diff --git a/.github/workflows/signaloid-python.yaml b/.github/workflows/signaloid-python.yaml
index e531056..ff1b778 100644
--- a/.github/workflows/signaloid-python.yaml
+++ b/.github/workflows/signaloid-python.yaml
@@ -20,7 +20,7 @@ jobs:
         args:
           - host: ubuntu-22.04
           - host: macos-14
-        python-version: ["3.10", "3.11", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
 
     runs-on:  ${{ matrix.args.host }}
 
diff --git a/poetry.lock b/poetry.lock
index b6f0c6b..e383b20 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,48 +2,48 @@
 
 [[package]]
 name = "black"
-version = "25.12.0"
+version = "26.3.1"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.10"
 groups = ["dev"]
 files = [
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 ]
 
 [package.dependencies]
 click = ">=8.0.0"
 mypy-extensions = ">=0.4.3"
 packaging = ">=22.0"
-pathspec = ">=0.9.0"
+pathspec = ">=1.0.0"
 platformdirs = ">=2"
-pytokens = ">=0.3.0"
+pytokens = ">=0.4.0,<0.5.0"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
@@ -51,7 +51,7 @@ typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 colorama = ["colorama (>=0.4.3)"]
 d = ["aiohttp (>=3.10)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
-uvloop = ["uvloop (>=0.15.2)"]
+uvloop = ["uvloop (>=0.15.2) ; sys_platform != \"win32\"", "winloop (>=0.5.0) ; sys_platform == \"win32\""]
 
 [[package]]
 name = "click"
@@ -75,7 +75,7 @@ description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 groups = ["dev"]
-markers = "platform_system == \"Windows\" or sys_platform == \"win32\""
+markers = "sys_platform == \"win32\" or platform_system == \"Windows\""
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
@@ -88,6 +88,7 @@ description = "Python library for calculating contours of 2D quadrilateral grids
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
+markers = "python_version == \"3.10\""
 files = [
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@@ -158,6 +159,99 @@ mypy = ["bokeh", "contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.15.0)", "
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
 
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.11"
+groups = ["main"]
+markers = "python_version >= \"3.11\" and python_version < \"3.15\""
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+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["bokeh", "contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.17.0)", "types-Pillow"]
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+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -165,6 +259,7 @@ description = "Composable style cycles"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "python_version < \"3.15\""
 files = [
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+version = "4.62.1"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.10"
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@@ -302,6 +398,7 @@ description = "A fast implementation of the Cassowary constraint solver"
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
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 ]
 
 [package.dependencies]
@@ -582,11 +694,11 @@ kiwisolver = ">=1.3.1"
 numpy = ">=1.23"
 packaging = ">=20.0"
 pillow = ">=8"
-pyparsing = ">=2.3.1"
+pyparsing = ">=3"
 python-dateutil = ">=2.7"
 
 [package.extras]
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+dev = ["meson-python (>=0.13.1,<0.17.0)", "pybind11 (>=2.13.2,!=2.13.3)", "setuptools (>=64)", "setuptools_scm (>=7)"]
 
 [[package]]
 name = "mccabe"
@@ -676,57 +788,151 @@ files = [
 
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 description = "Fundamental package for array computing in Python"
 optional = false
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 ]
+markers = {main = "python_version < \"3.15\""}
 
 [[package]]
 name = "pathspec"
@@ -942,6 +1149,7 @@ description = "pyparsing - Classes and methods to define and execute parsing gra
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "python_version < \"3.15\""
 files = [
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 [[package]]
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-python-versions = ">=3.9"
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-iniconfig = ">=1"
-packaging = ">=20"
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 pluggy = ">=1.5,<2"
 pygments = ">=2.7.2"
 tomli = {version = ">=1", markers = "python_version < \"3.11\""}
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 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 groups = ["main"]
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+    {file = "tomli-2.4.1.tar.gz", hash = "sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f"},
 ]
 
 [[package]]
@@ -1170,5 +1463,5 @@ test = ["pytest (>=6.0.0)", "setuptools (>=77)"]
 
 [metadata]
 lock-version = "2.1"
-python-versions = ">=3.10,<3.14"
-content-hash = "2d14fca2d693ab3004bdb3ccdc09b928a6afa20d178c4d5a108a6c6bda94431c"
+python-versions = ">=3.10"
+content-hash = "7ab9d2ffdde44a811ea851d73b6eb2c13709eb838352f0f481c85b70c239bdc8"
diff --git a/pyproject.toml b/pyproject.toml
index d2bb43d..b8ba773 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,21 +19,30 @@ style = "pep440"
 [tool.poetry.group.dev.dependencies]
 wheel = "^0.46.3"
 types-toml = "^0.10.8.20240310"
-pytest = "^8.4.2"
+pytest = "^9.0.2"
 mypy = "^1.19.1"
 flake8 = "^7.3.0"
 toml = "^0.10.2"
-black = {version = "^25.11.0", python = ">=3.10"}
+black = "^26.3.1"
 
 [build-system]
 requires = ["poetry-core>=1.0.0", "poetry-dynamic-versioning>=1.0.0,<2.0.0"]
 build-backend = "poetry_dynamic_versioning.backend"
 
 [tool.poetry.dependencies]
-python = ">=3.10,<3.14"
-numpy = "~2.0.2"
-matplotlib = "~3.9.4"
-Pillow = "~12.1.1"
+python = ">=3.10"
+numpy = [
+	{ version = ">=2.0.0", python = ">=3.10,<3.13" },
+	{ version = ">=2.1.0", python = ">=3.10,<3.14" },
+	{ version = ">=2.4.1", python = ">=3.11,<3.15" },
+]
+matplotlib = [
+	{ version = ">=3.9.0", python = ">=3.10,<3.13" },
+	{ version = ">=3.10.0", python = ">=3.10,<3.14" },
+	{ version = ">=3.10.5", python = ">=3.10,<3.15" },
+]
+pillow = ">=12.1.1"
+pyyaml = ">=6.0.0"
 
 [tool.poetry.scripts]
 signaloid-uxdata-toolkit = "signaloid.uxdata_toolkit:main"
diff --git a/src/signaloid/circuitpython/plot_wrapper.py b/src/signaloid/circuitpython/plot_wrapper.py
index 1f2ebd7..e1eb829 100644
--- a/src/signaloid/circuitpython/plot_wrapper.py
+++ b/src/signaloid/circuitpython/plot_wrapper.py
@@ -38,7 +38,6 @@
     PlotData,
 )
 
-
 # The background color of the plot
 BG_COLOR = 0xFFFFFF
 BG_COLOR_STR = f"{BG_COLOR:06x}"
diff --git a/src/signaloid/distributional/distributional.py b/src/signaloid/distributional/distributional.py
index 554227c..d88e46c 100644
--- a/src/signaloid/distributional/distributional.py
+++ b/src/signaloid/distributional/distributional.py
@@ -256,6 +256,36 @@ def has_special_values(self) -> bool:
             or self.pos_inf_dirac_delta.mass > 0
         )
 
+    @classmethod
+    def from_samples(cls, samples: np.ndarray | list[float]) -> "DistributionalValue":
+        """
+        Construct a DistributionalValue from an array of float samples.
+
+        Each sample becomes a Dirac delta with equal mass (1 / n_total).
+        Non-finite values (NaN, -Inf, +Inf) are included and will be
+        separated by the sort() method when the DistributionalValue is
+        processed.
+
+        Args:
+            samples: 1-D array of float samples (may contain NaN/Inf).
+
+        Returns:
+            A DistributionalValue instance.
+
+        Raises:
+            ValueError: If the samples array is empty.
+        """
+        samples = np.asarray(samples, dtype=np.float64)
+        n_total = len(samples)
+        if n_total == 0:
+            raise ValueError("samples array must not be empty.")
+
+        mass_per_sample = 1.0 / n_total
+        dirac_deltas = [DiracDelta(float(s), mass=mass_per_sample) for s in samples]
+
+        dist = cls(dirac_deltas=dirac_deltas)
+        return dist
+
     def __repr__(self) -> str:
         """Constructs the representation type for the `DistributionalValue`.
 
diff --git a/src/signaloid/distributional/distributional_test.py b/src/signaloid/distributional/distributional_test.py
index 82b0155..63a0fa8 100644
--- a/src/signaloid/distributional/distributional_test.py
+++ b/src/signaloid/distributional/distributional_test.py
@@ -18,9 +18,14 @@
 #   FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 #   DEALINGS IN THE SOFTWARE.
 
+
 from __future__ import annotations
+
 import csv
+import os
 import unittest
+
+import numpy as np
 from signaloid.distributional.dirac_delta import DiracDelta
 from signaloid.distributional.distributional import DistributionalValue
 
@@ -36,8 +41,13 @@ def read_string_bytes_pairs_from_csv(
     Returns:
         pairs: list of tuples of (string_value, bytearray_value)
     """
+    __location__ = os.path.realpath(
+        os.path.join(os.getcwd(), os.path.dirname(__file__))
+    )
+    csv_filepath = os.path.join(__location__, csv_filename)
+
     pairs: list[tuple[str, bytes]] = []
-    with open(csv_filename, "r") as csvfile:
+    with open(csv_filepath, "r") as csvfile:
         reader = csv.reader(csvfile)
         for row in reader:
             if len(row) == 2:
@@ -50,7 +60,7 @@ def read_string_bytes_pairs_from_csv(
 class TestUxParsing(unittest.TestCase):
     def test_parse_ux_strings_values(
         self,
-        input_filename: str = "src/signaloid/distributional/test_ux_value_pairs.csv",
+        input_filename: str = "./test_ux_value_pairs.csv",
     ) -> None:
         """
         Test parsing Ux string values and converting them to Ux bytes
@@ -70,7 +80,7 @@ def test_parse_ux_strings_values(
 
     def test_parse_ux_bytes_values(
         self,
-        input_filename: str = "src/signaloid/distributional/test_ux_value_pairs.csv",
+        input_filename: str = "./test_ux_value_pairs.csv",
     ) -> None:
         """
         Test parsing Ux bytes and converting them to Ux strings
@@ -359,5 +369,64 @@ def test_check_is_full_valid_TTR(self):
         self.assertTrue(dist.check_is_full_valid_TTR())
 
 
+class TestDistributionalValueFromSamples(unittest.TestCase):
+    """Tests for DistributionalValue.from_samples()."""
+
+    def test_basic_finite_samples(self) -> None:
+        """from_samples should produce a valid DistributionalValue."""
+        np.random.seed(42)
+        samples = np.random.normal(0, 1, 100)
+        dist = DistributionalValue.from_samples(samples)
+
+        self.assertEqual(dist.UR_order, 100)
+        self.assertIsNotNone(dist.mean)
+        self.assertFalse(dist.has_special_values)
+
+    def test_special_values_separated(self) -> None:
+        """NaN, -Inf, +Inf should be separated after sort()."""
+        samples = np.array(
+            [1.0, 2.0, 3.0, np.nan, np.nan, -np.inf, np.inf, np.inf, np.inf, 4.0]
+        )
+        dist = DistributionalValue.from_samples(samples)
+        dist.sort()
+
+        self.assertTrue(dist.has_special_values)
+        self.assertAlmostEqual(dist.nan_dirac_delta.mass, 2 / 10)
+        self.assertAlmostEqual(dist.neg_inf_dirac_delta.mass, 1 / 10)
+        self.assertAlmostEqual(dist.pos_inf_dirac_delta.mass, 3 / 10)
+
+    def test_equal_mass_dirac_deltas(self) -> None:
+        """Each sample should become a Dirac delta with mass 1/n."""
+        samples = [1.0, 2.0, 3.0, 4.0]
+        dist = DistributionalValue.from_samples(samples)
+
+        for dd in dist.dirac_deltas:
+            self.assertAlmostEqual(dd.mass, 0.25)
+
+    def test_empty_samples_raises(self) -> None:
+        """An empty array should raise ValueError."""
+        with self.assertRaises(ValueError):
+            DistributionalValue.from_samples(np.array([]))
+
+    def test_all_nan_samples(self) -> None:
+        """All-NaN samples should have nan_mass == 1 after sort."""
+        dist = DistributionalValue.from_samples(np.full(50, np.nan))
+        dist.sort()
+
+        self.assertTrue(dist.has_special_values)
+        self.assertAlmostEqual(dist.nan_dirac_delta.mass, 1.0)
+        self.assertEqual(len(dist.finite_dirac_deltas), 0)
+
+    def test_all_identical_samples(self) -> None:
+        """All-identical samples should combine to one Dirac delta."""
+        dist = DistributionalValue.from_samples(np.full(100, 3.14))
+        dist.combine_dirac_deltas()
+
+        finite = dist.finite_dirac_deltas
+        self.assertEqual(len(finite), 1)
+        self.assertAlmostEqual(finite[0].position, 3.14)
+        self.assertAlmostEqual(finite[0].mass, 1.0)
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/src/signaloid/distributional_information_plotting/README.md b/src/signaloid/distributional_information_plotting/README.md
index e20f147..936c8a3 100644
--- a/src/signaloid/distributional_information_plotting/README.md
+++ b/src/signaloid/distributional_information_plotting/README.md
@@ -1,2 +1,99 @@
-### `PlotHistogramDiracDeltas` class:
-Histogram plotter class. Exposes `plot_histogram_dirac_deltas()`, which takes a `DistributionalValue` list and plots each DistributionalValue as a histogram.
+# Distributional Information Plotting
+
+Tools for visualising and sampling from Signaloid distributional data.
+
+## Plotting a Ux-string
+
+Parse a Ux-encoded string into a `DistributionalValue`, build a `PlotData` object, and pass it to `plot()`:
+
+```python
+from signaloid.distributional.distributional import DistributionalValue
+from signaloid.distributional_information_plotting.plot_histogram_dirac_deltas import PlotData
+from signaloid.distributional_information_plotting.plot_wrapper import plot
+
+ux_string = "0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000"
+
+dist_value = DistributionalValue.parse(ux_string)
+if dist_value is None:
+    raise ValueError(f"Failed to parse Ux string: {ux_string}")
+
+plot_data = PlotData(dist_value)
+
+# Display interactively
+plot(plot_data)
+
+# Or save to a file
+plot(plot_data, path="output.png", save=True)
+```
+
+## Plotting from raw float samples
+
+If you already have an array of float samples (e.g. from Monte Carlo simulation), use `DistributionalValue.from_samples()` to build a distributional value and then pass it to `PlotData`:
+
+```python
+import numpy as np
+from signaloid.distributional.distributional import DistributionalValue
+from signaloid.distributional_information_plotting.plot_histogram_dirac_deltas import PlotData
+from signaloid.distributional_information_plotting.plot_wrapper import plot
+
+samples = np.random.normal(0, 1, 10_000)
+
+dist_value = DistributionalValue.from_samples(samples)
+plot_data = PlotData(dist_value)
+plot(plot_data, path="output.png", save=True)
+```
+
+Non-finite values (`NaN`, `-Inf`, `+Inf`) in the samples array are automatically separated and displayed in a dedicated special-values panel alongside the main histogram.
+
+## Customising the plot
+
+The `plot()` function accepts several optional parameters:
+
+```python
+plot(
+    plot_data,
+    path="output.png",                   # Output file path
+    save=True,                            # Save to file (False = show interactively)
+    plot_expected_value_line=True,         # Vertical line at the mean
+    x_lim=(-5, 5),                        # Custom x-axis limits
+    y_lim=(0, 0.5),                       # Custom y-axis limits
+    x_label="My Variable",               # Custom x-axis label
+    x_tick_label_rotation=45,             # Rotate x-axis tick labels
+    font_size=20,                         # Font size for labels
+    matplotlib_rc_params_override={...},  # Custom matplotlib rc params
+)
+```
+
+## Sampling from a Ux-string
+
+Generate random samples from a Ux-encoded distribution:
+
+```python
+from signaloid.distributional_information_plotting.sample_generator import sample_generator
+
+ux_string = "0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000"
+
+samples = sample_generator(ux_string, n_samples=1000)
+```
+
+Distributions that contain non-finite Dirac deltas (`NaN`, `-Inf`, `+Inf`) are handled via mixture sampling: each sample is drawn from either the finite part (via inverse CDF) or the non-finite part (categorically), proportional to their respective masses.
+
+## CLI usage
+
+These tools are also available via the `signaloid-uxdata-toolkit` command-line interface:
+
+```bash
+# Plot a distribution
+signaloid-uxdata-toolkit plot --ux-data=0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000
+
+# Save plot to file
+signaloid-uxdata-toolkit plot -o output.png --ux-data=0.40007Ux...
+
+# Generate samples
+signaloid-uxdata-toolkit sample --ux-data=0.40007Ux... --num-samples 100
+
+# Save samples to file
+signaloid-uxdata-toolkit sample -o samples.txt --ux-data=0.40007Ux... --num-samples 100
+```
+
+> **Note:** Use `=` syntax (`--ux-data=...`) for Ux-strings that start with `-`, otherwise the shell may interpret them as flags.
diff --git a/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas.py b/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas.py
index d9b7c5f..2c1feca 100644
--- a/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas.py
+++ b/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas.py
@@ -53,6 +53,34 @@ def __init__(
 
         self._construct_plot_data()
 
+    # Maximum number of bins for plotting
+    MAX_BINS: int = 1024
+
+    @classmethod
+    def from_samples(
+        cls,
+        samples: np.ndarray | list[float],
+        plotting_resolution: int | None = None,
+    ) -> PlotData:
+        """
+        Construct a PlotData from an array of float samples.
+
+        Creates a `DistributionalValue` from the samples (each sample
+        becomes an equal-weight Dirac delta) and then builds the plot
+        data through the standard TTR binning pipeline.
+
+        Args:
+            samples: 1-D array of float samples (may contain NaN/Inf).
+            plotting_resolution: Number of bins for the plot (must be a
+                power of 2). If `None`, automatically determined from
+                the data.
+
+        Returns:
+            A PlotData instance ready to be passed to plot().
+        """
+        dist = DistributionalValue.from_samples(samples)
+        return cls(dist, plotting_resolution=plotting_resolution)
+
     @property
     def positions(self) -> np.ndarray:
         """The boundary positions list.
@@ -185,31 +213,45 @@ def _determine_boundary_positions(
 
         if not use_ttr_binning:
             # Determine the 'NaN'-valued boundary points from adjacent Dirac deltas.
-            for i in range(2, number_of_boundaries - 1, 2):
-                if np.isnan(boundary_positions[i]):
-                    boundary_positions[i] = (
-                        boundary_probabilities[i - 1] * boundary_positions[i - 1]
-                        + boundary_probabilities[i + 1] * boundary_positions[i + 1]
-                    ) / (boundary_probabilities[i - 1] + boundary_probabilities[i + 1])
+            # Even indices (2, 4, ...) are the NaN-valued boundaries between
+            # odd-indexed Dirac delta positions.
+            even = slice(2, number_of_boundaries - 1, 2)
+            left_prob = boundary_probabilities[1 : number_of_boundaries - 2 : 2]
+            right_prob = boundary_probabilities[3:number_of_boundaries:2]
+            left_pos = boundary_positions[1 : number_of_boundaries - 2 : 2]
+            right_pos = boundary_positions[3:number_of_boundaries:2]
+            nan_mask = np.isnan(boundary_positions[even])
+            weighted_avg = (left_prob * left_pos + right_prob * right_pos) / (
+                left_prob + right_prob
+            )
+            boundary_positions[even] = np.where(
+                nan_mask, weighted_avg, boundary_positions[even]
+            )
 
             return (boundary_positions, boundary_probabilities)
 
         # First handle internal boundary positions.
         for n in range(exponent):
             step = 2**n
-            for i in range(2 ** (n + 1), number_of_boundaries - 1, 2 ** (n + 2)):
-                boundary_probabilities[i] = (
-                    boundary_probabilities[i - step] + boundary_probabilities[i + step]
-                )
-                boundary_positions[i] = (
-                    boundary_probabilities[i - step] * boundary_positions[i - step]
-                    + boundary_probabilities[i + step] * boundary_positions[i + step]
-                ) / boundary_probabilities[i]
+            indices = np.arange(2 ** (n + 1), number_of_boundaries - 1, 2 ** (n + 2))
+            if len(indices) == 0:
+                continue
+            left = indices - step
+            right = indices + step
+            boundary_probabilities[indices] = (
+                boundary_probabilities[left] + boundary_probabilities[right]
+            )
+            boundary_positions[indices] = (
+                boundary_probabilities[left] * boundary_positions[left]
+                + boundary_probabilities[right] * boundary_positions[right]
+            ) / boundary_probabilities[indices]
 
         # Above process might not produce a strictly increasing sequence of
         # positions if not a valid TTR, and it will leave 'NaN'-valued
         # boundary points if the number of Dirac deltas is not a power of 2.
         # Handle both cases by sweeping over the boundary positions.
+        # Note: this fixup must remain sequential because each corrected
+        # position feeds into the next check.
         for i in range(2, number_of_boundaries - 1, 2):
             if (
                 np.isnan(boundary_positions[i])
@@ -340,16 +382,17 @@ def _get_binning(
         bin_widths[1:-1] = boundary_positions[2:-1] - boundary_positions[1:-2]
         bin_heights = np.array([np.nan] * numberOfBins)
 
-        for i in range(1, number_of_finite_dirac_deltas - 1):
-            averageHeight = finite_sorted_dirac_deltas[i].mass / (
-                bin_widths[2 * i] + bin_widths[2 * i + 1]
-            )
-            bin_heights[2 * i] = (
-                averageHeight * bin_widths[2 * i + 1] / bin_widths[2 * i]
-            )
-            bin_heights[2 * i + 1] = (
-                averageHeight * bin_widths[2 * i] / bin_widths[2 * i + 1]
-            )
+        # Vectorise internal bin height computation for Dirac deltas 1..N-2.
+        if number_of_finite_dirac_deltas > 2:
+            internal = np.arange(1, number_of_finite_dirac_deltas - 1)
+            masses = np.array([finite_sorted_dirac_deltas[i].mass for i in internal])
+            left_idx = 2 * internal
+            right_idx = left_idx + 1
+            w_left = bin_widths[left_idx]
+            w_right = bin_widths[right_idx]
+            avg_h = masses / (w_left + w_right)
+            bin_heights[left_idx] = avg_h * w_right / w_left
+            bin_heights[right_idx] = avg_h * w_left / w_right
 
         boundary_positions, bin_widths, bin_heights = PlotData._handle_extremal_bins(
             finite_sorted_dirac_deltas,
@@ -428,15 +471,10 @@ def _bin_pdf_expected_dirac_delta(
             expected_dirac_delta: The expected Dirac delta in the format np.array([position, mass]).
         """
 
-        moment_sum = 0.0
-        probability_sum = 0.0
-
-        for i, (bin_width, bin_height) in enumerate(zip(bin_widths, bin_heights)):
-            probability = bin_width * bin_height
-            probability_sum += probability
-            moment_sum += (
-                probability * (boundary_positions[i + 1] + boundary_positions[i]) / 2
-            )
+        probabilities = bin_widths * bin_heights
+        probability_sum = float(np.sum(probabilities))
+        bin_centres = (boundary_positions[1:] + boundary_positions[:-1]) / 2
+        moment_sum = float(np.sum(probabilities * bin_centres))
 
         expected_dirac_delta = DiracDelta(
             moment_sum / probability_sum, mass=probability_sum
@@ -542,12 +580,15 @@ def _construct_plot_data(self) -> None:
             self.masses = np.array([finite_dirac_deltas[0].mass], dtype=np.float64)
             return
 
-        # Set plot resolution to (N*2) where N is machine representation
+        # Set plot resolution to (2 * N) where N is machine representation,
+        # capped at MAX_BINS.
         machine_representation = 2 ** math.floor(math.log(self.dist.UR_order, 2))
         self.plotting_resolution = int(
-            machine_representation * 2
+            min(machine_representation * 2, self.MAX_BINS)
             if self.plotting_resolution is None
-            else min((machine_representation * 2), self.plotting_resolution)
+            else min(
+                machine_representation * 2, self.plotting_resolution, self.MAX_BINS
+            )
         )
         log2_of_plotting_resolution = self.plotting_resolution.bit_length() - 1
         self.plotting_ttr_order = log2_of_plotting_resolution - 1
diff --git a/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas_test.py b/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas_test.py
index 396b69b..db20121 100644
--- a/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas_test.py
+++ b/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas_test.py
@@ -18,11 +18,11 @@
 #   FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 #   DEALINGS IN THE SOFTWARE.
 
+
 import random
 import unittest
 
 import numpy as np
-
 from signaloid.distributional.dirac_delta import DiracDelta
 from signaloid.distributional_information_plotting.plot_histogram_dirac_deltas import (
     PlotData,
@@ -159,6 +159,130 @@ def test_create_binning_property_preserve_ttr(self) -> None:
                 )
 
 
+class TestPlotDataFromSamples(unittest.TestCase):
+    """Tests for PlotData.from_samples() (delegates to DistributionalValue)."""
+
+    def test_basic_finite_samples(self) -> None:
+        """from_samples should produce valid PlotData from finite floats."""
+        np.random.seed(42)
+        samples = np.random.normal(0, 1, 1000)
+        pd = PlotData.from_samples(samples)
+
+        self.assertGreater(len(pd.positions), 0)
+        self.assertGreater(len(pd.masses), 0)
+        self.assertFalse(pd.dist.has_special_values)
+        self.assertAlmostEqual(pd.dist.nan_dirac_delta.mass, 0.0)
+        self.assertAlmostEqual(pd.dist.neg_inf_dirac_delta.mass, 0.0)
+        self.assertAlmostEqual(pd.dist.pos_inf_dirac_delta.mass, 0.0)
+        self.assertIsNotNone(pd.dist.mean)
+
+    def test_density_integrates_to_approximately_one(self) -> None:
+        """Histogram density should integrate to ~1 when all samples are finite."""
+        np.random.seed(42)
+        samples = np.random.normal(5, 2, 10_000)
+        pd = PlotData.from_samples(samples)
+
+        bin_widths = pd.positions[1:] - pd.positions[:-1]
+        total_area = float(np.sum(bin_widths * pd.masses))
+        self.assertAlmostEqual(total_area, 1.0, places=1)
+
+    def test_special_value_masses(self) -> None:
+        """NaN, -Inf, +Inf masses should match their proportions."""
+        samples = np.array(
+            [1.0, 2.0, 3.0, np.nan, np.nan, -np.inf, np.inf, np.inf, np.inf, 4.0]
+        )
+        pd = PlotData.from_samples(samples)
+
+        self.assertTrue(pd.dist.has_special_values)
+        self.assertAlmostEqual(pd.dist.nan_dirac_delta.mass, 2 / 10)
+        self.assertAlmostEqual(pd.dist.neg_inf_dirac_delta.mass, 1 / 10)
+        self.assertAlmostEqual(pd.dist.pos_inf_dirac_delta.mass, 3 / 10)
+
+    def test_all_identical_samples(self) -> None:
+        """All-identical finite samples should produce a single Dirac delta."""
+        samples = np.full(100, 3.14)
+        pd = PlotData.from_samples(samples)
+
+        self.assertEqual(len(pd.positions), 1)
+        self.assertAlmostEqual(pd.positions[0], 3.14)
+        self.assertAlmostEqual(pd.masses[0], 1.0)
+
+    def test_empty_samples_raises(self) -> None:
+        """An empty array should raise ValueError."""
+        with self.assertRaises(ValueError):
+            PlotData.from_samples(np.array([]))
+
+    def test_plotting_resolution_is_power_of_two(self) -> None:
+        """The plotting_resolution should be a power of 2."""
+        np.random.seed(42)
+        samples = np.random.normal(0, 1, 1000)
+        pd = PlotData.from_samples(samples)
+
+        self.assertIsNotNone(pd.plotting_resolution)
+        assert pd.plotting_resolution is not None
+        self.assertEqual(pd.plotting_resolution & (pd.plotting_resolution - 1), 0)
+
+    def test_custom_plotting_resolution(self) -> None:
+        """plotting_resolution parameter should control the number of bins."""
+        np.random.seed(42)
+        samples = np.random.normal(0, 1, 1000)
+        pd = PlotData.from_samples(samples, plotting_resolution=32)
+
+        self.assertIsNotNone(pd.plotting_resolution)
+        assert pd.plotting_resolution is not None
+        self.assertLessEqual(pd.plotting_resolution, 32)
+
+    def test_mean_value_close_to_sample_mean(self) -> None:
+        """mean_value should be close to the mean of the finite samples."""
+        np.random.seed(42)
+        samples = np.random.normal(5, 1, 500)
+        pd = PlotData.from_samples(samples)
+
+        finite = samples[np.isfinite(samples)]
+        self.assertIsNotNone(pd.dist.mean)
+        assert pd.dist.mean is not None
+        self.assertAlmostEqual(pd.dist.mean, float(np.mean(finite)), places=5)
+
+    def test_plot_from_samples_succeeds(self) -> None:
+        """plot() should succeed when given PlotData built from samples."""
+        import matplotlib
+
+        matplotlib.use("Agg")
+        from signaloid.distributional_information_plotting.plot_wrapper import (
+            plot,
+        )
+
+        np.random.seed(42)
+        samples = np.random.normal(0, 1, 500)
+        pd = PlotData.from_samples(samples)
+
+        result = plot(pd, path="/dev/null", save=True)
+        self.assertTrue(result)
+
+    def test_plot_from_samples_with_special_values_succeeds(self) -> None:
+        """plot() should succeed for samples containing NaN/Inf."""
+        import matplotlib
+
+        matplotlib.use("Agg")
+        from signaloid.distributional_information_plotting.plot_wrapper import (
+            plot,
+        )
+
+        np.random.seed(42)
+        samples = np.concatenate(
+            [
+                np.random.normal(0, 1, 400),
+                np.full(50, np.nan),
+                np.full(25, np.inf),
+                np.full(25, -np.inf),
+            ]
+        )
+        pd = PlotData.from_samples(samples)
+
+        result = plot(pd, path="/dev/null", save=True)
+        self.assertTrue(result)
+
+
 def dirac_deltas_to_ttr(
     dirac_deltas: list[DiracDelta], order: int, count: int = 0
 ) -> list[DiracDelta]:
diff --git a/src/signaloid/distributional_information_plotting/plot_wrapper.py b/src/signaloid/distributional_information_plotting/plot_wrapper.py
index 2c339af..f47f38b 100644
--- a/src/signaloid/distributional_information_plotting/plot_wrapper.py
+++ b/src/signaloid/distributional_information_plotting/plot_wrapper.py
@@ -23,6 +23,8 @@
 from typing import Any
 import matplotlib
 import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+import numpy as np
 
 from signaloid.distributional_information_plotting.plot_histogram_dirac_deltas import (
     PlotData,
@@ -83,6 +85,9 @@ def plot(
         "ytick.right": True,
     }
 
+    if save:
+        matplotlib.use("Agg")
+
     if matplotlib_rc_params_override is not None:
         matplotlib_rcParams_update_defaults.update(matplotlib_rc_params_override)
 
@@ -115,16 +120,39 @@ def plot(
             arrowprops={"arrowstyle": "->", "facecolor": "black", "lw": 3},
         )
     else:
-        # Plot the binning.
-        plt.bar(
-            x=plot_data.positions[:-1],
-            height=plot_data.masses,
-            width=plot_data.widths,
-            align="edge",
-            edgecolor="#33A333",
+        # Plot the binning: filled step area + step outline + bin edges.
+        ax = plt.gca()
+
+        # Plot the binning: filled step area + step outline + bin edges.
+        step_patch = ax.stairs(
+            plot_data.masses,
+            plot_data.positions,
+            fill=True,
             facecolor="#33A333" + "40",
             hatch="\\",
         )
+        step_patch.set_edgecolor("#33A333")
+        step_patch.set_linewidth(0)
+
+        ax.stairs(
+            plot_data.masses,
+            plot_data.positions,
+            fill=False,
+            color="#33A333",
+            linewidth=1.0,
+        )
+
+        # Vertical lines at internal bin edges via LineCollection (faster than plt.vlines).
+        internal_edges = plot_data.positions[1:-1]
+        edge_heights = np.maximum(plot_data.masses[:-1], plot_data.masses[1:])
+        n_edges = len(internal_edges)
+        segments = np.empty((n_edges, 2, 2))
+        segments[:, 0, 0] = internal_edges
+        segments[:, 0, 1] = 0
+        segments[:, 1, 0] = internal_edges
+        segments[:, 1, 1] = edge_heights
+        lc = LineCollection(segments.tolist(), colors="#33A333", linewidths=0.5)
+        ax.add_collection(lc)
 
     # Default kwargs for plt.annotate
     annotation_default_args: dict[str, Any] = {
diff --git a/src/signaloid/distributional_information_plotting/plot_wrapper_test.py b/src/signaloid/distributional_information_plotting/plot_wrapper_test.py
index a2df7c4..d5d5d46 100644
--- a/src/signaloid/distributional_information_plotting/plot_wrapper_test.py
+++ b/src/signaloid/distributional_information_plotting/plot_wrapper_test.py
@@ -18,12 +18,15 @@
 #   FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 #   DEALINGS IN THE SOFTWARE.
 
+
 from __future__ import annotations
+
 import csv
 import os
 import shutil
 import tempfile
 import unittest
+
 from signaloid.distributional.distributional import DistributionalValue
 from signaloid.distributional_information_plotting.plot_histogram_dirac_deltas import (
     PlotData,
@@ -41,8 +44,13 @@ def read_ux_strings_from_csv(csv_filename: str) -> list[str] | None:
     Returns:
         ux_strings: list of Ux strings
     """
+    __location__ = os.path.realpath(
+        os.path.join(os.getcwd(), os.path.dirname(__file__))
+    )
+    csv_filepath = os.path.join(__location__, csv_filename)
+
     ux_strings: list[str] = []
-    with open(csv_filename, "r") as csvfile:
+    with open(csv_filepath, "r") as csvfile:
         reader = csv.reader(csvfile)
         for row in reader:
             ux_strings.append(row[0])
@@ -61,12 +69,11 @@ def tearDown(self) -> None:
 
     def test_plot_wrapper(
         self,
-        input_filename: str = "src/signaloid/distributional/test_ux_value_pairs.csv",
+        input_filename: str = "../distributional/test_ux_value_pairs.csv",
     ) -> None:
         """
         Test parsing and plotting Ux Strings
         """
-
         # Read Ux strings from test data csv
         ux_strings = read_ux_strings_from_csv(input_filename)
         self.assertIsNotNone(ux_strings)
diff --git a/src/signaloid/distributional_information_plotting/sample_generator.py b/src/signaloid/distributional_information_plotting/sample_generator.py
index 6009be6..55765f5 100644
--- a/src/signaloid/distributional_information_plotting/sample_generator.py
+++ b/src/signaloid/distributional_information_plotting/sample_generator.py
@@ -179,6 +179,8 @@ def generate_samples(
     Returns:
         Array of samples drawn from the distribution.
     """
+    if len(cdf_values) != len(boundary_positions):
+        raise ValueError("cdf_values and boundary_positions length does not match.")
     us = np.random.uniform(0, 1, size=n)
     indices = np.searchsorted(cdf_values, us, side="right") - 1
     indices = np.clip(indices, 0, len(cdf_values) - 2)
diff --git a/src/signaloid/distributional_information_plotting/sample_generator_test.py b/src/signaloid/distributional_information_plotting/sample_generator_test.py
index 6288f23..b649489 100644
--- a/src/signaloid/distributional_information_plotting/sample_generator_test.py
+++ b/src/signaloid/distributional_information_plotting/sample_generator_test.py
@@ -20,12 +20,12 @@
 
 
 import unittest
+
+import numpy as np
+from signaloid.distributional.distributional import DistributionalValue
 from signaloid.distributional_information_plotting.sample_generator import (
     sample_generator,
 )
-from signaloid.distributional.distributional import DistributionalValue
-import numpy as np
-
 
 SAMPLE_UX_STRING = "-0.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
 SAMPLE_UX_STRING_WITH_SPECIAL_VALUES = "nanUx0400000000000000017FF8000000000000000000063FF0000000000000155555555555550040080000000000001555555555555500000000000000000015555555555555007FF80000000000001555555555555500FFF000000000000015555555555555007FF00000000000001555555555555500"
@@ -34,8 +34,6 @@
 # ============================================================================
 # Unit Tests: sample_generator
 # ============================================================================
-
-
 class TestSampleGenerator(unittest.TestCase):
     """Tests for the sample_generator function."""
 
diff --git a/src/signaloid/uxdata_toolkit_test.py b/src/signaloid/uxdata_toolkit_test.py
index 6a970ae..d26df88 100644
--- a/src/signaloid/uxdata_toolkit_test.py
+++ b/src/signaloid/uxdata_toolkit_test.py
@@ -27,11 +27,9 @@
 from pathlib import Path
 
 import numpy as np
-
 import signaloid.uxdata_toolkit as toolkit
 from signaloid.distributional.distributional import DistributionalValue
 
-
 # ============================================================================
 # Constants
 # ============================================================================
@@ -44,8 +42,6 @@
 # ============================================================================
 # Unit Tests: Argument Parsing
 # ============================================================================
-
-
 class TestArgumentParsing(unittest.TestCase):
     """Tests for CLI argument parsing."""
 
@@ -127,8 +123,6 @@ def test_sample_negative_num_samples_rejected(self) -> None:
 # ============================================================================
 # Unit Tests: command_plot
 # ============================================================================
-
-
 class TestCommandPlot(unittest.TestCase):
     """Tests for the plot command handler."""
 
@@ -190,8 +184,6 @@ def test_plot_with_special_values(self) -> None:
 # ============================================================================
 # Unit Tests: command_sample
 # ============================================================================
-
-
 class TestCommandSample(unittest.TestCase):
     """Tests for the sample command handler."""
 
@@ -281,8 +273,6 @@ def test_sample_with_special_values(self) -> None:
 # ============================================================================
 # Unit Tests: main()
 # ============================================================================
-
-
 class TestMain(unittest.TestCase):
     """Tests for the main dispatch function."""
 
@@ -294,8 +284,6 @@ def test_main_no_command_exits(self) -> None:
 # ============================================================================
 # Integration Tests
 # ============================================================================
-
-
 class TestIntegrationPlot(unittest.TestCase):
     """Integration tests for the full plot pipeline."""
 
@@ -358,6 +346,7 @@ def test_sample_statistical_sanity(self) -> None:
         with tempfile.TemporaryDirectory() as tmp_dir:
             output = str(Path(tmp_dir) / "stats_samples.txt")
             n_samples = 100_000
+            np.random.seed(42)
             args = toolkit.parse_arguments(
                 [
                     "sample",
@@ -385,7 +374,8 @@ def test_sample_statistical_sanity(self) -> None:
 
                 self.assertLess(np.abs(np.mean(data) - dist_value.mean), threshold_mean)
                 self.assertLess(
-                    np.abs(np.std(data) - np.sqrt(dist_value.variance)), threshold_std
+                    np.abs(np.std(data) - np.sqrt(dist_value.variance)),
+                    threshold_std,
                 )