Beginning Python for Bioinformatics

I posted about a week ago that Packt Publishing had invited me to review Python Testing Beginner’s Guide by Daniel Arbuckle. Having finished reading the book (I must admit that I haven’t tried all the code in it), I can say that I have an excellent initial impression of the book.

PTBG is not a long book and the topic is divided in 10 chapters and one appendix. One of the first things that I liked about the book is that there’s no introduction (or something similar) to Python. It just goes straight to the point assuming that you have some good understanding of the language and everything that surrounds it. In the past I was frustrated with some “Introduction to X with Python” that wasted precious space talking over and over about a topic, learning Python, better covered in many other books. PTBG does not waste time and space introducing its main topic which is testing, and in my opinion that’s the best approach, even though it might look a little bit abrupt by some.

The language and text in the book is clear and very pleasant. PTBG is a very well written book and I really enjoyed its style. The first chapters of the book cover Python testing using doctests. For someone like me that didn’t write so many tests in the normal software development workflow (I know I should write more tests), this section seems like a really nice introduction to the topic, with well thought real-life like examples and a good flow on the explanation of the different features. One small complain that I have is that for a beginner sometimes the code listed in the examples might seem a little bit confusing, and maybe the addition of line numbers might have helped a bit here. But at the same I understand that this is normal style of some Packt books.

After the doctests section, PTBG gets into more advanced techniques, covering a little bit mock objects with Mocker, then moving into unittest and nose. The latter is a Python tool that allows for managing, running and automating tests. Also covered is Twill, another third-party library that allows for testing of web applications.

One full chapter is devoted to test-driven development, with a complete walkthrough of this approach. This gives a wrap-up of most of the techniques and modules covered in the book, but there’s still space for another chapter that shows how beautifully doctests, unittest and nose can be fully integrated and help the development of applications using the test-driven approach.

Overall, I really enjoyed PTBG. As I mentioned, test driven development was never a high priority in the application I usually developed with Python. But certainly this book can be a good starting point for some Python test beginners to incorporate these techniques in their usual development workflow. Scientific software is also a perfect niche for this type of approach and we should do what is possible in order to avoid the nightmares of the past.

I was invited by Packt Publishing to review Python Testing Beginner’s Guide by Daniel Arbuckle. This is a book on one of the most important aspects of scientific programming (even though the majority of scientific software don’t have any testing routines): code testing, checking if your code actually does what is intended to do. I can say I’m not really an expert on testing so I guess I’m the right audience for it:

You’ll learn about several of Python’s automated testing tools, and you’ll learn about the philosophies and methodologies that they were designed to support, like unit testing and test-driven development. When you’re done, you’ll be able to produce thoroughly tested code faster and more easily than ever before, and you’ll be able to do it in a way that doesn’t distract you from your “real” programming.

Packt also supplied a preview/sample chapter that you can download here.

I hope to get a review ready by the end of the week. before the Ontario Institute of Cancer Research retreat, otherwise I will try to post a full review next week.

Let me start by saying that Python for Bioinformatics (Chapman & Hall/Crc Mathematical & Computational Biology) is a massive book, massive in a way that it contains a lot of material. I still didn’t have enough time to check everything, but I’m well into the first section of the book that gives an initial view of Python and how to program it.

The initial section of the book is well written (I’m not going criticize the book in terms of good/poor English, as I’m not well qualified to do that), and gives a clear perspective on how to program Python for scientists, who are the main target demographic of the book. Of course, it always help to have some basic knowledge of command line shells, but the book also includes some explanations of IDLE and other Python-capable IDEs. I cannot say that I read this section with the enough care and attention, but what I can say is that you won’t miss a beat with PfB, as it has more material than I expected. I still have to start with the more advanced topics, like BioPython and so forth, what I plan to do in the coming month, and as I don’t have a lot of experience with BioPython, I’m looking forward to it.

On the other hand I have a small-ish complaint, that maybe is more about style than substance. I don’t like the design of the book, the way the code interleaves with the text and the way the code explanations are presented. Most of the code blocks are followed by a careful explanation, but this explanation works as a figure label for the code block. That is quite annoying because there are too many stops in the text fluidity as one tends to lose attention to it (my case, not exactly everyone’s).

Another minor detail is the use of “he” every time scientists are referred (one example is on page 3 on the second phrase of the introduction). The (politically) correct would be to use “he or she” or “she or he” (but that’s OK with me).

I will try to post more complete reviews of the sections that I don’t master. I would also like to thank Sebastian for sending me a copy of the book.

So, I’m closing the blog, maybe for good, maybe not. I haven’t been updating it and some other responsibilities are consuming my spare time.

I would like to thank everyone that contributed, commented and read it. You have my deepest appreciation. My work is fulfilled if I helped at least one person along the way.

Cheers
Paulo

PS: there’s the wiki, so register and help me improve it.
PS II: sorry that I couldn’t finish the last project. Maybe some other time.

I’m slowly moving the posts from the blog to a wiki. It makes easier to display post series and allows people to modify/enhance/discuss.

The wiki address is http://wiki.genedrift.org.

Thanks to the comments and suggestions to the last post, it’s possible to make now a more pythonic and clearly generic database update class. Let’s check how the “generic” update/edit entry function is currently:

def update_data(self, values_list):
    '''edits and updates fields'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()

    cursor.execute("UPDATE bac SET  projects = ?, comments = ?, temperature = ?, cell = ?, box = ?, tubes = ?, chromosome = ?, sdate = ?, clone = ?, source
	= ?, location1 = ?, startpos = ?, endpos = ?,
	gene = ?, genelink = ?, dnaex = ?, validation = ?, pcr = ?, refs = ?, antibiotic = ? WHERE idbac = ?",
    values_list['projects'], values_list['comments'], values_list['temperature'], values_list['cell'], values_list['box'], values_list['tubes'],
    values_list['chromo'], values_list['date'], values_list['clone'], values_list['source'], values_list['location'], values_list['start']
    values_list['end'], values_list['gene'], values_list['genelink'], values_list['dna'], values_list['validation'], values_list['pcr'],
    values_list['refs'], values_list['antibiotic'], values_list['idbac']))

    database.commit()
    database.close()

which is really ugly and, although it works, is not really useful outside this small project. Based on the comments the best option was to use placeholders and a dictionary, similar to the approach used on the insert data function. Pre-formatting a string to have both the field name to be updated and a placeholder (for instance :idbac) that will receive the values

update = ','.join(['%s=:%s' % (y, y) for y in values_list])

where update is the string we want and values_list is the dictionary with all the key-value pairs. I tried this approach, using this structure in the generic function, but then I decided that the best alternative was to put this join in the derived class function and pre-populate the string with the values and then send this string directly to the update function. In the end I opted to use this

update = ','.join(['%s=\"%s\"' % (y, values_list[y]) for y in values_list])

The latter is slightly different to what was suggested. The original one would create a tuple with the keys from the dictionary, making for instance sdate:sdate. With all these place holders just pass the dictionary and you have all the values inserted. This would be handy if the insert string was being created on the “generic” function. If we move this to the derived class, we can use the the alternative, keeping in mind that the values parsed should be surrounded by quotes, otherwise the SQL UPDATE statement will have problems with spaces and other foreign characters that should not be there. So instead of placeholders we will have gene:"PTEN" and we can attache this joined string to the actual commands. We then can move all the machinery from the “generic” function that can be written as

def update_data(self, update_string):
    '''edits and updates fields'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()
    cursor.execute(update_string)

    database.commit()
    database.close()

That’s it, very elegant (we will see the derived class in the next post). And finishing our generic class, we would need a delete function, so the user can eliminate entries that he/she doesn’t want anymore. It’s also a very simple function

def delete_data(self, delete_string):
    '''deletes one field'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()
    cursor.execute(delete_string)

    database.commit()
    database.close()

We will check the delete string next time. Again, I would like to thank for all the comments, it has been really helpful for me.

Previously in the series:
Part 1
Part 2
Part 3
Part 4
Part 5
Part 6
Part 7

And we’re back. After a couple of weeks of inactivity we will get back to our small soap-opera pf Python, wxPython and SQLite. Continuing in our database management code let’s check two other functions that changed since our first inception of the code. The first one is the insert_data function that looks like this now

def insert_data(self, values_list, insert_string):
    '''inserts data in the database'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()

    cursor.execute(insert_string % self.table_name, values_list)

    database.commit()
    database.close()

Basically no changes, apart from the obvious check for the current running operating system, which was explained in the last post. The other function to check is the update_data. This function is new and it wasn’t in the first version, but as it can be seen it has a problem being a “generic” function, because it contains information pertained to the table and database being used in the interface. This function basically received information that needs to be updated in the table’s fields and by using the SQL UPDATE ... SET edits and updates data in the changed fields. I have tried several different syntaxes to make the execute generic, mainly trying to pre-format the string without success. IF anyone reading this can help, I’d appreciate.

def update_data(self, values_list):
    '''edits and updates fields'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()

    cursor.execute("UPDATE bac SET  projects = ?, comments = ?, temperature = ?, cell = ?, box = ?, tubes = ?, chromosome = ?, sdate = ?, clone = ?, source = ?, location1 = ?, startpos = ?, endpos = ?,
	gene = ?, genelink = ?, dnaex = ?, validation = ?, pcr = ?, refs = ?, antibiotic = ? WHERE idbac = ?",
    values_list['projects'], values_list['comments'], values_list['temperature'], values_list['cell'], values_list['box'], values_list['tubes'],
    values_list['chromo'], values_list['date'], values_list['clone'], values_list['source'], values_list['location'], values_list['start'],  values_list['end'],
    values_list['gene'], values_list['genelink'], values_list['dna'], values_list['validation'], values_list['pcr'],
    values_list['refs'], values_list['antibiotic'], values_list['idbac']))

    database.commit()
    database.close()

Anyway, I will explain the logic of the command (OK for a stop gap, but not as a definite solution). values_list is a dictionary that is passed to the function and contains the field names as keys and the new/changed information as values. The execute method simply parses the values from each key in the update string which is then sent to the database and table to be changed. Everything is committed and the database is closed.

As this is a “generic” function from a “generic” class the ideal scenario would be to the function to receive a pre-formatted string with all the information, as in the insert data function, and update the information in the database.

I would like to thank in advance anyone that can comment on this. Next time we will continue checking the generic class and finalize this part in order to start with the interface build process.

Previously in the series:
Part 1
Part 2
Part 3
Part 4
Part 5
Part 6

Let’s get back to our SQLite and wxPython project. We haven’t seen anything on wxPython yet, and we will check the interface only on the next post. For now, let’s see some extra code added to the SQLite access class. Remember that we have a generic class and one class derived from it that would work on accessing specific tables in our database file.

When we last covered the db access routines, there was no search for an entry (the function returned everything in the table no matter what), there was no update function in case someone would want to modify an entry and there was no delete method if you wanted to delete something. In the meantime, I added all of this functionality (and some other) to the generic class and extended it to the class derived from it. Let’s check how the generic class is now (you will notice that there is an issue in one of the methods, if someone can help me I’d appreciate. More details later.)

class DB_Generic():
    '''generic class to add DB functionality'''
    def __init__(self, table_name, db_path = ''):
        #par= name of the table to be used
        self.table_name = table_name
        if len(db_path) > 0:
            self.db_path = db_path
            print db_path

    def get_data_generic(self, range = 1, bac_to_get = 0):
        '''gets the data from the database'''       

        if sys.platform == 'darwin':
            (cursor, database) = link_db(self.db_path)
        else:
            (cursor, database) = link_db()

        if range == 1:
            cursor.execute("""SELECT * FROM %s""" % self.table_name)
        elif range == 2:
            cursor.execute("""SELECT * FROM %s where idbac = %d""" % (self.table_name, bac_to_get))

        table_data = cursor.fetchall()
        raw_data = []
        for i in table_data:
            raw_data.append(list(i))

        self.table_data = raw_data
        database.close()

    def insert_data(self, values_list, insert_string):
        '''inserts data in the database'''

        if sys.platform == 'darwin':
            (cursor, database) = link_db(self.db_path)
        else:
            (cursor, database) = link_db()

        cursor.execute(insert_string % self.table_name, values_list)

        database.commit()
        database.close()

    def update_data(self, values_list):
        '''edits and updates fields'''

        if sys.platform == 'darwin':
            (cursor, database) = link_db(self.db_path)
        else:
            (cursor, database) = link_db()

        #change this to generic!!!!!!!!!!!!
        cursor.execute("UPDATE bac SET  projects = ?, comments = ?, temperature = ?, cell = ?, box = ?, tubes = ?, chromosome = ?, sdate = ?, clone = ?, source = ?, location1 = ?, startpos = ?, endpos = ?,
		gene = ?, genelink = ?, dnaex = ?, validation = ?, pcr = ?, refs = ?, antibiotic = ? WHERE idbac = ?",
        (values_list['projects'], values_list['comments'], values_list['temperature'], values_list['cell'], values_list['box'], values_list['tubes'],
         values_list['chromo'], values_list['date'], values_list['clone'], values_list['source'], values_list['location'], values_list['start'], values_list['end'],
         values_list['gene'], values_list['genelink'], values_list['dna'], values_list['validation'], values_list['pcr'],
         values_list['refs'], values_list['antibiotic'], values_list['idbac']))

        database.commit()
        database.close()

    def delete_data(self, delete_string):
        '''deletes one field'''

        if sys.platform == 'darwin':
            (cursor, database) = link_db(self.db_path)
        else:
            (cursor, database) = link_db()
        cursor.execute(delete_string)

        database.commit()
        database.close()

In the next couple of posts we’ll dissect each function and see what’s going on. The class definition wasn’t changed, so we start with get_data_generic

def get_data_generic(self, range = 1, bac_to_get = 0):
	'''gets the data from the database'''       

	if sys.platform == 'darwin':
		(cursor, database) = link_db(self.db_path)
	else:
		(cursor, database) = link_db()

	if range == 1:
		cursor.execute("""SELECT * FROM %s""" % self.table_name)
	elif range == 2:
		cursor.execute("""SELECT * FROM %s where idbac = %d""" % (self.table_name, bac_to_get))

	table_data = cursor.fetchall()
	raw_data = []
	for i in table_data:
		raw_data.append(list(i))

	self.table_data = raw_data
	database.close()

The first difference we notice here is the sys.platform usage. This is required if we intend to package our application as an OS X app, using py2app. When a Python/wxPython application is packaged in OS X, the actual application executable is inside the a directory named after the application (or whatever you set up). In our case here we don’t provide a way for the Python script to receive the path and name for the database on a command line, as we expect it to be in the executable’s current directory. Because of that we need to provide a “config” file (in our case here a one-line text file with the database path) inside the application wrapper, something we will see in the end of the series.

Another modification here is the range parameter and the addition of the bac_to_get parameter. Notice that both parameters have a value assigned to it. This means that they are optional, the function’s call can pass them or not. If it doesn’t pass, their value will be the one assigned on the function declaration. So, here if we are interested in getting all bacs, range will have the value of 1 and we don’t need to worry about it. If we want an specific bac we will pass range as 2 and then pass the bac_to_get ID to be returned.

A final change/addition is that we added a new select statement for the cases when range equals 2. This time we are adding the bac ID to be returned.

Previously in the series:
Part 1
Part 2
Part 3
Part 4
Part 5

Just illustrating my point (or lack of), an animation about the commits of RoR to its repository. Notice the jump after it was migrated to Github

Ruby on Rails from Ilya Grigorik on Vimeo.

Sorry for the non-Python post.

I start this post with an apology. I usually don’t rant or vent here, which are feelings that I usually reserve to my personal blog.

I don’t use BioPython, never used it. I have it installed in my systems, but I never wrote a piece of code importing BioPython routines. But I subscribe to their mailing lists, both user and developer. I maybe have written once to the list, and I just follow the discussions there.

Since last year one of the main topics has been the possibility of moving BioPython from CVS to another version control system. Yes, you read it right. It’s 2009 and BioPython uses CVS and their version control system. Soon, CVS will be like typewriters and LPs to young developers. Last stable release of CVS was sometime in 2005, what in interwebs time is equivalent to something like 1972. Since 2005, Subversion has taken the world of version control by storm, and Git is getting also very strong, not to mention Bazaar, Darcs, Mercurial and some others that I might not be aware of.

This is a discussion that have been dragging for sometime in the list. And it’s a shame, a clear lack of leadership from whoever is (not) leading the project. BioRuby is Git, BioPerl SVN and BioPython is CVS, because they “need to care for the legacy developers”. It’s like MSFT keeping two copies of the Notepad executable because they needed to cater to legacy applications, but with a different scale of course. With the current Python steam in the non-bioinformatics and bioinformatics community is very sad to see BioPython not evolving (before you ask me, no, I’m not interested in helping, not the way things are now). Perl which is language forever-in-waiting for its holy grail (Perl 6) has a strong community behind it, and more important an excellent leadership, that’s not scare of making decisions.

So, if you’re still using CVS, it’s 2009!