patientzero

# Sebastian P. Bayerl
FB_TEST = ['68m', '26m', '54m', '24fc', '34m', '24ma']
FB_DEV_SPK = ['46ma', '27f', '24fb', '37m', '29mc', '32m', '57m']
FB_TRAIN_SPK = ['24fa', '62m', '50fa', '29ma', '68m', '64m', '35ma', '27ma', '29mb', '28m', '26m',
                          '46mb', '43m', '54m', '26f', '60m', '35mb', '24fc', '34m', '62f', '24ma', '24mb',
                          '25m', '39f', '54f', '50fb']
FB_TRAIN_DEV = FB_TRAIN_SPK + FB_DEV_SPK

patientzero

import pandas as pd

def load_cbt_annotations(path):
    import json
    with open(path) as f:
        x = json.load(f)
    # make session numeric
    df = pd.DataFrame()
    for k, v in x.items():
        while v:

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def morse_code_to_wav(morse_str, sr=8000, short_burst_len=0.1, long_burst_len=0.2,
                      pause_len=0.05, frequency=500, output_file_name=None):
    """
    @param morse_str: str containing . for short burst - for long burst and space for pause between words
    @param sr: sampling frequency of the desired wav output
    @param output_file_name: path to wavfile output
    """
    amplitude = np.iinfo(np.int16).max * 0.7
    t_pause = np.linspace(0, pause_len, int(sr * pause_len))
    t_pause[:] = 0

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#!/bin/bash
# based on Medium Blogpost :https://medium.com/@jimkang/complete-uninstall-remove-vscode-mac-5e48bef3bdec
rm -rf $HOME/Library/Application\ Support/Code
rm -rf $HOME/.vscode
rm -rf /Applications/Visual\ Studio\ Code.app

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from sklearn.model_selection import GroupKFold
# define number of splits
n_splits = 10
# all data in a list
pics = list(data_dir.glob('**/*.png'))
# matching labels in list
labels = [pic.parent.stem for pic in pics] 
# get all groups that should not be in the 
groups = [pic.stem.split('_')[1] for pic in pics]
gkf = GroupKFold(n_splits=n_splits)

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ffmpeg

Converting Audio into Different Formats / Sample Rates

Minimal example: transcode from MP3 to WMA:
ffmpeg -i input.mp3 output.wma

You can get the list of supported formats with:
ffmpeg -formats

Convert WAV to MP3, mix down to mono (use 1 audio channel), set bit rate to 64 kbps and sample rate to 22050 Hz:

patientzero

{"lastUpload":"2020-12-21T11:42:03.823Z","extensionVersion":"v3.4.3"}

patientzero

### Keybase proof

I hereby claim:

  * I am patientzero on github.
  * I am sebastianbayerl (https://keybase.io/sebastianbayerl) on keybase.
  * I have a public key ASBAzeywZrXnBpjPs4rL5teZKmxbF8f7LP8G-xJHUr5WZQo

To claim this, I am signing this object:

patientzero

HOWTO Configure Atlassian Jira to use Letsencrypt certificate with default Tomcat

This is a primer for installing a Letsencrypt certificate on a Jira server that is running the Jira provided, default Tomcat for serving webpages.

I found lots of information about how to do it using a free-standing Tomcat or nginx, but nothing about this particular combination. I hope it helps you!

Obviously, in all the examples, you need to replace jira.example.com with your own domain! And (duh) you need to use your own password, not 1234

You need to have installed Java (outside the scope of this document). Then in your user's shell RC file and probably root's RC file, add

patientzero

from scipy.io.wavfile import read,write
from pylab import plot,show,subplot,specgram
import pylab as pl
# Simple versions: 
rate, data = read('wiederholung.wav')
_=specgram(data, NFFT=256, Fs=rate, noverlap=0,cmap='inferno',scale_by_freq=False) # small window

# Custom functions: 
# Source : http://qaru.site/questions/804495/cutting-of-unused-frequencies-in-specgram-matplotlib