Python Tutorial

最新推荐文章于 2023-08-14 16:54:40 发布

james20141606

最新推荐文章于 2023-08-14 16:54:40 发布

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本文链接：https://blog.youkuaiyun.com/james20141606/article/details/105876803

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This is one of the chapter in Bioinformatics basic course in Tsinghua University. You may also find it here
mainly from my dear colleague Binbin Shi.

The related jupyter file

Python Tutorial

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Install Anaconda Python

URL: (https://www.anaconda.com/download/)

Easy install of data science packages (binary distribution)
Package management with

conda

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Install Python packages using conda:

conda install h5py

Update a package to the latest version:

conda update h5py

Install Python packages using pip:

pip install h5py

Update a package using pip:

pip install --upgrade h5py

Python language tips

Compatibility between Python 3.x and Python 2.x

Biggest difference: print is a function rather than statement in Python 3

This does not work in Python 3

print 1, 2, 3

Solution: use the __future__ module

from __future__ import print_function
# this works both in Python 2 and Python 3
print(1, 2, 3)

Second biggest difference: some package/function names in the standard library are changed

Python 2 => Python 3

cStringIO => io.StringIO
Queue => queue
cPickle => pickle
ConfigParser => configparser
HTMLParser => html.parser
SocketServer => socketserver
SimpleHTTPServer => http.server

Solution: use the six module

Refer to (https://docs.python.org/3/library/future.html) for usage of the __future__ module.
Refer to (https://pythonhosted.org/six/) for usage of the six module.

Get away from IndentationError

Python forces usage of tabs/spaces to indent code

# use a tab
for i in range(3):
    print(i)
# use 2 spaces
for i in range(3):
  print(i)
# use 4 spaces
for i in range(3):
    print(i)

Best practice: always use 4 spaces. You can set whether to use spaces(soft tabs) or tabs for indentation.

In vim editor, use :set list to inspect incorrect number of spaces/tabs.

Add Shebang and encoding at the beginning of executable scripts

Create a file named welcome.py

#! /usr/bin/env python
# -*- coding: UTF-8 -*-
print('welcome to python!')

Then set the python script as executable:

chmod +x welcome.py

Now you can run the script without specifying the Python interpreter:

./welcome.py

All variables, functions, classes are dynamic objects

class MyClass():
    def __init__(self, name):
        self.name = name
# assign an integer to a
a = 1
print(type(a))
# assign a string to a
a = 'abc'
print(type(a))
# assign a function to a
a = range
print(type(a))
print(a(10))
# assign a class to a
a = MyClass
print(type(a))
b = a('myclass')
print(b.name)
# assign an instance of a class to a
a = MyClass('myclass')
print(b.name)
# get type of a
print(type(a))

All python variables are pointers/references

a = [1, 2, 3]
print('a = ', a)
# this add another refrence to the list
b = a
print('b = ', b)
# this will change contents of both a and b
b[2] = 4
print('a = ', a)
print('b = ', b)

Use `deepcopy` if you really want to COPY a variable

from copy import deepcopy
a = {'A': [1], 'B': [2], 'C': [3]}
print(a)
# shallow copy
b = dict(a)
# modify elements of b will change contents of a
b['A'].append(2)
print('a = ', a)
print('b = ', b)
# this also does not work
c = {k:v for k, v in a}
c['A'].append(3)
print('a = ', a)
print('c = ', c)
# recurrently copy every object of a
d = deepcopy(a)
# modify elements of c will not change contents of a
d['A'].append(2)
print('a = ', a)
print('d = ', d)

What if I accidentally overwrite my builtin functions?

You can refer to (https://docs.python.org/2/library/functions.html) for builtin functions in the standard library.

A = [1, 2, 3, 4]
# Ops! the builtin function sum is overwritten by a number
sum = sum(A)
# this will raise an error because sum is not a function now
print(sum(A))
# recover the builtin function into the current environment
from __builtin__ import sum
# this works because sum is a function
print(sum(A))

Note: in Python 3, you should import from builtins rather than __builtin__

from builtins import sum

`int` is of arbitrary precision in Python!

In Pyhton:

print(2**10000)

In R:

print(2^10000)

Easiest way to swap values of two variables

In C/C++:

int a = 1, b = 2, t;
t = a;
a = b;
b = t;

In Python:

a = 1
b = 2
b, a = a, b
print(a, b)

List comprehension

Use for-loops:

a = []
for i in range(10):
    a.append(i + 10)
print(a)

Use list comprehension

a = [i + 10 for i in range(10)]
print(a)

Dict comprehension

Use for-loops:

a = {}
for i in range(10):
    a[i] = chr(ord('A') + i) 
print(a)

Use dict comprehension:

a = {i:chr(ord('A') + i) for i in range(10)}
print(a)

For the one-liners

Use ‘;’ instead of ‘\n’:

# print the first column of each line
python -c 'import sys; print("\n".join(line.split("\t")[0] for line in sys.stdin))'

For more examples of one-liners, please refer to (https://wiki.python.org/moin/Powerful%20Python%20One-Liners).

Read from standard input

import sys
# read line by line
for line in sys.stdin:
    print(line)

Order of dict keys are NOT as you expected

a = {'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5, 'F': 6}
# not in lexicographical order
print([key for key in a])
# now in lexicographical order
print([key for key in sorted(a)])

Use enumerate() to add a number during iteration

A = ['a', 'b', 'c', 'd']
for i, a in enumerate(A):
    print(i, a)

Reverse a list

# a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
a = range(10)
print(a)
print(a[::-1])

Strings are immutable in Python

a = 'ABCDF'
# will raise an Error
a[4] = 'E'
# convert str to bytearray
b = bytearray(a)
# bytearray are mutable
b[4] = 'E'
# convert bytearray to str
print(str(b))

tuples are hashable while lists are not hashable

# create dict using tuples as keys
d = {
    ('chr1', 1000, 2000): 'featureA',
    ('chr1', 2000, 3000): 'featureB',
    ('chr1', 3000, 4000): 'featureC',
    ('chr1', 4000, 5000): 'featureD',
    ('chr1', 5000, 6000): 'featureE',
    ('chr1', 6000, 7000): 'featureF'
}
# query the dict using tuples
print(d[('chr1', 3000, 4000)])
print(d[('chr1', 6000, 7000)])
# will raise an error
d = {['chr1', 1000, 2000]: 'featureA'}

Use itertools

Nested loops in a more concise way:

A = [1, 2, 3]
B = ['a', 'b', 'c']
C = ['i', 'j', 'k']
D = ['x', 'y', 'z']
# Use nested for-loops
for a in A:
    for b in B:
        for c in C:
            for d in D:
                print(a, b, c, d)
# Use itertools.product
import itertools
for a, b, c, d in itertools.product(A, B, C, D):
    print(a, b, c, d)

Get all combinations of a list:

A = ['A', 'B', 'C', 'D']
# Use itertools.combinations
import itertools
for a, b, c in itertools.combinations(A, 3):
    print(a, b, c)

Convert iterables to lists

import itertools
A = [1, 2, 3]
B = ['a', 'b', 'c']
a = itertools.product(A, B)
# a is a iterable rather than a list
print(a)
# a is a list now
a = list(a)
print(a)

Use the zip() function to transpose nested lists/tuples/iterables

records = [
    ('chr1', 1000, 2000),
    ('chr1', 2000, 3000),
    ('chr1', 3000, 4000),
    ('chr1', 4000, 5000),
    ('chr1', 5000, 6000),
    ('chr1', 6000, 7000)
]
# iterate by rows
for chrom, start, end in records:
    print(chrom, start, end)
# extract columns
chroms, starts, ends = zip(*records)
# build records from columns
# now records2 is the same as records
records2 = zip(chroms, starts, ends)
print(records)

Global and local variables

# a is global
a = 1
def print_local():
    # a is local
    a = 2
    print(a)

def print_global():
    # a is global
    global a
    a = 2
    print(a)

# print global variable
print(a)
# print local variable from function
print_local()
# a is unchanged
print(a)
# change and print global from function
print_global()
# a is changed
print(a)

Use defaultdict

Use dict:

d = {}
d['a'] = []
d['b'] = []
d['c'] = []
# extend list with new elements
d['a'] += [1, 2]
d['b'] += [3, 4, 5]
d['c'] += [6]
for key, val in d.items():
    print(key, val)

Use defaultdict:

from collections import defaultdict
# a new list is created automatically when new elements are added
d = defaultdict(list)
# extend list with new elements
d['a'] += [1, 2]
d['b'] += [3, 4, 5]
d['c'] += [6]
for key, val in d.items():
    print(key, val)

Use generators

Example: read a large FASTA file

def append_extra_line(f):
    """Yield an empty line after the last line in the file
    """
    for line in f:
        yield line
    yield ''

def read_fasta(filename):
    with open(filename, 'r') as f:
        name = None
        seq = ''
        for line in append_extra_line(f):
            if line.startswith('>') or (len(line) == 0):
                if (len(seq) > 0) and (name is not None):
                    yield (name, seq)
                if line.startswith('>'):
                    name = line.strip()[1:].split()[0]
                    seq = ''
            else:
                if name is None:
                    raise ValueError('the first line does not start with ">"')
                seq += line.strip()
# print sequence name and length of each 
for name, seq in read_fasta('test.fa'):
    print(name, len(seq))

Turn off annoying KeyboardInterrupt and BrokenPipe Error

Without exception handling (press Ctrl+C):

import time
time.sleep(300)

With exception handling (press Ctrl+C):

import time
import errno

try:
    time.sleep(300)
except KeyboardInterrupt:
    sys.exit(1)
except OSError as e:
    if e.errno == errno.EPIPE:
        sys.exit(-e.errno)

Class and instance variables

class MyClass():
    name = 'class_name'
    def __init__(self, name):
        self.name = name

    def change_name(self, name):
        self.name = name

# print class variable
print(MyClass.name)
# create an instance from MyClass
a = MyClass('instance_name')
# print instance name
print(a.name)
# change instance name
a.change_name('instance_new_name')
print(a.name)
print(MyClass.name)
# change class name
MyClass.name = 'class_new_name'
print(a.name)
print(MyClass.name)

Useful Python packages for data analysis

Browser-based interactive programming in Python: jupyter

URL: (http://jupyter.org/)

Start jupyter notebook

jupyter notebook --no-browser

Jupyter notebooks manager

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Jupyter process manager

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Jupyter notebook

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Integrate with matplotlib

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Browser-based text editor

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Browser-based terminal

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Display image

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Display dataframe

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Display audio

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Embedded markdown

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Python packages for scientific computing

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Vector arithmetics: numpy

URL: (http://www.numpy.org/)

Example:

import numpy as np
# create an empty matrix of shape (5, 4)
X = np.zeros((5, 4), dtype=np.int32)
# create an array of length 5: [0, 1, 2, 3, 4]
y = np.arange(5)
# create an array of length 4: [0, 1, 2, 3]
z = np.arange(4)
# set Row 1 to [0, 1, 2, 3]
X[0] = np.arange(4)
# set Row 2 to [1, 1, 1, 1]
X[1] = 1
# add 1 to all elements
X += 1
# add y to each row of X
X += y.reshape((-1, 1))
# add z to each column of X
X += z.reshape((1, -1))
# get row sums => 
row_sums = X.sum(axis=1)
# get column sums
col_sums = X.sum(axis=0)
# matrix multiplication
A = X.dot(X.T)
# save matrix to text file
np.savetxt('data.txt', A)

Numerical analysis (probability distribution, signal processing, etc.): scipy

URL: (https://www.scipy.org/)

scipy.stats contains a large number probability distributions:
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Unified interface for all probability distributions:
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Just-in-time (JIT) compiler for vector arithmetics

URL: (https://numba.pydata.org/)

Compile python for-loops to native code to achive similar performance to C/C++ code.
Example:

from numba import jit
from numpy import arange

# jit decorator tells Numba to compile this function.
# The argument types will be inferred by Numba when function is called.
@jit
def sum2d(arr):
    M, N = arr.shape
    result = 0.0
    for i in range(M):
        for j in range(N):
            result += arr[i,j]
    return result

a = arange(9).reshape(3,3)
print(sum2d(a))

Library for symbolic computation: sympy

URL: (http://www.sympy.org/en/index.html)

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Operation on data frames: pandas

URL: (http://pandas.pydata.org/pandas-docs/stable/)

Example:

import pandas as pd
# read a bed file
genes = pd.read_table('gene.bed', header=None, sep='\t',
                     names=('chrom', 'start', 'end', 'gene_id', 'score', 'strand', 'biotype'))
# get all gene IDs
gene_ids = genes['gene_id']
# set gene_id as index
genes.index = genes['gene_id']
# get row with given gene_id
gene = genes.loc['ENSG00000212325.1']
# get rows with biotype = 'protein_coding'
genes_selected = genes[genes['biotype'] == 'protein_coding']]
# get protein coding genes in chr1
genes_selected = genes.query('(biotype == "protein_coding") and (chrom == "chr1")')
# count genes for each biotype
biotype_counts = genes.groupby('biotype')['gene_id'].count()
# add a column for gene length
genes['length'] = genes['end'] - genes['start']
# calculate average gene length for each chromosome and biotype
length_table = genes.pivot_table(values='length', index='biotype', columns='chrom')
# save DataFrame to Excel file
length_table.to_excel('length_table.xlsx')

Basic graphics and plotting: matplotlib

URL: (https://matplotlib.org/contents.html)

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Statistical data visualization: seaborn

URL: (https://seaborn.pydata.org/)

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Interactive programming in Python: ipython

URL: (http://ipython.org/ipython-doc/stable/index.html)

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Statistical tests: statsmodels

URL: (https://www.statsmodels.org/stable/index.html)

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Machine learning algorithms: scikit-learn

URL: (http://scikit-learn.org/)

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Example:

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, accuracy_score

# generate ramdom data
X, y = make_classification(n_samples=1000, n_classes=2, n_features=10)
# split dataset into training and test dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# create an classifier object
model = LogisticRegression()
# training the classifier
model.fit(X_train, y_train)
# predict outcomes on the test dataset
y_pred = model.predict(X_test)
# evalualte the classification performance
print('roc_auc_score = %f'%roc_auc_score(y_test, y_pred))
print('accuracy_score = %f'%accuracy_score(y_test, y_pred))

Natural language analysis: gensim

URL: (https://radimrehurek.com/gensim/)

HTTP library: requests

URL: (http://docs.python-requests.org/en/master/)

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Lightweight Web framework: flask

URL: (http://flask.pocoo.org/)

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Deep learning framework: tensorflow

URL: (http://tensorflow.org/)

High-level deep learning framework: keras

URL: (https://keras.io/)

Operation on sequence and alignment formats: biopython

URL: (http://biopython.org/)

from Bio import SeqIO
for record in SeqIO.parse('test.fa', 'fasta'):
    print(record.id, len(record.seq))

from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
sequences = [
    SeqRecord(Seq('ACCGGTATCTATATCCCCGAGAGGAATGGGTCAGACATGGACCTAC'), id='A', description=''),
    SeqRecord(Seq('TTACAATGTGGCAGTGAACGCGTGACAATCCTCCCCGTTGGACAT'), id='B', description=''),
    SeqRecord(Seq('CAAAGCTGCATCGAATTGTCGAGACAACACTAGATTTAAGCGCA'), id='C', description=''),
    SeqRecord(Seq('CGCCCGCGAGGGCAATCAGGACGGATTTACGGAT'), id='D', description=''),
    SeqRecord(Seq('CCGCCCACGCTCCCGTTTTCTTCCATACCTGTCC'), id='E', description='')
]
with open('test_out.fa', 'w') as f:
    SeqIO.write(sequences, f, 'fasta')

Operation on genomic formats (BigWig,etc.): bx-python

Operation on HDF5 files: h5py

URL: (https://www.h5py.org/)

Save data to an HDF5 file

import h5py
import numpy as np
# generate data
chroms = ['chr1', 'chr2', 'chr3']
chrom_sizes = {
    'chr1': 15000,
    'chr2': 12000,
    'chr3': 11000
}
coverage = {}
counts = {}
for chrom, size in chrom_sizes.items():
    coverage[chrom] = np.random.randint(10, 1000, size=size)
    counts[chrom] = np.random.randint(1000, size=size)%coverage[chrom]
# save data to an HDF5 file
with h5py.File('dataset.h5', 'w') as f:
    for chrom in chrom_sizes:
        g = f.create_group(chrom)
        g.create_dataset('coverage', data=coverage[chrom])
        g.create_dataset('counts', data=counts[chrom])

h5ls -r dataset.h5

/                        Group
/chr1                    Group
/chr1/counts             Dataset {15000}
/chr1/coverage           Dataset {15000}
/chr2                    Group
/chr2/counts             Dataset {12000}
/chr2/coverage           Dataset {12000}
/chr3                    Group
/chr3/counts             Dataset {11000}
/chr3/coverage           Dataset {11000}

Read data from an HDF file:

import h5py
# read data from an HDF5 file
with h5py.File('dataset.h5', 'r') as f:
    coverage = {}
    counts = {}
    for chrom in f.keys():
        coverage[chrom] = f[chrom + '/coverage'][:]
        counts[chrom] = f[chrom + '/counts'][:]

Mixed C/C++ and python programming: cython

URL: (http://cython.org/)

import numpy as np
cimport numpy as np
cimport cython
from cython.parallel import prange
from cython.parallel cimport parallel
cimport openmp

@cython.boundscheck(False) # turn off bounds-checking for entire function
@cython.wraparound(False)  # turn off negative index wrapping for entire function
def compute_mse_grad_linear_ard(np.ndarray[np.float64_t, ndim=1] w,
        np.ndarray[np.float64_t, ndim=2] X1,
        np.ndarray[np.float64_t, ndim=2] X2,
        np.ndarray[np.float64_t, ndim=2] Kinv1,
        np.ndarray[np.float64_t, ndim=2] K2,
        np.ndarray[np.float64_t, ndim=2] a,
        np.ndarray[np.float64_t, ndim=2] err,
        np.ndarray[np.float64_t, ndim=2] mask=None):
    '''Compute the gradients of MSE on the test samples with respect to relevance vector w.
    :param w: 1D array of shape [n_features]
    :return: gradients of MSE wrt. 2, 1D array of shape [n_features]
    '''
    cdef np.int64_t N1, N2, p
    cdef np.int64_t k, i, j, m
    N1 = X1.shape[0]
    N2 = X2.shape[0]
    p = X2.shape[1]

    cdef np.ndarray[np.float64_t, ndim=2] K2Kinv1 = K2.dot(Kinv1)
    cdef np.ndarray[np.float64_t, ndim=1] mse_grad = np.zeros_like(w)

    #cdef np.ndarray[np.float64_t, ndim=3] K1_grad = np.zeros((p, N1, N1), dtype=np.float64)
    #cdef np.ndarray[np.float64_t, ndim=3] K2_grad = np.zeros((p, N2, N1), dtype=np.float64)
    #cdef np.ndarray[np.float64_t, ndim=3] K_grad =  np.zeros((p, N2, N1), dtype=np.float64)
    cdef np.int64_t max_n_threads = openmp.omp_get_max_threads()
    cdef np.ndarray[np.float64_t, ndim=3] K1_grad = np.zeros((max_n_threads, N1, N1), dtype=np.float64)
    cdef np.ndarray[np.float64_t, ndim=3] K2_grad = np.zeros((max_n_threads, N2, N1), dtype=np.float64)
    cdef np.ndarray[np.float64_t, ndim=3] K_grad  = np.zeros((max_n_threads, N1, N1), dtype=np.float64)

    cdef np.int64_t thread_id
    with nogil, parallel():
        for k in prange(p):
            thread_id = openmp.omp_get_thread_num()
            # compute K1_grad
            for i in range(N1):
                for j in range(N1):
                    K1_grad[thread_id, i, j] = 2.0*w[k]*X1[i, k]*X1[j, k]
            # compute K2_grad
            for i in range(N2):
                for j in range(N1):
                    K2_grad[thread_id, i, j] = 2.0*w[k]*X2[i, k]*X1[j, k]
            # compute K_grad
            for i in range(N2):
                for j in range(N1):
                    K_grad[thread_id, i, j] = K2_grad[thread_id, i, j]
                    for m in range(N1):
                        K_grad[thread_id, i, j] += K2Kinv1[i, m]*K1_grad[thread_id, m, j]
            # compute mse_grad
            for i in range(N2):
                for j in range(N1):
                    mse_grad[k] += err[i, 0]*K_grad[thread_id, i, j]*a[j, 0]
    return mse_grad, K_grad

Progress bar: tqdm

URL: (https://pypi.python.org/pypi/tqdm)

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Example Python scripts

View a table in a pretty way

The original table is ugly:

head -n 15 metadata.tsv

Output:

File accession	File format	Output type	Experiment accession	Assay	Biosample term id
ENCFF983DFB	fastq	reads	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF590TBW	fastq	reads	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF258RWG	bam	unfiltered alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF468LRV	bam	unfiltered alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF216EBS	bam	alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF232QFN	bam	unfiltered alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF682NGE	bam	alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF328UKA	bam	unfiltered alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF165COO	bam	alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF466OLG	bam	alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF595HIY	bigBed narrowPeak	peaks	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF494CKB	bigWig	fold change over control	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF308BXW	bigWig	fold change over control	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF368IHM	bed narrowPeak	peaks	ENCSR429XTR	ChIP-seq	EFO:0002067

Now display the table more clearly:

head -n 15 metadata.tsv | tvi -d $'\t' -j center

Output:

File accession    File format          Output type        Experiment accession  Assay   Biosample term id
 ENCFF983DFB         fastq                reads               ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF590TBW         fastq                reads               ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF258RWG          bam         unfiltered alignments       ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF468LRV          bam         unfiltered alignments       ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF216EBS          bam               alignments            ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF232QFN          bam         unfiltered alignments       ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF682NGE          bam               alignments            ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF328UKA          bam         unfiltered alignments       ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF165COO          bam               alignments            ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF466OLG          bam               alignments            ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF595HIY   bigBed narrowPeak          peaks               ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF494CKB        bigWig       fold change over control     ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF308BXW        bigWig       fold change over control     ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF368IHM    bed narrowPeak            peaks               ENCSR429XTR      ChIP-seq    EFO:0002067

You can also get some help by typing tvi -h:

usage: tvi [-h] [-d DELIMITER] [-j {left,right,center}] [-s SEPARATOR]
           [infile]

Print tables pretty

positional arguments:
  infile                input file, default is stdin

optional arguments:
  -h, --help            show this help message and exit
  -d DELIMITER          delimiter of fields of input. Default is white space.
  -j {left,right,center}
                        justification, either left, right or center. Default
                        is left
  -s SEPARATOR          separator of fields in output

tvi.py

#! /usr/bin/env python

import sys
import argparse
import os
from cStringIO import StringIO

def main():
	parser = argparse.ArgumentParser(description='Print tables pretty')
	parser.add_argument('infile', type=str, nargs='?',
					 help='input file, default is stdin')
	parser.add_argument('-d', dest='delimiter', type=str,
					 required=False,
					 help='delimiter of fields of input. Default is white space.')
	parser.add_argument('-j', dest='justify', type=str,
					 required=False, default='left',
					 choices=['left', 'right', 'center'],
					 help='justification, either left, right or center. Default is left')
	parser.add_argument('-s', dest='separator', type=str,
					 required=False, default=' ',
					 help='separator of fields in output')
	args = parser.parse_args()

	table = []
	maxwidth = []

	# default is to read from stdin
	fin = sys.stdin
	if args.infile:
		try:
			fin = open(args.infile, 'rt')
		except IOError as e:
			sys.stderr.write('Error: %s: %s\n'%(e.strerror, args.infile))
			sys.exit(e.errno)

	for line in fin:
		fields = None
		# split line by delimiter
		if args.delimiter:
			fields = line.strip().split(args.delimiter)
		else:
			fields = line.strip().split()
		for i in xrange(len(fields)):
			width = len(fields[i])
			if (i+1) > len(maxwidth):
				maxwidth.append(width)
			else:
				if width > maxwidth[i]:
					maxwidth[i] = width
		table.append(fields)
	fin.close()

	try:
		for fields in table:
			line = StringIO()
			for i in xrange(len(fields)):
				# format field with different justification
				nSpace = maxwidth[i] - len(fields[i])
				if args.justify == 'left':
					line.write(fields[i])
					for j in xrange(nSpace):
						line.write(' ')
				elif args.justify == 'right':
					for j in xrange(nSpace):
						line.write(' ')
					line.write(fields[i])
				elif args.justify == 'center':
					for j in xrange(nSpace/2):
						line.write(' ')
					line.write(fields[i])
					for j in xrange(nSpace - nSpace/2):
						line.write(' ')

				line.write(args.separator)
			print line.getvalue()
			line.close()
	except IOError:
		sys.exit(-1)

if __name__ == '__main__':
	main()

Generate a random FASTA file

seqgen.py

#! /usr/bin/env python

import sys
import argparse
import textwrap
import random

def write_fasta(fout, seq, name='seq', description=None):
	if description:
		fout.write('>' + name + ' ' + description + '\n')
	else:
		fout.write('>' + name + '\n')
	fout.write(textwrap.fill(seq) + '\n')

def main():
	parser = argparse.ArgumentParser(description='Generate sequences and output in various formats')
	parser.add_argument('-n', '--number', dest='number', type=int, required=False,
					 default=10, help='Number of sequences to generate')
	parser.add_argument('--min-length', dest='min_length', type=int, required=False,
					 default=30, help='Minimal length')
	parser.add_argument('--max-length', dest='max_length', type=int, required=False,
					 default=50, help='Maximal length')
	parser.add_argument('-l', '--length', type=int, required=False,
					 help='Fixed length. If specified, --min-length and --max-length will be ignored.')
	parser.add_argument('-a', '--alphabet', type=str, required=False,
					 default='ATGC', help='Letters to used in the sequences')
	parser.add_argument('-f', '--format', type=str, required=False,
					 choices=['fasta', 'text'], default='fasta', help='Output formats')
	parser.add_argument('-o', '--outfile', type=argparse.FileType('w'), required=False,
					 default=sys.stdout, help='Output file name')
	parser.add_argument('-p', '--prefix', type=str, required=False,
					 default='RN_', help='Prefix of sequence names for fasta format')
	args = parser.parse_args()

	rand = random.Random()
	for i in xrange(args.number):
		if args.length:
			length = args.length
		else:
			length = rand.randint(args.min_length, args.max_length)
		seq = bytearray(length)
		for j in xrange(length):
			seq[j] = rand.choice(args.alphabet)
		if args.format == 'fasta':
			write_fasta(args.outfile, str(seq), args.prefix + '%08d'%i)
		else:
			args.outfile.write(seq + '\n')
	args.outfile.close()

if __name__ == '__main__':
	main()

Weekly tasks

All files you need for completing the tasks can be found at: weekly_tasks.zip

Task 1: run examples (Python tips, numpy, pandas) in this tutorial

Install Anaconda on your PC. Try to understand example code and run in Jupyter or IPython.

Task 2: write a Python program to convert a GTF file to BED12 format

Please refer to (https://genome.ucsc.edu/FAQ/FAQformat.html#format1) for BED12 format and refer to (https://www.ensembl.org/info/website/upload/gff.html) for GTF format.
GTF example:

chr1	HAVANA	gene	29554	31109	.	+	.	gene_id "ENSG00000243485.5"; gene_type "lincRNA"; gene_name "MIR1302-2HG"; level 2; tag "ncRNA_host"; havana_gene "OTTHUMG00000000959.2";
chr1	HAVANA	transcript	29554	31097	.	+	.	gene_id "ENSG00000243485.5"; transcript_id "ENST00000473358.1"; gene_type "lincRNA"; gene_name "MIR1302-2HG"; transcript_type "lincRNA"; transcript_name "MIR1302-2HG-202"; level 2; transcript_support_level "5"; tag "not_best_in_genome_evidence"; tag "dotter_confirmed"; tag "basic"; havana_gene "OTTHUMG00000000959.2"; havana_transcript "OTTHUMT00000002840.1";
chr1	HAVANA	exon	29554	30039	.	+	.	gene_id "ENSG00000243485.5"; transcript_id "ENST00000473358.1"; gene_type "lincRNA"; gene_name "MIR1302-2HG"; transcript_type "lincRNA"; transcript_name "MIR1302-2HG-202"; exon_number 1; exon_id "ENSE00001947070.1"; level 2; transcript_support_level "5"; tag "not_best_in_genome_evidence"; tag "dotter_confirmed"; tag "basic"; havana_gene "OTTHUMG00000000959.2"; havana_transcript "OTTHUMT00000002840.1";

BED12 example:

chr1	67522353	67532326	ENST00000230113	0	+	0	0	0	5	45,60,97,64,221,	0,5024,7299,7961,9752,
chr1	39249837	39257649	ENST00000289890	0	-	0	0	0	3	365,78,115,	0,4304,7697,
chr1	144245237	144250279	ENST00000294715	0	-	0	0	0	3	78,135,55,	0,448,4987,
chr1	15111814	15152464	ENST00000310916	0	-	0	0	0	6	5993,578,121,88,146,174,	0,6512,8762,9157,12413,40476,
chr1	34975698	34978706	ENST00000311990	0	-	0	0	0	3	1704,154,29,	0,2232,2979,

The GTF file is weekly_tasks/gencode.v27.long_noncoding_RNAs.gtf.
Each line in the output file is a transcript with the 4th columns as transcript ID
The version number of the transcript ID should be stripped (e.g. ENST00000473358.1 => ENST00000473358).
The output file is sorted first by transcript IDs and then by chromosome in lexicographical order.
Column 5, 7, 8, 9 in the BED12 file should be set to 0.
Please do NOT use any external tools (e.g. sort, awk, etc.) in your program other than Python.
An example output can be found in weekly_tasks/transcripts.bed.

Hint: use dict, list, tuple, str.split, re.match, sorted.

Task 3: write a Python program to add a prefix to all directories

Each prefix is a two-digit number starting from 00 and ‘-’. If the number is less than 10, a single ‘0’ letter should be filled.
The files/directories should be numbered according to the lexicographical order.
For example, if the original directory structure is:

.
├── A
│   ├── A
│   │   ├── A
│   │   ├── B
│   │   └── C
│   ├── B
│   │   └── A
│   └── C
│       └── A
├── B
│   ├── A
│   └── B
└── C
    ├── A
    └── B
        └── A

then you should get the following directory structure after renaming:

.
├── 00-A
│   ├── 00-A
│   │   ├── 00-A
│   │   ├── 01-B
│   │   └── 02-C
│   ├── 01-B
│   │   └── 00-A
│   └── 02-C
│       └── 00-A
├── 01-B
│   ├── 00-A
│   └── 01-B
└── 02-C
    ├── 00-A
    └── 01-B
        └── 00-A

The original directories can be found in weekly_tasks/original_dirs.
The root directory (i.e. original_dirs) should not be renamed.
You can use tree command to display the directory structure as shown above.
An example result can be found in weekly_tasks/renamed_dirs.
Hint: use os.listdir, os.rename, str.format, sorted, yield.