diff --git a/src/pg_rad/path/path.py b/src/pg_rad/path/path.py
index 5f44860..e750ba2 100644
--- a/src/pg_rad/path/path.py
+++ b/src/pg_rad/path/path.py
@@ -5,9 +5,7 @@ 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
 
 logger = logging.getLogger(__name__)
 
@@ -44,8 +42,7 @@ class Path:
     def __init__(
             self,
             coord_list: Sequence[tuple[float, float]],
-            z: float = 0,
-            path_simplify: bool = False
+            z: float = 0
                  ):
         """Construct a path of sequences based on a list of coordinates.
 
@@ -53,8 +50,6 @@ class Path:
             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:
@@ -63,12 +58,6 @@ class Path:
 
         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)
 
@@ -102,83 +91,6 @@ class Path:
         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("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 is None:
-        logger.warning("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",