View lifeweeks.py
from datetime import date, timedelta | |
import math | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from matplotlib.patches import Rectangle | |
from matplotlib.collections import PatchCollection | |
mean_le = math.ceil(78.54) # for men in US | |
birthday = (1986, 12, 19) | |
deathday = (mean_le + birthday[0], 12, 19) |
View simple.slim
initialize() { | |
initializeMutationRate(1e-8); | |
initializeMutationType("m1", 0.5, "f", 0.0); | |
initializeGenomicElementType("g1", m1, 1); | |
initializeGenomicElement(g1, 0, 99999); | |
initializeRecombinationRate(1e-8); | |
} | |
1 early() { |
View fix_spines.py
def fix_spines(ax, connect=True, x=True): | |
""" | |
Beautifies spines by stopping them at the last tick mark. If connect=False, | |
also stops them at the first tick mark. For y-axis only, set x=False. | |
""" | |
ylim = ax.get_ylim() | |
xlim = ax.get_xlim() | |
yticks = ax.get_yticks() | |
xticks = ax.get_xticks() |
View Snakefile
import numpy as np | |
np.random.seed(1) | |
DATADIR = "sim_results/" | |
SLIM = "/home/vsb/src/SLiM_build/slim " | |
## Parameters | |
nreps = range(50) | |
# ------- Shared Parameters ------- |
View Snakefile
import numpy as np | |
np.random.seed(1) | |
DATADIR = "sim_results/" | |
SLIM = "/home/vsb/src/SLiM_build/slim " | |
## Parameters | |
nreps = range(50) | |
# ------- Shared Parameters ------- |
View Snakefile
import numpy as np | |
import slper.slimfile as sf | |
np.random.seed(1) | |
DATADIR = "../data/sims/" | |
SLIM = "/home/vsb/src/SLiM_build/slim " | |
## Parameters | |
nreps = range(50) |
View split.slim
initialize() { | |
defineConstant('tmu', 1e-8); | |
defineConstant('nmu', 1e-8); | |
defineConstant('rbp', 1e-8); | |
defineConstant('N', 1000); | |
defineConstant('alpha', 0.01); | |
defineConstant('nrep', 1); | |
defineConstant("seed", getSeed()); | |
defineConstant("data_dir", '../data/sims/'); |
View pairwise_cov.r
library(tidyverse) | |
library(MASS) | |
pcov <- function(x) { | |
xs <- scale(x, scale=FALSE) | |
dd <- as.integer(!is.na(x)) | |
dim(dd) <- dim(x) | |
denom <- (t(dd) %*% dd) - 1L | |
no_obs <- denom == 0L | |
xs[is.na(xs)] <- 0 |
View closure.R
library(purrr) | |
foo <- function(x) { | |
return(function(y) { | |
y + x | |
}) | |
} | |
args <- list(1, 2) | |
foos_map <- map(args, foo) |
View foo.R
Title: Using Bioconductor to Analyze your 23andme Data | |
Bioconductor is one of the open source projects of which I am most | |
fond. The documentation is excellent, the community wonderful, the | |
development fast-paced, and the software *very* well written. | |
There's a new package in the development branch (due to be released as | |
2.10 very soon) called `gwascat`. `gwascat` is a package that serves | |
as an interface to the [NHGRI's](http://www.genome.gov/) database of | |
genome-wide association studies. |
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