README.md

In Ilia-Kosenkov/Dipol2Red: Dipol-2 Data Reduction Pipeline

Dipol2Red

Dipol2Red is a package for reducing data obtained with a double-imaging polarimeter Dipol-2 (and any later versions of Dipol family polarimeters).

The input data are the differences (in magnitudes) between ordinary and extraordinary rays of a given target on the image and respective timestamps (or any other sortable numeric identifier). The data are generated as a result of subsequent observations with different phase plate angle (22.5 deg step). Every 16 measurement correspond to one full plate rotation, every 4 measurements produce one (independent polarization measurement).

Such stream of 4n data is transformed into polarization measurement. If no grouping is provided, all data are used to compute one average (over the whole data set). Alternatively, data can be grouped by 4m subsequent observations (using dplyr::group_by), and the averaging procedure is applied to each group separately, providing the best control.

Example usage

library(tibble, quietly = TRUE, warn.conflicts = FALSE)
library(dplyr, quietly = TRUE, warn.conflicts = FALSE)
library(Dipol2Red, quietly = TRUE, warn.conflicts = FALSE)

• Simulating one observation with 1 % polarization in x direction. 4 measurements corresponding to plate angles 0, 22.5, 45, 67.5 deg.

  r x_1 <- tibble( JD = c(1, 2, 3, 4), Obs = 2.5 * log10(c(102, 100, 98, 100))) fsigma_2(x_1)

  ## # A tibble: 1 × 11
  ##      JD    Px    Py     P    SG     A  SG_A   Itt     N Ratio               Q
  ##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl> <list<dbl[,2]>>
  ## 1   2.5  1.00     0  1.00     0     0     0     1     1     0         [2 × 2]
  

• Simulating total polarization of 1 % and equal x and y components.

  r x_2 <- tibble( JD = 10 + c(1, 2, 3, 4), Obs = 2.5 * log10(c(101.4142, 101.4142, 98.5858, 98.5858))) fsigma_2(x_2)

  ## # A tibble: 1 × 11
  ##      JD    Px    Py     P    SG     A  SG_A   Itt     N Ratio               Q
  ##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl> <list<dbl[,2]>>
  ## 1  12.5 0.707 0.707  1.00     0  22.5     0     1     1     0         [2 × 2]
  

• Combining two datasets to obtain average over several observations.

  r x_3 <- bind_rows(x_1, x_2) fsigma_2(x_3)

  ## # A tibble: 1 × 11
  ##      JD    Px    Py     P    SG     A  SG_A   Itt     N Ratio               Q
  ##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl> <list<dbl[,2]>>
  ## 1   7.5 0.854 0.354 0.924 0.271  11.2  8.16     2     2     0         [2 × 2]
  

• Assigning groups, separating observations by Run. Recreates two original measurements. fsigma_2 automatically detects grouping variables and appends these values to the output.

  r x_4 <- mutate(x_3, Run = factor(if_else(JD < 10, "First", "Second"))) x_4 <- group_by(x_4, Run) fsigma_2(x_4)

  ## # A tibble: 2 × 12
  ##      JD    Px    Py     P    SG     A  SG_A   Itt     N Ratio            Q Run  
  ##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl> <list<dbl[,> <fct>
  ## 1   2.5 1.00  0      1.00     0   0       0     1     1     0      [2 × 2] First
  ## 2  12.5 0.707 0.707  1.00     0  22.5     0     1     1     0      [2 × 2] Seco…
  

Ilia-Kosenkov/Dipol2Red documentation built on Dec. 16, 2021, 5:45 p.m.