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412 lines (320 loc) · 12.8 KB
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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, sizes=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.
sizes: 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 successive 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 type(data) in [list,tuple]:
if type(data[0]) in [list,tuple]:
sizes = [len(l) for l 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
_sizes = np.ones(1)*self._data.size
# Check item sizes and get items count
if sizes is not None:
if type(sizes) is int:
if (self._size % sizes) != 0:
raise ValueError("Cannot partition data as requested")
self._count = self._size//sizes
_sizes = np.ones(self._count,dtype=int)*(self._size//self._count)
else:
_sizes = np.array(sizes, copy=False)
self._count = len(sizes)
if _sizes.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 = _sizes.cumsum()
self._items[1:,0] += C[:-1]
self._items[0:,1] += C
else:
self._data = np.zeros(512, dtype=dtype)
self._items = np.zeros((64,2), 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 itemsize(self):
""" Individual item sizes """
return self._items[:self._count,1] - self._items[:self._count,0]
@property
def size(self):
""" Number of base elements, in memory. """
return self._size
@property
def dtype(self):
""" Describes the format of the elements in the buffer. """
return self._data.dtype
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 type(key) is 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 type(key) is 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 sizeable")
if type(key) is 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]
self._data[dstart:dstop] = data
elif type(key) is slice:
istart, istop, step = key.indices(len(self))
if istart > istop:
istart,istop = istop,istart
if istart == istop:
dstart = self._items[key][0]
dstop = self._items[key][1]
self._data[dstart:dstop] = data
else:
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]
self._data[dstart:dstop] = data
elif key is Ellipsis:
self.data[...] = data
elif type(key) is 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 type(key) is 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 type(key) is 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]
# Ellipsis
elif key is Ellipsis:
istart,istop = 0, len(self)
dstart, dstop = 0, self.size
# Error
else:
raise TypeError("List deletion indices must be integers")
# Remove data
size = self._size - (dstop-dstart)
self._data[dstart:dstart+size] = self._data[dstop:dstop+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, sizes=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.
sizes: 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 successive 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 RuntimeError("List is not sizeable")
if type(data) in [list,tuple] and type(data[0]) in [list,tuple]:
sizes = [len(l) for l 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 sizes is not None:
if type(sizes) is int:
if (size % sizes) != 0:
raise ValueError("Cannot partition data as requested")
_count = size//sizes
_sizes = np.ones(_count,dtype=int)*(size//_count)
else:
_sizes = np.array(sizes,copy=False)
_count = len(sizes)
if _sizes.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
self._data[dstart+size:self._size+size] = self._data[dstart:self._size]
# Update moved items
I = self._items[istart:self._count]+size
self._items[istart+_count:self._count+_count] = I
# 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 = _sizes.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, sizes=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.
sizes: 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 successive 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, sizes)
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