re-implement re-embeddings (#1679)

* fix mispelling

* re-implement re-embedding

* always load base embedding to fetch counts

* format

* lint

* fix tests

* lint

* fix accept handling

* test log

* more debug

* more

* more

* more

* more

* remove logging

* logging

* jsonify

* remove debugging logs

* lint

* clean up errors a bit

* fix issue found in PR review

* PR review changes

server/data_anndata/anndata_adaptor.py

@@ -1,7 +1,6 @@
 import warnings
 
 import numpy as np
-import pandas as pd
 from pandas.core.dtypes.dtypes import CategoricalDtype
 import anndata
 from scipy import sparse
@@ -314,16 +313,15 @@ class AnndataAdaptor(DataAdaptor):
             raise FilterError("Error parsing filter")
         with ServerTiming.time("layout.compute"):
             X_umap = scanpy_umap(self.data, obs_mask)
-            normalized_layout = DataAdaptor.normalize_embedding(X_umap)
 
         # Server picks reemedding name, which must not collide with any other
-        # embedding name generated by this backed.
+        # embedding name generated by this backend.
         name = f"reembed:{method}_{datetime.now().isoformat(timespec='milliseconds')}"
         dims = [f"{name}_0", f"{name}_1"]
-        df = pd.DataFrame(normalized_layout, columns=dims)
-        fbs = encode_matrix_fbs(df, col_idx=df.columns, row_idx=None)
-        schema = {"name": name, "type": "float32", "dims": dims}
-        return (schema, fbs)
+        layout_schema = {"name": name, "type": "float32", "dims": dims}
+        self.schema["layout"]["obs"].append(layout_schema)
+        self.data.obsm[f"X_{name}"] = X_umap
+        return layout_schema
 
     def compute_diffexp_ttest(self, maskA, maskB, top_n=None, lfc_cutoff=None):
         if top_n is None: