Source code for tables.expression

# -*- coding: utf-8 -*-

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#
# License: BSD
# Created: June 12, 2009
# Author: Francesc Alted - faltet@pytables.com
#
# $Id$
#
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"""Here is defined the Expr class."""

from __future__ import print_function
from __future__ import absolute_import
import sys
import warnings

import numpy as np
import tables as tb
from numexpr.necompiler import getContext, getExprNames, getType, NumExpr
from numexpr.expressions import functions as numexpr_functions
from .exceptions import PerformanceWarning
from .parameters import IO_BUFFER_SIZE, BUFFER_TIMES
import six
from six.moves import range
from six.moves import zip


[docs]class Expr(object): """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. >>> 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]) >>> 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]]) >>> sum(expr) array([ 8, 12]) .. 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, uservars=None, **kwargs): self.append_mode = False """The append mode for user-provided output containers.""" self.maindim = 0 """Common main dimension for inputs in expression.""" self.names = [] """The names of variables in expression (list).""" self.out = None """The user-provided container (if any) for the expression outcome.""" self.o_start = None """The start range selection for the user-provided output.""" self.o_stop = None """The stop range selection for the user-provided output.""" self.o_step = None """The step range selection for the user-provided output.""" self.shape = None """Common shape for the arrays in expression.""" self.start, self.stop, self.step = (None,) * 3 self.start = None """The start range selection for the input.""" self.stop = None """The stop range selection for the input.""" self.step = None """The step range selection for the input.""" self.values = [] """The values of variables in expression (list).""" self._compiled_expr = None """The compiled expression.""" self._single_row_out = 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 = getContext(kwargs) self.names, _ = getExprNames(expr, context) # Raise a ValueError in case we have unsupported objects for name, var in six.iteritems(vars_): 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 six.iteritems(vars_): 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, getType(type_)) for (name, type_) in zip(self.names, types_)] # Compile the expression self._compiled_expr = 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, uservars, depth=2): """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.keys())[: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 numexpr_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
[docs] def set_inputs_range(self, start=None, stop=None, step=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
[docs] def set_output(self, out, append_mode=False): """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
[docs] def set_output_range(self, start=None, stop=None, step=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, object_): """Calculate the number of rows that will fit in a buffer.""" # Compute the rowsize for the *leading* dimension shape_ = list(object_.shape) if shape_: shape_[0] = 1 rowsize = np.prod(shape_) * object_.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("""\ The object ``%s`` is exceeding the maximum recommended rowsize (%d 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.""" % (object, maxrowsize), PerformanceWarning) return nrowsinbuf def _guess_shape(self): """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, maindim, itermode=False): """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)
[docs] def eval(self): """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 setOuput() 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
[docs] def __iter__(self): """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 for row in rout: yield row # 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, 10000) 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() ## Local Variables: ## mode: python ## py-indent-offset: 4 ## tab-width: 4 ## fill-column: 72 ## End: