Style scGPT script

src/methods/scgpt/script.py

@@ -1,22 +1,21 @@
-import scgpt
 import sys
-import gdown
 import tempfile
-import torch
+
 import anndata as ad
+import gdown
+import scgpt
+import torch
 
 ## VIASH START
 # Note: this section is auto-generated by viash at runtime. To edit it, make changes
 # in config.vsh.yaml and then run `viash config inject config.vsh.yaml`.
 par = {
-  'input': 'resources_test/.../input.h5ad',
-  'output': 'output.h5ad',
-  "model" : "scGPT_human",
-  "n_hvg": 3000
-}
-meta = {
-  'name': 'scgpt'
+    "input": "resources_test/.../input.h5ad",
+    "output": "output.h5ad",
+    "model": "scGPT_human",
+    "n_hvg": 3000,
 }
+meta = {"name": "scgpt"}
 ## VIASH END
 
 print(f"====== scGPT version {scgpt.__version__} ======", flush=True)
@@ -39,33 +38,33 @@
 print("\n>>> Preprocessing data...", flush=True)
 if par["n_hvg"]:
     print(f"Selecting top {par['n_hvg']} highly variable genes", flush=True)
-    idx = adata.var["hvg_score"].to_numpy().argsort()[::-1][:par["n_hvg"]]
+    idx = adata.var["hvg_score"].to_numpy().argsort()[::-1][: par["n_hvg"]]
     adata = adata[:, idx].copy()
 
 print(adata, flush=True)
 
 print(f"\n>>> Downloading '{par['model']}' model...", flush=True)
 model_drive_ids = {
-    "scGPT_human" : "1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y",
-    "scGPT_CP" : "1_GROJTzXiAV8HB4imruOTk6PEGuNOcgB"
+    "scGPT_human": "1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y",
+    "scGPT_CP": "1_GROJTzXiAV8HB4imruOTk6PEGuNOcgB",
 }
 drive_path = f"https://drive.google.com/drive/folders/{model_drive_ids[par['model']]}"
 model_dir = tempfile.TemporaryDirectory()
 print(f"Downloading from '{drive_path}'", flush=True)
-gdown.download_folder(drive_path, output=model_dir.name, quiet = True)
+gdown.download_folder(drive_path, output=model_dir.name, quiet=True)
 print(f"Model directory: '{model_dir.name}'", flush=True)
 
 print("\n>>> Embedding data...", flush=True)
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
+device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"Device: '{device}'", flush=True)
 embedded = scgpt.tasks.embed_data(
     adata,
     model_dir.name,
     gene_col="feature_name",
     batch_size=64,
-    use_fast_transformer=False, # Disable fast-attn as not installed
-    device = device,
-    return_new_adata = True
+    use_fast_transformer=False,  # Disable fast-attn as not installed
+    device=device,
+    return_new_adata=True,
 )
 
 print("\n>>> Storing output...", flush=True)