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Merge pull request #1335 from ellisonbg/nbtoolbar...
Merge pull request #1335 from ellisonbg/nbtoolbar Add a toolbar with icons to the notebook interface. There are for now icons for the most important actions, we will slowly consider the addition of new ones later as necessary. But this should make everyday usage much more fluid for mouse-based users. The new toolbar and the header bar have also been made collapsible, which allows users to have a very compact view with only minimal vertical space devoted to the UI and most of the screen available as usable work space.

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data.py
106 lines | 3.1 KiB | text/x-python | PythonLexer
# encoding: utf-8
"""Utilities for working with data structures like lists, dicts and tuples.
"""
#-----------------------------------------------------------------------------
# Copyright (C) 2008-2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distributed as part of this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
import types
#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------
def uniq_stable(elems):
"""uniq_stable(elems) -> list
Return from an iterable, a list of all the unique elements in the input,
but maintaining the order in which they first appear.
A naive solution to this problem which just makes a dictionary with the
elements as keys fails to respect the stability condition, since
dictionaries are unsorted by nature.
Note: All elements in the input must be valid dictionary keys for this
routine to work, as it internally uses a dictionary for efficiency
reasons."""
unique = []
unique_dict = {}
for nn in elems:
if nn not in unique_dict:
unique.append(nn)
unique_dict[nn] = None
return unique
def sort_compare(lst1, lst2, inplace=1):
"""Sort and compare two lists.
By default it does it in place, thus modifying the lists. Use inplace = 0
to avoid that (at the cost of temporary copy creation)."""
if not inplace:
lst1 = lst1[:]
lst2 = lst2[:]
lst1.sort(); lst2.sort()
return lst1 == lst2
def list2dict(lst):
"""Takes a list of (key,value) pairs and turns it into a dict."""
dic = {}
for k,v in lst: dic[k] = v
return dic
def list2dict2(lst, default=''):
"""Takes a list and turns it into a dict.
Much slower than list2dict, but more versatile. This version can take
lists with sublists of arbitrary length (including sclars)."""
dic = {}
for elem in lst:
if type(elem) in (types.ListType,types.TupleType):
size = len(elem)
if size == 0:
pass
elif size == 1:
dic[elem] = default
else:
k,v = elem[0], elem[1:]
if len(v) == 1: v = v[0]
dic[k] = v
else:
dic[elem] = default
return dic
def flatten(seq):
"""Flatten a list of lists (NOT recursive, only works for 2d lists)."""
return [x for subseq in seq for x in subseq]
def get_slice(seq, start=0, stop=None, step=1):
"""Get a slice of a sequence with variable step. Specify start,stop,step."""
if stop == None:
stop = len(seq)
item = lambda i: seq[i]
return map(item,xrange(start,stop,step))
def chop(seq, size):
"""Chop a sequence into chunks of the given size."""
chunk = lambda i: seq[i:i+size]
return map(chunk,xrange(0,len(seq),size))