Merge pull request #20 from pim-n/remove-path-simplify

Remove path simplify

.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 +23,27 @@ 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]
 ```
 
 ## 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 +5,12 @@ build-backend = "setuptools.build_meta"
 [tool.setuptools.packages.find]
 where = ["src"]
 
+[tool.setuptools.package-data]
+"pg_rad.data" = ["*.csv"]
+
 [project]
 name = "pg-rad"
-version = "0.2.0"
+version = "0.2.1"
 authors = [
   { name="Pim Nelissen", email="pi0274ne-s@student.lu.se" },
 ]
@@ -18,7 +21,6 @@ dependencies = [
     "matplotlib>=3.9.2",
     "numpy>=2",
     "pandas>=2.3.1",
-    "piecewise_regression==1.5.0",
     "pyyaml>=6.0.2"
 ]
 license = "MIT"
@@ -29,4 +31,4 @@ 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/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
 
-    return df
+    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,)
+
+__all__ = ['ConvergenceError', 'DataLoadError', 'InvalidCSVError',
+           'exceptions']

src/pg_rad/exceptions/exceptions.py

@@ -1,8 +1,10 @@
 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."""
+    """Raised when a file is not a valid CSV."""

src/pg_rad/isotopes/__init__.py

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

src/pg_rad/isotopes/isotope.py

@@ -5,7 +5,7 @@ class Isotope:
         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,
@@ -20,4 +20,4 @@ class Isotope:
 
         self.name = name
         self.E = E
-        self.b = b
+        self.b = b

src/pg_rad/landscape/__init__.py

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

src/pg_rad/landscape/landscape.py

@@ -1,38 +1,46 @@
+import logging
+
 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
+from pg_rad.objects import PointSource
+
+logger = logging.getLogger(__name__)
+
 
 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.
+        air_density (float, optional): Air density, kg/m^3. Defaults to 1.243.
-        size (int | tuple[int, int, int], optional): Size of the world. Defaults to 500.
+        size (int | tuple[int, int, int], optional): Size of the world.
-        scale (str, optional): The scale of the size argument passed. Defaults to 'meters'.
+        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',
+            scale: str = '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.")
+            raise TypeError("size must be integer or a tuple of 3 integers.")
 
         self.air_density = air_density
         self.scale = scale
 
         self.path: Path = None
         self.sources: list[PointSource] = []
-    
+        logger.debug("Landscape initialized.")
-    def plot(self, z = 0):
+
+    def plot(self, z: float | int = 0):
         """Plot a slice of the world at a height `z`.
 
         Args:
@@ -40,7 +48,7 @@ class Landscape:
 
         Returns:
             fig, ax: Matplotlib figure objects.
-        """        
+        """
         x_lim, y_lim, _ = self.world.shape
 
         fig, ax = plt.subplots()
@@ -49,9 +57,9 @@ class Landscape:
         ax.set_xlabel(f"X [{self.scale}]")
         ax.set_ylabel(f"Y [{self.scale}]")
 
-        if not self.path == None:
+        if self.path is not 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(
@@ -73,19 +81,19 @@ class Landscape:
                 )
 
                 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
+            *sources (pg_rad.sources.PointSource): One or more sources,
-            Source1, Source2, ...
+            passed as Source1, Source2, ...
         Raises:
-            ValueError: If the source is outside the boundaries of the landscape.
+            ValueError: If the source is outside the boundaries of the
-        """        
+            landscape.
-
+        """
         max_x, max_y, max_z = self.world.shape[:3]
 
         if any(
@@ -103,24 +111,23 @@ class Landscape:
         Set the path in the landscape.
         """
         self.path = path
-    
+
-def create_landscape_from_path(path: Path, max_z = 500):
+
+def create_landscape_from_path(path: Path, max_z: float | int = 50):
     """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.
+        max_z (int, optional): Height of the world. Defaults to 50 meters.
 
     Returns:
-        landscape (pg_rad.landscape.Landscape): A landscape with dimensions based on the provided Path.
+        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(
+    landscape = Landscape(size=(max_x, max_y, max_z))
-        size = (max_x, max_y, max_z)
-        )
-    
     landscape.path = path
-    return landscape
+    return landscape

src/pg_rad/logging/__init__.py

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

src/pg_rad/logging/logger.py

@@ -1,17 +1,21 @@
 import logging
-import logging.config
 import pathlib
 
 import yaml
 
-def setup_logger(name):
+
-    logger = logging.getLogger(name)
+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}.")
 
     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)
-    
+
+    config["loggers"]["root"]["level"] = log_level
+
     logging.config.dictConfig(config)
-    return logger

src/pg_rad/objects/__init__.py

@@ -0,0 +1,13 @@
+# do not expose internal logger when running mkinit
+__ignore__ = ["logger"]
+
+from pg_rad.objects import detectors
+from pg_rad.objects import objects
+from pg_rad.objects import sources
+
+from pg_rad.objects.detectors import (Detector,)
+from pg_rad.objects.objects import (BaseObject,)
+from pg_rad.objects.sources import (PointSource,)
+
+__all__ = ['BaseObject', 'Detector', 'PointSource', 'detectors', 'objects',
+           'sources']

src/pg_rad/objects/objects.py

@@ -1,7 +1,8 @@
 import math
 from typing import Self
 
-class Object:
+
+class BaseObject:
     def __init__(
             self,
             x: float,
@@ -16,18 +17,20 @@ class Object:
             x (float): X coordinate.
             y (float): Y coordinate.
             z (float): Z coordinate.
-            name (str, optional): Name for the object. Defaults to "Unnamed object".
+            name (str, optional): Name for the object.
-            color (str, optional): Matplotlib compatible color string. Defaults to "red".
+            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/sources.py

@@ -1,8 +1,14 @@
-from pg_rad.objects import Object
+import logging
-from pg_rad.isotope import Isotope
 
-class PointSource(Object):
+from .objects import BaseObject
+from pg_rad.isotopes import Isotope
+
+logger = logging.getLogger(__name__)
+
+
+class PointSource(BaseObject):
     _id_counter = 1
+
     def __init__(
             self,
             x: float,
@@ -23,9 +29,10 @@ class PointSource(Object):
             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".
+            color (str, optional): Matplotlib compatible color string.
+            Defaults to "red".
         """
-        
+
         self.id = PointSource._id_counter
         PointSource._id_counter += 1
 
@@ -39,5 +46,12 @@ class PointSource(Object):
         self.isotope = isotope
         self.color = color
 
+        logger.debug(f"Source created: {self.name}")
+
     def __repr__(self):
-        return f"PointSource(name={self.name}, pos={(self.x, self.y, self.z)}, isotope={self.isotope.name}, A={self.activity} MBq)"
+        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 +1,121 @@
+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
+                 ):
+        """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.
+        """
+
+        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
+
+        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

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 +1,27 @@
 import numpy as np
 import pytest
 
-from pg_rad.objects import Source
+from pg_rad.sources import PointSource
 
 @pytest.fixture
 def test_sources():
     pos_a = np.random.rand(3)
     pos_b = np.random.rand(3)
 
-    a = Source(*tuple(pos_a), strength = None)
+    a = PointSource(*tuple(pos_a), strength = None)
-    b = Source(*tuple(pos_b), strength = None)
+    b = PointSource(*tuple(pos_b), strength = None)
 
     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 from PointSource A to PointSource 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) 
+    """Verify whether distance between two PointSources is calculated correctly."""
-    is calculated correctly._"""
 
     pos_a, pos_b, a, b = test_sources