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# See Yang et al Nature 2012 https://www.nature.com/articles/nature11401
# supplement page 18

invnorm <- function(x) {
    qnorm((rank(x, na.last="keep")-0.5)/sum(!is.na(x)))
}

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Simple Installation Instructions

Here are simple instructions for building and installing Shairport Sync on a Raspberry Pi B, 2B, 3B, 3B+ or 4B. It is assumed that the Pi is running Raspbian Buster Lite – a GUI isn't needed, since Shairport Sync runs as a daemon program. For a more thorough treatment, please go to the README.md page.

In the commands below, note the convention that a # prompt means you are in superuser mode and a $ prompt means you are in a regular unprivileged user mode. You can use sudo ("SUperuser DO") to temporarily promote yourself from user to superuser, if permitted. For example, if you want to execute apt-get update in superuser mode and you are in user mode, enter sudo apt-get update.

Configure and Update

Do the usual update and upgrade:

# apt-get update

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roundFormatC <- function(x, digits) {
    formatCDigits <- digits
    if(digits <= 0) {
        formatCDigits <- 0
    }
    formatC(round(x, digits), formatCDigits, format = "f")
}

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# Via https://stackoverflow.com/a/46416222/199475
pvalue.extreme.z <- function(z) {
   log.pvalue <- log(2) + pnorm(abs(z), lower.tail = FALSE, log.p = TRUE)
   log10.pvalue <- log.pvalue/log(10) ## from natural log to log10
   mantissa <- 10^(log10.pvalue %% 1)
   exponent <- log10.pvalue %/% 1
   return(list(mantissa=mantissa,exponent=exponent))
}

# Modified to the t-score approach from the Z-score based approach on https://stackoverflow.com/a/46416222/199475

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!pip install pathlib
!pip install fastai
!apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python
import cv2
from os import path
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())

accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'

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package main

import (
    "io"
    "os"
    "fmt"
    "log"
    "bytes"
    "image"
    "image/png"

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# Define the function
loghistplot  <- function(data) {
  
  require(ggplot2); require(gridExtra) # load packages

  names(data) <- c('x','y') # rename columns

  # get min and max axis values
  min_x <- min(data$x)
  max_x <- max(data$x)

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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#import data
mnist = input_data.read_data_sets("data/", one_hot=True)

sess = tf.InteractiveSession()

# Create the model
x = tf.placeholder(tf.float32, [None, 784])