hadley

ks.default <- function(rows) seq(2, max(3, rows %/% 4))

many_kmeans <- function(x, ks = ks.default(nrow(x)), ...) {
  ldply(seq_along(ks), function(i) {
    cl <- kmeans(x, centers = ks[i], ...)
    data.frame(obs = seq_len(nrow(x)), i = i, k = ks[i], cluster = cl$cluster)
  })
}

all_hclust <- function(x, ks = ks.default(nrow(x)), point.dist = "euclidean", cluster.dist = "ward") {

gjkerns

# Simulation study for sample size between/within
# got Treat + Sham between subjects
# got Time within subjects

nPerGroup <- 30
nTime     <- 4
muTreat   <- c(37, 32, 20, 15)
muSham    <- c(37, 32, 25, 22)
stdevs    <- c(12, 10, 8, 6)
stdiff    <- 9

CristinaSolana

1. Clone your fork:

git clone [email protected]:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

seberg

def rolling_window(array, window=(0,), asteps=None, wsteps=None, axes=None, toend=True):
    """Create a view of `array` which for every point gives the n-dimensional
    neighbourhood of size window. New dimensions are added at the end of
    `array` or after the corresponding original dimension.
    
    Parameters
    ----------
    array : array_like
        Array to which the rolling window is applied.
    window : int or tuple

fabianp

# .. Memory benchmarks for SciPy's Singular Value Decomposition ..
# .. Author: Fabian Pedregosa <[email protected]>

import numpy as np
from scipy.sparse import linalg as splinalg
from scipy import sparse, linalg
import pylab as pl
from memory_profiler import memory_usage

dims = np.arange(500, 1500, 20)

ashwin

%-----------------------------------------------------------------------------%

% Letter class
\documentclass[a4paper, 10pt]{letter}

% Name of sender
\name{Joe Fox}

% Signature of sender
\signature{Joe Fox}

xuanlongma

require(ncdf)
require(raster)

## Input: a netCDF file
file.nc <- 'rain.nc'

## Output: a GeoTIFF file
file.tiff <- 'rain.tiff'

## Import netCDF

bertbalcaen

ffmpeg -i shame-run.mov -r 24/1 test/output%03d.jpg

mabdrabo

import pyaudio
import wave
 
FORMAT = pyaudio.paInt16
CHANNELS = 2
RATE = 44100
CHUNK = 1024
RECORD_SECONDS = 5
WAVE_OUTPUT_FILENAME = "file.wav"