Python numpy.result_type方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def cartesian_prod(arrays, out=None, order = 'C'):
    '''
    This function is similar to lib.cartesian_prod of PySCF, except the output can be in Fortran or in C order
    '''
    arrays = [np.asarray(x) for x in arrays]
    dtype = np.result_type(*arrays)
    nd = len(arrays)
    dims = [nd] + [len(x) for x in arrays]

    if out is None:
        out = np.empty(dims, dtype)
    else:
        out = np.ndarray(dims, dtype, buffer=out)
    tout = out.reshape(dims)

    shape = [-1] + [1] * nd
    for i, arr in enumerate(arrays):
        tout[i] = arr.reshape(shape[:nd-i])

    return tout.reshape((nd,-1),order=order).T 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def _conc_mos(moi, moj, compact=False):
    if numpy.result_type(moi, moj) != numpy.double:
        compact = False
    nmoi = moi.shape[1]
    nmoj = moj.shape[1]
    if compact and iden_coeffs(moi, moj):
        ijmosym = 's2'
        nij_pair = nmoi * (nmoi+1) // 2
        moij = numpy.asarray(moi, order='F')
        ijshape = (0, nmoi, 0, nmoi)
    else:
        ijmosym = 's1'
        nij_pair = nmoi * nmoj
        moij = numpy.asarray(numpy.hstack((moi,moj)), order='F')
        ijshape = (0, nmoi, nmoi, nmoi+nmoj)
    return ijmosym, nij_pair, moij, ijshape 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def __array__(self, dtype=None, copy=True):
        fill_value = self.fill_value

        if self.sp_index.ngaps == 0:
            # Compat for na dtype and int values.
            return self.sp_values
        if dtype is None:
            # Can NumPy represent this type?
            # If not, `np.result_type` will raise. We catch that
            # and return object.
            if is_datetime64_any_dtype(self.sp_values.dtype):
                # However, we *do* special-case the common case of
                # a datetime64 with pandas NaT.
                if fill_value is NaT:
                    # Can't put pd.NaT in a datetime64[ns]
                    fill_value = np.datetime64('NaT')
            try:
                dtype = np.result_type(self.sp_values.dtype, type(fill_value))
            except TypeError:
                dtype = object

        out = np.full(self.shape, fill_value, dtype=dtype)
        out[self.sp_index.to_int_index().indices] = self.sp_values
        return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:  # pragma: no cover
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def _align(terms):
    """Align a set of terms"""
    try:
        # flatten the parse tree (a nested list, really)
        terms = list(com.flatten(terms))
    except TypeError:
        # can't iterate so it must just be a constant or single variable
        if isinstance(terms.value, pd.core.generic.NDFrame):
            typ = type(terms.value)
            return typ, _zip_axes_from_type(typ, terms.value.axes)
        return np.result_type(terms.type), None

    # if all resolved variables are numeric scalars
    if all(term.is_scalar for term in terms):
        return _result_type_many(*(term.value for term in terms)).type, None

    # perform the main alignment
    typ, axes = _align_core(terms)
    return typ, axes 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def _filter_special_cases(f):
    @wraps(f)
    def wrapper(terms):
        # single unary operand
        if len(terms) == 1:
            return _align_core_single_unary_op(terms[0])

        term_values = (term.value for term in terms)

        # only scalars or indexes
        if all(isinstance(term.value, pd.Index) or term.isscalar for term in
               terms):
            return np.result_type(*term_values), None

        # no pandas objects
        if not _any_pandas_objects(terms):
            return np.result_type(*term_values), None

        return f(terms)
    return wrapper 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def _align(terms):
    """Align a set of terms"""
    try:
        # flatten the parse tree (a nested list, really)
        terms = list(com.flatten(terms))
    except TypeError:
        # can't iterate so it must just be a constant or single variable
        if isinstance(terms.value, pd.core.generic.NDFrame):
            typ = type(terms.value)
            return typ, _zip_axes_from_type(typ, terms.value.axes)
        return np.result_type(terms.type), None

    # if all resolved variables are numeric scalars
    if all(term.isscalar for term in terms):
        return np.result_type(*(term.value for term in terms)).type, None

    # perform the main alignment
    typ, axes = _align_core(terms)
    return typ, axes 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def _promote_like_lnp(fun, inexact=False):
  """Decorator that promotes the arguments of `fun` to `lnp.result_type(*args)`.

  lnp and onp have different type promotion semantics; this decorator allows
  tests make an onp reference implementation act more like an lnp
  implementation.
  """
  def wrapper(*args, **kw):
    flat_args = tf.nest.flatten(args)
    if inexact and not any(lnp.issubdtype(lnp.result_type(x), lnp.inexact)
                           for x in flat_args):
      dtype = lnp.result_type(lnp.float_, *flat_args)
    else:
      dtype = lnp.result_type(*flat_args)
    args = tf.nest.map_structure(lambda a: onp.asarray(a, dtype), args)
    return fun(*args, **kw)
  return wrapper 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def testBitwiseOp(self, onp_op, lnp_op, rng_factory, shapes, dtypes):
    rng = rng_factory()
    args_maker = self._GetArgsMaker(rng, shapes, dtypes)
    has_python_scalar = jtu.PYTHON_SCALAR_SHAPE in shapes
    self._CheckAgainstNumpy(onp_op, lnp_op, args_maker, check_dtypes=True)
    if onp_op == onp.bitwise_not and has_python_scalar:
      # For bitwise_not with a Python `int`, npe.jit may choose a different
      # dtype for the `int` from onp's choice, which may result in a different
      # result value, so we skip _CompileAndCheck.
      return
    # Numpy does value-dependent dtype promotion on Python/numpy/array scalars
    # which `jit` can't do (when np.result_type is called inside `jit`, tensor
    # values are not available), so we skip dtype check in this case.
    check_dtypes = not(set(shapes) & set([jtu.NUMPY_SCALAR_SHAPE,
                                          jtu.PYTHON_SCALAR_SHAPE, ()]))
    self._CompileAndCheck(lnp_op, args_maker, check_dtypes=check_dtypes) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def testDot(self, lhs_shape, lhs_dtype, rhs_shape, rhs_dtype, rng_factory):
    rng = rng_factory()
    args_maker = lambda: [rng(lhs_shape, lhs_dtype), rng(rhs_shape, rhs_dtype)]
    tol = {onp.float16: 1e-2, onp.float32: 1e-5, onp.float64: 1e-14,
           onp.complex128: 1e-14}
    if jtu.device_under_test() == "tpu":
      tol[onp.float32] = tol[onp.complex64] = 2e-1
    def onp_dot(x, y):
      x = x.astype(onp.float32) if lhs_dtype == lnp.bfloat16 else x
      y = y.astype(onp.float32) if rhs_dtype == lnp.bfloat16 else y
      # `onp.dot(x, y).dtype` sometimes differs from `onp.result_type(x, y)`
      # (e.g. when x is float64[] and y is complex64[3,3], or when x is
      # float16[3,3] and y is int64[]). We ignore this corner case and pretend
      # that they agree.
      return onp.dot(x, y).astype(onp.result_type(x, y))
    self._CheckAgainstNumpy(onp_dot, lnp.dot, args_maker,
                            check_dtypes=True, tol=tol)
    # We disable dtype check in the following cases because `np.dot` does
    # value-dependent type promotion in those cases.
    check_dtypes = () not in (lhs_shape, rhs_shape)
    self._CompileAndCheck(lnp.dot, args_maker, check_dtypes=check_dtypes,
                          atol=tol, rtol=tol, check_incomplete_shape=True) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def testQuantile(self, op, a_rng, q_rng, a_shape, a_dtype, q_shape, q_dtype,
                   axis, keepdims):
    if op == "quantile" and numpy_version < (1, 15):
      raise SkipTest("Numpy < 1.15 does not have np.quantile")
    if op == "median":
      args_maker = lambda: [a_rng(a_shape, a_dtype)]
    else:
      args_maker = lambda: [a_rng(a_shape, a_dtype), q_rng(q_shape, q_dtype)]

    def onp_fun(*args):
      args = [x if lnp.result_type(x) != lnp.bfloat16 else
              onp.asarray(x, onp.float32) for x in args]
      return getattr(onp, op)(*args, axis=axis, keepdims=keepdims)
    lnp_fun = partial(getattr(lnp, op), axis=axis, keepdims=keepdims)
    # TODO(phawkins): we currently set dtype=False because we aren't as
    # aggressive about promoting to float64. It's not clear we want to mimic
    # Numpy here.
    tol_spec = {onp.float32: 2e-4, onp.float64: 5e-6}
    tol = max(jtu.tolerance(a_dtype, tol_spec),
              jtu.tolerance(q_dtype, tol_spec))
    self._CheckAgainstNumpy(onp_fun, lnp_fun, args_maker, check_dtypes=False,
                            tol=tol)
    self._CompileAndCheck(lnp_fun, args_maker, check_dtypes=True, rtol=tol) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def eval_mat(self, cell, ao_kpts, weight, rho, vxc,
                 non0tab=None, xctype='LDA', spin=0, verbose=None):
        nkpts = len(ao_kpts)
        nao = ao_kpts[0].shape[-1]
        dtype = numpy.result_type(*ao_kpts)
        mat = numpy.empty((nkpts,nao,nao), dtype=dtype)
        for k in range(nkpts):
            mat[k] = eval_mat(cell, ao_kpts[k], weight, rho, vxc,
                              non0tab, xctype, spin, verbose)
        return mat 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def _fxc_mat(self, cell, ao_kpts, wv, non0tab, xctype, ao_loc):
        nkpts = len(ao_kpts)
        nao = ao_kpts[0].shape[-1]
        dtype = numpy.result_type(*ao_kpts)
        mat = numpy.empty((nkpts,nao,nao), dtype=dtype)
        for k in range(nkpts):
            mat[k] = _fxc_mat(cell, ao_kpts[k], wv, non0tab, xctype, ao_loc)
        return mat 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import result_type [as 別名]
def _rotate_mo(mo_coeff, mo_occ, dx):
    mo = []
    p1 = 0
    dtype = numpy.result_type(dx, *mo_coeff)
    for k, occ in enumerate(mo_occ):
        nmo = occ.size
        no = numpy.count_nonzero(occ > 0)
        nv = nmo - no
        p0, p1 = p1, p1 + nv * no
        dr = numpy.zeros((nmo,nmo), dtype=dtype)
        dr[no:,:no] = dx[p0:p1].reshape(nv,no)
        dr[:no,no:] =-dx[p0:p1].reshape(nv,no).conj().T
        mo.append(numpy.dot(mo_coeff[k], scipy.linalg.expm(dr)))
    return mo