Tuesday, January 31, 2017

Improving the epub format -- hacking your ebooks

From a reader.

I recently purchased a copy of 'Modern Python Cookbook' but I found that the code listings in the epub file were indented which caused a problem when reading on my tablet. (I reverted to epub as the PDF version froze in the Bookari ereader software.)

I unzipped the epub file, created and ran the following script to 'unindent' the code listings then rezipped. (I also tweaked the epub.css file slightly.)

Script:

import os
import codecs
from textwrap import dedent
from bs4 import BeautifulSoup

ENCODING = 'utf8'

def dedent_page(filepath):
    soup = load_soup(filepath)
    code = soup.findAll('pre')
    for c in code:
        # Dedent twice to cater for 'blank' lines with spaces.
        c.string = dedent(dedent(c.text))
    save_soup(filepath, unicode(soup))

def load_soup(filepath):
    with codecs.open(filepath, encoding = ENCODING) as f:
        return BeautifulSoup(f)

def save_soup(filepath, soup):
    with codecs.open(filepath, mode = 'w', encoding = ENCODING) as f:
        f.write(unicode(soup))

if __name__ == "__main__":

    FOLDER = r'ebook\OEBPS'

    html_files = [fn for fn in os.listdir(FOLDER) if fn.endswith('.html')]
    total_files = len(html_files)
    for i, file_name in enumerate(html_files):
        print 'Processing file %s (%s/%s)' % (file_name, i + 1, total_files)
        dedent_page(os.path.join(FOLDER, file_name))

Tuesday, January 17, 2017

Irrelevant Feature Comparison

A Real Email.
So, please consider creating a blog post w/ a title something like "Solving the Fred Flintstone Problem using Monads in Python and Haskell"
First. There's this: https://pypi.python.org/pypi/PyMonad/ and this: http://www.valuedlessons.com/2008/01/monads-in-python-with-nice-syntax.html. Also, see https://en.wikipedia.org/wiki/Type_class. I think this has been covered nicely.

I can't improve on what's been presented.

Second. I don't see any problems that are solved well by monads in Python. In a lazy, optimized, functional language, monads can be used bind operations into ordered sequences. This is why file parsing and file writing examples of monads abound. They can also be used to bind a number of types so that operator overloading in the presence of strict type checking can be implemented. None of this seems helpful in Python.

Perhaps monads will be helpful with Python type hints. I'll wait and see if a monad definition shows up in the typing module. There, it may be a useful tool for handling dynamic type bindings.

Third. This request is perilously close to a "head-to-head" comparison between languages. The question says "problem", but it is similar to asking to see the exact same algorithm implemented in two different languages. It makes as much sense as comparing Python's built-in complex type with Java's built-in complex type (which Java doesn't have.)

Here's the issue. I replace Fred Flintstone with "Parse JSON Notation".  This is a cool application of monads to recognize the various sub-classes of JSON syntax and emit the correctly-structured document.  See http://fssnip.net/bq/title/JSON-parsing-with-monads.  In Python, this is import json. This isn't informative about the language. If we look at the Python code, we see some operations that might be considered as eligible for a rewrite using monads. But Python isn't compiled and doesn't have the same type-checking issues. The point is that Python has alternatives to monads.

Fourth. It's just asking about a not-required feature to a language. In the spirit of showing the not-required-in-Python features, I'll show the not-required-in-Python GOTO.

Here it is:

def goto(destination):
    global next
    next = destination

def min_none(sequence):
    try:
        return min(sequence)
    except ValueError:
        return None
        
def execute(program, debug=False, stmt=None):
    global next, context
    if stmt is None:
        stmt = min(program.keys())
        context = {'goto': goto}
    while stmt is not None:
        next = min_none(list(filter(lambda x: x>stmt, program.keys())))
        if debug:
            print(">>>", program[stmt])
        exec(program[stmt], globals(), context)
        stmt = next
            
example = {
100: "a = 10",
200: "if a == 0: goto(500)",
250: "print(a)",
300: "a = a - 1",
400: "goto(200)",
500: "print('done'()",
}

execute(example)

This shows how we can concoct an additional feature that isn't really needed in Python.

Given this, we can now compare the GOTO between Python, BASIC, and Haskell. Or maybe we can look at Monads in BASIC vs. Haskell. 

Monday, January 9, 2017

The Depths of Degradation or How to Reduce

Let's talk real-world functional programming. Disclosure: I'm a fan of functional programming in Python. (This: https://www.packtpub.com/application-development/functional-python-programming)

The usual culprits for functional programming are map(), filter(), generator functions, and the various comprehensions. This is very pleasant and can lead to succinct, expressive code.

The reduce operation, however, is sometimes slippery.  The obvious reductions are sum() and prod().  Some slightly less obvious reductions are these three:

sum0 = lambda s: sum(1 for _ in s)
sum1 = lambda s: sum(s)
sum2 = lambda s: sum(n**2 for n in s)

The first is essentially len(s), but stated more formally. It shows how we can add in filter or transformations. If we're working with a collections.Counter object, we can rewrite these three to work with the values() of a counter. This allows us to have a statistics library that works with a sequence of simple items or a Counter of binned items.

(I've left it as an exercise for the reader to create the summaries of Counters.)

The Health Check Question

The context is an RESTful application's /health end-point. When a client does a GET to /health, we want to provide status of the components on which the app depends as well as a summary.

The details are created like this:

components = (component() for component in COMPONENT_LIST)
init_components = [thing.init_app(app) for thing in components]
details = [component.health() for component in init_components]

We have a list of class definitions for each component. We can create instances of each class. We can initialize these by providing the RESTful app. Finally, we can create a list of the various health end-point status codes.

There's a class definition for other RESTful API's. The health check does a transitive GET to a /health end-point. These are all more-or-less identical.

There are also class definitions for the database and the cache and other non-RESTful components. It's all very pretty and very functional.

Note that the three statements aren't adjacent. They're scattered around to fit better with the way Flask works. The component list is in one place. The initialization happens before the first request. The details are computed as requested.

Also. We don't really use a simple list for the details. It's actually a mapping from which we will derive a vector. I've left that detail out because it's a relatively simple complication.

Representation of Health

We represent health with a simple enumeration of values:

from enum import Enum
class Status(Enum):
    OK = "OK"
    DEGRADED = "DEGRADED"
    DOWN = "DOWN"

This provides the essential definition of health for our purposes. We don't drag around details of the degradation; that's something that we have to determine by looking at our consoles and logs and stuff.  Degradation is (a) rare, and (b) nuanced. Some degradations are mere annoyances: one of the servers is being restarted. Other degradations are hints that something else might be going on that needs investigation: database primary server is down and we're running on a secondary.

Summarizing Health

A subset of the details vector, then, looks like this: [Status.OK, Status.OK, Status.DEGRADED].

How can we summarize this?

First, we need some rules.  Like these:

class Status(Enum):
    OK = "OK"
    DEGRADED = "DEGRADED"
    DOWN = "DOWN"

    def depth(self, other):
        if self == self.OK:
            return {self.OK: self.OK,
                    self.DEGRADED: self.DEGRADED,
                    self.DOWN: self.DEGRADED}[other]
        elif self == self.DEGRADED:
            return {self.OK: self.DEGRADED,
                    self.DEGRADED: self.DEGRADED,
                    self.DOWN: self.DEGRADED}[other]
        elif self == self.DOWN:
            return {self.OK: self.DEGRADED,
                    self.DEGRADED: self.DEGRADED,
                    self.DOWN: self.DOWN}[other]


The depth() method implements a comparison operator that defines the relationships. This can be visualized as a table.

depthOKDEGRADEDDOWN
OKOKDEGRADEDDOWN
DEGRADEDDEGRADEDDEGRADEDDEGRADED
DOWNDOWNDEGRADED DOWN


This allows us to define a function that uses reduce to summarize the vector of status values.

from functools import reduce
def summary(sequence): 
    return reduce(lambda a, b: a.depth(b), sequence)

The reduce() function applies a binary operator between items in a vector. We've used lambda a, b: a.depth(b) to turn the the depth() method into a binary operator so it can be used with reduce.

The summary() function is a "depth-reduction" of a vector of status objects. It's defined independently of the actual status objects. The relationships among the status levels are embedded in the class definition where they belong. The actual details of status are pleasantly opaque.

And.

We have an example of map-reduce outside the sphere of big data.

The Integer Alternative

The health rules as shown above are kind of complex. Could they be simplified? The answer is no.

Here's an alternative -- which does not do what we want.

class Status2(IntEnum):
    OK = 1
    DEGRADED = 2
    DOWN = 3
    
summary2 = lambda sequence: max(sequence)

This works in some cases, but it doesn't work in others. Another alternative is to change the order to be OK=1, DOWN=2, DEGRADED=3. This doesn't work, either. I'll leave it as an exercise to write out some of the various combinations of values and see how these differ.

JSON Representation

The final detail is JSONification of the status vector and the summary.

json.dumps({"status": summary(vector).name, "details": [s.name for s in vector]})

This converts the various Status objects to text items that fit the Swagger specification for our /health end-points. The .name attribute reference is required to get the string labels from the enum. An alternative is to customize the JSON encoder to recognize the Enum objects and extract their names.

Conclusion

Map-Reduce is easy. It surfaces in a number of places. The idea helps encapsulate summarization rules.

Tuesday, January 3, 2017

The "Build Script" Idea

In compiled languages, the build script or makefile is pretty important. Java has maven (and gradle and ant) for this job.

Python doesn't really have much for this. Mostly because it's needless.

However.

Some folks like the idea of a build script. I've been asked for suggestions.

First and foremost: Go Slow. A build script is not essential. It's barely even helpful. Python isn't Java. There's no maven/gradle/ant nonsense because it isn't necessary. Make is a poor choice of tools for reasons we'll see below.

For folks new to Python, here's the step that's sometimes important.

python setup.py sdist bdist_wheel upload

This uses the source distribution tools (sdist) to build a "wheel" out of the source code. That's the only thing that's important, and even that's optional. The source is all that really exists, and a Git Pull is the only thing that's truly required.

Really. There's no compilation, and there's no reason to do any processing prior to uploading source.

For folks experienced with Python, this may be obvious. For folks not so experienced, it's difficult to emphasize enough that Python is just source. No "class" files. No "jar" files. No "war" files. No "ear" files. None of that. A wheel is a Zip archive that follows some simple conventions.

Some Preliminary Steps

A modicum of care is a good idea before simply uploading something. There are a few steps that make some sense.

  1. Run pylint to check for obvious code problems. A low pylint score indicates that the code needs to be cleaned up. There's no magically ideal number, but with a few judicious "disable" comments, it's easy to get to 10.00.
  2. Run mypy to check the type hints. If mypy complains, you've got potentially serious problems.
  3. Run py.test and get a coverage report. There's no magically perfect test coverage number: more is better. Even 100% line-of-code coverage doesn't necessarily mean that all of the potential combinations of logic paths have been covered.
  4. Run sphinx to create documentation.
Only py.test has a simple pass-fail aspect. If the unit tests don't pass: that's a clear problem. 

The Script

Using make doesn't work out terribly well. It can be used, but it seems to me to be too confusing to set up properly.

Why? Because we don't have the kind of simple file relationships with which make works out so nicely. If we had simple *.c -> *.o -> *.ar kinds of relationships, make would be perfect. We don't have that, and this seems to make make more trouble than it's worth.  Both pylint and py.test keep history as well as produce reports. Sphinx is make-like already, which is why I'm leery of layering on the complexity.

My preference is something like this:

import pytest
from pylint import epylint as lint
import sphinx
from mypy.api import api

(pylint_stdout, pylint_stderr) = lint.py_run('*.py', return_std=True)
print(pylint_stdout.getvalue())

result = mypy.api.run('*.py')

pytest.main(["futurize_both/tests"])

sphinx.main(['source', 'build/html', '-b', 'singlehtml'])

The point here is to simply run the four tools and then look at the output to see what needs to be fixed. Circumstances will dictate changes to the parameters being used. New features will need different reports than bug fixes. Some parts of a project will have different focus than other parts. Conversion from Python 2 to Python 3 will indicate a shift in focus, also.

The idea of a one-size-fits-all script seems inappropriate. These tools are sophisticated. Each has a distinctive feature set. Tweaking the parameters by editing the build script seems like a simple, flexible solution. I'm not comfortable defining parameter-parsing options for this, since each project I work on seems to be unique.

Important. Right now, mypy-lang in the PyPI repository and mypy in GitHub differ. The GitHub version includes an api module; the PyPI release does not include this. This script may not work for you, depending on which mypy release you're using. This will change in the future, making things nicer. Until then, you may want to run mypy "the hard way" using subprocess.check_call().

In enterprise software development environments, it can make sense to set some thresholds for pylint and pytest coverage. It is very helpful to include type hints everywhere, also. In this context, it might make sense to parse the output from lint, mypy, and py.test to stop processing if some quality thresholds are met.

As noted above: Go Slow. This kind of tool automation isn't required and might actually be harmful if done badly. Arguing over pylint metrics isn't as helpful as writing unit test cases. I worry about teams developing an inappropriate focus on pylint or coverage reports -- and the associated numerology -- to the exclusion of sensible automated testing.

I think tools like https://pypi.python.org/pypi/pytest-bdd might be of more value than a simplistic "automated" tool chain. Automation doesn't seem as helpful as clarity in test design. I like the BDD idea with Gherkin test specifications because the Given-When-Then story outline seems to be very helpful for test design.