Mainly Tech projects on Python and Electronic Design Automation.

Friday, April 10, 2009

Memoization and stack use.

I was caught out today when investigating how to apply memoization to
allow computation of larger values of a recursive function.



Mutual Recursion


I had thought up a new task for Rosetta Code called href="http://www.rosettacode.org/wiki/Mutual_Recursion">Mutual
Recursion, as I had remembered that some languages needed
 at least the signature of a function to be defined before it
could be called, and so creating mutually recursive functions would
allow you to compare the languages in an interesting way - which is
what R.C. is all about.



For the example for implementation I used href="http://en.wikipedia.org/wiki/Hofstadter_sequence#Hofstadter_Female_and_Male_sequences"
target="_blank">Hoffstadters Female and Male
sequences, and produced the following href="http://www.rosettacode.org/wiki/Mutual_Recursion#Python">Python
definitions:


def  color="#008080">F(n): return 1  color="#804040">if n == 0  color="#804040">else n - M(F(n-1))
color="#804040">def color="#008080">M(n): return 0 color="#804040">if n == 0 color="#804040">else n - F(M(n-1))


It seems that other members of R.C. liked the task and soon added many
examples in other languages.



Apart from the computation of the series for 0<=n<20, I
did try to compute F(200) but it blew the stack so I went to bed and
decided to memoize the functions another day.



target="_blank">Wot, no memoize?


Memoization came very easily to mind, so I thought that Python would
have such available as a decorator waiting to be applied. No such luck,
I would have to craft my own . I did remember correctly however that
there was some  helper function that made wrapped functions
look a lot more like what had been wrapped, and so used functools.wrap:


from functools  color="#a020f0">import wraps

color="#804040">def color="#008080">memoize0(func):
' color="#ff00ff"> Adds cache as attribute to wrapped function for ease of access'
color="#a020f0">@wraps(func)
color="#804040">def color="#008080">wrapper(*args):
argsl = tuple(args)
color="#804040">if argsl color="#804040">in wrapper._cache:
color="#804040">return wrapper._cache[argsl]
color="#804040">else:
color="#804040">return wrapper._cache.setdefault(argsl, func(*args))
wrapper._cache = dict()
color="#804040">return wrapper

color="#804040">def color="#008080">memoize(func):
' color="#ff00ff"> Creates closure around locally created cache'
cache = dict()
color="#a020f0">@wraps(func)
color="#804040">def color="#008080">wrapper(*args):
argsl = tuple(args)
color="#804040">if argsl color="#804040">in cache:
color="#804040">return cache[argsl]
color="#804040">else:
color="#804040">return cache.setdefault(argsl, func(*args))
color="#804040">return wrapper




I haven't got over my (probably infantile) lust for attaching
attributes to functions so justified memoize0 as it allows you to look
at the contents of the cache. Combined with the fact that you cannot do
a memoization decorator without applying it to a href="http://en.wikipedia.org/wiki/Fibonacci_function">Fibonacci
number function:


if __name__ == ' color="#ff00ff">__main__':
color="#a020f0">@memoize0
color="#804040">def color="#008080">fib(n): return n color="#804040">if n color="#804040">in (0,1) color="#804040">else fib(n - 1) + fib(n - 2)
color="#a020f0">@memoize0
color="#804040">def color="#008080">fib2(n): color="#804040">return n color="#804040">if n color="#804040">in (0,1) color="#804040">else fib2(n - 1) + fib2(n - 2)
color="#804040">assert fib._cache color="#804040">is color="#804040">not fib2._cache
fib(50); fib2(50)
color="#804040">assert fib._cache == fib2._cache color="#804040">and (fib._cache color="#804040">is color="#804040">not fib2._cache)




It is important that caches are not shared for mutually recursive
functions.

A blown stack


I had initial success with the Male and Female sequence and was easily
able to compute F(200) with:



@ color="#008080">memoize
color="#804040">def color="#008080">F(n):
' color="#ff00ff"> Hofstadters Female Male sequences'
color="#804040">return 1 color="#804040">if n == 0 color="#804040">else n - M(F(n-1))

color="#a020f0">@memoize
color="#804040">def color="#008080">M(n):
' color="#ff00ff"> Hofstadters Female Male sequences'
color="#804040">return 0 color="#804040">if n == 0 color="#804040">else n - F(M(n-1))

color="#804040">assert F(200) == 124




But I got cocky, as F(2000) ran out of stack.



A little thought made me realise that I only had a cached results for F
and M up to around 200 and so a call of F(2000) was going to eat up
huge amounts of stack before recursing down to cached values.

With that knowledge in mind I thought I might gradually jump to higher
values of  n and, indeed, the following worked:


    print ([F(n)  color="#804040">for n  color="#804040">in range(0,20000+1,100)])




In real-world applicatrions, you might have to have some scheme to
reduce or limit cache size if memory rather than the stack becomes an
issue, and I have a great solution that I have just completed in the
margin :-)



- Paddy.

Thursday, April 02, 2009

(Ab)use of Pythons in-built data structures

I like to use Pythons in-built data structures quit a lot, and tend to
force myself to ask whether I  should create my own classes,
which allows you to use meaningful names for fields and add
comments/docstrings to the datastructure but usually at the cost of
adding more lines of text.



After writing about Huffman codes and posting solutions to href="http://www.rosettacode.org/w/index.php?title=Huffman_codes&oldid=27804#Python">Rosetta
code and href="http://paddy3118.blogspot.com/2009/03/huffman-encoding-in-python.html">my
blog:


 29  color="#804040">def  color="#008080">codecreate(symbol2weights, tutor= False):
color="#804040"> 30 ''' color="#ff00ff"> Huffman encode the given dict mapping symbols to weights '''
color="#804040"> 31 global decode
color="#804040"> 32
color="#804040"> 33 heap = [ [float(wt), [[sym, []]], repr(sym)] color="#804040">for sym, wt color="#804040">in symbol2weights.iteritems() ]
color="#804040"> 34 heapify(heap)
color="#804040"> 35 if tutor: color="#804040">print " color="#ff00ff">ENCODING:", sorted(symbol2weights.iteritems())
color="#804040"> 36 while len(heap) >1:
color="#804040"> 37 lo = heappop(heap)
color="#804040"> 38 hi = heappop(heap)
color="#804040"> 39 color="#804040">if tutor: color="#804040">print " color="#ff00ff"> COMBINING:", lo, ' color="#6a5acd">\n AND:', hi
color="#804040"> 40 color="#804040">for i color="#804040">in lo[1]: i[1].insert(0, ' color="#ff00ff">0')
color="#804040"> 41 color="#804040">for i color="#804040">in hi[1]: i[1].insert(0, ' color="#ff00ff">1')
color="#804040"> 42 lohi = [ lo[0] + hi[0] ] + [lo[1] + hi[1]]
color="#804040"> 43 lohi.append(' color="#ff00ff">(%s if nextbit() else %s)' % (hi[2], lo[2]))
color="#804040"> 44 color="#804040">if tutor: color="#804040">print " color="#ff00ff"> PRODUCING:", lohi, ' color="#6a5acd">\n'
color="#804040"> 45 heappush(heap, lohi)
color="#804040"> 46 wt, codes, decoder = heappop(heap)
color="#804040"> 47 decode = eval(' color="#ff00ff">lambda :' + decoder, globals())
color="#804040"> 48 decode.__doc__ = decoder
color="#804040"> 49 for i color="#804040">in codes: i[1] = ''.join(i[1])
color="#804040"> 50 #for i in codes: i[::] = i[:2]
color="#804040"> 51 return sorted(codes, key= color="#804040">lambda x: (len(x[-1]), x))
color="#804040"> 52


Someone posted a href="http://www.rosettacode.org/w/index.php?title=Huffman_codes&oldid=27804#Java">
Java solution, that was over three times as long. Instead of
just counting and comparing line counts,  I took a closer
look to see what the extra code was for.


import java.util.*;

color="#2e8b57">abstract color="#2e8b57">class HuffmanTree color="#2e8b57">implements Comparable<HuffmanTree> {
color="#2e8b57">public color="#2e8b57">int frequency; color="#0000ff">// the frequency of this tree
color="#2e8b57">public HuffmanTree( color="#2e8b57">int freq) { frequency = freq; }

color="#0000ff">// compares on the frequency
color="#2e8b57">public color="#2e8b57">int compareTo(HuffmanTree tree) {
color="#804040">return frequency - tree.frequency;
}
}

color="#2e8b57">class HuffmanLeaf color="#2e8b57">extends HuffmanTree {
color="#2e8b57">public color="#2e8b57">char value; color="#0000ff">// the character this leaf represents

color="#2e8b57">public HuffmanLeaf( color="#2e8b57">int freq, color="#2e8b57">char val) {
color="#2e8b57">super(freq);
value = val;
}
}

color="#2e8b57">class HuffmanNode color="#2e8b57">extends HuffmanTree {
color="#2e8b57">public HuffmanTree left, right; color="#0000ff">// subtrees

color="#2e8b57">public HuffmanNode(HuffmanTree l, HuffmanTree r) {
color="#2e8b57">super(l.frequency + r.frequency);
left = l;
right = r;
}
}

color="#2e8b57">public color="#2e8b57">class HuffmanCode {
color="#0000ff">// input is an array of frequencies, indexed by character code
color="#2e8b57">public color="#2e8b57">static HuffmanTree buildTree( color="#2e8b57">int[] charFreqs) {
PriorityQueue<HuffmanTree> trees = color="#804040">new PriorityQueue<HuffmanTree>();
color="#0000ff">// initially, we have a forest of leaves
color="#0000ff">// one for each non-empty character
color="#804040">for ( color="#2e8b57">int i = color="#ff00ff">0; i < charFreqs.length; i++)
color="#804040">if (charFreqs[i] > color="#ff00ff">0)
trees.offer( color="#804040">new HuffmanLeaf(charFreqs[i], ( color="#2e8b57">char)i));

color="#804040">assert trees.size() > color="#ff00ff">0;
color="#0000ff">// loop until there is only one tree left
color="#804040">while (trees.size() > color="#ff00ff">1) {
color="#0000ff">// two trees with least frequency
HuffmanTree a = trees.poll();
HuffmanTree b = trees.poll();

color="#0000ff">// put into new node and re-insert into queue
trees.offer( color="#804040">new HuffmanNode(a, b));
}
color="#804040">return trees.poll();
}

color="#2e8b57">public color="#2e8b57">static color="#2e8b57">void printCodes(HuffmanTree tree, Stack<Character> prefix) {
color="#804040">assert tree != color="#ff00ff">null;
color="#804040">if (tree color="#804040">instanceof HuffmanLeaf) {
HuffmanLeaf leaf = (HuffmanLeaf)tree;

color="#0000ff">// print out character and frequency
System.out.print(leaf.value + color="#ff00ff">"\t color="#ff00ff">" + leaf.frequency + color="#ff00ff">"\t color="#ff00ff">");

color="#0000ff">// print out code for this leaf, which is just the prefix
color="#804040">for ( color="#2e8b57">char bit : prefix)
System.out.print(bit);
System.out.println();

} color="#804040">else color="#804040">if (tree color="#804040">instanceof HuffmanNode) {
HuffmanNode node = (HuffmanNode)tree;

color="#0000ff">// traverse left
prefix.push( color="#ff00ff">'0');
printCodes(node.left, prefix);
prefix.pop();

color="#0000ff">// traverse right
prefix.push( color="#ff00ff">'1');
printCodes(node.right, prefix);
prefix.pop();
}
}

color="#2e8b57">public color="#2e8b57">static color="#2e8b57">void main(String[] args) {
String test = color="#ff00ff">"this is an example for huffman encoding";

color="#0000ff">// we will assume that all our characters will have
color="#0000ff">// code less than 256, for simplicity
color="#2e8b57">int[] charFreqs = color="#804040">new color="#2e8b57">int[ color="#ff00ff">256];
color="#0000ff">// read each character and record the frequencies
color="#804040">for ( color="#2e8b57">char c : test.toCharArray())
charFreqs[c]++;

color="#0000ff">// build tree
HuffmanTree tree = buildTree(charFreqs);

color="#0000ff">// print out results
System.out.println( color="#ff00ff">"SYMBOL\t color="#ff00ff">WEIGHT\t color="#ff00ff">HUFFMAN CODE");
printCodes(tree, color="#804040">new Stack<Character>());
}
}




I
noticed that the Java was tidy, and that they had defined their own
classes for a HuffmanLeaf structure used when constructing a
HuffmanTree.



In my original Python, I used a nested list as
the equivalent to the HuffmanLeaf of the Java example. It worked, but I
had to introduce 'magic constants' to access the fields of the Python
leaf , which is also un-named as a structure in the program (lline 33
of
the Python code creates a list of Leaf structures):


33  heap = [ [float(wt), [sym, []]]  color="#804040">for sym, wt  color="#804040">in symbol2weights.iteritems() ]




Now I didn't want to go to the trouble of creating my own
classes, but that's were the new href="http://docs.python.org/dev/py3k/library/collections.html#collections.namedtuple">namedtuple
class factory of Python 2.6 came to the rescue.

namedtuple


namedtuple
will generate a subtype of the tuple class that allows the fields of
the tuple to be named and accessed via subscription, [], or as if the
fields are instance variable names.



I modified my Python program to use two named tuples:


  1. For the Leaf structure as a whole in line 3.

  2. For a component of the Leaf structure that holds the symbol
    and the Huffman code (accumulated so far), in line 4.



 1  color="#a020f0">from collections  color="#a020f0">import namedtuple
color="#804040"> 2
color="#804040"> 3 Leaf = namedtuple(' color="#ff00ff">Leaf', 'weight, symbols')
color="#804040"> 4 SH = namedtuple(' color="#ff00ff">SH', 'sym, huff')
color="#804040"> 5
color="#804040"> 6 def color="#008080">codecreate2(symbol2weights, tutor= False):
color="#804040"> 7 ''' color="#ff00ff"> Huffman codecreate2 the given dict mapping symbols to weights '''
color="#804040"> 8 heap = [ Leaf(weight=float(wt), symbols=[ SH(sym, []) ])
color="#804040"> 9 color="#804040">for sym, wt color="#804040">in symbol2weights.iteritems() ]
color="#804040">10 heapify(heap)
color="#804040">11 if tutor: color="#804040">print " color="#ff00ff">ENCODING:", sorted(symbol2weights.iteritems())
color="#804040">12 while len(heap) >1:
color="#804040">13 lo = heappop(heap)
color="#804040">14 hi = heappop(heap)
color="#804040">15 color="#804040">if tutor: color="#804040">print " color="#ff00ff"> COMBINING:", lo, ' color="#6a5acd">\n AND:', hi
color="#804040">16 color="#804040">for sh color="#804040">in lo.symbols: sh.huff.insert(0, ' color="#ff00ff">0')
color="#804040">17 color="#804040">for sh color="#804040">in hi.symbols: sh.huff.insert(0, ' color="#ff00ff">1')
color="#804040">18 lohi = Leaf(weight = lo.weight + hi.weight,
color="#804040">19 symbols = lo.symbols + hi.symbols)
color="#804040">20 color="#804040">if tutor: color="#804040">print " color="#ff00ff"> PRODUCING:", lohi, ' color="#6a5acd">\n'
color="#804040">21 heappush(heap, lohi)
color="#804040">22 symbols = heappop(heap).symbols
color="#804040">23 symbols = [SH(sym, ''.join(huff)) color="#804040">for sym, huff color="#804040">in symbols]
color="#804040">24 return sorted(symbols, key= color="#804040">lambda sh: (len(sh.huff), sh))
color="#804040">25




The
class generation is succinct in lines 3 and 4, and I use the field
names for clarity for example when creating the original list of Leaves
that will form the heap in line 8

Printing namedtuples


The print statements stay the same, but before you got output like this:

style="font-family: monospace;"> style="font-family: monospace;">...

  COMBINING: [2.5, ['C', []]] style="font-family: monospace;">
       
AND: [5.0, ['A', []]]


 
PRODUCING: [7.5, ['C', ['0']], ['A', ['1']]]
style="font-family: monospace;">
...



Now you get the clearer:

style="font-family: monospace;"> ENCODING:
[('A', '5'), ('B', '25'), ('C', '2.5'), ('D', '12.5')]

  COMBINING: Leaf(weight=2.5, symbols=[SH(sym='C', huff=[])])

       
AND: Leaf(weight=5.0, symbols=[SH(sym='A', huff=[])])

  PRODUCING: Leaf(weight=7.5, symbols=[SH(sym='C',
huff=['0']), SH(sym='A', huff=['1'])])



  COMBINING: Leaf(weight=7.5, symbols=[SH(sym='C',
huff=['0']), SH(sym='A', huff=['1'])])

       
AND: Leaf(weight=12.5, symbols=[SH(sym='D', huff=[])])

 
PRODUCING: Leaf(weight=20.0, symbols=[SH(sym='C', huff=['0', '0']),
SH(sym='A', huff=['0', '1']), SH(sym='D', huff=['1'])])



 
COMBINING: Leaf(weight=20.0, symbols=[SH(sym='C', huff=['0', '0']),
SH(sym='A', huff=['0', '1']), SH(sym='D', huff=['1'])])

       
AND: Leaf(weight=25.0, symbols=[SH(sym='B', huff=[])])

 
PRODUCING: Leaf(weight=45.0, symbols=[SH(sym='C', huff=['0', '0',
'0']), SH(sym='A', huff=['0', '0', '1']), SH(sym='D', huff=['0', '1']),
SH(sym='B', huff=['1'])])



Conclusion


 Although
you can do so much with Python lists, if each position in the list has
a fixed meaning then you might be best to use tuples, and if you should
be using tuples, then namedtuples can make your code more readable
without bloating it with long class definitions.



- Paddy.

Saturday, March 28, 2009

Huffman Encoding in Python



Huffman encoding came up on Rosetta Code.
Huffman encoding is a way to assign binary codes to symbols that reduces the overall number of bits used to encode a typical string of of those symbols.
For example, if you use letters as symbols and have details of the frequency of occurence of those letters in typical strings, then you could just encode each letter with a fixed number of bits, such as in ASCII codes. You can do better than this by encoding more frequently occurring letters such as e and a, with smaller bit strings; and less frequently occurring letters such as q and x with longer bit strings.
Any string of letters will be encoded as a string of bits that are no-longer of the same length per letter. To successfully decode such as string, the smaller codes assigned to letters such as 'e' cannot occur as a prefix in the larger codes such as that for 'x'.
If you were to assign a code 01 for 'e' and code 011 for 'x', then if the bits to decode started as 011... then you would not know iif you should decode an 'e' or an 'x'.
The Huffman coding scheme takes each symbol and its weight (or frequency of occurrence), and generates proper encodings for each symbol taking account of the weights of each symbol, so that higher weighted symbols have less bits in their encoding. (See the WP article for more information).
A Huffman encoding can be computed by first creating a tree of nodes:
  1. Create a leaf node for each symbol and add it to the priority queue.
  2. While there is more than one node in the queue:
    1. Remove the node of highest priority (lowest probability) twice to get two nodes.
    2. Create a new internal node with these two nodes as children and with probability equal to the sum of the two nodes' probabilities.
    3. Add the new node to the queue.
  3. The remaining node is the root node and the tree is complete.
Traverse the constructed binary tree from root to leaves assigning and accumulating a '0' for one branch and a '1' for the other at each node. The accumulated zeroes and ones at each leaf constitute a Huffman encoding for those symbols and weights.

In Python

I orginally gave an an example that matched a definition that was later found to be insufficient, so substituted my own definition above.. My first Python solution  on RC to the wrong definition, did have the advantage, (as I saw it), of not having to traverse a tree.
My 'true' Huffman code creator assembles each symbol and its weight into the following structure initially (the leaf structure):
[ weight, [ symbol, []]]
The weight applies to every (in this case only one), of the [symbol, []] pairs after it in the same list.
The empty list is used to accumulate the Huffman code for the symbol as we manipulate the heap, without having to walk a constructed tree structure.

There are two types of input to the program that I am running examples with:

  1. A string of space separated symbol, weight pairs, as used in small examples.
  2. A sample of text for which letters and letter frequencies are extracted.
The if statement at line 23  allows me to switch between the two types of input whilst exploring the algorithm.
The tutor argument to the encode function shows what is happening in the loop around the heap pops
 1 
 2 from heapq import heappush, heappop, heapify
 3 
 4 def codecreate(symbol2weights, tutor= False):
 5     ''' Huffman encode the given dict mapping symbols to weights '''
 6     heap = [ [float(wt), [sym, []]] for sym, wt in symbol2weights.iteritems() ]
 7     heapify(heap)
 8     if tutor: print "ENCODING:", sorted(symbol2weights.iteritems())
 9     while len(heap) >1:
10         lo = heappop(heap)
11         hi = heappop(heap)
12         if tutor: print "  COMBINING:", lo, '\n        AND:', hi
13         for i in lo[1:]: i[1].insert(0, '0')
14         for i in hi[1:]: i[1].insert(0, '1')
15         lohi = [ lo[0] + hi[0] ] + lo[1:] + hi[1:]
16         if tutor: print "  PRODUCING:", lohi, '\n'
17         heappush(heap, lohi)
18     codes = heappop(heap)[1:]
19     for i in codes: i[1] = ''.join(i[1])
20     return sorted(codes, key=lambda x: (len(x[-1]), x))
21 
22 # Input types
23 if 1:
24     readin = "B 25   C 2.5 D  12.5 A 5 \n"
25     #readin = "a .1 b .15 c .3 d .16 e .29" # Wikipedia sample
26     #readin = "a1 .4 a2 .35 a3 .2 a4 .05" # Wikipedia sample
27     #readin = "A 50 B 25 C 12.5 D 12.5" # RC example
28 
29     cleaned = readin.strip().split()
30     symbol2weights = dict((symbol, wt)
31                          for symbol, wt in zip(cleaned[0::2], cleaned[1::2]) )
32 else:
33     astring = "this is an example for huffman encoding"
34     symbol2weights = dict((ch, astring.count(ch)) for ch in set(astring)) # for astring
35 
36 huff = codecreate(symbol2weights, True)
37 print "\nSYMBOL\tWEIGHT\tHUFFMAN CODE"
38 for h in huff:
39     print "%s\t%s\t%s" % (h[0], symbol2weights[h[0]], h[1])

A run, with the tutor enabled gives the following output:
ENCODING: [('A', '5'), ('B', '25'), ('C', '2.5'), ('D', '12.5')]
  COMBINING: [2.5, ['C', []]]
        AND: [5.0, ['A', []]]
  PRODUCING: [7.5, ['C', ['0']], ['A', ['1']]]

  COMBINING: [7.5, ['C', ['0']], ['A', ['1']]]
        AND: [12.5, ['D', []]]
  PRODUCING: [20.0, ['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]]

  COMBINING: [20.0, ['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]]
        AND: [25.0, ['B', []]]
  PRODUCING: [45.0, ['C', ['0', '0', '0']], ['A', ['0', '0', '1']], ['D', ['0', '1']], ['B', ['1']]]


SYMBOL    WEIGHT    HUFFMAN CODE
B         25        1
D         12.5      01
A         5         001
C         2.5       000


Encode/Decode Round-tripping

I realised that I could use a method similar to how I accumulate the codes in the heap loop, to generate a single function that can recognise a single symbol from the beginning of the encoded symbols. By using the function in a loop, I could regenerate the symbol list.

In the codecreate function, the leaf structure is modified:
[weight, [ [symbol, []] ], repr(sym)]
Their is an extra level of list around the [symbol, code accumulation list pair]  as well as a new item: 'repr(sym)' it is the third item in the outer list and will always be the  function to generate the item as accumulated so far.. This function starts off by returning just the symbol and an outer if/then/else expression is added as we go around the heap loop (see line 43)

After the outer expression is accumulated, it is turned into a lambda expression, the string eval'd, and assigned to a global variable (see line 47)

I use function probchoice (from earlier RC work), to create an arbitrary sequence of symbols to in the given weighting then encode and decode it as well as giving some stats on space saving.

  1 
  2 from heapq import heappush, heappop, heapify
  3 import random, bisect
  4 
  5 
  6 # Helper routine for generating test sequences
  7 def probchoice(items, probs):
  8   '''\
  9   Splits the interval 0.0-1.0 in proportion to probs
 10   then finds where each random.random() choice lies
 11   (This routine, probchoice, was released under the
 12   GNU Free Documentation License 1.2)
 13   '''
 14 
 15   prob_accumulator = 0
 16   accumulator = []
 17   for p in probs:
 18     prob_accumulator += p
 19     accumulator.append(prob_accumulator)
 20 
 21   while True:
 22     r = random.random()
 23     yield items[bisect.bisect(accumulator, r)]
 24 
 25 
 26 # placeholder
 27 decode = lambda : None
 28 
 29 def codecreate(symbol2weights, tutor= False):
 30     ''' Huffman encode the given dict mapping symbols to weights '''
 31     global decode
 32 
 33     heap = [ [float(wt), [[sym, []]], repr(sym)] for sym, wt in symbol2weights.iteritems() ]
 34     heapify(heap)
 35     if tutor: print "ENCODING:", sorted(symbol2weights.iteritems())
 36     while len(heap) >1:
 37         lo = heappop(heap)
 38         hi = heappop(heap)
 39         if tutor: print "  COMBINING:", lo, '\n        AND:', hi
 40         for i in lo[1]: i[1].insert(0, '0')
 41         for i in hi[1]: i[1].insert(0, '1')
 42         lohi = [ lo[0] + hi[0] ] + [lo[1] + hi[1]]
 43         lohi.append('(%s if nextbit() else %s)' % (hi[2], lo[2]))
 44         if tutor: print "  PRODUCING:", lohi, '\n'
 45         heappush(heap, lohi)
 46     wt, codes, decoder = heappop(heap)
 47     decode = eval('lambda :' + decoder, globals())
 48     decode.__doc__ = decoder
 49     for i in codes: i[1] = ''.join(i[1])
 50     #for i in codes: i[::] = i[:2]
 51     return sorted(codes, key=lambda x: (len(x[-1]), x))
 52 
 53 # Input types
 54 if 1:
 55     tutor = True
 56     sequencecount = 50
 57     readin = "B 25   C 2.5 D  12.5 A 5 \n"
 58     #readin = "a .1 b .15 c .3 d .16 e .29" # Wikipedia sample
 59     #readin = "a1 .4 a2 .35 a3 .2 a4 .05" # Wikipedia sample
 60     #readin = "A 50 B 25 C 12.5 D 12.5" # RC example
 61 
 62     cleaned = readin.strip().split()
 63     symbol2weights = dict((symbol, wt)
 64                          for symbol, wt in zip(cleaned[0::2], cleaned[1::2]) )
 65 else:
 66     tutor = False
 67     sequencecount = 500
 68     astring = "this is an example for huffman encoding"
 69     symbol2weights = dict((ch, astring.count(ch)) for ch in set(astring)) # for astring
 70 
 71 huff = codecreate(symbol2weights, tutor= tutor)
 72 print "\nSYMBOL\tWEIGHT\tHUFFMAN CODE"
 73 for h in huff:
 74     print "%s\t%s\t%s" % (h[0], symbol2weights[h[0]], h[1])
 75 
 76 ##
 77 ## encode-decode check
 78 ##
 79 symbol2code = dict(huff)
 80 symbols, weights = zip(*symbol2weights.iteritems())
 81 # normalize weights
 82 weights = [float(wt) for wt in weights]
 83 tot = sum(weights)
 84 weights = [wt/tot for wt in weights]
 85 # Generate a sequence
 86 nxt = probchoice(symbols, weights).next
 87 symbolsequence = [nxt() for i in range(sequencecount)]
 88 # encode it
 89 bitsequence = ''.join(symbol2code[sym] for sym in symbolsequence)
 90 
 91 sslen, slen, blen = len(symbolsequence), len(symbols), len(bitsequence)
 92 countlen = len(bin(slen-1)[2:])
 93 print '''
 94 
 95 
 96 ROUND-TRIPPING
 97 ==============
 98 I have generated a random sequence of %i symbols to the given weights.
 99 If I use a binary count to encode each of the %i symbols I would need
100 %i * %i = %i bits to encode the sequence.
101 Using the Huffman code, I need only %i bits.
102 ''' % (sslen, slen, sslen, countlen, sslen * countlen, blen )
103 
104 ## decoding
105 nextbit = (bit=='1' for bit in bitsequence).next
106 
107 decoded = []
108 try:
109     while 1:
110         decoded.append(decode())
111 except StopIteration:
112     pass
113 
114 print "Comparing the decoded sequence with the original I get:", decoded == symbolsequence

This short run is in tutor mode, so you can track the accumulation of the decode function:
ENCODING: [('A', '5'), ('B', '25'), ('C', '2.5'), ('D', '12.5')]
  COMBINING: [2.5, [['C', []]], "'C'"]
        AND: [5.0, [['A', []]], "'A'"]
  PRODUCING: [7.5, [['C', ['0']], ['A', ['1']]], "('A' if nextbit() else 'C')"]

  COMBINING: [7.5, [['C', ['0']], ['A', ['1']]], "('A' if nextbit() else 'C')"]
        AND: [12.5, [['D', []]], "'D'"]
  PRODUCING: [20.0, [['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]], "('D' if nextbit() else ('A' if nextbit() else 'C'))"]

  COMBINING: [20.0, [['C', ['0', '0']], ['A', ['0', '1']], ['D', ['1']]], "('D' if nextbit() else ('A' if nextbit() else 'C'))"]
        AND: [25.0, [['B', []]], "'B'"]
  PRODUCING: [45.0, [['C', ['0', '0', '0']], ['A', ['0', '0', '1']], ['D', ['0', '1']], ['B', ['1']]], "('B' if nextbit() else ('D' if nextbit() else ('A' if nextbit() else 'C')))"]


SYMBOL    WEIGHT    HUFFMAN CODE
B         25        1
D         12.5      01
A         5         001
C         2.5       000



ROUND-TRIPPING
==============
I have generated a random sequence of 50 symbols to the given weights.
If I use a binary count to encode each of the 4 symbols I would need
50 * 2 = 100 bits to encode the sequence.
Using the Huffman code, I need only 90 bits.

Comparing the decoded sequence with the original I get: True

And if I change line 54 to be False, I get the following:
SYMBOL    WEIGHT    HUFFMAN CODE
     6    101
n    4    010
a    3    1001
e    3    1100
f    3    1101
h    2    0001
i    3    1110
m    2    0010
o    2    0011
s    2    0111
g    1    00000
l    1    00001
p    1    01100
r    1    01101
t    1    10000
u    1    10001
x    1    11110
c    1    111110
d    1    111111



ROUND-TRIPPING
==============
I have generated a random sequence of 500 symbols to the given weights.
If I use a binary count to encode each of the 19 symbols I would need
500 * 5 = 2500 bits to encode the sequence.
Using the Huffman code, I need only 2012 bits.

Comparing the decoded sequence with the original I get: True


Note: The purpose of the program is to teach me more about Huffman coding and is not an exercise in speed!

I am the author of all the Python on this page. The diagram is from Wikipedia. Please refrain from passing-off my code as your own (that one is mainly for students).

Saturday, March 21, 2009

Batch Process Runner in bash shell - Forgotten Enhancements

A comment on an href="http://paddy3118.blogspot.com/2007/12/batch-process-runner-in-bash-shell.html">earlier
post caused me to dig out this enhancement to my original
batch process runner.



What you do is create a file of one-liner commands that you would enter
at a command prompt, for example, this is file style="font-weight: bold;">process_list.txt :


1  color="#0000ff"># (Comments have '#' at the left margin)
color="#804040">2 sleep 4; echo slept for 4 at line 2; csdf_sdf_sd; exit 19
color="#804040">3 sleep 1; cause-an-error; echo slept for 1 at line 3
color="#804040">4 sleep 3; echo color="#ff00ff">'slept for 3 at line 4'
color="#804040">5 sleep 7; echo color="#ff00ff">"slept for 7 at line 5"
color="#804040">6 # Whee!
color="#804040">7 sleep 9; another-erro; echo slept for 9 at line 7
color="#804040">8 sleep 5; echo color="#ff00ff">'slept "for 5" at line 8'


A bit of warning, bash returns the exit code of this laast command it
executes, earlier exit codes won't be seen.



Give my script the number of processes to run in parallel, N, followed
by the above file, and it will create N '.job'  files out of
the non-comment lines of process_list.txt and execute them as
background jobs, with their output sent to '.n' files.:


bash$ ./process_list_runner.sh 2 process_list.txt

## STARTING 6 Processes from file: process_list.txt, 2 at a time with id plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP

# 2 Jobs in background. 6/6 started. 2009-03-21-05:36:36

## FINISHED, (stats in plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.csv)

bash$ style="font-weight: bold;">ls -1 plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.*
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.1
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.2
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.3
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.4
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.5
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.6
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.csv
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.job_0
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP.job_1
bash$




The script does what it can to create a csv file of run stats, which,
with a bit of tidying up in calc produces the following:




frame="void" rules="none">
width="340"> width="110"> width="115"> width="73">























































































































width="1409">##
STARTING 6 Processes from file: process_list.txt
size="4">2 at a time with id
plr_HPDV8025EA_2009-03-21-05_36_19_PADDYS-HPLAPTOP
























align="center" bgcolor="#ccffff" height="123"
valign="middle">JOB
align="right" bgcolor="#ccffff" valign="middle">LINE align="center" bgcolor="#ccffff" valign="middle">Command
being timed
align="center" bgcolor="#ccffff" valign="middle">User
time (seconds)
align="center" bgcolor="#ccffff" valign="middle">System
time (seconds)
align="center" bgcolor="#ccffff" valign="middle">Percent
of CPU this job got
align="center" bgcolor="#ccffff" valign="middle">Elapsed
(wall clock) time (h:mm:ss or m:ss)
align="center" bgcolor="#ccffff" valign="middle">Maximum
resident set size (kbytes)
align="center" bgcolor="#ccffff" valign="middle">Major
(requiring I/O) page faults
align="center" bgcolor="#ccffff" valign="middle">Signals
delivered
align="center" bgcolor="#ccffff" valign="middle">Page
size (bytes)
align="center" bgcolor="#ccffff" valign="middle">Exit
status
sdnum="2057;" align="center" bgcolor="#ffffcc"
height="18">1
sdnum="2057;" align="center" bgcolor="#ffffcc">2 bgcolor="#ffffcc">sleep 4; echo slept for 4 at line 2;
csdf_sdf_sd; exit 19
sdnum="2057;" align="center" bgcolor="#ffffcc">0.13 sdnum="2057;" align="center" bgcolor="#ffffcc">0.15 sdnum="2057;0;0.00%" align="center" bgcolor="#ffffcc">6.00% sdval="0.0000503472222222222" sdnum="2057;0;MM:SS.00"
align="center" bgcolor="#ffffcc">00:04.35
sdval="760832" sdnum="2057;" align="center"
bgcolor="#ffffcc">760832
sdnum="2057;" align="center" bgcolor="#ffffcc">3277 sdnum="2057;" align="center" bgcolor="#ffffcc">6 sdval="65536" sdnum="2057;" align="center"
bgcolor="#ffffcc">65536
sdnum="2057;" align="center" bgcolor="#ffffcc">19
sdnum="2057;" align="center" bgcolor="#ccccff"
height="17">2
sdnum="2057;" align="center" bgcolor="#ccccff">3 bgcolor="#ccccff">sleep 1; cause-an-error; echo slept for 1
at line 3
sdnum="2057;" align="center" bgcolor="#ccccff">0.12 sdnum="2057;" align="center" bgcolor="#ccccff">0.09 sdnum="2057;0;0.00%" align="center" bgcolor="#ccccff">16.00% sdval="0.0000152777777777778" sdnum="2057;0;MM:SS.00"
align="center" bgcolor="#ccccff">00:01.32
sdval="761088" sdnum="2057;" align="center"
bgcolor="#ccccff">761088
sdnum="2057;" align="center" bgcolor="#ccccff">3273 sdnum="2057;" align="center" bgcolor="#ccccff">6 sdval="65536" sdnum="2057;" align="center"
bgcolor="#ccccff">65536
sdnum="2057;" align="center" bgcolor="#ccccff">0
sdnum="2057;" align="center" bgcolor="#ffffcc"
height="17">3
sdnum="2057;" align="center" bgcolor="#ffffcc">4 bgcolor="#ffffcc">sleep 3; echo 'slept for 3 at line 4' sdnum="2057;" align="center" bgcolor="#ffffcc">0.07 sdnum="2057;" align="center" bgcolor="#ffffcc">0.06 sdnum="2057;0;0.00%" align="center" bgcolor="#ffffcc">4.00% sdval="0.0000377314814814815" sdnum="2057;0;MM:SS.00"
align="center" bgcolor="#ffffcc">00:03.26
sdval="617216" sdnum="2057;" align="center"
bgcolor="#ffffcc">617216
sdnum="2057;" align="center" bgcolor="#ffffcc">2639 sdnum="2057;" align="center" bgcolor="#ffffcc">3 sdval="65536" sdnum="2057;" align="center"
bgcolor="#ffffcc">65536
sdnum="2057;" align="center" bgcolor="#ffffcc">0
sdnum="2057;" align="center" bgcolor="#ccccff"
height="17">4
sdnum="2057;" align="center" bgcolor="#ccccff">5 bgcolor="#ccccff">sleep 7; echo "slept for 7 at line 5" sdnum="2057;" align="center" bgcolor="#ccccff">0.07 sdnum="2057;" align="center" bgcolor="#ccccff">0.07 sdnum="2057;0;0.00%" align="center" bgcolor="#ccccff">2.00% sdval="0.000084837962962963" sdnum="2057;0;MM:SS.00"
align="center" bgcolor="#ccccff">00:07.33
sdval="617216" sdnum="2057;" align="center"
bgcolor="#ccccff">617216
sdnum="2057;" align="center" bgcolor="#ccccff">2644 sdnum="2057;" align="center" bgcolor="#ccccff">3 sdval="65536" sdnum="2057;" align="center"
bgcolor="#ccccff">65536
sdnum="2057;" align="center" bgcolor="#ccccff">0
sdnum="2057;" align="center" bgcolor="#ffffcc"
height="18">5
sdnum="2057;" align="center" bgcolor="#ffffcc">7 bgcolor="#ffffcc">sleep 9; another-erro; echo slept for 9 at
line 7
sdnum="2057;" align="center" bgcolor="#ffffcc">0.09 sdnum="2057;" align="center" bgcolor="#ffffcc">0.13 sdnum="2057;0;0.00%" align="center" bgcolor="#ffffcc">2.00% sdval="0.000106597222222222" sdnum="2057;0;MM:SS.00"
align="center" bgcolor="#ffffcc">00:09.21
sdval="760064" sdnum="2057;" align="center"
bgcolor="#ffffcc">760064
sdnum="2057;" align="center" bgcolor="#ffffcc">3274 sdnum="2057;" align="center" bgcolor="#ffffcc">6 sdval="65536" sdnum="2057;" align="center"
bgcolor="#ffffcc">65536
sdnum="2057;" align="center" bgcolor="#ffffcc">0
sdnum="2057;" align="center" bgcolor="#ccccff"
height="17">6
sdnum="2057;" align="center" bgcolor="#ccccff">8 bgcolor="#ccccff">sleep 5; echo 'slept "for 5" at line 8' sdnum="2057;" align="center" bgcolor="#ccccff">0.1 sdnum="2057;" align="center" bgcolor="#ccccff">0.06 sdnum="2057;" align="center" bgcolor="#ccccff">0.03 sdval="0.0000622685185185185" sdnum="2057;0;MM:SS.00"
align="center" bgcolor="#ccccff">00:05.38
sdval="617216" sdnum="2057;" align="center"
bgcolor="#ccccff">617216
sdnum="2057;" align="center" bgcolor="#ccccff">2639 sdnum="2057;" align="center" bgcolor="#ccccff">3 sdval="65536" sdnum="2057;" align="center"
bgcolor="#ccccff">65536
sdnum="2057;" align="center" bgcolor="#ccccff">0




The script itself is:


 1  color="#0000ff">#!/bin/bash 
color="#804040"> 2 #!/opt/TWWfsw/bin/bash -
color="#804040"> 3
color="#804040"> 4 set color="#008080">-u
color="#804040"> 5
color="#804040"> 6 ##
color="#804040"> 7 ## process_runner.sh <concurrent> <total procs>
color="#804040"> 8 ##
color="#804040"> 9 ## Example script given the maximum number of processes to
color="#804040"> 10 ## run concurrently, c, and the total number of processes, n
color="#804040"> 11 ## runs at most c, processes in the background until all n
color="#804040"> 12 ## Have been run.
color="#804040"> 13 ##
color="#804040"> 14 ## Author Donald 'Paddy' McCarthy Dec. 17 2007
color="#804040"> 15 ##
color="#804040"> 16
color="#804040"> 17 # how many processes to run in parallel
color="#804040"> 18 concurrent= color="#a020f0">$1
color="#804040"> 19 # File of commands
color="#804040"> 20 proclist= color="#804040">" color="#a020f0">$2"
color="#804040"> 21
color="#804040"> 22 ##
color="#804040"> 23 ##
color="#804040"> 24
color="#804040"> 25 # main loop wait time between checking background procs.
color="#804040"> 26 tick= color="#ff00ff">1
color="#804040"> 27 # Unique_id for process files of this run
color="#804040"> 28 unique_id=plr_ color="#6a5acd">`date +${ color="#a020f0">USER// color="#804040">/ color="#a020f0">}_%F-%T_ color="#a020f0">${HOSTNAME color="#804040">// color="#804040">/ color="#a020f0">}`
color="#804040"> 29 unique_id= color="#a020f0">${unique_id color="#804040">//: color="#804040">/_ color="#a020f0">}
color="#804040"> 30
color="#804040"> 31 function read_proclistfile color="#6a5acd">{
color="#804040"> 32 # read processes from file ignoring comment lines
color="#804040"> 33 maxprocs= color="#ff00ff">0
color="#804040"> 34 local -i color="#008080">linenumber= color="#ff00ff">0
color="#804040"> 35 while color="#804040"> color="#804040">read color="#804040">; color="#804040"> color="#804040">do
color="#804040"> 36 (( color="#008080">linenumber+= color="#ff00ff">1))
color="#804040"> 37 # echo [ "${REPLY:0:1}" == "#" ] $maxprocs $REPLY
color="#804040"> 38 if color="#804040">[ color="#804040">" color="#a020f0">${REPLY color="#804040">: color="#ff00ff">0: color="#ff00ff">1} color="#804040">" color="#804040">!= color="#804040">" color="#ff00ff">#" color="#804040">] color="#804040">; color="#804040">then
color="#804040"> 39 ((maxprocs+ color="#804040">= color="#ff00ff">1))
color="#804040"> 40 allprocs color="#804040">[ color="#a020f0">$maxprocs color="#804040">] color="#804040">= color="#804040">" color="#a020f0">$REPLY"
color="#804040"> 41 allprocline color="#804040">[ color="#a020f0">$maxprocs color="#804040">] color="#804040">= color="#a020f0">$linenumber
color="#804040"> 42 # echo $linenumber $maxprocs $REPLY
color="#804040"> 43 fi
color="#804040"> 44 done color="#804040">< color="#a020f0">$proclist
color="#804040"> 45 }
color="#804040"> 46
color="#804040"> 47 read_proclistfile
color="#804040"> 48
color="#804040"> 49 printf color="#804040">" color="#6a5acd">\n## STARTING %i Processes from file: %s, %i at a time with id %s color="#6a5acd">\n\n" \
color="#804040"> 50 $maxprocs color="#a020f0">$proclist color="#a020f0">$concurrent color="#a020f0">$unique_id
color="#804040"> 51
color="#804040"> 52
color="#804040"> 53
color="#804040"> 54 function assemble_job color="#6a5acd">{
color="#804040"> 55 #
color="#804040"> 56 local color="#008080">plr_unique_id= color="#804040">" color="#a020f0">$1"; color="#804040">local color="#008080">plr_ran=" color="#a020f0">$2"; color="#804040">local color="#008080">plr_proc= color="#804040">" color="#a020f0">$3"; color="#804040">local color="#008080">plr_procline= color="#804040">" color="#a020f0">$4"; color="#804040">local color="#008080">plr_proclist= color="#804040">" color="#a020f0">$5"
color="#804040"> 57 cat color="#804040"><<!
color="#804040"> 58
color="#804040"> 59 # color="#a020f0">$plr_unique_id color="#ff00ff"> $plr_ran color="#ff00ff">
color="#804040"> 60 ` color="#804040">local color="#6a5acd">`
color="#804040"> 61
color="#804040"> 62 trap 'error= color="#6a5acd">\$?;printf " color="#6a5acd">\n\n##STATISTICS For Job %i color="#6a5acd">\n" color="#a020f0">$plr_ran; printf "# Line %i of file %s color="#6a5acd">\n\n" color="#a020f0">$plr_procline color="#ff00ff"> $plr_proclist color="#ff00ff">; exit \$ color="#ff00ff">error' EXIT
color="#804040"> 63
color="#804040"> 64
color="#804040"> 65 $plr_proc
color="#804040"> 66
color="#804040"> 67 !
color="#804040"> 68 }
color="#804040"> 69
color="#804040"> 70 function print_runstats color="#6a5acd">{
color="#804040"> 71 printf color="#804040">' color="#ff00ff"># %i Jobs in background. %i/%i started. %s\r color="#804040">' \
color="#804040"> 72 ` color="#804040">jobs color="#6a5acd"> -r color="#804040">| color="#6a5acd">wc -l` $ran color="#a020f0">$maxprocs color="#804040">" color="#6a5acd">`date +%F-%T` color="#804040">"
color="#804040"> 73 }
color="#804040"> 74
color="#804040"> 75 # Bash array running keeps track of the background process numbers
color="#804040"> 76 # Start with nothing running (sentinel value will not be a process number
color="#804040"> 77 for color="#6a5acd">((i= color="#ff00ff">0; i color="#804040">< color="#a020f0">$concurrent color="#804040">; i+ color="#804040">= color="#ff00ff">1 )) color="#804040">; color="#804040">do running color="#804040">[ color="#a020f0">$i] color="#804040">= color="#ff00ff">123456789 color="#804040">; color="#804040">done
color="#804040"> 78
color="#804040"> 79 ran= color="#ff00ff">0
color="#804040"> 80 until
color="#804040"> 81 while color="#804040">[ color="#a020f0">$ran -lt color="#a020f0">$maxprocs color="#804040">] color="#804040">; color="#804040">do
color="#804040"> 82 for color="#6a5acd">((p= color="#ff00ff">0; p color="#804040">< color="#a020f0">$concurrent color="#804040">; p+ color="#804040">= color="#ff00ff">1 )) color="#804040">; color="#804040">do
color="#804040"> 83 proc= color="#a020f0">${running color="#a020f0">[$p color="#a020f0">]}
color="#804040"> 84 # Over all running processes...
color="#804040"> 85 # $proc still running?
color="#804040"> 86 ps -p color="#a020f0">$proc | color="#804040">fgrep color="#a020f0">$proc >/dev/null
color="#804040"> 87 if color="#804040">[ color="#a020f0">$? -ne color="#804040">' color="#ff00ff">0' color="#804040">] color="#804040">; color="#804040">then
color="#804040"> 88 # Not found i.e. its finished
color="#804040"> 89 # So start the next
color="#804040"> 90 ((ran+ color="#804040">= color="#ff00ff">1))
color="#804040"> 91 #(assemble_job ) >$unique_id.job_$p
color="#804040"> 92 ( assemble_job color="#804040">" color="#a020f0">$unique_id color="#804040">" color="#804040">" color="#a020f0">$ran" color="#804040">" color="#a020f0">${allprocs color="#a020f0">[$ran color="#a020f0">]} color="#804040">" \
color="#804040"> 93 ${ color="#a020f0">allprocline color="#a020f0">[$ran color="#a020f0">]} color="#a020f0">$proclist color="#804040">) color="#804040">> color="#a020f0">$unique_id.job_ color="#a020f0">$p
color="#804040"> 94 chmod +x color="#a020f0">$unique_id.job_ color="#a020f0">$p
color="#804040"> 95 # run one job in background collecting run stats
color="#804040"> 96 (/bin/ color="#804040">time -v -a -o color="#a020f0">$unique_id. color="#a020f0">$ran < /dev/null color="#a020f0">$unique_id.job_ color="#a020f0">$p 2>&1 color="#804040">) color="#804040">> color="#a020f0">$unique_id. color="#a020f0">$ran &
color="#804040"> 97 running color="#804040">[ color="#a020f0">$p] color="#804040">= color="#a020f0">$!
color="#804040"> 98 runningprocnum color="#804040">[ color="#a020f0">$p] color="#804040">= color="#a020f0">$ran
color="#804040"> 99 if color="#804040">[ color="#a020f0">$ran -ge color="#a020f0">$maxprocs color="#804040">] color="#804040">; color="#804040">then color="#804040">break color="#ff00ff">1; color="#804040">fi
color="#804040">100 #exit
color="#804040">101 fi
color="#804040">102 done
color="#804040">103 sleep color="#a020f0">$tick
color="#804040">104 # Status
color="#804040">105 print_runstats
color="#804040">106 done
color="#804040">107
color="#804040">108 ## break 1 gets us here!
color="#804040">109
color="#804040">110 # Keep on printing status while there are background processes
color="#804040">111 # even though there are no more to start.
color="#804040">112 sleep color="#a020f0">$tick
color="#804040">113 # Status
color="#804040">114 print_runstats
color="#804040">115 do color="#804040">[ color="#6a5acd">`jobs color="#6a5acd"> -r color="#804040">| color="#6a5acd">wc -l` -eq color="#ff00ff">0 ]
color="#804040">116 done
color="#804040">117 wait
color="#804040">118
color="#804040">119 function stats2csv color="#6a5acd">{
color="#804040">120 # Gather overall stats in csv file
color="#804040">121
color="#804040">122 # CSV Heading
color="#804040">123 printf color="#804040">" color="#6a5acd">\n## STARTING %i Processes from file: %s color="#6a5acd">\n%i at a time with id %s color="#6a5acd">\n\n" \
color="#804040">124 $maxprocs color="#a020f0">$proclist color="#a020f0">$concurrent color="#a020f0">$unique_id color="#804040">> color="#a020f0">$unique_id.csv
color="#804040">125 # CSV Column labels
color="#804040">126 gawk color="#804040">' color="#ff00ff">/^##STATISTICS For Job/{s++}
color="#804040">127 s&&/^# Line .*of file/{printf "JOB,LINE";next}
color="#804040">128 s&&/: /{l=$0;sub(/: .*/,"");$1=$1;printf",%s",$0}
color="#804040">129 s&&l~/Exit status:/{s=0;print"";l="";exit}
color="#804040">130 color="#804040">' color="#a020f0">$unique_id. color="#804040">[ color="#ff00ff">0-9]* color="#804040">>> color="#a020f0">$unique_id.csv
color="#804040">131
color="#804040">132 # Stick allprocs into env for later gawk substitution of command
color="#804040">133 for i color="#804040">in color="#a020f0">${!allprocs color="#a020f0">[@] color="#a020f0">}; color="#804040">do
color="#804040">134 declare color="#008080">-x _ap color="#a020f0">$i= color="#804040">" color="#a020f0">${allprocs color="#a020f0">[$i color="#a020f0">]} color="#804040">"
color="#804040">135 done
color="#804040">136 #env|grep '_ap.=' ; debug
color="#804040">137 # CSV Rows
color="#804040">138 gawk color="#804040">' color="#ff00ff">/^##STATISTICS For Job/{s++;j=$NF}
color="#804040">139 s&&/^# Line .*of file/{printf "%s,%s",j,$3;next}
color="#804040">140 s&&/Command [^:]*: /{printf",%s",ENVIRON["_ap" j]; next}
color="#804040">141 s&&/: /{l=$0;sub(/.*: /,"");printf",%s",$0}
color="#804040">142 s&&l~/Exit status:/{s=0;print"";l="";nextfile}
color="#804040">143 color="#804040">' color="#a020f0">$unique_id. color="#804040">[ color="#ff00ff">0-9]* color="#804040">>> color="#a020f0">$unique_id.csv
color="#804040">144 }
color="#804040">145
color="#804040">146 stats2csv
color="#804040">147
color="#804040">148 printf color="#804040">" color="#6a5acd">\n## FINISHED, (stats in %s) color="#6a5acd">\n\n" color="#a020f0">$unique_id.csv
color="#804040">149
color="#804040">150 exit color="#ff00ff">0
color="#804040">151
color="#804040">152
color="#804040">153
color="#804040">154


I remember writing this with the help of the advanced bash tutorial
which is great, and looking back at this program, I would need the
tutorials help if I needed to extend it!



Why not in Python?


 Of course I thought of doing it in Python, but bash's job
control features were used to flesh out the initial idea and it grew to
become the finished article. Looking back at this program as it's over
a year old, I find that I like bash's job control, and gawk one-liners
 (but not the readability) :-)



- Paddy.



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Wednesday, March 04, 2009

Psion Netbook

I was just reading about Intel's claim that the term Netbook was generic. It seems to be bullying to me.



So, in support of 'our Psion' I got out my old Psion Series 3 for nostalgias sake, and again marvelled at its design.



P.S. Offers of a complementary Netbook would be gratefully received :-)



- Paddy.

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