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!!! note
To get started quickly, you may copy and modify one of the example configs found [here](quickstart.md#example-configs).
The config file must be a [YAML](https://yaml.org/) file. YAML is a serialization language that works with key-value pairs, but in a syntax more readable than some other alternatives. In YAML, the indentation matters. I
The remainder of this chapter will explain the different required and optionals keys, what they represent, and allowed values.
## Required keys
### Simulation options
The first step is to name the simulation, and define the speed of the vehicle (assumed constant) and acquisition time.
#### Landscape name
The name is a string, which may include spaces, numbers and special characters.
Examples:
```yaml
name: test_landscape
```
```yaml
name: Test Landscape 1
```
#### Acquisition time
The acquisition time of the detector in seconds.
Example:
```yaml
acquisition_time: 1
```
!!! note
All units in the config file must be specified in SI units, e.g. meters and seconds, unless the key contains a unit itself (e.g. `activity_MBq` means activity in MegaBequerels).
#### Vehicle speed
The speed of the vehicle in m/s. Currently, the vehicle speed must be assumed constant. An example could be
```yaml
speed: 13.89 # this is approximately 50 km/h
```
!!! note
The text after the `#` signifies a comment. PG-RAD will ignore this, but it can be helpful for yourself to write notes.
### Path
The `path` keyword is used to create a path for the detector to travel along. There are two ways to specify a path; from experimental data or by specifying a procedural path.
#### Path - Experimental data
Currently the only supported coordinate format is the RT90 (East, North) coordinate system. If you have experimental data in CSV format with columns for these coordinates, then you can load that path into PG-RAD as follows:
```yaml
path:
file: path/to/experimental_data.csv
east_col_name: East
north_col_name: North
```
#### Path - Procedural path
Alternatively, you can let PG-RAD generate a path for you. A procedural path can be specified with at least two subkeys: `length` and `segments`.
Currently supported segments are: `straight`, `turn_left` and `turn_right`, and are provided in a list under the `segments` subkey as follows:
```yaml
path:
segments:
- straight
- turn_left
- straight
```
The length must also be specified, using the `length` subkey. `length` can be specified in two ways: a list with the same length as the `segments` list
```yaml
path:
segments:
- straight
- turn_left
- straight
length:
- 500
- 250
- 500
```
which will assign that length (meters) to each segment. Alternatively, a single number can be passed:
```yaml
path:
segments:
- straight
- turn_left
- straight
length: 1250
```
Setting the length for the total path will cause PG-RAD to *randomly assign* portions of the total length to each segment.
Finally, there is also an option to specify the turn angle in degrees:
```yaml
path:
segments:
- straight
- turn_left: 90
- straight
length: 1250
```
Like with the lengths, if a turn segment has no angle specified, a random one (within pre-defined limits) will be taken.
!!! warning
Letting PG-RAD randomly assign lengths and angles can cause (expected) issues. That is because of physics restrictions. If the combination of length, angle (radius) and velocity of the vehicle is such that the centrifugal force makes it impossible to take this turn, PG-RAD will raise an error. To fix it, you can 1) reduce the speed; 2) define a smaller angle for the turn; or 3) assign more length to the turn segment.
!!! info
For more information about how procedural roads are generated, including the random sampling of lengths and angles, see X
### Sources
Currently, the only type of source supported is a point source. Point sources can be added under the `sources` key, where the **subkey is the name** of the source:
```yaml
sources:
my_source: ...
```
the source name should not contain spaces or special characters other than `_` or `-`. There are three required subkeys under `sources.my_source`, which are: `activity_MBq`, `isotope` and `position`.
#### Source activity
The source activity is in MegaBequerels and must be a strictly positive number:
```yaml
sources:
my_source:
activity_MBq: 100
```
#### Source isotope
The isotope for the point source. This must be a string, following the naming convention of the symbol followed by the number of nucleons, e.g. `Cs137`:
```yaml
sources:
my_source:
activity_MBq: 100
isotope: Cs137
```
!!! info
Currently the following isotopes are supported: `Cs137`
#### Source position
There are two ways to specify the source position. Either with absolute (x,y,z) coordinates
```yaml
sources:
my_source:
activity_MBq: 100
isotope: Cs137
position: [0, 0, 0]
```
or relative to the path, using the subkeys `along_path`, `dist_from_path` and `side`
```yaml
sources:
my_source:
activity_MBq: 100
isotope: Cs137
position:
along_path: 100
dist_from_path: 50
side: left
```
Note that side is relative to the direction of travel. The path will by default start at (x,y) = (0,0) and initial heading is parallel to the x-axis.
### Detector
The final required key is the `detector`. Currently, only isotropic detectors are supported. Nonetheless, you must specify it with `name`, `is_isotropic` and `efficiency`:
```yaml
detector:
name: test
is_isotropic: True
efficiency: 0.02
```
Note there are some existing detectors available, where efficiency is not required and will be looked up by PG-RAD itself:
```yaml
detector:
name: NaIR
is_isotropic: True
```
## Optional keys
The following subkeys are optional and should be put under the `options` key.
```yaml
options:
air_density_kg_per_m3: 1.243
seed: 1234
```

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{
"cells": [
{
"cell_type": "markdown",
"id": "1a063d05",
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"source": [
"# Pseudo-ramdom procedural roads\n",
"\n",
"Suppose one wishes to describe a road between A and B in terms of segments\n",
"\n",
"$$\n",
"\\text{straight, turn left, straight, turn right, straight}\n",
"$$\n",
"\n",
"Let's see how we can create a random road of length $L$ from a pre-determined set of prefabs.\n",
"\n",
"#### Random apportionment of total length\n",
"\n",
"Suppose we want to build a road of length $L$ out of $K$ segments. The total number of waypoints $N$ depends on the step size $\\Delta s$:\n",
"\n",
"$$\n",
"N = \\frac{L}{\\Delta s}.\n",
"$$\n",
"\n",
"Let $\\left( p_1, p_2, \\dots, p_K \\right)$ represent the proportion of $N$ that each prefab will be assigned, where $\\sum p_i = 1$. One useful distribution here is the [Dirichlet distribution](https://en.wikipedia.org/wiki/Dirichlet_distribution), which is parametrized by a vector $\\mathbf{\\alpha} = \\left(\\alpha_1, \\alpha_2, \\dots, \\alpha_K \\right)$. The special case where all $\\alpha_i$, the scalar parameter $\\alpha$ is called a *concentration parameter*. Setting the same $\\alpha$ across the entire parameter space makes the distribution symmetric, meaning no prior assumptions are made regarding the proportion of $N$ that will be assigned to each segment. $\\alpha = 1$ leads to what is known as a flat Dirichlet distribution, whereas higher values lead to more dense and evenly distributed $\\left( p_1, p_2, \\dots, p_K \\right)$. On the other hand, keeping $\\alpha \\leq 1$ gives a sparser distribution which can lead to larger variance in apportioned number of waypoints to $\\left( p_1, p_2, \\dots, p_K \\right)$.\n",
"\n",
"#### Expectation value and variance of Dirichlet distribution\n",
"\n",
"Suppose we draw our samples for proportion of length from the Dirichlet distribution\n",
"\n",
"$$\n",
"(p_1, p_2, \\ldots, p_K) \\sim \\text{Dirichlet}(\\alpha, \\alpha, \\ldots, \\alpha)\n",
"$$\n",
"\n",
"with $\\alpha _{0}=\\sum _{i=1}^{K}\\alpha _{i}$, the mean and variance are then\n",
"\n",
"$$\n",
"\\operatorname {E} [p_{i}]={\\frac {\\alpha _{i}}{\\alpha _{0}}}, \\; \\operatorname {Var} [p_{i}]={\\frac {\\alpha _{i}(\\alpha _{0}-\\alpha _{i})}{\\alpha _{0}^{2}(\\alpha _{0}+1)}}.\n",
"$$\n",
"\n",
"If $\\alpha$ is a scalar, then $\\alpha _{0}= K \\alpha$ and the above simplifies to\n",
"\n",
"$$\n",
"\\operatorname {E} [p_{i}]={\\frac {\\alpha}{K \\alpha}}={\\frac {1}{K}}, \\; \\operatorname {Var} [p_{i}]={\\frac {\\alpha(K \\alpha -\\alpha)}{(K \\alpha)^{2}(K \\alpha +1)}}.\n",
"$$\n",
"\n",
"We see that $\\operatorname {Var} [p_{i}] \\propto \\frac{1}{\\alpha}$ meaning that the variance reduces with increasing $\\alpha$. We can simply scale the proportions\n",
"\n",
"$$\n",
"(N \\cdot p_1, N \\cdot p_2, \\ldots, N \\cdot p_K)\n",
"$$\n",
"\n",
"to get the randomly assigned number of waypoints for each prefab. We now have a distribution which can give randomly assigned lengths to a given list of prefabs, with a parameter to control the degree of randomness. With a large concentration parameter $\\alpha$, the distribution of lengths will be more uniform, with each prefab getting $N \\cdot \\operatorname {E} [p_{i}]={\\frac {N}{K}}$ waypoints assigned to it. Likewise, keeping $\\alpha$ low increases variance and allows for a more random assignment of proportions of waypoints to each prefab segment.\n",
"\n",
"#### Random angles\n",
"\n",
"Suppose a turn of a pre-defined arc length $l$ made of $N/K$ waypoints. If one wants to create a random angle, one has to keep in mind that the minimum radius $R_{min}$ depends on the speed of the vehicle and the weather conditions:\n",
"\n",
"$$\n",
"R_{\\text{min,vehicle}} = \\frac{v^2}{g\\mu},\n",
"$$\n",
"\n",
"where\n",
"- $v$ is the velocity of the vehicle in $\\text{m/s}$,\n",
"- $g$ is the gravitational acceleration (about $9.8$ $\\text{m/s}^{2}$), and\n",
"- $\\mu$ is the friction coefficient (about $0.7$ for dry asphalt).\n",
"\n",
"A regular turn (not a U-turn or roundabout) should also have an lower and upper limit on the angle, say, 30 degrees to 90 degrees for a conservative estimate. In terms of radii, it becomes\n",
"\n",
"$$\n",
"R_{\\text{min}} = \\max\\left(R_{\\text{min,vehicle}}, \\frac{l}{\\pi/2}\\right)\n",
"$$\n",
"\n",
"and\n",
"\n",
"$$\n",
"R_{\\text{max}} = \\frac{l}{\\pi/6}.\n",
"$$\n",
"\n",
"We then sample\n",
"\n",
"$$\n",
"R \\sim \\text{Uniform}\\left(R_{\\text{min}}, R_{\\text{max\\_angle}}\\right)\n",
"$$\n",
"\n",
"and obtain a random radius for a turn of arc length $l$ with limits to ensure the radius is large enough given the velocity of the vehicle. Finally, the curvature profile is related to the radius by\n",
"\n",
"$$\n",
"\\kappa = \\frac{1}{R}\n",
"$$\n",
"\n",
"which means that the curvature profile of a turn is simply a vector $\\mathbf{\\kappa} = (1/R, \\dots, 1/R)$ with a length of $N/K$ waypoints."
]
}
],
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Primary Gamma RADiation Landscapes (PG-RAD) is a Python package for research in source localization. It can simulate mobile gamma spectrometry data acquired from vehicle-borne detectors along a predefined path (e.g. a road).
## Requirements
## About
PG-RAD requires Python `3.12`. The guides below assume a unix-like system.
This software has been developed as part of dissertation work for the degree of master of Computational Science and Physics at Lund University, Sweden. The work has been done at the department of Medical Radiation Physics (MSF), Faculty of Medicine. The radiological emergency preparedness research group of MSF is assigned by the Swedish Radiation Safety Authority (SSM) to aid in preparation for effective mitigation of radiological or nuclear disasters on Swedish soil.
## Installation (CLI)
## Value proposition
<!--pipx seems like a possible option to install python package in a contained environment on unix-->
PG-RAD is a toolbox that allows for simulation of detector response for a wide variety of source localization scenarios. The strength of the software lies in its simple and minimal configuration and user input, while its flexibility allows for reconstruction of specific scenarios with relative ease. PG-RAD is also general enough that novel methods such as UAV-borne detectors can be simulated and evaluated.
Lorem ipsum
User input takes the form of an input file (YAML), describing the path, detector and source(s), and optional parameters. The output of the program is visualizations of the world (the path and sources), as well as the detector count rate as a function of distance travelled along the path.
## Installation (Python module)
If you are interested in using PG-RAD in another Python project, create a virtual environment first:
```
python3 -m venv .venv
```
Then install PG-RAD in it:
```
source .venv/bin/activate
(.venv) pip install git+https://github.com/pim-n/pg-rad
Users can provide experimental / geographical coordinates representing real roads. Alternatively, users can let PG-RAD generate a procedural road, where the user can easily control what that road should look like. The user can specify a single point source, several point sources, as well as a field of radioactive material covering a large area.
```
See how to get started with PG-RAD with your own Python code [here](pg-rad-in-python).

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## Requirements
PG-RAD requires Python `>=3.12.4` and `<3.13`. It has been tested on `3.12.9`. The guides below assume a unix-like system. You can check the Python version you have installed as follows:
```
python --version
```
If you don't have the right version installed there are various ways to get a compatible version, such as [pyenv](https://github.com/pyenv/pyenv?tab=readme-ov-file#installation).
## Installation (CLI)
<!--pipx seems like a possible option to install python package in a contained environment on unix-->
Lorem ipsum
## Installation (Python module)
If you are interested in using PG-RAD in another Python project, create a virtual environment first:
```
python -m venv .venv
```
Then install PG-RAD in it:
```
source .venv/bin/activate
(.venv) pip install git+https://github.com/pim-n/pg-rad
```
See how to get started with PG-RAD with your own Python code [here](pg-rad-in-python).
## For developers
```
git clone https://github.com/pim-n/pg-rad
cd pg-rad
git checkout dev
```
or
```
git@github.com:pim-n/pg-rad.git
cd pg-rad
git checkout dev
```

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---
title: Using PG-RAD in CLI
---
Lorem ipsum.

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## Installation
See the [installation guide](installation.md).
## Test your installation
First, see if PG-RAD is available on your system by typing
```
pgrad --help
```
You should get output along the lines of
```
usage: pg-rad [-h] ...
Primary Gamma RADiation landscape tool
...
```
If you get something like `pgrad: command not found`, please consult the [installation guide](installation.md).
You can run a quick test scenario as follows:
```
pgrad --test
```
This should produce a plot of a scenario containing a single point source and a path.
## Running PG-RAD
In order to use the CLI for your own simulations, you need to provide a *config file*. To run with your config, run
```
pgrad --config path/to/my_config.yml
```
where `path/to/my_config.yml` points to your config file.
## Example configs
The easiest way is to take one of these example configs, and adjust them as needed. Alternatively, there is a detailed guide on how to write your own config file [here](config-spec.md).
=== "Example 1"
The position can be defined relative to the path. `along_path` means at what distance traveled along the path the source is found. If the path is 200 meters long and `along_path` is `100` then the source is halfway along the path. `dist_from_path` is the distance in meters from the path. `side` is the side of the path the source is located. This is relative to the direction the path is traveled.
``` yaml
name: Example 1
speed: 13.89
acquisition_time: 1
path:
file: path/to/exp_coords.csv
east_col_name: East
north_col_name: North
sources:
source1:
activity_MBq: 1000
isotope: CS137
position:
along_path: 100
dist_from_path: 50
side: left
detector:
name: dummy
is_isotropic: True
```
=== "Example 2"
The position can also just be defined with (x,y,z) coordinates.
``` yaml
name: Example 2
speed: 13.89
acquisition_time: 1
path:
file: path/to/exp_coords.csv
east_col_name: East
north_col_name: North
sources:
source1:
activity_MBq: 1000
isotope: CS137
position: [104.3, 32.5, 0]
source2:
activity_MBq: 100
isotope: CS137
position: [0, 0, 0]
detector:
name: dummy
is_isotropic: True
```
=== "Example 3"
This is an example of a procedural path with random apportionment of total length and random angles being assigned to turns. The parameter `alpha` is optional, and is related to randomness. A higher value leads to more uniform apportionment of lengths and a lower value to more random apportionment. More information about `alpha` can be found [here](pg-rad-config-spec.md).
``` yaml
name: Example 3
speed: 8.33
acquisition_time: 1
path:
length: 1000
segments:
- straight
- turn_left
- straight
alpha: 100
sources:
source1:
activity_MBq: 1000
isotope: CS137
position: [0, 0, 0]
detector:
name: dummy
is_isotropic: True
```
=== "Example 4"
This is an example of a procedural path that is partially specified. Note that turn_left now is a key for the corresponding angle of 45 degrees. The length is still divided randomly
``` yaml
name: Example 4
speed: 8.33
acquisition_time: 1
path:
length: 1000
segments:
- straight
- turn_left: 45
- straight
sources:
source1:
activity_MBq: 1000
isotope: CS137
position: [0, 0, 0]
detector:
name: dummy
is_isotropic: True
```
=== "Example 5"
This is an example of a procedural path that is fully specified. See how length is now a list matching the length of the segments.
``` yaml
name: Example 5
speed: 8.33
acquisition_time: 1
path:
length:
- 400
- 200
- 400
segments:
- straight
- turn_left: 45
- straight
sources:
source1:
activity_MBq: 1000
isotope: CS137
position: [0, 0, 0]
detector:
name: dummy
is_isotropic: True
```