In this quick tour, we highlight the ease of creating and training a TNN model with only a few lines of code.
+
+#Train your own TNN model
+
+Below is a minimal example of using TopoModelX to load a simplicial complex dataset, define a simplicial attention network (SAN), and perform a forward pass:
+
+
+```bash
+import numpy as np
+import torch
+from topomodelx.datasets.graph import karate_club
+from topomodelx.nn.simplicial.san import SAN
+from topomodelx.utils.sparse import from_sparse
+
+# Step 1: Load the Karate Club dataset
+dataset = karate_club(complex_type="simplicial")
+
+# Step 2: Prepare Laplacians and node/edge features
+laplacian_down = from_sparse(dataset.down_laplacian_matrix(rank=1))
+laplacian_up = from_sparse(dataset.up_laplacian_matrix(rank=1))
+incidence_0_1 = from_sparse(dataset.incidence_matrix(rank=1))
+
+x_0 = torch.tensor(np.stack(list(dataset.get_simplex_attributes("node_feat").values())))
+x_1 = torch.tensor(np.stack(list(dataset.get_simplex_attributes("edge_feat").values())))
+x = x_1 + torch.sparse.mm(incidence_0_1.T, x_0)
+
+# Step 3: Define the network
+class Network(torch.nn.Module):
+    def __init__(self, in_channels, hidden_channels, out_channels):
+        super().__init__()
+        self.base_model = SAN(in_channels, hidden_channels, n_layers=2)
+        self.linear = torch.nn.Linear(hidden_channels, out_channels)
+
+    def forward(self, x, laplacian_up, laplacian_down):
+        x = self.base_model(x, laplacian_up, laplacian_down)
+        return torch.sigmoid(self.linear(x))
+
+# Step 4: Initialize the network and perform a forward pass
+model = Network(in_channels=x.shape[-1], hidden_channels=16, out_channels=2)
+y_hat_edge = model(x, laplacian_up=laplacian_up, laplacian_down=laplacian_down)
+   ```
+
+## 🤖 Installing TopoModelX
+
+`TopoModelX` is available on PyPI and can be installed using `pip`. 
+
+
+### Install with pip
+
+To install `TopoModelX` and its dependencies, run the following command:
+
+```bash
+pip install topomodelx
+```
+Then install torch, torch-scatter, torch-sparse with or without CUDA depending on your needs.
+
+      ```bash
+      pip install torch==2.0.1 --extra-index-url https://download.pytorch.org/whl/${CUDA}
+      pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.1+${CUDA}.html
+      pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html
+      ```
+where `${CUDA}` should be replaced by either `cpu`, `cu102`, `cu113`, or `cu115` depending on your PyTorch installation (`torch.version.cuda`).
 
-_**Note:** TMX is still under development._
 
 ## 🦾 Contributing to TMX
 
@@ -98,9 +155,19 @@ Then:
 
 ## 🔍 References ##
 
-To learn more about the topological deep learning blueprint:
+TMX is a part of TopoX, a suite of Python packages for machine learning on topological domains. If you find TMX useful please consider citing our software paper:
+
+- Hajij et al. 2023. TopoX: a suite of Python packages for machine learning on topological domains
+
+To learn more about the blueprint topological deep learning that topomodelx follows :
+
+- Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, Michael T. Schaub.  
+  [Topological Deep Learning: Going Beyond Graph Data](https://arxiv.org/abs/2206.00606) (arXiv) • [Topological Deep Learning: A Book](https://tdlbook.org/)
+
+TMX topological neural networks are surveyed in:
+
+- Papillon et al. 2023. Architectures of Topological Deep Learning: A Survey on Topological Neural Networks.
 
-- Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, Michael T. Schaub. [Topological Deep Learning: Going Beyond Graph Data](https://arxiv.org/abs/2206.00606).
 ```
 @misc{hajij2023topological,
       title={Topological Deep Learning: Going Beyond Graph Data},
@@ -110,6 +177,21 @@ To learn more about the topological deep learning blueprint:
       archivePrefix={arXiv},
       primaryClass={cs.LG}
 }
+
+@article{hajij2024topox,
+  title={TopoX: a suite of Python packages for machine learning on topological domains},
+  author={PYT-Team},
+  journal={arXiv preprint arXiv:2402.02441},
+  year={2024}
+}
+
+@article{papillon2023architectures,
+  title={Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks},
+  author={Papillon, Mathilde and Sanborn, Sophia and Hajij, Mustafa and Miolane, Nina},
+  journal={arXiv preprint arXiv:2304.10031},
+  year={2023}
+}
+
 ```
 ## Funding
 

pyproject.toml

@@ -26,7 +26,7 @@ classifiers = [
 requires-python = ">= 3.10"
 dependencies=[
     "tqdm",
-    "numpy",
+    "numpy<2",
     "scipy",
     "requests",
     "scikit-learn",
@@ -67,8 +67,10 @@ dev = ["TopoModelX[test, lint]"]
 all = ["TopoModelX[dev, doc]"]
 
 [project.urls]
-homepage="https://github.com/pyt-team/TopoModelX"
-repository="https://github.com/pyt-team/TopoModelX"
+homepage = "https://github.com/pyt-team/TopoModelX"
+repository = "https://github.com/pyt-team/TopoModelX"
+documentation = "https://pyt-team.github.io/TopoModelX"
+issues = "https://github.com/pyt-team/TopoModelX/issues"
 
 [tool.ruff]
 target-version = "py310"
@@ -93,7 +95,7 @@ select = [
     "PERF", # performance rules
     "RUF", # miscellaneous rules
 ]
-ignore = ["E501"] # line too long
+ignore = ["E501", "SIM115"] # line too long
 
 [tool.ruff.lint.pydocstyle]
 convention = "numpy"