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#!/bin/bash | |
# Usage | |
if [ ${#@} == 0 ] | |
then | |
echo "Usage: $0 notification_msg [command] [icon-path]" | |
echo "* notification: <Mensage for notification>" | |
echo "* [command] : <Command to execute>" | |
echo "* [icon-path] : <Path to icon path, Default Star icon>" | |
fi |
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find . -regex ".*\.\(png\|jpg\|gif\|jpeg\)$" | grep -v "[0-9]x[0-9]" | xargs -I {} cp -i {} ~/Desktop/tmp/ |
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#!/usr/bin/python3 | |
''' | |
Toggle the audio output devices among the available | |
''' | |
import pulsectl | |
def main(): | |
pulse = pulsectl.Pulse('my-client') | |
# Create list of sink objects |
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
Find all hardlinked files in an specific path | |
Hardlinked files share inode, however the same inode can be found in | |
different partitions, as such, besides the inode it is needed to check the | |
device number where the file exists | |
See -printf option %D | |
https://www.computerhope.com/unix/ufind.htm | |
""" |
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import argparse | |
parser = argparse.ArgumentParser() | |
# Positional argument | |
parser.add_argument('echo', help='Response text') | |
# Optional arguments | |
parser.add_argument('--value', type=int, default=0) | |
parser.add_argument('--flag', action='store_true', help='Flag, true if present') | |
args = parser.parse_args() | |
print(args.echo) |
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function [nElementsNorm, xCenters, heightsNorm, edges] = hist_norm(data, nBins) | |
% | |
% HIST_NORM(DATA, NBINS) computes the Histogram for the vector DATA | |
% using NBINS, and normalize it so the area occupied by the bars with heigth | |
% NELEMENTSNORM with center at XCENTERS sums up to 1. | |
% The resulting histogram (i.e., the barrs) can be plotted with | |
% bar(xCenters, nElementsNorm) | |
% alternatively, the empirical PDF can be plotted with | |
% area(heightsNorm,edges) |
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
# This is the code for the simulation and figures described in the post: | |
# "Multiple comparisons problem, visual approach" in | |
# https://www.castoriscausa.com/posts/2020/06/19/multiple-comparisons-visual/ | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import scipy.stats as stats |
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[Unit] | |
Description= Controls the power of the bluetooth controller when sleep and wake | |
Before=sleep.target | |
StopWhenUnneeded=yes | |
[Service] | |
Type=oneshot | |
RemainAfterExit=yes | |
# Uses bluetoothctl to change the power of bluetooth the controller | |
ExecStart=/usr/bin/bash -c "bluetoothctl power off" |
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09:58:35 From Sylvain Baillet To Everyone: | |
Hi everyone and welcome! We’ll be starting shortly. | |
10:30:13 From Shresth Prasad To Everyone: | |
Can I assume that mne python is a subset of Brainstrom ? | |
10:30:56 From Sezen Asan To Everyone: | |
Will we have access to the powerpoint after the presentation? I am slow to take notes... | |
10:32:03 From Gabriela Cintra Januário To Everyone: | |
Good morning! Are you going to talk also about NIRSTORM? | |
10:32:49 From Kirandeep Kaur To Everyone: | |
I have been using brainstorm to compute functional connectivity in epilepsy subjects. I would like to understand what the threshold values indicate in the connectivity plots, how do we interpret them. Thanks! |
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