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?
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))'
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
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
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))
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')
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))
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'][:]
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
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
