Merge pull request #47 from theislab/refactor

Adding tests and interface improvements

.gitignore

@@ -27,4 +27,4 @@ __pycache__/
 /.idea/
 /.vscode/
 
-**/lightning_logs/** 
+**/lightning_logs/**

README.md

@@ -33,6 +33,10 @@ pip install geome
 1. Install the latest development version:
 
 ```bash
+mamba create -n geome  python=3.11
+mamba activate geome
+mamba install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia
+pip install torch-scatter torch-sparse torch-cluster -f https://data.pyg.org/whl/torch-2.1.0+cu121.html
 pip install git+https://github.com/theislab/geome.git@main
 ```
 

docs/conf.py

@@ -19,7 +19,8 @@
 
 
 # -- Project information -----------------------------------------------------
-
+# NOTE: If you installed your project in editable mode, this might be stale.
+#       If this is the case, reinstall it to refresh the metadata
 # info = metadata("geome")
 # project_name = info["Name"]
 project_name = "geome"
@@ -114,12 +115,14 @@
 #
 html_theme = "furo"
 html_static_path = ["_static"]
+html_css_files = ["css/custom.css"]
 html_title = project_name
 
 html_theme_options = {
     "repository_url": repository_url,
     "use_repository_button": True,
     "path_to_docs": "docs/",
+    "navigation_with_keys": False,
 }
 
 pygments_style = "default"

docs/notebooks/1_iterables_and_iterators.ipynb

@@ -51,10 +51,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from geome import iterables, transforms, ann2data\n",
+    "from geome import iterables, ann2data\n",
     "import squidpy as sq\n",
-    "import numpy as np\n",
-    "from anndata import AnnData"
+    "import numpy as np"
    ]
   },
   {
@@ -162,7 +161,7 @@
    ],
    "source": [
     "split_adatas = list(to_iterable(adata))  # split by cluster\n",
-    "assert len(split_adatas) == len(adata.obs[\"Cluster\"].cat.categories) # ensure all clusters have their own adata\n",
+    "assert len(split_adatas) == len(adata.obs[\"Cluster\"].cat.categories)  # ensure all clusters have their own adata\n",
     "split_adatas[:3]  # show first 3"
    ]
   },
@@ -183,7 +182,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "assert all(len(ad.obs[\"Cluster\"].cat.categories) == len(adata.obs[\"Cluster\"].cat.categories) for ad in split_adatas)  # ensure all splits have the same category"
+    "assert all(\n",
+    "    len(ad.obs[\"Cluster\"].cat.categories) == len(adata.obs[\"Cluster\"].cat.categories) for ad in split_adatas\n",
+    ")  # ensure all splits have the same category"
    ]
   },
   {
@@ -297,7 +298,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "assert all(np.allclose(r1.x, r2.x) for r1, r2 in zip(result1, result2)) and all(np.allclose(r1.x, r3.x) for r1, r3 in zip(result1, result3))"
+    "assert all(np.allclose(r1.x, r2.x) for r1, r2 in zip(result1, result2)) and all(\n",
+    "    np.allclose(r1.x, r3.x) for r1, r3 in zip(result1, result3)\n",
+    ")"
    ]
   }
  ],

docs/notebooks/2_transforms_and_preprocessing.ipynb

@@ -37,32 +37,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from geome import iterables, transforms, ann2data\n",
+    "from geome import transforms\n",
     "import squidpy as sq\n",
-    "import numpy as np\n",
     "from anndata import AnnData"
    ]
   },
@@ -76,19 +66,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load some simple adata sample\n",
-    "def create_adata():\n",
-    "    adata = sq.datasets.mibitof()\n",
-    "    simple_adata = AnnData(adata.X)\n",
-    "    simple_adata.obs[\"Cluster\"] = adata.obs[\"Cluster\"]\n",
-    "    simple_adata.obsp[\"connectivities\"] = adata.obsp[\"connectivities\"]\n",
-    "    return simple_adata\n",
     "\n",
-    "adata = create_adata()"
+    "adata = sq.datasets.mibitof()"
    ]
   },
   {
@@ -102,18 +85,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "AnnData object with n_obs × n_vars = 3309 × 36\n",
-       "    obs: 'Cluster'\n",
-       "    obsp: 'connectivities'"
+       "    obs: 'row_num', 'point', 'cell_id', 'X1', 'center_rowcoord', 'center_colcoord', 'cell_size', 'category', 'donor', 'Cluster', 'batch', 'library_id'\n",
+       "    var: 'mean-0', 'std-0', 'mean-1', 'std-1', 'mean-2', 'std-2'\n",
+       "    uns: 'Cluster_colors', 'batch_colors', 'neighbors', 'spatial', 'umap'\n",
+       "    obsm: 'X_scanorama', 'X_umap', 'spatial'\n",
+       "    obsp: 'connectivities', 'distances'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -124,13 +110,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "adds_edge_index = transforms.AddEdgeIndex(\n",
-    "    adj_matrix_loc=\"obsp/connectivities\", edge_index_key=\"edge_index\", overwrite=True\n",
-    ")"
+    "sq_neighbors_args = {\"radius\": 4.0, \"coord_type\": \"generic\"}\n",
+    "adds_edge_index = transforms.AddEdgeIndex(edge_index_key=\"edge_index\", edge_weight_key=\"edge_weight\", func_args=sq_neighbors_args, spatial_key=\"spatial\", key_added=\"added\")"
    ]
   },
   {
@@ -142,19 +127,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "AnnData object with n_obs × n_vars = 3309 × 36\n",
-       "    obs: 'Cluster'\n",
-       "    uns: 'edge_index'\n",
-       "    obsp: 'connectivities'"
+       "    obs: 'row_num', 'point', 'cell_id', 'X1', 'center_rowcoord', 'center_colcoord', 'cell_size', 'category', 'donor', 'Cluster', 'batch', 'library_id'\n",
+       "    var: 'mean-0', 'std-0', 'mean-1', 'std-1', 'mean-2', 'std-2'\n",
+       "    uns: 'Cluster_colors', 'batch_colors', 'neighbors', 'spatial', 'umap', 'added_neighbors', 'edge_index', 'edge_weight'\n",
+       "    obsm: 'X_scanorama', 'X_umap', 'spatial'\n",
+       "    obsp: 'connectivities', 'distances', 'added_connectivities', 'added_distances'"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -165,42 +152,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "multiple_transforms = transforms.Compose(  # you can get creative with this\n",
     "    [\n",
-    "        transforms.AddEdgeIndex(adj_matrix_loc=\"obsp/connectivities\", edge_index_key=\"edge_index\", overwrite=True),\n",
-    "        transforms.AddEdgeWeight(\n",
-    "            weight_matrix_loc=\"obsp/connectivities\",\n",
-    "            edge_index_key=\"edge_index\",\n",
-    "            edge_weight_key=\"edge_weight\",\n",
-    "            overwrite=True,\n",
-    "        ),\n",
+    "        transforms.AddAdjMatrix(func_args=sq_neighbors_args, key_added=\"added2\", spatial_key=\"spatial\"),\n",
+    "        transforms.AddEdgeIndexFromAdj(adj_matrix_loc=\"obsp/added2_connectivities\", edge_index_key=\"edge_index2\", edge_weight_key=\"edge_weight2\"),\n",
     "    ]\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([71500])"
+       "AnnData object with n_obs × n_vars = 3309 × 36\n",
+       "    obs: 'row_num', 'point', 'cell_id', 'X1', 'center_rowcoord', 'center_colcoord', 'cell_size', 'category', 'donor', 'Cluster', 'batch', 'library_id'\n",
+       "    var: 'mean-0', 'std-0', 'mean-1', 'std-1', 'mean-2', 'std-2'\n",
+       "    uns: 'Cluster_colors', 'batch_colors', 'neighbors', 'spatial', 'umap', 'added_neighbors', 'edge_index', 'edge_weight', 'added2_neighbors', 'edge_index2', 'edge_weight2'\n",
+       "    obsm: 'X_scanorama', 'X_umap', 'spatial'\n",
+       "    obsp: 'connectivities', 'distances', 'added_connectivities', 'added_distances', 'added2_connectivities', 'added2_distances'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "res = multiple_transforms(adata)\n",
-    "res.uns[\"edge_weight\"].shape"
+    "res"
    ]
   }
  ],