The Kitchin Research Group

I just learned of for/else loops (http://pyvideo.org/video/1780/transforming-code-into-beautiful-idiomatic-pytho). They are interesting enough to write about. The idea is that there is an "else" clause of a for loop that is only executed if the loop completes without a break statement. The use case is to avoid using a flag. For example, let us say we want to loop through a list and determine if a number exists. Here is a typical way you might think to do it:

def f():
    flag = False
    for i in range(10):
        if i == 5:
            flag = True
            break

    return flag

print f()

True

A for/else loop does this in a different way. Essentially, the else clause runs if the loop completes, otherwise if the break occurs it is skipped. In this example the break statement occurs, so the else statement is skipped.

def f():
    for i in range(10):
        if i == 5:
            break
    else:
        return False

    return True

print f()

True

In this example no break statement occurs, so the else clause is executed.

def f():
    for i in range(10):
        if i == 15:
            break
    else:
        return False

    return True

print f()

False

It is hard to say if this is an improvement over the flag. They both use the same number of lines of code, and I find it debatable if the else statement is intuitive in its meaning. Maybe if there were multiple potential breaks this would be better.

Needless to say, go watch http://pyvideo.org/video/1780/transforming-code-into-beautiful-idiomatic-pytho. You will learn a lot of interesting things!

Copyright (C) 2013 by John Kitchin. See the License for information about copying.

org-mode source

The boolean logic commands and and or have return values in python. Let us first review briefly what these operators do by examples. The typical usage is in conditional statements. First, we look at what kind of values evaluate to "True" or "False" in python. Anything that is "empty" usually evaluates to False, along with the integer 0 and the boolean value of False.

for value in ('', 0, None, [], (), {}, False):
    if value:
        print value, "True"
    else:
        print value, "False"

 False
0 False
None False
[] False
() False
{} False
False False

Objects that are not empty evaluate to "True", along with numbers not equal to 0, and the boolean value True.

for value in (' ', 2, 1, "a", [1], (3,), True):
    if value:
        print value, "True"
    else:
        print value, "False"

  True
2 True
1 True
a True
[1] True
(3,) True
True True

The and and or operators compare two objects. and evaluates to "True" if both objects evaluate to "True" and or evaluates to "True" if either object evaluates to "True". Here are some examples.

a = None
b = 5

if a and b:
    print True
else:
    print False

False

a = None
b = 5

if a or b:
    print True
else:
    print False

True

Now the interesting part. The and and or operators actually return values! With the and operator, each argument is evaluated, and if they all evaluate to True, the last argument is returned. Otherwise the first False argument is returned.

a = 1
b = 5
print a and b
print b and a
print a and False
print a and True
print a and None
print False and a
print None and a
print True and 'a' and 0 and True # first False item is zero

5
1
False
True
None
False
None
0

The or operator returns the first True value or the last value if nothing is True. Note that if a True value is found, the values in the expressions after that value are not evaluated.

print 2 or False
print 0 or False
print 0 or False or 4 or {}

2
False
4

One way you might see this is to set variables depending on what command-line arguments were used in a script. For example:

import sys

# replace this:
if 'plot' in sys.argv:
    PLOT = True
else:
    PLOT = False

# with this
PLOT = 'plot' in sys.argv or False

# later in your code:
if PLOT: 
    # do something to make a plot

Now we get to the and-or trick. The trick is to assign a variable one value if some boolean value is True, and another value if the expression is False.

a = True
b = True

if a and b:
    c = "value1"
else: 
    c = "value2"

print c

value1

We can replace the if/else code above with this one line expression:

a = True
b = True

c = (a and b) and "value1" or "value2"
print c

value1

There is a problem. If the first value evaluates to False, you will not get what you expect:

a = True
b = True

c = (a and b) and None or "value2"
print c

value2

In this case, (a and b) evaluates to True, so we would expect the value of c to be the first value. However, None evaluates to False, so the or operator returns the first "True" value, which is the second value. We have to modify the code so that both the or arguments are True. We do this by putting both arguments inside a list, which will then always evaluate to True. This will assign the first list to c if the expression is True, and the second list if it is False. We wrap the whole thing in parentheses, and then index the returned list to get the contents of the list.

a = True
b = True

c = ((a and b) and [None] or ["value2"])[0]

print c

None

a = True
b = True

c = (not (a and b) and [None] or ["value2"])[0]

print c

value2

This is definitely a trick. I find the syntax difficult to read, especially compared to the more verbose if/else statements. It is interesting though, and there might be places where the return value of the boolean operators might be useful, now that you know you can get them.

Here is a tough example of using this to update a dictionary entry. Previously we used a dictionary to count unique entries in a list.

d = {}

d['key'] = (d.get('key', None) and [d['key'] + 1] or [1])[0]

print d

d['key'] = (d.get('key', None) and [d['key'] + 1] or [1])[0]
print d

{'key': 1}
{'key': 2}

This works because the .get function on a dictionary returns None if the key does not exist, resulting in assigning the value of 1 to that key, or it returns something that evaluates to True if the key does exist, so the key gets incremented.

Let us see this trick in action. Before we used if/else statements to achieve our goal, checking if the key is in the dictionary and incrementing by one if it is, and if not, setting the key to 1.

L = ['a', 'a', 'b','d', 'e', 'b', 'e', 'a']

# old method
d = {}
for el in L:
    if el in d:
        d[el] += 1
    else:
        d[el] = 1

print d

{'a': 3, 'b': 2, 'e': 2, 'd': 1}

Here is the new method:

# new method:
L = ['a', 'a', 'b','d', 'e', 'b', 'e', 'a']
d = {}
for el in L:
    d[el] = (d.get(el, None) and [d[el] + 1] or [1])[0]
print d

{'a': 3, 'b': 2, 'e': 2, 'd': 1}

We have in (an admittedly hard to read) a single single line replaced the if/else statement! I have for a long time thought this should possible. I am somewhat disappointed that it is not easier to read though.

Update 7/8/2013

a = True
c = "value1" if a else "value2"
print c

value1

a = ''
c = "value1" if a else "value2"
print c

value2

L = ['a', 'a', 'b','d', 'e', 'b', 'e', 'a']
d = {}
for el in L:
    d[el] = (d[el] + 1) if (el in d) else 1
print d

{'a': 3, 'b': 2, 'e': 2, 'd': 1}

from collections import defaultdict
print int()

L = ['a', 'a', 'b','d', 'e', 'b', 'e', 'a']
d = defaultdict(int)
for el in L:
    d[el] += 1
print d

0
defaultdict(<type 'int'>, {'a': 3, 'b': 2, 'e': 2, 'd': 1})

Copyright (C) 2013 by John Kitchin. See the License for information about copying.

org-mode source

Suppose you have a module that you import a class from, and the class defines some methods that you want to memoize. You do not want to modify the source code, maybe because it is not your code, or because you do not want to maintain it, etc… Here is one way to modify the class functions at runtime. We will use the memoize decorator and replace the class function definition with the wrapped function that caches the results. We also allow arbitrary arguments and keyword arguments. A subtle wrinkle here is that you cannot use a dictionary as a key to a dictionary because dictionaries are not hashable. We use the pickle module to created a string that should uniquely represent the args and keyword args, and we use that string as the key.

class Calculator:
    def __init__(self):
        pass

    def calculate(self, a):
        'returns the answer to everything'
        return 42

    def method_2(self, *args, **kwargs):
        return (args, kwargs)


import pickle

from functools import wraps
def memoize(func):
    cache = {}
    @wraps(func)
    def wrap(*args,**kwargs):
        key = pickle.dumps((args, kwargs))
        if key not in cache:
            print 'Running func with ', args
            cache[key] = func(*args, **kwargs)
        else:
            print 'result in cache'
        return cache[key]
    return wrap

# now monkey patch/decorate the class function
Calculator.calculate = memoize(Calculator.calculate)
Calculator.method_2 = memoize(Calculator.method_2)

calc = Calculator()
print calc.calculate(3)
print calc.calculate(3)
print calc.calculate(4)
print calc.calculate(3)


print calc.method_2()
print calc.method_2()

print calc.method_2(1,2)
print calc.method_2(1,2)

print calc.method_2(1,2,a=5)
print calc.method_2(1,2,a=5)

Running func with  (<__main__.Calculator instance at 0x0000000001E9B3C8>, 3)
42
result in cache
42
Running func with  (<__main__.Calculator instance at 0x0000000001E9B3C8>, 4)
42
result in cache
42
Running func with  (<__main__.Calculator instance at 0x0000000001E9B3C8>,)
((), {})
result in cache
((), {})
Running func with  (<__main__.Calculator instance at 0x0000000001E9B3C8>, 1, 2)
((1, 2), {})
result in cache
((1, 2), {})
Running func with  (<__main__.Calculator instance at 0x0000000001E9B3C8>, 1, 2)
((1, 2), {'a': 5})
result in cache
((1, 2), {'a': 5})

This particular memoize decorator is not persistent; the data is only stored in memory. You would have to write the data out to a file and reread the file to make it persistent.

It is not obvious this practice is good; you have in essence changed the behavior of the original function in a way that may be hard to debug, and could conceivably be incompatible with the documentation of the function.

An alternative approach is writing another function that wraps the code you want, and memoize that function.

class Calculator:
    def __init__(self):
        pass

    def calculate(self, a):
        'returns the answer to everything'
        return 42



from functools import wraps
def memoize(func):
    cache = {}
    @wraps(func)
    def wrap(*args):
        if args not in cache:
            print 'Running func with ', args
            cache[args] = func(*args)
        else:
            print 'result in cache'
        return cache[args]
    return wrap

calc = Calculator()

@memoize
def my_calculate(a):
    return calc.calculate(a)

print my_calculate(3)
print my_calculate(3)
print my_calculate(4)
print my_calculate(3)

Running func with  (3,)
42
result in cache
42
Running func with  (4,)
42
result in cache
42

It is debatable whether this is cleaner. One argument for this is that it does not monkey with the original code at all.

Copyright (C) 2013 by John Kitchin. See the License for information about copying.

org-mode source

Sometimes a function is expensive (time-consuming) to run, and you would like to save all the results of the function having been run to avoid having to rerun them. This is called memoization. A wrinkle on this problem is to save the results in a file so that later you can come back to a function and not have to run simulations over again.

In python, a good way to do this is to "decorate" your function. This way, you write the function to do what you want, and then "decorate" it. The decoration wraps your function and in this case checks if the arguments you passed to the function are already stored in the cache. If so, it returns the result, if not it runs the function. The memoize decorator below was adapted from here.

from functools import wraps
def memoize(func):
    cache = {}
    @wraps(func)
    def wrap(*args):
        if args not in cache:
            print 'Running func'
            cache[args] = func(*args)
        else:
            print 'result in cache'
        return cache[args]
    return wrap

@memoize
def myfunc(a):
    return a**2

print myfunc(2)
print myfunc(2)

print myfunc(3)
print myfunc(2)

Running func
4
result in cache
4
Running func
9
result in cache
4

The example above shows the principle, but each time you run that script you start from scratch. If those were expensive calculations that would not be desirable. Let us now write out the cache to a file. We use a simple pickle file to store the results.

import os, pickle
from functools import wraps
def memoize(func):
    if os.path.exists('memoize.pkl'):
        print 'reading cache file'
        with open('memoize.pkl') as f:
            cache = pickle.load(f)
    else:
        cache = {}
    @wraps(func)
    def wrap(*args):
        if args not in cache:
            print 'Running func'
            cache[args] = func(*args)
            # update the cache file
            with open('memoize.pkl', 'wb') as f:
                pickle.dump(cache, f)
        else:
            print 'result in cache'
        return cache[args]
    return wrap

@memoize
def myfunc(a):
    return a**2


print myfunc(2)
print myfunc(2)

print myfunc(3)
print myfunc(2)

reading cache file
result in cache
4
result in cache
4
result in cache
9
result in cache
4

Now you can see if we run this script a few times, the results are read from the cache file.

Copyright (C) 2013 by John Kitchin. See the License for information about copying.

org-mode source

One reason to interact with Aspen via python is to use external solvers to drive the simulations. Aspen has some built-in solvers, but it does not have everything. You may also want to integrate additional calculations, e.g. capital costs, water usage, etc… and integrate those results into a report.

Here is a simple example where we use fsolve to find the temperature of the flash tank that will give a vapor phase mole fraction of ethanol of 0.8. It is a simple example, but it illustrates the possibility.

import os
import win32com.client as win32
aspen = win32.Dispatch('Apwn.Document')

aspen.InitFromArchive2(os.path.abspath('data\Flash_Example.bkp'))

from scipy.optimize import fsolve

def func(flashT):
    flashT = float(flashT) # COM objects do not understand numpy types
    aspen.Tree.FindNode('\Data\Blocks\FLASH\Input\TEMP').Value = flashT
    aspen.Engine.Run2()
    y = aspen.Tree.FindNode('\Data\Streams\VAPOR\Output\MOLEFRAC\MIXED\ETHANOL').Value
    return y - 0.8

sol, = fsolve(func, 150.0)
print 'A flash temperature of {0:1.2f} degF will have y_ethanol = 0.8'.format(sol)

A flash temperature of 157.38 degF will have y_ethanol = 0.8

One unexpected detail was that the Aspen COM objects cannot be assigned numpy number types, so it was necessary to recast the argument as a float. Otherwise, this worked about as expected for an fsolve problem.

Copyright (C) 2013 by John Kitchin. See the License for information about copying.

org-mode source