In the directory I have say, 30 txt files each containing two columns of numbers with roughly 6000 numbers in each column. What i want to do is to import the first 3 txt files, process the data which gives me the desired output, then i want to move onto the next 3 txt files.
The directory looks like:
file0a
file0b
file0c
file1a
file1b
file1c ... and so on.
I don't want to import all of the txt files simultaneously, I want to import the first 3, process the data, then the next 3 and so forth. I was thinking of making a dictionary - though i have a feeling this might involve writing each file name in the dictionary, which would take far too long.
EDIT:
For those that are interested, I think i have come up with a work around. Any feedback would greatly be appreciated, since i'm not sure if this is the quickest way to do things or the most pythonic.
import glob
def chunks(l,n):
for i in xrange(0,len(l),n):
yield l[i:i+n]
Data = []
txt_files = glob.iglob("./*.txt")
for data in txt_files:
d = np.loadtxt(data, dtype = np.float64)
Data.append(d)
Data_raw_all = list(chunks(Data,3))
Here the list 'Data' is all of the text files from the directory, and 'Data_raw_all' uses the function 'chunks' to group the elements in 'Data' into sets of 3. This way you can selecting one element in Data_raw_all selects the corresponding 3 text files in the directory.
First of all, I have nothing original to include here and I definitely do not want to claim credit for it at all because it all comes from the Python Cookbook 3rd Ed and from this wonderful presentation on generators by David Beazley (one of the co-authors of the aforementioned Cookbook). However, I think you might really benefit from the examples given in the slideshow on generators.
What Beazley does is chain a bunch of generators together in order to do the following:
yields filenames matching a given filename pattern.
yields open file objects from a sequence of filenames.
concatenates a sequence of generators into a single sequence
greps a series of lines for those that match a regex pattern
All of these code examples are located here. The beauty of this method is that the chained generators simply chew up the next pieces of information: they don't load all files into memory in order to process all the data. It's really a nice solution.
Anyway, if you read through the slideshow, I believe it will give you a blueprint for exactly what you want to do: you just have to change it for the information you are seeking.
In short, check out the slideshow linked above and follow along and it should provide a blueprint for solving your problem.
I'm presuming you want to hardcode as few of the file names as possible. Therefore most of this code is for generating the filenames. The files are then opened with a with statement.
Example code:
from itertools import cycle, count
root = "UVF2CNa"
for n in count(1):
for char in cycle("abc"):
first_part = "{}{}{}".format(root, n, char)
try:
with open(first_part + "i") as i,\
open(first_part + "j") as j,\
open(first_part + "k") as k:
# do stuff with files i, j and k here
pass
except FileNotFoundError:
# deal with this however
pass
Related
I'am trying to get lines from a text file (.log) into a .txt document.
I need get into my .txt file the same data. But the line itself is sometimes different. From what I have seen on internet, it's usualy done with a pattern that will anticipate how the line is made.
1525:22Player 11 spawned with userinfo: \team\b\forcepowers\0-5-030310001013001131\ip\46.98.134.211:24806\rate\25000\snaps\40\cg_predictItems\1\char_color_blue\34\char_color_green\34\char_color_red\34\color1\65507\color2\14942463\color3\2949375\color4\2949375\handicap\100\jp\0\model\desann/default\name\Faybell\pbindicator\1\saber1\saber_malgus_broken\saber2\none\sex\male\ja_guid\420D990471FC7EB6B3EEA94045F739B7\teamoverlay\1
The line i'm working with usualy looks like this. The data i'am trying to collect are :
\ip\0.0.0.0
\name\NickName_of_the_player
\ja_guid\420D990471FC7EB6B3EEA94045F739B7
And print these data, inside a .txt file. Here is my current code.
As explained above, i'am unsure about what keyword to use for my research on google. And how this could be called (Because the string isn't the same?)
I have been looking around alot, and most of the test I have done, have allowed me to do some things, but i'am not yet able to do as explained above. So i'am in hope for guidance here :) (Sorry if i'am noobish, I understand alot how it works, I just didn't learned language in school, I mostly do small scripts, and usualy they work fine, this time it's way harder)
def readLog(filename):
with open(filename,'r') as eventLog:
data = eventLog.read()
dataList = data.splitlines()
return dataList
eventLog = readLog('games.log')
You'll need to read the files in "raw" mode rather than as strings. When reading the file from disk, use open(filename,'rb'). To use your example, I ran
text_input = r"1525:22Player 11 spawned with userinfo: \team\b\forcepowers\0-5-030310001013001131\ip\46.98.134.211:24806\rate\25000\snaps\40\cg_predictItems\1\char_color_blue\34\char_color_green\34\char_color_red\34\color1\65507\color2\14942463\color3\2949375\color4\2949375\handicap\100\jp\0\model\desann/default\name\Faybell\pbindicator\1\saber1\saber_malgus_broken\saber2\none\sex\male\ja_guid\420D990471FC7EB6B3EEA94045F739B7\teamoverlay\1"
text_as_array = text_input.split('\\')
You'll need to know which columns contain the strings you care about. For example,
with open('output.dat','w') as fil:
fil.write(text_as_array[6])
You can figure these array positions from the sample string
>>> text_as_array[6]
'46.98.134.211:24806'
>>> text_as_array[34]
'Faybell'
>>> text_as_array[44]
'420D990471FC7EB6B3EEA94045F739B7'
If the column positions are not consistent but the key-value pairs are always adjacent, we can leverage that
>>> text_as_array.index("ip")
5
>>> text_as_array[text_as_array.index("ip")+1]
'46.98.134.211:24806'
I have hundred of thousands of data text files to read. As of now, I'm importing the data from text files every time I run the code. Perhaps the easy solution would be to simply reformat the data into a file faster to read.
Anyway, right now every text files I have look like:
User: unknown
Title : OE1_CHANNEL1_20181204_103805_01
Sample data
Wavelength OE1_CHANNEL1
185.000000 27.291955
186.000000 27.000877
187.000000 25.792290
188.000000 25.205620
189.000000 24.711882
.
.
.
The code where I read and import the txt files is:
# IMPORT DATA
path = 'T2'
if len(sys.argv) == 2:
path = sys.argv[1]
files = os.listdir(path)
trans_import = []
for index, item in enumerate(files):
trans_import.append(np.loadtxt(path+'/'+files[1], dtype=float, skiprows=4, usecols=(0,1)))
The resulting array looks in the variable explorer as:
{ndarray} = [[185. 27.291955]\n [186. 27.000877]\n ... ]
I'm wondering, how I could speed up this part? It takes a little too long as of now just to import ~4k text files. There are 841 lines inside every text files (spectrum). The output I get with this code is 841 * 2 = 1682. Obviously, it considers the \n as a line...
It would probably be much faster if you had one large file instead of many small ones. This is generally more efficient. Additionally, you might get a speedup from just saving the numpy array directly and loading that .npy file in instead of reading in a large text file. I'm not as sure about the last part though. As always when time is a concern, I would try both of these options and then measure the performance improvement.
If for some reason you really can't just have one large text file / .npy file, you could also probably get a speedup by using, e.g., multiprocessing to have multiple workers reading in the files at the same time. Then you can just concatenate the matrices together at the end.
Not your primary question but since it seems to be an issue - you can rewrite the text files to not have those extra newlines, but I don't think np.loadtxt can ignore them. If you're open to using pandas, though, pandas.read_csv with skip_blank_lines=True should handle that for you. To get a numpy.ndarray from a pandas.DataFrame, just do dataframe.values.
Let use pandas.read_csv (with C speed) instead of numpy.loadtxt. This is a very helpful post:
http://akuederle.com/stop-using-numpy-loadtxt
I need to extract the last number in the last line of each text file in a directory. Can someone get me started on this in Python? The data is information formatted as follows:
# time 'A' 'B'
0.000000E+00 10000 0
1.000000E+05 7742 2263
where the '#' column is empty in each file. The filenames obey the following naming convention:
for i in `seq 1 100`; for j in `seq 1 101`; for letter in {A..D};
filename = $letter${j}_${i}.txt
These files contain the resulting data from running simulations in KaSim (Kappa language). I want to take the averages of subsets of the extracted numbers and plot some results.
Matlab can't handle the set of 50,000 files I'm dealing with. I'm relatively new to Python but I have experience in Matlab and R. I want to do the data extraction through Python and the analysis in Matlab or R.
Thanks for any help.
This code should get you started. As far as the directory has only those files for which you need the last number, the naming convention can be ignored. Because, you can rather look up all of the file in that directory.
import glob
last_numbers = []
for filename in glob.glob("/path/to/directory/*"): # dont forget this ending * (its wild character)
last_number = file.open(filename).readlines()[-1].split(" ")[-1]
# in case last line is empty line '\n' and your interest is in last second line then it should be '.readlines()[-2].split(" ")[-1]'
last_numbers.append(last_number)
I have a folder with multiple subfolders that all contain several files. I am looking to write a matlab code that will insert a commented out "disclaimer" on the top of every relevant code [c, python (.py not .pyc), .urdf, .xml (.launch, .xacro, .config)]
My current thought process is to first list out every subfolder within the main folder. Then search within each subfolder for the relevant codes. If a relevant code is found, the disclaimer is commented in the top of the code... (each language has a different disclaimer)
I am having a hard time piecing this all together.. any help?
data_dir = 'C:thedirectorytomainfolder':
topLevelFolder = data_dir;
if topLevelFolder == 0
return;
end
% Get list of all subfolders.
allSubFolders = genpath(topLevelFolder);
remain = allSubFolders;
listOfFolderNames = {};
while true
[singleSubFolder, remain] = strtok(remain, ';');
if isempty(singleSubFolder)
break;
end
listOfFolderNames = [listOfFolderNames singleSubFolder];
end
numberOfFolders = length(listOfFolderNames)
%% Process all (wanted) files in those folders
for k = 1 : numberOfFolders
% Get this folder and print it out.
thisFolder = listOfFolderNames{k};
fprintf('Processing folder %s\n', thisFolder);
% Get .xml files.
filePattern = sprintf('%s/*.xml', thisFolder);
baseFileNames = dir(filePattern);
filePattern = sprintf('%s/*.c', thisFolder);
baseFileNames = [baseFileNames; dir(filePattern)];
numberOfImageFiles = length(baseFileNames)
I'm having a hard time reading each relevant file and inserting the correct comment code at the beginning of the file... any help?
Most of matlab's methods for reading text files assume you are trying to load in primarily numeric data but one of them might still work for you.
Sometimes it's easier to fopen the file and then read lines with fgetl of fread. Because you're doing low-level IO you have to test for the end of file too with while ~feof or somesuch. You could store each line in a cell array, prepend it with a cell array of your disclaimer and then write back out with fwrite, converting the cell back to a string with char.
It'll be pretty cumbersome. Does it have to be matlab? If you have the option it might be quicker to do it in a different language - it would be less than twenty lines in shell, and ruby/python/perl are all more geared up for text processing, which isn't matlab's strongest point.
I'm trying to write a script in Python for sorting through files (photos, videos), checking metadata of each, finding and moving all duplicates to a separate directory. Got stuck with the metadata checking part. Tried os.stat - doesn't return True for duplicate files. Ideally, I should be able to do something like :
if os.stat("original.jpg")== os.stat("duplicate.jpg"):
shutil.copy("duplicate.jpg","C:\\Duplicate Folder")
Pointers anyone?
There's a few things you can do. You can compare the contents or hash of each file or you can check a few select properties from the os.stat result, ex
def is_duplicate(file1, file2):
stat1, stat2 = os.stat(file1), os.stat(file2)
return stat1.st_size==stat2.st_size and stat1.st_mtime==stat2.st_mtime
A basic loop using a set to keep track of already encountered files:
import glob
import hashlib
uniq = set()
for fname in glob.glob('*.txt'):
with open(fname,"rb") as f:
sig = hashlib.sha256(f.read()).digest()
if sig not in uniq:
uniq.add(sig)
print fname
else:
print fname, " (duplicate)"
Please note as with any hash function there is a slight chance of collision. That is two different files having the same digest. Depending your needs, this is acceptable of not.
According to Thomas Pornin in an other answer :
"For instance, with SHA-256 (n=256) and one billion messages (p=109) then the probability [of collision] is about 4.3*10-60."
Given your need, if you have to check for additional properties in order to identify "true" duplicates, change the sig = ....line to whatever suits you. For example, if you need to check for "same content" and "same owner" (st_uidas returned by os.stat()), write:
sig = ( hashlib.sha256(f.read()).digest(),
os.stat(fname).st_uid )
If two files have the same md5 they are exact duplicates.
from hashlib import md5
with open(file1, "r") as original:
original_md5 = md5(original.read()).hexdigest()
with open(file2, "r") as duplicate:
duplicate_md5 = md5(duplicate.read()).hexdigest()
if original_md5 == duplicate_md5:
do_stuff()
In your example you're using jpg file in that case you want to call the method open with its second argument equals to rb. For that see the documentation for open
os.stat offers information about some file's metadata and features, including the creation time. That is not a good approach in order to find out if two files are the same.
For instance: Two files can be the same and have different time creation. Hence, comparing stats will fail here. Sylvain Leroux approach is the best one when combining performance and accuracy, since it is very rare two different files has the same hash.
So, unless you have an incredibly large amount of data and a repeated file will cause a system fatality, this is the way to go.
If that your case (it not seems to be), well ... the only way you can be 100% sure two file are the same is iterating and perform a comparison byte per byte.