mirror of
https://github.com/chanzuckerberg/cellxgene.git
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fb61bd6e9c
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.
107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
import sys
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import argparse
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import random
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import time
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import numpy as np
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import local_server.compute.diffexp_generic as diffexp_generic
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from local_server.common.config.app_config import AppConfig
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from local_server.data_common.matrix_loader import MatrixDataLoader
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def main():
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parser = argparse.ArgumentParser("A command to test diffexp")
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parser.add_argument("dataset", help="name of a dataset to load")
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parser.add_argument("-na", "--numA", type=int, help="number of rows in group A")
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parser.add_argument("-nb", "--numB", type=int, help="number of rows in group B")
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parser.add_argument("-va", "--varA", help="obs variable:value to use for group A")
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parser.add_argument("-vb", "--varB", help="obs variable:value to use for group B")
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parser.add_argument("-t", "--trials", default=1, type=int, help="number of trials")
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parser.add_argument(
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"-a", "--alg", choices=("default", "generic"), default="default", help="algorithm to use"
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)
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parser.add_argument("-s", "--show", default=False, action="store_true", help="show the results")
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parser.add_argument(
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"-n", "--new-selection", default=False, action="store_true", help="change the selection between each trial"
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)
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parser.add_argument("--seed", default=1, type=int, help="set the random seed")
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args = parser.parse_args()
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app_config = AppConfig()
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app_config.update_server_config(single_dataset__datapath=args.dataset)
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app_config.update_server_config(app__verbose=True)
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app_config.complete_config()
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loader = MatrixDataLoader(args.dataset)
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adaptor = loader.open(app_config)
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random.seed(args.seed)
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np.random.seed(args.seed)
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rows = adaptor.get_shape()[0]
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if args.numA:
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filterA = random.sample(range(rows), args.numA)
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elif args.varA:
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vname, vval = args.varA.split(":")
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filterA = get_filter_from_obs(adaptor, vname, vval)
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else:
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print("must supply numA or varA")
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sys.exit(1)
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if args.numB:
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filterB = random.sample(range(rows), args.numB)
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elif args.varB:
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vname, vval = args.varB.split(":")
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filterB = get_filter_from_obs(adaptor, vname, vval)
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else:
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print("must supply numB or varB")
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sys.exit(1)
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for i in range(args.trials):
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if args.new_selection:
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if args.numA:
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filterA = random.sample(range(rows), args.numA)
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if args.numB:
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filterB = random.sample(range(rows), args.numB)
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maskA = np.zeros(rows, dtype=bool)
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maskA[filterA] = True
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maskB = np.zeros(rows, dtype=bool)
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maskB[filterB] = True
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t1 = time.time()
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if args.alg == "default":
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results = adaptor.compute_diffexp_ttest(maskA, maskB)
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elif args.alg == "generic":
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results = diffexp_generic.diffexp_ttest(adaptor, maskA, maskB)
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t2 = time.time()
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print("TIME=", t2 - t1)
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if args.show:
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for res in results:
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print(res)
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def get_filter_from_obs(adaptor, obsname, obsval):
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attrs = adaptor.get_obs_columns()
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if obsname not in attrs:
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print(f"Unknown obs attr {obsname}: expected on of {attrs}")
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sys.exit(1)
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obsvals = adaptor.query_obs_array(obsname)[:]
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obsval = type(obsvals[0])(obsval)
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vfilter = np.where(obsvals == obsval)[0]
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if len(vfilter) == 0:
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u = np.unique(obsvals)
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print(f"Unknown value in variable {obsname}:{obsval}: expected one of {list(u)}")
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sys.exit(1)
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return vfilter
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if __name__ == "__main__":
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main()
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