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.
2026-09-28 05:48:12 +08:00 · 2021-02-18 12:58:22 -08:00
parent 036b5f8c0f
commit fb61bd6e9c
153 changed files with 14027 additions and 46 deletions
@@ -0,0 +1,106 @@
+import sys
+import argparse
+import random
+import time
+import numpy as np
+
+import local_server.compute.diffexp_generic as diffexp_generic
+
+from local_server.common.config.app_config import AppConfig
+from local_server.data_common.matrix_loader import MatrixDataLoader
+
+
+def main():
+    parser = argparse.ArgumentParser("A command to test diffexp")
+    parser.add_argument("dataset", help="name of a dataset to load")
+    parser.add_argument("-na", "--numA", type=int, help="number of rows in group A")
+    parser.add_argument("-nb", "--numB", type=int, help="number of rows in group B")
+    parser.add_argument("-va", "--varA", help="obs variable:value to use for group A")
+    parser.add_argument("-vb", "--varB", help="obs variable:value to use for group B")
+    parser.add_argument("-t", "--trials", default=1, type=int, help="number of trials")
+    parser.add_argument(
+        "-a", "--alg", choices=("default", "generic"), default="default", help="algorithm to use"
+    )
+    parser.add_argument("-s", "--show", default=False, action="store_true", help="show the results")
+    parser.add_argument(
+        "-n", "--new-selection", default=False, action="store_true", help="change the selection between each trial"
+    )
+    parser.add_argument("--seed", default=1, type=int, help="set the random seed")
+
+    args = parser.parse_args()
+
+    app_config = AppConfig()
+    app_config.update_server_config(single_dataset__datapath=args.dataset)
+    app_config.update_server_config(app__verbose=True)
+    app_config.complete_config()
+
+    loader = MatrixDataLoader(args.dataset)
+    adaptor = loader.open(app_config)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    rows = adaptor.get_shape()[0]
+
+    if args.numA:
+        filterA = random.sample(range(rows), args.numA)
+    elif args.varA:
+        vname, vval = args.varA.split(":")
+        filterA = get_filter_from_obs(adaptor, vname, vval)
+    else:
+        print("must supply numA or varA")
+        sys.exit(1)
+
+    if args.numB:
+        filterB = random.sample(range(rows), args.numB)
+    elif args.varB:
+        vname, vval = args.varB.split(":")
+        filterB = get_filter_from_obs(adaptor, vname, vval)
+    else:
+        print("must supply numB or varB")
+        sys.exit(1)
+
+    for i in range(args.trials):
+        if args.new_selection:
+            if args.numA:
+                filterA = random.sample(range(rows), args.numA)
+            if args.numB:
+                filterB = random.sample(range(rows), args.numB)
+
+        maskA = np.zeros(rows, dtype=bool)
+        maskA[filterA] = True
+        maskB = np.zeros(rows, dtype=bool)
+        maskB[filterB] = True
+
+        t1 = time.time()
+        if args.alg == "default":
+            results = adaptor.compute_diffexp_ttest(maskA, maskB)
+        elif args.alg == "generic":
+            results = diffexp_generic.diffexp_ttest(adaptor, maskA, maskB)
+
+        t2 = time.time()
+        print("TIME=", t2 - t1)
+
+    if args.show:
+        for res in results:
+            print(res)
+
+
+def get_filter_from_obs(adaptor, obsname, obsval):
+    attrs = adaptor.get_obs_columns()
+    if obsname not in attrs:
+        print(f"Unknown obs attr {obsname}: expected on of {attrs}")
+        sys.exit(1)
+    obsvals = adaptor.query_obs_array(obsname)[:]
+    obsval = type(obsvals[0])(obsval)
+
+    vfilter = np.where(obsvals == obsval)[0]
+    if len(vfilter) == 0:
+        u = np.unique(obsvals)
+        print(f"Unknown value in variable {obsname}:{obsval}: expected one of {list(u)}")
+        sys.exit(1)
+
+    return vfilter
+
+
+if __name__ == "__main__":
+    main()