A few thingz

Joseph Basquin



Working on PDF files with Python

There are many solutions to work on PDF files with Python. Depending on whether you need to read, parse data, extract tables, modify (split, merge, crop...), or create a new PDF, you will need different tools.

Here is a quick diagram of some common tools I have used:

If you need to extract data from image PDF files, it's a whole different story, and you might need to use OCR libraries like (Py)Tesseract or other tools.

Have some specific data conversion / extraction needs? Please contact me for consulting - a little script can probably automate hours of manual processing in a few seconds!

N-dimensional array data store (with labeled indexing)

What am I trying to do?

I'm currently looking for the perfect data structure for an ongoing R&D task.

I need to work with a data store as a n-dimensional array x (of dimension 4 or more) such that:

Possible solutions

I'm looking for a good and lightweight solution.
To keep things simple, I deliberately avoid (for now):

method ragged non-consecutive indexing numpy arithm. random access for 100 GB data store notes
xarray ? no
sparse ? no
Pandas DataFrame + Numpy ndarray ? ? (*) (**)
Tensorflow tf.ragged.constant ? ? ?
Sqlite + Numpy ndarray ? ? ? ? to be tested

(*) serialization with parquet: doesn't accept 2D or 3D arrays:

import numpy as np, pandas as pd
x = pd.DataFrame(columns=['a', 'b'])
for i in range(100):
    x.loc['t%i' % i] = [np.random.rand(100, 100), np.random.rand(2000)]
# pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column a with type object')

(**) serialization with hdf5: currently not working:

import numpy as np, pandas as pd
store = pd.HDFStore("store.h5")
df = pd.DataFrame(columns=['a', 'b'])
df.loc['t1'] = {'a': np.random.rand(100, 100), 'b': np.random.rand(2000)}
store.append('test', df)
# TypeError: Cannot serialize the column [a] because its data contents are not [string] but [mixed] object dtype

Contact me if you have ideas!


https://stackoverflow.com/questions/72733385/data-structure-for-sparse-n-dimensional-array-tensor-such-a0-and-a1, https://stackoverflow.com/questions/72737525/pandas-rows-containing-numpy-ndarrays-various-shapes, https://stackoverflow.com/questions/72742007/pandas-dataframe-containing-numpy-ndarray-and-mean, https://stackoverflow.com/questions/72742843/100gb-data-store-pandas-dataframe-of-numpy-ndarrays-loading-only-a-small-part

Python + TensorFlow + GPU + CUDA + CUDNN setup with Ubuntu

Every time I setup Python + TensorFlow on a new machine with a fresh Ubuntu install, I have to spend some time again and again on this topic, and do some trial and error (yes I'm speaking about such issues). So here is a little HOWTO, once for all.

Important fact: we need to install the specific version number of CUDA and CUDNN relative to a particular version of TensorFlow, otherwise it will fail, with errors like libcudnn.so.7: cannot open shared object file: No such file or directory.

For example, for TensorFlow 2.3, we have to use CUDA 10.1 and CUDNN 7.6 (see here).

Here is how to install on a Ubuntu 18.04:

pip3 install --upgrade pip   # it was mandatory to upgrade for me
pip3 install keras tensorflow==2.3.0

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt install cuda-10-1 nvidia-driver-430

To test if the NVIDIA driver is properly installed, you can run nvidia-smi (I noticed a reboot was necessary).

Then download "Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.1" on https://developer.nvidia.com/rdp/cudnn-archive (you need to create an account there), and then:

sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.1_amd64.deb     

That's it! Reboot the computer, launch Python 3 and do:

import tensorflow
tensorflow.test.gpu_device_name()     # also, tensorflow.test.is_gpu_available() should give True

The last line should display the right GPU device name. If you get an empty string instead, it means your GPU isn't used by TensorFlow!


Quick-tip: Rebooter une Livebox avec un script Python

Petite astuce utile pour rebooter une Livebox Play en 4 lignes de code :

import requests
r = requests.post("")
h = {'Content-Type': 'application/json; charset=UTF-8', 'X-Context': r.json()['data']['contextID']}
s = requests.post("", headers=h, cookies=r.cookies)

Avec une Livebox 4 ou 5, voici la méthode :

import requests
session = requests.Session()
auth = '{"service":"sah.Device.Information","method":"createContext","parameters":{"applicationName":"so_sdkut","username":"admin","password":"LEMOTDEPASSEICI"}}'
r = session.post('', data=auth, headers={'Content-Type': 'application/x-sah-ws-1-call+json', 'Authorization': 'X-Sah-Login'})
h = {'X-Context': r.json()['data']['contextID'], 'X-Prototype-Version': '1.7', 'Content-Type': 'application/x-sah-ws-1-call+json; charset=UTF-8', 'Accept': 'text/javascript'}
s = session.post("", headers=h, data='{"parameters":{}}')

Inspiré de ce post avec curl, de ce projet (la même chose en ... 99 lignes de code ;)) et la librairie sysbus.

NB: cette méthode de reboot change l'IP de la Livebox au redémarrage.

nFreezer, a secure remote backup tool

So you make backups of your sensitive data on a remote server. How to be sure that it is really safe on the destination server?

By safe, I mean "safe even if a malicious user gains access" on the destination server; here we're looking for a solution such that, even if a hacker attacks your server (and installs compromised software on it), they cannot read your data.

You might think that using SFTP/SSH (and/or rsync, or sync programs) and using an encrypted filesystem on the server is enough. In fact, no: there will be a short time during which the data will be processed unencrypted on the remote server (at the output of the SSH layer, and before arriving at the filesystem encryption layer).

How to solve this problem? By using an encrypted-at-rest backup program: the data is encrypted locally, and is never decrypted on the remote server.

I created nFreezer for this purpose.

Main features:

More about this on nFreezer.

  By the way I just published another (local) backup tool on PyPi: backupdisk, that you can install with pip install diskbackup. It allows you to quickly backup your disk to an external USB HDD in one-line:

diskbackup.backup(src=r'D:\Documents', dest=r'I:\Documents', exclude=['.mp4'])

Update: many thanks to @Korben for his article nFreezer – De la sauvegarde chiffrée de bout en bout (December 12, 2020).

Vversioning, a quick and dirty code versioning system

For some projects you need a real code versioning system (like git or similar tools).

But for some others, typically micro-size projects, you sometimes don't want to use a complex tool. You might want to move to git later when the project gets bigger, or when you want to publish it online, but using git for any small project you begin creates a lot of friction for some users like me. Examples:

User A: "I rarely use git. If I use it for this project, will I remember which commands to use in 5 years when I'll want to reopen this project? Or will I get stuck with git wtf and unable to quickly see the different versions?".

User B: "I want to be able to see the different versions of my code even if no software like git is installed (ex: using my parents' computer)."

User C: "My project is just a single file. I don't want to use a complex versioning system for this. How can I archive the versions?"

For this reason, I just made this:


It is a (quick and dirty) versioning system, done in less than 50 lines of Python code.

sdfgh – encrypted notepad: don't trust it, just read its 65 lines of code

Why another lightweight notepad with encryption?

Well, I needed an encrypted notepad for personal notes that has these features:

Here it is:


An improved Launchpad for Ableton Live

The Novation Launchpad is a great controller for Ableton Live, especially because it allows you to play / jam / record without having to look at the computer screen.

Except for one thing: you can play a clip, record a clip, stop a clip... but you cannot delete a clip. This limitation can be annoying, because sometimes when playing with your synth/guitar/whatever you need to record many takes before having the right one, and you also want to be able to immediately delete the bad recordings. Strangely, this was not possible with the Launchpad (now possible with Launchpad Pro, but it is more than twice as expensive).

So here is a "MIDI remote script" (just a little .py file) that you can copy in C:\ProgramData\Ableton\Live 10 Suite\Resources\MIDI Remote Scripts\Launchpad (for Windows, or the equivalent folder on Mac), that adds this feature to the Launchpad: the bottom right button will be a "delete the currently selected clip" button.

Bonus: this script also transforms the last row of the Launchpad into "stop clip" buttons, which is quite useful.

Download: MainSelectorComponent.py



An attempt to generate random data with audio (and your computer's built-in microphone)

You probably know that generating some real random data is not so easy to do with a computer. How to design a good Random Number Generator (or a pseudo-random one) is a math topic that you can work years on ; it's also something very important for real-life applications such as security/cryptography, for example when you need to generate strong passwords.

Usually (and this is true in general in cryptography), designing your own algorithm is bad, because unless you're a professional in this subject and your algorithm has been approved by peers, you're guaranteed to have flaws in it, that could be exploited.

But here, for fun (don't use it for critical applications!), let's try to generate 100 MB of good random data.

1) Record 20 minutes of audio in 96khz 16bit mono with your computer's built-in microphone. Try to set the mic input level so that the average volume is neither 0 dB (saturation) nor -60 dB (too quiet). Something around -10 dB looks good. What kind of audio should you record? Nothing special, just the noise in your room is ok. You will get around 20*60*96000*2 ~ 220 MB of data. In these 220 MB, only the half will be really useful (because many values in the signal - an array of 16-bit integers - won't use the full 16-bit amplitude: many integers "encoding" the signal might be for example of absolute value < 1024, i.e. will provide only 10 bits)

2) Now let's shuffle these millions of bits of data with some Python code:

from scipy.io import wavfile
import numpy as np
import functools

sr, x = wavfile.read('sound.wav')  # read a mono audio file, recorded with your computer's built-in microphone

L = []  # list of bits
for i in range(len(x)):
    bits = format(abs(x[i]), "b")  # get binary representation of the data
                                   # don't use "016b" format because it would create a bias: small integers (those not using
                                   # the full bit 16-bit amplitude) would have many leading 0s!
    L += map(int, bits)[1:]        # discard the first bit, which is always 1!

print L.count(1)
print L.count(0)  # check if it's equidistributed in 0s and 1s

n = 2 ** int(np.log2(len(L)))
L = L[:n]  # crop the array of bits so that the length is a power of 2; well the only requirement is that len(L) is coprime with p (see below)

# The trick is: don't use **consecutive bits**, as it would recreate something close to the input audio data. 
# Let's take one bit every 96263 bits instead! Why 96263? Because it's a prime number, then we are guaranteed that
# 0 * 96263 mod n, 1 * 96263 mod n, 2 * 96263 mod n, ..., (n-1) * 96263 mod n will cover [0, 1, ..., n-1].  (**)
# This is true since 96263 is coprime with n. In math language: 96253 is a "generator" of (Z/nZ, +).

p = 96263  # The higher this prime number, the better the shuffling of the bits! 
           # If you have at least one minute of audio, you have at least 45 millions of useful bits already, 
           # so you could take p = 41716139 (just a random prime number I like around 40M)

M = set()
with open('random.raw', 'wb') as f:
    for i in range(0, n, 8):
        M.update(set([(k * p) % n for k in range(i, i+8)]))  # this is optional, here just to prove that our math claim (**) is true
        c = [L[(k * p) % n] for k in range(i, i+8)]   # take 8 bits, in shuffled order
        char = chr(functools.reduce(lambda a, b: a * 2 + b, c))  # create a char with it

print M  == set(range(n))  # True, this shows that the assertion (**) before is true. Math rulez!

Done, your random.raw file is filled with random data!


How to create symbolic links with Windows Explorer?

Quick tip: here is how to create symlinks in Windows without using any command line tool.

Create symbolic links with a GUI

1) If you have Python installed, create mklinkgui.py:

import win32clipboard    # pip install pywin32 if you haven't installed it already
import sys, os, subprocess
fname = sys.argv[1]
filenames = win32clipboard.GetClipboardData(win32clipboard.CF_HDROP)
for filename in filenames:
    base = os.path.basename(filename)
    link = os.path.join(fname, base)
    subprocess.Popen('mklink %s "%s" "%s"' % ('/d' if os.path.isdir(filename) else '', link, filename), shell=True)

2) Open regedit and

How to use it?

Working with audio files in Python, advanced use cases (24-bit, 32-bit, cue and loop markers, etc.)

Python comes with the built-in wave module and for most use cases, it's enough to read and write .wav audio files.

But in some cases, you need to be able to work with 24 or 32-bit audio files, to read cue markers, loop markers or other metadata (required for example when designing a sampler software). As I needed this for various projects such as SamplerBox, here are some contributions I made:

  1. The Python standard library's wave module doesn't read cue markers and doesn't support 24-bit files. Here is an updated module:

    wave.py (enhanced)

    that adds some little useful things. (See Revision #1 to see diff with the original stdlib code).

    Usage example:

    from wave import open
    f = open('Take1.wav')

    If you're familiar with main Python repositery contributions (I'm not), feel free to include these additions there.

  2. The module scipy.io.wavfile is very useful too. So here is an enhanced version:

    wavfile.py (enhanced)

    Among other things, it adds 24-bit and 32-bit IEEE support, cue marker & cue marker labels support, pitch metadata, etc.

    Usage example:

    from wavfile import read, write
    sr, samples, br, cue, cuelabels, cuelist, loops, f0 = read('Take1.wav', readmarkers=True, 
        readmarkerlabels=True, readmarkerslist=True, readpitch=True, readloops=True)
    write('Take2.wav', sr, samples, bitrate=br, markers=cue, loops=loops, pitch=130.82)

    Here is a Github post and pull-request about a hypothetical merge to Scipy.

Here is how loop markers look like in the good old (non open-source but soooo useful) SoundForge:

Lastly, this is how to convert a WAV to MP3 with pydub, for future reference. As usual, do pip install pydub and make sure ffmpeg is in the system path. Then:

from pydub import AudioSegment
song = AudioSegment.from_wav("test.wav")
song.export("test.mp3", format="mp3", bitrate="256k")

will convert a WAV file to MP3.

Interested for future evolutions and other audio programming tools?

Get the reverb impulse response of a church

I recently recorded an impulse response of the reverb of a 14th-century church (more or less the footprint of the sound ambiance of the building). Here is how I did it.

Quite a lot of reverb, that's exactly what we want to catch with an IR!

Then, of course, we can do some cleaning, fade out, etc.

But what is this useful for? You can use this Impulse Response in any music production software (the VST SIR1 is quite good and freeware) , and make any of your recordings (voice, instrument, etc.) sound like if they were recorded in this church. This is the magic of convolution reverb!

Useful trick when you record your own IR: play sweep0.wav in the building instead of sweep.wav. The initial "beep" is helpful to see exactly where things begin. If you don't do that, as the sweep begins with very low frequencies (starting from 20 Hz), you won't know exactly where is the beginning of your microphone-recording. Once your recording is done, you can trim the soundfile by making it begin exactly 10 seconds after the short beep.

Some related reading in this topic, and this blog post.

My personal blog.


Data / AI / Python consulting and freelancing.

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