Keybase proof
I hereby claim:
- I am cdiener on github.
- I am cdiener (https://keybase.io/cdiener) on keybase.
- I have a public key ASBZVRZQ9U_qJu_jYWdikE66fJ9Vbl6g-YWN92NVOcc8kgo
To claim this, I am signing this object:
"""Set up Qiime 2 on Google colab. | |
Do not use this on o local machine, especially not as an admin! | |
""" | |
import os | |
import sys | |
from subprocess import Popen, PIPE | |
r = Popen(["pip", "install", "rich"]) |
I hereby claim:
To claim this, I am signing this object:
Para las siguientes clases vamos a usar Python como nuestro lenguaje de programación preferido. En particular, vamos a usar la versión 3 de Python y las libretas de Jupyter. Para ya tener una instalación funcional en la clase, aquí hay unas pistas para la instalación. Para la instalación en Windows y Mac vamos a usar la versión de Anaconda mientras que para Linux usamos la versión nativa de Python.
Lo primero que tienes que saber sobre la instalación de docker es que para Mac y windows hay dos versiones de docker:
La versión nativa (opción 2) tiene menos overhead y corre más rapido pero pone mas restricciones a su OS. Por el momento yo recomiendo que usan esta versión en Mac y Linux y la version legacy (opcion 1) en Windows.
library(data.table) | |
library(ggplot2) | |
library(magrittr) | |
library(pbapply) | |
large <- fread("ERR260132_genes.csv") | |
#' Sample a rarefied version of a count vector. | |
#' | |
#' @param x A named vector of counts. |
{ | |
"workbench.colorTheme": "Sublime Material Theme - Dark", | |
"workbench.iconTheme": "material-icon-theme", | |
"editor.fontFamily": "'Fira Mono', monospace", | |
"editor.fontSize": 17, | |
"editor.rulers": [80], | |
"window.zoomLevel": 0, | |
"window.menuBarVisibility": "toggle", | |
// Settings for Python |
import json | |
import pandas as pd | |
from sys import argv, exit | |
def benchmark_to_df(json_file): | |
with open(json_file) as jf: | |
content = json.load(jf) | |
df = pd.DataFrame(columns=("test", "time [ms]")) | |
for b in content["benchmarks"]: |
ES <- function(p, w, pws, both=FALSE) { | |
n <- length(pws) | |
nr <- sum(abs(w[pws == p])) | |
nh <- sum(pws == p) | |
scores <- vector(length=n) | |
scores[pws == p] <- abs(w[pws == p])/nr | |
scores[pws != p] <- -1/(n - nh) | |
r <- range(cumsum(scores)) | |
i <- which.max(abs(r)) |
#/usr/bin/env python | |
from cobra.test import create_test_model | |
from cobra.flux_analysis import single_gene_deletion | |
cobra_model = create_test_model("textbook") | |
dels = {"b0008": 0.87, "b0114": 0.71, "b0116": 0.56, "b2276": 0.11, "b1779": 0.00} | |
rates, statuses = single_gene_deletion(cobra_model, gene_list=dels.keys(), | |
method="moma", solver="mosek") |
Rcpp::sourceCpp("matrix_reduce.cpp") | |
#' Reduces an ExpressionSet by an n-to-n map of features to groups. All entries | |
#' in \code{features} must exist in \code{eset}. \code{features} and | |
#' \code{groups} must have the same length. | |
#' | |
#' @param eset An ExpressionSet object. | |
#' @param features A character vector of features to be grouped. | |
#' @param groups A factor or character vector mapping the entries in | |
#' \code{features} to groups. |