Develop Reference

Is there any feasible solution to read WOT battle results .dat files?

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.

openpyxl blocking excel file after first read

I am trying to overwrite a value in a given cell using openpyxl. I have two sheets. One is called Raw, it is populated by API calls. Second is Data that is fed off of Raw sheet. Two sheets have exactly identical shape (cols/rows). I am doing a comparison of the two to see if there is a bay assignment in Raw. If there is - grab it to Data sheet. If both Raw and Data have the value in that column missing - then run a complex Algo (irrelevant for this question) to assign bay number based on logic.
I am having problems with rewriting Excel using openpyxl.
Here's example of my code.
data_df = pd.read_excel('Algo Build v23test.xlsx', sheet_name='MondayData')
raw_df = pd.read_excel('Algo Build v23test.xlsx', sheet_name='MondayRaw')
no_bay_res = data_df[data_df['Bay assignment'].isnull()].reset_index() #grab rows where there is no bay assignment in a specific column
book = load_workbook("Algo Build v23test.xlsx")
sheet = book["MondayData"]
for index, reservation in no_bay_res.iterrows():
idx = int(reservation['index'])
if pd.isna(raw_df.iloc[idx, 13]):
continue
else:
value = raw_df.iat[idx,13]
data_df.iloc[idx, 13] = value
sheet.cell(idx+2, 14).value = int(value)
book.save("Algo Build v23test.xlsx")
book.close()
print(value) #302
Now the problem is that it seems that book.close() is not working. Book is still callable in python. Now, it overwrites Excel totally fine. However, if I try to run these two lines again
data_df = pd.read_excel('Algo Build v23test.xlsx', sheet_name='MondayData')
raw_df = pd.read_excel('Algo Build v23test.xlsx', sheet_name='MondayRaw')
I am getting datasets full of NULL values, except for the value that was replaced. (attached the image).
However, if I open that Excel file manually from the folder and save it (CTRL+S) and try running the code again - it works properly. Weirdest problem.
I need to loop the code above for Monday-Sunday, so I need it to be able to read the data again without manually resaving the file.

Due to some reason, pandas will read all the formulas as NaN after the file been used in the script by openpyxl until the file has been opened, saved and closed. Here's the code that helps do that within the script. However, it is rather slow.
import xlwings as xl
def df_from_excel(path, sheet_name):
app = xl.App(visible=False)
book = app.books.open(path)
book.save()
app.kill()
return pd.read_excel(path, sheet_name)

I got the same problem, the only workaround I found is to terminate the excel.exe manually from taskmanager. After that everything went fine.

Mongoexport exporting invalid json files

I collected some tweets from the twitter API and stored it to mongodb, I tried exporting the data to a JSON file and didn't have any issues there, until I tried to make a python script to read the JSON and convert it to a csv. I get this traceback error with my code:
json.decoder.JSONDecodeError: Extra data: line 367 column 1 (char 9745)
So, after digging around the internet I was pointed to check the actual JSON data in an online validator, which I did. This gave me the error of:
Multiple JSON root elements
from the site https://jsonformatter.curiousconcept.com/
Here are pictures of the 1st/2nd object beginning/end of the file:
or a link to the data here
Now, the problem is, I haven't found anything on the internet of how to handle that error. I'm not sure if it's an error with the data I've collected, exported, or if I just don't know how to work with it.
My end game with these tweets is to make a network graph. I was looking at either Networkx or Gephi, which is why I'd like to get a csv file.

Robert Moskal is right. If you can address the issue at source and use --jsonArray flag when you use mongoexport then it will make the problem easier i guess. If you can't address it at source then read the below points.
The code below will extract you the individual json objects from the given file and convert them to python dictionaries.
You can then apply your CSV logic to each individual dictionary.
If you are using csv module then I would say use unicodecsv module as it would handle the unicode data in your json objects.
import json
with open('path_to_your_json_file', 'rb') as infile:
json_block = []
for line in infile:
json_block.append(line)
if line.startswith('}'):
json_dict = json.loads(''.join(json_block))
json_block = []
print json_dict
If you want to convert it to CSV using pandas you can use the below code:
import json, pandas as pd
with open('path_to_your_json_file', 'rb') as infile:
json_block = []
dictlist=[]
for line in infile:
json_block.append(line)
if line.startswith('}'):
json_dict = json.loads(''.join(json_block))
dictlist.append(json_dict)
json_block = []
df = pd.DataFrame(jsonlist)
df.to_csv('out.csv',encoding='utf-8')
If you want to flatten out the json object you can use pandas.io.json.json_normalize() method.

Elaborating on #MYGz suggestion to use --jsonArray
Your post doesn't show how you exported the data from mongo. If you use the following via the terminal, you will get valid json from mongodb:
mongoexport --collection=somecollection --db=somedb --jsonArray --out=validfile.json
Replace somecollection, somedb and validfile.json with your target collection, target database, and desired output filename respectively.
The following: mongoexport --collection=somecollection --db=somedb --out=validfile.json...will NOT give you the results you are looking for because:
By default mongoexport writes data using one JSON document for every
MongoDB document. Ref

A bit late reply, and I am not sure it was available the time this question was posted. Anyway, now there is a simple way to import the mongoexport json data as follows:
df = pd.read_json(filename, lines=True)
mongoexport provides each line as a json objects itself, instead of the whole file as json.

PyFITS: hdulist.writeto()

I'm extracting extensions from a multi-extension FITS file, manipulate the data, and save the data (with the extension's header information) to a new FITS file.
To my knowledge pyfits.writeto() does the task. However, when I give it a data parameter in the form of an array, it gives me the error:
'AttributeError: 'numpy.ndarray' object has no attribute 'lower''
Here is a sample of my code:
'file = 'hst_11166_54_wfc3_ir_f110w_drz.fits'
hdulist = pyfits.open(dir + file)'
sci = hdulist[1].data # science image data
exp = hdulist[5].data # exposure time data
sci = sci*exp # converts electrons/second to electrons
file = 'test_counts.fits'
hdulist.writeto(file,sci,clobber=True)
hdulist.close()
I appreciate any help with this. Thanks in advance.

You're confusing the HDUList.writeto method, and the writeto function.
What you're calling is a method on the HDUList object that is returned when you call pyfits.open. You can think of this object as something like a file handle to your original drizzled FITS file. You can manipulate this object in place and either write it out to a new file or save updates in place (if you open the file in mode='update').
The writeto function on the other hand is not tied to any existing file. It's just a high-level function for writing an array out to a file. In your example you could write your array of electron counts out like:
pyfits.writeto(filename, data)
This will create a single-HDU FITS file with the array data in the PRIMARY HDU.
Do be aware of the admonishment at the top of this section of the docs: http://docs.astropy.org/en/v1.0.3/io/fits/index.html#convenience-functions
The functions like pyfits.writeto are there for convenience in interactive work, but are not recommendable for use in code that will be run repeatedly, as in a script. Instead have a look at these instructions to start.

It is probably because you should use hdulist.writeto(file, clobber=True). There is only one required argument:
https://pythonhosted.org/pyfits/api_docs/api_hdulists.html#pyfits.HDUList.writeto
If you give a second argument, it is used for output_verify which should be a string, not a numpy array. This probably explains your AttributeError ....

Pyshp shapefile reader not working

import shapefile
r = shapefile.Reader("C:\Users\Me\Desktop\py\mis.dbf")
That is as far as I get, must be something simple I don't know about. I have already spent a embarrassing amount of time on this little thing. Could one of you more knowlegeable ones tell me what I missed?

It looks like you're good to go unless you're getting an error that you didn't mention.
First of all you're looking at the dbf file which contains the shapefile attributes (similar to a spreadsheet). But that doesn't matter because the Reader ignores extensions and will try to find the .shp and .shx files as well containing the geometry and geometry record index as well.
If you're just interested in the attributes try the following after you above example:
# Print the dbf field names
print [f[0] for f in r.fields]
# Print the first record:
print r.record(0)
# Loop through all the records using an interator:
for rec in r.iterRecords(): print rec