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 numpy.issubdtype(nparr.dtype, int):
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)
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
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)
def append(self, sequence):
"""Add a sequence of data to the end of the dataset.
The sequence must have the same type as the array; otherwise a
TypeError is raised. In the same way, the dimensions of the
sequence must conform to the shape of the array, that is, all
dimensions must match, with the exception of the enlargeable
dimension, which can be of any length (even 0!). If the shape
of the sequence is invalid, a ValueError is raised.
"""
self._g_check_open()
self._v_file._check_writable()
# Convert the sequence into a NumPy object
nparr = convert_to_np_atom2(sequence, self.atom)
# Check if it has a consistent shape with underlying EArray
self._check_shape_append(nparr)
# If the size of the nparr is zero, don't do anything else
if nparr.size > 0:
self._append(nparr)
def append(self, sequence):
"""Add a sequence of data to the end of the dataset.
The sequence must have the same type as the array; otherwise a
TypeError is raised. In the same way, the dimensions of the
sequence must conform to the shape of the array, that is, all
dimensions must match, with the exception of the enlargeable
dimension, which can be of any length (even 0!). If the shape
of the sequence is invalid, a ValueError is raised.
"""
self._g_check_open()
self._v_file._check_writable()
# Convert the sequence into a NumPy object
nparr = convert_to_np_atom2(sequence, self.atom)
# Check if it has a consistent shape with underlying EArray
self._check_shape_append(nparr)
# If the size of the nparr is zero, don't do anything else
if nparr.size > 0:
self._append(nparr)
def append(self, sequence):
"""Add a sequence of data to the end of the dataset.
This method appends the objects in the sequence to a *single row* in
this array. The type and shape of individual objects must be compliant
with the atoms in the array. In the case of serialized objects and
variable length strings, the object or string to append is itself the
sequence.
"""
self._g_check_open()
self._v_file._check_writable()
# Prepare the sequence to convert it into a NumPy object
atom = self.atom
if not hasattr(atom, 'size'): # it is a pseudo-atom
sequence = atom.toarray(sequence)
statom = atom.base
else:
try: # fastest check in most cases
len(sequence)
except TypeError:
raise TypeError("argument is not a sequence")
statom = atom
if len(sequence) > 0:
# The sequence needs to be copied to make the operation safe
# to in-place conversion.
nparr = convert_to_np_atom2(sequence, statom)
nobjects = self._getnobjects(nparr)
else:
nobjects = 0
nparr = None
self._append(nparr, nobjects)
self.nrows += 1
def append(self, sequence):
"""Add a sequence of data to the end of the dataset.
This method appends the objects in the sequence to a *single row* in
this array. The type and shape of individual objects must be compliant
with the atoms in the array. In the case of serialized objects and
variable length strings, the object or string to append is itself the
sequence.
"""
self._g_check_open()
self._v_file._check_writable()
# Prepare the sequence to convert it into a NumPy object
atom = self.atom
if not hasattr(atom, 'size'): # it is a pseudo-atom
sequence = atom.toarray(sequence)
statom = atom.base
else:
try: # fastest check in most cases
len(sequence)
except TypeError:
raise TypeError("argument is not a sequence")
statom = atom
if len(sequence) > 0:
# The sequence needs to be copied to make the operation safe
# to in-place conversion.
nparr = convert_to_np_atom2(sequence, statom)
nobjects = self._getnobjects(nparr)
else:
nobjects = 0
nparr = None
self._append(nparr, nobjects)
self.nrows += 1
