saketkc

a=[]
while STDIN.readline.chomp!="42"
	a.push($_)
end
a.each  { |s| puts s }

brantfaircloth

import sys
import time
from Bio import Entrez

Entrez.email = "your.email@domain.tld"
if not Entrez.email:
    print "you must add your email address"
    sys.exit(2)

# create an empty list we will fill with the gene names

brandon-rhodes

# Inspired by the following sentence that I ran across this morning:
#
#    "f_lineno is the current line number of the frame - writing to
#     this from within a trace function jumps to the given line
#     (only for the bottom-most frame). A debugger can implement a
#     Jump command (aka Set Next Statement) by writing to f_lineno."
#
#    https://docs.python.org/2/reference/datamodel.html
#
# There is an older implementation of a similar idea:

twiecki

import dask
import dask.array as da
import dask.dataframe as dd
import sparse

@dask.delayed(pure=True)
def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9):
    return sparse.COO.from_numpy((np.dot(chunk1, chunk2.T) > corr_thresh))
        
def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9):

fperez

Polyglot Data Science with IPython & friends

Author: Fernando Pérez.

A demonstration of how to use Python, Julia, Fortran and R cooperatively to analyze data, in the same process.

This is supported by the IPython kernel and a few extensions that take advantage of IPython's magic system to provide low-level integration between Python and other languages.

See the companion notebook for data preparation and setup.

brentp

library(limma)

GROUP="62976"
# targets.txt has columns of "FileName" and "Condition" e.g.
"""
FileName    Condition
data/scrubbed/LT001098RU_COPD.45015.txt COPD
data/scrubbed/LT001600RL_ILD.45015.txt  ILD
data/scrubbed/LT003990RU_CTRL.45015.txt CTRL
data/scrubbed/LT004173LL_ILD.45015.txt  ILD

dgrtwo

library(tidyverse)

# Data is downloaded from here:
# https://www.kaggle.com/c/digit-recognizer
kaggle_data <- read_csv("~/Downloads/train.csv")

pixels_gathered <- kaggle_data %>%
  mutate(instance = row_number()) %>%
  gather(pixel, value, -label, -instance) %>%
  extract(pixel, "pixel", "(\\d+)", convert = TRUE)

lmcinnes

mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

fperez