---
title: "'fourierin' package"
author: "Guillermo Basulto-Elias"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{'fourierin' package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

This is a package in `R` to numerically calculate Fourier-type integrals of multivariate functions with compact support evaluated at regular grids. Specifically, integrals of the type

$$
I \left[f(t), \boldsymbol{a}, \boldsymbol{b};r, s \right]
  = \left[ \frac{|s|}{(2\pi)^{1 - r}}\right]^{n/2}
  \int_{a_1}^{b_1}\int_{a_2}^{b_2}\cdots\int_{a_n}^{b_n}
  f(\boldsymbol{t})e^{\imath s 
  \langle \boldsymbol{w}, \boldsymbol{t}\rangle} \text{d}\boldsymbol{t},
$$

<!--
<img src="eq01_main_eq.png" width="600px" />
-->

where,

$\boldsymbol{a} = (a_1, \ldots, a_n)$, 
$\boldsymbol{b} = (b_1, \ldots, b_n)$, 
$\boldsymbol{t} = (t^{(1)}, \ldots, t^{(n)})$, 
$\boldsymbol{w} = (w^{(1)}, \ldots, w^{(n)})$, 
$a_l \leq t^{(l)} \leq b_l$,
$c_l \leq w^{(l)} \leq c_l$.

<!--
<img src="eq02_details.png" width="200px" />
-->

Common values for _r_ are -1, 0 and -1, while common values for _s_ are _-2\pi_, -1, 1 and _2\pi_. For example, if _f_ is a density function, _s = 1_ and _r = 1_ could be used to obtain the characteristic function of _f_. Conversely, if _f_ is the characteristic function of a probability density function, then _r = -1_ and _s = -1_ could be used to recover the density.

The implementation of this algorithm is the one described in [Inverarity (2002), Fast Computation of multidimensional Integrals](http://epubs.siam.org/doi/abs/10.1137/S106482750138647X).

Some examples (also found in documentation).

## Example 0: Speed tests

Below it is an example of univariate continuous Fourier transform.

```{r, fig.height= 5, fig.width= 7}

library(fourierin)
library(dplyr)
library(purrr)
library(ggplot2)

## Test speed at several resolutions
resolution <- 2^(3:8)

## Function to be tested
myfnc <- function(t) exp(-t^2/2)

## Aux. function
compute_times <- function(resol){
  reps <- 100
  rbenchmark::benchmark(
    yes = fourierin_1d(f = myfnc, -5, 5, -3, 3, -1, -1, resol),
    no = fourierin_1d(f = myfnc, -5, 5, -3, 3, -1, -1, resol,
                      use_fft = FALSE),
    replications = reps) %>%
    as.data.frame() %>%
    select(FFT = test, time = elapsed) %>%
    mutate(time = time*1e3/reps, resolution = resol)
}

resolution %>%
    map_df(compute_times) %>%
    mutate(resolution = as.factor(resolution)) %>%
    ggplot(aes(resolution, time, color = FFT)) +
    geom_point(size = 2, aes(shape = FFT)) +
    geom_line(aes(linetype = FFT, group = FFT)) +
    ylab("time (in milliseconds)")

```

Now we present an example using a bivariate function.

```{r, fig.height= 5, fig.width= 7}
## Load packages
library(fourierin)
library(dplyr)
library(purrr)
library(ggplot2)

## Test speed at several resolutions
resolution <- 2^(3:7)

## Bivariate function to be tested
myfnc <- function(x) dnorm(x[, 1])*dnorm(x[, 2])

## Aux. function
compute_times <- function(resol){
  reps <- 1
  resol <- rep(resol, 2)
  rbenchmark::benchmark(
    yes = fourierin(myfnc,
                      lower_int = c(-8, -6), upper_int = c(6, 8),
                      lower_eval = c(-4, -4), upper_eval = c(4, 4),
                      const_adj = 1, freq_adj = 1,
                      resolution = resol),
    no = fourierin(myfnc,
                      lower_int = c(-8, -6), upper_int = c(6, 8),
                      lower_eval = c(-4, -4), upper_eval = c(4, 4),
                      const_adj = 1, freq_adj = 1,
                      resolution = resol, use_fft = FALSE),
    replications = reps) %>%
    as.data.frame() %>%
    select(FFT = test, time = elapsed) %>%
    mutate(time = time/reps, resolution = resol)
}

## Values
comparison <- 
    resolution %>%
    map_df(compute_times) 

fctr_order <-
    unique(comparison$resolution) %>%
    paste(., ., sep = "x")

## Plot
comparison %>%
    mutate(resolution = paste(resolution, resolution, sep = "x"),
           resolution = ordered(resolution, levels = fctr_order)) %>%
    ggplot(aes(resolution, time, color = FFT)) +
    geom_point(size = 2, aes(shape = FFT)) +
    geom_line(aes(linetype = FFT, group = FFT)) +
    ylab("time (in seconds)")

```



## Example 1: Recovering standard normal from its characteristic function

The density function of a random variable can be recovered using the [Fourier inversion theorem](https://en.wikipedia.org/wiki/Fourier_inversion_theorem). The characteristic function of a standard normal distribution is 
$$
\phi(t) = \exp(-t^2/2) \;\; t \in \mathbb{R}
$$

The due to the smoothness and symmetry of both, the characteristic function and the standard normal density, it can be well approximated even with a low resolution.

```{r, fig.height= 5, fig.width= 7}
library(fourierin)
library(dplyr)
library(ggplot2)

## Characteristic function of std. normal.
fnc <- function(t) exp(-t^2/2)

## Compute integral
out <- fourierin(f = fnc, lower_int = -5, upper_int = 5,
                 lower_eval = -5, upper_eval = 5,
                 const_adj = -1, freq_adj = -1, resolution = 64)

## Extract values and compute true values of the density
df1 <- out %>%
    as_data_frame() %>%
    mutate(values = Re(values),
           density = "approximated")

df2 <- data_frame(w = seq(min(df1$w), max(df1$w), len = 150),
                  values = dnorm(w),
                  density = "true")

bind_rows(df1, df2) %>%
    ggplot(aes(w, values, color = density)) +
    geom_line(aes(linetype = density)) +
    xlab("x") + ylab("f(x)")

```

We now present a second example: recovering the density of a $\chi^2$ distribution using its characteristic function. The corresponding density is not symmetric and its support is not the entire real line, thus a higher resolution might be required to achieve a good approximation.

```{r, fig.height= 5, fig.width= 7}

library(fourierin)
library(dplyr)
library(purrr)
library(ggplot2)

## Set functions
df <- 5                                        
cf <- function(t) (1 - 2i*t)^(-df/2)
dens <- function(x) dchisq(x, df)


## Set resolutions
resolutions <- 2^(6:8)

## Compute integral given the resoltion
recover_f <- function(resol){
    ## Get grid and density values
    out <- fourierin(f = cf, lower_int = -10, upper_int = 10,
                     lower_eval = -3, upper_eval = 20,
                     const_adj = -1, freq_adj = -1,
                     resolution = resol)
    ## Return in dataframe format
    out %>%
        as_data_frame() %>%
        transmute(
            x = w,
            values = Re(values),
            resolution = resol)
}

## Density approximations
vals <- map_df(resolutions, recover_f)

## True values
true <- data_frame(x = seq(min(vals$x), max(vals$x), length = 150),
                   values = dens(x))

vals %>%
    mutate(resolution = ordered(resolution,
                                labels = resolutions)) %>%
    ggplot(aes(x, values)) +
    geom_line(aes(color = resolution)) +
    geom_line(data = true, aes(color = "true values"))

```


## Example 2: Computing characteristic function of a gamma density

```{r, fig.height= 5, fig.width= 7}

library(fourierin)
library(tidyr)
library(dplyr)
library(purrr)
library(ggplot2)

## Compute integral
shape <- 5
rate <- 3
myfnc <- function(t) dgamma(t, shape, rate)

## Function to compute characteristic function for a given resolution.
approximate_CF <- function (resol) {
    out <- fourierin(f = myfnc, -0, 8, -5, 5, 1, 1, resol)
    
    out %>%
        as_data_frame() %>%
        transmute(t = w,
                  real = Re(values),
                  imaginary = Im(values),
                  resolution = as.character(resol)) %>%
        gather(CF, values, -t, -resolution)
}

## Evaluate approxs. to CF for different resulutions
resolution <- 2^c(6, 7)
CF <- map_df(resolution, approximate_CF)

## Evaluate true values of characteristic function
true_cf <- function(t, shape, rate) (1 - 1i*t/rate)^-shape
true <- data_frame(t = seq(min(CF$t), max(CF$t), length = 150),
                   values = true_cf(t, shape, rate),
                   real = Re(values),
                   imaginary = Im(values),
                   resolution = "true values") %>%
    select(-values) %>%
    gather(CF, values, -t, -resolution)

## Plot values
CF %>%
    ggplot(aes(t, values, color = resolution,
               group = resolution)) +
    geom_line(aes()) +
    geom_line(data = true) + 
    facet_grid(~CF) + 
    theme(legend.position = "bottom")

```

## Example 3: Recovering std. normal from its characteristic function

Testing example.

```{r, fig.height= 5, fig.width= 7, eval = FALSE}

## Load packages
library(fourierin)
library(tidyr)
library(dplyr)
library(purrr)
library(lattice)
library(ggplot2)

## Set functions to be tested with their corresponding parameters.
mu <- c(-1, 1)
sig <- matrix(c(3, -1, -1, 2), 2, 2)

## Multivariate normal density, x is n x d
f <- function(x) {
    ## Auxiliar values
    d <- ncol(x)
    z <- sweep(x, 2, mu, "-")
    ## Get numerator and denominator of normal density
    num <- exp(-0.5*rowSums(z * (z %*% solve(sig))))
    denom <- sqrt((2*pi)^d*det(sig))
    return(num/denom)
}

## Characteristic function, s is n x d
phi <- function (s) {
    complex(modulus = exp(-0.5*rowSums(s*(s %*% sig))),
            argument = s %*% mu)
}

## Evaluate characteristic function for a given resolution.
eval <- fourierin(f,
                  lower_int = c(-8, -6), upper_int = c(6, 8),
                  lower_eval = c(-4, -4), upper_eval = c(4, 4),
                  const_adj = 1, freq_adj = 1, resolution = 2*c(64, 64),
                  use_fft = T)

## --- Little test
dat <- eval %>%
    with(crossing(y = w2, x = w1) %>%
         mutate(approximated = c(values))) %>%
    mutate(true = phi(matrix(c(x, y), ncol = 2)),
           difference = approximated - true) %>%
    gather(value, z, -x, -y) %>%
    mutate(real = Re(z), imaginary = Im(z)) %>%
    select(-z) %>%
    gather(part, z, -x, -y, -value)


## Surface plot
wireframe(z ~ x*y | value*part, data = dat,          
          scales =
              list(arrows=FALSE, cex= 0.45,
                   col = "black", font = 3, tck = 1),
          screen = list(z = 90, x = -74),
          colorkey = FALSE,
          shade=TRUE,
          light.source= c(0,10,10),
          shade.colors = function(irr, ref,
                                  height, w = 0.4)
              grey(w*irr + (1 - w)*(1 - (1 - ref)^0.4)),
          aspect = c(1, 0.65))


## Contours of values
dat %>%
    filter(value != "difference") %>%
    ggplot(aes(x, y, z = z)) +
    geom_tile(aes(fill = z)) +
    facet_grid(part ~ value) +
    scale_fill_distiller(palette = "Reds")

## Contour of differences
dat %>%
    filter(value == "difference") %>%
    ggplot(aes(x, y, z = z)) +
    geom_tile(aes(fill = z)) +
    facet_grid(part ~ value) +
    scale_fill_distiller(palette = "Spectral")

```


