I am trying to concatenate a list of wav files into a single continuous wav file. I have used the following snippet but the output is not correct, sounds almost like the files are on top of each other. audio_files is a list with .wav filenames that are playing as expected.
This is my current code:
outfile = "sounds.wav"
data= []
for wav_file in audio_files:
w = wave.open(wav_file, 'rb')
data.append( [w.getparams(), w.readframes(w.getnframes())] )
w.close()
output = wave.open(outfile, 'wb')
output.setparams(data[0][0])
output.writeframes(data[0][1])
output.writeframes(data[1][1])
output.close()
I am assuming that you were using this question as your reference. However, the code that you have taken only takes into account 2 wav files because that is what the original question had asked.
Although I am not if this will fix the problem of the sound being on top of each other, you should iterate through each item in your data list.
output = wave.open(outfile, 'wb')
output.setparams(data[0][0])
for params,frames in data:
output.writeframes(frames)
output.close()
to ensure you are putting in the frames from each file you have.
One thing to keep in mind is that the params for your new wav files might be specific to each file and you might want to check that getparams() is returning similar results for each file.
Related
I am fairly new to programming, so bear with me!
We have a task at school which we are made to clean up three text files ("Balance1", "Saving", and "Withdrawal") and append them together into a new file. These files are just names and sums of money listed downwards, but some of it is jumbled. This is my code for the first file Balance1:
with open('Balance1.txt', 'r+') as f:
f_contents = f.readlines()
# Then I start cleaning up the lines. Here I edit Anna's savings to an integer.
f_contents[8] = "Anna, 600000"
# Here I delete the blank lines and edit in the 50000 to Philip.
del f_contents[3]
del f_contents[3]
In the original text file Anna's savings is written like this: "Anna, six hundred thousand" and we have to make it look clean, so its rather "NAME, SUM (as integer). When I print this as a list it looks good, but after I have done this with all three files I try to append them together in a file called "Balance.txt" like this:
filenames = ["Balance1.txt", "Saving.txt", "Withdrawal.txt"]
with open("Balance.txt", "a") as outfile:
for filename in filenames:
with open(filename) as infile:
contents = infile.read()
outfile.write(contents)
When I check the new text file "Balance" it has appended them together, but just as they were in the beginning and not with my edits. So it is not "cleaned up". Can anyone help me understand why this happens, and what I have to do so it appends the edited and clean versions?
In the first part, where you do the "editing" of Balance.txt` file, this is what happens:
You open the file in read mode
You load the data into memory
You edit the in memory data
And voila.
You never persisted the changes to any file on the disk. So when in the second part you read the content of all the files, you will read the data that was originally there.
So if you want to concatenate the edited data, you have 2 choices:
Pre-process the data by creating 3 final correct files (editing Balance1.txt and persisting it to another file, say Balance1_fixed.txt) and then in the second part, concatenate: ["Balance1_fixed.txt", "Saving.txt", "Withdrawal.txt"]. Total of 4 data file openings, more IO.
Use only the second loop you have, and correct the contents before writing it to the outfile. You can use readlines() like you did first, edit the specific line and then use writelines(). Total of 3 data file openings, less IO than previous option
I am new here to try to solve one of my interesting questions in World of Tanks. I heard that every battle data is reserved in the client's disk in the Wargaming.net folder because I want to make a batch of data analysis for our clan's battle performances.
image
It is said that these .dat files are a kind of json files, so I tried to use a couple of lines of Python code to read but failed.
import json
f = open('ex.dat', 'r', encoding='unicode_escape')
content = f.read()
a = json.loads(content)
print(type(a))
print(a)
f.close()
The code is very simple and obviously fails to make it. Well, could anyone tell me the truth about that?
Added on Feb. 9th, 2022
After I tried another set of codes via Jupyter Notebook, it seems like something can be shown from the .dat files
import struct
import numpy as np
import matplotlib.pyplot as plt
import io
with open('C:/Users/xukun/Desktop/br/ex.dat', 'rb') as f:
fbuff = io.BufferedReader(f)
N = len(fbuff.read())
print('byte length: ', N)
with open('C:/Users/xukun/Desktop/br/ex.dat', 'rb') as f:
data =struct.unpack('b'*N, f.read(1*N))
The result is a set of tuple but I have no idea how to deal with it now.
Here's how you can parse some parts of it.
import pickle
import zlib
file = '4402905758116487.dat'
cache_file = open(file, 'rb') # This can be improved to not keep the file opened.
# Converting pickle items from python2 to python3 you need to use the "bytes" encoding or "latin1".
legacyBattleResultVersion, brAllDataRaw = pickle.load(cache_file, encoding='bytes', errors='ignore')
arenaUniqueID, brAccount, brVehicleRaw, brOtherDataRaw = brAllDataRaw
# The data stored inside the pickled file will be a compressed pickle again.
vehicle_data = pickle.loads(zlib.decompress(brVehicleRaw), encoding='latin1')
account_data = pickle.loads(zlib.decompress(brAccount), encoding='latin1')
brCommon, brPlayersInfo, brPlayersVehicle, brPlayersResult = pickle.loads(zlib.decompress(brOtherDataRaw), encoding='latin1')
# Lastly you can print all of these and see a lot of data inside.
The response contains a mixture of more binary files as well as some data captured from the replays.
This is not a complete solution but it's a decent start to parsing these files.
First you can look at the replay file itself in a text editor. But it won't show the code at the beginning of the file that has to be cleaned out. Then there is a ton of info that you have to read in and figure out but it is the stats for each player in the game. THEN it comes to the part that has to do with the actual replay. You don't need that stuff.
You can grab the player IDs and tank IDs from WoT developer area API if you want.
After loading the pickle files like gabzo mentioned, you will see that it is simply a list of values and without knowing what the value is referring to, its hard to make sense of it. The identifiers for the values can be extracted from your game installation:
import zipfile
WOT_PKG_PATH = "Your/Game/Path/res/packages/scripts.pkg"
BATTLE_RESULTS_PATH = "scripts/common/battle_results/"
archive = zipfile.ZipFile(WOT_PKG_PATH, 'r')
for file in archive.namelist():
if file.startswith(BATTLE_RESULTS_PATH):
archive.extract(file)
You can then decompile the python files(uncompyle6) and then go through the code to see the identifiers for the values.
One thing to note is that the list of values for the main pickle objects (like brAccount from gabzo's code) always has a checksum as the first value. You can use this to check whether you have the right order and the correct identifiers for the values. The way these checksums are generated can be seen in the decompiled python files.
I have been tackling this problem for some time (albeit in Rust): https://github.com/dacite/wot-battle-results-parser/tree/main/datfile_parser.
I apologize if this is a very beginner-ish question. But I have a multivariate data set from reddit ( https://files.pushshift.io/reddit/submissions/), but the files are way too big. Is it possible to downsample one of these files down to 20% or less, and either save it as a new file (json or csv) or directly read it as a pandas dataframe? Any help will be very appreciated!
Here is my attempt thus far
def load_json_df(filename, num_bytes = -1):
'''Load the first `num_bytes` of the filename as a json blob, convert each line into a row in a Pandas data frame.'''
fs = open(filename, encoding='utf-8')
df = pd.DataFrame([json.loads(x) for x in fs.readlines(num_bytes)])
fs.close()
return df
january_df = load_json_df('RS_2019-01.json')
january_df.sample(frac=0.2)
However this gave me a memory error while trying to open it. Is there a way to downsample it without having to open the entire file?
The problem is, it is not possible to determine exactly what the 20% of the data is. In order to do that you must first read the entire length of the file and only then you can get an idea of what a 20% would look like.
Reading a large file into memory all at once throws this error generally. You can process this by reading the file line-by-line with below code:
data = []
counter = 0
with open('file') as f:
for line in f:
data.append(json.loads(line))
counter +=1
You should then be able to do this
df = pd.DataFrame([x for x in data]) #you can set a range here with counter/5 if you want to get 20%
I downloaded first of the files, i.e. https://files.pushshift.io/reddit/submissions/RS_2011-01.bz2
decompressed it and looked at the contents. As it happens, it is not a proper JSON but rather JSON-lines - a series of JSON objects, one per line (see http://jsonlines.org/ ). This means you can just cut out as many lines as you want, using any tool you want (for example, a text editor). Or you can just process the file sequentially in your Python script, taking into account every fifth line, like this:
with open('RS_2019-01.json', 'r') as infile:
for i, line in enumerate(infile):
if i % 5 == 0:
j = json.loads(line)
# process the data here
Objective
I'm trying to extract the GPS "Latitude" and "Longitude" data from a bunch of JPG's and I have been successful so far but my main problem is that when I try to write the coordinates to a text file for example I see that only 1 set of coordinates was written compared to my console output which shows that every image was extracted. Here is an example: Console Output and here is my text file that is supposed be a mirror output along my console: Text file
I don't fully understand whats the problem and why it won't just write all of them instead of one. I believe it is being overwritten somehow or the 'GPSPhoto' module is causing some issues.
Code
from glob import glob
from GPSPhoto import gpsphoto
# Scan jpg's that are located in the same directory.
data = glob("*.jpg")
# Scan contents of images and GPS values.
for x in data:
data = gpsphoto.getGPSData(x)
data = [data.get("Latitude"), data.get("Longitude")]
print("\nsource: {}".format(x), "\n ↪ {}".format(data))
# Write coordinates to a text file.
with open('output.txt', 'w') as f:
print('Coordinates:', data, file=f)
I have tried pretty much everything that I can think of including: changing the write permissions, not using glob, no loops, loops, lists, no lists, different ways to write to the file, etc.
Any help is appreciated because I am completely lost at this point. Thank you.
You're replacing the data variable each time through the loop, not appending to a list.
all_coords = []
for x in data:
data = gpsphoto.getGPSData(x)
all_coords.append([data.get("Latitude"), data.get("Longitude")])
with open('output.txt', 'w') as f:
print('Coordinates:', all_coords, file=f)
I have three sound files for example a.wav, b.wav and c.wav . I want to write them into a single file for example all.xmv (extension could be different too) and when I need I want to extract one of them and I want to play it (for example I want to play a.wav and extract it form all.xmv).
How can I do it in python. I have heard that there is a function named blockwrite in Delphi and it does the thing that I want. Is there a function in python that is like blockwrite in Delphi or how can I write these files and play them?
Would standard tar/zip files work for you?
http://docs.python.org/library/zipfile.html
http://docs.python.org/library/tarfile.html
If the archive idea (which is btw, the best answer to your question) doesn't suit you, you can fuse the data from several files in one file, e.g. by writing consecutive blocks of binary data (thus creating an uncompressed archive!)
Let paths be a list of files that should be concatenated:
import io
import os
offsets = [] # the offsets that should be kept for later file navigation
last_offset = 0
fout = io.FileIO(out_path, 'w')
for path in paths:
f = io.FileIO(path) # stream IO
fout.write(f.read())
f.close()
last_offset += os.path.getsize(path)
offsets.append(last_offset)
fout.close()
# Pseudo: write the offsets to separate file e.g. by pickling
# ...
# reading the data, given that offsets[] list is available
file_ID = 10 # e.g. you need to read 10th file
f = io.FileIO(path)
f.seek(offsets[file_ID - 1]) # seek to required position
read_size = offsets[filed_ID] - offsets[file_ID - 1] # get the file size
data = f.read(read_size) # here we are!
f.close()