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#!/bin/bash
# --- Version history ---
# mrn: sleep for $freq instead of 3 seconds (forked)
# 0.4: added variable to store file path, and $2 for base file name
# added variable to store desired reporting interval
# 0.3: added $1 to send in process ID at run time.
# 0.2: switched to $SECONDS for the loop. works.
# 0.1: didn't work well at all.
# --- Version history ---
#! /usr/bin/env python
# ID mapping using mygene
# https://pypi.python.org/pypi/mygene
# http://nbviewer.ipython.org/gist/newgene/6771106
# http://mygene-py.readthedocs.org/en/latest/
# 08/30/14
__author__ = 'tommy'
@vals
vals / Clusters from dendrograms.ipynb
Created June 28, 2014 23:37
IPython notebook illustrating how to extract cluster elements in Python Dendrograms
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@ej3
ej3 / com.sonatype.nexus.plist
Last active January 29, 2021 04:06
run nexus from brew on osx
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>Label</key>
<string>com.sonatype.nexus</string>
<key>ProgramArguments</key>
<array>
<string>/usr/local/opt/nexus/bin/nexus</string>
<string>start</string>
@fperez
fperez / ProgrammaticNotebook.ipynb
Last active March 20, 2025 03:57
Creating an IPython Notebook programatically
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@wandernauta
wandernauta / sp
Last active June 3, 2025 10:02
sp is a command-line client for Spotify's dbus interface. Play, pause, skip and search tracks from the comfort of your command line.
#!/usr/bin/env bash
#
# This is sp, the command-line Spotify controller. It talks to a running
# instance of the Spotify Linux client over dbus, providing an interface not
# unlike mpc.
#
# Put differently, it allows you to control Spotify without leaving the comfort
# of your command line, and without a custom client or Premium subscription.
#
@why-not
why-not / gist:4582705
Last active November 6, 2024 04:35
Pandas recipe. I find pandas indexing counter intuitive, perhaps my intuitions were shaped by many years in the imperative world. I am collecting some recipes to do things quickly in pandas & to jog my memory.
"""making a dataframe"""
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
"""quick way to create an interesting data frame to try things out"""
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd'])
"""convert a dictionary into a DataFrame"""
"""make the keys into columns"""
df = pd.DataFrame(dic, index=[0])
@chitchcock
chitchcock / 20111011_SteveYeggeGooglePlatformRant.md
Created October 12, 2011 15:53
Stevey's Google Platforms Rant

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real