Parsing 8-byte Hex Integers in R

Introduction

In this post, we’ll explore how to parse 8-byte hex integers in R. The problem arises when working with GPS track files that use a custom binary specification to represent latitude, longitude, and timestamps as 8-byte signed integers. We’ll delve into the world of bitwise operations, bit manipulation, and two’s complement representation to convert these raw hex values into meaningful numeric data.

Background

To understand this problem, we need to review some fundamental concepts in computer science:

• Two’s Complement Representation: In binary arithmetic, negative numbers are represented using two’s complement. The process involves inverting the bits of the positive equivalent and adding 1.
• Bitwise Operations: Bitwise operations allow us to manipulate individual bits within a binary number. Common operations include AND, OR, XOR, and NOT.

The GPS track files use big-endian byte order for the integers, which means that the most significant bit (MSB) is stored in the highest-order byte, followed by the next 7 bits, and so on.

Raw Hex Values

Let’s take a closer look at the raw hex values provided:

# Raw hex values
lat = as.raw(c(0x00, 0x00, 0x00, 0x00, 0x16, 0x7f, 0x4b, 0xcc))
lon = as.raw(c(0xff, 0xff, 0xff, 0xff, 0xb8, 0xb8, 0x07, 0x23))
time = as.raw(c(0x00, 0x00, 0x01, 0x78, 0xe0, 0xbb, 0x08, 0x28))

These raw hex values represent the signed integers in two’s complement representation.

Converting Raw Hex Values

To convert these raw hex values into meaningful numeric data, we can use bitwise operations and bit manipulation. We’ll focus on converting the raw hex value lat first.

Step 1: Convert Raw Hex to Bits

We start by converting the raw hex value to bits using rawToBits. This function returns a vector of 0s and 1s representing the original binary data:

# Convert raw hex to bits
bits <- sapply(lat, function(y) rev(as.integer(rawToBits(y))))

Note that we use rev to reverse the order of the bits because R’s rawToBits function returns the bits in reverse order.

Step 2: Summation for Two’s Complement

We then perform a summation on the bits, excluding the MSB and subtracting the value of the MSB (which is 1). This step converts the raw hex value to its two’s complement representation:

# Perform summation for two's complement
sum_bits <- sum(bits[-1] * 2^(62:0)) - bits[1] * 2^63

This calculation gives us the equivalent integer value represented in two’s complement.

Step 3: Repeat for lon and time

We repeat these steps for lon and time, converting their raw hex values to bits, performing the summation, and obtaining the final integer values:

# Repeat steps for lon and time
sum_lon <- sum(sapply(lon, function(z) {
    bits <- sapply(z, function(y) rev(as_integer(rawToBits(y))))
    return(sum(bits[-1] * 2^(62:0)) - bits[1] * 2^63)
}))

sum_time <- sum(sapply(time, function(z) {
    bits <- sapply(z, function(y) rev(as_integer(rawToBits(y))))
    return(sum(bits[-1] * 2^(62:0)) - bits[1] * 2^63)
}))

Vectorized Version

We can optimize this process by vectorizing the calculations. We create a new function parser that takes a list of raw hex values as input and returns a vector of equivalent integers:

# Create vectorized parser function
parser <- function(x) {
    sapply(x, function(z) {
        bits <- sapply(z, function(y) rev(as_integer(rawToBits(y))))
        return(sum(bits[-1] * 2^(62:0)) - bits[1] * 2^63)
    })
}

We can use this function to parse multiple raw hex values in a single call:

# Parse list of raw hex values
result <- parser(list(lat, lon, time))

Conclusion

In conclusion, we’ve explored how to parse 8-byte hex integers in R using bitwise operations and bit manipulation. We converted the raw hex values lat, lon, and time into meaningful numeric data by performing a summation for two’s complement representation.

By following this process and creating a vectorized parser function, you can efficiently handle multiple raw hex values and convert them to equivalent integers in R.

Last modified on 2024-11-23