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# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2014, Nicolas P. Rougier. All rights reserved.
# Distributed under the terms of the new BSD License.
# -----------------------------------------------------------------------------
"""
An ArrayList is a strongly typed list whose type can be anything that can be
interpreted as a numpy data type.
Example
-------
>>> L = ArrayList( [[0], [1,2], [3,4,5], [6,7,8,9]] )
>>> print L
[ [0] [1 2] [3 4 5] [6 7 8 9] ]
>>> print L.data
[0 1 2 3 4 5 6 7 8 9]
You can add several items at once by specifying common or individual size: a
single scalar means all items are the same size while a list of sizes is used
to specify individual item sizes.
Example
-------
>>> L = ArrayList( np.arange(10), [3,3,4])
>>> print L
[ [0 1 2] [3 4 5] [6 7 8 9] ]
>>> print L.data
[0 1 2 3 4 5 6 7 8 9]
"""
import numpy as np
class ArrayList(object):
"""
An ArrayList is a strongly typed list whose type can be anything that can
be interpreted as a numpy data type.
"""
def __init__(self, data=None, itemsize=None, dtype=float,
sizeable=True, writeable=True):
""" Create a new buffer using given data and sizes or dtype
Parameters
----------
data : array_like
An array, any object exposing the array interface, an object
whose __array__ method returns an array, or any (nested) sequence.
itemsize: int or 1-D array
If `itemsize is an integer, N, the array will be divided
into elements of size N. If such partition is not possible,
an error is raised.
If `itemsize` is 1-D array, the array will be divided into
elements whose succesive sizes will be picked from itemsize.
If the sum of itemsize values is different from array size,
an error is raised.
dtype: np.dtype
Any object that can be interpreted as a numpy data type.
sizeable : boolean
Indicate whether item can be appended/inserted/deleted
writeable : boolean
Indicate whether content can be changed
"""
self._sizeable = sizeable
self._writeable = writeable
if data is not None:
if isinstance(data, (list, tuple)):
if isinstance(data[0], (list, tuple)):
itemsize = [len(sublist) for sublist in data]
data = [item for sublist in data for item in sublist]
self._data = np.array(data, copy=False)
self._size = self._data.size
# Default is one group with all data inside
_itemsize = np.ones(1) * self._data.size
# Check item sizes and get items count
if itemsize is not None:
if isinstance(itemsize, int):
if (self._size % itemsize) != 0:
raise ValueError("Cannot partition data as requested")
self._count = self._size // itemsize
_itemsize = np.ones(
self._count, dtype=int) * (self._size // self._count)
else:
_itemsize = np.array(itemsize, copy=False)
self._count = len(itemsize)
if _itemsize.sum() != self._size:
raise ValueError("Cannot partition data as requested")
else:
self._count = 1
# Store items
self._items = np.zeros((self._count, 2), int)
C = _itemsize.cumsum()
self._items[1:, 0] += C[:-1]
self._items[0:, 1] += C
else:
self._data = np.zeros(1, dtype=dtype)
self._items = np.zeros((1, 2), dtype=int)
self._size = 0
self._count = 0
@property
def data(self):
""" The array's elements, in memory. """
return self._data[:self._size]
@property
def size(self):
""" Number of base elements, in memory. """
return self._size
@property
def itemsize(self):
""" Individual item sizes """
return self._items[:self._count, 1] - self._items[:self._count, 0]
@property
def dtype(self):
""" Describes the format of the elements in the buffer. """
return self._data.dtype
def reserve(self, capacity):
""" Set current capacity of the underlying array"""
if capacity >= self._data.size:
capacity = int(2 ** np.ceil(np.log2(capacity)))
self._data = np.resize(self._data, capacity)
def __len__(self):
""" x.__len__() <==> len(x) """
return self._count
def __str__(self):
s = '[ '
for item in self:
s += str(item) + ' '
s += ']'
return s
def __getitem__(self, key):
""" x.__getitem__(y) <==> x[y] """
if isinstance(key, int):
if key < 0:
key += len(self)
if key < 0 or key >= len(self):
raise IndexError("Tuple index out of range")
dstart = self._items[key][0]
dstop = self._items[key][1]
return self._data[dstart:dstop]
elif isinstance(key, slice):
istart, istop, step = key.indices(len(self))
if istart > istop:
istart, istop = istop, istart
dstart = self._items[istart][0]
if istart == istop:
dstop = dstart
else:
dstop = self._items[istop - 1][1]
return self._data[dstart:dstop]
elif isinstance(key, str):
return self._data[key][:self._size]
elif key is Ellipsis:
return self.data
else:
raise TypeError("List indices must be integers")
def __setitem__(self, key, data):
""" x.__setitem__(i, y) <==> x[i]=y """
if not self._writeable:
raise AttributeError("List is not writeable")
if isinstance(key, (int, slice)):
if isinstance(key, int):
if key < 0:
key += len(self)
if key < 0 or key > len(self):
raise IndexError("List assignment index out of range")
dstart = self._items[key][0]
dstop = self._items[key][1]
istart = key
elif isinstance(key, slice):
istart, istop, step = key.indices(len(self))
if istart == istop:
return
if istart > istop:
istart, istop = istop, istart
if istart > len(self) or istop > len(self):
raise IndexError("Can only assign iterable")
dstart = self._items[istart][0]
if istart == istop:
dstop = dstart
else:
dstop = self._items[istop - 1][1]
if hasattr(data, "__len__"):
if len(data) == dstop - dstart: # or len(data) == 1:
self._data[dstart:dstop] = data
else:
self.__delitem__(key)
self.insert(istart, data)
else: # we assume len(data) = 1
if dstop - dstart == 1:
self._data[dstart:dstop] = data
else:
self.__delitem__(key)
self.insert(istart, data)
elif key is Ellipsis:
self.data[...] = data
elif isinstance(key, str):
self._data[key][:self._size] = data
else:
raise TypeError("List assignment indices must be integers")
def __delitem__(self, key):
""" x.__delitem__(y) <==> del x[y] """
if not self._sizeable:
raise AttributeError("List is not sizeable")
# Deleting a single item
if isinstance(key, int):
if key < 0:
key += len(self)
if key < 0 or key > len(self):
raise IndexError("List deletion index out of range")
istart, istop = key, key + 1
dstart, dstop = self._items[key]
# Deleting several items
elif isinstance(key, slice):
istart, istop, step = key.indices(len(self))
if istart > istop:
istart, istop = istop, istart
if istart == istop:
return
dstart = self._items[istart][0]
dstop = self._items[istop - 1][1]
elif key is Ellipsis:
istart = 0
istop = len(self)
dstart = 0
dstop = self.size
# Error
else:
raise TypeError("List deletion indices must be integers")
# Remove data
size = self._size - (dstop - dstart)
self._data[
dstart:dstart + self._size - dstop] = self._data[dstop:self._size]
self._size -= dstop - dstart
# Remove corresponding items
size = self._count - istop
self._items[istart:istart + size] = self._items[istop:istop + size]
# Update other items
size = dstop - dstart
self._items[istart:istop + size + 1] -= size, size
self._count -= istop - istart
def insert(self, index, data, itemsize=None):
""" Insert data before index
Parameters
----------
index : int
Index before which data will be inserted.
data : array_like
An array, any object exposing the array interface, an object
whose __array__ method returns an array, or any (nested) sequence.
itemsize: int or 1-D array
If `itemsize is an integer, N, the array will be divided
into elements of size N. If such partition is not possible,
an error is raised.
If `itemsize` is 1-D array, the array will be divided into
elements whose succesive sizes will be picked from itemsize.
If the sum of itemsize values is different from array size,
an error is raised.
"""
if not self._sizeable:
raise AttributeError("List is not sizeable")
if isinstance(data, (list, tuple)) and isinstance(data[0], (list, tuple)): # noqa
itemsize = [len(sublist) for sublist in data]
data = [item for sublist in data for item in sublist]
data = np.array(data, copy=False).ravel()
size = data.size
# Check item size and get item number
if itemsize is not None:
if isinstance(itemsize, int):
if (size % itemsize) != 0:
raise ValueError("Cannot partition data as requested")
_count = size // itemsize
_itemsize = np.ones(_count, dtype=int) * (size // _count)
else:
_itemsize = np.array(itemsize, copy=False)
_count = len(itemsize)
if _itemsize.sum() != size:
raise ValueError("Cannot partition data as requested")
else:
_count = 1
# Check if data array is big enough and resize it if necessary
if self._size + size >= self._data.size:
capacity = int(2 ** np.ceil(np.log2(self._size + size)))
self._data = np.resize(self._data, capacity)
# Check if item array is big enough and resize it if necessary
if self._count + _count >= len(self._items):
capacity = int(2 ** np.ceil(np.log2(self._count + _count)))
self._items = np.resize(self._items, (capacity, 2))
# Check index
if index < 0:
index += len(self)
if index < 0 or index > len(self):
raise IndexError("List insertion index out of range")
# Inserting
if index < self._count:
istart = index
dstart = self._items[istart][0]
dstop = self._items[istart][1]
# Move data
Z = self._data[dstart:self._size]
self._data[dstart + size:self._size + size] = Z
# Update moved items
items = self._items[istart:self._count] + size
self._items[istart + _count:self._count + _count] = items
# Appending
else:
dstart = self._size
istart = self._count
# Only one item (faster)
if _count == 1:
# Store data
self._data[dstart:dstart + size] = data
self._size += size
# Store data location (= item)
self._items[istart][0] = dstart
self._items[istart][1] = dstart + size
self._count += 1
# Several items
else:
# Store data
dstop = dstart + size
self._data[dstart:dstop] = data
self._size += size
# Store items
items = np.ones((_count, 2), int) * dstart
C = _itemsize.cumsum()
items[1:, 0] += C[:-1]
items[0:, 1] += C
istop = istart + _count
self._items[istart:istop] = items
self._count += _count
def append(self, data, itemsize=None):
"""
Append data to the end.
Parameters
----------
data : array_like
An array, any object exposing the array interface, an object
whose __array__ method returns an array, or any (nested) sequence.
itemsize: int or 1-D array
If `itemsize is an integer, N, the array will be divided
into elements of size N. If such partition is not possible,
an error is raised.
If `itemsize` is 1-D array, the array will be divided into
elements whose succesive sizes will be picked from itemsize.
If the sum of itemsize values is different from array size,
an error is raised.
"""
self.insert(len(self), data, itemsize)
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