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/test/performance/create_test_matrix.py

@@ -0,0 +1,44 @@
+import anndata
+import argparse
+import random
+import scipy
+import numpy as np
+
+
+def main():
+    parser = argparse.ArgumentParser("A command to generate test h5ad files")
+    parser.add_argument("output", help="Name of the output file")
+    parser.add_argument("nobs", type=int, help="Number of observations (rows)")
+    parser.add_argument("nvar", type=int, help="Number of variables (columns)")
+    parser.add_argument("-n", "--nnz-percent", type=float, default=100, help="percent of non-zeros")
+    parser.add_argument("-c", "--col-shift", action="store_true", help="add a random value to each column")
+    parser.add_argument("--seed", type=int, default=None, help="add a random value to each column")
+
+    args = parser.parse_args()
+    create_test_h5ad(args.output, args.nobs, args.nvar, args.nnz_percent, args.col_shift, args.seed)
+
+
+def create_test_h5ad(outfile, nobs, nvar, nnz_percent=100, apply_col_shift=False, seed=None):
+    random.seed(seed)
+    np.random.seed(seed)
+    x = create_X_array(nobs, nvar, nnz_percent, apply_col_shift)
+    obsm = {"X_random": np.random.rand(nobs, 2).astype(np.float32)}
+    adata = anndata.AnnData(x, obsm=obsm)
+    adata.write(outfile)
+
+
+def create_X_array(nobs, nvar, nnz_percent, apply_col_shift):
+    if nnz_percent < 100:
+        array = scipy.sparse.random(nobs, nvar, nnz_percent * 0.01, dtype=np.float32, format="csc")
+    else:
+        array = np.random.rand(nobs, nvar).astype(np.float32)
+
+    if apply_col_shift:
+        col_shift = np.random.rand((nvar))
+        array += col_shift
+
+    return array
+
+
+if __name__ == "__main__":
+    main()