test_transmute.py 文件源码

def main():
    parser = argparse.ArgumentParser(description=__doc__)

    parser.add_argument('degree', type=int)
    parser.add_argument('prec', type=int, default=50)
    parser.add_argument('data', help="""Data of matrix to compute exp of. Should
    be in scipy sparse csr format.""")
    parser.add_argument('time', type=float)
    parser.add_argument('--expr', type=lambda e: sympify(e, locals=globals()), help="""Precomputed CRAM
    expression. Should have the same prec as 'prec'. If not provided, will be
    computed from scratch.""")
    parser.add_argument('--log-level', default=None, choices=['debug', 'info',
        'warning', 'error', 'critical'])
    # TODO: Add options for arguments to pass to various functions as needed.
    try:
        import argcomplete
        argcomplete.autocomplete(parser)
    except ImportError:
        pass
    args = parser.parse_args()

    if args.log_level:
        logger.setLevel(getattr(logging, args.log_level.upper()))

    res = run_transmute_test(args.data, args.degree, args.prec,
        args.time,expr=args.expr,  _print=True)

    print("Column sums (min, max):")
    errors = {}
    colsums = {}
    for r in sorted(res):
        if res[r] is None:
            print('Could not compute', r)
            continue
        colsums[r] = np.sum(res[r], axis=1)
        errors[r] = np.max(colsums[r]) - np.min(colsums[r])
    for r in sorted(errors, key=lambda i:errors[i], reverse=True):
        print(r, np.min(colsums[r]), np.max(colsums[r]))