46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
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#!/usr/bin/env python
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from __future__ import print_function
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import os
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import sys
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import argparse
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import numpy as np
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# Parse arguments.
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parser = argparse.ArgumentParser()
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parser.add_argument('-m', '--model', nargs='+', required=True,
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help="Models to average")
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parser.add_argument('-o', '--output', required=True,
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help="Output path")
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args = parser.parse_args()
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# *average* holds the model matrix.
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average = dict()
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# No. of models.
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n = len(args.model)
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for filename in args.model:
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print("Loading {}".format(filename))
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with open(filename, "rb") as mfile:
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# Loads matrix from model file.
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m = np.load(mfile)
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for k in m:
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if k != "history_errs":
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# Initialize the key.
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if k not in average:
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average[k] = m[k]
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# Add to the appropriate value.
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elif average[k].shape == m[k].shape and "special" not in k:
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average[k] += m[k]
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# Actual averaging.
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for k in average:
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if "special" not in k:
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average[k] /= n
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# Save averaged model to file.
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print("Saving to {}".format(args.output))
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np.savez(args.output, **average)
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