Python numpy.atleast_1d方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def byte_array(pattern):
    """Convert the pattern to a byte array.

    Parameters
    ----------
    pattern : ~numpy.ndarray, bytes, int, or iterable of int
        Pattern to convert.  If a `~numpy.ndarray` or `bytes` instance,
        a byte array view is taken.  If an (iterable of) int, the integers
        need to be unsigned 32 bit and will be interpreted as little-endian.

    Returns
    -------
    byte_array : `~numpy.ndarray` of byte
        With any elements of pattern stored in little-endian order.
    """
    if isinstance(pattern, (np.ndarray, bytes)):
        # Quick turn-around for input that is OK already:
        return np.atleast_1d(pattern).view('u1')

    pattern = np.array(pattern, ndmin=1)
    if (pattern.dtype.kind not in 'uif'
            or pattern.min() < 0
            or pattern.max() >= 1 << 32):
        raise ValueError('values have to fit in 32 bit unsigned int.')
    return pattern.astype('<u4').view('u1') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def do(self, a, b, tags):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def blackbody_radiance(self, T, spectral=True):
        """Calculate integrated radiance for blackbody at temperature T

        :param T: Temperature [K].  This can be either a python number, or
            a numpy ndarray, on a ureg quantity encompassing either.
        :param spectral: Parameter to control whether to return spectral
            radiance or radiance.  See self.integrate_radiances for
            details.

        Returns quantity ndarray with blackbody radiance in desired unit.
        Note that this is an ndarray with dimension (1,) even if you
        passin a scalar.
        """
        try:
            T = T.to("K")
        except AttributeError:
            T = ureg.Quantity(T, "K")
        T = ureg.Quantity(numpy.atleast_1d(T), T.u)
        # fails if T is multidimensional
        shp = T.shape
        return self.integrate_radiances(
            self.frequency, planck_f(
                self.frequency[numpy.newaxis, :],
                T.reshape((-1,))[:, numpy.newaxis]),
                spectral=spectral).reshape(shp) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def normalize_channels_parameter(channels, channel_count):
    if channels is None:
        if channel_count == 1:
            return [0], True
        else:
            return list(range(channel_count)), False

    indices = np.arange(channel_count)
    indices = indices[channels]
    indices = np.atleast_1d(indices)

    if isinstance(channels, slice):
        return indices.tolist(), False

    channels = np.asarray(channels)
    if not np.issubdtype(channels.dtype, np.number):
        raise TypeError('`channels` should be None or int or slice or list of int')
    if channels.ndim == 0:
        assert len(indices) == 1
        return indices.tolist(), True
    return indices.tolist(), False 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def _validate_partition_arguments(a, kth, axis, kind, order, kw):
    a = astensor(a)
    if axis is None:
        a = a.flatten()
        axis = 0
    else:
        axis = validate_axis(a.ndim, axis)
    if isinstance(kth, (Base, Entity)):
        kth = astensor(kth)
        _check_kth_dtype(kth.dtype)
    else:
        kth = np.atleast_1d(kth)
        kth = _validate_kth_value(kth, a.shape[axis])
    if kth.ndim > 1:
        raise ValueError('object too deep for desired array')
    if kind != 'introselect':
        raise ValueError('{} is an unrecognized kind of select'.format(kind))
    # if a is structure type and order is not None
    order = validate_order(a.dtype, order)
    need_align = kw.pop('need_align', None)
    if len(kw) > 0:
        raise TypeError('partition() got an unexpected keyword '
                        'argument \'{}\''.format(next(iter(kw))))

    return a, kth, axis, kind, order, need_align 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def lp2lp(b, a, wo=1.0):
    """
    Transform a lowpass filter prototype to a different frequency.

    Return an analog low-pass filter with cutoff frequency `wo`
    from an analog low-pass filter prototype with unity cutoff frequency, in
    transfer function ('ba') representation.

    """
    a, b = map(atleast_1d, (a, b))
    try:
        wo = float(wo)
    except TypeError:
        wo = float(wo[0])
    d = len(a)
    n = len(b)
    M = max((d, n))
    pwo = pow(wo, numpy.arange(M - 1, -1, -1))
    start1 = max((n - d, 0))
    start2 = max((d - n, 0))
    b = b * pwo[start1] / pwo[start2:]
    a = a * pwo[start1] / pwo[start1:]
    return normalize(b, a) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def _check_func(checker, argname, thefunc, x0, args, numinputs,
                output_shape=None):
    res = atleast_1d(thefunc(*((x0[:numinputs],) + args)))
    if (output_shape is not None) and (shape(res) != output_shape):
        if (output_shape[0] != 1):
            if len(output_shape) > 1:
                if output_shape[1] == 1:
                    return shape(res)
            msg = "%s: there is a mismatch between the input and output " \
                  "shape of the '%s' argument" % (checker, argname)
            func_name = getattr(thefunc, '__name__', None)
            if func_name:
                msg += " '%s'." % func_name
            else:
                msg += "."
            msg += 'Shape should be %s but it is %s.' % (output_shape, shape(res))
            raise TypeError(msg)
    if issubdtype(res.dtype, inexact):
        dt = res.dtype
    else:
        dt = dtype(float)
    return shape(res), dt 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def _chk2_asarray(a, b, axis):
    if axis is None:
        a = np.ravel(a)
        b = np.ravel(b)
        outaxis = 0
    else:
        a = np.asarray(a)
        b = np.asarray(b)
        outaxis = axis

    if a.ndim == 0:
        a = np.atleast_1d(a)
    if b.ndim == 0:
        b = np.atleast_1d(b)

    return a, b, outaxis 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def _munp(self, n, beta, m):
        """
        Returns the n-th non-central moment of the crystalball function.
        """
        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) + _norm_pdf_C * _norm_cdf(beta))

        def n_th_moment(n, beta, m):
            """
            Returns n-th moment. Defined only if n+1 < m
            Function cannot broadcast due to the loop over n
            """
            A = (m/beta)**m * np.exp(-beta**2 / 2.0)
            B = m/beta - beta
            rhs = 2**((n-1)/2.0) * sc.gamma((n+1)/2) * (1.0 + (-1)**n * sc.gammainc((n+1)/2, beta**2 / 2))
            lhs = np.zeros(rhs.shape)
            for k in range(n + 1):
                lhs += sc.binom(n, k) * B**(n-k) * (-1)**k / (m - k - 1) * (m/beta)**(-m + k + 1)
            return A * lhs + rhs

        return N * _lazywhere(np.atleast_1d(n + 1 < m),
                              (n, beta, m),
                              np.vectorize(n_th_moment, otypes=[np.float]),
                              np.inf) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def _parse(self):
        element = np.atleast_1d(_read_dump('dump.element', 'unicode'))
        b = np.atleast_2d(_read_dump('dump.sna'))
        db = np.atleast_2d(_read_dump('dump.snad'))
        vb = np.atleast_2d(_read_dump('dump.snav'))
        return b, db, vb, element 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def test_working_type():
    # Which type do input types with slope and inter cast to in numpy?
    # Wrapper function because we need to use the dtype str for comparison.  We
    # need this because of the very confusing np.int32 != np.intp (on 32 bit).
    def wt(*args, **kwargs):
        return np.dtype(working_type(*args, **kwargs)).str
    d1 = np.atleast_1d
    for in_type in NUMERIC_TYPES:
        in_ts = np.dtype(in_type).str
        assert_equal(wt(in_type), in_ts)
        assert_equal(wt(in_type, 1, 0), in_ts)
        assert_equal(wt(in_type, 1.0, 0.0), in_ts)
        in_val = d1(in_type(0))
        for slope_type in NUMERIC_TYPES:
            sl_val = slope_type(1) # no scaling, regardless of type
            assert_equal(wt(in_type, sl_val, 0.0), in_ts)
            sl_val = slope_type(2) # actual scaling
            out_val = in_val / d1(sl_val)
            assert_equal(wt(in_type, sl_val), out_val.dtype.str)
            for inter_type in NUMERIC_TYPES:
                i_val = inter_type(0) # no scaling, regardless of type
                assert_equal(wt(in_type, 1, i_val), in_ts)
                i_val = inter_type(1) # actual scaling
                out_val = in_val - d1(i_val)
                assert_equal(wt(in_type, 1, i_val), out_val.dtype.str)
                # Combine scaling and intercept
                out_val = (in_val - d1(i_val)) / d1(sl_val)
                assert_equal(wt(in_type, sl_val, i_val), out_val.dtype.str)
    # Confirm that type codes and dtypes work as well
    f32s = np.dtype(np.float32).str
    assert_equal(wt('f4', 1, 0), f32s)
    assert_equal(wt(np.dtype('f4'), 1, 0), f32s) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def _ftype4scaled_finite(tst_arr, slope, inter, direction='read',
                         default=np.float32):
    """ Smallest float type for scaling of `tst_arr` that does not overflow
    """
    assert direction in ('read', 'write')
    if not default in OK_FLOATS and default is np.longdouble:
        # Omitted longdouble
        return default
    def_ind = OK_FLOATS.index(default)
    # promote to arrays to avoid numpy scalar casting rules
    tst_arr = np.atleast_1d(tst_arr)
    slope = np.atleast_1d(slope)
    inter = np.atleast_1d(inter)
    warnings.filterwarnings('ignore', '.*overflow.*', RuntimeWarning)
    try:
        for ftype in OK_FLOATS[def_ind:]:
            tst_trans = tst_arr.copy()
            slope = slope.astype(ftype)
            inter = inter.astype(ftype)
            if direction == 'read': # as in reading of image from disk
                if slope != 1.0:
                    tst_trans = tst_trans * slope
                if inter != 0.0:
                    tst_trans = tst_trans + inter
            elif direction == 'write':
                if inter != 0.0:
                    tst_trans = tst_trans - inter
                if slope != 1.0:
                    tst_trans = tst_trans / slope
            if np.all(np.isfinite(tst_trans)):
                return ftype
    finally:
        warnings.filters.pop(0)
    raise ValueError('Overflow using highest floating point type') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def create_hdf5(self):
        '''Creates an HDF5 file to store the data '''
        # if the hdf5 file does not exist, create it
        file_exists = os.path.exists(self.fn_hdf5)
        if file_exists:
            raise IOError('Cannot create hdf5 file: {0} '
                          'it already exists!'.format(self.fn_hdf5))

        self.mesh.write_hdf5(self.fn_hdf5, 'mesh/')
        self.mesh_roi.write_hdf5(self.fn_hdf5, 'mesh_roi/')
        self.poslist._write_conductivity_to_hdf5(self.fn_hdf5)
        with h5py.File(self.fn_hdf5, 'a') as f:
            f.create_dataset('roi', data=np.atleast_1d(np.array(self.roi, dtype=int))) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def record_data_matrix(self, data, name, group):
        ''' Appends or create data to the HDF5 file 

        Parameters:
        -------------
        data: np.ndarray
            Data to be appended. Will be appended along the first dimension
        name: str
            Name of data seet
        group: str
            Group where to place data set
        '''
        data = np.array(data).squeeze()
        data = np.atleast_1d(data)
        with h5py.File(self.fn_hdf5, 'a') as f:
            try:
                g = f.create_group(group)
            except:
                g = f[group]
            if name not in g.keys():
                g.create_dataset(name,
                                 shape=(0, ) + data.shape,
                                 maxshape=(None, ) + data.shape,
                                 dtype=data.dtype,
                                 chunks=(1, ) + data.shape)

            dset = g[name]
            dset.resize((dset.shape[0] + 1, ) + data.shape)
            dset[-1, ...] = data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import atleast_1d [as 別名]
def run_simulation(self, random_vars):
        poslist = self._update_poslist(random_vars)
        cond = poslist.cond2elmdata(self.mesh)
        v = fem.tdcs(
            self.mesh, cond, self.el_currents,
            self.el_tags, units='mm')


        self.mesh.nodedata = [v]
        cropped = self.mesh.crop_mesh(self.roi)
        v_c = cropped.nodedata[0]
        self.mesh.nodedata = []

        qois = []
        for qoi_name, qoi_f in self.qoi_function.items():
            qois.append(qoi_f(v_c, random_vars))

        self.record_data_matrix(random_vars, 'random_var_samples', '/')
        self.record_data_matrix(v.value, 'v_samples', 'mesh/data_matrices')
        self.record_data_matrix(v_c.value, 'v_samples', 'mesh_roi/data_matrices')
        for qoi_name, qoi_v in zip(self.qoi_function.keys(), qois):
            self.record_data_matrix(
                qoi_v, qoi_name + '_samples', 'mesh_roi/data_matrices')

        del cropped
        del cond
        del v
        del v_c

        return np.atleast_1d(qois[0]).reshape(-1)