Split out the local backend (#2052)

This splits the backend into two parts: the local backend for desktop cellxgene and the AWS backend for hosted cellxgene. The local backend is in local_server while the hosted remains in server. The general idea is to copy everything from server to local_server, pull unneeded stuff out of local_server, and keep server as-is for this PR. Not touching server means all the infra and deployment code will continue working just as it did before so we can make those changes incrementally.

local_server/compute/scanpy.py

@@ -0,0 +1,53 @@
+import importlib
+import numpy as np
+
+"""
+Wrapper for various scanpy modules.  Will raise NotImplementedError if the scanpy
+module is not installed/available
+"""
+
+
+def get_scanpy_module():
+    try:
+        sc = importlib.import_module("scanpy")
+        # Future: we could enforce versions here, eg, lookat sc.__version__
+        return sc
+    except ModuleNotFoundError as e:
+        raise NotImplementedError("Please install scanpy to enable UMAP re-embedding") from e
+    except Exception as e:
+        # will capture other ImportError corner cases
+        raise NotImplementedError() from e
+
+
+def scanpy_umap(adata, obs_mask=None, pca_options={}, neighbors_options={}, umap_options={}):
+    """
+    Given adata and an obs mask, return a new embedding for adata[obs_mask, :]
+    as an ndarray of shape (len(obs_mask), N), where N>=2.
+
+    Do NOT mutate adata.
+    """
+
+    # backed mode is incompatible with the current implementation
+    if adata.isbacked:
+        raise NotImplementedError("Backed mode is incompatible with re-embedding")
+
+    # safely get scanpy module, which may not be present.
+    sc = get_scanpy_module()
+
+    # https://github.com/theislab/anndata/issues/311
+    obs_mask = slice(None) if obs_mask is None else obs_mask
+    adata = adata[obs_mask, :].copy()
+
+    for k in list(adata.obsm.keys()):
+        del adata.obsm[k]
+    for k in list(adata.uns.keys()):
+        del adata.uns[k]
+
+    sc.pp.pca(adata, zero_center=None, n_comps=min(adata.n_vars - 1, 50), **pca_options)
+    sc.pp.neighbors(adata, **neighbors_options)
+    sc.tl.umap(adata, **umap_options)
+
+    umap = adata.obsm["X_umap"]
+    result = np.full((obs_mask.shape[0], umap.shape[1]), np.NaN)
+    result[obs_mask] = umap
+    return result