Merge pull request #38 from pim-n/feature-plotter

Config parser, plotting, simulation engine

.github/workflows/ci-docs.yml

@@ -17,7 +17,7 @@ jobs:
           git config user.email 41898282+github-actions[bot]@users.noreply.github.com
       - uses: actions/setup-python@v5
         with:
-          python-version: 3.x
+          python-version: 3.12.9
       - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
       - uses: actions/cache@v4
         with:

.github/workflows/ci-tests.yml

@@ -0,0 +1,33 @@
+name: Tests
+on:
+  pull_request:
+    branches: ["main", "dev"]
+  workflow_dispatch:
+
+jobs:
+  tests:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ ubuntu-latest, windows-latest ]
+        python-version: [ '3.12' ]
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: pip
+      - name: Install Dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e .[dev]
+      - name: Run linting
+        run: |
+          flake8 ./src/
+
+      - name: Run tests
+        run: |
+          pytest     

.gitignore

@@ -1,3 +1,6 @@
+# Custom
+dev-tools/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[codz]
@@ -173,7 +176,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
 # Abstra
 # Abstra is an AI-powered process automation framework.

README.md

@@ -1,3 +1,5 @@
+[![Python 3.12](https://img.shields.io/badge/python-3.12-blue.svg)](https://www.python.org/downloads/release/python-312/)
+[![Tests](https://github.com/pim-n/pg-rad/actions/workflows/ci-tests.yml/badge.svg)](https://github.com/pim-n/pg-rad/actions/workflows/ci-tests.yml)
 # pg-rad
 Primary Gamma RADiation landscape - Development
 
@@ -23,13 +25,35 @@ With Python verion `>=3.12.4` and `<3.13`, create a virtual environment and inst
 ```
 python3 -m venv .venv
 source .venv/bin/activate
-(venv) pip install -e .[dev]
+```
+
+With the virtual environment activated, run:
+
+```
+pip install -e .[dev]
+```
+
+## Running example landscape
+
+The example landscape can be generated using the command-line interface. Still in the virtual environment, run
+
+```
+pgrad --test --loglevel DEBUG
 ```
 
 ## Tests
 
-Tests can be run with `pytest` from the root directory of the repository.
+Tests can be run with `pytest` from the root directory of the repository. With the virtual environment activated, run:
 
 ```
-(venv) pytest
+pytest
 ```
+
+## Local viewing of documentation
+
+PG-RAD uses [Material for MkDocs](https://squidfunk.github.io/mkdocs-material/) for generating documentation. It can be locally viewed by (in the venv) running:
+```
+mkdocs serve
+```
+
+where you can add the `--livereload` flag to automatically update the documentation as you write to the Markdown files.

docs/javascripts/mathjax.js

@@ -0,0 +1,18 @@
+window.MathJax = {
+  tex: {
+    inlineMath: [['$', '$'], ["\\(", "\\)"]],
+    displayMath: [['$$', '$$'], ["\\[", "\\]"]],
+    processEscapes: true,
+    processEnvironments: true
+  },
+  options: {
+    processHtmlClass: "arithmatex"
+  }
+};
+
+document$.subscribe(() => { 
+  MathJax.startup.output.clearCache()
+  MathJax.typesetClear()
+  MathJax.texReset()
+  MathJax.typesetPromise()
+})

docs/pg-rad-in-python.md

@@ -1,5 +0,0 @@
----
-title: Using PG-RAD as a module
----
-
-Consult the API documentation in the side bar.

mkdocs.yml

@@ -28,8 +28,16 @@ markdown_extensions:
   - pymdownx.inlinehilite
   - pymdownx.snippets
   - pymdownx.superfences
+  - pymdownx.arithmatex:
+      generic: true
+
+extra_javascript:
+  - javascripts/mathjax.js
+  - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
 
 plugins:
+- mkdocs-jupyter:
+    execute: false
 - mkdocstrings:
     enabled: !ENV [ENABLE_MKDOCSTRINGS, true]
     default_handler: python

pyproject.toml

@@ -5,9 +5,13 @@ build-backend = "setuptools.build_meta"
 [tool.setuptools.packages.find]
 where = ["src"]
 
+[tool.setuptools.package-data]
+"pg_rad.data" = ["*.csv"]
+"pg_rad.configs" = ["*.yml"]
+
 [project]
 name = "pg-rad"
-version = "0.2.0"
+version = "0.2.1"
 authors = [
   { name="Pim Nelissen", email="pi0274ne-s@student.lu.se" },
 ]
@@ -18,15 +22,19 @@ dependencies = [
     "matplotlib>=3.9.2",
     "numpy>=2",
     "pandas>=2.3.1",
-    "piecewise_regression==1.5.0",
+    "pyyaml>=6.0.2",
-    "pyyaml>=6.0.2"
+    "scipy>=1.15.0"
 ]
 license = "MIT"
 license-files = ["LICEN[CS]E*"]
 
+[project.scripts]
+pgrad = "pg_rad.main:main"
+
 [project.urls]
 Homepage = "https://github.com/pim-n/pg-rad"
 Issues = "https://github.com/pim-n/pg-rad/issues"
 
 [project.optional-dependencies]
-dev = ["pytest", "notebook", "mkdocs-material", "mkdocstrings-python"]
+
+dev = ["pytest", "mkinit", "notebook", "mkdocs-material", "mkdocstrings-python", "mkdocs-jupyter", "flake8"]

src/pg_rad/configs/__init__.py

@@ -0,0 +1 @@
+__all__ = []

src/pg_rad/configs/defaults.py

@@ -0,0 +1,18 @@
+# --- Physics defaults ---
+DEFAULT_AIR_DENSITY = 1.243  # kg/m^3, Skåne
+
+# --- Simulation defaults ---
+DEFAULT_SEED = None
+DEFAULT_ACQUISITION_TIME = 1.0
+
+# --- Source defaults ---
+DEFAULT_PATH_HEIGHT = 0.0
+DEFAULT_SOURCE_HEIGHT = 0.0
+
+# --- Segmented road defaults ---
+DEFAULT_MIN_TURN_ANGLE = 30.
+DEFAULT_MAX_TURN_ANGLE = 90.
+
+DEFAULT_FRICTION_COEFF = 0.7      # dry asphalt
+DEFAULT_GRAVITATIONAL_ACC = 9.81  # m/s^2
+DEFAULT_ALPHA = 100.

src/pg_rad/configs/filepaths.py

@@ -0,0 +1,3 @@
+ATTENUATION_TABLE = 'attenuation_table.csv'
+TEST_EXP_DATA = 'test_path_coords.csv'
+LOGGING_CONFIG = 'logging.yml'

src/pg_rad/data/__init__.py

@@ -0,0 +1 @@
+__all__ = []

src/pg_rad/data/attenuation_table.csv

@@ -0,0 +1,39 @@
+energy_mev,mu
+1.00000E-03,3.606E+03
+1.50000E-03,1.191E+03
+2.00000E-03,5.279E+02
+3.00000E-03,1.625E+02
+3.20290E-03,1.340E+02
+3.20290E-03,1.485E+02
+4.00000E-03,7.788E+01
+5.00000E-03,4.027E+01
+6.00000E-03,2.341E+01
+8.00000E-03,9.921E+00
+1.00000E-02,5.120E+00
+1.50000E-02,1.614E+00
+2.00000E-02,7.779E-01
+3.00000E-02,3.538E-01
+4.00000E-02,2.485E-01
+5.00000E-02,2.080E-01
+6.00000E-02,1.875E-01
+8.00000E-02,1.662E-01
+1.00000E-01,1.541E-01
+1.50000E-01,1.356E-01
+2.00000E-01,1.233E-01
+3.00000E-01,1.067E-01
+4.00000E-01,9.549E-02
+5.00000E-01,8.712E-02
+6.00000E-01,8.055E-02
+8.00000E-01,7.074E-02
+1.00000E+00,6.358E-02
+1.25000E+00,5.687E-02
+1.50000E+00,5.175E-02
+2.00000E+00,4.447E-02
+3.00000E+00,3.581E-02
+4.00000E+00,3.079E-02
+5.00000E+00,2.751E-02
+6.00000E+00,2.522E-02
+8.00000E+00,2.225E-02
+1.00000E+01,2.045E-02
+1.50000E+01,1.810E-02
+2.00000E+01,1.705E-02

src/pg_rad/dataloader.py

@@ -1,26 +0,0 @@
-import pandas as pd
-
-from pg_rad.logger import setup_logger
-from pg_rad.exceptions import DataLoadError, InvalidCSVError
-
-logger = setup_logger(__name__)
-
-def load_data(filename: str) -> pd.DataFrame:
-    logger.debug(f"Attempting to load data from {filename}")
-
-    try:
-        df = pd.read_csv(filename, delimiter=',')
-
-    except FileNotFoundError as e:
-        logger.error(f"File not found: {filename}")
-        raise DataLoadError(f"File does not exist: {filename}") from e
-
-    except pd.errors.ParserError as e:
-        logger.error(f"Invalid CSV format: {filename}")
-        raise InvalidCSVError(f"Invalid CSV file: {filename}") from e
-
-    except Exception as e:
-        logger.exception(f"Unexpected error while loading {filename}")
-        raise DataLoadError("Unexpected error while loading data") from e
-
-    return df

src/pg_rad/dataloader/__init__.py

@@ -0,0 +1,8 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+
+from pg_rad.dataloader import dataloader
+
+from pg_rad.dataloader.dataloader import (load_data,)
+
+__all__ = ['dataloader', 'load_data']

src/pg_rad/dataloader/dataloader.py

@@ -0,0 +1,22 @@
+import logging
+
+import pandas as pd
+
+
+logger = logging.getLogger(__name__)
+
+
+def load_data(filename: str) -> pd.DataFrame:
+    logger.debug(f"Attempting to load file: {filename}")
+
+    try:
+        df = pd.read_csv(filename, delimiter=',')
+    except FileNotFoundError as e:
+        logger.critical(e)
+        raise
+    except pd.errors.ParserError as e:
+        logger.critical(e)
+        raise
+
+    logger.debug(f"File loaded: {filename}")
+    return df

src/pg_rad/exceptions.py

@@ -1,8 +0,0 @@
-class ConvergenceError(Exception):
-    """Raised when an algorithm fails to converge."""
-
-class DataLoadError(Exception):
-    """Base class for data loading errors."""
-
-class InvalidCSVError(DataLoadError):
-    """Raised when a file is not a valid CSV."""

src/pg_rad/exceptions/__init__.py

@@ -0,0 +1,10 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+
+from pg_rad.exceptions import exceptions
+
+from pg_rad.exceptions.exceptions import (ConvergenceError, DataLoadError,
+                                          InvalidCSVError, OutOfBoundsError,)
+
+__all__ = ['ConvergenceError', 'DataLoadError', 'InvalidCSVError',
+           'OutOfBoundsError', 'exceptions']

src/pg_rad/exceptions/exceptions.py

@@ -0,0 +1,33 @@
+class ConvergenceError(Exception):
+    """Raised when an algorithm fails to converge."""
+
+
+class DataLoadError(Exception):
+    """Base class for data loading errors."""
+
+
+class InvalidCSVError(DataLoadError):
+    """Raised when a file is not a valid CSV."""
+
+
+class OutOfBoundsError(Exception):
+    """Raised when an object is attempted to be placed out of bounds."""
+
+
+class MissingConfigKeyError(KeyError):
+    """Raised when a (nested) config key is missing in the config."""
+    def __init__(self, key, subkey=None):
+        if subkey:
+            self.message = f"Missing key in {key}: {', '.join(list(subkey))}"
+        else:
+            self.message = f"Missing key: {key}"
+
+        super().__init__(self.message)
+
+
+class DimensionError(ValueError):
+    """Raised if dimensions or coordinates do not match the system."""
+
+
+class InvalidIsotopeError(ValueError):
+    """Raised if attempting to load an isotope that is not valid."""

src/pg_rad/inputparser/parser.py

@@ -0,0 +1,316 @@
+import logging
+import os
+from typing import Any, Dict, List, Union
+
+import yaml
+
+from pg_rad.exceptions.exceptions import MissingConfigKeyError, DimensionError
+from pg_rad.configs import defaults
+
+from .specs import (
+    MetadataSpec,
+    RuntimeSpec,
+    SimulationOptionsSpec,
+    SegmentSpec,
+    PathSpec,
+    ProceduralPathSpec,
+    CSVPathSpec,
+    SourceSpec,
+    AbsolutePointSourceSpec,
+    RelativePointSourceSpec,
+    SimulationSpec,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+class ConfigParser:
+    _ALLOWED_ROOT_KEYS = {
+        "name",
+        "speed",
+        "acquisition_time",
+        "path",
+        "sources",
+        "options",
+    }
+
+    def __init__(self, config_source: str):
+        self.config = self._load_yaml(config_source)
+
+    def parse(self) -> SimulationSpec:
+        self._warn_unknown_keys(
+            section="global",
+            provided=set(self.config.keys()),
+            allowed=self._ALLOWED_ROOT_KEYS,
+        )
+
+        metadata = self._parse_metadata()
+        runtime = self._parse_runtime()
+        options = self._parse_options()
+        path = self._parse_path()
+        sources = self._parse_point_sources()
+
+        return SimulationSpec(
+            metadata=metadata,
+            runtime=runtime,
+            options=options,
+            path=path,
+            point_sources=sources,
+        )
+
+    def _load_yaml(self, config_source: str) -> Dict[str, Any]:
+        if os.path.exists(config_source):
+            with open(config_source) as f:
+                data = yaml.safe_load(f)
+        else:
+            data = yaml.safe_load(config_source)
+
+        if not isinstance(data, dict):
+            raise ValueError(
+                "Provided path or string is not a valid YAML representation."
+            )
+
+        return data
+
+    def _parse_metadata(self) -> MetadataSpec:
+        try:
+            return MetadataSpec(
+                name=self.config["name"]
+            )
+        except KeyError as e:
+            raise MissingConfigKeyError("global", {"name"}) from e
+
+    def _parse_runtime(self) -> RuntimeSpec:
+        required = {"speed", "acquisition_time"}
+        missing = required - self.config.keys()
+        if missing:
+            raise MissingConfigKeyError("global", missing)
+
+        return RuntimeSpec(
+            speed=float(self.config["speed"]),
+            acquisition_time=float(self.config["acquisition_time"]),
+        )
+
+    def _parse_options(self) -> SimulationOptionsSpec:
+        options = self.config.get("options", {})
+
+        allowed = {"air_density", "seed"}
+        self._warn_unknown_keys(
+            section="options",
+            provided=set(options.keys()),
+            allowed=allowed,
+        )
+
+        air_density = options.get("air_density", defaults.DEFAULT_AIR_DENSITY)
+        seed = options.get("seed")
+
+        if not isinstance(air_density, float) or air_density <= 0:
+            raise ValueError(
+                "options.air_density must be a positive float in kg/m^3."
+            )
+        if (
+            seed is not None or
+            (isinstance(seed, int) and seed <= 0)
+        ):
+            raise ValueError("Seed must be a positive integer value.")
+
+        return SimulationOptionsSpec(
+            air_density=air_density,
+            seed=seed,
+        )
+
+    def _parse_path(self) -> PathSpec:
+        allowed_csv = {"file", "east_col_name", "north_col_name", "z"}
+        allowed_proc = {"segments", "length", "z"}
+
+        path = self.config.get("path")
+        if path is None:
+            raise MissingConfigKeyError("global", {"path"})
+
+        if not isinstance(path, dict):
+            raise ValueError("Path must be a dictionary.")
+
+        if "file" in path:
+            self._warn_unknown_keys(
+                section="path (csv)",
+                provided=set(path.keys()),
+                allowed=allowed_csv,
+            )
+
+            return CSVPathSpec(
+                file=path["file"],
+                east_col_name=path["east_col_name"],
+                north_col_name=path["north_col_name"],
+                z=path.get("z", 0),
+            )
+
+        if "segments" in path:
+            self._warn_unknown_keys(
+                section="path (procedural)",
+                provided=set(path.keys()),
+                allowed=allowed_proc,
+            )
+            return self._parse_procedural_path(path)
+
+        raise ValueError("Invalid path configuration.")
+
+    def _parse_procedural_path(
+        self,
+        path: Dict[str, Any]
+    ) -> ProceduralPathSpec:
+        raw_segments = path.get("segments")
+
+        if not isinstance(raw_segments, List):
+            raise ValueError("path.segments must be a list of segments.")
+
+        raw_length = path.get("length")
+
+        if raw_length is None:
+            raise MissingConfigKeyError("path", {"length"})
+
+        if isinstance(raw_length, int | float):
+            raw_length = [float(raw_length)]
+
+        segments = self._process_segment_angles(raw_segments)
+        lengths = self._process_segment_lengths(raw_length, len(segments))
+        resolved_segments = self._combine_segments_lengths(segments, lengths)
+
+        return ProceduralPathSpec(
+            segments=resolved_segments,
+            z=path.get("z", defaults.DEFAULT_PATH_HEIGHT),
+        )
+
+    def _process_segment_angles(
+        self,
+        raw_segments: List[Union[str, dict]]
+    ) -> List[Dict[str, Any]]:
+
+        normalized = []
+
+        for segment in raw_segments:
+
+            if isinstance(segment, str):
+                normalized.append({"type": segment, "angle": None})
+
+            elif isinstance(segment, dict):
+                if len(segment) != 1:
+                    raise ValueError("Invalid segment definition.")
+                seg_type, angle = list(segment.items())[0]
+                normalized.append({"type": seg_type, "angle": angle})
+
+            else:
+                raise ValueError("Invalid segment entry format.")
+
+        return normalized
+
+    def _process_segment_lengths(
+        self,
+        raw_length_list: List[Union[int, float]],
+        num_segments: int
+    ) -> List[float]:
+        num_lengths = len(raw_length_list)
+
+        if num_lengths == num_segments or num_lengths == 1:
+            return raw_length_list
+        else:
+            raise ValueError(
+                "Path length must either be a single number or a list with "
+                "number of elements equal to the number of segments."
+            )
+
+    def _combine_segments_lengths(
+        self,
+        segments: List[Dict[str, Any]],
+        lengths: List[float],
+    ) -> List[SegmentSpec]:
+
+        resolved = []
+
+        for seg, length in zip(segments, lengths):
+            angle = seg["angle"]
+
+            if angle is not None and not self._is_turn(seg["type"]):
+                raise ValueError(
+                    f"A {seg['type']} segment does not support an angle."
+                )
+
+            resolved.append(
+                SegmentSpec(
+                    type=seg["type"],
+                    length=length,
+                    angle=angle,
+                )
+            )
+
+        return resolved
+
+    @staticmethod
+    def _is_turn(segment_type: str) -> bool:
+        return segment_type in {"turn_left", "turn_right"}
+
+    def _parse_point_sources(self) -> List[SourceSpec]:
+        source_dict = self.config.get("sources", {})
+        specs: List[SourceSpec] = []
+
+        for name, params in source_dict.items():
+
+            required = {"activity_MBq", "isotope", "position"}
+            missing = required - params.keys()
+            if missing:
+                raise MissingConfigKeyError(name, missing)
+
+            activity = params.get("activity_MBq")
+            isotope = params.get("isotope")
+
+            if not isinstance(activity, int | float) or activity <= 0:
+                raise ValueError(
+                    f"sources.{name}.activity_MBq must be positive value "
+                    "in MegaBequerels."
+                )
+
+            position = params.get("position")
+
+            if isinstance(position, list):
+                if len(position) != 3:
+                    raise DimensionError(
+                        "Absolute position must be [x, y, z]."
+                    )
+
+                specs.append(
+                    AbsolutePointSourceSpec(
+                        name=name,
+                        activity_MBq=float(activity),
+                        isotope=isotope,
+                        x=float(position[0]),
+                        y=float(position[1]),
+                        z=float(position[2]),
+                    )
+                )
+
+            elif isinstance(position, dict):
+                specs.append(
+                    RelativePointSourceSpec(
+                        name=name,
+                        activity_MBq=float(activity),
+                        isotope=isotope,
+                        along_path=float(position["along_path"]),
+                        dist_from_path=float(position["dist_from_path"]),
+                        side=position["side"],
+                        z=position.get("z", defaults.DEFAULT_SOURCE_HEIGHT)
+                    )
+                )
+
+            else:
+                raise ValueError(
+                    f"Invalid position format for source '{name}'."
+                    )
+
+        return specs
+
+    def _warn_unknown_keys(self, section: str, provided: set, allowed: set):
+        unknown = provided - allowed
+        if unknown:
+            logger.warning(
+                f"Unknown keys in '{section}' section: {unknown}"
+            )

src/pg_rad/inputparser/specs.py

@@ -0,0 +1,76 @@
+from abc import ABC
+from dataclasses import dataclass
+
+
+@dataclass
+class MetadataSpec:
+    name: str
+
+
+@dataclass
+class RuntimeSpec:
+    speed: float
+    acquisition_time: float
+
+
+@dataclass
+class SimulationOptionsSpec:
+    air_density: float = 1.243
+    seed: int | None = None
+
+
+@dataclass
+class SegmentSpec:
+    type: str
+    length: float
+    angle: float | None
+
+
+@dataclass
+class PathSpec(ABC):
+    pass
+
+
+@dataclass
+class ProceduralPathSpec(PathSpec):
+    segments: list[SegmentSpec]
+    z: int | float
+
+
+@dataclass
+class CSVPathSpec(PathSpec):
+    file: str
+    east_col_name: str
+    north_col_name: str
+    z: int | float
+
+
+@dataclass
+class SourceSpec(ABC):
+    activity_MBq: float
+    isotope: str
+    name: str
+
+
+@dataclass
+class AbsolutePointSourceSpec(SourceSpec):
+    x: float
+    y: float
+    z: float
+
+
+@dataclass
+class RelativePointSourceSpec(SourceSpec):
+    along_path: float
+    dist_from_path: float
+    side: str
+    z: float
+
+
+@dataclass
+class SimulationSpec:
+    metadata: MetadataSpec
+    runtime: RuntimeSpec
+    options: SimulationOptionsSpec
+    path: PathSpec
+    point_sources: list[SourceSpec]

src/pg_rad/isotope.py

@@ -1,23 +0,0 @@
-class Isotope:
-    """Represents the essential information of an isotope.
-
-    Args:
-        name (str): Full name (e.g. Caesium-137).
-        E (float): Energy of the primary gamma in keV.
-        b (float): Branching ratio for the gamma at energy E.
-    """  
-    def __init__(
-            self,
-            name: str,
-            E: float,
-            b: float
-    ):        
-        if E <= 0:
-            raise ValueError("primary_gamma must be a positive energy (keV).")
-
-        if not (0 <= b <= 1):
-            raise ValueError("branching_ratio_pg must be a ratio (0 <= b <= 1)")
-
-        self.name = name
-        self.E = E
-        self.b = b

src/pg_rad/isotopes/__init__.py

@@ -0,0 +1,9 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+
+from pg_rad.isotopes import isotope
+
+from pg_rad.isotopes.isotope import (CS137, Isotope, get_isotope,
+                                     preset_isotopes,)
+
+__all__ = ['CS137', 'Isotope', 'get_isotope', 'isotope', 'preset_isotopes']

src/pg_rad/isotopes/isotope.py

@@ -0,0 +1,51 @@
+from typing import Dict, Type
+
+from pg_rad.exceptions.exceptions import InvalidIsotopeError
+from pg_rad.physics.attenuation import get_mass_attenuation_coeff
+
+
+class Isotope:
+    """Represents the essential information of an isotope.
+
+    Args:
+        name (str): Full name (e.g. Caesium-137).
+        E (float): Energy of the primary gamma in keV.
+        b (float): Branching ratio for the gamma at energy E.
+    """
+    def __init__(
+            self,
+            name: str,
+            E: float,
+            b: float
+    ):
+        if E <= 0:
+            raise ValueError("primary_gamma must be a positive energy (keV).")
+
+        if not (0 <= b <= 1):
+            raise ValueError("branching_ratio_pg must be a ratio b in [0,1]")
+
+        self.name = name
+        self.E = E
+        self.b = b
+        self.mu_mass_air = get_mass_attenuation_coeff(E / 1000)
+
+
+class CS137(Isotope):
+    def __init__(self):
+        super().__init__(
+            name="Cs-137",
+            E=661.66,
+            b=0.851
+        )
+
+
+preset_isotopes: Dict[str, Type[Isotope]] = {
+    "CS137": CS137
+}
+
+
+def get_isotope(isotope_str: str) -> Isotope:
+    """Lazy factory function to create isotope objects."""
+    if isotope_str not in preset_isotopes:
+        raise InvalidIsotopeError(f"Unknown isotope: {isotope_str}")
+    return preset_isotopes[isotope_str]()

src/pg_rad/landscape.py

@@ -1,126 +0,0 @@
-from matplotlib import pyplot as plt
-from matplotlib.patches import Circle
-import numpy as np
-
-from pg_rad.path import Path
-from pg_rad.sources import PointSource
-
-class Landscape:
-    """A generic Landscape that can contain a Path and sources.
-
-    Args:
-        air_density (float, optional): Air density in kg / m^3. Defaults to 1.243.
-        size (int | tuple[int, int, int], optional): Size of the world. Defaults to 500.
-        scale (str, optional): The scale of the size argument passed. Defaults to 'meters'.
-    """    
-    def __init__(
-            self,
-            air_density: float = 1.243,
-            size: int | tuple[int, int, int] = 500,
-            scale = 'meters',
-            ):    
-        if isinstance(size, int):
-            self.world = np.zeros((size, size, size))
-        elif isinstance(size, tuple) and len(size) == 3:
-            self.world = np.zeros(size)
-        else:
-            raise TypeError("size must be an integer or a tuple of 3 integers.")
-
-        self.air_density = air_density
-        self.scale = scale
-
-        self.path: Path = None
-        self.sources: list[PointSource] = []
-    
-    def plot(self, z = 0):
-        """Plot a slice of the world at a height `z`.
-
-        Args:
-            z (int, optional): Height of slice. Defaults to 0.
-
-        Returns:
-            fig, ax: Matplotlib figure objects.
-        """        
-        x_lim, y_lim, _ = self.world.shape
-
-        fig, ax = plt.subplots()
-        ax.set_xlim(right=x_lim)
-        ax.set_ylim(top=y_lim)
-        ax.set_xlabel(f"X [{self.scale}]")
-        ax.set_ylabel(f"Y [{self.scale}]")
-
-        if not self.path == None:
-            ax.plot(self.path.x_list, self.path.y_list, 'bo-')
-        
-        for s in self.sources:
-            if np.isclose(s.z, z):
-                dot = Circle(
-                        (s.x, s.y),
-                        radius=5,
-                        color=s.color,
-                        zorder=5
-                )
-
-                ax.text(
-                    s.x + 0.06,
-                    s.y + 0.06,
-                    s.name,
-                    color=s.color,
-                    fontsize=10,
-                    ha="left",
-                    va="bottom",
-                    zorder=6
-                )
-
-                ax.add_patch(dot)
-                
-        return fig, ax
-
-    def add_sources(self, *sources: PointSource):
-        """Add one or more point sources to the world.
-
-        Args:
-            *sources (pg_rad.sources.PointSource): One or more sources, passed as
-            Source1, Source2, ...
-        Raises:
-            ValueError: If the source is outside the boundaries of the landscape.
-        """        
-
-        max_x, max_y, max_z = self.world.shape[:3]
-
-        if any(
-            not (0 <= source.x < max_x and
-                 0 <= source.y < max_y and
-                 0 <= source.z < max_z)
-            for source in sources
-        ):
-            raise ValueError("One or more sources are outside the landscape!")
-
-        self.sources.extend(sources)
-
-    def set_path(self, path: Path):
-        """
-        Set the path in the landscape.
-        """
-        self.path = path
-    
-def create_landscape_from_path(path: Path, max_z = 500):
-    """Generate a landscape from a path, using its dimensions to determine
-    the size of the landscape.
-
-    Args:
-        path (Path): A Path object describing the trajectory.
-        max_z (int, optional): Height of the world. Defaults to 500 meters.
-
-    Returns:
-        landscape (pg_rad.landscape.Landscape): A landscape with dimensions based on the provided Path.
-    """    
-    max_x = np.ceil(max(path.x_list))
-    max_y = np.ceil(max(path.y_list))
-
-    landscape = Landscape(
-        size = (max_x, max_y, max_z)
-        )
-    
-    landscape.path = path
-    return landscape

src/pg_rad/landscape/builder.py

@@ -0,0 +1,172 @@
+import logging
+from typing import Self
+
+from .landscape import Landscape
+from pg_rad.dataloader.dataloader import load_data
+from pg_rad.objects.sources import PointSource
+from pg_rad.exceptions.exceptions import OutOfBoundsError
+from pg_rad.utils.projection import rel_to_abs_source_position
+from pg_rad.inputparser.specs import (
+    SimulationSpec,
+    CSVPathSpec,
+    AbsolutePointSourceSpec,
+    RelativePointSourceSpec
+)
+
+from pg_rad.path.path import Path, path_from_RT90
+
+from road_gen.generators.segmented_road_generator import SegmentedRoadGenerator
+
+
+logger = logging.getLogger(__name__)
+
+
+class LandscapeBuilder:
+    def __init__(self, name: str = "Unnamed landscape"):
+        self.name = name
+        self._path = None
+        self._point_sources = []
+        self._size = None
+        self._air_density = 1.243
+
+        logger.debug(f"LandscapeBuilder initialized: {self.name}")
+
+    def set_air_density(self, air_density: float | None) -> Self:
+        """Set the air density of the world."""
+        if air_density and isinstance(air_density, float):
+            self._air_density = air_density
+        return self
+
+    def set_landscape_size(self, size: tuple[int, int, int]) -> Self:
+        """Set the size of the landscape in meters (x,y,z)."""
+        if self._path and any(p > s for p, s in zip(self._path.size, size)):
+            raise OutOfBoundsError(
+                "Cannot set landscape size smaller than the path."
+            )
+
+        self._size = size
+        logger.debug("Size of the landscape has been updated.")
+
+        return self
+
+    def get_path(self):
+        return self._path
+
+    def set_path_from_segments(
+        self,
+        sim_spec: SimulationSpec
+    ):
+
+        segments = sim_spec.path.segments
+        types = [s.type for s in segments]
+        lengths = [s.length for s in segments]
+        angles = [s.angle for s in segments]
+
+        sg = SegmentedRoadGenerator(
+            length=lengths,
+            ds=sim_spec.runtime.speed * sim_spec.runtime.acquisition_time,
+            velocity=sim_spec.runtime.speed,
+            seed=sim_spec.options.seed
+        )
+
+        x, y = sg.generate(
+            segments=types,
+            lengths=lengths,
+            angles=angles
+        )
+
+        self._path = Path(list(zip(x, y)))
+        self._fit_landscape_to_path()
+        return self
+
+    def set_path_from_experimental_data(
+        self,
+        spec: CSVPathSpec
+    ) -> Self:
+        df = load_data(spec.file)
+        self._path = path_from_RT90(
+            df=df,
+            east_col=spec.east_col_name,
+            north_col=spec.north_col_name,
+            z=spec.z
+        )
+
+        self._fit_landscape_to_path()
+
+        return self
+
+    def set_point_sources(
+        self,
+        *sources: AbsolutePointSourceSpec | RelativePointSourceSpec
+    ):
+        """Add one or more point sources to the world.
+
+        Args:
+            *sources (AbsolutePointSourceSpec | RelativePointSourceSpec):
+                One or more sources, passed as SourceSpecs tuple
+        Raises:
+            OutOfBoundsError: If any source is outside the boundaries of the
+            landscape.
+        """
+
+        for s in sources:
+            if isinstance(s, AbsolutePointSourceSpec):
+                pos = (s.x, s.y, s.z)
+            elif isinstance(s, RelativePointSourceSpec):
+                path = self.get_path()
+                pos = rel_to_abs_source_position(
+                    x_list=path.x_list,
+                    y_list=path.y_list,
+                    path_z=path.z,
+                    along_path=s.along_path,
+                    side=s.side,
+                    dist_from_path=s.dist_from_path)
+            if any(
+                p < 0 or p >= s for p, s in zip(pos, self._size)
+            ):
+                raise OutOfBoundsError(
+                    "One or more sources attempted to "
+                    "be placed outside the landscape."
+                    )
+
+            self._point_sources.append(PointSource(
+                activity_MBq=s.activity_MBq,
+                isotope=s.isotope,
+                position=pos,
+                name=s.name
+            ))
+
+    def _fit_landscape_to_path(self) -> None:
+        """The size of the landscape will be updated if
+        1) _size is not set, or
+        2) _size is too small to contain the path."""
+
+        needs_resize = (
+            not self._size
+            or any(p > s for p, s in zip(self._path.size, self._size))
+        )
+
+        if needs_resize:
+            if not self._size:
+                logger.debug("Because no Landscape size was set, "
+                             "it will now set to path dimensions.")
+            else:
+                logger.warning(
+                    "Path exceeds current landscape size. "
+                    "Landscape size will be expanded to accommodate path."
+                )
+
+            max_size = max(self._path.size)
+            self.set_landscape_size((max_size, max_size))
+
+    def build(self):
+        landscape = Landscape(
+            name=self.name,
+            path=self._path,
+            point_sources=self._point_sources,
+            size=self._size,
+            air_density=self._air_density
+        )
+
+        logger.info(f"Landscape built successfully: {landscape.name}")
+        return landscape

src/pg_rad/landscape/director.py

@@ -0,0 +1,49 @@
+from importlib.resources import files
+import logging
+
+from pg_rad.configs.filepaths import TEST_EXP_DATA
+from .builder import LandscapeBuilder
+from pg_rad.inputparser.specs import (
+    SimulationSpec,
+    CSVPathSpec,
+    ProceduralPathSpec
+)
+from pg_rad.objects.sources import PointSource
+
+
+logger = logging.getLogger(__name__)
+
+
+class LandscapeDirector:
+    def __init__(self):
+        logger.debug("LandscapeDirector initialized.")
+
+    @staticmethod
+    def build_test_landscape():
+        fp = files('pg_rad.data').joinpath(TEST_EXP_DATA)
+        source = PointSource(
+            activity_MBq=100E9,
+            isotope="CS137",
+            position=(0, 0, 0)
+        )
+        lb = LandscapeBuilder("Test landscape")
+        lb.set_air_density(1.243)
+        lb.set_path_from_experimental_data(fp, z=0)
+        lb.set_point_sources(source)
+        landscape = lb.build()
+        return landscape
+
+    @staticmethod
+    def build_from_config(config: SimulationSpec):
+        lb = LandscapeBuilder(config.metadata.name)
+        lb.set_air_density(config.options.air_density)
+
+        if isinstance(config.path, CSVPathSpec):
+            lb.set_path_from_experimental_data(spec=config.path)
+        elif isinstance(config.path, ProceduralPathSpec):
+            lb.set_path_from_segments(
+                sim_spec=config,
+            )
+        lb.set_point_sources(*config.point_sources)
+        landscape = lb.build()
+        return landscape

src/pg_rad/landscape/landscape.py

@@ -0,0 +1,38 @@
+import logging
+
+from pg_rad.path.path import Path
+from pg_rad.objects.sources import PointSource
+
+
+logger = logging.getLogger(__name__)
+
+
+class Landscape:
+    """
+    A generic Landscape that can contain a Path and sources.
+    """
+    def __init__(
+            self,
+            name: str,
+            path: Path,
+            air_density: float,
+            point_sources: list[PointSource],
+            size: tuple[int, int, int]
+            ):
+        """Initialize a landscape.
+
+        Args:
+            name (str): Name of the landscape.
+            path (Path): The path of the detector.
+            air_density (float): Air density in kg/m^3.
+            point_sources (list[PointSource]): List of point sources.
+            size (tuple[int, int, int]): Size of the world.
+        """
+
+        self.name = name
+        self.path = path
+        self.point_sources = point_sources
+        self.size = size
+        self.air_density = air_density
+
+        logger.debug(f"Landscape created: {self.name}")

src/pg_rad/logger.py

@@ -1,17 +0,0 @@
-import logging
-import logging.config
-import pathlib
-
-import yaml
-
-def setup_logger(name):
-    logger = logging.getLogger(name)
-
-    base_dir = pathlib.Path(__file__).resolve().parent
-    config_file = base_dir / "configs" / "logging.yml"
-
-    with open(config_file) as f:
-        config = yaml.safe_load(f)
-    
-    logging.config.dictConfig(config)
-    return logger

src/pg_rad/logger/__init__.py

@@ -0,0 +1,5 @@
+from pg_rad.logger import logger
+
+from pg_rad.logger.logger import (setup_logger,)
+
+__all__ = ['logger', 'setup_logger']

src/pg_rad/logger/logger.py

@@ -0,0 +1,22 @@
+import logging.config
+from importlib.resources import files
+
+import yaml
+
+from pg_rad.configs.filepaths import LOGGING_CONFIG
+
+
+def setup_logger(log_level: str = "WARNING"):
+    levels = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
+
+    if log_level not in levels:
+        raise ValueError(f"Log level must be one of {levels}.")
+
+    config_file = files('pg_rad.configs').joinpath(LOGGING_CONFIG)
+
+    with open(config_file) as f:
+        config = yaml.safe_load(f)
+
+    config["loggers"]["root"]["level"] = log_level
+
+    logging.config.dictConfig(config)

src/pg_rad/main.py

@@ -0,0 +1,125 @@
+import argparse
+import logging
+import sys
+
+from pandas.errors import ParserError
+from yaml import YAMLError
+
+from pg_rad.exceptions.exceptions import (
+    MissingConfigKeyError,
+    OutOfBoundsError,
+    DimensionError,
+    InvalidIsotopeError
+)
+from pg_rad.logger.logger import setup_logger
+from pg_rad.inputparser.parser import ConfigParser
+from pg_rad.landscape.director import LandscapeDirector
+from pg_rad.plotting.result_plotter import ResultPlotter
+from pg_rad.simulator.engine import SimulationEngine
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        prog="pg-rad",
+        description="Primary Gamma RADiation landscape tool"
+    )
+    parser.add_argument(
+        "--config",
+        help="Build from a config file."
+    )
+    parser.add_argument(
+        "--test",
+        action="store_true",
+        help="Load and run the test landscape."
+    )
+    parser.add_argument(
+        "--loglevel",
+        default="INFO",
+        choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+    )
+    parser.add_argument(
+        "--saveplot",
+        action="store_true",
+        help="Save the plot or not."
+    )
+
+    args = parser.parse_args()
+    setup_logger(args.loglevel)
+    logger = logging.getLogger(__name__)
+
+    if args.test:
+        test_yaml = """
+        name: Test landscape
+        speed: 8.33
+        acquisition_time: 1
+
+        path:
+            length: 1000
+            segments:
+                - straight
+
+        sources:
+            test_source:
+                activity_MBq: 1000
+                position: [500, 100, 0]
+                isotope: CS137
+        """
+
+        cp = ConfigParser(test_yaml).parse()
+        landscape = LandscapeDirector.build_from_config(cp)
+
+        output = SimulationEngine(
+            landscape=landscape,
+            runtime_spec=cp.runtime,
+            sim_spec=cp.options
+        ).simulate()
+
+        plotter = ResultPlotter(landscape, output)
+        plotter.plot()
+
+    elif args.config:
+        try:
+            cp = ConfigParser(args.config).parse()
+            landscape = LandscapeDirector.build_from_config(cp)
+
+            output = SimulationEngine(
+                landscape=landscape,
+                runtime_spec=cp.runtime,
+                sim_spec=cp.options
+            ).simulate()
+
+            plotter = ResultPlotter(landscape, output)
+            plotter.plot()
+        except (
+            MissingConfigKeyError,
+            KeyError,
+            YAMLError,
+        ):
+            logger.critical(
+                "The provided config file is invalid. "
+                "Check the log above. You can consult the documentation for "
+                "an explanation of how to define a config file."
+                )
+            sys.exit(1)
+        except (
+            OutOfBoundsError,
+            DimensionError,
+            InvalidIsotopeError,
+            ValueError
+        ) as e:
+            logger.critical(e)
+            logger.critical(
+                "One or more items in config are not specified correctly. "
+                "Please consult this log and fix the problem."
+                )
+            sys.exit(1)
+
+        except (
+            FileNotFoundError,
+            ParserError
+        ):
+            sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

src/pg_rad/objects.py

@@ -1,33 +0,0 @@
-import math
-from typing import Self
-
-class Object:
-    def __init__(
-            self,
-            x: float,
-            y: float,
-            z: float,
-            name: str = "Unnamed object",
-            color: str = 'grey'):
-        """
-        A generic object.
-
-        Args:
-            x (float): X coordinate.
-            y (float): Y coordinate.
-            z (float): Z coordinate.
-            name (str, optional): Name for the object. Defaults to "Unnamed object".
-            color (str, optional): Matplotlib compatible color string. Defaults to "red".
-        """
-        
-        self.x = x
-        self.y = y
-        self.z = z
-        self.name = name
-        self.color = color
-    
-    def distance_to(self, other: Self) -> float:
-        return math.dist(
-            (self.x, self.y, self.z),
-            (other.x, other.y, other.z),
-        )

src/pg_rad/objects/__init__.py

@@ -0,0 +1,11 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+
+from pg_rad.objects import objects
+from pg_rad.objects import sources
+
+from pg_rad.objects.objects import (BaseObject,)
+from pg_rad.objects.sources import (PointSource,)
+
+__all__ = ['BaseObject', 'PointSource', 'objects',
+           'sources']

src/pg_rad/objects/objects.py

@@ -0,0 +1,44 @@
+from typing import Self
+
+import numpy as np
+
+from pg_rad.exceptions.exceptions import DimensionError
+
+
+class BaseObject:
+    def __init__(
+            self,
+            pos: tuple[float, float, float] = (0, 0, 0),
+            name: str = "Unnamed object",
+            color: str = 'grey'):
+        """
+        A generic object.
+
+        Args:
+            pos (tuple[float, float, float]): Position vector (x,y,z).
+            name (str, optional): Name for the object.
+            Defaults to "Unnamed object".
+            color (str, optional): Matplotlib compatible color string.
+            Defaults to "red".
+        """
+
+        if len(pos) != 3:
+            raise DimensionError("Position must be tuple of length 3 (x,y,z).")
+        self.pos = pos
+        self.name = name
+        self.color = color
+
+    def distance_to(self, other: Self | tuple) -> float:
+        if isinstance(other, tuple) and len(other) == 3:
+            r = np.linalg.norm(
+                np.subtract(self.pos, other)
+                )
+        else:
+            try:
+                r = np.linalg.norm(
+                    np.subtract(self.pos, other.pos)
+                    )
+            except AttributeError as e:
+                raise e("other must be an object in the world \
+                    or a position tuple (x,y,z).")
+        return r

src/pg_rad/objects/sources.py

@@ -0,0 +1,54 @@
+import logging
+
+from .objects import BaseObject
+from pg_rad.isotopes.isotope import Isotope, get_isotope
+
+logger = logging.getLogger(__name__)
+
+
+class PointSource(BaseObject):
+    _id_counter = 1
+
+    def __init__(
+            self,
+            activity_MBq: int,
+            isotope: str,
+            position: tuple[float, float, float] = (0, 0, 0),
+            name: str | None = None,
+            color: str = 'red'
+            ):
+        """A point source.
+
+        Args:
+            activity_MBq (int): Activity A in MBq.
+            isotope (Isotope): The isotope.
+            pos (tuple[float, float, float], optional):
+                Position of the PointSource.
+            name (str, optional): Can give the source a unique name.
+                If not provided, point sources are sequentially
+                named: Source1, Source2, ...
+            color (str, optional): Matplotlib compatible color string
+        """
+
+        self.id = PointSource._id_counter
+        PointSource._id_counter += 1
+
+        # default name derived from ID if not provided
+        if name is None:
+            name = f"Source {self.id}"
+
+        super().__init__(position, name, color)
+
+        self.activity = activity_MBq
+        self.isotope: Isotope = get_isotope(isotope)
+
+        logger.debug(f"Source created: {self.name}")
+
+    def __repr__(self):
+        x, y, z = self.position
+        repr_str = (f"PointSource(name={self.name}, "
+                    + f"pos={(x, y, z)}, "
+                    + f"A={self.activity} MBq), "
+                    + f"isotope={self.isotope.name}.")
+
+        return repr_str

src/pg_rad/path.py

@@ -1,187 +0,0 @@
-from collections.abc import Sequence
-import math
-
-from matplotlib import pyplot as plt
-import numpy as np
-import pandas as pd
-import piecewise_regression
-
-from pg_rad.exceptions import ConvergenceError
-from pg_rad.logger import setup_logger
-
-logger = setup_logger(__name__)
-
-class PathSegment:
-    def __init__(self, a: tuple[float, float], b: tuple[float, float]):
-        """A straight Segment of a Path, from (x_a, y_a) to (x_b, y_b).
-
-        Args:
-            a (tuple[float, float]): The starting point (x_a, y_a).
-            b (tuple[float, float]): The final point (x_b, y_b).
-        """        
-        self.a = a
-        self.b = b
-
-    def get_length(self) -> float:
-        return math.dist(self.a, self.b)
-    
-    length = property(get_length)
-
-    def __str__(self) -> str:
-        return str(f"({self.a}, {self.b})")
-    
-    def __getitem__(self, index) -> float:
-        if index == 0:
-            return self.a
-        elif index == 1:
-            return self.b
-        else:
-            raise IndexError
-
-class Path:
-    def __init__(
-            self,
-            coord_list: Sequence[tuple[float, float]],
-            z: float = 0,
-            path_simplify = False
-                 ):
-        """Construct a path of sequences based on a list of coordinates.
-
-        Args:
-            coord_list (Sequence[tuple[float, float]]): List of x,y coordinates.
-            z (float, optional): Height of the path. Defaults to 0.
-            path_simplify (bool, optional): Whether to pg_rad.path.simplify_path(). Defaults to False.
-        """        
-        
-        if len(coord_list) < 2:
-            raise ValueError("Must provide at least two coordinates as a list of tuples, e.g. [(x1, y1), (x2, y2)]")
-
-        x, y = tuple(zip(*coord_list))
-    
-        if path_simplify:
-            try:
-                x, y = simplify_path(list(x), list(y))
-            except ConvergenceError:
-                logger.warning("Continuing without simplifying path.")
-
-        self.x_list = list(x)
-        self.y_list = list(y)
-
-        coord_list = list(zip(x, y))
-
-        self.segments = [PathSegment(i, ip1) for i, ip1 in zip(coord_list, coord_list[1:])]
-
-        self.z = z
-
-    def get_length(self) -> float:
-        return sum([s.length for s in self.segments])
-    
-    length = property(get_length)
-
-    def __getitem__(self, index) -> PathSegment:
-        return self.segments[index]
-
-    def __str__(self) -> str:
-        return str([str(s) for s in self.segments])
-    
-    def plot(self, **kwargs):
-        """
-        Plot the path using matplotlib.
-        """  
-        plt.plot(self.x_list, self.y_list, **kwargs)
-
-def simplify_path(
-        x: Sequence[float],
-        y: Sequence[float],
-        keep_endpoints_equal: bool = False,
-        n_breakpoints: int = 3
-        ):
-    """From full resolution x and y arrays, return a piecewise linearly approximated/simplified pair of x and y arrays.
-
-    This function uses the `piecewise_regression` package. From a full set of
-    coordinate pairs, the function fits linear sections, automatically finding
-    the number of breakpoints and their positions.
-
-    On why the default value of n_breakpoints is 3, from the `piecewise_regression`
-    docs:
-    "If you do not have (or do not want to use) initial guesses for the number
-    of breakpoints, you can set it to n_breakpoints=3, and the algorithm will
-    randomly generate start_values. With a 50% chance, the bootstrap restarting
-    algorithm will either use the best currently converged breakpoints or
-    randomly generate new start_values, escaping the local optima in two ways in
-    order to find better global optima."
-
-    Args:
-        x (Sequence[float]): Full list of x coordinates.
-        y (Sequence[float]): Full list of y coordinates.
-        keep_endpoints_equal (bool, optional): Whether or not to force start
-        and end to be exactly equal to the original. This will worsen the linear
-        approximation at the beginning and end of path. Defaults to False.
-        n_breakpoints (int, optional): Number of breakpoints. Defaults to 3.
-
-    Returns:
-        x (list[float]): Reduced list of x coordinates.
-        y (list[float]): Reduced list of y coordinates.
-
-    Raises:
-        ConvergenceError: If the fitting algorithm failed to simplify the path.
-
-    Reference:
-        Pilgrim, C., (2021). piecewise-regression (aka segmented regression) in Python. Journal of Open Source Software, 6(68), 3859, https://doi.org/10.21105/joss.03859.
-    """
-    
-    logger.debug(f"Attempting piecewise regression on path.")
-
-    pw_fit = piecewise_regression.Fit(x, y, n_breakpoints=n_breakpoints)
-    pw_res = pw_fit.get_results()
-
-    if pw_res == None:
-        logger.error("Piecewise regression failed to converge.")
-        raise ConvergenceError("Piecewise regression failed to converge.") 
-    
-    est = pw_res['estimates']
-
-    # extract and sort breakpoints
-    breakpoints_x = sorted(
-        v['estimate'] for k, v in est.items() if k.startswith('breakpoint')
-    )
- 
-    x_points = [x[0]] + breakpoints_x + [x[-1]]
-
-    y_points = pw_fit.predict(x_points)
-
-    if keep_endpoints_equal:
-        logger.debug("Forcing endpoint equality.")
-        y_points[0] = y[0]
-        y_points[-1] = y[-1]
-
-    logger.info(
-        f"Piecewise regression reduced path from {len(x)-1} to {len(x_points)-1} segments."
-    )
-
-    return x_points, y_points
-
-def path_from_RT90(
-        df: pd.DataFrame,
-        east_col: str = "East",
-        north_col: str = "North",
-        **kwargs
-        ) -> Path:
-    """Construct a path from East and North formatted coordinates (RT90) in a Pandas DataFrame.
-
-    Args:
-        df (pandas.DataFrame): DataFrame containing at least the two columns noted in the cols argument.
-        east_col (str): The column name for the East coordinates.
-        north_col (str): The column name for the North coordinates.
-
-    Returns:
-        Path: A Path object built from the aquisition coordinates in the DataFrame.
-    """    
-    
-    east_arr = np.array(df[east_col]) - min(df[east_col])
-    north_arr = np.array(df[north_col]) - min(df[north_col])
-
-    coord_pairs = list(zip(east_arr, north_arr))
-
-    path = Path(coord_pairs, **kwargs)
-    return path

src/pg_rad/path/__init__.py

@@ -0,0 +1,8 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+
+from pg_rad.path import path
+
+from pg_rad.path.path import (Path, PathSegment, path_from_RT90,)
+
+__all__ = ['Path', 'PathSegment', 'path', 'path_from_RT90']

src/pg_rad/path/path.py

@@ -0,0 +1,130 @@
+from collections.abc import Sequence
+import logging
+import math
+
+from matplotlib import pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+logger = logging.getLogger(__name__)
+
+
+class PathSegment:
+    def __init__(self, a: tuple[float, float], b: tuple[float, float]):
+        """A straight Segment of a Path, from (x_a, y_a) to (x_b, y_b).
+
+        Args:
+            a (tuple[float, float]): The starting point (x_a, y_a).
+            b (tuple[float, float]): The final point (x_b, y_b).
+        """
+        self.a = a
+        self.b = b
+
+    def get_length(self) -> float:
+        return math.dist(self.a, self.b)
+
+    length = property(get_length)
+
+    def __str__(self) -> str:
+        return str(f"({self.a}, {self.b})")
+
+    def __getitem__(self, index) -> float:
+        if index == 0:
+            return self.a
+        elif index == 1:
+            return self.b
+        else:
+            raise IndexError
+
+
+class Path:
+    def __init__(
+            self,
+            coord_list: Sequence[tuple[float, float]],
+            z: float = 0.,
+            z_box: float = 50.
+                 ):
+        """Construct a path of sequences based on a list of coordinates.
+
+        Args:
+            coord_list (Sequence[tuple[float, float]]): List of x,y
+            coordinates.
+            z (float, optional): position of the path in z-direction in meters.
+                Defaults to 0 meters.
+            z_box (float, optional): How much empty space to set
+                above the path in meters. Defaults to 50 meters.
+        """
+
+        if len(coord_list) < 2:
+            raise ValueError("Must provide at least two coordinates as a \
+                of tuples, e.g. [(x1, y1), (x2, y2)]")
+
+        x, y = tuple(zip(*coord_list))
+
+        self.x_list = list(x)
+        self.y_list = list(y)
+
+        coord_list = list(zip(x, y))
+
+        self.segments = [
+            PathSegment(i, ip1)
+            for i, ip1 in
+            zip(coord_list, coord_list[1:])
+            ]
+
+        self.z = z
+        self.size = (
+            np.ceil(max(self.x_list)),
+            np.ceil(max(self.y_list)),
+            z + z_box
+        )
+
+        logger.debug("Path created.")
+
+    def get_length(self) -> float:
+        return sum([s.length for s in self.segments])
+
+    length = property(get_length)
+
+    def __getitem__(self, index) -> PathSegment:
+        return self.segments[index]
+
+    def __str__(self) -> str:
+        return str([str(s) for s in self.segments])
+
+    def plot(self, **kwargs):
+        """
+        Plot the path using matplotlib.
+        """
+        plt.plot(self.x_list, self.y_list, **kwargs)
+
+
+def path_from_RT90(
+        df: pd.DataFrame,
+        east_col: str = "East",
+        north_col: str = "North",
+        **kwargs
+        ) -> Path:
+    """Construct a path from East and North formatted coordinates (RT90)
+    in a Pandas DataFrame.
+
+    Args:
+        df (pandas.DataFrame): DataFrame containing at least the two columns
+        noted in the cols argument.
+        east_col (str): The column name for the East coordinates.
+        north_col (str): The column name for the North coordinates.
+
+    Returns:
+        Path: A Path object built from the aquisition coordinates in the
+        DataFrame.
+    """
+
+    east_arr = np.array(df[east_col]) - min(df[east_col])
+    north_arr = np.array(df[north_col]) - min(df[north_col])
+
+    coord_pairs = list(zip(east_arr, north_arr))
+
+    path = Path(coord_pairs, **kwargs)
+    logger.debug("Loaded path from provided RT90 coordinates.")
+    return path

src/pg_rad/physics/__init__.py

@@ -0,0 +1,12 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+from pg_rad.physics import attenuation
+from pg_rad.physics import fluence
+
+from pg_rad.physics.attenuation import (get_mass_attenuation_coeff,)
+from pg_rad.physics.fluence import (calculate_fluence_along_path,
+                                    calculate_fluence_at, phi,)
+
+__all__ = ['attenuation', 'calculate_fluence_along_path',
+           'calculate_fluence_at', 'fluence', 'get_mass_attenuation_coeff',
+           'phi']

src/pg_rad/physics/attenuation.py

@@ -0,0 +1,17 @@
+from importlib.resources import files
+
+from pandas import read_csv
+from scipy.interpolate import interp1d
+
+from pg_rad.configs.filepaths import ATTENUATION_TABLE
+
+
+def get_mass_attenuation_coeff(
+        *args
+        ) -> float:
+    csv = files('pg_rad.data').joinpath(ATTENUATION_TABLE)
+    data = read_csv(csv)
+    x = data["energy_mev"].to_numpy()
+    y = data["mu"].to_numpy()
+    f = interp1d(x, y)
+    return f(*args)

src/pg_rad/physics/fluence.py

@@ -0,0 +1,99 @@
+from typing import Tuple, TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from pg_rad.landscape.landscape import Landscape
+
+
+def phi(
+        r: float,
+        activity: float | int,
+        branching_ratio: float,
+        mu_mass_air: float,
+        air_density: float,
+        ) -> float:
+    """Compute the contribution of a single point source to the
+    primary photon fluence rate phi at position (x,y,z).
+
+    Args:
+        r (float): [m] Distance to the point source.
+        activity (float | int): [Bq] Activity of the point source.
+        branching_ratio (float): Branching ratio for the photon energy E_gamma.
+        mu_mass_air (float): [cm^2/g] Mass attenuation coefficient for air.
+        air_density (float): [kg/m^3] Air density.
+
+    Returns:
+        phi (float): [s^-1 m^-2] Primary photon fluence rate at distance r from
+        a point source.
+    """
+
+    # Linear photon attenuation coefficient in m^-1.
+    mu_mass_air *= 0.1
+    mu_air = mu_mass_air * air_density
+
+    phi_r = (
+        activity
+        * branching_ratio
+        * np.exp(-mu_air * r)
+        / (4 * np.pi * r**2)
+    )
+
+    return phi_r
+
+
+def calculate_fluence_at(landscape: "Landscape", pos: np.ndarray, scaling=1E6):
+    """Compute fluence at an arbitrary position in the landscape.
+
+    Args:
+        landscape (Landscape): The landscape to compute.
+        pos (np.ndarray): (N, 3) array of positions.
+
+    Returns:
+        total_phi (np.ndarray): (N,) array of fluences.
+    """
+    pos = np.atleast_2d(pos)
+    total_phi = np.zeros(pos.shape[0])
+
+    for source in landscape.point_sources:
+        r = np.linalg.norm(pos - np.array(source.pos), axis=1)
+        r = np.maximum(r, 1E-3)  # enforce minimum distance of 1cm
+
+        phi_source = phi(
+            r=r,
+            activity=source.activity * scaling,
+            branching_ratio=source.isotope.b,
+            mu_mass_air=source.isotope.mu_mass_air,
+            air_density=landscape.air_density
+        )
+
+        total_phi += phi_source
+
+    return total_phi
+
+
+def calculate_fluence_along_path(
+    landscape: "Landscape",
+    points_per_segment: int = 10
+) -> Tuple[np.ndarray, np.ndarray]:
+    path = landscape.path
+    num_segments = len(path.segments)
+
+    xnew = np.linspace(
+        path.x_list[0],
+        path.x_list[-1],
+        num=num_segments*points_per_segment)
+
+    ynew = np.interp(xnew, path.x_list, path.y_list)
+
+    z = np.full(xnew.shape, path.z)
+    full_positions = np.c_[xnew, ynew, z]
+    phi_result = calculate_fluence_at(landscape, full_positions)
+
+    dist_travelled = np.linspace(
+        full_positions[0, 0],
+        path.length,
+        len(phi_result)
+    )
+
+    return dist_travelled, phi_result

src/pg_rad/plotting/__init__.py

@@ -0,0 +1,7 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+from pg_rad.plotting import landscape_plotter
+
+from pg_rad.plotting.landscape_plotter import (LandscapeSlicePlotter,)
+
+__all__ = ['LandscapeSlicePlotter', 'landscape_plotter']

src/pg_rad/plotting/landscape_plotter.py

@@ -0,0 +1,115 @@
+import logging
+
+from matplotlib import pyplot as plt
+from matplotlib.axes import Axes
+from matplotlib.patches import Circle
+
+from numpy import median
+
+from pg_rad.landscape.landscape import Landscape
+
+logger = logging.getLogger(__name__)
+
+
+class LandscapeSlicePlotter:
+    def plot(
+        self,
+        landscape: Landscape,
+        z: int = 0,
+        show: bool = True,
+        save: bool = False,
+        ax: Axes | None = None
+    ):
+        """Plot a top-down slice of the landscape at a height z.
+
+        Args:
+            landscape (Landscape): the landscape to plot
+            z (int, optional): Height at which to plot slice. Defaults to 0.
+            show (bool, optional): Show the plot. Defaults to True.
+            save (bool, optional): Save the plot. Defaults to False.
+        """        """
+
+        """
+        self.z = z
+
+        if not ax:
+            fig, ax = plt.subplots()
+
+        self._draw_base(ax, landscape)
+        self._draw_path(ax, landscape)
+        self._draw_point_sources(ax, landscape)
+
+        ax.set_aspect("equal")
+
+        if save and not ax:
+            landscape_name = landscape.name.lower().replace(' ', '_')
+            filename = f"{landscape_name}_z{self.z}.png"
+            plt.savefig(filename)
+            logger.info("Plot saved to file: "+filename)
+
+        if show and not ax:
+            plt.show()
+
+        return ax
+
+    def _draw_base(self, ax, landscape: Landscape):
+        width, height = landscape.size[:2]
+
+        ax.set_xlim(right=max(width, .5*height))
+
+        # if the road is very flat, we center it vertically (looks better)
+        if median(landscape.path.y_list) == 0:
+            h = max(height, .5*width)
+            ax.set_ylim(bottom=-h//2,
+                        top=h//2)
+        else:
+            ax.set_ylim(top=max(height, .5*width))
+
+        ax.set_xlabel("X [m]")
+        ax.set_ylabel("Y [m]")
+        ax.set_title(f"Landscape (top-down, z = {self.z})")
+
+    def _draw_path(self, ax, landscape):
+        if landscape.path.z <= self.z:
+            ax.plot(
+                landscape.path.x_list,
+                landscape.path.y_list,
+                linestyle='-',
+                marker='|',
+                markersize=3,
+                linewidth=1
+            )
+        else:
+            logger.warning(
+                "Path is above the slice height z."
+                "It will not show on the plot."
+                )
+
+    def _draw_point_sources(self, ax, landscape):
+        for s in landscape.point_sources:
+            x, y, z = s.pos
+            if z <= self.z:
+                dot = Circle(
+                        (x, y),
+                        radius=5,
+                        color=s.color,
+                        zorder=5
+                )
+
+                ax.text(
+                    x + 0.06,
+                    y + 0.06,
+                    s.name+", z="+str(z),
+                    color=s.color,
+                    fontsize=10,
+                    ha="left",
+                    va="bottom",
+                    zorder=6
+                )
+
+                ax.add_patch(dot)
+            else:
+                logger.warning(
+                    f"Source {s.name} is above slice height z."
+                    "It will not show on the plot."
+                    )

src/pg_rad/plotting/result_plotter.py

@@ -0,0 +1,100 @@
+from matplotlib import pyplot as plt
+from matplotlib.gridspec import GridSpec
+
+from .landscape_plotter import LandscapeSlicePlotter
+from pg_rad.simulator.outputs import SimulationOutput
+from pg_rad.landscape.landscape import Landscape
+
+
+class ResultPlotter:
+    def __init__(self, landscape: Landscape, output: SimulationOutput):
+        self.landscape = landscape
+        self.count_rate_res = output.count_rate
+        self.source_res = output.sources
+
+    def plot(self, landscape_z: float = 0):
+        fig = plt.figure(figsize=(12, 10), constrained_layout=True)
+        fig.suptitle(self.landscape.name)
+        gs = GridSpec(
+            3,
+            2,
+            width_ratios=[0.5, 0.5],
+            height_ratios=[0.7, 0.15, 0.15],
+            hspace=0.2)
+
+        ax1 = fig.add_subplot(gs[0, 0])
+        self._draw_count_rate(ax1)
+
+        ax2 = fig.add_subplot(gs[0, 1])
+        self._plot_landscape(ax2, landscape_z)
+
+        ax3 = fig.add_subplot(gs[1, :])
+        self._draw_table(ax3)
+
+        ax4 = fig.add_subplot(gs[2, :])
+        self._draw_source_table(ax4)
+
+        plt.tight_layout()
+        plt.show()
+
+    def _plot_landscape(self, ax, z):
+        lp = LandscapeSlicePlotter()
+        ax = lp.plot(landscape=self.landscape, z=z, ax=ax, show=False)
+        return ax
+
+    def _draw_count_rate(self, ax):
+        x = self.count_rate_res.arc_length
+        y = self.count_rate_res.count_rate
+        ax.plot(x, y, label='Count rate', color='r')
+        ax.set_title('Count rate')
+        ax.set_xlabel('Arc length s [m]')
+        ax.set_ylabel('Counts')
+        ax.legend()
+
+    def _draw_table(self, ax):
+        ax.set_axis_off()
+        ax.set_title('Simulation parameters')
+        cols = ('Parameter', 'Value')
+        data = [
+            ["Air density (kg/m^3)", round(self.landscape.air_density, 3)],
+            ["Total path length (m)", round(self.landscape.path.length, 3)]
+        ]
+
+        ax.table(
+            cellText=data,
+            colLabels=cols,
+            loc='center'
+        )
+
+        return ax
+
+    def _draw_source_table(self, ax):
+        ax.set_axis_off()
+        ax.set_title('Point sources')
+
+        cols = (
+            'Name',
+            'Isotope',
+            'Activity (MBq)',
+            'Position (m)',
+            'Dist. to path (m)'
+        )
+
+        # this formats position to tuple
+        data = [
+            [
+                s.name,
+                s.isotope,
+                s.activity,
+                "("+", ".join(f"{val:.2f}" for val in s.position)+")",
+                round(s.dist_from_path, 2)
+            ]
+            for s in self.source_res
+        ]
+        ax.table(
+            cellText=data,
+            colLabels=cols,
+            loc='center'
+        )
+
+        return ax

src/pg_rad/simulator/engine.py

@@ -0,0 +1,63 @@
+from typing import List
+
+from pg_rad.landscape.landscape import Landscape
+from pg_rad.simulator.outputs import (
+    CountRateOutput,
+    SimulationOutput,
+    SourceOutput
+)
+from pg_rad.physics.fluence import calculate_fluence_along_path
+from pg_rad.utils.projection import minimal_distance_to_path
+from pg_rad.inputparser.specs import RuntimeSpec, SimulationOptionsSpec
+
+
+class SimulationEngine:
+    """Takes a fully built landscape and produces results."""
+    def __init__(
+        self,
+        landscape: Landscape,
+        runtime_spec=RuntimeSpec,
+        sim_spec=SimulationOptionsSpec
+    ):
+
+        self.landscape = landscape
+        self.runtime_spec = runtime_spec
+        self.sim_spec = sim_spec
+
+    def simulate(self) -> SimulationOutput:
+        """Compute everything and return structured output."""
+
+        count_rate_results = self._calculate_count_rate_along_path()
+        source_results = self._calculate_point_source_distance_to_path()
+
+        return SimulationOutput(
+            name=self.landscape.name,
+            count_rate=count_rate_results,
+            sources=source_results
+        )
+
+    def _calculate_count_rate_along_path(self) -> CountRateOutput:
+        arc_length, phi = calculate_fluence_along_path(self.landscape)
+        return CountRateOutput(arc_length, phi)
+
+    def _calculate_point_source_distance_to_path(self) -> List[SourceOutput]:
+
+        path = self.landscape.path
+        source_output = []
+        for s in self.landscape.point_sources:
+            dist_to_path = minimal_distance_to_path(
+                path.x_list,
+                path.y_list,
+                path.z,
+                s.pos)
+
+            source_output.append(
+                SourceOutput(
+                    s.name,
+                    s.isotope.name,
+                    s.activity,
+                    s.pos,
+                    dist_to_path)
+            )
+
+        return source_output

src/pg_rad/simulator/outputs.py

@@ -0,0 +1,25 @@
+from typing import List, Tuple
+
+from dataclasses import dataclass
+
+
+@dataclass
+class CountRateOutput:
+    arc_length: List[float]
+    count_rate: List[float]
+
+
+@dataclass
+class SourceOutput:
+    name: str
+    isotope: str
+    activity: float
+    position: Tuple[float, float, float]
+    dist_from_path: float
+
+
+@dataclass
+class SimulationOutput:
+    name: str
+    count_rate: CountRateOutput
+    sources: List[SourceOutput]

src/pg_rad/sources.py

@@ -1,43 +0,0 @@
-from pg_rad.objects import Object
-from pg_rad.isotope import Isotope
-
-class PointSource(Object):
-    _id_counter = 1
-    def __init__(
-            self,
-            x: float,
-            y: float,
-            z: float,
-            activity: int,
-            isotope: Isotope,
-            name: str | None = None,
-            color: str = "red"):
-        """A point source.
-
-        Args:
-            x (float): X coordinate.
-            y (float): Y coordinate.
-            z (float): Z coordinate.
-            activity (int): Activity A in MBq.
-            isotope (Isotope): The isotope.
-            name (str | None, optional): Can give the source a unique name.
-            Defaults to None, making the name sequential.
-            (Source-1, Source-2, etc.).
-            color (str, optional): Matplotlib compatible color string. Defaults to "red".
-        """
-        
-        self.id = PointSource._id_counter
-        PointSource._id_counter += 1
-
-        # default name derived from ID if not provided
-        if name is None:
-            name = f"Source {self.id}"
-
-        super().__init__(x, y, z, name, color)
-
-        self.activity = activity
-        self.isotope = isotope
-        self.color = color
-
-    def __repr__(self):
-        return f"PointSource(name={self.name}, pos={(self.x, self.y, self.z)}, isotope={self.isotope.name}, A={self.activity} MBq)"

src/pg_rad/utils/projection.py

@@ -0,0 +1,168 @@
+from typing import List, Tuple
+
+import numpy as np
+
+from pg_rad.exceptions.exceptions import OutOfBoundsError
+
+
+def rel_to_abs_source_position(
+    x_list: List,
+    y_list: List,
+    path_z: int | float,
+    along_path: int | float,
+    side: str,
+    dist_from_path: int | float,
+    z_rel: int | float = 0
+):
+    path = np.column_stack((x_list, y_list))
+    segments = np.diff(path, axis=0)
+    segment_lengths = np.hypot(segments[:, 0], segments[:, 1])
+
+    arc_length = np.cumsum(segment_lengths)
+    arc_length = np.insert(arc_length, 0, 0)
+
+    # find index of the segment where the source should be placed
+    s_i = np.searchsorted(arc_length, along_path, side='right') - 1
+    if not 0 <= s_i < len(segments):
+        raise OutOfBoundsError(
+            "along_path must be less than the length of the path!"
+            )
+
+    # fraction of segment length where to place the point source
+    segment_fraction = (
+        (along_path - arc_length[s_i])
+        / segment_lengths[s_i]
+    )
+
+    # tangential vector at point where point source to be placed
+    point_on_path = path[s_i] + segment_fraction * segments[s_i]
+    tangent_vec = segments[s_i] / segment_lengths[s_i]
+
+    if side not in {"left", "right"}:
+        raise ValueError("side must be 'left' or 'right'")
+
+    orientation = 1 if side == "left" else -1
+    perp_vec = orientation * np.array([-tangent_vec[1], tangent_vec[0]])
+
+    x_abs, y_abs = point_on_path + dist_from_path * perp_vec
+    z_abs = path_z + z_rel
+
+    return x_abs, y_abs, z_abs
+
+
+def minimal_distance_to_path(
+    x_list: List[float],
+    y_list: List[float],
+    path_z: float,
+    pos: Tuple[float, float, float]
+) -> float:
+    """
+    Compute minimal Euclidean distance from pos (x,y,z)
+    to interpolated polyline path at height path_z.
+    """
+    x, y, z = pos
+
+    path = np.column_stack((x_list, y_list))
+    point_xy = np.array([x, y])
+
+    segments = np.diff(path, axis=0)
+    segment_lengths = np.hypot(segments[:, 0], segments[:, 1])
+
+    min_dist = np.inf
+
+    for p0, seg, seg_len in zip(path[:-1], segments, segment_lengths):
+        if seg_len == 0:
+            continue
+
+        t = np.dot(point_xy - p0, seg) / (seg_len ** 2)
+        t = np.clip(t, 0.0, 1.0)
+
+        projection_xy = p0 + t * seg
+
+        dx, dy = point_xy - projection_xy
+        dz = z - path_z
+
+        dist = np.sqrt(dx**2 + dy**2 + dz**2)
+
+        if dist < min_dist:
+            min_dist = dist
+
+    return min_dist
+
+
+def abs_to_rel_source_position(
+    x_list: List,
+    y_list: List,
+    path_z: int | float,
+    pos: Tuple[float, float, float],
+) -> Tuple[float, float, str, float]:
+    """
+    Convert absolute (x,y,z) position into:
+        along_path,
+        dist_from_path,
+        side ("left" or "right"),
+        z_rel
+    """
+
+    x, y, z = pos
+
+    path = np.column_stack((x_list, y_list))
+    point = np.array([x, y])
+
+    segments = np.diff(path, axis=0)
+    segment_lengths = np.hypot(segments[:, 0], segments[:, 1])
+
+    arc_length = np.cumsum(segment_lengths)
+    arc_length = np.insert(arc_length, 0, 0)
+
+    min_dist = np.inf
+    best_projection = None
+    best_s_i = None
+    best_fraction = None
+    best_signed_dist = None
+
+    for (i, (p0, seg, seg_len)) in enumerate(
+        zip(path[:-1], segments, segment_lengths)
+    ):
+        if seg_len == 0:
+            continue
+
+        # project point onto infinite line
+        t = np.dot(point - p0, seg) / (seg_len ** 2)
+
+        # clamp to segment
+        t_clamped = np.clip(t, 0.0, 1.0)
+
+        projection = p0 + t_clamped * seg
+        diff_vec = point - projection
+        dist = np.linalg.norm(diff_vec)
+
+        if dist < min_dist:
+            min_dist = dist
+            best_projection = projection
+            best_s_i = i
+            best_fraction = t_clamped
+
+            # tangent and perpendicular
+            tangent_vec = seg / seg_len
+            perp_vec = np.array([-tangent_vec[1], tangent_vec[0]])
+
+            signed_dist = np.dot(diff_vec, perp_vec)
+            best_signed_dist = signed_dist
+
+    if best_projection is None:
+        raise ValueError("Could not project point onto path.")
+
+    # Compute along_path
+    along_path = (
+        arc_length[best_s_i] + best_fraction * segment_lengths[best_s_i]
+    )
+
+    # Side and distance
+    side = "left" if best_signed_dist > 0 else "right"
+    dist_from_path = abs(best_signed_dist)
+
+    # z relative
+    z_rel = z - path_z
+
+    return along_path, dist_from_path, side, z_rel

src/road_gen/generators/base_road_generator.py

@@ -0,0 +1,55 @@
+import secrets
+
+import numpy as np
+
+
+class BaseRoadGenerator:
+    """A base generator object for generating a road of a specified length."""
+    def __init__(
+            self,
+            length: int | float,
+            ds: int | float,
+            velocity: int | float,
+            mu: float = 0.7,
+            g: float = 9.81,
+            seed: int | None = None
+            ):
+        """Initialize a BaseGenerator with a given or random seed.
+
+        Args:
+            length (int | float): The total length of the road in meters.
+            ds (int | float): The step size in meters.
+            velocity (int | float): Velocity in meters per second.
+            mu (float): Coefficient of friction. Defaults to 0.7 (dry asphalt).
+            g (float): Acceleration due to gravity (m/s^2). Defaults to 9.81.
+            seed (int | None, optional): Set a seed for the generator.
+                Defaults to a random seed.
+        """
+        if seed is None:
+            seed = secrets.randbits(32)
+
+        if not isinstance(seed, int):
+            raise TypeError("seed must be an integer or None.")
+
+        if not isinstance(length, int | float):
+            raise TypeError("Length must be an integer or float in meters.")
+
+        if not isinstance(ds, int | float):
+            raise TypeError("Step size must be integer or float in meters.")
+
+        if not isinstance(velocity, int | float):
+            raise TypeError(
+                "Velocity must be integer or float in meters per second."
+            )
+
+        self.length = length
+        self.ds = ds
+
+        self.velocity = velocity
+        self.min_radius = (velocity ** 2) / (g * mu)
+
+        self.seed = seed
+        self._rng = np.random.default_rng(seed)
+
+    def generate(self):
+        pass

src/road_gen/generators/segmented_road_generator.py

@@ -0,0 +1,143 @@
+import logging
+from typing import Tuple
+
+import numpy as np
+
+from .base_road_generator import BaseRoadGenerator
+from road_gen.prefabs import prefabs
+from road_gen.integrator.integrator import integrate_road
+
+from pg_rad.configs import defaults
+
+
+logger = logging.getLogger(__name__)
+
+
+class SegmentedRoadGenerator(BaseRoadGenerator):
+    def __init__(
+        self,
+        length: int | float | list[int | float],
+        ds: int | float,
+        velocity: int | float,
+        mu: float = 0.7,
+        g: float = 9.81,
+        seed: int | None = None
+    ):
+        """Initialize a SegmentedRoadGenerator with given or random seed.
+
+        Args:
+            length (int | float): The total length of the road in meters.
+            ds (int | float): The step size in meters.
+            velocity (int | float): Velocity in meters per second.
+            mu (float): Coefficient of friction. Defaults to 0.7 (dry asphalt).
+            g (float): Acceleration due to gravity (m/s^2). Defaults to 9.81.
+            seed (int | None, optional): Set a seed for the generator.
+                Defaults to random seed.
+        """
+
+        if isinstance(length, list):
+            length = sum(
+                [seg_len for seg_len in length if seg_len is not None]
+            )
+        super().__init__(length, ds, velocity, mu, g, seed)
+
+    def generate(
+            self,
+            segments: list[str],
+            lengths: list[int | float] | None = None,
+            angles: list[int | float] | None = None,
+            alpha: float = defaults.DEFAULT_ALPHA,
+            min_turn_angle: float = defaults.DEFAULT_MIN_TURN_ANGLE,
+            max_turn_angle: float = defaults.DEFAULT_MAX_TURN_ANGLE
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """Generate a curvature profile from a list of segments.
+
+        Args:
+            segments (list[str]): List of segments.
+            alpha (float, optional): Dirichlet concentration parameter.
+                A higher value leads to more uniform apportionment of the
+                length amongst the segments, while a lower value allows more
+                random apportionment. Defaults to 1.0.
+            min_turn_angle (float, optional): Minimum turn angle in degrees for
+                random sampling of turn radius. Does nothing if `angle_list` is
+                provided or no `turn_*` segement is specified in the `segments`
+                list.
+            min_turn_angle (float, optional): Maximum turn angle in degrees for
+                random sampling of turn radius. Does nothing if `angle_list` is
+                provided or no `turn_*` segement is specified in the `segments`
+                list.
+        Raises:
+            ValueError: Raised when a turn
+                is too tight given its segment length and the velocity.
+                To fix this, you can try to reduce the amount of segments or
+                increase length. Increasing alpha
+                (Dirichlet concentration parameter) can also help because this
+                reduces the odds of very small lengths being assigned to
+                turn segments.
+
+        Returns:
+            Tuple[np.ndarray, np.ndarray]: x and y coordinates of the
+                waypoints describing the random road.
+        """
+        existing_prefabs = prefabs.PREFABS.keys()
+        if not all(segment in existing_prefabs for segment in segments):
+            raise ValueError(
+                "Invalid segment type provided. Available choices"
+                f"{existing_prefabs}"
+            )
+
+        self.segments = segments
+        self.alpha = alpha
+        num_points = np.ceil(self.length / self.ds).astype(int)
+
+        # divide num_points into len(segments) randomly sized parts.
+        if isinstance(self.length, list):
+            parts = self.length
+        else:
+            parts = self._rng.dirichlet(
+                np.full(len(segments), alpha),
+                size=1)[0]
+            parts = parts * num_points
+            parts = np.round(parts).astype(int)
+
+            # correct round off so the sum of parts is still total length L.
+            if sum(parts) != num_points:
+                parts[0] += num_points - sum(parts)
+
+        curvature = np.zeros(num_points)
+        current_index = 0
+
+        for seg_name, seg_length in zip(segments, parts):
+            seg_function = prefabs.PREFABS[seg_name]
+
+            if seg_name == 'straight':
+                curvature_s = seg_function(seg_length)
+            else:
+                R_min_angle = seg_length / np.deg2rad(max_turn_angle)
+                R_max_angle = seg_length / np.deg2rad(min_turn_angle)
+
+                # physics limit
+                R_min = max(self.min_radius, R_min_angle)
+
+                if R_min > R_max_angle:
+                    raise ValueError(
+                        f"{seg_name} with length {seg_length} does not have "
+                        "a possible radius. The minimum for the provided "
+                        "velocity and friction coefficient is "
+                        f"{self.min_radius}, but the possible range is "
+                        f"({R_min}, {R_max_angle})"
+                    )
+
+                rand_radius = self._rng.uniform(R_min, R_max_angle)
+
+                if seg_name.startswith("u_turn"):
+                    curvature_s = seg_function(rand_radius)
+                else:
+                    curvature_s = seg_function(seg_length, rand_radius)
+
+            curvature[current_index:(current_index + seg_length)] = curvature_s
+            current_index += seg_length
+
+            x, y = integrate_road(curvature)
+
+        return x * self.ds, y * self.ds

src/road_gen/integrator/integrator.py

@@ -0,0 +1,21 @@
+import numpy as np
+
+
+def integrate_road(curvature: np.ndarray, ds: float = 1.0):
+    """Integrate along the curvature field to obtain X and Y coordinates.
+
+    Args:
+        curvature (np.ndarray): The curvature field.
+        ds (float, optional): _description_. Defaults to 1.0.
+
+    Returns:
+        x, y (np.ndarray): X and Y waypoints.
+    """
+
+    theta = np.zeros(len(curvature))
+    theta[1:] = np.cumsum(curvature[:-1] * ds)
+
+    x = np.cumsum(np.cos(theta) * ds)
+    y = np.cumsum(np.sin(theta) * ds)
+
+    return x, y

src/road_gen/prefabs/prefabs.py

@@ -0,0 +1,20 @@
+import numpy as np
+
+
+def straight(length: int) -> np.ndarray:
+    return np.zeros(length)
+
+
+def turn_left(length: int, radius: float) -> np.ndarray:
+    return np.full(length, 1.0 / radius)
+
+
+def turn_right(length: int, radius: float) -> np.ndarray:
+    return -turn_left(length, radius)
+
+
+PREFABS = {
+    'straight': straight,
+    'turn_left': turn_left,
+    'turn_right': turn_right,
+}

tests/test_attenuation_functions.py

@@ -0,0 +1,29 @@
+import pytest
+
+from pg_rad.physics import get_mass_attenuation_coeff
+
+
+@pytest.mark.parametrize("energy,mu", [
+    (1.00000E-03, 3.606E+03),
+    (1.00000E-02, 5.120E+00),
+    (1.00000E-01, 1.541E-01),
+    (1.00000E+00, 6.358E-02),
+    (1.00000E+01, 2.045E-02) 
+])
+def test_exact_attenuation_retrieval(energy, mu):
+    """
+    Test if retrieval of values that are exactly in the table is correct.
+    """
+    func_mu = get_mass_attenuation_coeff(energy)
+    assert pytest.approx(func_mu, rel=1E-6) == mu
+
+
+@pytest.mark.parametrize("energy,mu", [
+    (0.662, 0.0778)
+])
+def test_attenuation_interpolation(energy, mu):
+    """
+    Test retreival for Cs-137 mass attenuation coefficient.
+    """
+    interp_mu = get_mass_attenuation_coeff(energy)
+    assert pytest.approx(interp_mu, rel=1E-2) == mu

tests/test_fluence_rate.py

@@ -0,0 +1,53 @@
+import numpy as np
+import pytest
+
+from pg_rad.inputparser.parser import ConfigParser
+from pg_rad.landscape.director import LandscapeDirector
+from pg_rad.physics import calculate_fluence_at
+
+
+@pytest.fixture
+def phi_ref(test_landscape):
+    source = test_landscape.point_sources[0]
+
+    r = np.linalg.norm(np.array([10, 10, 0]) - np.array(source.pos))
+
+    A = source.activity * 1E6
+    b = source.isotope.b
+    mu_air = source.isotope.mu_mass_air * test_landscape.air_density
+    mu_air *= 0.1
+
+    return A * b * np.exp(-mu_air * r) / (4 * np.pi * r**2)
+
+
+@pytest.fixture
+def test_landscape():
+
+    test_yaml = """
+    name: Test landscape
+    speed: 8.33
+    acquisition_time: 1
+
+    path:
+        length: 1000
+        segments:
+            - straight
+
+    sources:
+        test_source:
+            activity_MBq: 100
+            position: [0, 0, 0]
+            isotope: CS137
+    """
+
+    cp = ConfigParser(test_yaml).parse()
+    landscape = LandscapeDirector.build_from_config(cp)
+    return landscape
+
+
+def test_single_source_fluence(phi_ref, test_landscape):
+    phi = calculate_fluence_at(
+        test_landscape,
+        np.array([10, 10, 0]),
+        )
+    assert pytest.approx(phi[0], rel=1E-3) == phi_ref

tests/test_path_functionality.py

@@ -1,47 +0,0 @@
-import pathlib
-
-import numpy as np
-import pytest
-
-from pg_rad.dataloader import load_data
-from pg_rad.path import Path, path_from_RT90
-
-@pytest.fixture
-def test_df():
-    csv_path = pathlib.Path(__file__).parent / "data/coordinates.csv"
-    return load_data(csv_path)
-
-def test_piecewise_regression(test_df):
-    """_Verify whether the intermediate points deviate less than 0.1 SD._"""
-        
-    p_full = path_from_RT90(
-        test_df,
-        east_col="East",
-        north_col="North",
-        simplify_path=False
-    )
- 
-    p_simpl = path_from_RT90(
-        test_df,
-        east_col="East",
-        north_col="North",
-        simplify_path=True
-    )
-
-    x_f = np.array(p_full.x_list)
-    y_f = np.array(p_full.y_list)
-
-    x_s = np.array(p_simpl.x_list)
-    y_s = np.array(p_simpl.y_list)
-
-    sd = np.std(y_f)
-
-    for xb, yb in zip(x_s[1:-1], y_s[1:-1]):
-        # find nearest original x index
-        idx = np.argmin(np.abs(x_f - xb))
-        deviation = abs(yb - y_f[idx])
-
-        assert deviation < 0.1 * sd, (
-            f"Breakpoint deviation too large: {deviation:.4f} "
-            f"(threshold {0.1 * sd:.4f}) at x={xb:.2f}"
-            )

tests/test_sources.py

@@ -1,28 +1,35 @@
 import numpy as np
 import pytest
 
-from pg_rad.objects import Source
+from pg_rad.objects import PointSource
+
 
 @pytest.fixture
 def test_sources():
+    iso = "CS137"
     pos_a = np.random.rand(3)
     pos_b = np.random.rand(3)
 
-    a = Source(*tuple(pos_a), strength = None)
+    a = PointSource(position=pos_a, activity_MBq=None, isotope=iso)
-    b = Source(*tuple(pos_b), strength = None)
+    b = PointSource(position=pos_b, activity_MBq=None, isotope=iso)
 
     return pos_a, pos_b, a, b
 
+
 def test_if_distances_equal(test_sources):
-    """_Verify whether from object A to object B is the same as B to A._"""
+    """
+    Verify whether distance from PointSource A to B is the same as B to A.
+    """
 
     _, _, a, b = test_sources
 
     assert a.distance_to(b) == b.distance_to(a)
 
+
 def test_distance_calculation(test_sources):
-    """_Verify whether distance between two static objects (e.g. sources) 
+    """
-    is calculated correctly._"""
+    Verify whether distance between PointSources is calculated correctly.
+    """
 
     pos_a, pos_b, a, b = test_sources