def call(self, space, args_w): w_obj = args_w[0] out = None if len(args_w) > 1: out = args_w[1] if space.is_w(out, space.w_None): out = None w_obj = convert_to_array(space, w_obj) dtype = w_obj.get_dtype() if dtype.is_flexible_type(): raise OperationError(space.w_TypeError, space.wrap('Not implemented for this type')) if (self.int_only and not dtype.is_int_type() or not self.allow_bool and dtype.is_bool_type() or not self.allow_complex and dtype.is_complex_type()): raise OperationError( space.w_TypeError, space.wrap("ufunc %s not supported for the input type" % self.name)) calc_dtype = find_unaryop_result_dtype( space, w_obj.get_dtype(), promote_to_float=self.promote_to_float, promote_bools=self.promote_bools) if out is not None: if not isinstance(out, W_NDimArray): raise OperationError(space.w_TypeError, space.wrap('output must be an array')) res_dtype = out.get_dtype() #if not w_obj.get_dtype().can_cast_to(res_dtype): # raise operationerrfmt(space.w_TypeError, # "Cannot cast ufunc %s output from dtype('%s') to dtype('%s') with casting rule 'same_kind'", self.name, w_obj.get_dtype().name, res_dtype.name) elif self.bool_result: res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype else: res_dtype = calc_dtype if self.complex_to_float and calc_dtype.is_complex_type(): if calc_dtype.name == 'complex64': res_dtype = interp_dtype.get_dtype_cache( space).w_float32dtype else: res_dtype = interp_dtype.get_dtype_cache( space).w_float64dtype if w_obj.is_scalar(): w_val = self.func(calc_dtype, w_obj.get_scalar_value().convert_to(calc_dtype)) if out is None: return w_val if out.is_scalar(): out.set_scalar_value(w_val) else: out.fill(res_dtype.coerce(space, w_val)) return out shape = shape_agreement(space, w_obj.get_shape(), out, broadcast_down=False) return loop.call1(space, shape, self.func, calc_dtype, res_dtype, w_obj, out)
def setslice(self, space, arr): impl = arr.implementation if impl.is_scalar(): self.fill(impl.get_scalar_value()) return shape = shape_agreement(space, self.get_shape(), arr) if impl.storage == self.storage: impl = impl.copy(space) loop.setslice(space, shape, self, impl)
def setslice(self, space, arr): if len(arr.get_shape()) > 0 and len(self.get_shape()) == 0: raise oefmt(space.w_ValueError, "could not broadcast input array from shape " "(%s) into shape ()", ','.join([str(x) for x in arr.get_shape()])) shape = shape_agreement(space, self.get_shape(), arr) impl = arr.implementation if impl.storage == self.storage: impl = impl.copy(space) loop.setslice(space, shape, self, impl)
def setslice(self, space, arr): if len(arr.get_shape()) > 0 and len(self.get_shape()) == 0: raise oefmt( space.w_ValueError, "could not broadcast input array from shape " "(%s) into shape ()", ','.join([str(x) for x in arr.get_shape()])) shape = shape_agreement(space, self.get_shape(), arr) impl = arr.implementation if impl.storage == self.storage: impl = impl.copy(space) loop.setslice(space, shape, self, impl)
def call(self, space, args_w): w_obj = args_w[0] out = None if len(args_w) > 1: out = args_w[1] if space.is_w(out, space.w_None): out = None w_obj = convert_to_array(space, w_obj) dtype = w_obj.get_dtype() if dtype.is_flexible_type(): raise OperationError(space.w_TypeError, space.wrap('Not implemented for this type')) if (self.int_only and not dtype.is_int_type() or not self.allow_bool and dtype.is_bool_type() or not self.allow_complex and dtype.is_complex_type()): raise OperationError(space.w_TypeError, space.wrap( "ufunc %s not supported for the input type" % self.name)) calc_dtype = find_unaryop_result_dtype(space, w_obj.get_dtype(), promote_to_float=self.promote_to_float, promote_bools=self.promote_bools) if out is not None: if not isinstance(out, W_NDimArray): raise OperationError(space.w_TypeError, space.wrap( 'output must be an array')) res_dtype = out.get_dtype() #if not w_obj.get_dtype().can_cast_to(res_dtype): # raise operationerrfmt(space.w_TypeError, # "Cannot cast ufunc %s output from dtype('%s') to dtype('%s') with casting rule 'same_kind'", self.name, w_obj.get_dtype().name, res_dtype.name) elif self.bool_result: res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype else: res_dtype = calc_dtype if self.complex_to_float and calc_dtype.is_complex_type(): if calc_dtype.name == 'complex64': res_dtype = interp_dtype.get_dtype_cache(space).w_float32dtype else: res_dtype = interp_dtype.get_dtype_cache(space).w_float64dtype if w_obj.is_scalar(): w_val = self.func(calc_dtype, w_obj.get_scalar_value().convert_to(calc_dtype)) if out is None: return w_val if out.is_scalar(): out.set_scalar_value(w_val) else: out.fill(res_dtype.coerce(space, w_val)) return out shape = shape_agreement(space, w_obj.get_shape(), out, broadcast_down=False) return loop.call1(space, shape, self.func, calc_dtype, res_dtype, w_obj, out)
def setslice(self, space, arr): if len(arr.get_shape()) > len(self.get_shape()): # record arrays get one extra dimension if not self.dtype.is_record() or \ len(arr.get_shape()) > len(self.get_shape()) + 1: raise oefmt(space.w_ValueError, "could not broadcast input array from shape " "(%s) into shape (%s)", ','.join([str(x) for x in arr.get_shape()]), ','.join([str(x) for x in self.get_shape()]), ) shape = shape_agreement(space, self.get_shape(), arr) impl = arr.implementation if impl.storage == self.storage: impl = impl.copy(space) loop.setslice(space, shape, self, impl)
def setslice(self, space, arr): if len(arr.get_shape()) > len(self.get_shape()): # record arrays get one extra dimension if not self.dtype.is_record() or \ len(arr.get_shape()) > len(self.get_shape()) + 1: raise oefmt( space.w_ValueError, "could not broadcast input array from shape " "(%s) into shape (%s)", ','.join([str(x) for x in arr.get_shape()]), ','.join([str(x) for x in self.get_shape()]), ) shape = shape_agreement(space, self.get_shape(), arr) impl = arr.implementation if impl.storage == self.storage: impl = impl.copy(space) loop.setslice(space, shape, self, impl)
def where(space, w_arr, w_x=None, w_y=None): """where(condition, [x, y]) Return elements, either from `x` or `y`, depending on `condition`. If only `condition` is given, return ``condition.nonzero()``. Parameters ---------- condition : array_like, bool When True, yield `x`, otherwise yield `y`. x, y : array_like, optional Values from which to choose. `x` and `y` need to have the same shape as `condition`. Returns ------- out : ndarray or tuple of ndarrays If both `x` and `y` are specified, the output array contains elements of `x` where `condition` is True, and elements from `y` elsewhere. If only `condition` is given, return the tuple ``condition.nonzero()``, the indices where `condition` is True. See Also -------- nonzero, choose Notes ----- If `x` and `y` are given and input arrays are 1-D, `where` is equivalent to:: [xv if c else yv for (c,xv,yv) in zip(condition,x,y)] Examples -------- >>> np.where([[True, False], [True, True]], ... [[1, 2], [3, 4]], ... [[9, 8], [7, 6]]) array([[1, 8], [3, 4]]) >>> np.where([[0, 1], [1, 0]]) (array([0, 1]), array([1, 0])) >>> x = np.arange(9.).reshape(3, 3) >>> np.where( x > 5 ) (array([2, 2, 2]), array([0, 1, 2])) >>> x[np.where( x > 3.0 )] # Note: result is 1D. array([ 4., 5., 6., 7., 8.]) >>> np.where(x < 5, x, -1) # Note: broadcasting. array([[ 0., 1., 2.], [ 3., 4., -1.], [-1., -1., -1.]]) NOTE: support for not passing x and y is unsupported """ if space.is_none(w_y): if space.is_none(w_x): raise OperationError( space.w_NotImplementedError, space.wrap("1-arg where unsupported right now")) raise OperationError( space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments")) if space.is_none(w_x): raise OperationError( space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments")) arr = convert_to_array(space, w_arr) x = convert_to_array(space, w_x) y = convert_to_array(space, w_y) if x.is_scalar() and y.is_scalar() and arr.is_scalar(): if arr.get_dtype().itemtype.bool(arr.get_scalar_value()): return x return y dtype = ufuncs.find_binop_result_dtype(space, x.get_dtype(), y.get_dtype()) shape = shape_agreement(space, arr.get_shape(), x) shape = shape_agreement(space, shape, y) out = W_NDimArray.from_shape(space, shape, dtype) return loop.where(space, out, shape, arr, x, y, dtype)
def where(space, w_arr, w_x=None, w_y=None): """where(condition, [x, y]) Return elements, either from `x` or `y`, depending on `condition`. If only `condition` is given, return ``condition.nonzero()``. Parameters ---------- condition : array_like, bool When True, yield `x`, otherwise yield `y`. x, y : array_like, optional Values from which to choose. `x` and `y` need to have the same shape as `condition`. Returns ------- out : ndarray or tuple of ndarrays If both `x` and `y` are specified, the output array contains elements of `x` where `condition` is True, and elements from `y` elsewhere. If only `condition` is given, return the tuple ``condition.nonzero()``, the indices where `condition` is True. See Also -------- nonzero, choose Notes ----- If `x` and `y` are given and input arrays are 1-D, `where` is equivalent to:: [xv if c else yv for (c,xv,yv) in zip(condition,x,y)] Examples -------- >>> np.where([[True, False], [True, True]], ... [[1, 2], [3, 4]], ... [[9, 8], [7, 6]]) array([[1, 8], [3, 4]]) >>> np.where([[0, 1], [1, 0]]) (array([0, 1]), array([1, 0])) >>> x = np.arange(9.).reshape(3, 3) >>> np.where( x > 5 ) (array([2, 2, 2]), array([0, 1, 2])) >>> x[np.where( x > 3.0 )] # Note: result is 1D. array([ 4., 5., 6., 7., 8.]) >>> np.where(x < 5, x, -1) # Note: broadcasting. array([[ 0., 1., 2.], [ 3., 4., -1.], [-1., -1., -1.]]) NOTE: support for not passing x and y is unsupported """ if space.is_none(w_y): if space.is_none(w_x): raise OperationError(space.w_NotImplementedError, space.wrap("1-arg where unsupported right now")) raise OperationError(space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments")) if space.is_none(w_x): raise OperationError(space.w_ValueError, space.wrap("Where should be called with either 1 or 3 arguments")) arr = convert_to_array(space, w_arr) x = convert_to_array(space, w_x) y = convert_to_array(space, w_y) if x.is_scalar() and y.is_scalar() and arr.is_scalar(): if arr.get_dtype().itemtype.bool(arr.get_scalar_value()): return x return y dtype = interp_ufuncs.find_binop_result_dtype(space, x.get_dtype(), y.get_dtype()) shape = shape_agreement(space, arr.get_shape(), x) shape = shape_agreement(space, shape, y) out = W_NDimArray.from_shape(space, shape, dtype) return loop.where(out, shape, arr, x, y, dtype)
def call(self, space, args_w): if len(args_w) > 2: [w_lhs, w_rhs, w_out] = args_w else: [w_lhs, w_rhs] = args_w w_out = None w_lhs = convert_to_array(space, w_lhs) w_rhs = convert_to_array(space, w_rhs) w_ldtype = w_lhs.get_dtype() w_rdtype = w_rhs.get_dtype() if w_ldtype.is_str_type() and w_rdtype.is_str_type() and \ self.comparison_func: pass elif (w_ldtype.is_str_type() or w_rdtype.is_str_type()) and \ self.comparison_func and w_out is None: return space.wrap(False) elif (w_ldtype.is_flexible_type() or \ w_rdtype.is_flexible_type()): raise OperationError(space.w_TypeError, space.wrap( 'unsupported operand dtypes %s and %s for "%s"' % \ (w_rdtype.get_name(), w_ldtype.get_name(), self.name))) if self.are_common_types(w_ldtype, w_rdtype): if not w_lhs.is_scalar() and w_rhs.is_scalar(): w_rdtype = w_ldtype elif w_lhs.is_scalar() and not w_rhs.is_scalar(): w_ldtype = w_rdtype if (self.int_only and (not w_ldtype.is_int_type() or not w_rdtype.is_int_type()) or not self.allow_bool and (w_ldtype.is_bool_type() or w_rdtype.is_bool_type()) or not self.allow_complex and (w_ldtype.is_complex_type() or w_rdtype.is_complex_type())): raise OperationError(space.w_TypeError, space.wrap("Unsupported types")) calc_dtype = find_binop_result_dtype(space, w_ldtype, w_rdtype, promote_to_float=self.promote_to_float, promote_bools=self.promote_bools) if space.is_none(w_out): out = None elif not isinstance(w_out, W_NDimArray): raise OperationError(space.w_TypeError, space.wrap( 'output must be an array')) else: out = w_out calc_dtype = out.get_dtype() if self.comparison_func: res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype else: res_dtype = calc_dtype if w_lhs.is_scalar() and w_rhs.is_scalar(): arr = self.func(calc_dtype, w_lhs.get_scalar_value().convert_to(calc_dtype), w_rhs.get_scalar_value().convert_to(calc_dtype) ) if isinstance(out, W_NDimArray): if out.is_scalar(): out.set_scalar_value(arr) else: out.fill(arr) else: out = arr return out new_shape = shape_agreement(space, w_lhs.get_shape(), w_rhs) new_shape = shape_agreement(space, new_shape, out, broadcast_down=False) return loop.call2(space, new_shape, self.func, calc_dtype, res_dtype, w_lhs, w_rhs, out)
def call(self, space, args_w): if len(args_w) > 2: [w_lhs, w_rhs, w_out] = args_w else: [w_lhs, w_rhs] = args_w w_out = None w_lhs = convert_to_array(space, w_lhs) w_rhs = convert_to_array(space, w_rhs) w_ldtype = w_lhs.get_dtype() w_rdtype = w_rhs.get_dtype() if w_ldtype.is_str_type() and w_rdtype.is_str_type() and \ self.comparison_func: pass elif (w_ldtype.is_str_type() or w_rdtype.is_str_type()) and \ self.comparison_func and w_out is None: return space.wrap(False) elif (w_ldtype.is_flexible_type() or \ w_rdtype.is_flexible_type()): raise OperationError(space.w_TypeError, space.wrap( 'unsupported operand dtypes %s and %s for "%s"' % \ (w_rdtype.get_name(), w_ldtype.get_name(), self.name))) if self.are_common_types(w_ldtype, w_rdtype): if not w_lhs.is_scalar() and w_rhs.is_scalar(): w_rdtype = w_ldtype elif w_lhs.is_scalar() and not w_rhs.is_scalar(): w_ldtype = w_rdtype if (self.int_only and (not w_ldtype.is_int_type() or not w_rdtype.is_int_type()) or not self.allow_bool and (w_ldtype.is_bool_type() or w_rdtype.is_bool_type()) or not self.allow_complex and (w_ldtype.is_complex_type() or w_rdtype.is_complex_type())): raise OperationError(space.w_TypeError, space.wrap("Unsupported types")) calc_dtype = find_binop_result_dtype( space, w_ldtype, w_rdtype, promote_to_float=self.promote_to_float, promote_bools=self.promote_bools) if space.is_none(w_out): out = None elif not isinstance(w_out, W_NDimArray): raise OperationError(space.w_TypeError, space.wrap('output must be an array')) else: out = w_out calc_dtype = out.get_dtype() if self.comparison_func: res_dtype = interp_dtype.get_dtype_cache(space).w_booldtype else: res_dtype = calc_dtype if w_lhs.is_scalar() and w_rhs.is_scalar(): arr = self.func(calc_dtype, w_lhs.get_scalar_value().convert_to(calc_dtype), w_rhs.get_scalar_value().convert_to(calc_dtype)) if isinstance(out, W_NDimArray): if out.is_scalar(): out.set_scalar_value(arr) else: out.fill(arr) else: out = arr return out new_shape = shape_agreement(space, w_lhs.get_shape(), w_rhs) new_shape = shape_agreement(space, new_shape, out, broadcast_down=False) return loop.call2(space, new_shape, self.func, calc_dtype, res_dtype, w_lhs, w_rhs, out)
def call(self, space, args_w): if len(args_w) > 2: [w_lhs, w_rhs, w_out] = args_w else: [w_lhs, w_rhs] = args_w w_out = None w_lhs = convert_to_array(space, w_lhs) w_rhs = convert_to_array(space, w_rhs) w_ldtype = w_lhs.get_dtype() w_rdtype = w_rhs.get_dtype() if w_ldtype.is_str() and w_rdtype.is_str() and \ self.comparison_func: pass elif (w_ldtype.is_str() or w_rdtype.is_str()) and \ self.comparison_func and w_out is None: return space.wrap(False) elif w_ldtype.is_flexible() or w_rdtype.is_flexible(): if self.comparison_func: if self.name == 'equal' or self.name == 'not_equal': res = w_ldtype.eq(space, w_rdtype) if not res: return space.wrap(self.name == 'not_equal') else: return space.w_NotImplemented else: raise oefmt(space.w_TypeError, 'unsupported operand dtypes %s and %s for "%s"', w_rdtype.get_name(), w_ldtype.get_name(), self.name) if self.are_common_types(w_ldtype, w_rdtype): if not w_lhs.is_scalar() and w_rhs.is_scalar(): w_rdtype = w_ldtype elif w_lhs.is_scalar() and not w_rhs.is_scalar(): w_ldtype = w_rdtype calc_dtype = find_binop_result_dtype(space, w_ldtype, w_rdtype, promote_to_float=self.promote_to_float, promote_bools=self.promote_bools) if (self.int_only and (not w_ldtype.is_int() or not w_rdtype.is_int() or not calc_dtype.is_int()) or not self.allow_bool and (w_ldtype.is_bool() or w_rdtype.is_bool()) or not self.allow_complex and (w_ldtype.is_complex() or w_rdtype.is_complex())): raise oefmt(space.w_TypeError, "ufunc '%s' not supported for the input types", self.name) if space.is_none(w_out): out = None elif not isinstance(w_out, W_NDimArray): raise oefmt(space.w_TypeError, 'output must be an array') else: out = w_out calc_dtype = out.get_dtype() if self.comparison_func: res_dtype = descriptor.get_dtype_cache(space).w_booldtype else: res_dtype = calc_dtype if w_lhs.is_scalar() and w_rhs.is_scalar(): arr = self.func(calc_dtype, w_lhs.get_scalar_value().convert_to(space, calc_dtype), w_rhs.get_scalar_value().convert_to(space, calc_dtype) ) if isinstance(out, W_NDimArray): if out.is_scalar(): out.set_scalar_value(arr) else: out.fill(space, arr) else: out = arr return out new_shape = shape_agreement(space, w_lhs.get_shape(), w_rhs) new_shape = shape_agreement(space, new_shape, out, broadcast_down=False) return loop.call2(space, new_shape, self.func, calc_dtype, res_dtype, w_lhs, w_rhs, out)
def __init__(self, space, w_seq, w_flags, w_op_flags, w_op_dtypes, w_casting, w_op_axes, w_itershape, buffersize=0, order=NPY.KEEPORDER, allow_backward=True): self.external_loop = False self.buffered = False self.tracked_index = '' self.common_dtype = False self.delay_bufalloc = False self.grow_inner = False self.ranged = False self.refs_ok = False self.reduce_ok = False self.zerosize_ok = False self.index_iter = None self.done = False self.first_next = True self.op_axes = [] self.allow_backward = allow_backward if not space.is_w(w_casting, space.w_None): self.casting = space.str_w(w_casting) else: self.casting = 'safe' # convert w_seq operands to a list of W_NDimArray if space.isinstance_w(w_seq, space.w_tuple) or \ space.isinstance_w(w_seq, space.w_list): w_seq_as_list = space.listview(w_seq) self.seq = [ convert_to_array(space, w_elem) if not space.is_none(w_elem) else None for w_elem in w_seq_as_list ] else: self.seq = [convert_to_array(space, w_seq)] if order == NPY.ANYORDER: # 'A' means "'F' order if all the arrays are Fortran contiguous, # 'C' order otherwise" order = NPY.CORDER for s in self.seq: if s and not (s.get_flags() & NPY.ARRAY_F_CONTIGUOUS): break else: order = NPY.FORTRANORDER elif order == NPY.KEEPORDER: # 'K' means "as close to the order the array elements appear in # memory as possible", so match self.order to seq.order order = NPY.CORDER for s in self.seq: if s and not (s.get_order() == NPY.FORTRANORDER): break else: order = NPY.FORTRANORDER self.order = order parse_func_flags(space, self, w_flags) self.op_flags = parse_op_arg(space, 'op_flags', w_op_flags, len(self.seq), parse_op_flag) # handle w_op_axes oa_ndim = -1 if not space.is_none(w_op_axes): oa_ndim = self.set_op_axes(space, w_op_axes) self.ndim = calculate_ndim(self.seq, oa_ndim) # handle w_op_dtypes part 1: creating self.dtypes list from input if not space.is_none(w_op_dtypes): w_seq_as_list = space.listview(w_op_dtypes) self.dtypes = [ decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list ] if len(self.dtypes) != len(self.seq): raise oefmt( space.w_ValueError, "op_dtypes must be a tuple/list matching the number of ops" ) else: self.dtypes = [] # handle None or writable operands, calculate my shape outargs = [ i for i in range(len(self.seq)) if self.seq[i] is None or self.op_flags[i].rw == 'w' ] if len(outargs) > 0: out_shape = shape_agreement_multiple( space, [self.seq[i] for i in outargs]) else: out_shape = None if space.isinstance_w(w_itershape, space.w_tuple) or \ space.isinstance_w(w_itershape, space.w_list): self.shape = [space.int_w(i) for i in space.listview(w_itershape)] else: self.shape = shape_agreement_multiple(space, self.seq, shape=out_shape) if len(outargs) > 0: # Make None operands writeonly and flagged for allocation if len(self.dtypes) > 0: out_dtype = self.dtypes[outargs[0]] else: out_dtype = None for i in range(len(self.seq)): if self.seq[i] is None: self.op_flags[i].allocate = True continue if self.op_flags[i].rw == 'w': continue out_dtype = find_binop_result_dtype( space, self.seq[i].get_dtype(), out_dtype) for i in outargs: if self.seq[i] is None: # XXX can we postpone allocation to later? self.seq[i] = W_NDimArray.from_shape( space, self.shape, out_dtype) else: if not self.op_flags[i].broadcast: # Raises if output cannot be broadcast try: shape_agreement(space, self.shape, self.seq[i], False) except OperationError as e: raise oefmt( space.w_ValueError, "non-broadcastable" " output operand with shape %s doesn't match " "the broadcast shape %s", str(self.seq[i].get_shape()), str(self.shape)) if self.tracked_index != "": order = self.order if order == NPY.KEEPORDER: order = self.seq[0].implementation.order if self.tracked_index == "multi": backward = False else: backward = ((order == NPY.CORDER and self.tracked_index != 'C') or (order == NPY.FORTRANORDER and self.tracked_index != 'F')) self.index_iter = IndexIterator(self.shape, backward=backward) # handle w_op_dtypes part 2: copy where needed if possible if len(self.dtypes) > 0: for i in range(len(self.seq)): self_d = self.dtypes[i] seq_d = self.seq[i].get_dtype() if not self_d: self.dtypes[i] = seq_d elif self_d != seq_d: impl = self.seq[i].implementation if self.buffered or 'r' in self.op_flags[i].tmp_copy: if not can_cast_array(space, self.seq[i], self_d, self.casting): raise oefmt( space.w_TypeError, "Iterator operand %d" " dtype could not be cast from %s to %s" " according to the rule '%s'", i, space.str_w(seq_d.descr_repr(space)), space.str_w(self_d.descr_repr(space)), self.casting) order = support.get_order_as_CF(impl.order, self.order) new_impl = impl.astype(space, self_d, order).copy(space) self.seq[i] = W_NDimArray(new_impl) else: raise oefmt( space.w_TypeError, "Iterator " "operand required copying or buffering, " "but neither copying nor buffering was " "enabled") if 'w' in self.op_flags[i].rw: if not can_cast_type(space, self_d, seq_d, self.casting): raise oefmt( space.w_TypeError, "Iterator" " requested dtype could not be cast from " " %s to %s, the operand %d dtype, accord" "ing to the rule '%s'", space.str_w(self_d.descr_repr(space)), space.str_w(seq_d.descr_repr(space)), i, self.casting) elif self.buffered and not (self.external_loop and len(self.seq) < 2): for i in range(len(self.seq)): if i not in outargs: self.seq[i] = self.seq[i].descr_copy(space, w_order=space.wrap( self.order)) self.dtypes = [s.get_dtype() for s in self.seq] else: #copy them from seq self.dtypes = [s.get_dtype() for s in self.seq] # create an iterator for each operand self.iters = [] for i in range(len(self.seq)): it = self.get_iter(space, i) it.contiguous = False self.iters.append((it, it.reset())) if self.external_loop: coalesce_axes(self, space)
def __init__(self, space, w_seq, w_flags, w_op_flags, w_op_dtypes, w_casting, w_op_axes, w_itershape, buffersize=0, order='K'): from pypy.module.micronumpy.ufuncs import find_binop_result_dtype self.order = order self.external_loop = False self.buffered = False self.tracked_index = '' self.common_dtype = False self.delay_bufalloc = False self.grow_inner = False self.ranged = False self.refs_ok = False self.reduce_ok = False self.zerosize_ok = False self.index_iter = None self.done = False self.first_next = True self.op_axes = [] # convert w_seq operands to a list of W_NDimArray if space.isinstance_w(w_seq, space.w_tuple) or \ space.isinstance_w(w_seq, space.w_list): w_seq_as_list = space.listview(w_seq) self.seq = [convert_to_array(space, w_elem) if not space.is_none(w_elem) else None for w_elem in w_seq_as_list] else: self.seq = [convert_to_array(space, w_seq)] parse_func_flags(space, self, w_flags) self.op_flags = parse_op_arg(space, 'op_flags', w_op_flags, len(self.seq), parse_op_flag) # handle w_op_axes oa_ndim = -1 if not space.is_none(w_op_axes): oa_ndim = self.set_op_axes(space, w_op_axes) self.ndim = calculate_ndim(self.seq, oa_ndim) # handle w_op_dtypes part 1: creating self.dtypes list from input if not space.is_none(w_op_dtypes): w_seq_as_list = space.listview(w_op_dtypes) self.dtypes = [decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list] if len(self.dtypes) != len(self.seq): raise oefmt(space.w_ValueError, "op_dtypes must be a tuple/list matching the number of ops") else: self.dtypes = [] # handle None or writable operands, calculate my shape outargs = [i for i in range(len(self.seq)) if self.seq[i] is None or self.op_flags[i].rw == 'w'] if len(outargs) > 0: out_shape = shape_agreement_multiple(space, [self.seq[i] for i in outargs]) else: out_shape = None if space.isinstance_w(w_itershape, space.w_tuple) or \ space.isinstance_w(w_itershape, space.w_list): self.shape = [space.int_w(i) for i in space.listview(w_itershape)] else: self.shape = shape_agreement_multiple(space, self.seq, shape=out_shape) if len(outargs) > 0: # Make None operands writeonly and flagged for allocation if len(self.dtypes) > 0: out_dtype = self.dtypes[outargs[0]] else: out_dtype = None for i in range(len(self.seq)): if self.seq[i] is None: self.op_flags[i].allocate = True continue if self.op_flags[i].rw == 'w': continue out_dtype = find_binop_result_dtype( space, self.seq[i].get_dtype(), out_dtype) for i in outargs: if self.seq[i] is None: # XXX can we postpone allocation to later? self.seq[i] = W_NDimArray.from_shape(space, self.shape, out_dtype) else: if not self.op_flags[i].broadcast: # Raises if ooutput cannot be broadcast shape_agreement(space, self.shape, self.seq[i], False) if self.tracked_index != "": if self.order == "K": self.order = self.seq[0].implementation.order if self.tracked_index == "multi": backward = False else: backward = self.order != self.tracked_index self.index_iter = IndexIterator(self.shape, backward=backward) # handle w_op_dtypes part 2: copy where needed if possible if len(self.dtypes) > 0: for i in range(len(self.seq)): selfd = self.dtypes[i] seq_d = self.seq[i].get_dtype() if not selfd: self.dtypes[i] = seq_d elif selfd != seq_d: if not 'r' in self.op_flags[i].tmp_copy: raise oefmt(space.w_TypeError, "Iterator operand required copying or " "buffering for operand %d", i) impl = self.seq[i].implementation new_impl = impl.astype(space, selfd) self.seq[i] = W_NDimArray(new_impl) else: #copy them from seq self.dtypes = [s.get_dtype() for s in self.seq] # create an iterator for each operand self.iters = [] for i in range(len(self.seq)): it = get_iter(space, self.order, self.seq[i], self.shape, self.dtypes[i], self.op_flags[i], self) it.contiguous = False self.iters.append((it, it.reset())) if self.external_loop: coalesce_axes(self, space)
def __init__(self, space, w_seq, w_flags, w_op_flags, w_op_dtypes, w_casting, w_op_axes, w_itershape, buffersize=0, order='K'): self.order = order self.external_loop = False self.buffered = False self.tracked_index = '' self.common_dtype = False self.delay_bufalloc = False self.grow_inner = False self.ranged = False self.refs_ok = False self.reduce_ok = False self.zerosize_ok = False self.index_iter = None self.done = False self.first_next = True self.op_axes = [] # convert w_seq operands to a list of W_NDimArray if space.isinstance_w(w_seq, space.w_tuple) or \ space.isinstance_w(w_seq, space.w_list): w_seq_as_list = space.listview(w_seq) self.seq = [ convert_to_array(space, w_elem) if not space.is_none(w_elem) else None for w_elem in w_seq_as_list ] else: self.seq = [convert_to_array(space, w_seq)] parse_func_flags(space, self, w_flags) self.op_flags = parse_op_arg(space, 'op_flags', w_op_flags, len(self.seq), parse_op_flag) # handle w_op_axes oa_ndim = -1 if not space.is_none(w_op_axes): oa_ndim = self.set_op_axes(space, w_op_axes) self.ndim = calculate_ndim(self.seq, oa_ndim) # handle w_op_dtypes part 1: creating self.dtypes list from input if not space.is_none(w_op_dtypes): w_seq_as_list = space.listview(w_op_dtypes) self.dtypes = [ decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list ] if len(self.dtypes) != len(self.seq): raise oefmt( space.w_ValueError, "op_dtypes must be a tuple/list matching the number of ops" ) else: self.dtypes = [] # handle None or writable operands, calculate my shape outargs = [ i for i in range(len(self.seq)) if self.seq[i] is None or self.op_flags[i].rw == 'w' ] if len(outargs) > 0: out_shape = shape_agreement_multiple( space, [self.seq[i] for i in outargs]) else: out_shape = None if space.isinstance_w(w_itershape, space.w_tuple) or \ space.isinstance_w(w_itershape, space.w_list): self.shape = [space.int_w(i) for i in space.listview(w_itershape)] else: self.shape = shape_agreement_multiple(space, self.seq, shape=out_shape) if len(outargs) > 0: # Make None operands writeonly and flagged for allocation if len(self.dtypes) > 0: out_dtype = self.dtypes[outargs[0]] else: out_dtype = None for i in range(len(self.seq)): if self.seq[i] is None: self.op_flags[i].allocate = True continue if self.op_flags[i].rw == 'w': continue out_dtype = find_binop_result_dtype( space, self.seq[i].get_dtype(), out_dtype) for i in outargs: if self.seq[i] is None: # XXX can we postpone allocation to later? self.seq[i] = W_NDimArray.from_shape( space, self.shape, out_dtype) else: if not self.op_flags[i].broadcast: # Raises if ooutput cannot be broadcast shape_agreement(space, self.shape, self.seq[i], False) if self.tracked_index != "": if self.order == "K": self.order = self.seq[0].implementation.order if self.tracked_index == "multi": backward = False else: backward = self.order != self.tracked_index self.index_iter = IndexIterator(self.shape, backward=backward) # handle w_op_dtypes part 2: copy where needed if possible if len(self.dtypes) > 0: for i in range(len(self.seq)): selfd = self.dtypes[i] seq_d = self.seq[i].get_dtype() if not selfd: self.dtypes[i] = seq_d elif selfd != seq_d: if not 'r' in self.op_flags[i].tmp_copy: raise oefmt( space.w_TypeError, "Iterator operand required copying or " "buffering for operand %d", i) impl = self.seq[i].implementation new_impl = impl.astype(space, selfd) self.seq[i] = W_NDimArray(new_impl) else: #copy them from seq self.dtypes = [s.get_dtype() for s in self.seq] # create an iterator for each operand self.iters = [] for i in range(len(self.seq)): it = get_iter(space, self.order, self.seq[i], self.shape, self.dtypes[i], self.op_flags[i], self) it.contiguous = False self.iters.append((it, it.reset())) if self.external_loop: coalesce_axes(self, space)
def __init__( self, space, w_seq, w_flags, w_op_flags, w_op_dtypes, w_casting, w_op_axes, w_itershape, buffersize=0, order=NPY.KEEPORDER, allow_backward=True, ): self.external_loop = False self.buffered = False self.tracked_index = "" self.common_dtype = False self.delay_bufalloc = False self.grow_inner = False self.ranged = False self.refs_ok = False self.reduce_ok = False self.zerosize_ok = False self.index_iter = None self.done = False self.first_next = True self.op_axes = [] self.allow_backward = allow_backward if not space.is_w(w_casting, space.w_None): self.casting = space.str_w(w_casting) else: self.casting = "safe" # convert w_seq operands to a list of W_NDimArray if space.isinstance_w(w_seq, space.w_tuple) or space.isinstance_w(w_seq, space.w_list): w_seq_as_list = space.listview(w_seq) self.seq = [ convert_to_array(space, w_elem) if not space.is_none(w_elem) else None for w_elem in w_seq_as_list ] else: self.seq = [convert_to_array(space, w_seq)] if order == NPY.ANYORDER: # 'A' means "'F' order if all the arrays are Fortran contiguous, # 'C' order otherwise" order = NPY.CORDER for s in self.seq: if s and not (s.get_flags() & NPY.ARRAY_F_CONTIGUOUS): break else: order = NPY.FORTRANORDER elif order == NPY.KEEPORDER: # 'K' means "as close to the order the array elements appear in # memory as possible", so match self.order to seq.order order = NPY.CORDER for s in self.seq: if s and not (s.get_order() == NPY.FORTRANORDER): break else: order = NPY.FORTRANORDER self.order = order parse_func_flags(space, self, w_flags) self.op_flags = parse_op_arg(space, "op_flags", w_op_flags, len(self.seq), parse_op_flag) # handle w_op_axes oa_ndim = -1 if not space.is_none(w_op_axes): oa_ndim = self.set_op_axes(space, w_op_axes) self.ndim = calculate_ndim(self.seq, oa_ndim) # handle w_op_dtypes part 1: creating self.dtypes list from input if not space.is_none(w_op_dtypes): w_seq_as_list = space.listview(w_op_dtypes) self.dtypes = [decode_w_dtype(space, w_elem) for w_elem in w_seq_as_list] if len(self.dtypes) != len(self.seq): raise oefmt(space.w_ValueError, "op_dtypes must be a tuple/list matching the number of ops") else: self.dtypes = [] # handle None or writable operands, calculate my shape outargs = [i for i in range(len(self.seq)) if self.seq[i] is None or self.op_flags[i].rw == "w"] if len(outargs) > 0: out_shape = shape_agreement_multiple(space, [self.seq[i] for i in outargs]) else: out_shape = None if space.isinstance_w(w_itershape, space.w_tuple) or space.isinstance_w(w_itershape, space.w_list): self.shape = [space.int_w(i) for i in space.listview(w_itershape)] else: self.shape = shape_agreement_multiple(space, self.seq, shape=out_shape) if len(outargs) > 0: # Make None operands writeonly and flagged for allocation if len(self.dtypes) > 0: out_dtype = self.dtypes[outargs[0]] else: out_dtype = None for i in range(len(self.seq)): if self.seq[i] is None: self.op_flags[i].allocate = True continue if self.op_flags[i].rw == "w": continue out_dtype = find_binop_result_dtype(space, self.seq[i].get_dtype(), out_dtype) for i in outargs: if self.seq[i] is None: # XXX can we postpone allocation to later? self.seq[i] = W_NDimArray.from_shape(space, self.shape, out_dtype) else: if not self.op_flags[i].broadcast: # Raises if output cannot be broadcast try: shape_agreement(space, self.shape, self.seq[i], False) except OperationError as e: raise oefmt( space.w_ValueError, "non-broadcastable" " output operand with shape %s doesn't match " "the broadcast shape %s", str(self.seq[i].get_shape()), str(self.shape), ) if self.tracked_index != "": order = self.order if order == NPY.KEEPORDER: order = self.seq[0].implementation.order if self.tracked_index == "multi": backward = False else: backward = (order == NPY.CORDER and self.tracked_index != "C") or ( order == NPY.FORTRANORDER and self.tracked_index != "F" ) self.index_iter = IndexIterator(self.shape, backward=backward) # handle w_op_dtypes part 2: copy where needed if possible if len(self.dtypes) > 0: for i in range(len(self.seq)): self_d = self.dtypes[i] seq_d = self.seq[i].get_dtype() if not self_d: self.dtypes[i] = seq_d elif self_d != seq_d: impl = self.seq[i].implementation if self.buffered or "r" in self.op_flags[i].tmp_copy: if not can_cast_array(space, self.seq[i], self_d, self.casting): raise oefmt( space.w_TypeError, "Iterator operand %d" " dtype could not be cast from %s to %s" " according to the rule '%s'", i, space.str_w(seq_d.descr_repr(space)), space.str_w(self_d.descr_repr(space)), self.casting, ) order = support.get_order_as_CF(impl.order, self.order) new_impl = impl.astype(space, self_d, order).copy(space) self.seq[i] = W_NDimArray(new_impl) else: raise oefmt( space.w_TypeError, "Iterator " "operand required copying or buffering, " "but neither copying nor buffering was " "enabled", ) if "w" in self.op_flags[i].rw: if not can_cast_type(space, self_d, seq_d, self.casting): raise oefmt( space.w_TypeError, "Iterator" " requested dtype could not be cast from " " %s to %s, the operand %d dtype, accord" "ing to the rule '%s'", space.str_w(self_d.descr_repr(space)), space.str_w(seq_d.descr_repr(space)), i, self.casting, ) elif self.buffered and not (self.external_loop and len(self.seq) < 2): for i in range(len(self.seq)): if i not in outargs: self.seq[i] = self.seq[i].descr_copy(space, w_order=space.wrap(self.order)) self.dtypes = [s.get_dtype() for s in self.seq] else: # copy them from seq self.dtypes = [s.get_dtype() for s in self.seq] # create an iterator for each operand self.iters = [] for i in range(len(self.seq)): it = self.get_iter(space, i) it.contiguous = False self.iters.append((it, it.reset())) if self.external_loop: coalesce_axes(self, space)