https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/ https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data
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# git clone https://github.com/NVlabs/stylegan2 | |
import os | |
import numpy as np | |
from scipy.interpolate import interp1d | |
from scipy.io import wavfile | |
import matplotlib.pyplot as plt | |
import PIL.Image | |
import moviepy.editor | |
import dnnlib |
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-- specify input and output directories | |
set infile_directory to "/Users/doug/Desktop/inputs/" | |
set outfile_directory to "/Users/doug/Desktop/outputs/" | |
-- get the basenames of each input file | |
tell application "System Events" | |
set infile_list to files of folder infile_directory | |
end tell | |
-- process each input file |
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# wavfile.py (Enhanced) | |
# Date: 20190213_2328 Joseph Ernest | |
# | |
# URL: https://gist.github.com/josephernest/3f22c5ed5dabf1815f16efa8fa53d476 | |
# Source: scipy/io/wavfile.py | |
# | |
# Added: | |
# * read: also returns bitrate, cue markers + cue marker labels (sorted), loops, pitch | |
# See https://web.archive.org/web/20141226210234/http://www.sonicspot.com/guide/wavefiles.html#labl | |
# * read: 24 bit & 32 bit IEEE files support (inspired from wavio_weckesser.py from Warren Weckesser) |
Following this guide will set up a local Elasticsearch with Kibana and Marvel using Homebrew and Homebrew Cask
If you already have Java
installed on your system, skip steps Install Cask and Install Java
If you already have Java
and Homebrew
installed on your system, skip steps Prerequisites, start at Install Elasticsearch and Kibana after running $ brew update
$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"