Base Scaffolding for main

.gitignore

@@ -1,2 +1,3 @@
 **/raw
-data/
+data/
+bu/

mantra/simplicial.py

@@ -1,64 +0,0 @@
-"""
-Simplical dataset, downloads the processed data from Zenodo into torch geometric 
-dataset that can be used in conjunction to dataloaders. 
-
-NOTE: Code untested until we have the zenodo database running or another place
-retrieve the data from.
-"""
-
-import os
-import json
-
-from torch_geometric.data import (
-    Data,
-    InMemoryDataset,
-    download_url,
-    extract_zip,
-)
-
-
-class SimplicialDataset(InMemoryDataset):
-    url = "my.cool.url.com"
-
-    def __init__(
-        self,
-        root,
-        transform=None,
-        pre_transform=None,
-        pre_filter=None,
-    ):
-        self.manifold = "2"
-        root += f"/simplicial"
-        super().__init__(root, transform, pre_transform, pre_filter)
-        self.load(self.processed_paths[0])
-
-    @property
-    def raw_file_names(self):
-        return [
-            f"{self.manifold}_manifold.json",
-        ]
-
-    @property
-    def processed_file_names(self):
-        return [f"{self.manifold}_manifold.pt"]
-
-    def download(self) -> None:
-        path = download_url(self.url, self.raw_dir)
-        extract_zip(path, self.raw_dir)
-        os.unlink(path)
-
-    def process(self):
-
-        # After download and extraction, we expect a json file with
-        # a list of json objects.
-        inputs = json.load(self.raw_paths[0])
-
-        data_list = [Data(**el) for el in inputs]
-
-        if self.pre_filter is not None:
-            data_list = [data for data in data_list if self.pre_filter(data)]
-
-        if self.pre_transform is not None:
-            data_list = [self.pre_transform(data) for data in data_list]
-
-        self.save(data_list, self.processed_paths[0])