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__next__
Get the next element of the array during an iteration. The element is returned as an object of the current flavor.
def __next__(self): """Get the next element of the array during an iteration. The element is returned as an object of the current flavor. """ # this could probably be sped up for long iterations by reusing the # listarr buffer if self._nrowsread >= self._stop: self._init = False self.listarr = None # fixes issue #308 raise StopIteration # end of iteration else: # Read a chunk of rows if self._row + 1 >= self.nrowsinbuf or self._row < 0: self._stopb = self._startb + self._step * self.nrowsinbuf # Protection for reading more elements than needed if self._stopb > self._stop: self._stopb = self._stop listarr = self._read(self._startb, self._stopb, self._step) # Swap the axes to easy the return of elements if self.extdim > 0: listarr = listarr.swapaxes(self.extdim, 0) self.listarr = internal_to_flavor(listarr, self.flavor) self._row = -1 self._startb = self._stopb self._row += 1 self.nrow += self._step self._nrowsread += self._step # Fixes bug #968132 # if self.listarr.shape: if self.shape: return self.listarr[self._row] else: return self.listarr # Scalar case
(self)
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727,980
tables.array
__repr__
This provides more metainfo in addition to standard __str__
"""Here is defined the Array class.""" import operator import sys import numpy as np from . import hdf5extension from .filters import Filters from .flavor import flavor_of, array_as_internal, internal_to_flavor from .leaf import Leaf from .utils import (is_idx, convert_to_np_atom2, SizeType, lazyattr, byteorders, quantize) # default version for ARRAY objects # obversion = "1.0" # initial version # obversion = "2.0" # Added an optional EXTDIM attribute # obversion = "2.1" # Added support for complex datatypes # obversion = "2.2" # This adds support for time datatypes. # obversion = "2.3" # This adds support for enumerated datatypes. obversion = "2.4" # Numeric and numarray flavors are gone. class Array(hdf5extension.Array, Leaf): """This class represents homogeneous datasets in an HDF5 file. This class provides methods to write or read data to or from array objects in the file. This class does not allow you neither to enlarge nor compress the datasets on disk; use the EArray class (see :ref:`EArrayClassDescr`) if you want enlargeable dataset support or compression features, or CArray (see :ref:`CArrayClassDescr`) if you just want compression. An interesting property of the Array class is that it remembers the *flavor* of the object that has been saved so that if you saved, for example, a list, you will get a list during readings afterwards; if you saved a NumPy array, you will get a NumPy object, and so forth. Note that this class inherits all the public attributes and methods that Leaf (see :ref:`LeafClassDescr`) already provides. However, as Array instances have no internal I/O buffers, it is not necessary to use the flush() method they inherit from Leaf in order to save their internal state to disk. When a writing method call returns, all the data is already on disk. Parameters ---------- parentnode The parent :class:`Group` object. .. versionchanged:: 3.0 Renamed from *parentNode* to *parentnode* name : str The name of this node in its parent group. obj The array or scalar to be saved. Accepted types are NumPy arrays and scalars as well as native Python sequences and scalars, provided that values are regular (i.e. they are not like ``[[1,2],2]``) and homogeneous (i.e. all the elements are of the same type). .. versionchanged:: 3.0 Renamed form *object* into *obj*. title A description for this node (it sets the ``TITLE`` HDF5 attribute on disk). byteorder The byteorder of the data *on disk*, specified as 'little' or 'big'. If this is not specified, the byteorder is that of the given `object`. track_times Whether time data associated with the leaf are recorded (object access time, raw data modification time, metadata change time, object birth time); default True. Semantics of these times depend on their implementation in the HDF5 library: refer to documentation of the H5O_info_t data structure. As of HDF5 1.8.15, only ctime (metadata change time) is implemented. .. versionadded:: 3.4.3 """ # Class identifier. _c_classid = 'ARRAY' @lazyattr def dtype(self): """The NumPy ``dtype`` that most closely matches this array.""" return self.atom.dtype @property def nrows(self): """The number of rows in the array.""" if self.shape == (): return SizeType(1) # scalar case else: return self.shape[self.maindim] @property def rowsize(self): """The size of the rows in bytes in dimensions orthogonal to *maindim*.""" maindim = self.maindim rowsize = self.atom.size for i, dim in enumerate(self.shape): if i != maindim: rowsize *= dim return rowsize @property def size_in_memory(self): """The size of this array's data in bytes when it is fully loaded into memory.""" return self.nrows * self.rowsize def __init__(self, parentnode, name, obj=None, title="", byteorder=None, _log=True, _atom=None, track_times=True): self._v_version = None """The object version of this array.""" self._v_new = new = obj is not None """Is this the first time the node has been created?""" self._v_new_title = title """New title for this node.""" self._obj = obj """The object to be stored in the array. It can be any of numpy, list, tuple, string, integer of floating point types, provided that they are regular (i.e. they are not like ``[[1, 2], 2]``). .. versionchanged:: 3.0 Renamed form *_object* into *_obj*. """ self._v_convert = True """Whether the ``Array`` object must be converted or not.""" # Miscellaneous iteration rubbish. self._start = None """Starting row for the current iteration.""" self._stop = None """Stopping row for the current iteration.""" self._step = None """Step size for the current iteration.""" self._nrowsread = None """Number of rows read up to the current state of iteration.""" self._startb = None """Starting row for current buffer.""" self._stopb = None """Stopping row for current buffer. """ self._row = None """Current row in iterators (sentinel).""" self._init = False """Whether we are in the middle of an iteration or not (sentinel).""" self.listarr = None """Current buffer in iterators.""" # Documented (*public*) attributes. self.atom = _atom """An Atom (see :ref:`AtomClassDescr`) instance representing the *type* and *shape* of the atomic objects to be saved. """ self.shape = None """The shape of the stored array.""" self.nrow = None """On iterators, this is the index of the current row.""" self.extdim = -1 # ordinary arrays are not enlargeable """The index of the enlargeable dimension.""" # Ordinary arrays have no filters: leaf is created with default ones. super().__init__(parentnode, name, new, Filters(), byteorder, _log, track_times) def _g_create(self): """Save a new array in file.""" self._v_version = obversion try: # `Leaf._g_post_init_hook()` should be setting the flavor on disk. self._flavor = flavor = flavor_of(self._obj) nparr = array_as_internal(self._obj, flavor) except Exception: # XXX # Problems converting data. Close the node and re-raise exception. self.close(flush=0) raise # Raise an error in case of unsupported object if nparr.dtype.kind in ['V', 'U', 'O']: # in void, unicode, object raise TypeError("Array objects cannot currently deal with void, " "unicode or object arrays") # Decrease the number of references to the object self._obj = None # Fix the byteorder of data nparr = self._g_fix_byteorder_data(nparr, nparr.dtype.byteorder) # Create the array on-disk try: # ``self._v_objectid`` needs to be set because would be # needed for setting attributes in some descendants later # on (self._v_objectid, self.shape, self.atom) = self._create_array( nparr, self._v_new_title, self.atom) except Exception: # XXX # Problems creating the Array on disk. Close node and re-raise. self.close(flush=0) raise # Compute the optimal buffer size self.nrowsinbuf = self._calc_nrowsinbuf() # Arrays don't have chunkshapes (so, set it to None) self._v_chunkshape = None return self._v_objectid def
(self)
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727,981
tables.array
__setitem__
Set a row, a range of rows or a slice in the array. It takes different actions depending on the type of the key parameter: if it is an integer, the corresponding array row is set to value (the value is broadcast when needed). If key is a slice, the row slice determined by it is set to value (as usual, if the slice to be updated exceeds the actual shape of the array, only the values in the existing range are updated). If value is a multidimensional object, then its shape must be compatible with the shape determined by key, otherwise, a ValueError will be raised. Furthermore, NumPy-style fancy indexing, where a list of indices in a certain axis is specified, is also supported. Note that only one list per selection is supported right now. Finally, NumPy-style point and boolean selections are supported as well. Examples -------- :: a1[0] = 333 # assign an integer to a Integer Array row a2[0] = 'b' # assign a string to a string Array row a3[1:4] = 5 # broadcast 5 to slice 1:4 a4[1:4:2] = 'xXx' # broadcast 'xXx' to slice 1:4:2 # General slice update (a5.shape = (4,3,2,8,5,10). a5[1, ..., ::2, 1:4, 4:] = numpy.arange(1728, shape=(4,3,2,4,3,6)) a6[1, [1,5,10], ..., -1] = arr # fancy selection a7[np.where(a6[:] > 4)] = 4 # point selection + broadcast a8[arr > 4] = arr2 # boolean selection
def __setitem__(self, key, value): """Set a row, a range of rows or a slice in the array. It takes different actions depending on the type of the key parameter: if it is an integer, the corresponding array row is set to value (the value is broadcast when needed). If key is a slice, the row slice determined by it is set to value (as usual, if the slice to be updated exceeds the actual shape of the array, only the values in the existing range are updated). If value is a multidimensional object, then its shape must be compatible with the shape determined by key, otherwise, a ValueError will be raised. Furthermore, NumPy-style fancy indexing, where a list of indices in a certain axis is specified, is also supported. Note that only one list per selection is supported right now. Finally, NumPy-style point and boolean selections are supported as well. Examples -------- :: a1[0] = 333 # assign an integer to a Integer Array row a2[0] = 'b' # assign a string to a string Array row a3[1:4] = 5 # broadcast 5 to slice 1:4 a4[1:4:2] = 'xXx' # broadcast 'xXx' to slice 1:4:2 # General slice update (a5.shape = (4,3,2,8,5,10). a5[1, ..., ::2, 1:4, 4:] = numpy.arange(1728, shape=(4,3,2,4,3,6)) a6[1, [1,5,10], ..., -1] = arr # fancy selection a7[np.where(a6[:] > 4)] = 4 # point selection + broadcast a8[arr > 4] = arr2 # boolean selection """ self._g_check_open() # Create an array compliant with the specified slice nparr = convert_to_np_atom2(value, self.atom) if nparr.size == 0: return # truncate data if least_significant_digit filter is set # TODO: add the least_significant_digit attribute to the array on disk if (self.filters.least_significant_digit is not None and not np.issubdtype(nparr.dtype, np.signedinteger)): nparr = quantize(nparr, self.filters.least_significant_digit) try: startl, stopl, stepl, shape = self._interpret_indexing(key) self._write_slice(startl, stopl, stepl, shape, nparr) except TypeError: # Then, try with a point-wise selection try: coords = self._point_selection(key) self._write_coords(coords, nparr) except TypeError: selection, reorder, shape = self._fancy_selection(key) self._write_selection(selection, reorder, shape, nparr)
(self, key, value)
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727,982
tables.leaf
__str__
The string representation for this object is its pathname in the HDF5 object tree plus some additional metainfo.
def csformula(expected_mb): """Return the fitted chunksize for expected_mb.""" # For a basesize of 8 KB, this will return: # 8 KB for datasets <= 1 MB # 1 MB for datasets >= 10 TB basesize = 8 * 1024 # 8 KB is a good minimum return basesize * int(2**math.log10(expected_mb))
(self)
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727,983
tables.leaf
_calc_chunkshape
Calculate the shape for the HDF5 chunk.
def _calc_chunkshape(self, expectedrows, rowsize, itemsize): """Calculate the shape for the HDF5 chunk.""" # In case of a scalar shape, return the unit chunksize if self.shape == (): return (SizeType(1),) # Compute the chunksize MB = 1024 * 1024 expected_mb = (expectedrows * rowsize) // MB chunksize = calc_chunksize(expected_mb) complib = self.filters.complib if (complib is not None and complib.startswith("blosc2") and self._c_classid in ('TABLE', 'CARRAY', 'EARRAY')): # Blosc2 can introspect into blocks, so we can increase the # chunksize for improving HDF5 perf for its internal btree. # For the time being, this has been implemented efficiently # just for tables, but in the future *Array objects could also # be included. # Use a decent default value for chunksize chunksize *= 16 # Now, go explore the L3 size and try to find a smarter chunksize if 'l3_cache_size' in cpu_info: # In general, is a good idea to set the chunksize equal to L3 l3_cache_size = cpu_info['l3_cache_size'] # cpuinfo sometimes returns cache sizes as strings (like, # "4096 KB"), so refuse the temptation to guess and use the # value only when it is an actual int. # Also, sometimes cpuinfo does not return a correct L3 size; # so in general, enforcing L3 > L2 is a good sanity check. l2_cache_size = cpu_info.get('l2_cache_size', "Not found") if (type(l3_cache_size) is int and type(l2_cache_size) is int and l3_cache_size > l2_cache_size): chunksize = l3_cache_size # In Blosc2, the chunksize cannot be larger than 2 GB - BLOSC2_MAX_BUFFERSIZE if chunksize > 2**31 - 32: chunksize = 2**31 - 32 maindim = self.maindim # Compute the chunknitems chunknitems = chunksize // itemsize # Safeguard against itemsizes being extremely large if chunknitems == 0: chunknitems = 1 chunkshape = list(self.shape) # Check whether trimming the main dimension is enough chunkshape[maindim] = 1 newchunknitems = np.prod(chunkshape, dtype=SizeType) if newchunknitems <= chunknitems: chunkshape[maindim] = chunknitems // newchunknitems else: # No, so start trimming other dimensions as well for j in range(len(chunkshape)): # Check whether trimming this dimension is enough chunkshape[j] = 1 newchunknitems = np.prod(chunkshape, dtype=SizeType) if newchunknitems <= chunknitems: chunkshape[j] = chunknitems // newchunknitems break else: # Ops, we ran out of the loop without a break # Set the last dimension to chunknitems chunkshape[-1] = chunknitems return tuple(SizeType(s) for s in chunkshape)
(self, expectedrows, rowsize, itemsize)
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727,984
tables.leaf
_calc_nrowsinbuf
Calculate the number of rows that fits on a PyTables buffer.
def _calc_nrowsinbuf(self): """Calculate the number of rows that fits on a PyTables buffer.""" params = self._v_file.params # Compute the nrowsinbuf rowsize = self.rowsize buffersize = params['IO_BUFFER_SIZE'] if rowsize != 0: nrowsinbuf = buffersize // rowsize else: nrowsinbuf = 1 # Safeguard against row sizes being extremely large if nrowsinbuf == 0: nrowsinbuf = 1 # If rowsize is too large, issue a Performance warning maxrowsize = params['BUFFER_TIMES'] * buffersize if rowsize > maxrowsize: warnings.warn("""\ The Leaf ``%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 *main* dimension of this leave. Alternatively, in case you have specified a very small/large chunksize, you may want to increase/decrease it.""" % (self._v_pathname, maxrowsize), PerformanceWarning) return nrowsinbuf
(self)
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727,985
tables.array
_check_shape
Test that nparr shape is consistent with underlying object. If not, try creating a new nparr object, using broadcasting if necessary.
def _check_shape(self, nparr, slice_shape): """Test that nparr shape is consistent with underlying object. If not, try creating a new nparr object, using broadcasting if necessary. """ if nparr.shape != (slice_shape + self.atom.dtype.shape): # Create an array compliant with the specified shape narr = np.empty(shape=slice_shape, dtype=self.atom.dtype) # Assign the value to it. It will raise a ValueError exception # if the objects cannot be broadcast to a single shape. narr[...] = nparr return narr else: return nparr
(self, nparr, slice_shape)
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727,986
tables.leaf
_f_close
Close this node in the tree. This method has the behavior described in :meth:`Node._f_close`. Besides that, the optional argument flush tells whether to flush pending data to disk or not before closing.
def _f_close(self, flush=True): """Close this node in the tree. This method has the behavior described in :meth:`Node._f_close`. Besides that, the optional argument flush tells whether to flush pending data to disk or not before closing. """ if not self._v_isopen: return # the node is already closed or not initialized # Only do a flush in case the leaf has an IO buffer. The # internal buffers of HDF5 will be flushed afterwards during the # self._g_close() call. Avoiding an unnecessary flush() # operation accelerates the closing for the unbuffered leaves. if flush and hasattr(self, "_v_iobuf"): self.flush() # Close the dataset and release resources self._g_close() # Close myself as a node. super()._f_close()
(self, flush=True)
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727,987
tables.node
_f_copy
Copy this node and return the new node. Creates and returns a copy of the node, maybe in a different place in the hierarchy. newparent can be a Group object (see :ref:`GroupClassDescr`) or a pathname in string form. If it is not specified or None, the current parent group is chosen as the new parent. newname must be a string with a new name. If it is not specified or None, the current name is chosen as the new name. If recursive copy is stated, all descendants are copied as well. If createparents is true, the needed groups for the given new parent group path to exist will be created. Copying a node across databases is supported but can not be undone. Copying a node over itself is not allowed, nor it is recursively copying a node into itself. These result in a NodeError. Copying over another existing node is similarly not allowed, unless the optional overwrite argument is true, in which case that node is recursively removed before copying. Additional keyword arguments may be passed to customize the copying process. For instance, title and filters may be changed, user attributes may be or may not be copied, data may be sub-sampled, stats may be collected, etc. See the documentation for the particular node type. Using only the first argument is equivalent to copying the node to a new location without changing its name. Using only the second argument is equivalent to making a copy of the node in the same group.
def _f_copy(self, newparent=None, newname=None, overwrite=False, recursive=False, createparents=False, **kwargs): """Copy this node and return the new node. Creates and returns a copy of the node, maybe in a different place in the hierarchy. newparent can be a Group object (see :ref:`GroupClassDescr`) or a pathname in string form. If it is not specified or None, the current parent group is chosen as the new parent. newname must be a string with a new name. If it is not specified or None, the current name is chosen as the new name. If recursive copy is stated, all descendants are copied as well. If createparents is true, the needed groups for the given new parent group path to exist will be created. Copying a node across databases is supported but can not be undone. Copying a node over itself is not allowed, nor it is recursively copying a node into itself. These result in a NodeError. Copying over another existing node is similarly not allowed, unless the optional overwrite argument is true, in which case that node is recursively removed before copying. Additional keyword arguments may be passed to customize the copying process. For instance, title and filters may be changed, user attributes may be or may not be copied, data may be sub-sampled, stats may be collected, etc. See the documentation for the particular node type. Using only the first argument is equivalent to copying the node to a new location without changing its name. Using only the second argument is equivalent to making a copy of the node in the same group. """ self._g_check_open() srcfile = self._v_file srcparent = self._v_parent srcname = self._v_name dstparent = newparent dstname = newname # Set default arguments. if dstparent is None and dstname is None: raise NodeError("you should specify at least " "a ``newparent`` or a ``newname`` parameter") if dstparent is None: dstparent = srcparent if dstname is None: dstname = srcname # Get destination location. if hasattr(dstparent, '_v_file'): # from node dstfile = dstparent._v_file dstpath = dstparent._v_pathname elif hasattr(dstparent, 'startswith'): # from path dstfile = srcfile dstpath = dstparent else: raise TypeError("new parent is not a node nor a path: %r" % (dstparent,)) # Validity checks on arguments. if dstfile is srcfile: # Copying over itself? srcpath = srcparent._v_pathname if dstpath == srcpath and dstname == srcname: raise NodeError( "source and destination nodes are the same node: ``%s``" % self._v_pathname) # Recursively copying into itself? if recursive: self._g_check_not_contains(dstpath) # Note that the previous checks allow us to go ahead and create # the parent groups if `createparents` is true. `dstParent` is # used instead of `dstPath` because it may be in other file, and # to avoid accepting `Node` objects when `createparents` is # true. dstparent = srcfile._get_or_create_path(dstparent, createparents) self._g_check_group(dstparent) # Is it a group? # Copying to another file with undo enabled? if dstfile is not srcfile and srcfile.is_undo_enabled(): warnings.warn("copying across databases can not be undone " "nor redone from this database", UndoRedoWarning) # Copying over an existing node? self._g_maybe_remove(dstparent, dstname, overwrite) # Copy the node. # The constructor of the new node takes care of logging. return self._g_copy(dstparent, dstname, recursive, **kwargs)
(self, newparent=None, newname=None, overwrite=False, recursive=False, createparents=False, **kwargs)
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727,988
tables.node
_f_delattr
Delete a PyTables attribute from this node. If the named attribute does not exist, an AttributeError is raised.
def _f_delattr(self, name): """Delete a PyTables attribute from this node. If the named attribute does not exist, an AttributeError is raised. """ delattr(self._v_attrs, name)
(self, name)
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727,989
tables.node
_f_getattr
Get a PyTables attribute from this node. If the named attribute does not exist, an AttributeError is raised.
def _f_getattr(self, name): """Get a PyTables attribute from this node. If the named attribute does not exist, an AttributeError is raised. """ return getattr(self._v_attrs, name)
(self, name)
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727,990
tables.node
_f_isvisible
Is this node visible?
def _f_isvisible(self): """Is this node visible?""" self._g_check_open() return isvisiblepath(self._v_pathname)
(self)
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727,991
tables.node
_f_move
Move or rename this node. Moves a node into a new parent group, or changes the name of the node. newparent can be a Group object (see :ref:`GroupClassDescr`) or a pathname in string form. If it is not specified or None, the current parent group is chosen as the new parent. newname must be a string with a new name. If it is not specified or None, the current name is chosen as the new name. If createparents is true, the needed groups for the given new parent group path to exist will be created. Moving a node across databases is not allowed, nor it is moving a node *into* itself. These result in a NodeError. However, moving a node *over* itself is allowed and simply does nothing. Moving over another existing node is similarly not allowed, unless the optional overwrite argument is true, in which case that node is recursively removed before moving. Usually, only the first argument will be used, effectively moving the node to a new location without changing its name. Using only the second argument is equivalent to renaming the node in place.
def _f_move(self, newparent=None, newname=None, overwrite=False, createparents=False): """Move or rename this node. Moves a node into a new parent group, or changes the name of the node. newparent can be a Group object (see :ref:`GroupClassDescr`) or a pathname in string form. If it is not specified or None, the current parent group is chosen as the new parent. newname must be a string with a new name. If it is not specified or None, the current name is chosen as the new name. If createparents is true, the needed groups for the given new parent group path to exist will be created. Moving a node across databases is not allowed, nor it is moving a node *into* itself. These result in a NodeError. However, moving a node *over* itself is allowed and simply does nothing. Moving over another existing node is similarly not allowed, unless the optional overwrite argument is true, in which case that node is recursively removed before moving. Usually, only the first argument will be used, effectively moving the node to a new location without changing its name. Using only the second argument is equivalent to renaming the node in place. """ self._g_check_open() file_ = self._v_file oldparent = self._v_parent oldname = self._v_name # Set default arguments. if newparent is None and newname is None: raise NodeError("you should specify at least " "a ``newparent`` or a ``newname`` parameter") if newparent is None: newparent = oldparent if newname is None: newname = oldname # Get destination location. if hasattr(newparent, '_v_file'): # from node newfile = newparent._v_file newpath = newparent._v_pathname elif hasattr(newparent, 'startswith'): # from path newfile = file_ newpath = newparent else: raise TypeError("new parent is not a node nor a path: %r" % (newparent,)) # Validity checks on arguments. # Is it in the same file? if newfile is not file_: raise NodeError("nodes can not be moved across databases; " "please make a copy of the node") # The movement always fails if the hosting file can not be modified. file_._check_writable() # Moving over itself? oldpath = oldparent._v_pathname if newpath == oldpath and newname == oldname: # This is equivalent to renaming the node to its current name, # and it does not change the referenced object, # so it is an allowed no-op. return # Moving into itself? self._g_check_not_contains(newpath) # Note that the previous checks allow us to go ahead and create # the parent groups if `createparents` is true. `newparent` is # used instead of `newpath` to avoid accepting `Node` objects # when `createparents` is true. newparent = file_._get_or_create_path(newparent, createparents) self._g_check_group(newparent) # Is it a group? # Moving over an existing node? self._g_maybe_remove(newparent, newname, overwrite) # Move the node. oldpathname = self._v_pathname self._g_move(newparent, newname) # Log the change. if file_.is_undo_enabled(): self._g_log_move(oldpathname)
(self, newparent=None, newname=None, overwrite=False, createparents=False)
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727,992
tables.node
_f_remove
Remove this node from the hierarchy. If the node has children, recursive removal must be stated by giving recursive a true value; otherwise, a NodeError will be raised. If the node is a link to a Group object, and you are sure that you want to delete it, you can do this by setting the force flag to true.
def _f_remove(self, recursive=False, force=False): """Remove this node from the hierarchy. If the node has children, recursive removal must be stated by giving recursive a true value; otherwise, a NodeError will be raised. If the node is a link to a Group object, and you are sure that you want to delete it, you can do this by setting the force flag to true. """ self._g_check_open() file_ = self._v_file file_._check_writable() if file_.is_undo_enabled(): self._g_remove_and_log(recursive, force) else: self._g_remove(recursive, force)
(self, recursive=False, force=False)
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727,993
tables.node
_f_rename
Rename this node in place. Changes the name of a node to *newname* (a string). If a node with the same newname already exists and overwrite is true, recursively remove it before renaming.
def _f_rename(self, newname, overwrite=False): """Rename this node in place. Changes the name of a node to *newname* (a string). If a node with the same newname already exists and overwrite is true, recursively remove it before renaming. """ self._f_move(newname=newname, overwrite=overwrite)
(self, newname, overwrite=False)
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727,994
tables.node
_f_setattr
Set a PyTables attribute for this node. If the node already has a large number of attributes, a PerformanceWarning is issued.
def _f_setattr(self, name, value): """Set a PyTables attribute for this node. If the node already has a large number of attributes, a PerformanceWarning is issued. """ setattr(self._v_attrs, name, value)
(self, name, value)
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727,995
tables.array
_fancy_selection
Performs a NumPy-style fancy selection in `self`. Implements advanced NumPy-style selection operations in addition to the standard slice-and-int behavior. Indexing arguments may be ints, slices or lists of indices. Note: This is a backport from the h5py project.
def _fancy_selection(self, args): """Performs a NumPy-style fancy selection in `self`. Implements advanced NumPy-style selection operations in addition to the standard slice-and-int behavior. Indexing arguments may be ints, slices or lists of indices. Note: This is a backport from the h5py project. """ # Internal functions def validate_number(num, length): """Validate a list member for the given axis length.""" try: num = int(num) except TypeError: raise TypeError("Illegal index: %r" % num) if num > length - 1: raise IndexError("Index out of bounds: %d" % num) def expand_ellipsis(args, rank): """Expand ellipsis objects and fill in missing axes.""" n_el = sum(1 for arg in args if arg is Ellipsis) if n_el > 1: raise IndexError("Only one ellipsis may be used.") elif n_el == 0 and len(args) != rank: args = args + (Ellipsis,) final_args = [] n_args = len(args) for idx, arg in enumerate(args): if arg is Ellipsis: final_args.extend((slice(None),) * (rank - n_args + 1)) else: final_args.append(arg) if len(final_args) > rank: raise IndexError("Too many indices.") return final_args def translate_slice(exp, length): """Given a slice object, return a 3-tuple (start, count, step) This is for use with the hyperslab selection routines. """ start, stop, step = exp.start, exp.stop, exp.step if start is None: start = 0 else: start = int(start) if stop is None: stop = length else: stop = int(stop) if step is None: step = 1 else: step = int(step) if step < 1: raise IndexError("Step must be >= 1 (got %d)" % step) if stop == start: raise IndexError("Zero-length selections are not allowed") if stop < start: raise IndexError("Reverse-order selections are not allowed") if start < 0: start = length + start if stop < 0: stop = length + stop if not 0 <= start <= (length - 1): raise IndexError( "Start index %s out of range (0-%d)" % (start, length - 1)) if not 1 <= stop <= length: raise IndexError( "Stop index %s out of range (1-%d)" % (stop, length)) count = (stop - start) // step if (stop - start) % step != 0: count += 1 if start + count > length: raise IndexError( "Selection out of bounds (%d; axis has %d)" % (start + count, length)) return start, count, step # Main code for _fancy_selection mshape = [] selection = [] if not isinstance(args, tuple): args = (args,) args = expand_ellipsis(args, len(self.shape)) list_seen = False reorder = None for idx, (exp, length) in enumerate(zip(args, self.shape)): if isinstance(exp, slice): start, count, step = translate_slice(exp, length) selection.append((start, count, step, idx, "AND")) mshape.append(count) else: try: exp = list(exp) except TypeError: exp = [exp] # Handle scalar index as a list of length 1 mshape.append(0) # Keep track of scalar index for NumPy else: mshape.append(len(exp)) if len(exp) == 0: raise IndexError( "Empty selections are not allowed (axis %d)" % idx) elif len(exp) > 1: if list_seen: raise IndexError("Only one selection list is allowed") else: list_seen = True else: if (not isinstance(exp[0], (int, np.integer)) or (isinstance(exp[0], np.ndarray) and not np.issubdtype(exp[0].dtype, np.integer))): raise TypeError("Only integer coordinates allowed.") nexp = np.asarray(exp, dtype="i8") # Convert negative values nexp = np.where(nexp < 0, length + nexp, nexp) # Check whether the list is ordered or not # (only one unordered list is allowed) if len(nexp) != len(np.unique(nexp)): raise IndexError( "Selection lists cannot have repeated values") neworder = nexp.argsort() if (neworder.shape != (len(exp),) or np.sum(np.abs(neworder - np.arange(len(exp)))) != 0): if reorder is not None: raise IndexError( "Only one selection list can be unordered") corrected_idx = sum(1 for x in mshape if x != 0) - 1 reorder = (corrected_idx, neworder) nexp = nexp[neworder] for select_idx in range(len(nexp) + 1): # This crazy piece of code performs a list selection # using HDF5 hyperslabs. # For each index, perform a "NOTB" selection on every # portion of *this axis* which falls *outside* the list # selection. For this to work, the input array MUST be # monotonically increasing. if select_idx < len(nexp): validate_number(nexp[select_idx], length) if select_idx == 0: start = 0 count = nexp[0] elif select_idx == len(nexp): start = nexp[-1] + 1 count = length - start else: start = nexp[select_idx - 1] + 1 count = nexp[select_idx] - start if count > 0: selection.append((start, count, 1, idx, "NOTB")) mshape = tuple(x for x in mshape if x != 0) return selection, reorder, mshape
(self, args)
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727,996
tables.node
_g_check_group
null
def _g_check_group(self, node): # Node must be defined in order to define a Group. # However, we need to know Group here. # Using class_name_dict avoids a circular import. if not isinstance(node, class_name_dict['Node']): raise TypeError("new parent is not a registered node: %s" % node._v_pathname) if not isinstance(node, class_name_dict['Group']): raise TypeError("new parent node ``%s`` is not a group" % node._v_pathname)
(self, node)
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727,997
tables.node
_g_check_name
Check validity of name for this particular kind of node. This is invoked once the standard HDF5 and natural naming checks have successfully passed.
def _g_check_name(self, name): """Check validity of name for this particular kind of node. This is invoked once the standard HDF5 and natural naming checks have successfully passed. """ if name.startswith('_i_'): # This is reserved for table index groups. raise ValueError( "node name starts with reserved prefix ``_i_``: %s" % name)
(self, name)
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0.02119750902056694, 0.025607958436012268 ]
727,998
tables.node
_g_check_not_contains
null
def _g_check_not_contains(self, pathname): # The not-a-TARDIS test. ;) mypathname = self._v_pathname if (mypathname == '/' # all nodes fall below the root group or pathname == mypathname or pathname.startswith(mypathname + '/')): raise NodeError("can not move or recursively copy node ``%s`` " "into itself" % mypathname)
(self, pathname)
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727,999
tables.node
_g_check_open
Check that the node is open. If the node is closed, a `ClosedNodeError` is raised.
def _g_check_open(self): """Check that the node is open. If the node is closed, a `ClosedNodeError` is raised. """ if not self._v_isopen: raise ClosedNodeError("the node object is closed") assert self._v_file.isopen, "found an open node in a closed file"
(self)
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728,000
tables.leaf
_g_copy
null
def _g_copy(self, newparent, newname, recursive, _log=True, **kwargs): # Compute default arguments. start = kwargs.pop('start', None) stop = kwargs.pop('stop', None) step = kwargs.pop('step', None) title = kwargs.pop('title', self._v_title) filters = kwargs.pop('filters', self.filters) chunkshape = kwargs.pop('chunkshape', self.chunkshape) copyuserattrs = kwargs.pop('copyuserattrs', True) stats = kwargs.pop('stats', None) if chunkshape == 'keep': chunkshape = self.chunkshape # Keep the original chunkshape elif chunkshape == 'auto': chunkshape = None # Will recompute chunkshape # Fix arguments with explicit None values for backwards compatibility. if title is None: title = self._v_title if filters is None: filters = self.filters # Create a copy of the object. (new_node, bytes) = self._g_copy_with_stats( newparent, newname, start, stop, step, title, filters, chunkshape, _log, **kwargs) # Copy user attributes if requested (or the flavor at least). if copyuserattrs: self._v_attrs._g_copy(new_node._v_attrs, copyclass=True) elif 'FLAVOR' in self._v_attrs: if self._v_file.params['PYTABLES_SYS_ATTRS']: new_node._v_attrs._g__setattr('FLAVOR', self._flavor) new_node._flavor = self._flavor # update cached value # Update statistics if needed. if stats is not None: stats['leaves'] += 1 stats['bytes'] += bytes return new_node
(self, newparent, newname, recursive, _log=True, **kwargs)
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728,001
tables.node
_g_copy_as_child
Copy this node as a child of another group. Copies just this node into `newparent`, not recursing children nor overwriting nodes nor logging the copy. This is intended to be used when copying whole sub-trees.
def _g_copy_as_child(self, newparent, **kwargs): """Copy this node as a child of another group. Copies just this node into `newparent`, not recursing children nor overwriting nodes nor logging the copy. This is intended to be used when copying whole sub-trees. """ return self._g_copy(newparent, self._v_name, recursive=False, _log=False, **kwargs)
(self, newparent, **kwargs)
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728,002
tables.array
_g_copy_with_stats
Private part of Leaf.copy() for each kind of leaf.
def _g_copy_with_stats(self, group, name, start, stop, step, title, filters, chunkshape, _log, **kwargs): """Private part of Leaf.copy() for each kind of leaf.""" # Compute the correct indices. (start, stop, step) = self._process_range_read(start, stop, step) # Get the slice of the array # (non-buffered version) if self.shape: arr = self[start:stop:step] else: arr = self[()] # Build the new Array object. Use the _atom reserved keyword # just in case the array is being copied from a native HDF5 # with atomic types different from scalars. # For details, see #275 of trac. object_ = Array(group, name, arr, title=title, _log=_log, _atom=self.atom) nbytes = np.prod(self.shape, dtype=SizeType) * self.atom.size return (object_, nbytes)
(self, group, name, start, stop, step, title, filters, chunkshape, _log, **kwargs)
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728,003
tables.array
_g_create
Save a new array in file.
def _g_create(self): """Save a new array in file.""" self._v_version = obversion try: # `Leaf._g_post_init_hook()` should be setting the flavor on disk. self._flavor = flavor = flavor_of(self._obj) nparr = array_as_internal(self._obj, flavor) except Exception: # XXX # Problems converting data. Close the node and re-raise exception. self.close(flush=0) raise # Raise an error in case of unsupported object if nparr.dtype.kind in ['V', 'U', 'O']: # in void, unicode, object raise TypeError("Array objects cannot currently deal with void, " "unicode or object arrays") # Decrease the number of references to the object self._obj = None # Fix the byteorder of data nparr = self._g_fix_byteorder_data(nparr, nparr.dtype.byteorder) # Create the array on-disk try: # ``self._v_objectid`` needs to be set because would be # needed for setting attributes in some descendants later # on (self._v_objectid, self.shape, self.atom) = self._create_array( nparr, self._v_new_title, self.atom) except Exception: # XXX # Problems creating the Array on disk. Close node and re-raise. self.close(flush=0) raise # Compute the optimal buffer size self.nrowsinbuf = self._calc_nrowsinbuf() # Arrays don't have chunkshapes (so, set it to None) self._v_chunkshape = None return self._v_objectid
(self)
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728,004
tables.node
_g_del_location
Clear location-dependent attributes. This also triggers the removal of file references to this node.
def _g_del_location(self): """Clear location-dependent attributes. This also triggers the removal of file references to this node. """ node_manager = self._v_file._node_manager pathname = self._v_pathname if not self._v__deleting: node_manager.drop_from_cache(pathname) # Note: node_manager.drop_node do not removes the node form the # registry if it is still open node_manager.registry.pop(pathname, None) self._v_file = None self._v_isopen = False self._v_pathname = None self._v_name = None self._v_depth = None
(self)
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728,005
tables.leaf
_g_fix_byteorder_data
Fix the byteorder of data passed in constructors.
def _g_fix_byteorder_data(self, data, dbyteorder): """Fix the byteorder of data passed in constructors.""" dbyteorder = byteorders[dbyteorder] # If self.byteorder has not been passed as an argument of # the constructor, then set it to the same value of data. if self.byteorder is None: self.byteorder = dbyteorder # Do an additional in-place byteswap of data if the in-memory # byteorder doesn't match that of the on-disk. This is the only # place that we have to do the conversion manually. In all the # other cases, it will be HDF5 the responsible for doing the # byteswap properly. if dbyteorder in ['little', 'big']: if dbyteorder != self.byteorder: # if data is not writeable, do a copy first if not data.flags.writeable: data = data.copy() data.byteswap(True) else: # Fix the byteorder again, no matter which byteorder have # specified the user in the constructor. self.byteorder = "irrelevant" return data
(self, data, dbyteorder)
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728,006
tables.node
_g_getparent
The parent :class:`Group` instance
def _g_getparent(self): """The parent :class:`Group` instance""" (parentpath, nodename) = split_path(self._v_pathname) return self._v_file._get_node(parentpath)
(self)
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728,007
tables.node
_g_gettitle
A description of this node. A shorthand for TITLE attribute.
def _g_gettitle(self): """A description of this node. A shorthand for TITLE attribute.""" if hasattr(self._v_attrs, 'TITLE'): return self._v_attrs.TITLE else: return ''
(self)
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728,008
tables.node
_g_log_create
null
def _g_log_create(self): self._v_file._log('CREATE', self._v_pathname)
(self)
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728,009
tables.node
_g_log_move
null
def _g_log_move(self, oldpathname): self._v_file._log('MOVE', oldpathname, self._v_pathname)
(self, oldpathname)
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728,010
tables.node
_g_maybe_remove
null
def _g_maybe_remove(self, parent, name, overwrite): if name in parent: if not overwrite: raise NodeError( f"destination group ``{parent._v_pathname}`` already " f"has a node named ``{name}``; you may want to use the " f"``overwrite`` argument") parent._f_get_child(name)._f_remove(True)
(self, parent, name, overwrite)
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728,011
tables.node
_g_move
Move this node in the hierarchy. Moves the node into the given `newparent`, with the given `newname`. It does not log the change.
def _g_move(self, newparent, newname): """Move this node in the hierarchy. Moves the node into the given `newparent`, with the given `newname`. It does not log the change. """ oldparent = self._v_parent oldname = self._v_name oldpathname = self._v_pathname # to move the HDF5 node # Try to insert the node into the new parent. newparent._g_refnode(self, newname) # Remove the node from the new parent. oldparent._g_unrefnode(oldname) # Remove location information for this node. self._g_del_location() # Set new location information for this node. self._g_set_location(newparent, newname) # hdf5extension operations: # Update node attributes. self._g_new(newparent, self._v_name, init=False) # Move the node. # self._v_parent._g_move_node(oldpathname, self._v_pathname) self._v_parent._g_move_node(oldparent._v_objectid, oldname, newparent._v_objectid, newname, oldpathname, self._v_pathname) # Tell dependent objects about the new location of this node. self._g_update_dependent()
(self, newparent, newname)
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728,012
tables.array
_g_open
Get the metadata info for an array in file.
def _g_open(self): """Get the metadata info for an array in file.""" (oid, self.atom, self.shape, self._v_chunkshape) = self._open_array() self.nrowsinbuf = self._calc_nrowsinbuf() return oid
(self)
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728,013
tables.leaf
_g_post_init_hook
Code to be run after node creation and before creation logging. This method gets or sets the flavor of the leaf.
def _g_post_init_hook(self): """Code to be run after node creation and before creation logging. This method gets or sets the flavor of the leaf. """ super()._g_post_init_hook() if self._v_new: # set flavor of new node if self._flavor is None: self._flavor = internal_flavor else: # flavor set at creation time, do not log if self._v_file.params['PYTABLES_SYS_ATTRS']: self._v_attrs._g__setattr('FLAVOR', self._flavor) else: # get flavor of existing node (if any) if self._v_file.params['PYTABLES_SYS_ATTRS']: flavor = getattr(self._v_attrs, 'FLAVOR', internal_flavor) self._flavor = flavor_alias_map.get(flavor, flavor) else: self._flavor = internal_flavor
(self)
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728,014
tables.node
_g_pre_kill_hook
Code to be called before killing the node.
def _g_pre_kill_hook(self): """Code to be called before killing the node.""" pass
(self)
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728,015
tables.node
_g_remove
Remove this node from the hierarchy. If the node has children, recursive removal must be stated by giving `recursive` a true value; otherwise, a `NodeError` will be raised. If `force` is set to true, the node will be removed no matter it has children or not (useful for deleting hard links). It does not log the change.
def _g_remove(self, recursive, force): """Remove this node from the hierarchy. If the node has children, recursive removal must be stated by giving `recursive` a true value; otherwise, a `NodeError` will be raised. If `force` is set to true, the node will be removed no matter it has children or not (useful for deleting hard links). It does not log the change. """ # Remove the node from the PyTables hierarchy. parent = self._v_parent parent._g_unrefnode(self._v_name) # Close the node itself. self._f_close() # hdf5extension operations: # Remove the node from the HDF5 hierarchy. self._g_delete(parent)
(self, recursive, force)
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728,016
tables.node
_g_remove_and_log
null
def _g_remove_and_log(self, recursive, force): file_ = self._v_file oldpathname = self._v_pathname # Log *before* moving to use the right shadow name. file_._log('REMOVE', oldpathname) move_to_shadow(file_, oldpathname)
(self, recursive, force)
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728,017
tables.node
_g_set_location
Set location-dependent attributes. Sets the location-dependent attributes of this node to reflect that it is placed under the specified `parentnode`, with the specified `name`. This also triggers the insertion of file references to this node. If the maximum recommended tree depth is exceeded, a `PerformanceWarning` is issued.
def _g_set_location(self, parentnode, name): """Set location-dependent attributes. Sets the location-dependent attributes of this node to reflect that it is placed under the specified `parentnode`, with the specified `name`. This also triggers the insertion of file references to this node. If the maximum recommended tree depth is exceeded, a `PerformanceWarning` is issued. """ file_ = parentnode._v_file parentdepth = parentnode._v_depth self._v_file = file_ self._v_isopen = True root_uep = file_.root_uep if name.startswith(root_uep): # This has been called from File._get_node() assert parentdepth == 0 if root_uep == "/": self._v_pathname = name else: self._v_pathname = name[len(root_uep):] _, self._v_name = split_path(name) self._v_depth = name.count("/") - root_uep.count("/") + 1 else: # If we enter here is because this has been called elsewhere self._v_name = name self._v_pathname = join_path(parentnode._v_pathname, name) self._v_depth = parentdepth + 1 # Check if the node is too deep in the tree. if parentdepth >= self._v_maxtreedepth: warnings.warn("""\ node ``%s`` is exceeding the recommended maximum depth (%d);\ be ready to see PyTables asking for *lots* of memory and possibly slow I/O""" % (self._v_pathname, self._v_maxtreedepth), PerformanceWarning) if self._v_pathname != '/': file_._node_manager.cache_node(self, self._v_pathname)
(self, parentnode, name)
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728,018
tables.node
_g_settitle
null
def _g_settitle(self, title): self._v_attrs.TITLE = title
(self, title)
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728,019
tables.node
_g_update_dependent
Update dependent objects after a location change. All dependent objects (but not nodes!) referencing this node must be updated here.
def _g_update_dependent(self): """Update dependent objects after a location change. All dependent objects (but not nodes!) referencing this node must be updated here. """ if '_v_attrs' in self.__dict__: self._v_attrs._g_update_node_location(self)
(self)
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728,020
tables.node
_g_update_location
Update location-dependent attributes. Updates location data when an ancestor node has changed its location in the hierarchy to `newparentpath`. In fact, this method is expected to be called by an ancestor of this node. This also triggers the update of file references to this node. If the maximum recommended node depth is exceeded, a `PerformanceWarning` is issued. This warning is assured to be unique.
def _g_update_location(self, newparentpath): """Update location-dependent attributes. Updates location data when an ancestor node has changed its location in the hierarchy to `newparentpath`. In fact, this method is expected to be called by an ancestor of this node. This also triggers the update of file references to this node. If the maximum recommended node depth is exceeded, a `PerformanceWarning` is issued. This warning is assured to be unique. """ oldpath = self._v_pathname newpath = join_path(newparentpath, self._v_name) newdepth = newpath.count('/') self._v_pathname = newpath self._v_depth = newdepth # Check if the node is too deep in the tree. if newdepth > self._v_maxtreedepth: warnings.warn("""\ moved descendent node is exceeding the recommended maximum depth (%d);\ be ready to see PyTables asking for *lots* of memory and possibly slow I/O""" % (self._v_maxtreedepth,), PerformanceWarning) node_manager = self._v_file._node_manager node_manager.rename_node(oldpath, newpath) # Tell dependent objects about the new location of this node. self._g_update_dependent()
(self, newparentpath)
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728,021
tables.array
_init_loop
Initialization for the __iter__ iterator.
def _init_loop(self): """Initialization for the __iter__ iterator.""" self._nrowsread = self._start self._startb = self._start self._row = -1 # Sentinel self._init = True # Sentinel self.nrow = SizeType(self._start - self._step) # row number
(self)
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728,022
tables.array
_interpret_indexing
Internal routine used by __getitem__ and __setitem__
def _interpret_indexing(self, keys): """Internal routine used by __getitem__ and __setitem__""" maxlen = len(self.shape) shape = (maxlen,) startl = np.empty(shape=shape, dtype=SizeType) stopl = np.empty(shape=shape, dtype=SizeType) stepl = np.empty(shape=shape, dtype=SizeType) stop_None = np.zeros(shape=shape, dtype=SizeType) if not isinstance(keys, tuple): keys = (keys,) nkeys = len(keys) dim = 0 # Here is some problem when dealing with [...,...] params # but this is a bit weird way to pass parameters anyway for key in keys: ellipsis = 0 # Sentinel if isinstance(key, type(Ellipsis)): ellipsis = 1 for diml in range(dim, len(self.shape) - (nkeys - dim) + 1): startl[dim] = 0 stopl[dim] = self.shape[diml] stepl[dim] = 1 dim += 1 elif dim >= maxlen: raise IndexError("Too many indices for object '%s'" % self._v_pathname) elif is_idx(key): key = operator.index(key) # Protection for index out of range if key >= self.shape[dim]: raise IndexError("Index out of range") if key < 0: # To support negative values (Fixes bug #968149) key += self.shape[dim] start, stop, step = self._process_range( key, key + 1, 1, dim=dim) stop_None[dim] = 1 elif isinstance(key, slice): start, stop, step = self._process_range( key.start, key.stop, key.step, dim=dim) else: raise TypeError("Non-valid index or slice: %s" % key) if not ellipsis: startl[dim] = start stopl[dim] = stop stepl[dim] = step dim += 1 # Complete the other dimensions, if needed if dim < len(self.shape): for diml in range(dim, len(self.shape)): startl[dim] = 0 stopl[dim] = self.shape[diml] stepl[dim] = 1 dim += 1 # Compute the shape for the container properly. Fixes #1288792 shape = [] for dim in range(len(self.shape)): new_dim = len(range(startl[dim], stopl[dim], stepl[dim])) if not (new_dim == 1 and stop_None[dim]): shape.append(new_dim) return startl, stopl, stepl, shape
(self, keys)
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728,023
tables.leaf
_point_selection
Perform a point-wise selection. `key` can be any of the following items: * A boolean array with the same shape than self. Those positions with True values will signal the coordinates to be returned. * A numpy array (or list or tuple) with the point coordinates. This has to be a two-dimensional array of size len(self.shape) by num_elements containing a list of zero-based values specifying the coordinates in the dataset of the selected elements. The order of the element coordinates in the array specifies the order in which the array elements are iterated through when I/O is performed. Duplicate coordinate locations are not checked for. Return the coordinates array. If this is not possible, raise a `TypeError` so that the next selection method can be tried out. This is useful for whatever `Leaf` instance implementing a point-wise selection.
def _point_selection(self, key): """Perform a point-wise selection. `key` can be any of the following items: * A boolean array with the same shape than self. Those positions with True values will signal the coordinates to be returned. * A numpy array (or list or tuple) with the point coordinates. This has to be a two-dimensional array of size len(self.shape) by num_elements containing a list of zero-based values specifying the coordinates in the dataset of the selected elements. The order of the element coordinates in the array specifies the order in which the array elements are iterated through when I/O is performed. Duplicate coordinate locations are not checked for. Return the coordinates array. If this is not possible, raise a `TypeError` so that the next selection method can be tried out. This is useful for whatever `Leaf` instance implementing a point-wise selection. """ input_key = key if type(key) in (list, tuple): if isinstance(key, tuple) and len(key) > len(self.shape): raise IndexError(f"Invalid index or slice: {key!r}") # Try to convert key to a numpy array. If not possible, # a TypeError will be issued (to be catched later on). try: key = toarray(key) except ValueError: raise TypeError(f"Invalid index or slice: {key!r}") elif not isinstance(key, np.ndarray): raise TypeError(f"Invalid index or slice: {key!r}") # Protection against empty keys if len(key) == 0: return np.array([], dtype="i8") if key.dtype.kind == 'b': if not key.shape == self.shape: raise IndexError( "Boolean indexing array has incompatible shape") # Get the True coordinates (64-bit indices!) coords = np.asarray(key.nonzero(), dtype='i8') coords = np.transpose(coords) elif key.dtype.kind == 'i' or key.dtype.kind == 'u': if len(key.shape) > 2: raise IndexError( "Coordinate indexing array has incompatible shape") elif len(key.shape) == 2: if key.shape[0] != len(self.shape): raise IndexError( "Coordinate indexing array has incompatible shape") coords = np.asarray(key, dtype="i8") coords = np.transpose(coords) else: # For 1-dimensional datasets coords = np.asarray(key, dtype="i8") # handle negative indices base = coords if coords.base is None else coords.base if base is input_key: # never modify the original "key" data coords = coords.copy() idx = coords < 0 coords[idx] = (coords + self.shape)[idx] # bounds check if np.any(coords < 0) or np.any(coords >= self.shape): raise IndexError("Index out of bounds") else: raise TypeError("Only integer coordinates allowed.") # We absolutely need a contiguous array if not coords.flags.contiguous: coords = coords.copy() return coords
(self, key)
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728,024
tables.leaf
_process_range
null
def _process_range(self, start, stop, step, dim=None, warn_negstep=True): if dim is None: nrows = self.nrows # self.shape[self.maindim] else: nrows = self.shape[dim] if warn_negstep and step and step < 0: raise ValueError("slice step cannot be negative") # if start is not None: start = long(start) # if stop is not None: stop = long(stop) # if step is not None: step = long(step) return slice(start, stop, step).indices(int(nrows))
(self, start, stop, step, dim=None, warn_negstep=True)
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728,025
tables.leaf
_process_range_read
null
def _process_range_read(self, start, stop, step, warn_negstep=True): nrows = self.nrows if start is not None and stop is None and step is None: # Protection against start greater than available records # nrows == 0 is a special case for empty objects if 0 < nrows <= start: raise IndexError("start of range (%s) is greater than " "number of rows (%s)" % (start, nrows)) step = 1 if start == -1: # corner case stop = nrows else: stop = start + 1 # Finally, get the correct values (over the main dimension) start, stop, step = self._process_range(start, stop, step, warn_negstep=warn_negstep) return (start, stop, step)
(self, start, stop, step, warn_negstep=True)
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728,026
tables.array
_read
Read the array from disk without slice or flavor processing.
def _read(self, start, stop, step, out=None): """Read the array from disk without slice or flavor processing.""" nrowstoread = len(range(start, stop, step)) shape = list(self.shape) if shape: shape[self.maindim] = nrowstoread if out is None: arr = np.empty(dtype=self.atom.dtype, shape=shape) else: bytes_required = self.rowsize * nrowstoread # if buffer is too small, it will segfault if bytes_required != out.nbytes: raise ValueError(f'output array size invalid, got {out.nbytes}' f' bytes, need {bytes_required} bytes') if not out.flags['C_CONTIGUOUS']: raise ValueError('output array not C contiguous') arr = out # Protection against reading empty arrays if 0 not in shape: # Arrays that have non-zero dimensionality self._read_array(start, stop, step, arr) # data is always read in the system byteorder # if the out array's byteorder is different, do a byteswap if (out is not None and byteorders[arr.dtype.byteorder] != sys.byteorder): arr.byteswap(True) return arr
(self, start, stop, step, out=None)
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728,027
tables.array
_read_coords
Read a set of points defined by `coords`.
def _read_coords(self, coords): """Read a set of points defined by `coords`.""" nparr = np.empty(dtype=self.atom.dtype, shape=len(coords)) if len(coords) > 0: self._g_read_coords(coords, nparr) # For zero-shaped arrays, return the scalar if nparr.shape == (): nparr = nparr[()] return nparr
(self, coords)
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728,028
tables.array
_read_selection
Read a `selection`. Reorder if necessary.
def _read_selection(self, selection, reorder, shape): """Read a `selection`. Reorder if necessary. """ # Create the container for the slice nparr = np.empty(dtype=self.atom.dtype, shape=shape) # Arrays that have non-zero dimensionality self._g_read_selection(selection, nparr) # For zero-shaped arrays, return the scalar if nparr.shape == (): nparr = nparr[()] elif reorder is not None: # We need to reorder the array idx, neworder = reorder k = [slice(None)] * len(shape) k[idx] = neworder.argsort() # Apparently, a copy is not needed here, but doing it # for symmetry with the `_write_selection()` method. nparr = nparr[tuple(k)].copy() return nparr
(self, selection, reorder, shape)
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728,029
tables.array
_read_slice
Read a slice based on `startl`, `stopl` and `stepl`.
def _read_slice(self, startl, stopl, stepl, shape): """Read a slice based on `startl`, `stopl` and `stepl`.""" nparr = np.empty(dtype=self.atom.dtype, shape=shape) # Protection against reading empty arrays if 0 not in shape: # Arrays that have non-zero dimensionality self._g_read_slice(startl, stopl, stepl, nparr) # For zero-shaped arrays, return the scalar if nparr.shape == (): nparr = nparr[()] return nparr
(self, startl, stopl, stepl, shape)
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728,030
tables.array
_write_coords
Write `nparr` values in points defined by `coords` coordinates.
def _write_coords(self, coords, nparr): """Write `nparr` values in points defined by `coords` coordinates.""" if len(coords) > 0: nparr = self._check_shape(nparr, (len(coords),)) self._g_write_coords(coords, nparr)
(self, coords, nparr)
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728,031
tables.array
_write_selection
Write `nparr` in `selection`. Reorder if necessary.
def _write_selection(self, selection, reorder, shape, nparr): """Write `nparr` in `selection`. Reorder if necessary. """ nparr = self._check_shape(nparr, tuple(shape)) # Check whether we should reorder the array if reorder is not None: idx, neworder = reorder k = [slice(None)] * len(shape) k[idx] = neworder # For a reason a don't understand well, we need a copy of # the reordered array nparr = nparr[tuple(k)].copy() self._g_write_selection(selection, nparr)
(self, selection, reorder, shape, nparr)
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728,032
tables.array
_write_slice
Write `nparr` in a slice based on `startl`, `stopl` and `stepl`.
def _write_slice(self, startl, stopl, stepl, shape, nparr): """Write `nparr` in a slice based on `startl`, `stopl` and `stepl`.""" nparr = self._check_shape(nparr, tuple(shape)) countl = ((stopl - startl - 1) // stepl) + 1 self._g_write_slice(startl, stepl, countl, nparr)
(self, startl, stopl, stepl, shape, nparr)
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728,033
tables.leaf
close
Close this node in the tree. This method is completely equivalent to :meth:`Leaf._f_close`.
def close(self, flush=True): """Close this node in the tree. This method is completely equivalent to :meth:`Leaf._f_close`. """ self._f_close(flush)
(self, flush=True)
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728,034
tables.leaf
copy
Copy this node and return the new one. This method has the behavior described in :meth:`Node._f_copy`. Please note that there is no recursive flag since leaves do not have child nodes. .. warning:: Note that unknown parameters passed to this method will be ignored, so may want to double check the spelling of these (i.e. if you write them incorrectly, they will most probably be ignored). Parameters ---------- title The new title for the destination. If omitted or None, the original title is used. filters : Filters Specifying this parameter overrides the original filter properties in the source node. If specified, it must be an instance of the Filters class (see :ref:`FiltersClassDescr`). The default is to copy the filter properties from the source node. copyuserattrs You can prevent the user attributes from being copied by setting this parameter to False. The default is to copy them. start, stop, step : int Specify the range of rows to be copied; the default is to copy all the rows. stats This argument may be used to collect statistics on the copy process. When used, it should be a dictionary with keys 'groups', 'leaves' and 'bytes' having a numeric value. Their values will be incremented to reflect the number of groups, leaves and bytes, respectively, that have been copied during the operation. chunkshape The chunkshape of the new leaf. It supports a couple of special values. A value of keep means that the chunkshape will be the same than original leaf (this is the default). A value of auto means that a new shape will be computed automatically in order to ensure best performance when accessing the dataset through the main dimension. Any other value should be an integer or a tuple matching the dimensions of the leaf.
def copy(self, newparent=None, newname=None, overwrite=False, createparents=False, **kwargs): """Copy this node and return the new one. This method has the behavior described in :meth:`Node._f_copy`. Please note that there is no recursive flag since leaves do not have child nodes. .. warning:: Note that unknown parameters passed to this method will be ignored, so may want to double check the spelling of these (i.e. if you write them incorrectly, they will most probably be ignored). Parameters ---------- title The new title for the destination. If omitted or None, the original title is used. filters : Filters Specifying this parameter overrides the original filter properties in the source node. If specified, it must be an instance of the Filters class (see :ref:`FiltersClassDescr`). The default is to copy the filter properties from the source node. copyuserattrs You can prevent the user attributes from being copied by setting this parameter to False. The default is to copy them. start, stop, step : int Specify the range of rows to be copied; the default is to copy all the rows. stats This argument may be used to collect statistics on the copy process. When used, it should be a dictionary with keys 'groups', 'leaves' and 'bytes' having a numeric value. Their values will be incremented to reflect the number of groups, leaves and bytes, respectively, that have been copied during the operation. chunkshape The chunkshape of the new leaf. It supports a couple of special values. A value of keep means that the chunkshape will be the same than original leaf (this is the default). A value of auto means that a new shape will be computed automatically in order to ensure best performance when accessing the dataset through the main dimension. Any other value should be an integer or a tuple matching the dimensions of the leaf. """ return self._f_copy( newparent, newname, overwrite, createparents, **kwargs)
(self, newparent=None, newname=None, overwrite=False, createparents=False, **kwargs)
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728,035
tables.leaf
del_attr
Delete a PyTables attribute from this node. This method has the behavior described in :meth:`Node_f_delAttr`.
def del_attr(self, name): """Delete a PyTables attribute from this node. This method has the behavior described in :meth:`Node_f_delAttr`. """ self._f_delattr(name)
(self, name)
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728,036
tables.leaf
flush
Flush pending data to disk. Saves whatever remaining buffered data to disk. It also releases I/O buffers, so if you are filling many datasets in the same PyTables session, please call flush() extensively so as to help PyTables to keep memory requirements low.
def flush(self): """Flush pending data to disk. Saves whatever remaining buffered data to disk. It also releases I/O buffers, so if you are filling many datasets in the same PyTables session, please call flush() extensively so as to help PyTables to keep memory requirements low. """ self._g_flush()
(self)
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728,037
tables.leaf
get_attr
Get a PyTables attribute from this node. This method has the behavior described in :meth:`Node._f_getattr`.
def get_attr(self, name): """Get a PyTables attribute from this node. This method has the behavior described in :meth:`Node._f_getattr`. """ return self._f_getattr(name)
(self, name)
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728,038
tables.array
get_enum
Get the enumerated type associated with this array. If this array is of an enumerated type, the corresponding Enum instance (see :ref:`EnumClassDescr`) is returned. If it is not of an enumerated type, a TypeError is raised.
def get_enum(self): """Get the enumerated type associated with this array. If this array is of an enumerated type, the corresponding Enum instance (see :ref:`EnumClassDescr`) is returned. If it is not of an enumerated type, a TypeError is raised. """ if self.atom.kind != 'enum': raise TypeError("array ``%s`` is not of an enumerated type" % self._v_pathname) return self.atom.enum
(self)
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728,039
tables.leaf
isvisible
Is this node visible? This method has the behavior described in :meth:`Node._f_isvisible()`.
def isvisible(self): """Is this node visible? This method has the behavior described in :meth:`Node._f_isvisible()`. """ return self._f_isvisible()
(self)
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728,040
tables.array
iterrows
Iterate over the rows of the array. This method returns an iterator yielding an object of the current flavor for each selected row in the array. The returned rows are taken from the *main dimension*. If a range is not supplied, *all the rows* in the array are iterated upon - you can also use the :meth:`Array.__iter__` special method for that purpose. If you only want to iterate over a given *range of rows* in the array, you may use the start, stop and step parameters. Examples -------- :: result = [row for row in arrayInstance.iterrows(step=4)] .. versionchanged:: 3.0 If the *start* parameter is provided and *stop* is None then the array is iterated from *start* to the last line. In PyTables < 3.0 only one element was returned.
def iterrows(self, start=None, stop=None, step=None): """Iterate over the rows of the array. This method returns an iterator yielding an object of the current flavor for each selected row in the array. The returned rows are taken from the *main dimension*. If a range is not supplied, *all the rows* in the array are iterated upon - you can also use the :meth:`Array.__iter__` special method for that purpose. If you only want to iterate over a given *range of rows* in the array, you may use the start, stop and step parameters. Examples -------- :: result = [row for row in arrayInstance.iterrows(step=4)] .. versionchanged:: 3.0 If the *start* parameter is provided and *stop* is None then the array is iterated from *start* to the last line. In PyTables < 3.0 only one element was returned. """ try: (self._start, self._stop, self._step) = self._process_range( start, stop, step) except IndexError: # If problems with indexes, silently return the null tuple return () self._init_loop() return self
(self, start=None, stop=None, step=None)
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728,041
tables.leaf
move
Move or rename this node. This method has the behavior described in :meth:`Node._f_move`
def move(self, newparent=None, newname=None, overwrite=False, createparents=False): """Move or rename this node. This method has the behavior described in :meth:`Node._f_move` """ self._f_move(newparent, newname, overwrite, createparents)
(self, newparent=None, newname=None, overwrite=False, createparents=False)
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728,042
tables.array
read
Get data in the array as an object of the current flavor. The start, stop and step parameters can be used to select only a *range of rows* in the array. Their meanings are the same as in the built-in range() Python function, except that negative values of step are not allowed yet. Moreover, if only start is specified, then stop will be set to start + 1. If you do not specify neither start nor stop, then *all the rows* in the array are selected. The out parameter may be used to specify a NumPy array to receive the output data. Note that the array must have the same size as the data selected with the other parameters. Note that the array's datatype is not checked and no type casting is performed, so if it does not match the datatype on disk, the output will not be correct. Also, this parameter is only valid when the array's flavor is set to 'numpy'. Otherwise, a TypeError will be raised. When data is read from disk in NumPy format, the output will be in the current system's byteorder, regardless of how it is stored on disk. The exception is when an output buffer is supplied, in which case the output will be in the byteorder of that output buffer. .. versionchanged:: 3.0 Added the *out* parameter.
def read(self, start=None, stop=None, step=None, out=None): """Get data in the array as an object of the current flavor. The start, stop and step parameters can be used to select only a *range of rows* in the array. Their meanings are the same as in the built-in range() Python function, except that negative values of step are not allowed yet. Moreover, if only start is specified, then stop will be set to start + 1. If you do not specify neither start nor stop, then *all the rows* in the array are selected. The out parameter may be used to specify a NumPy array to receive the output data. Note that the array must have the same size as the data selected with the other parameters. Note that the array's datatype is not checked and no type casting is performed, so if it does not match the datatype on disk, the output will not be correct. Also, this parameter is only valid when the array's flavor is set to 'numpy'. Otherwise, a TypeError will be raised. When data is read from disk in NumPy format, the output will be in the current system's byteorder, regardless of how it is stored on disk. The exception is when an output buffer is supplied, in which case the output will be in the byteorder of that output buffer. .. versionchanged:: 3.0 Added the *out* parameter. """ self._g_check_open() if out is not None and self.flavor != 'numpy': msg = ("Optional 'out' argument may only be supplied if array " "flavor is 'numpy', currently is {}").format(self.flavor) raise TypeError(msg) (start, stop, step) = self._process_range_read(start, stop, step) arr = self._read(start, stop, step, out) return internal_to_flavor(arr, self.flavor)
(self, start=None, stop=None, step=None, out=None)
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728,043
tables.leaf
remove
Remove this node from the hierarchy. This method has the behavior described in :meth:`Node._f_remove`. Please note that there is no recursive flag since leaves do not have child nodes.
def remove(self): """Remove this node from the hierarchy. This method has the behavior described in :meth:`Node._f_remove`. Please note that there is no recursive flag since leaves do not have child nodes. """ self._f_remove(False)
(self)
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728,044
tables.leaf
rename
Rename this node in place. This method has the behavior described in :meth:`Node._f_rename()`.
def rename(self, newname): """Rename this node in place. This method has the behavior described in :meth:`Node._f_rename()`. """ self._f_rename(newname)
(self, newname)
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728,045
tables.leaf
set_attr
Set a PyTables attribute for this node. This method has the behavior described in :meth:`Node._f_setattr()`.
def set_attr(self, name, value): """Set a PyTables attribute for this node. This method has the behavior described in :meth:`Node._f_setattr()`. """ self._f_setattr(name, value)
(self, name, value)
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728,046
tables.leaf
truncate
Truncate the main dimension to be size rows. If the main dimension previously was larger than this size, the extra data is lost. If the main dimension previously was shorter, it is extended, and the extended part is filled with the default values. The truncation operation can only be applied to *enlargeable* datasets, else a TypeError will be raised.
def truncate(self, size): """Truncate the main dimension to be size rows. If the main dimension previously was larger than this size, the extra data is lost. If the main dimension previously was shorter, it is extended, and the extended part is filled with the default values. The truncation operation can only be applied to *enlargeable* datasets, else a TypeError will be raised. """ # A non-enlargeable arrays (Array, CArray) cannot be truncated if self.extdim < 0: raise TypeError("non-enlargeable datasets cannot be truncated") self._g_truncate(size)
(self, size)
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728,047
tables.atom
Atom
Defines the type of atomic cells stored in a dataset. The meaning of *atomic* is that individual elements of a cell can not be extracted directly by indexing (i.e. __getitem__()) the dataset; e.g. if a dataset has shape (2, 2) and its atoms have shape (3,), to get the third element of the cell at (1, 0) one should use dataset[1,0][2] instead of dataset[1,0,2]. The Atom class is meant to declare the different properties of the *base element* (also known as *atom*) of CArray, EArray and VLArray datasets, although they are also used to describe the base elements of Array datasets. Atoms have the property that their length is always the same. However, you can grow datasets along the extensible dimension in the case of EArray or put a variable number of them on a VLArray row. Moreover, they are not restricted to scalar values, and they can be *fully multidimensional objects*. Parameters ---------- itemsize : int For types with a non-fixed size, this sets the size in bytes of individual items in the atom. shape : tuple Sets the shape of the atom. An integer shape of N is equivalent to the tuple (N,). dflt Sets the default value for the atom. The following are the public methods and attributes of the Atom class. Notes ----- A series of descendant classes are offered in order to make the use of these element descriptions easier. You should use a particular Atom descendant class whenever you know the exact type you will need when writing your code. Otherwise, you may use one of the Atom.from_*() factory Methods. .. rubric:: Atom attributes .. attribute:: dflt The default value of the atom. If the user does not supply a value for an element while filling a dataset, this default value will be written to disk. If the user supplies a scalar value for a multidimensional atom, this value is automatically *broadcast* to all the items in the atom cell. If dflt is not supplied, an appropriate zero value (or *null* string) will be chosen by default. Please note that default values are kept internally as NumPy objects. .. attribute:: dtype The NumPy dtype that most closely matches this atom. .. attribute:: itemsize Size in bytes of a single item in the atom. Specially useful for atoms of the string kind. .. attribute:: kind The PyTables kind of the atom (a string). .. attribute:: shape The shape of the atom (a tuple for scalar atoms). .. attribute:: type The PyTables type of the atom (a string). Atoms can be compared with atoms and other objects for strict (in)equality without having to compare individual attributes:: >>> atom1 = StringAtom(itemsize=10) # same as ``atom2`` >>> atom2 = Atom.from_kind('string', 10) # same as ``atom1`` >>> atom3 = IntAtom() >>> atom1 == 'foo' False >>> atom1 == atom2 True >>> atom2 != atom1 False >>> atom1 == atom3 False >>> atom3 != atom2 True
class Atom(metaclass=MetaAtom): """Defines the type of atomic cells stored in a dataset. The meaning of *atomic* is that individual elements of a cell can not be extracted directly by indexing (i.e. __getitem__()) the dataset; e.g. if a dataset has shape (2, 2) and its atoms have shape (3,), to get the third element of the cell at (1, 0) one should use dataset[1,0][2] instead of dataset[1,0,2]. The Atom class is meant to declare the different properties of the *base element* (also known as *atom*) of CArray, EArray and VLArray datasets, although they are also used to describe the base elements of Array datasets. Atoms have the property that their length is always the same. However, you can grow datasets along the extensible dimension in the case of EArray or put a variable number of them on a VLArray row. Moreover, they are not restricted to scalar values, and they can be *fully multidimensional objects*. Parameters ---------- itemsize : int For types with a non-fixed size, this sets the size in bytes of individual items in the atom. shape : tuple Sets the shape of the atom. An integer shape of N is equivalent to the tuple (N,). dflt Sets the default value for the atom. The following are the public methods and attributes of the Atom class. Notes ----- A series of descendant classes are offered in order to make the use of these element descriptions easier. You should use a particular Atom descendant class whenever you know the exact type you will need when writing your code. Otherwise, you may use one of the Atom.from_*() factory Methods. .. rubric:: Atom attributes .. attribute:: dflt The default value of the atom. If the user does not supply a value for an element while filling a dataset, this default value will be written to disk. If the user supplies a scalar value for a multidimensional atom, this value is automatically *broadcast* to all the items in the atom cell. If dflt is not supplied, an appropriate zero value (or *null* string) will be chosen by default. Please note that default values are kept internally as NumPy objects. .. attribute:: dtype The NumPy dtype that most closely matches this atom. .. attribute:: itemsize Size in bytes of a single item in the atom. Specially useful for atoms of the string kind. .. attribute:: kind The PyTables kind of the atom (a string). .. attribute:: shape The shape of the atom (a tuple for scalar atoms). .. attribute:: type The PyTables type of the atom (a string). Atoms can be compared with atoms and other objects for strict (in)equality without having to compare individual attributes:: >>> atom1 = StringAtom(itemsize=10) # same as ``atom2`` >>> atom2 = Atom.from_kind('string', 10) # same as ``atom1`` >>> atom3 = IntAtom() >>> atom1 == 'foo' False >>> atom1 == atom2 True >>> atom2 != atom1 False >>> atom1 == atom3 False >>> atom3 != atom2 True """ @classmethod def prefix(cls): """Return the atom class prefix.""" cname = cls.__name__ return cname[:cname.rfind('Atom')] @classmethod def from_sctype(cls, sctype, shape=(), dflt=None): """Create an Atom from a NumPy scalar type sctype. Optional shape and default value may be specified as the shape and dflt arguments, respectively. Information in the sctype not represented in an Atom is ignored:: >>> import numpy as np >>> Atom.from_sctype(np.int16, shape=(2, 2)) Int16Atom(shape=(2, 2), dflt=0) >>> Atom.from_sctype('S5', dflt='hello') Traceback (most recent call last): ... ValueError: unknown NumPy scalar type: 'S5' >>> Atom.from_sctype('float64') Float64Atom(shape=(), dflt=0.0) """ if (not isinstance(sctype, type) or not issubclass(sctype, np.generic)): if "," in sctype: raise ValueError(f"unknown NumPy scalar type: {sctype!r}") try: dtype = np.dtype(sctype) except TypeError: raise ValueError(f"unknown NumPy scalar type: {sctype!r}") from None if issubclass(dtype.type, np.flexible) and dtype.itemsize > 0: raise ValueError(f"unknown NumPy scalar type: {sctype!r}") sctype = dtype.type return cls.from_dtype(np.dtype((sctype, shape)), dflt) @classmethod def from_dtype(cls, dtype, dflt=None): """Create an Atom from a NumPy dtype. An optional default value may be specified as the dflt argument. Information in the dtype not represented in an Atom is ignored:: >>> import numpy as np >>> Atom.from_dtype(np.dtype((np.int16, (2, 2)))) Int16Atom(shape=(2, 2), dflt=0) >>> Atom.from_dtype(np.dtype('float64')) Float64Atom(shape=(), dflt=0.0) Note: for easier use in Python 3, where all strings lead to the Unicode dtype, this dtype will also generate a StringAtom. Since this is only viable for strings that are castable as ascii, a warning is issued. >>> Atom.from_dtype(np.dtype('U20')) # doctest: +SKIP Atom.py:392: FlavorWarning: support for unicode type is very limited, and only works for strings that can be cast as ascii StringAtom(itemsize=20, shape=(), dflt=b'') """ basedtype = dtype.base if basedtype.names: raise ValueError("compound data types are not supported: %r" % dtype) if basedtype.shape != (): raise ValueError("nested data types are not supported: %r" % dtype) if basedtype.kind == 'S': # can not reuse something like 'string80' itemsize = basedtype.itemsize return cls.from_kind('string', itemsize, dtype.shape, dflt) elif basedtype.kind == 'U': # workaround for unicode type (standard string type in Python 3) warnings.warn("support for unicode type is very limited, and " "only works for strings that can be cast as ascii", FlavorWarning) itemsize = basedtype.itemsize // 4 assert str(itemsize) in basedtype.str, ( "something went wrong in handling unicode.") return cls.from_kind('string', itemsize, dtype.shape, dflt) # Most NumPy types have direct correspondence with PyTables types. return cls.from_type(basedtype.name, dtype.shape, dflt) @classmethod def from_type(cls, type, shape=(), dflt=None): """Create an Atom from a PyTables type. Optional shape and default value may be specified as the shape and dflt arguments, respectively:: >>> Atom.from_type('bool') BoolAtom(shape=(), dflt=False) >>> Atom.from_type('int16', shape=(2, 2)) Int16Atom(shape=(2, 2), dflt=0) >>> Atom.from_type('string40', dflt='hello') Traceback (most recent call last): ... ValueError: unknown type: 'string40' >>> Atom.from_type('Float64') Traceback (most recent call last): ... ValueError: unknown type: 'Float64' """ if type not in all_types: raise ValueError(f"unknown type: {type!r}") kind, itemsize = split_type(type) return cls.from_kind(kind, itemsize, shape, dflt) @classmethod def from_kind(cls, kind, itemsize=None, shape=(), dflt=None): """Create an Atom from a PyTables kind. Optional item size, shape and default valu
(nptype, shape, dflt)
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728,048
tables.atom
dispatched_cmp
null
def _cmp_dispatcher(other_method_name): """Dispatch comparisons to a method of the *other* object. Returns a new *rich comparison* method which dispatches calls to the method `other_method_name` of the *other* object. If there is no such method in the object, ``False`` is returned. This is part of the implementation of a double dispatch pattern. """ def dispatched_cmp(self, other): try: other_method = getattr(other, other_method_name) except AttributeError: return False return other_method(self) return dispatched_cmp
(self, other)
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728,049
tables.atom
__init__
null
def __init__(self, nptype, shape, dflt): if not hasattr(self, 'type'): raise NotImplementedError("``%s`` is an abstract class; " "please use one of its subclasses" % self.__class__.__name__) self.shape = shape = _normalize_shape(shape) """The shape of the atom (a tuple for scalar atoms).""" # Curiously enough, NumPy isn't generally able to accept NumPy # integers in a shape. ;( npshape = tuple(int(s) for s in shape) self.dtype = dtype = np.dtype((nptype, npshape)) """The NumPy dtype that most closely matches this atom.""" self.dflt = _normalize_default(dflt, dtype) """The default value of the atom. If the user does not supply a value for an element while filling a dataset, this default value will be written to disk. If the user supplies a scalar value for a multidimensional atom, this value is automatically *broadcast* to all the items in the atom cell. If dflt is not supplied, an appropriate zero value (or *null* string) will be chosen by default. Please note that default values are kept internally as NumPy objects."""
(self, nptype, shape, dflt)
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728,051
tables.atom
__repr__
null
def __repr__(self): args = f'shape={self.shape}, dflt={self.dflt!r}' if not hasattr(self.__class__.itemsize, '__int__'): # non-fixed args = f'itemsize={self.itemsize}, {args}' return f'{self.__class__.__name__}({args})'
(self)
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728,052
tables.atom
_get_init_args
Get a dictionary of instance constructor arguments. This implementation works on classes which use the same names for both constructor arguments and instance attributes.
def _get_init_args(self): """Get a dictionary of instance constructor arguments. This implementation works on classes which use the same names for both constructor arguments and instance attributes. """ signature = inspect.signature(self.__init__) parameters = signature.parameters args = [arg for arg, p in parameters.items() if p.kind is p.POSITIONAL_OR_KEYWORD] return {arg: getattr(self, arg) for arg in args if arg != 'self'}
(self)
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728,053
tables.atom
_is_equal_to_atom
Is this object equal to the given `atom`?
def _is_equal_to_atom(self, atom): """Is this object equal to the given `atom`?""" return (self.type == atom.type and self.shape == atom.shape and self.itemsize == atom.itemsize and np.all(self.dflt == atom.dflt))
(self, atom)
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728,054
tables.atom
copy
Get a copy of the atom, possibly overriding some arguments. Constructor arguments to be overridden must be passed as keyword arguments:: >>> atom1 = Int32Atom(shape=12) >>> atom2 = atom1.copy() >>> print(atom1) Int32Atom(shape=(12,), dflt=0) >>> print(atom2) Int32Atom(shape=(12,), dflt=0) >>> atom1 is atom2 False >>> atom3 = atom1.copy(shape=(2, 2)) >>> print(atom3) Int32Atom(shape=(2, 2), dflt=0) >>> atom1.copy(foobar=42) #doctest: +ELLIPSIS Traceback (most recent call last): ... TypeError: ...__init__() got an unexpected keyword argument 'foobar'
def copy(self, **override): """Get a copy of the atom, possibly overriding some arguments. Constructor arguments to be overridden must be passed as keyword arguments:: >>> atom1 = Int32Atom(shape=12) >>> atom2 = atom1.copy() >>> print(atom1) Int32Atom(shape=(12,), dflt=0) >>> print(atom2) Int32Atom(shape=(12,), dflt=0) >>> atom1 is atom2 False >>> atom3 = atom1.copy(shape=(2, 2)) >>> print(atom3) Int32Atom(shape=(2, 2), dflt=0) >>> atom1.copy(foobar=42) #doctest: +ELLIPSIS Traceback (most recent call last): ... TypeError: ...__init__() got an unexpected keyword argument 'foobar' """ newargs = self._get_init_args() newargs.update(override) return self.__class__(**newargs)
(self, **override)
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728,055
tables.atom
BoolAtom
Defines an atom of type bool.
class BoolAtom(Atom): """Defines an atom of type bool.""" kind = 'bool' itemsize = 1 type = 'bool' _deftype = 'bool8' _defvalue = False def __init__(self, shape=(), dflt=_defvalue): Atom.__init__(self, self.type, shape, dflt)
(shape=(), dflt=False)
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728,057
tables.atom
__init__
null
def __init__(self, shape=(), dflt=_defvalue): Atom.__init__(self, self.type, shape, dflt)
(self, shape=(), dflt=False)
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728,063
tables.description
BoolCol
Defines a non-nested column of a particular type. The constructor accepts the same arguments as the equivalent `Atom` class, plus an additional ``pos`` argument for position information, which is assigned to the `_v_pos` attribute and an ``attrs`` argument for storing additional metadata similar to `table.attrs`, which is assigned to the `_v_col_attrs` attribute.
from tables.description import BoolCol
(*args, **kwargs)
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728,064
tables.description
dispatched_cmp
null
def same_position(oldmethod): """Decorate `oldmethod` to also compare the `_v_pos` attribute.""" def newmethod(self, other): try: other._v_pos except AttributeError: return False # not a column definition return self._v_pos == other._v_pos and oldmethod(self, other) newmethod.__name__ = oldmethod.__name__ newmethod.__doc__ = oldmethod.__doc__ return newmethod
(self, other)
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728,065
tables.description
__init__
null
@classmethod def _subclass_from_prefix(cls, prefix): """Get a column subclass for the given `prefix`.""" cname = '%sCol' % prefix class_from_prefix = cls._class_from_prefix if cname in class_from_prefix: return class_from_prefix[cname] atombase = getattr(atom, '%sAtom' % prefix) class NewCol(cls, atombase): """Defines a non-nested column of a particular type. The constructor accepts the same arguments as the equivalent `Atom` class, plus an additional ``pos`` argument for position information, which is assigned to the `_v_pos` attribute and an ``attrs`` argument for storing additional metadata similar to `table.attrs`, which is assigned to the `_v_col_attrs` attribute. """ def __init__(self, *args, **kwargs): pos = kwargs.pop('pos', None) col_attrs = kwargs.pop('attrs', {}) offset = kwargs.pop('_offset', None) class_from_prefix = self._class_from_prefix atombase.__init__(self, *args, **kwargs) # The constructor of an abstract atom may have changed # the class of `self` to something different of `NewCol` # and `atombase` (that's why the prefix map is saved). if self.__class__ is not NewCol: colclass = class_from_prefix[self.prefix()] self.__class__ = colclass self._v_pos = pos self._v_offset = offset self._v_col_attrs = col_attrs __eq__ = same_position(atombase.__eq__) _is_equal_to_atom = same_position(atombase._is_equal_to_atom) # XXX: API incompatible change for PyTables 3 line # Overriding __eq__ blocks inheritance of __hash__ in 3.x # def __hash__(self): # return hash((self._v_pos, self.atombase)) if prefix == 'Enum': _is_equal_to_enumatom = same_position( atombase._is_equal_to_enumatom) NewCol.__name__ = cname class_from_prefix[prefix] = NewCol return NewCol
(self, *args, **kwargs)
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728,067
tables.description
__repr__
null
def __repr__(self): # Reuse the atom representation. atomrepr = super().__repr__() lpar = atomrepr.index('(') rpar = atomrepr.rindex(')') atomargs = atomrepr[lpar + 1:rpar] classname = self.__class__.__name__ if self._v_col_attrs: return (f'{classname}({atomargs}, pos={self._v_pos}' f', attrs={self._v_col_attrs})') return f'{classname}({atomargs}, pos={self._v_pos})'
(self)
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728,068
tables.description
_get_init_args
Get a dictionary of instance constructor arguments.
def _get_init_args(self): """Get a dictionary of instance constructor arguments.""" kwargs = {arg: getattr(self, arg) for arg in ('shape', 'dflt')} kwargs['pos'] = getattr(self, '_v_pos', None) return kwargs
(self)
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728,069
tables.description
_is_equal_to_atom
Is this object equal to the given `atom`?
def same_position(oldmethod): """Decorate `oldmethod` to also compare the `_v_pos` attribute.""" def newmethod(self, other): try: other._v_pos except AttributeError: return False # not a column definition return self._v_pos == other._v_pos and oldmethod(self, other) newmethod.__name__ = oldmethod.__name__ newmethod.__doc__ = oldmethod.__doc__ return newmethod
(self, other)
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728,071
tables.carray
CArray
This class represents homogeneous datasets in an HDF5 file. The difference between a CArray and a normal Array (see :ref:`ArrayClassDescr`), from which it inherits, is that a CArray has a chunked layout and, as a consequence, it supports compression. You can use datasets of this class to easily save or load arrays to or from disk, with compression support included. CArray includes all the instance variables and methods of Array. Only those with different behavior are mentioned here. Parameters ---------- parentnode The parent :class:`Group` object. .. versionchanged:: 3.0 Renamed from *parentNode* to *parentnode*. name : str The name of this node in its parent group. atom An `Atom` instance representing the *type* and *shape* of the atomic objects to be saved. shape The shape of the new array. title A description for this node (it sets the ``TITLE`` HDF5 attribute on disk). filters An instance of the `Filters` class that provides information about the desired I/O filters to be applied during the life of this object. chunkshape The shape of the data chunk to be read or written in a single HDF5 I/O operation. Filters are applied to those chunks of data. The dimensionality of `chunkshape` must be the same as that of `shape`. If ``None``, a sensible value is calculated (which is recommended). byteorder The byteorder of the data *on disk*, specified as 'little' or 'big'. If this is not specified, the byteorder is that of the platform. track_times Whether time data associated with the leaf are recorded (object access time, raw data modification time, metadata change time, object birth time); default True. Semantics of these times depend on their implementation in the HDF5 library: refer to documentation of the H5O_info_t data structure. As of HDF5 1.8.15, only ctime (metadata change time) is implemented. .. versionadded:: 3.4.3 Examples -------- See below a small example of the use of the `CArray` class. The code is available in ``examples/carray1.py``:: import numpy as np import tables as tb fileName = 'carray1.h5' shape = (200, 300) atom = tb.UInt8Atom() filters = tb.Filters(complevel=5, complib='zlib') h5f = tb.open_file(fileName, 'w') ca = h5f.create_carray(h5f.root, 'carray', atom, shape, filters=filters) # Fill a hyperslab in ``ca``. ca[10:60, 20:70] = np.ones((50, 50)) h5f.close() # Re-open a read another hyperslab h5f = tb.open_file(fileName) print(h5f) print(h5f.root.carray[8:12, 18:22]) h5f.close() The output for the previous script is something like:: carray1.h5 (File) '' Last modif.: 'Thu Apr 12 10:15:38 2007' Object Tree: / (RootGroup) '' /carray (CArray(200, 300), shuffle, zlib(5)) '' [[0 0 0 0] [0 0 0 0] [0 0 1 1] [0 0 1 1]]
class CArray(Array): """This class represents homogeneous datasets in an HDF5 file. The difference between a CArray and a normal Array (see :ref:`ArrayClassDescr`), from which it inherits, is that a CArray has a chunked layout and, as a consequence, it supports compression. You can use datasets of this class to easily save or load arrays to or from disk, with compression support included. CArray includes all the instance variables and methods of Array. Only those with different behavior are mentioned here. Parameters ---------- parentnode The parent :class:`Group` object. .. versionchanged:: 3.0 Renamed from *parentNode* to *parentnode*. name : str The name of this node in its parent group. atom An `Atom` instance representing the *type* and *shape* of the atomic objects to be saved. shape The shape of the new array. title A description for this node (it sets the ``TITLE`` HDF5 attribute on disk). filters An instance of the `Filters` class that provides information about the desired I/O filters to be applied during the life of this object. chunkshape The shape of the data chunk to be read or written in a single HDF5 I/O operation. Filters are applied to those chunks of data. The dimensionality of `chunkshape` must be the same as that of `shape`. If ``None``, a sensible value is calculated (which is recommended). byteorder The byteorder of the data *on disk*, specified as 'little' or 'big'. If this is not specified, the byteorder is that of the platform. track_times Whether time data associated with the leaf are recorded (object access time, raw data modification time, metadata change time, object birth time); default True. Semantics of these times depend on their implementation in the HDF5 library: refer to documentation of the H5O_info_t data structure. As of HDF5 1.8.15, only ctime (metadata change time) is implemented. .. versionadded:: 3.4.3 Examples -------- See below a small example of the use of the `CArray` class. The code is available in ``examples/carray1.py``:: import numpy as np import tables as tb fileName = 'carray1.h5' shape = (200, 300) atom = tb.UInt8Atom() filters = tb.Filters(complevel=5, complib='zlib') h5f = tb.open_file(fileName, 'w') ca = h5f.create_carray(h5f.root, 'carray', atom, shape, filters=filters) # Fill a hyperslab in ``ca``. ca[10:60, 20:70] = np.ones((50, 50)) h5f.close() # Re-open a read another hyperslab h5f = tb.open_file(fileName) print(h5f) print(h5f.root.carray[8:12, 18:22]) h5f.close() The output for the previous script is something like:: carray1.h5 (File) '' Last modif.: 'Thu Apr 12 10:15:38 2007' Object Tree: / (RootGroup) '' /carray (CArray(200, 300), shuffle, zlib(5)) '' [[0 0 0 0] [0 0 0 0] [0 0 1 1] [0 0 1 1]] """ # Class identifier. _c_classid = 'CARRAY' def __init__(self, parentnode, name, atom=None, shape=None, title="", filters=None, chunkshape=None, byteorder=None, _log=True, track_times=True): self.atom = atom """An `Atom` instance representing the shape, type of the atomic objects to be saved. """ self.shape = None """The shape of the stored array.""" self.extdim = -1 # `CArray` objects are not enlargeable by default """The index of the enlargeable dimension.""" # Other private attributes self._v_version = None """The object version of this array.""" self._v_new = new = atom is not None """Is this the first time the node has been created?""" self._v_new_title = title """New title for this node.""" self._v_convert = True """Whether the ``Array`` object must be converted or not.""" self._v_chunkshape = chunkshape """Private storage for the `chunkshape` property of the leaf.""" # Miscellaneous iteration rubbish. self._start = None """Starting row for the current iteration.""" self._stop = None """Stopping row for the current iteration.""" self._step = None """Step size for the current iteration.""" self._nrowsread = None """Number of rows read up to the current state of iteration.""" self._startb = None """Starting row for current buffer.""" self._stopb = None """Stopping row for current buffer. """ self._row = None """Current row in iterators (sentinel).""" self._init = False """Whether we are in the middle of an iteration or not (sentinel).""" self.listarr = None """Current buffer in iterators.""" if new: if not isinstance(atom, Atom): raise ValueError("atom parameter should be an instance of " "tables.Atom and you passed a %s." % type(atom)) if shape is None: raise ValueError("you must specify a non-empty shape") try: shape = tuple(shape) except TypeError: raise TypeError("`shape` parameter must be a sequence " "and you passed a %s" % type(shape)) self.shape = tuple(SizeType(s) for s in shape) if chunkshape is not None: try: chunkshape = tuple(chunkshape) except TypeError: raise TypeError( "`chunkshape` parameter must be a sequence " "and you passed a %s" % type(chunkshape)) if len(shape) != len(chunkshape): raise ValueError(f"the shape ({shape}) and chunkshape " f"({chunkshape}) ranks must be equal.") elif min(chunkshape) < 1: raise ValueError("chunkshape parameter cannot have " "zero-dimensions.") self._v_chunkshape = tuple(SizeType(s) for s in chunkshape) # The `Array` class is not abstract enough! :( super(Array, self).__init__(parentnode, name, new, filters, byteorder, _log, track_times) def _g_create(self): """Create a new array in file (specific part).""" if min(self.shape) < 1: raise ValueError( "shape parameter cannot have zero-dimensions.") # Finish the common part of creation process return self._g_create_common(self.nrows) def _g_create_common(self, expectedrows): """Create a new array in file (common part).""" self._v_version = obversion if self._v_chunkshape is None: # Compute the optimal chunk size self._v_chunkshape = self._calc_chunkshape( expectedrows, self.rowsize, self.atom.size) # Compute the optimal nrowsinbuf self.nrowsinbuf = self._calc_nrowsinbuf() # Correct the byteorder if needed if self.byteorder is None: self.byteorder = correct_byteorder(self.atom.type, sys.byteorder) try: # ``self._v_objectid`` needs to be set because would be # needed for setting attributes in some descendants later # on self._v_objectid = self._create_carray(self._v_new_title) except Exception: # XXX # Problems creating the Array on disk. Close node and re-raise. self.close(flush=0) raise return self._v_objectid def _g_copy_with_stats(self, group, name, start, stop, step,
(parentnode, name, atom=None, shape=None, title='', filters=None, chunkshape=None, byteorder=None, _log=True, track_times=True)
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728,074
tables.carray
__init__
null
def __init__(self, parentnode, name, atom=None, shape=None, title="", filters=None, chunkshape=None, byteorder=None, _log=True, track_times=True): self.atom = atom """An `Atom` instance representing the shape, type of the atomic objects to be saved. """ self.shape = None """The shape of the stored array.""" self.extdim = -1 # `CArray` objects are not enlargeable by default """The index of the enlargeable dimension.""" # Other private attributes self._v_version = None """The object version of this array.""" self._v_new = new = atom is not None """Is this the first time the node has been created?""" self._v_new_title = title """New title for this node.""" self._v_convert = True """Whether the ``Array`` object must be converted or not.""" self._v_chunkshape = chunkshape """Private storage for the `chunkshape` property of the leaf.""" # Miscellaneous iteration rubbish. self._start = None """Starting row for the current iteration.""" self._stop = None """Stopping row for the current iteration.""" self._step = None """Step size for the current iteration.""" self._nrowsread = None """Number of rows read up to the current state of iteration.""" self._startb = None """Starting row for current buffer.""" self._stopb = None """Stopping row for current buffer. """ self._row = None """Current row in iterators (sentinel).""" self._init = False """Whether we are in the middle of an iteration or not (sentinel).""" self.listarr = None """Current buffer in iterators.""" if new: if not isinstance(atom, Atom): raise ValueError("atom parameter should be an instance of " "tables.Atom and you passed a %s." % type(atom)) if shape is None: raise ValueError("you must specify a non-empty shape") try: shape = tuple(shape) except TypeError: raise TypeError("`shape` parameter must be a sequence " "and you passed a %s" % type(shape)) self.shape = tuple(SizeType(s) for s in shape) if chunkshape is not None: try: chunkshape = tuple(chunkshape) except TypeError: raise TypeError( "`chunkshape` parameter must be a sequence " "and you passed a %s" % type(chunkshape)) if len(shape) != len(chunkshape): raise ValueError(f"the shape ({shape}) and chunkshape " f"({chunkshape}) ranks must be equal.") elif min(chunkshape) < 1: raise ValueError("chunkshape parameter cannot have " "zero-dimensions.") self._v_chunkshape = tuple(SizeType(s) for s in chunkshape) # The `Array` class is not abstract enough! :( super(Array, self).__init__(parentnode, name, new, filters, byteorder, _log, track_times)
(self, parentnode, name, atom=None, shape=None, title='', filters=None, chunkshape=None, byteorder=None, _log=True, track_times=True)
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728,100
tables.carray
_g_copy_with_stats
Private part of Leaf.copy() for each kind of leaf.
def _g_copy_with_stats(self, group, name, start, stop, step, title, filters, chunkshape, _log, **kwargs): """Private part of Leaf.copy() for each kind of leaf.""" (start, stop, step) = self._process_range_read(start, stop, step) maindim = self.maindim shape = list(self.shape) shape[maindim] = len(range(start, stop, step)) # Now, fill the new carray with values from source nrowsinbuf = self.nrowsinbuf # The slices parameter for self.__getitem__ slices = [slice(0, dim, 1) for dim in self.shape] # This is a hack to prevent doing unnecessary conversions # when copying buffers self._v_convert = False # Build the new CArray object object = CArray(group, name, atom=self.atom, shape=shape, title=title, filters=filters, chunkshape=chunkshape, _log=_log) # Start the copy itself for start2 in range(start, stop, step * nrowsinbuf): # Save the records on disk stop2 = start2 + step * nrowsinbuf if stop2 > stop: stop2 = stop # Set the proper slice in the main dimension slices[maindim] = slice(start2, stop2, step) start3 = (start2 - start) // step stop3 = start3 + nrowsinbuf if stop3 > shape[maindim]: stop3 = shape[maindim] # The next line should be generalised if, in the future, # maindim is designed to be different from 0 in CArrays. # See ticket #199. object[start3:stop3] = self.__getitem__(tuple(slices)) # Activate the conversion again (default) self._v_convert = True nbytes = np.prod(self.shape, dtype=SizeType) * self.atom.size return (object, nbytes)
(self, group, name, start, stop, step, title, filters, chunkshape, _log, **kwargs)
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728,101
tables.carray
_g_create
Create a new array in file (specific part).
def _g_create(self): """Create a new array in file (specific part).""" if min(self.shape) < 1: raise ValueError( "shape parameter cannot have zero-dimensions.") # Finish the common part of creation process return self._g_create_common(self.nrows)
(self)
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728,102
tables.carray
_g_create_common
Create a new array in file (common part).
def _g_create_common(self, expectedrows): """Create a new array in file (common part).""" self._v_version = obversion if self._v_chunkshape is None: # Compute the optimal chunk size self._v_chunkshape = self._calc_chunkshape( expectedrows, self.rowsize, self.atom.size) # Compute the optimal nrowsinbuf self.nrowsinbuf = self._calc_nrowsinbuf() # Correct the byteorder if needed if self.byteorder is None: self.byteorder = correct_byteorder(self.atom.type, sys.byteorder) try: # ``self._v_objectid`` needs to be set because would be # needed for setting attributes in some descendants later # on self._v_objectid = self._create_carray(self._v_new_title) except Exception: # XXX # Problems creating the Array on disk. Close node and re-raise. self.close(flush=0) raise return self._v_objectid
(self, expectedrows)
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728,146
tables.exceptions
ClosedFileError
The operation can not be completed because the hosting file is closed. For instance, getting an existing node from a closed file is not allowed.
class ClosedFileError(ValueError): """The operation can not be completed because the hosting file is closed. For instance, getting an existing node from a closed file is not allowed. """ pass
null
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728,147
tables.exceptions
ClosedNodeError
The operation can not be completed because the node is closed. For instance, listing the children of a closed group is not allowed.
class ClosedNodeError(ValueError): """The operation can not be completed because the node is closed. For instance, listing the children of a closed group is not allowed. """ pass
null
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728,148
tables.description
Col
Defines a non-nested column. Col instances are used as a means to declare the different properties of a non-nested column in a table or nested column. Col classes are descendants of their equivalent Atom classes (see :ref:`AtomClassDescr`), but their instances have an additional _v_pos attribute that is used to decide the position of the column inside its parent table or nested column (see the IsDescription class in :ref:`IsDescriptionClassDescr` for more information on column positions). In the same fashion as Atom, you should use a particular Col descendant class whenever you know the exact type you will need when writing your code. Otherwise, you may use one of the Col.from_*() factory methods. Each factory method inherited from the Atom class is available with the same signature, plus an additional pos parameter (placed in last position) which defaults to None and that may take an integer value. This parameter might be used to specify the position of the column in the table. Besides, there are the next additional factory methods, available only for Col objects. The following parameters are available for most Col-derived constructors. Parameters ---------- itemsize : int For types with a non-fixed size, this sets the size in bytes of individual items in the column. shape : tuple Sets the shape of the column. An integer shape of N is equivalent to the tuple (N,). dflt Sets the default value for the column. pos : int Sets the position of column in table. If unspecified, the position will be randomly selected. attrs : dict Attribute metadata stored in the column (see :ref:`AttributeSetClassDescr`).
class Col(atom.Atom, metaclass=type): """Defines a non-nested column. Col instances are used as a means to declare the different properties of a non-nested column in a table or nested column. Col classes are descendants of their equivalent Atom classes (see :ref:`AtomClassDescr`), but their instances have an additional _v_pos attribute that is used to decide the position of the column inside its parent table or nested column (see the IsDescription class in :ref:`IsDescriptionClassDescr` for more information on column positions). In the same fashion as Atom, you should use a particular Col descendant class whenever you know the exact type you will need when writing your code. Otherwise, you may use one of the Col.from_*() factory methods. Each factory method inherited from the Atom class is available with the same signature, plus an additional pos parameter (placed in last position) which defaults to None and that may take an integer value. This parameter might be used to specify the position of the column in the table. Besides, there are the next additional factory methods, available only for Col objects. The following parameters are available for most Col-derived constructors. Parameters ---------- itemsize : int For types with a non-fixed size, this sets the size in bytes of individual items in the column. shape : tuple Sets the shape of the column. An integer shape of N is equivalent to the tuple (N,). dflt Sets the default value for the column. pos : int Sets the position of column in table. If unspecified, the position will be randomly selected. attrs : dict Attribute metadata stored in the column (see :ref:`AttributeSetClassDescr`). """ _class_from_prefix = {} # filled as column classes are created """Maps column prefixes to column classes.""" @classmethod def prefix(cls): """Return the column class prefix.""" cname = cls.__name__ return cname[:cname.rfind('Col')] @classmethod def from_atom(cls, atom, pos=None, _offset=None): """Create a Col definition from a PyTables atom. An optional position may be specified as the pos argument. """ prefix = atom.prefix() kwargs = atom._get_init_args() colclass = cls._class_from_prefix[prefix] return colclass(pos=pos, _offset=_offset, **kwargs) @classmethod def from_sctype(cls, sctype, shape=(), dflt=None, pos=None): """Create a `Col` definition from a NumPy scalar type `sctype`. Optional shape, default value and position may be specified as the `shape`, `dflt` and `pos` arguments, respectively. Information in the `sctype` not represented in a `Col` is ignored. """ newatom = atom.Atom.from_sctype(sctype, shape, dflt) return cls.from_atom(newatom, pos=pos) @classmethod def from_dtype(cls, dtype, dflt=None, pos=None, _offset=None): """Create a `Col` definition from a NumPy `dtype`. Optional default value and position may be specified as the `dflt` and `pos` arguments, respectively. The `dtype` must have a byte order which is irrelevant or compatible with that of the system. Information in the `dtype` not represented in a `Col` is ignored. """ newatom = atom.Atom.from_dtype(dtype, dflt) return cls.from_atom(newatom, pos=pos, _offset=_offset) @classmethod def from_type(cls, type, shape=(), dflt=None, pos=None): """Create a `Col` definition from a PyTables `type`. Optional shape, default value and position may be specified as the `shape`, `dflt` and `pos` arguments, respectively. """ newatom = atom.Atom.from_type(type, shape, dflt) return cls.from_atom(newatom, pos=pos) @classmethod def from_kind(cls, kind, itemsize=None, shape=(), dflt=None, pos=None): """Create a `Col` definition from a PyTables `kind`. Optional item size, shape, default value and position may be specified as the `itemsize`, `shape`, `dflt` and `pos` arguments, respectively. Bear in mind that not all columns support a default item size. """ newatom = atom.Atom.from_kind(kind, itemsize, shape, dflt) return cls.from_atom(newatom, pos=pos) @classmethod def _subclass_from_prefix(cls, prefix): """Get a column subclass for the given `prefix`.""" cname = '%sCol' % prefix class_from_prefix = cls._class_from_prefix if cname in class_from_prefix: return class_from_prefix[cname] atombase = getattr(atom, '%sAtom' % prefix) class NewCol(cls, atombase): """Defines a non-nested column of a particular type. The constructor accepts the same arguments as the equivalent `Atom` class, plus an additional ``pos`` argument for position information, which is assigned to the `_v_pos` attribute and an ``attrs`` argument for storing additional metadata similar to `table.attrs`, which is assigned to the `_v_col_attrs` attribute. """ def __init__(self, *args, **kwargs): pos = kwargs.pop('pos', None) col_attrs = kwargs.pop('attrs', {}) offset = kwargs.pop('_offset', None) class_from_prefix = self._class_from_prefix atombase.__init__(self, *args, **kwargs) # The constructor of an abstract atom may have changed # the class of `self` to something different of `NewCol` # and `atombase` (that's why the prefix map is saved). if self.__class__ is not NewCol: colclass = class_from_prefix[self.prefix()] self.__class__ = colclass self._v_pos = pos self._v_offset = offset self._v_col_attrs = col_attrs __eq__ = same_position(atombase.__eq__) _is_equal_to_atom = same_position(atombase._is_equal_to_atom) # XXX: API incompatible change for PyTables 3 line # Overriding __eq__ blocks inheritance of __hash__ in 3.x # def __hash__(self): # return hash((self._v_pos, self.atombase)) if prefix == 'Enum': _is_equal_to_enumatom = same_position( atombase._is_equal_to_enumatom) NewCol.__name__ = cname class_from_prefix[prefix] = NewCol return NewCol def __repr__(self): # Reuse the atom representation. atomrepr = super().__repr__() lpar = atomrepr.index('(') rpar = atomrepr.rindex(')') atomargs = atomrepr[lpar + 1:rpar] classname = self.__class__.__name__ if self._v_col_attrs: return (f'{classname}({atomargs}, pos={self._v_pos}' f', attrs={self._v_col_attrs})') return f'{classname}({atomargs}, pos={self._v_pos})' def _get_init_args(self): """Get a dictionary of instance constructor arguments.""" kwargs = {arg: getattr(self, arg) for arg in ('shape', 'dflt')} kwargs['pos'] = getattr(self, '_v_pos', None) return kwargs
(nptype, shape, dflt)
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728,156
tables.table
Cols
Container for columns in a table or nested column. This class is used as an *accessor* to the columns in a table or nested column. It supports the *natural naming* convention, so that you can access the different columns as attributes which lead to Column instances (for non-nested columns) or other Cols instances (for nested columns). For instance, if table.cols is a Cols instance with a column named col1 under it, the later can be accessed as table.cols.col1. If col1 is nested and contains a col2 column, this can be accessed as table.cols.col1.col2 and so on. Because of natural naming, the names of members start with special prefixes, like in the Group class (see :ref:`GroupClassDescr`). Like the Column class (see :ref:`ColumnClassDescr`), Cols supports item access to read and write ranges of values in the table or nested column. .. rubric:: Cols attributes .. attribute:: _v_colnames A list of the names of the columns hanging directly from the associated table or nested column. The order of the names matches the order of their respective columns in the containing table. .. attribute:: _v_colpathnames A list of the pathnames of all the columns under the associated table or nested column (in preorder). If it does not contain nested columns, this is exactly the same as the :attr:`Cols._v_colnames` attribute. .. attribute:: _v_desc The associated Description instance (see :ref:`DescriptionClassDescr`).
class Cols: """Container for columns in a table or nested column. This class is used as an *accessor* to the columns in a table or nested column. It supports the *natural naming* convention, so that you can access the different columns as attributes which lead to Column instances (for non-nested columns) or other Cols instances (for nested columns). For instance, if table.cols is a Cols instance with a column named col1 under it, the later can be accessed as table.cols.col1. If col1 is nested and contains a col2 column, this can be accessed as table.cols.col1.col2 and so on. Because of natural naming, the names of members start with special prefixes, like in the Group class (see :ref:`GroupClassDescr`). Like the Column class (see :ref:`ColumnClassDescr`), Cols supports item access to read and write ranges of values in the table or nested column. .. rubric:: Cols attributes .. attribute:: _v_colnames A list of the names of the columns hanging directly from the associated table or nested column. The order of the names matches the order of their respective columns in the containing table. .. attribute:: _v_colpathnames A list of the pathnames of all the columns under the associated table or nested column (in preorder). If it does not contain nested columns, this is exactly the same as the :attr:`Cols._v_colnames` attribute. .. attribute:: _v_desc The associated Description instance (see :ref:`DescriptionClassDescr`). """ @property def _v_table(self): """The parent Table instance (see :ref:`TableClassDescr`).""" return self._v__tableFile._get_node(self._v__tablePath) def __init__(self, table, desc): myDict = self.__dict__ myDict['_v__tableFile'] = table._v_file myDict['_v__tablePath'] = table._v_pathname myDict['_v_desc'] = desc myDict['_v_colnames'] = desc._v_names myDict['_v_colpathnames'] = table.description._v_pathnames # Put the column in the local dictionary for name in desc._v_names: if name in desc._v_types: myDict[name] = Column(table, name, desc) else: myDict[name] = Cols(table, desc._v_colobjects[name]) def _g_update_table_location(self, table): """Updates the location information about the associated `table`.""" myDict = self.__dict__ myDict['_v__tableFile'] = table._v_file myDict['_v__tablePath'] = table._v_pathname # Update the locations in individual columns. for colname in self._v_colnames: myDict[colname]._g_update_table_location(table) def __len__(self): """Get the number of top level columns in table.""" return len(self._v_colnames) def _f_col(self, colname): """Get an accessor to the column colname. This method returns a Column instance (see :ref:`ColumnClassDescr`) if the requested column is not nested, and a Cols instance (see :ref:`ColsClassDescr`) if it is. You may use full column pathnames in colname. Calling cols._f_col('col1/col2') is equivalent to using cols.col1.col2. However, the first syntax is more intended for programmatic use. It is also better if you want to access columns with names that are not valid Python identifiers. """ if not isinstance(colname, str): raise TypeError("Parameter can only be an string. You passed " "object: %s" % colname) if ((colname.find('/') > -1 and colname not in self._v_colpathnames) and colname not in self._v_colnames): raise KeyError(("Cols accessor ``%s.cols%s`` does not have a " "column named ``%s``") % (self._v__tablePath, self._v_desc._v_pathname, colname)) return self._g_col(colname) def _g_col(self, colname): """Like `self._f_col()` but it does not check arguments.""" # Get the Column or Description object inames = colname.split('/') cols = self for iname in inames: cols = cols.__dict__[iname] return cols def __getitem__(self, key): """Get a row or a range of rows from a table or nested column. If key argument is an integer, the corresponding nested type row is returned as a record of the current flavor. If key is a slice, the range of rows determined by it is returned as a structured array of the current flavor. Examples -------- :: record = table.cols[4] # equivalent to table[4] recarray = table.cols.Info[4:1000:2] Those statements are equivalent to:: nrecord = table.read(start=4)[0] nrecarray = table.read(start=4, stop=1000, step=2).field('Info') Here you can see how a mix of natural naming, indexing and slicing can be used as shorthands for the :meth:`Table.read` method. """ table = self._v_table nrows = table.nrows if is_idx(key): key = operator.index(key) # Index out of range protection if key >= nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += nrows (start, stop, step) = table._process_range(key, key + 1, 1) colgroup = self._v_desc._v_pathname if colgroup == "": # The root group return table.read(start, stop, step)[0] else: crecord = table.read(start, stop, step)[0] return crecord[colgroup] elif isinstance(key, slice): (start, stop, step) = table._process_range( key.start, key.stop, key.step) colgroup = self._v_desc._v_pathname if colgroup == "": # The root group return table.read(start, stop, step) else: crecarray = table.read(start, stop, step) if hasattr(crecarray, "field"): return crecarray.field(colgroup) # RecArray case else: return get_nested_field(crecarray, colgroup) # numpy case else: raise TypeError(f"invalid index or slice: {key!r}") def __setitem__(self, key, value): """Set a row or a range of rows in a table or nested column. If key argument is an integer, the corresponding row is set to value. If key is a slice, the range of rows determined by it is set to value. Examples -------- :: table.cols[4] = record table.cols.Info[4:1000:2] = recarray Those statements are equivalent to:: table.modify_rows(4, rows=record) table.modify_column(4, 1000, 2, colname='Info', column=recarray) Here you can see how a mix of natural naming, indexing and slicing can be used as shorthands for the :meth:`Table.modify_rows` and :meth:`Table.modify_column` methods. """ table = self._v_table nrows = table.nrows if is_idx(key): key = operator.index(key) # Index out of range protection if key >= nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += nrows (start, stop, step) = table._process_range(key, key + 1, 1) elif isinstance(key, slice): (start, stop, step) = table._process_range( key.start, key.stop, key.step) else: raise TypeError(f"invalid index or slice: {key!r}") # Actually modify the correct columns colgroup = self._v_desc._v_pathname if colgroup == "": # The root group table.modify_rows(start, stop, step,
(table, desc)
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728,157
tables.table
__getitem__
Get a row or a range of rows from a table or nested column. If key argument is an integer, the corresponding nested type row is returned as a record of the current flavor. If key is a slice, the range of rows determined by it is returned as a structured array of the current flavor. Examples -------- :: record = table.cols[4] # equivalent to table[4] recarray = table.cols.Info[4:1000:2] Those statements are equivalent to:: nrecord = table.read(start=4)[0] nrecarray = table.read(start=4, stop=1000, step=2).field('Info') Here you can see how a mix of natural naming, indexing and slicing can be used as shorthands for the :meth:`Table.read` method.
def __getitem__(self, key): """Get a row or a range of rows from a table or nested column. If key argument is an integer, the corresponding nested type row is returned as a record of the current flavor. If key is a slice, the range of rows determined by it is returned as a structured array of the current flavor. Examples -------- :: record = table.cols[4] # equivalent to table[4] recarray = table.cols.Info[4:1000:2] Those statements are equivalent to:: nrecord = table.read(start=4)[0] nrecarray = table.read(start=4, stop=1000, step=2).field('Info') Here you can see how a mix of natural naming, indexing and slicing can be used as shorthands for the :meth:`Table.read` method. """ table = self._v_table nrows = table.nrows if is_idx(key): key = operator.index(key) # Index out of range protection if key >= nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += nrows (start, stop, step) = table._process_range(key, key + 1, 1) colgroup = self._v_desc._v_pathname if colgroup == "": # The root group return table.read(start, stop, step)[0] else: crecord = table.read(start, stop, step)[0] return crecord[colgroup] elif isinstance(key, slice): (start, stop, step) = table._process_range( key.start, key.stop, key.step) colgroup = self._v_desc._v_pathname if colgroup == "": # The root group return table.read(start, stop, step) else: crecarray = table.read(start, stop, step) if hasattr(crecarray, "field"): return crecarray.field(colgroup) # RecArray case else: return get_nested_field(crecarray, colgroup) # numpy case else: raise TypeError(f"invalid index or slice: {key!r}")
(self, key)
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728,158
tables.table
__init__
null
def __init__(self, table, desc): myDict = self.__dict__ myDict['_v__tableFile'] = table._v_file myDict['_v__tablePath'] = table._v_pathname myDict['_v_desc'] = desc myDict['_v_colnames'] = desc._v_names myDict['_v_colpathnames'] = table.description._v_pathnames # Put the column in the local dictionary for name in desc._v_names: if name in desc._v_types: myDict[name] = Column(table, name, desc) else: myDict[name] = Cols(table, desc._v_colobjects[name])
(self, table, desc)
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728,159
tables.table
__len__
Get the number of top level columns in table.
def __len__(self): """Get the number of top level columns in table.""" return len(self._v_colnames)
(self)
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728,160
tables.table
__repr__
A detailed string representation for this object.
def __repr__(self): """A detailed string representation for this object.""" lines = [f'{self!s}'] for name in self._v_colnames: # Get this class name classname = getattr(self, name).__class__.__name__ # The type if name in self._v_desc._v_dtypes: tcol = self._v_desc._v_dtypes[name] # The shape for this column shape = (self._v_table.nrows,) + \ self._v_desc._v_dtypes[name].shape else: tcol = "Description" # Description doesn't have a shape currently shape = () lines.append(f" {name} ({classname}{shape}, {tcol})") return '\n'.join(lines) + '\n'
(self)
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728,161
tables.table
__setitem__
Set a row or a range of rows in a table or nested column. If key argument is an integer, the corresponding row is set to value. If key is a slice, the range of rows determined by it is set to value. Examples -------- :: table.cols[4] = record table.cols.Info[4:1000:2] = recarray Those statements are equivalent to:: table.modify_rows(4, rows=record) table.modify_column(4, 1000, 2, colname='Info', column=recarray) Here you can see how a mix of natural naming, indexing and slicing can be used as shorthands for the :meth:`Table.modify_rows` and :meth:`Table.modify_column` methods.
def __setitem__(self, key, value): """Set a row or a range of rows in a table or nested column. If key argument is an integer, the corresponding row is set to value. If key is a slice, the range of rows determined by it is set to value. Examples -------- :: table.cols[4] = record table.cols.Info[4:1000:2] = recarray Those statements are equivalent to:: table.modify_rows(4, rows=record) table.modify_column(4, 1000, 2, colname='Info', column=recarray) Here you can see how a mix of natural naming, indexing and slicing can be used as shorthands for the :meth:`Table.modify_rows` and :meth:`Table.modify_column` methods. """ table = self._v_table nrows = table.nrows if is_idx(key): key = operator.index(key) # Index out of range protection if key >= nrows: raise IndexError("Index out of range") if key < 0: # To support negative values key += nrows (start, stop, step) = table._process_range(key, key + 1, 1) elif isinstance(key, slice): (start, stop, step) = table._process_range( key.start, key.stop, key.step) else: raise TypeError(f"invalid index or slice: {key!r}") # Actually modify the correct columns colgroup = self._v_desc._v_pathname if colgroup == "": # The root group table.modify_rows(start, stop, step, rows=value) else: table.modify_column( start, stop, step, colname=colgroup, column=value)
(self, key, value)
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728,162
tables.table
__str__
The string representation for this object.
def __str__(self): """The string representation for this object.""" # The pathname descpathname = self._v_desc._v_pathname if descpathname: descpathname = "." + descpathname return (f"{self._v__tablePath}.cols{descpathname} " f"({self.__class__.__name__}), " f"{len(self._v_colnames)} columns")
(self)
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728,163
tables.table
_f_col
Get an accessor to the column colname. This method returns a Column instance (see :ref:`ColumnClassDescr`) if the requested column is not nested, and a Cols instance (see :ref:`ColsClassDescr`) if it is. You may use full column pathnames in colname. Calling cols._f_col('col1/col2') is equivalent to using cols.col1.col2. However, the first syntax is more intended for programmatic use. It is also better if you want to access columns with names that are not valid Python identifiers.
def _f_col(self, colname): """Get an accessor to the column colname. This method returns a Column instance (see :ref:`ColumnClassDescr`) if the requested column is not nested, and a Cols instance (see :ref:`ColsClassDescr`) if it is. You may use full column pathnames in colname. Calling cols._f_col('col1/col2') is equivalent to using cols.col1.col2. However, the first syntax is more intended for programmatic use. It is also better if you want to access columns with names that are not valid Python identifiers. """ if not isinstance(colname, str): raise TypeError("Parameter can only be an string. You passed " "object: %s" % colname) if ((colname.find('/') > -1 and colname not in self._v_colpathnames) and colname not in self._v_colnames): raise KeyError(("Cols accessor ``%s.cols%s`` does not have a " "column named ``%s``") % (self._v__tablePath, self._v_desc._v_pathname, colname)) return self._g_col(colname)
(self, colname)
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728,164
tables.table
_g_close
null
def _g_close(self): # First, close the columns (ie possible indices open) for col in self._v_colnames: colobj = self._g_col(col) if isinstance(colobj, Column): colobj.close() # Delete the reference to column del self.__dict__[col] else: colobj._g_close() self.__dict__.clear()
(self)
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