added atac_layer argument to train_model tasks and made tests for it

src/pyrovelocity/tasks/train.py

@@ -188,7 +188,7 @@ def train_dataset(
                     "batch_size": batch_size,
                 }
             )
-
+            
             adata, trained_model, posterior_samples = train_model(
                 adata=adata,
                 guide_type=guide_type,
@@ -279,6 +279,7 @@ def check_shared_time(posterior_samples, adata):
 @beartype
 def train_model(
     adata: str | AnnData,
+    atac_layer: Optional[str] = None,
     guide_type: str = "auto",
     model_type: str = "auto",
     batch_size: int = -1,
@@ -305,6 +306,7 @@ def train_model(
 
     Args:
         adata (str | AnnData): Path to a file that can be read to an AnnData object or an AnnData object.
+        atac_layer (Optional[str], optional): Name of AnnData layer that contains atac data, if present.
         guide_type (str, optional): The type of guide function for the Pyro model. Default is "auto".
         model_type (str, optional): The type of Pyro model. Default is "auto".
         batch_size (int, optional): Batch size for training. Default is -1, which indicates using the full dataset.
@@ -347,6 +349,9 @@ def train_model(
         >>> copy_raw_counts(adata)
         >>> _, model, posterior_samples = train_model(adata, use_gpu="auto", seed=99, max_epochs=200, loss_plot_path=loss_plot_path)
     """
+
+    if atac_layer:
+        logger.info("Multiome model not yet implemented. Proceeding without atac data.")
     if isinstance(adata, str):
         adata = load_anndata_from_path(adata)
 

src/pyrovelocity/tests/tasks/__init__.py

@@ -0,0 +1 @@
+"""Unit test package for pyrovelocity."""

src/pyrovelocity/tests/tasks/test_train_model.py

@@ -0,0 +1,12 @@
+"""Tests for `pyrovelocity._train_model` task."""
+
+from pyrovelocity.tasks.train import train_model
+from pyrovelocity.utils import generate_sample_data
+from pyrovelocity.tasks.preprocess import copy_raw_counts
+def test_train_model(tmp_path):
+    loss_plot_path = str(tmp_path) + "/loss_plot_docs.png"
+    print(loss_plot_path)
+    adata = generate_sample_data(random_seed=99)
+    copy_raw_counts(adata)
+    _, model, posterior_samples = train_model(adata, atac_layer = 'atac', 
+                                              use_gpu="auto", seed=99, max_epochs=200, loss_plot_path=loss_plot_path)