musevlt / origin Goto Github PK

tests/test_origin.py:99: 

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

muse_origin/steps.py:273: in __call__

    self.run(self.orig, *args, **kwargs)

muse_origin/steps.py:777: in run

    pmeansub=pmeansub,

muse_origin/lib_origin.py:68: in timed

    result = f(*args, **kw)

muse_origin/lib_origin.py:1131: in Correlation_GLR_test

    for i in range(Nz)

.tox/py36-cov/lib/python3.6/site-packages/joblib/parallel.py:1004: in __call__

    if self.dispatch_one_batch(iterator):

.tox/py36-cov/lib/python3.6/site-packages/joblib/parallel.py:835: in dispatch_one_batch

    self._dispatch(tasks)

.tox/py36-cov/lib/python3.6/site-packages/joblib/parallel.py:754: in _dispatch

    job = self._backend.apply_async(batch, callback=cb)

.tox/py36-cov/lib/python3.6/site-packages/joblib/_parallel_backends.py:209: in apply_async

    result = ImmediateResult(func)

.tox/py36-cov/lib/python3.6/site-packages/joblib/_parallel_backends.py:590: in __init__

    self.results = batch()

.tox/py36-cov/lib/python3.6/site-packages/joblib/parallel.py:256: in __call__

    for func, args, kwargs in self.items]

.tox/py36-cov/lib/python3.6/site-packages/joblib/parallel.py:256: in <listcomp>

    for func, args, kwargs in self.items]

muse_origin/lib_origin.py:1034: in _convolve_fsf

    cube_fsf = fftconvolve(cube, psf, mode='same')

.tox/py36-cov/lib/python3.6/site-packages/scipy/signal/signaltools.py:542: in fftconvolve

    ret = _freq_domain_conv(in1, in2, axes, shape, calc_fast_len=True)

.tox/py36-cov/lib/python3.6/site-packages/scipy/signal/signaltools.py:382: in _freq_domain_conv

    sp1 = fft(in1, fshape, axes=axes)

.tox/py36-cov/lib/python3.6/site-packages/scipy/fft/_backend.py:23: in __ua_function__

    return fn(*args, **kwargs)

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forward = True

x = array([[-0.37443433,  1.17728097,  0.17462521, ..., -0.92230921,

         0.15581388, -1.08591093],

       [ 0.1879498...332,  2.01586593],

       [ 0.36455996,  0.70848931, -0.01467468, ...,  0.3368053 ,

         1.820535  ,  0.31100637]])

s = [90, 108], axes = [0, 1], norm = 0, overwrite_x = False, workers = 1

    def r2cn(forward, x, s=None, axes=None, norm=None, overwrite_x=False,

             workers=None):

        """Return multi-dimensional discrete Fourier transform of real input"""

        tmp = _asfarray(x)

    

        if not np.isrealobj(tmp):

            raise TypeError("x must be a real sequence")

    

        shape, axes = _init_nd_shape_and_axes(tmp, s, axes)

        tmp, _ = _fix_shape(tmp, shape, axes)

        norm = _normalization(norm, forward)

        workers = _workers(workers)

    

        if len(axes) == 0:

            raise ValueError("at least 1 axis must be transformed")

    

        # Note: overwrite_x is not utilised

>       return pfft.r2c(tmp, axes, forward, norm, None, workers)

E       RuntimeError: unsupported data type