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@saketkc
saketkc / TEST.rb
Created Jul 14, 2011
CodeChef(SPOJ) Problem1 Ruby Solution
View TEST.rb
a=[]
while STDIN.readline.chomp!="42"
a.push($_)
end
a.each { |s| puts s }
@brantfaircloth
brantfaircloth / get_protein.py
Created Apr 3, 2011
Get protein sequences from Genbank given a genomic accession number and a gene name
View get_protein.py
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
View goto_powered.py
# 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:
@brentp
brentp / one-channel-agilent.R
Created Aug 17, 2011
use limma to normalize 1-channel agilent data and write out differentially expressed genes.
View one-channel-agilent.R
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
@twiecki
twiecki / dask_sparse_corr.py
Created Aug 17, 2018
Compute large, sparse correlation matrices in parallel using dask.
View dask_sparse_corr.py
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):
@lmcinnes
lmcinnes / flow_cytometry.ipynb
Created Sep 8, 2018
Flow Cytometry experiments with UMAP
View flow_cytometry.ipynb
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@fperez
fperez / README.md
Last active Jul 1, 2021
Polyglot Data Science with IPython
View README.md

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.

@dgrtwo
dgrtwo / mnist_pairs.R
Created May 31, 2017
Comparing pairs of MNIST digits based on one pixel
View mnist_pairs.R
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)
@fperez
fperez / ProgrammaticNotebook.ipynb
Last active Sep 2, 2021
Creating an IPython Notebook programatically
View ProgrammaticNotebook.ipynb
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@gizmaa
gizmaa / Plot_Examples.md
Last active Nov 12, 2021
Various Julia plotting examples using PyPlot