https://linuxconfig.org/how-to-install-kubernetes-on-ubuntu-18-04-bionic-beaver-linux
> apt-get install nfs-kernel-server
| # coding: utf-8 | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| # Dates (by month: October 2019 through September 2023) | |
| dates = ['Oct19', 'Nov19', 'Dec19', 'Jan20', 'Feb20', 'Mar20', 'Apr20', 'May20', 'Jun20', 'Jul20', 'Aug20', 'Sep20', 'Oct20', 'Nov20', 'Dec20', 'Jan21', 'Feb21', 'Mar21', 'Apr21', 'May21', 'Jun21', 'Jul21', 'Aug21', 'Sep21', 'Oct21', 'Nov21', 'Dec21', 'Jan22', 'Feb22', 'Mar22', 'Apr22', 'May22', 'Jun22', 'Jul22', 'Aug22', 'Sep22', 'Oct22', 'Nov22', 'Dec22', 'Jan23', 'Feb23', 'Mar23', 'Apr23', 'May23', 'Jun23', 'Jul23', 'Aug23', 'Sep23'] | |
| # Mileages (per month) | |
| mileages = [153.49, 100.30, 101.19, 80.82, 139.45, 125.23, 101.14, 97.93, 82.42, 49.97, 0.0, 0.0, 23.76, 8.41, 5.68, 33.96, 14.08, 17.80, 30.97, 40.24, 16.40, 29.37, 23.41, 30.10, 35.08, 18.06, 18.79, 52.87, 33.71, 58.27, 21.67, 25.81, 14.68, 16.24, 38.55, 41.04, 50.35, 8.96, 15.77, 14.19, 48.73, 64.55, 73.92, 63.36, 60.10, 62.67, 85.22, 101.28] |
https://linuxconfig.org/how-to-install-kubernetes-on-ubuntu-18-04-bionic-beaver-linux
> apt-get install nfs-kernel-server
| # converts vidfile.avi to vidfile.mov | |
| # re-encodes video using h264 codec | |
| # This is good for, e.g., creating a video to post to instagram. | |
| ffmpeg -i vidfile.avi -c:v h264 vidfile.mov | |
| # This is good for, e.g., creating a video to embed in PPTX. | |
| ffmpeg -i vidfile.avi -pix_fmt yuv420p vidfile.mov | |
| # see the list of available codecs |
| # This function was written as part of the analysis pipeline in | |
| # Lu et al 2018, IEEE ISBI: https://arxiv.org/abs/1803.07534 | |
| # | |
| # The purpose of this function was to convert the predicted mask | |
| # of cilia into an empirical probability density function that we | |
| # would then sample "seeds" from to extract patches of ciliary motion | |
| # (with high probability; hence, the empirical PDF) to feed to the | |
| # downstream Conv-LSTM for classification. The reason we didn't | |
| # just uniformly sample from the masked areas of cilia was to try | |
| # and minimize uncertainty in the mask edges (hence, the distance |
| #!/opt/python/bin/python | |
| # Installs a GPU-enabled OpenCV from the conda-forge feedstock. | |
| # Assumes you already have Python 3 installed and the conda-forge channel on. | |
| import argparse | |
| import os.path | |
| import subprocess | |
| import sys | |
| # These are the options we want to make sure are set to the values listed here. |
| import argparse | |
| import matplotlib.pyplot as plot | |
| from matplotlib import animation | |
| from mpl_toolkits.mplot3d.axes3d import Axes3D | |
| import numpy as np | |
| import scipy.io | |
| import ar.ar as AR | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description = 'Cilia AR Subspace Plotting', |
| # Register each worker. | |
| ./bootstrap-uga-general.sh | |
| # Make sure each worker is subscribed to the RHEL channels: | |
| # - RHEL Server Optional | |
| # - RHEL Server Supplementary | |
| # - RHN Tools for RHEL | |
| # Run on each worker. | |
| yum -y update |
Interfaces the AWS Dash button with the Hue lights.
DEPRECATED. Use this repo instead: https://github.com/quinngroup/python-packages
This is a list of all the Python packages I like to use.
| import argparse | |
| import bz2 | |
| import os | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description = 'FERET Parser', | |
| epilog = 'lol moar p1cz', add_help = 'How to use', | |
| prog = 'python feret.py <args>') | |
| # Required parameters. |