---
title: "Introduction to radsafer"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Intro_and_decay_family}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
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---
  
```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(radsafer)
```

The radsafer package was developed with these goals:
  
* Provide functions that are commonly used in radiation safety

* Provide easy access to data provided in the RadData package

* Share some less commonly-used functions that may be of significant value to workers in instrumentation and modeling

Related functions are identified as members of these families:
  
* decay corrections 

* radionuclides 

* rad measurements 

* mcnp tools 

A few functions stand alone.

This framework makes it a little easier to find the function you want - the help file for each function lists related functions.

### Decay Correction Functions

Radsafer includes several functions to manage radioactive decay corrections:
  
  **dk_correct** provides decay corrected activity or activity-dependent value, such as instrument response rate or dose rate. The computation is made either based on a single radionuclide, or based on user-provided half-life, with time unit. The differential time is either computed based on dates entered or time lapsed  based on the time unit. 

Obtain a correction factor for a source to be used today based on a calibration on `date1`. Allow `date2` to be the default system date.

```{r warning=FALSE}
dk_correct(half_life = 10,
           time_unit = "y",
           date1 = "2010-01-01")
```

Use this function to correct for the value needed on dates it was used. Let the function obtain the half-life from the ICRP Publication 107 decay data in the `RadData` R package. In the example, we have a disk source with an original count rate of 10000 cpm:
  
```{r warning=FALSE}
dk_correct(RN_select = "Sr-90",
           date1 = "2005-01-01",
           date2 = c("2009-01-01","2009-10-01"),
           A1 = 10000)
```

Reverse decay - find out what readings should have been in the past given today's reading of 3000

```{r warning=FALSE}
dk_correct(RN_select = "Cs-137", 
  date1 = "2019-01-01", 
  date2 = c("2009-01-01","1999-01-01"), 
  A1 = 3000)
```

Other decay functions answer the following questions:

* How long does it take to decay something with a given activity, or how old is a sample if it has decayed from? **dk_time**

* Given a percentage reduction in activity, how many half-lives have passed.**dk_pct_to_num_half_life**

* Given two or more data points, estimate the half-life: **half_life_2pt**

### radionuclides
Search by alpha, beta, photon or use the general screen option. 

`RN_search_phot_by_E` allows screening based on energy, half-life, and minimum probability. Also available are `RN_search_alpha_by_E`, `RN_search_beta_by_E`, and `bin_screen_phot`. `RN_bin_screen_phot` allows limiting searches to radionuclides with emissions in an energy bin of interest with additional filters for not having photons in other specified energy bins. Results for all these search functions may be plotted with `RN_plot_spectrum`.

Here's a search for photon energy between 0.99 and 1.01 MeV, half-life between 13 and 15 minutes, and probability at least 1e-3

```{r echo = TRUE}
search_results <- RN_search_phot_by_E(0.99, 1.01, 13 * 60, 15 * 60, 1e-3)
```

```{r echo = FALSE}
knitr::kable(search_results)
```

```{r out.width = '75%', fig.align='center', warning=FALSE, message=FALSE}
RN_plot_spectrum(search_results)
```

You can also plot a spectrum in one step, skipping the data save:

```{r echo = TRUE}
 RN_plot_spectrum(
   desired_RN = c("Pu-238", "Pu-239", "Am-241"), rad_type = "A",
   photon = FALSE, prob_cut = 0.01, log_plot = 0)
```

The `RN_index_screen` function helps find a radionuclide of interest based on decay mode, half-life, and total emission energy.

In this example, we search for radionuclides decaying by spontaneous fission with half-lives between 6 months and 2 years.

```{r echo = TRUE}
RNs_selected <- RN_index_screen(dk_mode = "SF", min_half_life_seconds = 0.5 * 3.153e7, max_half_life_seconds = 2 * 3.153e7)
```

```{r echo = FALSE}
knitr::kable(RNs_selected[, c(1:3)])
```

Other radionuclides family functions:
  
  * Obtain a specific activity with **RN_Spec_Act**.
  
  * Find a potential radionuclide parent with **RN_find_parent**.
  
```{r echo = TRUE}

RN_find_parent("Th-230")

```

### rad measurements functions
**air_dens_cf**
Correct _vented ion chamber readings_ based on difference in air pressure (readings in degrees Celsius and mm Hg):

```{r}
air_dens_cf(T.actual = 30, P.actual = 760, T.ref = 20, P.ref = 760)
```
Let's try it out combined with the instrument reading:

```{r}
rdg <- 100
(rdg_corrected <- rdg * air_dens_cf(T.actual = 30, P.actual = 760, T.ref = 20, P.ref = 760))
```

**neutron_geom_cf**
  
  Correct for _geometry_ when reading a close _neutron_ source. Example: neutron rem detector with a radius of 11 cm and source near surface:
  
```{r}
neutron_geom_cf(11.1, 11)
```

**disk_to_disk_solid_angle**
  
  Correct for a mismatch between the _source calibration_ of a _counting system_ and the item being measured. A significant factor in the counting efficiency is the solid angle from the source to the detector. You can also check for the impact of an item not being centered with the detector.

Example: You are counting an air sample with an active collection diameter of 45 mm, your detector has a radius of 25 mm and there is a gap between the two of 5 mm. (The function is based on radius, not diameter so be sure to divide the diameter by two.) The relative solid angle is:
  
```{r}
(as_rel_solid_angle <- as.numeric(disk_to_disk_solid_angle(r.source = 45/2, gap = 20, r.detector = 12.5, runs = 1e4, plot.opt = "n")))
```

An optional plot is available in 2D or 3D:
```{r out.width = '50%', fig.align='center'}
  library(ggplot2)
theme_update(# axis labels
             axis.title = element_text(size = 7),
             # tick labels
             axis.text = element_text(size = 5),
             # title 
             title = element_text(size = 5))
(as_rel_solid_angle <- as.numeric(disk_to_disk_solid_angle(r.source = 45/2, gap = 20, r.detector = 12.5, runs = 1e4, plot.opt = "3d")))
```

Continuing the example: the only calibration source you had available with the appropriate isotope has an active diameter of 20 mm. Is this a big deal? Let's estimate the relative solid angle of the calibration, then take a ratio of the two.

```{r}
(cal_rel_solid_angle <- disk_to_disk_solid_angle(r.source = 20, gap = 20, r.detector = 12.5, runs = 1e4, plot.opt = "n"))
```
Correct for the mismatch:
```{r}
(cf <- cal_rel_solid_angle / as_rel_solid_angle)
```
This makes sense - the air sample has particles originating outside the source radius, so more of them will be lost, thus an adjustment is needed for the activity measurement.

**scaler_sim**

_Scaler counts_: obtain quick distributions for parameters of interest:
```{r out.width = '50%', fig.align='center'}
scaler_sim(true_bkg = 50, true_samp = 10, ct_time = 20, trials = 1e5)
```

**rate_meter_sim**

_Rate meters_: In the ratemeter simulation, readings are plotted once per second for a default time of 600 seconds. The meter starts with a reading of zero and builds up based on the time constant. Resolution uncertainty is established to express the uncertainty from reading an analog scale, including the instability of its readings. Many standard references identify the precision or resolution uncertainty of analog readings as half of the smallest increment. This should be considered the single coverage uncertainty for a very stable reading. When a reading is not very stable, evaluation of the reading fluctuation is evaluated in terms of numbers of scale increments covered by meter indication over a reasonable evaluation period. Example with default time constant:
```{r out.width = '50%', fig.align='center'}
rate_meter_sim(cpm_equilibrium = 270, meter_scale_increments = seq(100, 1000, 20))
```

To estimate _time constant_, use `tau.estimate`

### Some stand-alone functions

#### Stay-time computation

Given a dose rate, dose allowed, and a safety margin (default = 20%), calculate stay time with: `stay_time`
```{r}
stay_time(dose_rate = 120, dose_allowed = 100, margin =  20)
```

#### half-value and tenth value computation

Given a set of radiation levels through varying thicknesses of material, compute the half-values, tenth-values, and homogeneity coefficients.

```{r echo = FALSE}
mm_Al <- 0:5
mR_h <- c(7.428, 6.272, 5.325,4.535, 3.878, 3.317)
```

```{r echo = TRUE}
hvl(x = mm_Al, y = mR_h) 
```

### mcnp tools functions

If you create MCNP inputs, these functions may be helpful:

**mcnp_sdef_erg_line** Obtain emission data from the `RadData` package for use with the radiation transport code, MCNP, when you want a "line" source from individual radionuclides. Use **mcnp_sdef_erg_hist** when you have histogram data and want to format it nicely for MCNP.

**mcnp_matrix_rotations**

* Determine the entries needed for MCNP _coordinate transformation rotation_ 

```{r}
mcnp_matrix_rotations("z", 90)
```

**mcnp_mesh_bins** This helps identify the x, y, or z parameter of a simple geometric mesh tally. Select a center of interest, the width of this center mesh, then minimum and maximum limits on the extent of the entire mesh. Repeat for the other dimensions. 
```{r}
mcnp_mesh_bins(target = 30, width = 10, lowest_less = 0, highest_less = 15, highest_high = 304.8, lowest_high = 250)
```

**mcnp_cone_angle** This is just the square of the tangent of the angle in radians, but the argument used here is degree.

```{r}
mcnp_cone_angle(30)
```

**mcnp_plot_out_spec**

For _MCNP outputs_, plot the results of a tally with _energy bins_. The fastest way to do this is with `mcnp_scan2plot`. 

Alternatively, if you want to get your data into R, first save it in a text file and import it to R. (Base R provides methods with `read.table` or you might prefer options from the `readr` or `readxl` packages.) Or you can copy and paste your data using `mcnp_scan_save`. You can then plot with `mcnp_plot_out_spec` (below) or design your own plot.

```{r echo = FALSE}
plot_data <- photons_cs137_hist
```
```{r out.width = '75%', fig.align='center', warning=FALSE, message=FALSE}
mcnp_plot_out_spec(photons_cs137_hist, 'example Cs-137 well irradiator')
```

