vsbuffalo

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)

vsbuffalo

initialize() {
  initializeMutationRate(1e-8);
  initializeMutationType("m1", 0.5, "f", 0.0);
  initializeGenomicElementType("g1", m1, 1);
  initializeGenomicElement(g1, 0, 99999);
  initializeRecombinationRate(1e-8);
}


1 early() {

vsbuffalo

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()

vsbuffalo

import numpy as np
np.random.seed(1)

DATADIR = "sim_results/"
SLIM = "/home/vsb/src/SLiM_build/slim "

## Parameters
nreps = range(50)

# ------- Shared Parameters -------

vsbuffalo

import numpy as np
np.random.seed(1)

DATADIR = "sim_results/"
SLIM = "/home/vsb/src/SLiM_build/slim "

## Parameters
nreps = range(50)

# ------- Shared Parameters -------

vsbuffalo

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)

vsbuffalo

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/');

vsbuffalo

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

vsbuffalo

library(purrr)

foo <- function(x) {
  return(function(y) {
    y + x
  })
}

args <- list(1, 2)
foos_map <- map(args, foo)

vsbuffalo

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.