XavierTolza

import numpy as np
from scipy.optimize import minimize
from scipy.signal import lfilter, butter

Fs = 50
Ts = 1 / Fs  # sec
tmax = 10  # sec
noise_level = 0.05
system_order = 1

XavierTolza

import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import minimize
from scipy.signal import lfilter

t = np.linspace(0, 10, 1000)
y = (1 - np.exp(-t / 1))
y += np.random.normal(0, 0.05, y.shape)

XavierTolza

# 720p nvidia
ffmpeg -i input.mkv -preset slow -codec:a libfdk_aac -b:a 128k -codec:v nvenc_h264 -pix_fmt yuv420p -b:v 2500k -minrate 1500k -maxrate 3000k -bufsize 5000k -vf scale=-2:720 -loglevel error -movflags +faststart -y output.mp4

# 720p cpu
ffmpeg -i input.mkv -preset slow -codec:a libfdk_aac -b:a 128k -codec:v h264 -pix_fmt yuv420p -b:v 2500k -minrate 1500k -maxrate 3000k -bufsize 5000k -vf scale=-2:720 -loglevel error -movflags +faststart -y output.mp4

# 720p cpu emby direct play (sur raspi 1 thread)
ffmpeg -i input.mkv -preset slow -c:a libopus -strict -2 -b:a 128k -codec:v libx264 -pix_fmt yuv420p -b:v 2500k -minrate 1500k -maxrate 3000k -bufsize 5000k -vf scale=-2:720 -loglevel error -movflags +faststart -y -threads 1 output.mp4 

XavierTolza

#!python3
import re
from argparse import ArgumentParser
from base64 import b64encode
from os.path import basename, abspath, dirname, join

parser = ArgumentParser()
parser.add_argument("filename")
args = parser.parse_args()

XavierTolza

#!python3
import urllib3
import json
import re
import os
user_agent = {'user-agent': 'Mozilla/5.0 (Windows NT 6.3; rv:36.0) Gecko/20100101 Firefox/36.0'}
http = urllib3.PoolManager(10, headers=user_agent)
url = "https://api.github.com/repos/ramboxapp/community-edition/releases"
data = json.loads(http.request('GET', url).data.decode("utf-8"))
assets = data[0]["assets"]

XavierTolza

sudo apt install -y git build-essential autoconf pkg-config libssl-dev libvncserver-dev
git clone https://github.com/LibVNC/x11vnc.git

XavierTolza

from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
from pandas import DataFrame
iris = datasets.load_iris()
iris = data1 = DataFrame(data= np.c_[iris['data'], iris['target']],
                            columns= iris['feature_names'] + ['class'])

def plot_pca(data,classes, classes_label=None):
    fig, (axe_2d,axe_info) = plt.subplots(1,2)