Merge pull request #19 from pim-n/feature-fluence-rate

- Added mass attenuation interpolator
- PG fluence rate function that can be called at any position in the landscape
- Rewrote landscape module to pseudo-builder pattern (LandscapeBuilder & LandscapeConstructor)
- Added entry point (CLI)
- Verified installation process and updated instructions accordingly
- Updated tests
- various bugfixes
- various minor improvements

.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:

.gitignore

@@ -1,3 +1,6 @@
+# Custom
+dev-tools/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[codz]

README.md

@@ -23,13 +23,35 @@ 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"]

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/__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

@@ -1,12 +1,14 @@
+import logging
+
 import pandas as pd
 
-from pg_rad.logger import setup_logger
 from pg_rad.exceptions import DataLoadError, InvalidCSVError
 
-logger = setup_logger(__name__)
+logger = logging.getLogger(__name__)
+
 
 def load_data(filename: str) -> pd.DataFrame:
-    logger.debug(f"Attempting to load data from {filename}")
+    logger.debug(f"Attempting to load file: {filename}")
 
     try:
         df = pd.read_csv(filename, delimiter=',')
@@ -23,4 +25,5 @@ def load_data(filename: str) -> pd.DataFrame:
         logger.exception(f"Unexpected error while loading {filename}")
         raise DataLoadError("Unexpected error while loading data") from e
 
+    logger.debug(f"File loaded: {filename}")
     return df

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

@@ -1,8 +1,14 @@
 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."""

src/pg_rad/isotopes/__init__.py

@@ -0,0 +1,10 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+
+from pg_rad.isotopes import isotope
+from pg_rad.isotopes import presets
+
+from pg_rad.isotopes.isotope import (Isotope,)
+from pg_rad.isotopes.presets import (CS137,)
+
+__all__ = ['CS137', 'Isotope', 'isotope', 'presets']

src/pg_rad/isotopes/isotope.py

@@ -1,3 +1,6 @@
+from pg_rad.physics import get_mass_attenuation_coeff
+
+
 class Isotope:
     """Represents the essential information of an isotope.
 
@@ -16,8 +19,9 @@ class Isotope:
             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)")
+            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)

src/pg_rad/isotopes/presets.py

@@ -0,0 +1,10 @@
+from .isotope import Isotope
+
+
+class CS137(Isotope):
+    def __init__(self):
+        super().__init__(
+            name="Cs-137",
+            E=661.66,
+            b=0.851
+        )

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/__init__.py

@@ -0,0 +1,11 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+
+from pg_rad.landscape import director
+from pg_rad.landscape import landscape
+
+from pg_rad.landscape.director import (LandscapeDirector,)
+from pg_rad.landscape.landscape import (Landscape, LandscapeBuilder,)
+
+__all__ = ['Landscape', 'LandscapeBuilder', 'LandscapeDirector', 'director',
+           'landscape']

src/pg_rad/landscape/director.py

@@ -0,0 +1,23 @@
+from importlib.resources import files
+import logging
+
+from pg_rad.configs.filepaths import TEST_EXP_DATA
+from pg_rad.isotopes import CS137
+from pg_rad.landscape.landscape import LandscapeBuilder
+from pg_rad.objects import PointSource
+
+
+class LandscapeDirector:
+    def __init__(self):
+        self.logger = logging.getLogger(__name__)
+        self.logger.debug("LandscapeDirector initialized.")
+
+    def build_test_landscape(self):
+        fp = files('pg_rad.data').joinpath(TEST_EXP_DATA)
+        source = PointSource(activity=100E9, isotope=CS137(), pos=(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

src/pg_rad/landscape/landscape.py

@@ -0,0 +1,161 @@
+import logging
+from typing import Self
+
+from pg_rad.dataloader import load_data
+from pg_rad.exceptions import OutOfBoundsError
+from pg_rad.objects import PointSource
+from pg_rad.path import Path, path_from_RT90
+from pg_rad.physics.fluence import phi_single_source
+
+logger = logging.getLogger(__name__)
+
+
+class Landscape:
+    """
+    A generic Landscape that can contain a Path and sources.
+    """
+    def __init__(
+            self,
+            name: str,
+            path: Path,
+            point_sources: list[PointSource] = [],
+            size: tuple[int, int, int] = [500, 500, 50],
+            air_density: float = 1.243
+            ):
+        """Initialize a landscape.
+
+        Args:
+            path (Path): A Path object.
+            point_sources (list[PointSource], optional): List of point sources.
+            air_density (float, optional): Air density in kg/m^3.
+                Defaults to 1.243.
+            size (tuple[int, int, int], optional): (x,y,z) dimensions of world
+                in meters. Defaults to [500, 500, 50].
+
+        Raises:
+            TypeError: _description_
+        """
+
+        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}")
+
+    def calculate_fluence_at(self, pos: tuple):
+        total_phi = 0.
+        for source in self.point_sources:
+            r = source.distance_to(pos)
+            phi_source = phi_single_source(
+                r=r,
+                activity=source.activity,
+                branching_ratio=source.isotope.b,
+                mu_mass_air=source.isotope.mu_mass_air,
+                air_density=self.air_density
+            )
+            total_phi += phi_source
+        return total_phi
+
+    def calculate_fluence_along_path(self):
+        pass
+
+
+class LandscapeBuilder:
+    def __init__(self, name: str = "Unnamed landscape"):
+        self.name = name
+        self._path = None
+        self._point_sources = []
+        self._size = None
+        self._air_density = None
+
+        logger.debug(f"LandscapeBuilder initialized: {self.name}")
+
+    def set_air_density(self, air_density) -> Self:
+        """Set the air density of the world."""
+        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 set_path_from_experimental_data(
+        self,
+        filename: str,
+        z: int,
+        east_col: str = "East",
+        north_col: str = "North"
+    ) -> Self:
+        df = load_data(filename)
+        self._path = path_from_RT90(
+            df=df,
+            east_col=east_col,
+            north_col=north_col,
+            z=z
+        )
+
+        # 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."
+                )
+
+            self.set_landscape_size(self._path.size)
+
+        return self
+
+    def set_point_sources(self, *sources):
+        """Add one or more point sources to the world.
+
+        Args:
+            *sources (pg_rad.sources.PointSource): One or more sources,
+            passed as Source1, Source2, ...
+        Raises:
+            OutOfBoundsError: If any source is outside the boundaries of the
+            landscape.
+        """
+
+        if any(
+            any(p < 0 or p >= s for p, s in zip(source.pos, self._size))
+            for source in sources
+        ):
+            raise OutOfBoundsError(
+                "One or more sources attempted to "
+                "be placed outside the landscape."
+                )
+
+        self._point_sources = sources
+
+    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/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 +1,33 @@
+import argparse
+
+from pg_rad.logger import setup_logger
+from pg_rad.landscape import LandscapeDirector
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        prog="pg-rad",
+        description="Primary Gamma RADiation landscape tool"
+    )
+
+    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"],
+    )
+
+    args = parser.parse_args()
+    setup_logger(args.loglevel)
+
+    if args.test:
+        landscape = LandscapeDirector().build_test_landscape()
+        print(landscape.name)
+
+
+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 +1,42 @@
+from typing import Self
+
+import numpy as np
+
+
+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 ValueError("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 +1,53 @@
+import logging
+
+from .objects import BaseObject
+from pg_rad.isotopes import Isotope
+
+logger = logging.getLogger(__name__)
+
+
+class PointSource(BaseObject):
+    _id_counter = 1
+
+    def __init__(
+            self,
+            activity: int,
+            isotope: Isotope,
+            pos: tuple[float, float, float] = (0, 0, 0),
+            name: str | None = None,
+            color: str = 'red'
+            ):
+        """A point source.
+
+        Args:
+            activity (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__(pos, name, color)
+
+        self.activity = activity
+        self.isotope = isotope
+
+        logger.debug(f"Source created: {self.name}")
+
+    def __repr__(self):
+        repr_str = (f"PointSource(name={self.name}, "
+                    + f"pos={(self.x, self.y, self.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 +1,10 @@
+# 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 (phi_single_source,)
+
+__all__ = ['attenuation', 'fluence', 'get_mass_attenuation_coeff',
+           'phi_single_source']

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 +1,37 @@
+import numpy as np
+
+
+def phi_single_source(
+        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 = (
+        activity
+        * branching_ratio
+        * np.exp(-mu_air * r)
+        / (4 * np.pi * r**2)
+    )
+
+    return phi

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 +1,75 @@
+import logging
+
+from matplotlib import pyplot as plt
+from matplotlib.patches import Circle
+
+from pg_rad.landscape import Landscape
+
+
+logger = logging.getLogger(__name__)
+
+
+class LandscapeSlicePlotter:
+    def plot(self, landscape: Landscape, z: int = 0):
+        """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.
+        """        """
+
+        """
+        self.z = z
+        fig, ax = plt.subplots()
+
+        self._draw_base(ax, landscape)
+        self._draw_path(ax, landscape)
+        self._draw_point_sources(ax, landscape)
+
+        ax.set_aspect("equal")
+        plt.show()
+
+    def _draw_base(self, ax, landscape):
+        width, height = landscape.size[:2]
+        ax.set_xlim(right=width)
+        ax.set_ylim(top=height)
+        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, 'bo-')
+        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:
+            if s.z <= self.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)
+            else:
+                logger.warning(
+                    f"Source {s.name} is above slice height z."
+                    "It will not show on the plot."
+                    )

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)"

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 +1,34 @@
+from math import dist, exp, pi
+
+import pytest
+
+from pg_rad.landscape import LandscapeDirector
+
+
+@pytest.fixture
+def phi_ref():
+    A = 100                             # MBq
+    b = 0.851
+    mu_mass_air = 0.0778                # cm^2/g
+    air_density = 1.243                 # kg/m^3
+    r = dist((0, 0, 0), (10, 10, 0))    # m
+
+    A *= 1E9                            # Convert to Bq
+    mu_mass_air *= 0.1                  # Convert to m^2/kg
+
+    mu_air = mu_mass_air * air_density  # [m^2/kg] x [kg/m^3] = [m^-1]
+
+    # [s^-1] x exp([m^-1] x [m]) / [m^-2] = [s^-1 m^-2]
+    phi = A * b * exp(-mu_air * r) / (4 * pi * r**2)
+    return phi
+
+
+@pytest.fixture
+def test_landscape():
+    landscape = LandscapeDirector().build_test_landscape()
+    return landscape
+
+
+def test_single_source_fluence(phi_ref, test_landscape):
+    phi = test_landscape.calculate_fluence_at((10, 10, 0))
+    assert pytest.approx(phi, 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 +1,36 @@
 import numpy as np
 import pytest
 
-from pg_rad.objects import Source
+from pg_rad.objects import PointSource
+from pg_rad.isotopes import CS137
+
 
 @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(pos=pos_a, activity=None, isotope=iso)
-    b = Source(*tuple(pos_b), strength = None)
+    b = PointSource(pos=pos_b, activity=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
 
@@ -33,4 +41,4 @@ def test_distance_calculation(test_sources):
     assert np.isclose(
         a.distance_to(b),
         np.sqrt(dx**2 + dy**2 + dz**2),
-        rtol = 1e-12)
+        rtol=1e-12)