I'm new to programming and python and I'm looking for a way to distinguish between two input formats in the same input file text file. For example, let's say I have an input file like so where values are comma-separated:
5
Washington,A,10
New York,B,20
Seattle,C,30
Boston,B,20
Atlanta,D,50
2
New York,5
Boston,10
Where the format is N followed by N lines of Data1, and M followed by M lines of Data2. I tried opening the file, reading it line by line and storing it into one single list, but I'm not sure how to go about to produce 2 lists for Data1 and Data2, such that I would get:
Data1 = ["Washington,A,10", "New York,B,20", "Seattle,C,30", "Boston,B,20", "Atlanta,D,50"]
Data2 = ["New York,5", "Boston,10"]
My initial idea was to iterate through the list until I found an integer i, remove the integer from the list and continue for the next i iterations all while storing the subsequent values in a separate list, until I found the next integer and then repeat. However, this would destroy my initial list. Is there a better way to separate the two data formats in different lists?
You could use itertools.islice and a list comprehension:
from itertools import islice
string = """
5
Washington,A,10
New York,B,20
Seattle,C,30
Boston,B,20
Atlanta,D,50
2
New York,5
Boston,10
"""
result = [[x for x in islice(parts, idx + 1, idx + 1 + int(line))]
for parts in [string.split("\n")]
for idx, line in enumerate(parts)
if line.isdigit()]
print(result)
This yields
[['Washington,A,10', 'New York,B,20', 'Seattle,C,30', 'Boston,B,20', 'Atlanta,D,50'], ['New York,5', 'Boston,10']]
For a file, you need to change it to:
with open("testfile.txt", "r") as f:
result = [[x for x in islice(parts, idx + 1, idx + 1 + int(line))]
for parts in [f.read().split("\n")]
for idx, line in enumerate(parts)
if line.isdigit()]
print(result)
You're definitely on the right track.
If you want to preserve the original list here, you don't actually have to remove integer i; you can just go on to the next item.
Code:
originalData = []
formattedData = []
with open("data.txt", "r") as f :
f = list(f)
originalData = f
i = 0
while i < len(f): # Iterate through every line
try:
n = int(f[i]) # See if line can be cast to an integer
originalData[i] = n # Change string to int in original
formattedData.append([])
for j in range(n):
i += 1
item = f[i].replace('\n', '')
originalData[i] = item # Remove newline char in original
formattedData[-1].append(item)
except ValueError:
print("File has incorrect format")
i += 1
print(originalData)
print(formattedData)
The following code will produce a list results which is equal to [Data1, Data2].
The code assumes that the number of entries specified is exactly the amount that there is. That means that for a file like this, it will not work.
2
New York,5
Boston,10
Seattle,30
The code:
# get the data from the text file
with open('filename.txt', 'r') as file:
lines = file.read().splitlines()
results = []
index = 0
while index < len(lines):
# Find the start and end values.
start = index + 1
end = start + int(lines[index])
# Everything from the start up to and excluding the end index gets added
results.append(lines[start:end])
# Update the index
index = end
i am trying to extract a specific line as variable in file.
this is content of my test.txt
#first set
Task Identification Number: 210CT1
Task title: Assignment 1
Weight: 25
fullMark: 100
Description: Program and design and complexity running time.
#second set
Task Identification Number: 210CT2
Task title: Assignment 2
Weight: 25
fullMark: 100
Description: Shortest Path Algorithm
#third set
Task Identification Number: 210CT3
Task title: Final Examination
Weight: 50
fullMark: 100
Description: Close Book Examination
this is my code
with open(home + '\\Desktop\\PADS Assignment\\test.txt', 'r') as mod:
for line in mod:
taskNumber , taskTile , weight, fullMark , desc = line.strip(' ').split(": ")
print(taskNumber)
print(taskTile)
print(weight)
print(fullMark)
print(description)
here is what i'm trying to do:
taskNumber is 210CT1
taskTitle is Assignment 1
weight is 25
fullMark is 100
desc is Program and design and complexity running time
and loop until the third set
but there's an error occurred in the output
ValueError: not enough values to unpack (expected 5, got 2)
Reponse for SwiftsNamesake
i tried out your code . i am still getting an error.
ValueError: too many values to unpack (expected 5)
this is my attempt by using your code
from itertools import zip_longest
def chunks(iterable, n, fillvalue=None):
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
with open(home + '\\Desktop\\PADS Assignment\\210CT.txt', 'r') as mod:
for group in chunks(mod.readlines(), 5+2, fillvalue=''):
# Choose the item after the colon, excluding the extraneous rows
# that don't have one.
# You could probably find a more elegant way of achieving the same thing
l = [item.split(': ')[1].strip() for item in group if ':' in item]
taskNumber , taskTile , weight, fullMark , desc = l
print(taskNumber , taskTile , weight, fullMark , desc, sep='|')
As previously mentioned, you need some sort of chunking. To chunk it usefully we'd also need to ignore the irrelevant lines of the file. I've implemented such a function with some nice Python witchcraft below.
It might also suit you to use a namedtuple to store the values. A namedtuple is a pretty simple type of object, that just stores a number of different values - for example, a point in 2D space might be a namedtuple with an x and a y field. This is the example given in the Python documentation. You should refer to that link for more info on namedtuples and their uses, if you wish. I've taken the liberty of making a Task class with the fields ["number", "title", "weight", "fullMark", "desc"].
As your variables are all properties of a task, using a named tuple might make sense in the interest of brevity and clarity.
Aside from that, I've tried to generally stick to your approach, splitting by the colon. My code produces the output
================================================================================
number is 210CT1
title is Assignment 1
weight is 25
fullMark is 100
desc is Program and design and complexity running time.
================================================================================
number is 210CT2
title is Assignment 2
weight is 25
fullMark is 100
desc is Shortest Path Algorithm
================================================================================
number is 210CT3
title is Final Examination
weight is 50
fullMark is 100
desc is Close Book Examination
which seems to be roughly what you're after - I'm not sure how strict your output requirements are. It should be relatively easy to modify to that end, though.
Here is my code, with some explanatory comments:
from collections import namedtuple
#defines a simple class 'Task' which stores the given properties of a task
Task = namedtuple("Task", ["number", "title", "weight", "fullMark", "desc"])
#chunk a file (or any iterable) into groups of n (as an iterable of n-tuples)
def n_lines(n, read_file):
return zip(*[iter(read_file)] * n)
#used to strip out empty lines and lines beginning with #, as those don't appear to contain any information
def line_is_relevant(line):
return line.strip() and line[0] != '#'
with open("input.txt") as in_file:
#filters the file for relevant lines, and then chunks into 5 lines
for task_lines in n_lines(5, filter(line_is_relevant, in_file)):
#for each line of the task, strip it, split it by the colon and take the second element
#(ie the remainder of the string after the colon), and build a Task from this
task = Task(*(line.strip().split(": ")[1] for line in task_lines))
#just to separate each parsed task
print("=" * 80)
#iterate over the field names and values in the task, and print them
for name, value in task._asdict().items():
print("{} is {}".format(name, value))
You can also reference each field of the Task, like this:
print("The number is {}".format(task.number))
If the namedtuple approach is not desired, feel free to replace the content of the main for loop with
taskNumber, taskTitle, weight, fullMark, desc = (line.strip().split(": ")[1] for line in task_lines)
and then your code will be back to normal.
Some notes on other changes I've made:
filter does what it says on the tin, only iterating over lines that meet the predicate (line_is_relevant(line) is True).
The * in the Task instantiation unpacks the iterator, so each parsed line is an argument to the Task constructor.
The expression (line.strip().split(": ")[1] for line in task_lines) is a generator. This is needed because we're doing multiple lines at once with task_lines, so for each line in our 'chunk' we strip it, split it by the colon and take the second element, which is the value.
The n_lines function works by passing a list of n references to the same iterator to the zip function (documentation). zip then tries to yield the next element from each element of this list, but as each of the n elements is an iterator over the file, zip yields n lines of the file. This continues until the iterator is exhausted.
The line_is_relevant function uses the idea of "truthiness". A more verbose way to implement it might be
def line_is_relevant(line):
return len(line.strip()) > 0 and line[0] != '#'
However, in Python, every object can implicitly be used in boolean logic expressions. An empty string ("") in such an expression acts as False, and a non-empty string acts as True, so conveniently, if line.strip() is empty it will act as False and line_is_relevant will therefore be False. The and operator will also short-circuit if the first operand is falsy, which means the second operand won't be evaluated and therefore, conveniently, the reference to line[0] will not cause an IndexError.
Ok, here's my attempt at a more extended explanation of the n_lines function:
Firstly, the zip function lets you iterate over more than one 'iterable' at once. An iterable is something like a list or a file, that you can go over in a for loop, so the zip function can let you do something like this:
>>> for i in zip(["foo", "bar", "baz"], [1, 4, 9]):
... print(i)
...
('foo', 1)
('bar', 4)
('baz', 9)
The zip function returns a 'tuple' of one element from each list at a time. A tuple is basically a list, except it's immutable, so you can't change it, as zip isn't expecting you to change any of the values it gives you, but to do something with them. A tuple can be used pretty much like a normal list apart from that. Now a useful trick here is using 'unpacking' to separate each of the bits of the tuple, like this:
>>> for a, b in zip(["foo", "bar", "baz"], [1, 4, 9]):
... print("a is {} and b is {}".format(a, b))
...
a is foo and b is 1
a is bar and b is 4
a is baz and b is 9
A simpler unpacking example, which you may have seen before (Python also lets you omit the parentheses () here):
>>> a, b = (1, 2)
>>> a
1
>>> b
2
Although the n-lines function doesn't use this. Now zip can also work with more than one argument - you can zip three, four or as many lists (pretty much) as you like.
>>> for i in zip([1, 2, 3], [0.5, -2, 9], ["cat", "dog", "apple"], "ABC"):
... print(i)
...
(1, 0.5, 'cat', 'A')
(2, -2, 'dog', 'B')
(3, 9, 'apple', 'C')
Now the n_lines function passes *[iter(read_file)] * n to zip. There are a couple of things to cover here - I'll start with the second part. Note that the first * has lower precedence than everything after it, so it is equivalent to *([iter(read_file)] * n). Now, what iter(read_file) does, is constructs an iterator object from read_file by calling iter on it. An iterator is kind of like a list, except you can't index it, like it[0]. All you can do is 'iterate over it', like going over it in a for loop. It then builds a list of length 1 with this iterator as its only element. It then 'multiplies' this list by n.
In Python, using the * operator with a list concatenates it to itself n times. If you think about it, this kind of makes sense as + is the concatenation operator. So, for example,
>>> [1, 2, 3] * 3 == [1, 2, 3] + [1, 2, 3] + [1, 2, 3] == [1, 2, 3, 1, 2, 3, 1, 2, 3]
True
By the way, this uses Python's chained comparison operators - a == b == c is equivalent to a == b and b == c, except b only has to be evaluated once,which shouldn't matter 99% of the time.
Anyway, we now know that the * operator copies a list n times. It also has one more property - it doesn't build any new objects. This can be a bit of a gotcha -
>>> l = [object()] * 3
>>> id(l[0])
139954667810976
>>> id(l[1])
139954667810976
>>> id(l[2])
139954667810976
Here l is three objects - but they're all in reality the same object (you might think of this as three 'pointers' to the same object). If you were to build a list of more complex objects, such as lists, and perform an in place operation like sorting them, it would affect all elements of the list.
>>> l = [ [3, 2, 1] ] * 4
>>> l
[[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1]]
>>> l[0].sort()
>>> l
[[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]]
So [iter(read_file)] * n is equivalent to
it = iter(read_file)
l = [it, it, it, it... n times]
Now the very first *, the one with the low precedence, 'unpacks' this, again, but this time doesn't assign it to a variable, but to the arguments of zip. This means zip receives each element of the list as a separate argument, instead of just one argument that is the list. Here is an example of how unpacking works in a simpler case:
>>> def f(a, b):
... print(a + b)
...
>>> f([1, 2]) #doesn't work
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: f() missing 1 required positional argument: 'b'
>>> f(*[1, 2]) #works just like f(1, 2)
3
So in effect, now we have something like
it = iter(read_file)
return zip(it, it, it... n times)
Remember that when you 'iterate' over a file object in a for loop, you iterate over each lines of the file, so when zip tries to 'go over' each of the n objects at once, it draws one line from each object - but because each object is the same iterator, this line is 'consumed' and the next line it draws is the next line from the file. One 'round' of iteration from each of its n arguments yields n lines, which is what we want.
Your line variable gets only Task Identification Number: 210CT1 as its first input. You're trying to extract 5 values from it by splitting it by :, but there are only 2 values there.
What you want is to divide your for loop into 5, read each set as 5 lines, and split each line by :.
The problem here is that you are spliting the lines by : and for each line there is only 1 : so there are 2 values.
In this line:
taskNumber , taskTile , weight, fullMark , desc = line.strip(' ').split(": ")
you are telling it that there are 5 values but it only finds 2 so it gives you an error.
One way to fix this is to run multiple for loops one for each value since you are not allowed to change the format of the file. I would use the first word and sort the data into different
import re
Identification=[]
title=[]
weight=[]
fullmark=[]
Description=[]
with open(home + '\\Desktop\\PADS Assignment\\test.txt', 'r') as mod::
for line in mod:
list_of_line=re.findall(r'\w+', line)
if len(list_of_line)==0:
pass
else:
if list_of_line[0]=='Task':
if list_of_line[1]=='Identification':
Identification.append(line[28:-1])
if list_of_line[1]=='title':
title.append(line[12:-1])
if list_of_line[0]=='Weight':
weight.append(line[8:-1])
if list_of_line[0]=='fullMark':
fullmark.append(line[10:-1])
if list_of_line[0]=='Description':
Description.append(line[13:-1])
print('taskNumber is %s' % Identification[0])
print('taskTitle is %s' % title[0])
print('Weight is %s' % weight[0])
print('fullMark is %s' %fullmark[0])
print('desc is %s' %Description[0])
print('\n')
print('taskNumber is %s' % Identification[1])
print('taskTitle is %s' % title[1])
print('Weight is %s' % weight[1])
print('fullMark is %s' %fullmark[1])
print('desc is %s' %Description[1])
print('\n')
print('taskNumber is %s' % Identification[2])
print('taskTitle is %s' % title[2])
print('Weight is %s' % weight[2])
print('fullMark is %s' %fullmark[2])
print('desc is %s' %Description[2])
print('\n')
of course you can use a loop for the prints but i was too lazy so i copy and pasted :).
IF YOU NEED ANY HELP OR HAVE ANY QUESTIONS PLEASE PLEASE ASK!!!
THIS CODE ASSUMES THAT YOU ARE NOT THAT ADVANCED IN CODING
Good Luck!!!
As another poster (#Cuber) has already stated, you're looping over the lines one-by-one, whereas the data sets are split across five lines. The error message is essentially stating that you're trying to unpack five values when all you have is two. Furthermore, it looks like you're only interested in the value on the right hand side of the colon, so you really only have one value.
There are multiple ways of resolving this issue, but the simplest is probably to group the data into fives (plus the padding, making it seven) and process it in one go.
First we'll define chunks, with which we'll turn this somewhat fiddly process into one elegant loop (from the itertools docs).
from itertools import zip_longest
def chunks(iterable, n, fillvalue=None):
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
Now, we'll use it with your data. I've omitted the file boilerplate.
for group in chunks(mod.readlines(), 5+2, fillvalue=''):
# Choose the item after the colon, excluding the extraneous rows
# that don't have one.
# You could probably find a more elegant way of achieving the same thing
l = [item.split(': ')[1].strip() for item in group if ':' in item]
taskNumber , taskTile , weight, fullMark , desc = l
print(taskNumber , taskTile , weight, fullMark , desc, sep='|')
The 2 in 5+2 is for the padding (the comment above and the empty line below).
The implementation of chunks may not make sense to you at the moment. If so, I'd suggest looking into Python generators (and the itertools documentation in particular, which is a marvellous resource). It's also a good idea to get your hands dirty and tinker with snippets inside the Python REPL.
You can still read in lines one by one, but you will have to help the code understand what it's parsing. We can use an OrderedDict to lookup the appropriate variable name.
import os
import collections as ct
def printer(dict_, lookup):
for k, v in lookup.items():
print("{} is {}".format(v, dict_[k]))
print()
names = ct.OrderedDict([
("Task Identification Number", "taskNumber"),
("Task title", "taskTitle"),
("Weight", "weight"),
("fullMark","fullMark"),
("Description", "desc"),
])
filepath = home + '\\Desktop\\PADS Assignment\\test.txt'
with open(filepath, "r") as f:
for line in f.readlines():
line = line.strip()
if line.startswith("#"):
header = line
d = {}
continue
elif line:
k, v = line.split(":")
d[k] = v.strip(" ")
else:
printer(d, names)
printer(d, names)
Output
taskNumber is 210CT3
taskTitle is Final Examination
weight is 50
fullMark is 100
desc is Close Book Examination
taskNumber is 210CT1
taskTitle is Assignment 1
weight is 25
fullMark is 100
desc is Program and design and complexity running time.
taskNumber is 210CT2
taskTitle is Assignment 2
weight is 25
fullMark is 100
desc is Shortest Path Algorithm
You're trying to get more data than is present on one line; the five pieces of data are on separate lines.
As SwiftsNamesake suggested, you can use itertools to group the lines:
import itertools
def keyfunc(line):
# Ignores comments in the data file.
if len(line) > 0 and line[0] == "#":
return True
# The separator is an empty line between the data sets, so it returns
# true when it finds this line.
return line == "\n"
with open(home + '\\Desktop\\PADS Assignment\\test.txt', 'r') as mod:
for k, g in itertools.groupby(mod, keyfunc):
if not k: # Does not process lines that are separators.
for line in g:
data = line.strip().partition(": ")
print(f"{data[0] is {data[2]}")
# print(data[0] + " is " + data[2]) # If python < 3.6
print("") # Prints a newline to separate groups at the end of each group.
If you want to use the data in other functions, output it as a dictionary from a generator:
from collections import OrderedDict
import itertools
def isSeparator(line):
# Ignores comments in the data file.
if len(line) > 0 and line[0] == "#":
return True
# The separator is an empty line between the data sets, so it returns
# true when it finds this line.
return line == "\n"
def parseData(data):
for line in data:
k, s, v = line.strip().partition(": ")
yield k, v
def readData(filePath):
with open(filePath, "r") as mod:
for key, g in itertools.groupby(mod, isSeparator):
if not key: # Does not process lines that are separators.
yield OrderedDict((k, v) for k, v in parseData(g))
def printData(data):
for d in data:
for k, v in d.items():
print(f"{k} is {v}")
# print(k + " is " + v) # If python < 3.6
print("") # Prints a newline to separate groups at the end of each group.
data = readData(home + '\\Desktop\\PADS Assignment\\test.txt')
printData(data)
Inspired by itertools-related solutions, here is another using the more_itertools.grouper tool from the more-itertools library. It behaves similarly to #SwiftsNamesake's chunks function.
import collections as ct
import more_itertools as mit
names = dict([
("Task Identification Number", "taskNumber"),
("Task title", "taskTitle"),
("Weight", "weight"),
("fullMark","fullMark"),
("Description", "desc"),
])
filepath = home + '\\Desktop\\PADS Assignment\\test.txt'
with open(filepath, "r") as f:
lines = (line.strip() for line in f.readlines())
for group in mit.grouper(7, lines):
for line in group[1:]:
if not line: continue
k, v = line.split(":")
print("{} is {}".format(names[k], v.strip()))
print()
Output
taskNumber is 210CT1
taskTitle is Assignment 1
weight is 25
fullMark is 100
desc is Program and design and complexity running time.
taskNumber is 210CT2
taskTitle is Assignment 2
weight is 25
fullMark is 100
desc is Shortest Path Algorithm
taskNumber is 210CT3
taskTitle is Final Examination
weight is 50
fullMark is 100
desc is Close Book Examination
Care was taken to print the variable name with the corresponding value.
I have lines of data from a csv file, the first line stored are the headers. For example:
first line -> [a,b,c,d,e]
second line -> [0,1,2,1,2]
third line -> [4,2,4,1,5]
Plus, i have strings with conditions related to the data in the following format:
condition = (((a = d) OR (a = c)) AND (c < e))
The output should be only line 3. How can I evaluate this conditions, and separate all the nested subconditions? I was thinking on a recursive function, reading through the parenthesis, but i have a mess in my code :(. Thanks for the answers and sorry my bad english!
PS: I wouldn't like to use pandas, or csv libraries
PS2: The condition above is just a example, there could be another more nested conditions like ((((a = d) AND (c > e)) OR (b = c)) AND (e < d)), or sometimes, simply (a = d)
Below is a quick solution:
from itertools import ifilter
with open('input.csv', 'r') as fi:
lines = ((rawline, map(int, rawline.split(','))) for rawline in fi.readlines()[1:])
results = ifilter(lambda (_, fds): (fds[0] == fds[3] or fds[0] == fds[2]) and (fds[2] < fds[4]), lines)
for (rawline, _) in results:
print rawline
With input.csv being:
a,b,c,d,e
0,1,2,1,2
4,2,4,1,5
The result output is:
4,2,4,1,5
Update: a shorter/compact implementation:
from itertools import ifilter
with open('input.csv', 'r') as fi:
results = ifilter(
lambda fds: (fds[0] == fds[3] or fds[0] == fds[2]) and (fds[2] < fds[4]),
(map(int, rawline.split(',')) for rawline in fi.readlines()[1:]))
for fields in results:
print ','.join(map(str,fields))
Better to create a new answer as the requirement has been updated/changed
The quickest solution for evaluating the conditions of string format is to use built-in function eval. In this way, you don't have to do the heavy/unaffordable parsing(lexical analysis and syntactic analysis)
Here is the sample code:
from itertools import ifilter
condition1 = '(((a = d) OR (a = c)) AND (c < e))'
def evalCondition(condition, *args):
'''
1) if you have condition format follow python grammar, then you don't need below replacement
2) assume there is no '>=' or '<=', otherwise, you have to use more sophisticated replacement method e.g. using regular exppression
'''
condition = condition.replace('=', '==').replace('OR', 'or').replace('AND', 'and')
a,b,c,d,e = args
return eval(condition)
with open('input.csv', 'r') as fi:
results = ifilter(
lambda fields: evalCondition(condition1, *fields),
(map(int, rawline.split(',')) for rawline in fi.readlines()[1:]))
for fields in results:
print ','.join(map(str,fields))
With input.csv being:
a,b,c,d,e
0,1,2,1,2
4,2,4,1,5
The result output is:
4,2,4,1,5
Does anyone have a clue how to increase the speed of this part of python code?
It was designed to deal with small files (with just a few lines, and for this is very fast) but i want to run it with big files (with ~50Gb, and millions of lines).
The main goal of this code is to get stings from a file (.txt) and search for these in a input file printing the the number of times that these occurred in the output file.
Here is the code: infile, seqList and out are determined by the optparse as Options in the beginning of the code (not shown)
def novo (infile, seqList, out) :
uDic = dict()
rDic = dict()
nmDic = dict()
with open(infile, 'r') as infile, open(seqList, 'r') as RADlist :
samples = [line.strip() for line in RADlist]
lines = [line.strip() for line in infile]
#Create dictionaires with all the samples
for i in samples:
uDic[i.replace(" ","")] = 0
rDic[i.replace(" ","")] = 0
nmDic[i.replace(" ","")] = 0
for k in lines:
l1 = k.split("\t")
l2 = l1[0].split(";")
l3 = l2[0].replace(">","")
if len(l1)<2:
continue
if l1[4] == "U":
for k in uDic.keys():
if k == l3:
uDic[k] += 1
if l1[4] == "R":
for j in rDic.keys():
if j == l3:
rDic[j] += 1
if l1[4] == "NM":
for h in nmDic.keys():
if h == l3:
nmDic[h] += 1
f = open(out, "w")
f.write("Sample"+"\t"+"R"+"\t"+"U"+"\t"+"NM"+"\t"+"TOTAL"+"\t"+"%R"+"\t"+"%U"+"\t"+"%NM"+"\n")
for i in samples:
U = int()
R = int()
NM = int ()
for k, j in uDic.items():
if k == i:
U = j
for o, p in rDic.items():
if o == i:
R = p
for y,u in nmDic.items():
if y == i:
NM = u
TOTAL = int(U + R + NM)
try:
f.write(i+"\t"+str(R)+"\t"+str(U)+"\t"+str(NM)+"\t"+str(TOTAL)+"\t"+str(float(R) / TOTAL)+"\t"+str(float(U) / TOTAL)+"\t"+str(float(NM) / TOTAL$
except:
continue
f.close()
With processing 50 GB files, the question is not how to make it faster, but how to make it runnable
at all.
The main problem is, you will run out of memory and shall modify the code to be processing the files
without having all the files in memory, but rather having in memory onle a line, which is needed.
Following code from your question is reading all the lines form two files:
with open(infile, 'r') as infile, open(seqList, 'r') as RADlist :
samples = [line.strip() for line in RADlist]
lines = [line.strip() for line in infile]
# at this moment you are likely to run out of memory already
#Create dictionaires with all the samples
for i in samples:
uDic[i.replace(" ","")] = 0
rDic[i.replace(" ","")] = 0
nmDic[i.replace(" ","")] = 0
#similar loop over `lines` comes later on
You shall defer reading the lines till the latest possible moment like this:
#Create dictionaires with all the samples
with open(seqList, 'r') as RADlist:
for samplelines in RADlist:
sample = sampleline.strip()
for i in samples:
uDic[i.replace(" ","")] = 0
rDic[i.replace(" ","")] = 0
nmDic[i.replace(" ","")] = 0
Note: did you want to use line.strip() or line.split()?
This way, you do not have to keep all the content in memory.
There are many more options for optimization, but this one will let you to take off and run.
It would make it much easier if you provided some sample inputs. Because you haven't I haven't tested this, but the idea is simple - iterate through each file only once, using iterators rather than reading the whole file into memory. Use the efficient collections.Counter object to handle the counting and minimise inner looping:
def novo (infile, seqList, out):
from collections import Counter
import csv
# Count
counts = Counter()
with open(infile, 'r') as infile:
for line in infile:
l1 = line.strip().split("\t")
l2 = l1[0].split(";")
l3 = l2[0].replace(">","")
if len(l1)<2:
continue
counts[(l1[4], l3)] += 1
# Produce output
types = ['R', 'U', 'NM']
with open(seqList, 'r') as RADlist, open(out, 'w') as outfile:
f = csv.writer(outfile, delimiter='\t')
f.writerow(types + ['TOTAL'] + ['%' + t for t in types])
for sample in RADlist:
sample = sample.strip()
countrow = [counts((t, sample)) for t in types]
total = sum(countrow)
f.writerow([sample] + countrow + [total] + [c/total for c in countrow])
samples = [line.strip() for line in RADlist]
lines = [line.strip() for line in infile]
If you convert your script into functions (it makes profiling easier), and then see what it does when you code profile it : I suggest using runsnake : runsnakerun
I would try replacing your loops with list & dictionary comprehensions:
For example, instead of
for i in samples:
uDict[i.replace(" ","")] = 0
Try:
udict = {i.replace(" ",""):0 for i in samples}
and similarly for the other dicts
I don't really follow what's going on in your "for k in lines" loop, but you only need l3 (and l2) when you have certain values for l1[4]. Why not check for those values before splitting and replacing?
Lastly, instead of looping through all the keys of a dict to see if a given element is in that dict, try:
if x in myDict:
myDict[x] = ....
for example:
for k in uDic.keys():
if k == l3:
uDic[k] += 1
can be replaced with:
if l3 in uDic:
uDic[l3] += 1
Other than that, try profiling.
1)Look into profilers and adjust the code that is taking the most time.
2)You could try optimizing some methods with Cython - use data from profiler to modify correct thing
3)It looks like you can use a counter instead of a dict for the output file, and a set for the input file -look into them.
set = set()
from Collections import Counter
counter = Counter() # Essentially a modified dict, that is optimized for counting...
# like counting occurences of strings in a text file
4) If you are reading 50GB of memory you won't be able to store it all in RAM (I'm assuming who knows what kind of computer you have), so generators should save your memory and time.
#change list comprehension to generators
samples = (line.strip() for line in RADlist)
lines = (line.strip() for line in infile)