Python W_NDimArray.from_shape_and_storage方法代码示例

# 需要导入模块: from pypy.module.micronumpy.base import W_NDimArray [as 别名]
# 或者: from pypy.module.micronumpy.base.W_NDimArray import from_shape_and_storage [as 别名]
def frombuffer(space, w_buffer, w_dtype=None, count=-1, offset=0):
    dtype = space.interp_w(descriptor.W_Dtype,
        space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
    if dtype.elsize == 0:
        raise oefmt(space.w_ValueError, "itemsize cannot be zero in type")

    try:
        buf = _getbuffer(space, w_buffer)
    except OperationError as e:
        if not e.match(space, space.w_TypeError):
            raise
        w_buffer = space.call_method(w_buffer, '__buffer__', 
                                    space.newint(space.BUF_FULL_RO))
        buf = _getbuffer(space, w_buffer)

    ts = buf.getlength()
    if offset < 0 or offset > ts:
        raise oefmt(space.w_ValueError,
                    "offset must be non-negative and no greater than "
                    "buffer length (%d)", ts)

    s = ts - offset
    if offset:
        buf = SubBuffer(buf, offset, s)

    n = count
    itemsize = dtype.elsize
    assert itemsize > 0
    if n < 0:
        if s % itemsize != 0:
            raise oefmt(space.w_ValueError,
                        "buffer size must be a multiple of element size")
        n = s / itemsize
    else:
        if s < n * itemsize:
            raise oefmt(space.w_ValueError,
                        "buffer is smaller than requested size")

    try:
        storage = buf.get_raw_address()
    except ValueError:
        a = W_NDimArray.from_shape(space, [n], dtype=dtype)
        loop.fromstring_loop(space, a, dtype, itemsize, buf.as_str())
        return a
    else:
        writable = not buf.readonly
    return W_NDimArray.from_shape_and_storage(space, [n], storage, storage_bytes=s,
                                dtype=dtype, w_base=w_buffer, writable=writable)

# 需要导入模块: from pypy.module.micronumpy.base import W_NDimArray [as 别名]
# 或者: from pypy.module.micronumpy.base.W_NDimArray import from_shape_and_storage [as 别名]
def _array(space, w_object, w_dtype=None, copy=True, w_order=None, subok=False):
    from pypy.module.micronumpy import strides

    # for anything that isn't already an array, try __array__ method first
    if not isinstance(w_object, W_NDimArray):
        w_array = try_array_method(space, w_object, w_dtype)
        if w_array is not None:
            # continue with w_array, but do further operations in place
            w_object = w_array
            copy = False
    if not isinstance(w_object, W_NDimArray):
        w_array = try_interface_method(space, w_object)
        if w_array is not None:
            w_object = w_array
            copy = False
    dtype = descriptor.decode_w_dtype(space, w_dtype)

    if space.is_none(w_order):
        order = 'C'
    else:
        order = space.str_w(w_order)
        if order == 'K':
            order = 'C'
        if order != 'C':  # or order != 'F':
            raise oefmt(space.w_ValueError, "Unknown order: %s", order)

    if isinstance(w_object, W_NDimArray):
        if (dtype is None or w_object.get_dtype() is dtype):
            if copy and (subok or type(w_object) is W_NDimArray):
                return w_object.descr_copy(space, w_order)
            elif not copy and (subok or type(w_object) is W_NDimArray):
                return w_object
        if subok and not type(w_object) is W_NDimArray:
            raise oefmt(space.w_NotImplementedError,
                "array(..., subok=True) only partially implemented")
        # we have a ndarray, but need to copy or change dtype
        if dtype is None:
            dtype = w_object.get_dtype()
        if dtype != w_object.get_dtype():
            # silently reject the copy value
            copy = True
        if copy:
            shape = w_object.get_shape()
            w_arr = W_NDimArray.from_shape(space, shape, dtype, order=order)
            if support.product(shape) == 1:
                w_arr.set_scalar_value(dtype.coerce(space,
                        w_object.implementation.getitem(0)))
            else:
                loop.setslice(space, shape, w_arr.implementation, w_object.implementation)
            return w_arr
        else:
            imp = w_object.implementation
            w_base = w_object
            if imp.base() is not None:
                w_base = imp.base()
            with imp as storage:
                sz = support.product(w_object.get_shape()) * dtype.elsize
                return W_NDimArray.from_shape_and_storage(space,
                    w_object.get_shape(), storage, dtype, storage_bytes=sz,
                    w_base=w_base, start=imp.start)
    else:
        # not an array
        shape, elems_w = strides.find_shape_and_elems(space, w_object, dtype)
    if dtype is None and space.isinstance_w(w_object, space.w_buffer):
        dtype = descriptor.get_dtype_cache(space).w_uint8dtype
    if dtype is None or (dtype.is_str_or_unicode() and dtype.elsize < 1):
        dtype = find_dtype_for_seq(space, elems_w, dtype)
        if dtype is None:
            dtype = descriptor.get_dtype_cache(space).w_float64dtype
        elif dtype.is_str_or_unicode() and dtype.elsize < 1:
            # promote S0 -> S1, U0 -> U1
            dtype = descriptor.variable_dtype(space, dtype.char + '1')

    w_arr = W_NDimArray.from_shape(space, shape, dtype, order=order)
    if support.product(shape) == 1: # safe from overflow since from_shape checks
        w_arr.set_scalar_value(dtype.coerce(space, elems_w[0]))
    else:
        loop.assign(space, w_arr, elems_w)
    return w_arr

# 需要导入模块: from pypy.module.micronumpy.base import W_NDimArray [as 别名]
# 或者: from pypy.module.micronumpy.base.W_NDimArray import from_shape_and_storage [as 别名]
def try_interface_method(space, w_object, copy):
    try:
        w_interface = space.getattr(w_object, space.wrap("__array_interface__"))
        if w_interface is None:
            return None, False
        version_w = space.finditem(w_interface, space.wrap("version"))
        if version_w is None:
            raise oefmt(space.w_ValueError, "__array_interface__ found without"
                        " 'version' key")
        if not space.isinstance_w(version_w, space.w_int):
            raise oefmt(space.w_ValueError, "__array_interface__ found with"
                        " non-int 'version' key")
        version = space.int_w(version_w)
        if version < 3:
            raise oefmt(space.w_ValueError,
                    "__array_interface__ version %d not supported", version)
        # make a view into the data
        w_shape = space.finditem(w_interface, space.wrap('shape'))
        w_dtype = space.finditem(w_interface, space.wrap('typestr'))
        w_descr = space.finditem(w_interface, space.wrap('descr'))
        w_data = space.finditem(w_interface, space.wrap('data'))
        w_strides = space.finditem(w_interface, space.wrap('strides'))
        if w_shape is None or w_dtype is None:
            raise oefmt(space.w_ValueError,
                    "__array_interface__ missing one or more required keys: shape, typestr"
                    )
        if w_descr is not None:
            raise oefmt(space.w_NotImplementedError,
                    "__array_interface__ descr not supported yet")
        if w_strides is None or space.is_w(w_strides, space.w_None):
            strides = None
        else:
            strides = [space.int_w(i) for i in space.listview(w_strides)]
        shape = [space.int_w(i) for i in space.listview(w_shape)]
        dtype = descriptor.decode_w_dtype(space, w_dtype)
        if dtype is None:
            raise oefmt(space.w_ValueError,
                    "__array_interface__ could not decode dtype %R", w_dtype
                    )
        if w_data is not None and (space.isinstance_w(w_data, space.w_tuple) or
                                   space.isinstance_w(w_data, space.w_list)):
            data_w = space.listview(w_data)
            w_data = rffi.cast(RAW_STORAGE_PTR, space.int_w(data_w[0]))
            read_only = space.is_true(data_w[1]) or copy
            offset = 0
            w_base = w_object
            if read_only:
                w_base = None
            return W_NDimArray.from_shape_and_storage(space, shape, w_data, 
                                dtype, w_base=w_base, strides=strides,
                                start=offset), read_only
        if w_data is None:
            w_data = w_object
        w_offset = space.finditem(w_interface, space.wrap('offset'))
        if w_offset is None:
            offset = 0
        else:
            offset = space.int_w(w_offset)
        #print 'create view from shape',shape,'dtype',dtype,'data',data
        if strides is not None:
            raise oefmt(space.w_NotImplementedError,
                   "__array_interface__ strides not fully supported yet") 
        arr = frombuffer(space, w_data, dtype, support.product(shape), offset)
        new_impl = arr.implementation.reshape(arr, shape)
        return W_NDimArray(new_impl), False
        
    except OperationError as e:
        if e.match(space, space.w_AttributeError):
            return None, False
        raise

# 需要导入模块: from pypy.module.micronumpy.base import W_NDimArray [as 别名]
# 或者: from pypy.module.micronumpy.base.W_NDimArray import from_shape_and_storage [as 别名]
def _array(space, w_object, w_dtype=None, copy=True, w_order=None, subok=False):

    from pypy.module.micronumpy.boxes import W_GenericBox
    # numpy testing calls array(type(array([]))) and expects a ValueError
    if space.isinstance_w(w_object, space.w_type):
        raise oefmt(space.w_ValueError, "cannot create ndarray from type instance")
    # for anything that isn't already an array, try __array__ method first
    dtype = descriptor.decode_w_dtype(space, w_dtype)
    if not isinstance(w_object, W_NDimArray):
        w_array = try_array_method(space, w_object, w_dtype)
        if w_array is None:
            if (    not space.isinstance_w(w_object, space.w_str) and 
                    not space.isinstance_w(w_object, space.w_unicode) and
                    not isinstance(w_object, W_GenericBox)):
                # use buffer interface
                w_object = _array_from_buffer_3118(space, w_object, dtype)
        else:
            # continue with w_array, but do further operations in place
            w_object = w_array
            copy = False
            dtype = w_object.get_dtype()
    if not isinstance(w_object, W_NDimArray):
        w_array, _copy = try_interface_method(space, w_object, copy)
        if w_array is not None:
            w_object = w_array
            copy = _copy
            dtype = w_object.get_dtype()

    if isinstance(w_object, W_NDimArray):
        npy_order = order_converter(space, w_order, NPY.ANYORDER)
        if (dtype is None or w_object.get_dtype() is dtype) and (subok or
                type(w_object) is W_NDimArray):
            flags = w_object.get_flags()
            must_copy = copy
            must_copy |= (npy_order == NPY.CORDER and not flags & NPY.ARRAY_C_CONTIGUOUS)
            must_copy |= (npy_order == NPY.FORTRANORDER and not flags & NPY.ARRAY_F_CONTIGUOUS)
            if must_copy:
                return w_object.descr_copy(space, space.wrap(npy_order))
            else:
                return w_object
        if subok and not type(w_object) is W_NDimArray:
            raise oefmt(space.w_NotImplementedError,
                "array(..., subok=True) only partially implemented")
        # we have a ndarray, but need to copy or change dtype
        if dtype is None:
            dtype = w_object.get_dtype()
        if dtype != w_object.get_dtype():
            # silently reject the copy value
            copy = True
        if copy:
            shape = w_object.get_shape()
            order = support.get_order_as_CF(w_object.get_order(), npy_order)
            w_arr = W_NDimArray.from_shape(space, shape, dtype, order=order)
            if support.product(shape) == 1:
                w_arr.set_scalar_value(dtype.coerce(space,
                        w_object.implementation.getitem(0)))
            else:
                loop.setslice(space, shape, w_arr.implementation, w_object.implementation)
            return w_arr
        else:
            imp = w_object.implementation
            w_base = w_object
            sz = w_base.get_size() * dtype.elsize
            if imp.base() is not None:
                w_base = imp.base()
                if type(w_base) is W_NDimArray:
                    sz = w_base.get_size() * dtype.elsize
                else:
                    # this must succeed (mmap, buffer, ...)
                    sz = space.int_w(space.call_method(w_base, 'size'))
            with imp as storage:
                return W_NDimArray.from_shape_and_storage(space,
                    w_object.get_shape(), storage, dtype, storage_bytes=sz,
                    w_base=w_base, strides=imp.strides, start=imp.start)
    else:
        # not an array
        npy_order = order_converter(space, w_order, NPY.CORDER)
        shape, elems_w = find_shape_and_elems(space, w_object, dtype)
    if dtype is None and space.isinstance_w(w_object, space.w_buffer):
        dtype = descriptor.get_dtype_cache(space).w_uint8dtype
    if dtype is None or (dtype.is_str_or_unicode() and dtype.elsize < 1):
        dtype = find_dtype_for_seq(space, elems_w, dtype)

    w_arr = W_NDimArray.from_shape(space, shape, dtype, order=npy_order)
    if support.product(shape) == 1: # safe from overflow since from_shape checks
        w_arr.set_scalar_value(dtype.coerce(space, elems_w[0]))
    else:
        loop.assign(space, w_arr, elems_w)
    return w_arr

# 需要导入模块: from pypy.module.micronumpy.base import W_NDimArray [as 别名]
# 或者: from pypy.module.micronumpy.base.W_NDimArray import from_shape_and_storage [as 别名]
def _array_from_buffer_3118(space, w_object, dtype):
    try:
        w_buf = space.call_method(space.builtin, "memoryview", w_object)
    except OperationError as e:
        if e.match(space, space.w_TypeError):
            # object does not have buffer interface
            return w_object
        raise
    format = space.getattr(w_buf,space.newbytes('format'))
    if format:
        descr = _descriptor_from_pep3118_format(space, space.str_w(format))
        if not descr:
            return w_object
        if dtype and descr:
            raise oefmt(space.w_NotImplementedError,
                "creating an array from a memoryview while specifying dtype "
                "not supported")
        if descr.elsize != space.int_w(space.getattr(w_buf, space.newbytes('itemsize'))): 
            msg = ("Item size computed from the PEP 3118 buffer format "
                  "string does not match the actual item size.")
            space.warn(space.wrap(msg), space.w_RuntimeWarning)
            return w_object
        dtype = descr 
    elif not dtype:
        dtype = descriptor.get_dtype_cache(space).w_stringdtype
        dtype.elsize = space.int_w(space.getattr(w_buf, space.newbytes('itemsize')))
    nd = space.int_w(space.getattr(w_buf, space.newbytes('ndim')))
    shape = [space.int_w(d) for d in space.listview(
                            space.getattr(w_buf, space.newbytes('shape')))]
    strides = []
    buflen = space.len_w(w_buf) * dtype.elsize
    if shape:
        strides = [space.int_w(d) for d in space.listview(
                            space.getattr(w_buf, space.newbytes('strides')))]
        if not strides:
            d = buflen
            strides = [0] * nd
            for k in range(nd):
                if shape[k] > 0:
                    d /= shape[k]
                    strides[k] = d
    else:
        if nd == 1:
            shape = [buflen / dtype.elsize, ]
            strides = [dtype.elsize, ]
        elif nd > 1:
            msg = ("ndim computed from the PEP 3118 buffer format "
                   "is greater than 1, but shape is NULL.")
            space.warn(space.wrap(msg), space.w_RuntimeWarning)
            return w_object
    try:
        w_data = rffi.cast(RAW_STORAGE_PTR, space.int_w(space.call_method(w_buf, '_pypy_raw_address')))
    except OperationError as e:
        if e.match(space, space.w_ValueError):
            return w_object
        else:
            raise e
    writable = not space.bool_w(space.getattr(w_buf, space.newbytes('readonly')))
    w_ret = W_NDimArray.from_shape_and_storage(space, shape, w_data,
               storage_bytes=buflen, dtype=dtype, w_base=w_object, 
               writable=writable, strides=strides)
    if w_ret:
        return w_ret
    return w_object

# 需要导入模块: from pypy.module.micronumpy.base import W_NDimArray [as 别名]
# 或者: from pypy.module.micronumpy.base.W_NDimArray import from_shape_and_storage [as 别名]
def _array(space, w_object, w_dtype=None, copy=True, w_order=None, subok=False):
    from pypy.module.micronumpy import strides

    # for anything that isn't already an array, try __array__ method first
    if not isinstance(w_object, W_NDimArray):
        w_array = try_array_method(space, w_object, w_dtype)
        if w_array is not None:
            # continue with w_array, but do further operations in place
            w_object = w_array
            copy = False

    dtype = descriptor.decode_w_dtype(space, w_dtype)

    if space.is_none(w_order):
        order = 'C'
    else:
        order = space.str_w(w_order)
        if order == 'K':
            order = 'C'
        if order != 'C':  # or order != 'F':
            raise oefmt(space.w_ValueError, "Unknown order: %s", order)

    if isinstance(w_object, W_NDimArray):
        if (dtype is None or w_object.get_dtype() is dtype):
            if copy and (subok or type(w_object) is W_NDimArray):
                return w_object.descr_copy(space, w_order)
            elif not copy and (subok or type(w_object) is W_NDimArray):
                return w_object
        # we have a ndarray, but need to copy or change dtype or create W_NDimArray
        if dtype is None:
            dtype = w_object.get_dtype()
        if dtype != w_object.get_dtype():
            # silently reject the copy value
            copy = True
        if copy:
            shape = w_object.get_shape()
            _elems_w = w_object.reshape(space, space.wrap(-1))
            elems_w = [None] * w_object.get_size()
            for i in range(len(elems_w)):
                elems_w[i] = _elems_w.descr_getitem(space, space.wrap(i))
        elif subok:
            raise oefmt(space.w_NotImplementedError, 
                "array(...copy=False, subok=True) not implemented yet")
        else:
            sz = support.product(w_object.get_shape()) * dtype.elsize
            return W_NDimArray.from_shape_and_storage(space,
                w_object.get_shape(),w_object.implementation.storage,
                dtype, storage_bytes=sz, w_base=w_object)
    else:
        # not an array
        shape, elems_w = strides.find_shape_and_elems(space, w_object, dtype)
    if dtype is None or (dtype.is_str_or_unicode() and dtype.elsize < 1):
        dtype = strides.find_dtype_for_seq(space, elems_w, dtype)
        if dtype is None:
            dtype = descriptor.get_dtype_cache(space).w_float64dtype
        elif dtype.is_str_or_unicode() and dtype.elsize < 1:
            # promote S0 -> S1, U0 -> U1
            dtype = descriptor.variable_dtype(space, dtype.char + '1')

    w_arr = W_NDimArray.from_shape(space, shape, dtype, order=order)
    if len(elems_w) == 1:
        w_arr.set_scalar_value(dtype.coerce(space, elems_w[0]))
    else:
        loop.assign(space, w_arr, elems_w)
    return w_arr