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Saket Choudhary saketkc

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gizmaa /
Last active Jan 22, 2022
Various Julia plotting examples using PyPlot
View tmux-cheatsheet.markdown

tmux shortcuts & cheatsheet

start new:


start new with session name:

tmux new -s myname
willurd /
Last active Jan 19, 2022
Big list of http static server one-liners

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000
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
# 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
"""Kernel K-means"""
# Author: Mathieu Blondel <>
# 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 / ProgrammaticNotebook.ipynb
Last active Dec 9, 2021
Creating an IPython Notebook programatically
View ProgrammaticNotebook.ipynb
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dgrtwo / mnist_pairs.R
Created May 31, 2017
Comparing pairs of MNIST digits based on one pixel
View mnist_pairs.R
# Data is downloaded from here:
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 /
Last active Jul 1, 2021
Polyglot Data Science with IPython

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.

lmcinnes / flow_cytometry.ipynb
Created Sep 8, 2018
Flow Cytometry experiments with UMAP
View flow_cytometry.ipynb
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twiecki /
Created Aug 17, 2018
Compute large, sparse correlation matrices in parallel using dask.
import dask
import dask.array as da
import dask.dataframe as dd
import sparse
def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9):
return sparse.COO.from_numpy((, chunk2.T) > corr_thresh))
def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9):