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.
$ python -m SimpleHTTPServer 8000
Saket Choudhary saketkc
cmparlettpelleriti
/ R_EigenPCA_Plots.R
Last active
April 19, 2024 10:45
Show students the relationship between Eigendecomp of Cor/Cov and the % variance explained for PCs
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| library(tidyverse) | |
| library(MASS) | |
| library(patchwork) | |
| cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") | |
| # generate data with given cor matrix | |
| a <- 0.9 | |
| s1 <- matrix(c(1,a, | |
| a,1), ncol = 2) |
gizmaa
/ Plot_Examples.md
Last active
April 12, 2024 14:18
Various Julia plotting examples using PyPlot
Last Update: May 13, 2019
Offline Version
fperez
/ ProgrammaticNotebook.ipynb
Last active
April 5, 2024 12:00
Creating an IPython Notebook programatically
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| """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 |
lmcinnes
/ flow_cytometry.ipynb
Created
September 8, 2018 22:19
Flow Cytometry experiments with UMAP
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dgrtwo
/ mnist_pairs.R
Created
May 31, 2017 18:56
Comparing pairs of MNIST digits based on one pixel
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| 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) |
brentp
/ one-channel-agilent.R
Created
August 17, 2011 22:58
use limma to normalize 1-channel agilent data and write out differentially expressed genes.
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| 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 |
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.
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