python类complex128()的实例源码

def test_basic(self):
        dts = [np.bool, np.int16, np.int32, np.int64, np.double, np.complex128,
               np.longdouble, np.clongdouble]
        for dt in dts:
            c = np.ones(53, dtype=np.bool)
            assert_equal(np.where( c, dt(0), dt(1)), dt(0))
            assert_equal(np.where(~c, dt(0), dt(1)), dt(1))
            assert_equal(np.where(True, dt(0), dt(1)), dt(0))
            assert_equal(np.where(False, dt(0), dt(1)), dt(1))
            d = np.ones_like(c).astype(dt)
            e = np.zeros_like(d)
            r = d.astype(dt)
            c[7] = False
            r[7] = e[7]
            assert_equal(np.where(c, e, e), e)
            assert_equal(np.where(c, d, e), r)
            assert_equal(np.where(c, d, e[0]), r)
            assert_equal(np.where(c, d[0], e), r)
            assert_equal(np.where(c[::2], d[::2], e[::2]), r[::2])
            assert_equal(np.where(c[1::2], d[1::2], e[1::2]), r[1::2])
            assert_equal(np.where(c[::3], d[::3], e[::3]), r[::3])
            assert_equal(np.where(c[1::3], d[1::3], e[1::3]), r[1::3])
            assert_equal(np.where(c[::-2], d[::-2], e[::-2]), r[::-2])
            assert_equal(np.where(c[::-3], d[::-3], e[::-3]), r[::-3])
            assert_equal(np.where(c[1::-3], d[1::-3], e[1::-3]), r[1::-3])

def test_sum_complex(self):
        for dt in (np.complex64, np.complex128, np.clongdouble):
            for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127,
                      128, 1024, 1235):
                tgt = dt(v * (v + 1) / 2) - dt((v * (v + 1) / 2) * 1j)
                d = np.empty(v, dtype=dt)
                d.real = np.arange(1, v + 1)
                d.imag = -np.arange(1, v + 1)
                assert_almost_equal(np.sum(d), tgt)
                assert_almost_equal(np.sum(d[::-1]), tgt)

            d = np.ones(500, dtype=dt) + 1j
            assert_almost_equal(np.sum(d[::2]), 250. + 250j)
            assert_almost_equal(np.sum(d[1::2]), 250. + 250j)
            assert_almost_equal(np.sum(d[::3]), 167. + 167j)
            assert_almost_equal(np.sum(d[1::3]), 167. + 167j)
            assert_almost_equal(np.sum(d[::-2]), 250. + 250j)
            assert_almost_equal(np.sum(d[-1::-2]), 250. + 250j)
            assert_almost_equal(np.sum(d[::-3]), 167. + 167j)
            assert_almost_equal(np.sum(d[-1::-3]), 167. + 167j)
            # sum with first reduction entry != 0
            d = np.ones((1,), dtype=dt) + 1j
            d += d
            assert_almost_equal(d, 2. + 2j)

def test_zero_division(self):
        with np.errstate(all="ignore"):
            for t in [np.complex64, np.complex128]:
                a = t(0.0)
                b = t(1.0)
                assert_(np.isinf(b/a))
                b = t(complex(np.inf, np.inf))
                assert_(np.isinf(b/a))
                b = t(complex(np.inf, np.nan))
                assert_(np.isinf(b/a))
                b = t(complex(np.nan, np.inf))
                assert_(np.isinf(b/a))
                b = t(complex(np.nan, np.nan))
                assert_(np.isnan(b/a))
                b = t(0.)
                assert_(np.isnan(b/a))

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
                      np.uint32, np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)

            tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
            assert_array_equal(np.cumsum(a, axis=0), tgt)

            tgt = np.array(
                [[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
            assert_array_equal(np.cumsum(a2, axis=0), tgt)

            tgt = np.array(
                [[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
            assert_array_equal(np.cumsum(a2, axis=1), tgt)

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                self.assertRaises(ArithmeticError, np.prod, a)
                self.assertRaises(ArithmeticError, np.prod, a2, 1)
            else:
                assert_equal(a.prod(axis=0), 26400)
                assert_array_equal(a2.prod(axis=0),
                                   np.array([50, 36, 84, 180], ctype))
                assert_array_equal(a2.prod(axis=-1),
                                   np.array([24, 1890, 600], ctype))

def test_basic(self):
        ba = [1, 2, 10, 11, 6, 5, 4]
        ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
        for ctype in [np.int16, np.uint16, np.int32, np.uint32,
                      np.float32, np.float64, np.complex64, np.complex128]:
            a = np.array(ba, ctype)
            a2 = np.array(ba2, ctype)
            if ctype in ['1', 'b']:
                self.assertRaises(ArithmeticError, np.cumprod, a)
                self.assertRaises(ArithmeticError, np.cumprod, a2, 1)
                self.assertRaises(ArithmeticError, np.cumprod, a)
            else:
                assert_array_equal(np.cumprod(a, axis=-1),
                                   np.array([1, 2, 20, 220,
                                             1320, 6600, 26400], ctype))
                assert_array_equal(np.cumprod(a2, axis=0),
                                   np.array([[1, 2, 3, 4],
                                             [5, 12, 21, 36],
                                             [50, 36, 84, 180]], ctype))
                assert_array_equal(np.cumprod(a2, axis=-1),
                                   np.array([[1, 2, 6, 24],
                                             [5, 30, 210, 1890],
                                             [10, 30, 120, 600]], ctype))

def test_basic(self):
        A = np.arange(100).reshape(10, 10)
        mA = matrix(A)

        mB = mA.copy()
        O = np.ones((10, 10), np.float64) * 0.1
        mB = mB + O
        assert_(mB.dtype.type == np.float64)
        assert_(np.all(mA != mB))
        assert_(np.all(mB == mA+0.1))

        mC = mA.copy()
        O = np.ones((10, 10), np.complex128)
        mC = mC * O
        assert_(mC.dtype.type == np.complex128)
        assert_(np.all(mA != mB))

def logscale_spec(spec, sr=44100, factor=20.):
    timebins, freqbins = np.shape(spec)

    scale = np.linspace(0, 1, freqbins) ** factor
    scale *= (freqbins-1)/max(scale)
    scale = np.unique(np.round(scale))

    # create spectrogram with new freq bins
    newspec = np.complex128(np.zeros([timebins, len(scale)]))
    for i in range(0, len(scale)):
      if i == len(scale)-1:
          newspec[:,i] = np.sum(spec[:,int(scale[i]):], axis=1)
      else:        
          newspec[:,i] = np.sum(spec[:,int(scale[i]):int(scale[i+1])], axis=1)

    # list center freq of bins
    allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1])
    freqs = []
    for i in range(0, len(scale)):
      if i == len(scale)-1:
          freqs += [np.mean(allfreqs[int(scale[i]):])]
      else:
          freqs += [np.mean(allfreqs[int(scale[i]):int(scale[i+1])])]

    return newspec, freqs

def one_particle(sp, chli, chlo, Es=None):
    """
    Single particle

    Parameters
    ----------
    s : Setup
        Scattering setup
    chi : int
        Input channel
    cho : int
        Output channel
    Es : ndarray
        Input energies
    """
    Es, T1 = tmatrix.one_particle(sp, chli, chlo, Es)

    S1 = np.zeros(T1.shape, dtype=np.complex128)

    if chli == chlo:
        S1 += 1

    S1 -= 2 * 1j * np.pi * T1

    return Es, S1

def compare_g2_fock_state_to_g2_coherent_state():

    N, U, = 2, 0
    gs = (.2, .1)

    model = scattering.Model(
        omegas=[0]*N,
        links=[(0, 1, 1)],
        U=[2*U]*N)

    channels = []
    channels.append(scattering.Channel(site=0, strength=gs[0]))
    channels.append(scattering.Channel(site=N-1, strength=gs[1]))

    setup = scattering.Setup(model, channels)

    Es = np.linspace(-3, 12, 1024)
    dE = 0

    g2f = np.zeros(Es.shape, dtype=np.complex128)
    g2c = np.zeros(Es.shape, dtype=np.complex128)

    for i, E in enumerate(Es):
        g2s[i], _, _ = g2.fock_state(setup, (0, 0), (1, 1), E, dE)

def test_noninteracting_dimer_eigenenergies():
    """Test"""
    m = scat.Model([0] * 2, [[0, 1, 1.1]], [0] * 2)
    channels = [
        scat.Channel(site=0, strength=1),
        scat.Channel(site=1, strength=1),
    ]
    sp = scat.Setup(m, channels)

    E1, _, _ = sp.eigenbasis(1)
    E2, _, _ = sp.eigenbasis(2)

    E12 = np.zeros((len(E2), ), dtype=np.complex128)
    for i in xrange(len(E1)):
        for j in xrange(len(E1)):
            E12[i + j] = E1[i] + E1[j]

    E12 = E12[np.argsort(np.real(E12))]
    E2 = E2[np.argsort(np.real(E2))]

    assert np.allcose(E2, E12), \
        'Non-interacting dimer, single particle energies do not coincide with the two-particle energies.'

def create(i, state0, basis0, basis1):
    """
    Create a boson on site <i>

    Parameters
    ----------
    i : int
        Site index
    state0 : ndarray
        Initial state
    basis0 : list
        Initial basis
    basis1 : list
        Final basis
    """
    mbasis = np.copy(basis0.vs)
    mbasis[:, i] += 1

    state1 = np.zeros((basis1.len,), dtype=np.complex128)

    index1 = basis1.index(mbasis)

    state1[index1] = state0 * np.sqrt(mbasis[:, i])

    return state1

def uniform(size, low=None, high=None, dtype=numpy.float32):
    if dtype is numpy.float32 or dtype is numpy.float64:
        if low is None:
            low = -numpy.sqrt(6. / sum(size))
        if high is None:
            high = numpy.sqrt(6. / sum(size))
        return numpy.asarray(
                numpy.random.uniform(
                        low=low,
                        high=high,
                        size=size
                ),
                dtype=dtype)
    elif dtype is numpy.complex64:
        return (uniform(size, low=low, high=high, dtype=numpy.float32) +
                1j * uniform(size, low=low, high=high,
                             dtype=numpy.float32)).astype(numpy.complex64)
    elif dtype is numpy.complex128:
        return (uniform(size, low=low, high=high, dtype=numpy.float64) +
                1j * uniform(size, low=low, high=high,
                             dtype=numpy.float64)).astype(numpy.complex128)
    else:
        raise ValueError('Requested dtype not available.')

def _ensure_numeric(x):
    if isinstance(x, np.ndarray):
        if is_integer_dtype(x) or is_bool_dtype(x):
            x = x.astype(np.float64)
        elif is_object_dtype(x):
            try:
                x = x.astype(np.complex128)
            except:
                x = x.astype(np.float64)
            else:
                if not np.any(x.imag):
                    x = x.real
    elif not (is_float(x) or is_integer(x) or is_complex(x)):
        try:
            x = float(x)
        except Exception:
            try:
                x = complex(x)
            except Exception:
                raise TypeError('Could not convert %s to numeric' % str(x))
    return x

# NA-friendly array comparisons

def test_complex_fixed(self):
        df = DataFrame(np.random.rand(4, 5).astype(np.complex64),
                       index=list('abcd'),
                       columns=list('ABCDE'))

        with ensure_clean_path(self.path) as path:
            df.to_hdf(path, 'df')
            reread = read_hdf(path, 'df')
            assert_frame_equal(df, reread)

        df = DataFrame(np.random.rand(4, 5).astype(np.complex128),
                       index=list('abcd'),
                       columns=list('ABCDE'))
        with ensure_clean_path(self.path) as path:
            df.to_hdf(path, 'df')
            reread = read_hdf(path, 'df')
            assert_frame_equal(df, reread)

def test_complex_table(self):
        df = DataFrame(np.random.rand(4, 5).astype(np.complex64),
                       index=list('abcd'),
                       columns=list('ABCDE'))

        with ensure_clean_path(self.path) as path:
            df.to_hdf(path, 'df', format='table')
            reread = read_hdf(path, 'df')
            assert_frame_equal(df, reread)

        df = DataFrame(np.random.rand(4, 5).astype(np.complex128),
                       index=list('abcd'),
                       columns=list('ABCDE'))

        with ensure_clean_path(self.path) as path:
            df.to_hdf(path, 'df', format='table', mode='w')
            reread = read_hdf(path, 'df')
            assert_frame_equal(df, reread)

def test_basic(self):
        dts = [np.bool, np.int16, np.int32, np.int64, np.double, np.complex128,
               np.longdouble, np.clongdouble]
        for dt in dts:
            c = np.ones(53, dtype=np.bool)
            assert_equal(np.where( c, dt(0), dt(1)), dt(0))
            assert_equal(np.where(~c, dt(0), dt(1)), dt(1))
            assert_equal(np.where(True, dt(0), dt(1)), dt(0))
            assert_equal(np.where(False, dt(0), dt(1)), dt(1))
            d = np.ones_like(c).astype(dt)
            e = np.zeros_like(d)
            r = d.astype(dt)
            c[7] = False
            r[7] = e[7]
            assert_equal(np.where(c, e, e), e)
            assert_equal(np.where(c, d, e), r)
            assert_equal(np.where(c, d, e[0]), r)
            assert_equal(np.where(c, d[0], e), r)
            assert_equal(np.where(c[::2], d[::2], e[::2]), r[::2])
            assert_equal(np.where(c[1::2], d[1::2], e[1::2]), r[1::2])
            assert_equal(np.where(c[::3], d[::3], e[::3]), r[::3])
            assert_equal(np.where(c[1::3], d[1::3], e[1::3]), r[1::3])
            assert_equal(np.where(c[::-2], d[::-2], e[::-2]), r[::-2])
            assert_equal(np.where(c[::-3], d[::-3], e[::-3]), r[::-3])
            assert_equal(np.where(c[1::-3], d[1::-3], e[1::-3]), r[1::-3])

def test_zero_division(self):
        with np.errstate(all="ignore"):
            for t in [np.complex64, np.complex128]:
                a = t(0.0)
                b = t(1.0)
                assert_(np.isinf(b/a))
                b = t(complex(np.inf, np.inf))
                assert_(np.isinf(b/a))
                b = t(complex(np.inf, np.nan))
                assert_(np.isinf(b/a))
                b = t(complex(np.nan, np.inf))
                assert_(np.isinf(b/a))
                b = t(complex(np.nan, np.nan))
                assert_(np.isnan(b/a))
                b = t(0.)
                assert_(np.isnan(b/a))

def test_basic(self):
        A = np.arange(100).reshape(10, 10)
        mA = matrix(A)

        mB = mA.copy()
        O = np.ones((10, 10), np.float64) * 0.1
        mB = mB + O
        assert_(mB.dtype.type == np.float64)
        assert_(np.all(mA != mB))
        assert_(np.all(mB == mA+0.1))

        mC = mA.copy()
        O = np.ones((10, 10), np.complex128)
        mC = mC * O
        assert_(mC.dtype.type == np.complex128)
        assert_(np.all(mA != mB))

def readBandCoeff(self, ispin=1, ikpt=1, iband=1, norm=False):
        '''
        Read the planewave coefficients of specified KS states.
        '''

        self.checkIndex(ispin, ikpt, iband)

        rec = self.whereRec(ispin, ikpt, iband)
        self._wfc.seek(rec * self._recl)

        nplw = self._nplws[ikpt - 1]
        dump = np.fromfile(self._wfc, dtype=self._WFPrec, count=nplw)

        cg = np.asarray(dump, dtype=np.complex128)
        if norm:
            cg /= np.linalg.norm(cg)
        return cg