estysdesu

# add a new default shell
sudo vim /etc/shells
chsh -s <path to shell>

estysdesu

##### Ubuntu/Debian Apt #####
# /etc/apt/apt.conf.d/proxy.conf
# https://www.serverlab.ca/tutorials/linux/administration-linux/how-to-set-the-proxy-for-apt-for-ubuntu-18-04/

Acquire {
  HTTP::proxy "<ip-addr>";
  HTTPS::proxy "<ip-addr>";
}

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#!/usr/bin/env python3

import shutil, subprocess

cmd = shutil.which("python3")
try:
	runner = subprocess.run(cmd, check=True)
except subprocess.CalledProcessError as e: # python3 is not found in path
    print(str(e))
    exit(1)

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#!/usr/bin/env python3

def window(data, w_size):
    """ Get all windows for a list with an odd window size. """
    if not getattr(data, '__iter__', False):
      raise ValueError("data must be an iterable")
    if w_size%2 != 1:
        raise ValueError("window size must be odd")
    if w_size > len(data):
        raise ValueError("window size must be less than the data size")

estysdesu

import numpy as np

t = np.linspace(0, 4*np.pi, 100)
y1 = np.sin(t)
randomizer = lambda x: x + np.random.choice([-1, 1])*np.random.rand()
# y2 = np.apply_along_axis(randomizer, 0, y1) # applies once on whole array instead of per element of array
vRandomizer = np.vectorize(randomizer)
y2 = vRandomizer(y1)

corr = np.corrcoef(y1, y2)[0, 1] # choose a value from the left --> right diagonal; either [0, 1] or [1, 0]

estysdesu

import numpy as np

x1 = np.ma.array([0, 1, 2, 3, 4])
x1.mask # --> [False]

x2 = np.ma.array([0, 1, 2, 3, 4], mask=[False]) # triggers mask expansion
x2.mask # --> [False, False, False, False, False]

estysdesu

import numpy as np

def drop_tracker(curArr, rmIndxs, dropTracker):
	"""
    Track values that are removed from an array.
   	dropTracker is a MaskedArray that curArr has derived from and slightly changed.
    Requires curArr pts to be closer to their complimentary dropTracker pts more than any other dropTracker pts.
    """
    assert type(dropTracker) is np.ma.MaskedArray
    newArr = np.delete(curArr, rmIndxs)

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# PyInstaller/bindepend.py
#-----------------------------------------------------------------------------
# Copyright (c) 2013-2018, PyInstaller Development Team.
#
# Distributed under the terms of the GNU General Public License with exception
# for distributing bootloader.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------

estysdesu

import numpy as np

def grad_n(y, x, n, edge_order=2):
    g = np.gradient(y, x, edge_order=edge_order)
    for _ in range(n-1):
        g = grad_n(g, x, 1, edge_order=edge_order)
    return g

estysdesu

#!/usr/bin/env python3

import window # https://gist.github.com/estysdesu/af7301fa17817c0d0ac7b5f8952c9bfe

def median_filt_1d(data, w_size):
    """ Median filter for 1d data. Edges are handled with a window shift. """
    windowed_data = window(data, w_size)
    w_half_size = w_size//2
    for i, w_data in enumerate(windowed_data):
        w_data_sort = sorted(w_data)