# Source code for tables.expression

    """Here is defined the Expr class."""

    from __future__ import annotations

    import sys
    import warnings
    from typing import Any, Union, TYPE_CHECKING
    from collections.abc import Iterator

    import numpy as np
    import numexpr as ne

    import tables as tb

    from .exceptions import PerformanceWarning
    from .parameters import IO_BUFFER_SIZE, BUFFER_TIMES

    if TYPE_CHECKING:
        from .array import Array
        from .table import Column
        from .tableextension import Row

    ContainerType = Union[np.ndarray, "Array", "Column"]

    class Expr:
        """A class for evaluating expressions with arbitrary array-like objects.

        Expr is a class for evaluating expressions containing array-like objects.
        With it, you can evaluate expressions (like "3 * a + 4 * b") that
        operate on arbitrary large arrays while optimizing the resources
        required to perform them (basically main memory and CPU cache memory).
        It is similar to the Numexpr package (see :ref:`[NUMEXPR] <NUMEXPR>`),
        but in addition to NumPy objects, it also accepts disk-based homogeneous
        arrays, like the Array, CArray, EArray and Column PyTables objects.

        .. warning::

            Expr class only offers a subset of the Numexpr features due to the
            complexity of implement some of them when dealing with huge amount of
            data.

        All the internal computations are performed via the Numexpr package,
        so all the broadcast and upcasting rules of Numexpr applies here too.
        These rules are very similar to the NumPy ones, but with some exceptions
        due to the particularities of having to deal with potentially very large
        disk-based arrays.  Be sure to read the documentation of the Expr
        constructor and methods as well as that of Numexpr, if you want to fully
        grasp these particularities.

        Parameters
        ----------
        expr : str
            This specifies the expression to be evaluated, such as "2 * a + 3 * b".
        uservars : dict
            This can be used to define the variable names appearing in *expr*.
            This mapping should consist of identifier-like strings pointing to any
            `Array`, `CArray`, `EArray`, `Column` or NumPy ndarray instances (or
            even others which will tried to be converted to ndarrays).  When
            `uservars` is not provided or `None`, the current local and global
            namespace is sought instead of `uservars`.  It is also possible to pass
            just some of the variables in expression via the `uservars` mapping,
            and the rest will be retrieved from the current local and global
            namespaces.
        kwargs : dict
            This is meant to pass additional parameters to the Numexpr kernel.
            This is basically the same as the kwargs argument in
            Numexpr.evaluate(), and is mainly meant for advanced use.

        Examples
        --------
        The following shows an example of using Expr::

            >>> f = tb.open_file('/tmp/test_expr.h5', 'w')
            >>> a = f.create_array('/', 'a', np.array([1,2,3]))
            >>> b = f.create_array('/', 'b', np.array([3,4,5]))
            >>> c = np.array([4,5,6])
            >>> expr = tb.Expr("2 * a + b * c")   # initialize the expression
            >>> expr.eval()                 # evaluate it
            array([14, 24, 36], dtype=int64)
            >>> sum(expr)                   # use as an iterator
            74

        where you can see that you can mix different containers in
        the expression (whenever shapes are consistent).

        You can also work with multidimensional arrays::

            >>> a2 = f.create_array('/', 'a2', np.array([[1,2],[3,4]]))
            >>> b2 = f.create_array('/', 'b2', np.array([[3,4],[5,6]]))
            >>> c2 = np.array([4,5])           # This will be broadcasted
            >>> expr = tb.Expr("2 * a2 + b2-c2")
            >>> expr.eval()
            array([[1, 3],
                   [7, 9]], dtype=int64)
            >>> sum(expr)
            array([ 8, 12], dtype=int64)
            >>> f.close()

        .. rubric:: Expr attributes

        .. attribute:: append_mode

            The append mode for user-provided output containers.

        .. attribute:: maindim

            Common main dimension for inputs in expression.

        .. attribute:: names

            The names of variables in expression (list).

        .. attribute:: out

            The user-provided container (if any) for the expression outcome.

        .. attribute:: o_start

            The start range selection for the user-provided output.

        .. attribute:: o_stop

            The stop range selection for the user-provided output.

        .. attribute:: o_step

            The step range selection for the user-provided output.

        .. attribute:: shape

            Common shape for the arrays in expression.

        .. attribute:: values

            The values of variables in expression (list).

        """

        _exprvars_cache = {}
        """Cache of variables participating in expressions.

        .. versionadded:: 3.0

        """

        def __init__(
            self,
            expr: str,
            uservars: dict[str, Any] | None = None,
            **kwargs,
        ) -> None:

            self.append_mode = False
            """The append mode for user-provided output containers."""
            self.maindim = 0
            """Common main dimension for inputs in expression."""
            self.names: list[str] = []
            """The names of variables in expression (list)."""
            self.out: ContainerType | None = None
            """The user-provided container (if any) for the expression outcome."""
            self.o_start: int | None = None
            """The start range selection for the user-provided output."""
            self.o_stop: int | None = None
            """The stop range selection for the user-provided output."""
            self.o_step: int | None = None
            """The step range selection for the user-provided output."""
            self.shape: tuple[int, ...] | None = None
            """Common shape for the arrays in expression."""
            self.start, self.stop, self.step = (None,) * 3
            self.start: int | None = None
            """The start range selection for the input."""
            self.stop: int | None = None
            """The stop range selection for the input."""
            self.step: int | None = None
            """The step range selection for the input."""
            self.values: list = []
            """The values of variables in expression (list)."""

            self._compiled_expr: ne.interpreter.NumExpr | None = None
            """The compiled expression."""
            self._single_row_out: np.ndarray | None = None
            """A sample of the output with just a single row."""

            # First, get the signature for the arrays in expression
            vars_ = self._required_expr_vars(expr, uservars)
            context = ne.necompiler.getContext(kwargs)
            self.names, _ = ne.necompiler.getExprNames(expr, context)

            # Raise a ValueError in case we have unsupported objects
            for name, var in vars_.items():
                if type(var) in (int, float, str):
                    continue
                if not isinstance(var, (tb.Leaf, tb.Column)):
                    if hasattr(var, "dtype"):
                        # Quacks like a NumPy object
                        continue
                    raise TypeError("Unsupported variable type: %r" % var)
                objname = var.__class__.__name__
                if objname not in ("Array", "CArray", "EArray", "Column"):
                    raise TypeError("Unsupported variable type: %r" % var)

            # NumPy arrays to be copied? (we don't need to worry about
            # PyTables objects, as the reads always return contiguous and
            # aligned objects, or at least I think so).
            for name, var in vars_.items():
                if isinstance(var, np.ndarray):
                    # See numexpr.necompiler.evaluate for a rational
                    # of the code below
                    if not var.flags.aligned:
                        if var.ndim != 1:
                            # Do a copy of this variable
                            var = var.copy()
                            # Update the vars_ dictionary
                            vars_[name] = var

            # Get the variables and types
            values = self.values
            types_ = []
            for name in self.names:
                value = vars_[name]
                if hasattr(value, "atom"):
                    types_.append(value.atom)
                elif hasattr(value, "dtype"):
                    types_.append(value)
                else:
                    # try to convert into a NumPy array
                    value = np.array(value)
                    types_.append(value)
                values.append(value)

            # Create a signature for the expression
            signature = [
                (name, ne.necompiler.getType(type_))
                for (name, type_) in zip(self.names, types_)
            ]

            # Compile the expression
            self._compiled_expr = ne.necompiler.NumExpr(expr, signature, **kwargs)

            # Guess the shape for the outcome and the maindim of inputs
            self.shape, self.maindim = self._guess_shape()

        # The next method is similar to their counterpart in `Table`, but
        # adapted to the `Expr` own requirements.
        def _required_expr_vars(
            self,
            expression: str,
            uservars: dict[str, Any] | None,
            depth: int = 2,
        ) -> dict[str, Any]:
            """Get the variables required by the `expression`.

            A new dictionary defining the variables used in the `expression`
            is returned.  Required variables are first looked up in the
            `uservars` mapping, then in the set of top-level columns of the
            table.  Unknown variables cause a `NameError` to be raised.

            When `uservars` is `None`, the local and global namespace where
            the API callable which uses this method is called is sought
            instead.  To disable this mechanism, just specify a mapping as
            `uservars`.

            Nested columns and variables with an ``uint64`` type are not
            allowed (`TypeError` and `NotImplementedError` are raised,
            respectively).

            `depth` specifies the depth of the frame in order to reach local
            or global variables.

            """
            # Get the names of variables used in the expression.
            exprvars_cache = self._exprvars_cache
            if expression not in exprvars_cache:
                # Protection against growing the cache too much
                if len(exprvars_cache) > 256:
                    # Remove 10 (arbitrary) elements from the cache
                    for k in list(exprvars_cache)[:10]:
                        del exprvars_cache[k]
                cexpr = compile(expression, "<string>", "eval")
                exprvars = [
                    var
                    for var in cexpr.co_names
                    if var not in ["None", "False", "True"]
                    and var not in ne.expressions.functions
                ]
                exprvars_cache[expression] = exprvars
            else:
                exprvars = exprvars_cache[expression]

            # Get the local and global variable mappings of the user frame
            # if no mapping has been explicitly given for user variables.
            user_locals, user_globals = {}, {}
            if uservars is None:
                user_frame = sys._getframe(depth)
                user_locals = user_frame.f_locals
                user_globals = user_frame.f_globals

            # Look for the required variables first among the ones
            # explicitly provided by the user.
            reqvars = {}
            for var in exprvars:
                # Get the value.
                if uservars is not None and var in uservars:
                    val = uservars[var]
                elif uservars is None and var in user_locals:
                    val = user_locals[var]
                elif uservars is None and var in user_globals:
                    val = user_globals[var]
                else:
                    raise NameError("name ``%s`` is not defined" % var)

                # Check the value.
                if hasattr(val, "dtype") and val.dtype.str[1:] == "u8":
                    raise NotImplementedError(
                        "variable ``%s`` refers to "
                        "a 64-bit unsigned integer object, that is "
                        "not yet supported in expressions, sorry; " % var
                    )
                elif hasattr(val, "_v_colpathnames"):  # nested column
                    # This branch is never reached because the compile step
                    # above already raise a ``TypeError`` for nested
                    # columns, but that could change in the future.  So it
                    # is best to let this here.
                    raise TypeError(
                        "variable ``%s`` refers to a nested column, "
                        "not allowed in expressions" % var
                    )
                reqvars[var] = val
            return reqvars

        def set_inputs_range(
            self,
            start: int | None = None,
            stop: int | None = None,
            step: int | None = None,
        ) -> None:
            """Define a range for all inputs in expression.

            The computation will only take place for the range defined by
            the start, stop and step parameters in the main dimension of
            inputs (or the leading one, if the object lacks the concept of
            main dimension, like a NumPy container).  If not a common main
            dimension exists for all inputs, the leading dimension will be
            used instead.

            """
            self.start = start
            self.stop = stop
            self.step = step

        def set_output(
            self, out: ContainerType, append_mode: bool = False
        ) -> None:
            """Set out as container for output as well as the append_mode.

            The out must be a container that is meant to keep the outcome of
            the expression.  It should be an homogeneous type container and
            can typically be an Array, CArray, EArray, Column or a NumPy ndarray.

            The append_mode specifies the way of which the output is filled.
            If true, the rows of the outcome are *appended* to the out container.
            Of course, for doing this it is necessary that out would have an
            append() method (like an EArray, for example).

            If append_mode is false, the output is set via the __setitem__()
            method (see the Expr.set_output_range() for info on how to select
            the rows to be updated).  If out is smaller than what is required
            by the expression, only the computations that are needed to fill
            up the container are carried out.  If it is larger, the excess
            elements are unaffected.

            """
            if not (hasattr(out, "shape") and hasattr(out, "__setitem__")):
                raise ValueError(
                    "You need to pass a settable multidimensional container "
                    "as output"
                )
            self.out = out
            if append_mode and not hasattr(out, "append"):
                raise ValueError(
                    "For activating the ``append`` mode, you need a container "
                    "with an `append()` method (like the `EArray`)"
                )
            self.append_mode = append_mode

        def set_output_range(
            self,
            start: int | None = None,
            stop: int | None = None,
            step: int | None = None,
        ) -> None:
            """Define a range for user-provided output object.

            The output object will only be modified in the range specified by the
            start, stop and step parameters in the main dimension of output (or the
            leading one, if the object does not have the concept of main dimension,
            like a NumPy container).

            """
            if self.out is None:
                raise IndexError(
                    "You need to pass an output object to `setOut()` first"
                )
            self.o_start = start
            self.o_stop = stop
            self.o_step = step

        # Although the next code is similar to the method in `Leaf`, it
        # allows the use of pure NumPy objects.
        def _calc_nrowsinbuf(self, obj: np.ndarray) -> int:
            """Calculate the number of rows that will fit in a buffer."""
            # Compute the rowsize for the *leading* dimension
            shape_ = list(obj.shape)
            if shape_:
                shape_[0] = 1

            rowsize = np.prod(shape_) * obj.dtype.itemsize

            # Compute the nrowsinbuf
            # Multiplying the I/O buffer size by 4 gives optimal results
            # in my benchmarks with `tables.Expr` (see ``bench/poly.py``)
            buffersize = IO_BUFFER_SIZE * 4
            nrowsinbuf = buffersize // rowsize

            # Safeguard against row sizes being extremely large
            if nrowsinbuf == 0:
                nrowsinbuf = 1
                # If rowsize is too large, issue a Performance warning
                maxrowsize = BUFFER_TIMES * buffersize
                if rowsize > maxrowsize:
                    warnings.warn(
                        f"""\
    The object ``{obj}`` is exceeding the maximum recommended rowsize ({maxrowsize}
    bytes); be ready to see PyTables asking for *lots* of memory and
    possibly slow I/O.  You may want to reduce the rowsize by trimming the
    value of dimensions that are orthogonal (and preferably close) to the
    *leading* dimension of this object.""",
                        PerformanceWarning,
                        stacklevel=2,
                    )

            return nrowsinbuf

        def _guess_shape(
            self,
        ) -> tuple[list[tuple[int, int]], int] | tuple[tuple, None]:
            """Guess the shape of the output of the expression."""
            # First, compute the maximum dimension of inputs and maindim
            # (if it exists)
            maxndim = 0
            maindims = []
            for val in self.values:
                # Get the minimum of the lengths
                if len(val.shape) > maxndim:
                    maxndim = len(val.shape)
                if hasattr(val, "maindim"):
                    maindims.append(val.maindim)
            if maxndim == 0:
                self._single_row_out = out = self._compiled_expr(*self.values)
                return (), None
            if maindims and [maindims[0]] * len(maindims) == maindims:
                # If all maindims detected are the same, use this as maindim
                maindim = maindims[0]
            else:
                # If not, the main dimension will be the default one
                maindim = 0

            # The slices parameter for inputs
            slices = (slice(None),) * maindim + (0,)

            # Now, collect the values in first row of arrays with maximum dims
            vals = []
            lens = []
            for val in self.values:
                shape = val.shape
                # Warning: don't use len(val) below or it will raise an
                # `Overflow` error on 32-bit platforms for large enough arrays.
                if shape != () and shape[maindim] == 0:
                    vals.append(val[:])
                    lens.append(0)
                elif len(shape) < maxndim:
                    vals.append(val)
                else:
                    vals.append(val.__getitem__(slices))
                    lens.append(shape[maindim])
            minlen = min(lens)
            self._single_row_out = out = self._compiled_expr(*vals)
            shape = list(out.shape)
            if minlen > 0:
                shape.insert(maindim, minlen)
            return shape, maindim

        def _get_info(
            self, shape: list[int], maindim: int | None, itermode: bool = False
        ) -> (
            tuple[int, list[int], int, int, int | None, int]
            | tuple[
                int,
                list[int],
                int,
                int,
                int | None,
                int,
                ContainerType,
                int,
                int,
                int,
                int,
            ]
        ):
            """Return various info needed for evaluating the computation loop."""
            # Compute the shape of the resulting container having
            # in account new possible values of start, stop and step in
            # the inputs range
            if maindim is not None:
                (start, stop, step) = slice(
                    self.start, self.stop, self.step
                ).indices(shape[maindim])
                shape[maindim] = min(shape[maindim], len(range(start, stop, step)))
                i_nrows = shape[maindim]
            else:
                start, stop, step = 0, 0, None
                i_nrows = 0

            if not itermode:
                # Create a container for output if not defined yet
                o_maindim = 0  # Default maindim
                if self.out is None:
                    out = np.empty(shape, dtype=self._single_row_out.dtype)
                    # Get the trivial values for start, stop and step
                    if maindim is not None:
                        (o_start, o_stop, o_step) = (0, shape[maindim], 1)
                    else:
                        (o_start, o_stop, o_step) = (0, 0, 1)
                else:
                    out = self.out
                    # Out container already provided.  Do some sanity checks.
                    if hasattr(out, "maindim"):
                        o_maindim = out.maindim

                    # Refine the shape of the resulting container having in
                    # account new possible values of start, stop and step in
                    # the output range
                    o_shape = list(out.shape)
                    s = slice(self.o_start, self.o_stop, self.o_step)
                    o_start, o_stop, o_step = s.indices(o_shape[o_maindim])
                    o_shape[o_maindim] = min(
                        o_shape[o_maindim], len(range(o_start, o_stop, o_step))
                    )

                    # Check that the shape of output is consistent with inputs
                    tr_oshape = list(o_shape)  # this implies a copy
                    olen_ = tr_oshape.pop(o_maindim)
                    tr_shape = list(shape)  # do a copy
                    if maindim is not None:
                        len_ = tr_shape.pop(o_maindim)
                    else:
                        len_ = 1
                    if tr_oshape != tr_shape:
                        raise ValueError(
                            "Shape for out container does not match expression"
                        )
                    # Force the input length to fit in `out`
                    if not self.append_mode and olen_ < len_:
                        shape[o_maindim] = olen_
                        stop = start + olen_

            # Get the positions of inputs that should be sliced (the others
            # will be broadcasted)
            ndim = len(shape)
            slice_pos = [
                i for i, val in enumerate(self.values) if len(val.shape) == ndim
            ]

            # The size of the I/O buffer
            nrowsinbuf = 1
            for i, val in enumerate(self.values):
                # Skip scalar values in variables
                if i in slice_pos:
                    nrows = self._calc_nrowsinbuf(val)
                    if nrows > nrowsinbuf:
                        nrowsinbuf = nrows

            if not itermode:
                return (
                    i_nrows,
                    slice_pos,
                    start,
                    stop,
                    step,
                    nrowsinbuf,
                    out,
                    o_maindim,
                    o_start,
                    o_stop,
                    o_step,
                )
            else:
                # For itermode, we don't need the out info
                return (i_nrows, slice_pos, start, stop, step, nrowsinbuf)

        def eval(self) -> ContainerType:  # noqa: A003
            """Evaluate the expression and return the outcome.

            Because of performance reasons, the computation order tries to go along
            the common main dimension of all inputs.  If not such a common main
            dimension is found, the iteration will go along the leading dimension
            instead.

            For non-consistent shapes in inputs (i.e. shapes having a different
            number of dimensions), the regular NumPy broadcast rules applies.
            There is one exception to this rule though: when the dimensions
            orthogonal to the main dimension of the expression are consistent, but
            the main dimension itself differs among the inputs, then the shortest
            one is chosen for doing the computations.  This is so because trying to
            expand very large on-disk arrays could be too expensive or simply not
            possible.

            Also, the regular Numexpr casting rules (which are similar to those of
            NumPy, although you should check the Numexpr manual for the exceptions)
            are applied to determine the output type.

            Finally, if the set_output() method specifying a user container has
            already been called, the output is sent to this user-provided
            container.  If not, a fresh NumPy container is returned instead.

            .. warning::

                When dealing with large on-disk inputs, failing to specify an
                on-disk container may consume all your available memory.

            """
            values, shape, maindim = self.values, self.shape, self.maindim

            # Get different info we need for the main computation loop
            (
                i_nrows,
                slice_pos,
                start,
                stop,
                step,
                nrowsinbuf,
                out,
                o_maindim,
                o_start,
                o_stop,
                o_step,
            ) = self._get_info(shape, maindim)

            if i_nrows == 0:
                # No elements to compute
                if start >= stop and self.start is not None:
                    return out
                else:
                    return self._single_row_out

            # Create a key that selects every element in inputs and output
            # (including the main dimension)
            i_slices = [slice(None)] * (maindim + 1)
            o_slices = [slice(None)] * (o_maindim + 1)

            # This is a hack to prevent doing unnecessary flavor conversions
            # while reading buffers
            for val in values:
                if hasattr(val, "maindim"):
                    val._v_convert = False

            # Start the computation itself
            for start2 in range(start, stop, step * nrowsinbuf):
                stop2 = start2 + step * nrowsinbuf
                if stop2 > stop:
                    stop2 = stop
                # Set the proper slice for inputs
                i_slices[maindim] = slice(start2, stop2, step)
                # Get the input values
                vals = []
                for i, val in enumerate(values):
                    if i in slice_pos:
                        vals.append(val.__getitem__(tuple(i_slices)))
                    else:
                        # A read of values is not apparently needed, as PyTables
                        # leaves seems to work just fine inside Numexpr
                        vals.append(val)
                # Do the actual computation for this slice
                rout = self._compiled_expr(*vals)
                # Set the values into the out buffer
                if self.append_mode:
                    out.append(rout)
                else:
                    # Compute the slice to be filled in output
                    start3 = o_start + (start2 - start) // step
                    stop3 = start3 + nrowsinbuf * o_step
                    if stop3 > o_stop:
                        stop3 = o_stop
                    o_slices[o_maindim] = slice(start3, stop3, o_step)
                    # Set the slice
                    out[tuple(o_slices)] = rout

            # Activate the conversion again (default)
            for val in values:
                if hasattr(val, "maindim"):
                    val._v_convert = True

            return out

        def __iter__(self) -> Iterator[Row]:
            """Iterate over the rows of the outcome of the expression.

            This iterator always returns rows as NumPy objects, so a possible out
            container specified in :meth:`Expr.set_output` method is ignored here.

            """
            values, shape, maindim = self.values, self.shape, self.maindim

            # Get different info we need for the main computation loop
            (i_nrows, slice_pos, start, stop, step, nrowsinbuf) = self._get_info(
                shape, maindim, itermode=True
            )

            if i_nrows == 0:
                # No elements to compute
                return

            # Create a key that selects every element in inputs
            # (including the main dimension)
            i_slices = [slice(None)] * (maindim + 1)

            # This is a hack to prevent doing unnecessary flavor conversions
            # while reading buffers
            for val in values:
                if hasattr(val, "maindim"):
                    val._v_convert = False

            # Start the computation itself
            for start2 in range(start, stop, step * nrowsinbuf):
                stop2 = start2 + step * nrowsinbuf
                if stop2 > stop:
                    stop2 = stop
                # Set the proper slice in the main dimension
                i_slices[maindim] = slice(start2, stop2, step)
                # Get the values for computing the buffer
                vals = []
                for i, val in enumerate(values):
                    if i in slice_pos:
                        vals.append(val.__getitem__(tuple(i_slices)))
                    else:
                        # A read of values is not apparently needed, as PyTables
                        # leaves seems to work just fine inside Numexpr
                        vals.append(val)
                # Do the actual computation
                rout = self._compiled_expr(*vals)
                # Return one row per call
                yield from rout

            # Activate the conversion again (default)
            for val in values:
                if hasattr(val, "maindim"):
                    val._v_convert = True

    if __name__ == "__main__":

        # shape = (10000,10000)
        shape = (10, 10_000)

        f = tb.open_file("/tmp/expression.h5", "w")

        # Create some arrays
        a = f.create_carray(f.root, "a", atom=tb.Float32Atom(dflt=1), shape=shape)
        b = f.create_carray(f.root, "b", atom=tb.Float32Atom(dflt=2), shape=shape)
        c = f.create_carray(f.root, "c", atom=tb.Float32Atom(dflt=3), shape=shape)
        out = f.create_carray(
            f.root, "out", atom=tb.Float32Atom(dflt=3), shape=shape
        )

        expr = Expr("a * b + c")
        expr.set_output(out)
        d = expr.eval()

        print("returned-->", repr(d))
        # print(`d[:]`)

        f.close()