From 7bd4f6d5bd10be250a22e631f4892e721f60ca45 Mon Sep 17 00:00:00 2001 From: Michael Selby <149940738+michaelselby-signaloid@users.noreply.github.com> Date: Fri, 13 Mar 2026 14:51:55 +0000 Subject: [PATCH] Updated to version --- .github/workflows/signaloid-python.yaml | 16 +- poetry.lock | 842 ++++++++---------- pyproject.toml | 20 +- .../circuitpython/extended_ulab_numpy.py | 2 +- src/signaloid/circuitpython/plot_wrapper.py | 3 +- src/signaloid/distributional/dirac_delta.py | 4 +- .../distributional/dirac_delta_test.py | 2 +- .../distributional/distributional.py | 11 +- .../distributional/distributional_test.py | 2 +- .../plot_histogram_dirac_deltas.py | 48 +- .../plot_histogram_dirac_deltas_test.py | 4 +- .../plot_wrapper.py | 2 +- .../plot_wrapper_test.py | 2 +- .../sample_generator.py | 216 +++++ .../sample_generator_test.py | 123 +++ src/signaloid/uxdata_toolkit.py | 244 +++-- src/signaloid/uxdata_toolkit_test.py | 393 ++++++++ 17 files changed, 1346 insertions(+), 588 deletions(-) create mode 100644 src/signaloid/distributional_information_plotting/sample_generator.py create mode 100644 src/signaloid/distributional_information_plotting/sample_generator_test.py create mode 100644 src/signaloid/uxdata_toolkit_test.py diff --git a/.github/workflows/signaloid-python.yaml b/.github/workflows/signaloid-python.yaml index b6438be..e531056 100644 --- a/.github/workflows/signaloid-python.yaml +++ b/.github/workflows/signaloid-python.yaml @@ -20,6 +20,7 @@ jobs: args: - host: ubuntu-22.04 - host: macos-14 + python-version: ["3.10", "3.11", "3.13"] runs-on: ${{ matrix.args.host }} @@ -31,6 +32,8 @@ jobs: uses: actions/checkout@v4 with: ref: ${{ github.head_ref }} + fetch-depth: 0 + fetch-tags: true - name: Debug run: | @@ -43,10 +46,10 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: ${{ matrix.python-version }} - name: Install Poetry Action - run: python -m pip install poetry==1.4.0 + run: python -m pip install poetry==1.8.0 - name: Print Poetry version run: poetry --version @@ -57,6 +60,9 @@ jobs: - name: Install run: poetry install + - name: Install Poetry plugins + run: poetry self add "poetry-dynamic-versioning[plugin]>=1.0.0,<2.0.0" + - name: Print Python Information run: | which python @@ -81,12 +87,12 @@ jobs: uses: actions/upload-artifact@v4 if: always() with: - name: "test-logs-${{matrix.args.host}}-${{ github.event.pull_request.head.sha || github.sha }}" + name: "test-logs-${{matrix.args.host}}-py${{ matrix.python-version }}-${{ github.event.pull_request.head.sha || github.sha }}" path: | ./src/signaloid/distributional_information_plotting/tests/testdata/Brown-Ham-model-actual-results_df.csv - name: Upload wheels - if: ${{ startsWith(matrix.args.host, 'ubuntu') }} + if: ${{ startsWith(matrix.args.host, 'ubuntu') && matrix.python-version == '3.11' }} uses: actions/upload-artifact@v4 with: name: "wheel-${{ github.event.pull_request.head.sha || github.sha }}" @@ -95,7 +101,7 @@ jobs: # Upload the artifact to the GitHub release - name: Upload Release Asset - if: ${{ github.event_name == 'release' && startsWith(matrix.args.host, 'ubuntu') }} + if: ${{ github.event_name == 'release' && startsWith(matrix.args.host, 'ubuntu') && matrix.python-version == '3.11' }} run: | gh release upload ${{ github.event.release.tag_name }} dist/* env: diff --git a/poetry.lock b/poetry.lock index 261d7a3..b6f0c6b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,39 +1,40 @@ -# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.3.2 and should not be changed by hand. 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] +[tool.poetry-dynamic-versioning] +enable = true +vcs = "git" +style = "pep440" + + [tool.poetry.group.dev.dependencies] wheel = "^0.46.3" -pex = "^2.87.0" types-toml = "^0.10.8.20240310" pytest = "^8.4.2" mypy = "^1.19.1" flake8 = "^7.3.0" toml = "^0.10.2" -black = {version = "^25.11.0", python = ">=3.9"} +black = {version = "^25.11.0", python = ">=3.10"} [build-system] -requires = ["poetry_core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +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 = ">=3.9,<3.14" +python = ">=3.10,<3.14" numpy = "~2.0.2" matplotlib = "~3.9.4" +Pillow = "~12.1.1" [tool.poetry.scripts] signaloid-uxdata-toolkit = "signaloid.uxdata_toolkit:main" @@ -36,7 +42,7 @@ signaloid-uxdata-toolkit = "signaloid.uxdata_toolkit:main" warn_unused_configs = true strict = true disallow_any_generics = false -python_version = "3.9" +python_version = "3.10" [[tool.mypy.overrides]] module = [ diff --git a/src/signaloid/circuitpython/extended_ulab_numpy.py b/src/signaloid/circuitpython/extended_ulab_numpy.py index 84f535d..4a6c383 100644 --- a/src/signaloid/circuitpython/extended_ulab_numpy.py +++ b/src/signaloid/circuitpython/extended_ulab_numpy.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025, Signaloid. +# Copyright (c) 2026, Signaloid. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), diff --git a/src/signaloid/circuitpython/plot_wrapper.py b/src/signaloid/circuitpython/plot_wrapper.py index 8ccce65..1f2ebd7 100644 --- a/src/signaloid/circuitpython/plot_wrapper.py +++ b/src/signaloid/circuitpython/plot_wrapper.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025, Signaloid. +# Copyright (c) 2026, Signaloid. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to @@ -38,6 +38,7 @@ PlotData, ) + # The background color of the plot BG_COLOR = 0xFFFFFF BG_COLOR_STR = f"{BG_COLOR:06x}" diff --git a/src/signaloid/distributional/dirac_delta.py b/src/signaloid/distributional/dirac_delta.py index 00ea644..7e1251d 100644 --- a/src/signaloid/distributional/dirac_delta.py +++ b/src/signaloid/distributional/dirac_delta.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025, Signaloid. +# Copyright (c) 2026, Signaloid. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to @@ -174,7 +174,7 @@ def __ne__(self, other: object) -> bool: return self.position != other.position def __ge__(self, other: "DiracDelta") -> bool: - """Checks if this Dirac Delta is greater than the given Dirac Delta. + """Checks if this Dirac Delta is greater than or equal to the given Dirac Delta. :param other: The second Dirac Delta to use for the comparison. :type other: DiracDelta diff --git a/src/signaloid/distributional/dirac_delta_test.py b/src/signaloid/distributional/dirac_delta_test.py index 6f46ba8..217c970 100644 --- a/src/signaloid/distributional/dirac_delta_test.py +++ b/src/signaloid/distributional/dirac_delta_test.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025, Signaloid. +# Copyright (c) 2026, Signaloid. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to diff --git a/src/signaloid/distributional/distributional.py b/src/signaloid/distributional/distributional.py index 546fff0..554227c 100644 --- a/src/signaloid/distributional/distributional.py +++ b/src/signaloid/distributional/distributional.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024, Signaloid. +# Copyright (c) 2026, Signaloid. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to @@ -191,7 +191,10 @@ def calculate_mean(self) -> float | None: total_weighted_position += dd.position * dd.mass total_mass += dd.mass - self._mean = total_weighted_position / total_mass + if total_mass == 0: + self._mean = np.nan + else: + self._mean = total_weighted_position / total_mass return self._mean @@ -286,7 +289,7 @@ def export(self, to_str: bool = True) -> str | bytes: Constructs the Ux string/Ux bytes with particle value for the `DistributionalValue`. Args: - to_str: Weather to export a Ux string or a Ux bytes + to_str: Whether to export a Ux string or a Ux bytes - True: Export a Ux string - False: Export a Ux bytes @@ -551,7 +554,7 @@ def mean_relative_diff(self, other: "DistributionalValue") -> float: The relative difference between the mean values (normalized by the mean value of the other `DistributionalValue`). """ - if self.mean is None or other.mean is None: + if self.mean is None or other.mean is None or other.mean == 0: return math.nan return abs((self.mean - other.mean) / other.mean) diff --git a/src/signaloid/distributional/distributional_test.py b/src/signaloid/distributional/distributional_test.py index a3e97b6..82b0155 100644 --- a/src/signaloid/distributional/distributional_test.py +++ b/src/signaloid/distributional/distributional_test.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024, Signaloid. +# Copyright (c) 2026, Signaloid. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to 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 5110306..d9b7c5f 100644 --- a/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas.py +++ b/src/signaloid/distributional_information_plotting/plot_histogram_dirac_deltas.py @@ -35,7 +35,9 @@ class PlotData: def __init__( - self, dist: DistributionalValue, plotting_resolution: int | None = None + self, + dist: DistributionalValue, + plotting_resolution: int | None = None, ) -> None: if dist.mean is None or dist.UR_order == 0: raise ValueError("Failed to load data") @@ -369,7 +371,7 @@ def _get_binning( return (boundary_positions, bin_widths, bin_heights) @staticmethod - def _create_binning( + def create_binning( finite_sorted_dirac_deltas: list[DiracDelta], exponent: int, use_ttr_binning: bool, @@ -557,21 +559,33 @@ def _construct_plot_data(self) -> None: "plot_histogram_dirac_deltas: plotting_resolution must be a power of 2!" ) - # Create the binning such that the average of two bins surrounding a Dirac delta - # is the Dirac delta itself. - boundary_positions, bin_widths, bin_heights = PlotData._create_binning( - finite_dirac_deltas, 0, False - ) + try: + # Create the binning such that the average of two bins surrounding a Dirac delta + # is the Dirac delta itself. + boundary_positions, bin_widths, bin_heights = PlotData.create_binning( + finite_dirac_deltas, 0, False + ) - # Find the TTR of the created binning. This is always a valid TTR. - ttr = PlotData._bin_pdf_to_ttr( - boundary_positions, bin_widths, bin_heights, self.plotting_ttr_order - ) + # Find the TTR of the created binning. This is always a valid TTR. + ttr = PlotData._bin_pdf_to_ttr( + boundary_positions, bin_widths, bin_heights, self.plotting_ttr_order + ) - # Create the binning from the obtained (valid) TTR using the TTR binning method. - boundary_positions, bin_widths, bin_heights = PlotData._create_binning( - ttr, self.plotting_ttr_order, True - ) + # Create the binning from the obtained (valid) TTR using the TTR binning method. + boundary_positions, bin_widths, bin_heights = PlotData.create_binning( + ttr, self.plotting_ttr_order, True + ) + + self.positions = boundary_positions + self.masses = bin_heights + except (ValueError, TypeError): + positions = np.array([dd.position for dd in finite_dirac_deltas]) + masses = np.array([dd.mass for dd in finite_dirac_deltas]) + + midpoints = (positions[:-1] + positions[1:]) / 2 + left = 2 * positions[0] - midpoints[0] + right = 2 * positions[-1] - midpoints[-1] + self.positions = np.concatenate(([left], midpoints, [right])) - self.positions = boundary_positions - self.masses = bin_heights + widths = np.diff(self.positions) + self.masses = masses / widths 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 8010b8d..396b69b 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 @@ -64,7 +64,7 @@ def test_create_binning_property_dirac_deltas_average_of_bins(self) -> None: input_dirac_delta_ensembles.append(dirac_deltas) for i, input_ensemble in enumerate(input_dirac_delta_ensembles): - boundary_positions, bin_widths, bin_heights = PlotData._create_binning( + boundary_positions, bin_widths, bin_heights = PlotData.create_binning( input_ensemble, 0, False ) @@ -133,7 +133,7 @@ def test_create_binning_property_preserve_ttr(self) -> None: for i, input_ttr in enumerate(input_ttrs): exponent = ttr_orders[i] - boundary_positions, bin_widths, bin_heights = PlotData._create_binning( + boundary_positions, bin_widths, bin_heights = PlotData.create_binning( input_ttr, exponent, True ) diff --git a/src/signaloid/distributional_information_plotting/plot_wrapper.py b/src/signaloid/distributional_information_plotting/plot_wrapper.py index ec6b68a..2c339af 100644 --- a/src/signaloid/distributional_information_plotting/plot_wrapper.py +++ b/src/signaloid/distributional_information_plotting/plot_wrapper.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024, Signaloid. +# Copyright (c) 2026, Signaloid. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to diff --git a/src/signaloid/distributional_information_plotting/plot_wrapper_test.py b/src/signaloid/distributional_information_plotting/plot_wrapper_test.py index 87edcd0..a2df7c4 100644 --- a/src/signaloid/distributional_information_plotting/plot_wrapper_test.py +++ b/src/signaloid/distributional_information_plotting/plot_wrapper_test.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024, Signaloid. +# Copyright (c) 2026, Signaloid. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to diff --git a/src/signaloid/distributional_information_plotting/sample_generator.py b/src/signaloid/distributional_information_plotting/sample_generator.py new file mode 100644 index 0000000..6009be6 --- /dev/null +++ b/src/signaloid/distributional_information_plotting/sample_generator.py @@ -0,0 +1,216 @@ +# Copyright (c) 2026, Signaloid. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to +# deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +# sell copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. + + +from __future__ import annotations +from signaloid.distributional.distributional import DistributionalValue +from signaloid.distributional_information_plotting.plot_histogram_dirac_deltas import ( + PlotData, +) +import sys + +if sys.implementation.name != "circuitpython": + # Use numpy for accelerated computing + import numpy as np +else: + # Use the extended version of ulab's numpy when running on CircuitPython + from signaloid.circuitpython.extended_ulab_numpy import np # type: ignore[no-redef] + + +def sample_generator(ux_data: str, n_samples: int) -> np.ndarray: + """ + Parse a Ux-encoded string and generate samples from the resulting distribution. + + Args: + ux_data: A Ux-string or Ux-bytes encoding a distributional value. + n_samples: Number of samples to generate. + + Returns: + Array of samples drawn from the distribution. + + Raises: + ValueError: If the Ux-data cannot be parsed into a DistributionalValue. + """ + if n_samples < 1: + raise ValueError(f"n_samples must be >= 1, got {n_samples}.") + + dist_value = DistributionalValue.parse(ux_data) + if dist_value is None: + raise ValueError("Failed to parse Ux-data into DistributionalValue.") + + samples = sample_from_distributional_value(dist_value, n_samples) + return samples + + +def sample_from_distributional_value( + distributional_value: DistributionalValue, n_samples: int +) -> np.ndarray: + """ + Generate samples from a DistributionalValue using inverse CDF sampling. + + For particle distributions (single Dirac delta), returns identical copies + of the particle position. For distributions with non-finite Dirac deltas + (NaN, -Inf, +Inf), samples are drawn from a mixture: with probability + equal to the total finite mass, a finite sample is drawn via inverse CDF; + otherwise a non-finite value is chosen based on its relative mass. + + Args: + distributional_value: The parsed distributional value to sample from. + n_samples: Number of samples to generate. + """ + if n_samples < 1: + raise ValueError(f"n_samples must be >= 1, got {n_samples}.") + + finite_deltas = distributional_value.finite_dirac_deltas + + # Collect non-finite (special) Dirac deltas with non-zero mass. + special_positions: list[float] = [] + special_masses: list[float] = [] + if distributional_value.has_special_values: + for dd in [ + distributional_value.nan_dirac_delta, + distributional_value.neg_inf_dirac_delta, + distributional_value.pos_inf_dirac_delta, + ]: + if dd.mass > 0: + special_positions.append(dd.position) + special_masses.append(dd.mass) + + total_finite_mass = sum(dd.mass for dd in finite_deltas) + total_special_mass = sum(special_masses) + total_mass = total_finite_mass + total_special_mass + + # If there are no finite deltas, sample entirely from special values. + if len(finite_deltas) == 0: + return _sample_special_values( + np.array(special_positions), + np.array(special_masses) / total_mass, + n_samples, + ) + + # If there is exactly one finite delta and no special values, return + # identical copies (particle distribution). + if len(finite_deltas) == 1 and total_special_mass == 0: + return np.full(n_samples, finite_deltas[0].position) + + # If there are no special values, sample entirely from finite deltas. + if total_special_mass == 0: + return _sample_finite(distributional_value, n_samples) + + # Mixed case: decide per-sample whether to draw from finite or special. + finite_prob = total_finite_mass / total_mass + us = np.random.uniform(0, 1, size=n_samples) + is_finite = us < finite_prob + + n_finite = int(np.sum(is_finite)) + n_special = n_samples - n_finite + + samples = np.empty(n_samples) + + if n_finite > 0: + if len(finite_deltas) == 1: + samples[is_finite] = finite_deltas[0].position + else: + samples[is_finite] = _sample_finite(distributional_value, n_finite) + + if n_special > 0: + special_probs = np.array(special_masses) / total_special_mass + samples[~is_finite] = _sample_special_values( + np.array(special_positions), special_probs, n_special + ) + + return samples + + +def _sample_finite( + distributional_value: DistributionalValue, n_samples: int +) -> np.ndarray: + """Sample from the finite part of a distributional value via inverse CDF.""" + distributional_value.drop_zero_mass_positions() + distributional_value.combine_dirac_deltas() + boundary_positions, bin_widths, bin_heights = PlotData.create_binning( + distributional_value.finite_dirac_deltas, 0, False + ) + cdf_values = generate_cdf_values(boundary_positions, bin_widths, bin_heights) + return generate_samples(cdf_values, boundary_positions, n_samples) + + +def _sample_special_values( + positions: np.ndarray, probabilities: np.ndarray, n_samples: int +) -> np.ndarray: + """Sample non-finite values (NaN, -Inf, +Inf) according to their masses.""" + cumulative = np.cumsum(probabilities) + indices = np.searchsorted(cumulative, np.random.uniform(0, 1, size=n_samples)) + indices = np.clip(indices, 0, len(positions) - 1) + return positions[indices] + + +def generate_samples( + cdf_values: np.ndarray, boundary_positions: np.ndarray, n: int +) -> np.ndarray: + """ + Generate samples via inverse CDF sampling with linear interpolation. + + Draws uniform random values and maps them through the inverse CDF by + locating the corresponding bin and linearly interpolating within it. + + Args: + cdf_values: Cumulative distribution function values at each bin boundary. + boundary_positions: Positions of the bin boundaries. + n: Number of samples to generate. + + Returns: + Array of samples drawn from the distribution. + """ + 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) + samples = boundary_positions[indices] + ( + boundary_positions[indices + 1] - boundary_positions[indices] + ) * (us - cdf_values[indices]) / (cdf_values[indices + 1] - cdf_values[indices]) + return np.asarray(samples) + + +def generate_cdf_values( + boundary_positions: np.ndarray, bin_widths: np.ndarray, bin_heights: np.ndarray +) -> np.ndarray: + """ + Compute CDF values at each bin boundary from a binned density histogram. + + The CDF is computed as the cumulative sum of bin areas (width * height), + starting from zero at the first boundary. + + Args: + boundary_positions: Positions of the bin boundaries. + bin_widths: Width of each bin. + bin_heights: Height (density) of each bin. + + Returns: + Array of CDF values at each bin boundary, starting at 0.0. + """ + cdf_values = np.zeros(len(boundary_positions)) + cdf_values[1:] = np.cumsum(bin_widths * bin_heights) + total_area = cdf_values[-1] + if total_area > 0.0: + # Normalize to ensure the CDF spans [0, 1], avoiding sampling issues + # when drawing u ~ U(0, 1) in generate_samples. + cdf_values /= total_area + cdf_values[-1] = 1.0 + return cdf_values diff --git a/src/signaloid/distributional_information_plotting/sample_generator_test.py b/src/signaloid/distributional_information_plotting/sample_generator_test.py new file mode 100644 index 0000000..6288f23 --- /dev/null +++ b/src/signaloid/distributional_information_plotting/sample_generator_test.py @@ -0,0 +1,123 @@ +# Copyright (c) 2026, Signaloid. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to +# deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +# sell copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. + + +import unittest +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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nanUx0400000000000000017FF8000000000000000000063FF0000000000000155555555555550040080000000000001555555555555500000000000000000015555555555555007FF80000000000001555555555555500FFF000000000000015555555555555007FF00000000000001555555555555500" + + +# ============================================================================ +# Unit Tests: sample_generator +# ============================================================================ + + +class TestSampleGenerator(unittest.TestCase): + """Tests for the sample_generator function.""" + + def test_particle_returns_identical_samples(self) -> None: + """A particle (single value) should return n identical samples.""" + particle_ux = "0.785398Ux0400000000000000003FE921FB54442D18000000013FE921FB54442D188000000000000000" + n = 10 + samples = sample_generator(particle_ux, n) + + self.assertEqual(len(samples), n) + self.assertTrue(np.all(samples == samples[0])) + + def test_distribution_returns_correct_number_of_samples(self) -> None: + """Should return exactly n_samples samples.""" + n = 100 + samples = sample_generator(SAMPLE_UX_STRING, n) + + self.assertEqual(len(samples), n) + + def test_samples_are_finite(self) -> None: + """All samples should be finite float values.""" + n = 1000 + samples = sample_generator(SAMPLE_UX_STRING, n) + + self.assertIsInstance(samples, np.ndarray) + self.assertTrue(np.all(np.isfinite(samples))) + + def test_invalid_ux_data_raises(self) -> None: + """Should raise ValueError for invalid Ux-data.""" + with self.assertRaises(ValueError): + sample_generator("invalid_data", 10) + + def test_zero_samples_raises(self) -> None: + """Should raise ValueError when n_samples is 0.""" + with self.assertRaises(ValueError): + sample_generator(SAMPLE_UX_STRING, 0) + + def test_negative_samples_raises(self) -> None: + """Should raise ValueError when n_samples is negative.""" + with self.assertRaises(ValueError): + sample_generator(SAMPLE_UX_STRING, -1) + + def test_special_values_returns_correct_count(self) -> None: + """Sampling from a distribution with NaN/Inf should return n samples.""" + n = 1000 + samples = sample_generator(SAMPLE_UX_STRING_WITH_SPECIAL_VALUES, n) + self.assertEqual(len(samples), n) + + def test_special_values_contains_non_finite(self) -> None: + """Samples from a distribution with NaN/Inf should include non-finite values.""" + n = 10_000 + np.random.seed(42) + samples = sample_generator(SAMPLE_UX_STRING_WITH_SPECIAL_VALUES, n) + n_non_finite = np.sum(~np.isfinite(samples)) + self.assertGreater(n_non_finite, 0) + + def test_special_values_contains_nan(self) -> None: + """Samples should include NaN when the distribution has NaN mass.""" + n = 10_000 + np.random.seed(42) + samples = sample_generator(SAMPLE_UX_STRING_WITH_SPECIAL_VALUES, n) + self.assertGreater(np.sum(np.isnan(samples)), 0) + + def test_statistical_sanity(self) -> None: + """Verify sample mean and std are close to distribution parameters.""" + n = 10_000 + np.random.seed(0) + samples = sample_generator(SAMPLE_UX_STRING, n) + + dist_value = DistributionalValue.parse(SAMPLE_UX_STRING) + if dist_value is None: + raise ValueError("Failed to parse Ux-data into DistributionalValue.") + + if dist_value.mean is not None and dist_value.variance is not None: + std = np.sqrt(dist_value.variance) + + threshold_mean = 5 * std / np.sqrt(n) + self.assertLess(np.abs(np.mean(samples) - dist_value.mean), threshold_mean) + + threshold_std = 5 * std / np.sqrt(2 * n) + self.assertLess(np.abs(np.std(samples) - std), threshold_std) + + +if __name__ == "__main__": + unittest.main() diff --git a/src/signaloid/uxdata_toolkit.py b/src/signaloid/uxdata_toolkit.py index 553756e..e85ece3 100644 --- a/src/signaloid/uxdata_toolkit.py +++ b/src/signaloid/uxdata_toolkit.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python3 - # Copyright (c) 2026, Signaloid. # # Permission is hereby granted, free of charge, to any person obtaining a copy @@ -25,27 +23,96 @@ A set of tools for working with Signaloid Ux data. +Ux-string format specification: + - Particle value (double in string format) + - "Ux" ( 2 chars) + - Representation type (uint8_t) ( 2 chars) + - Number of samples (uint64_t) ( 16 chars) (unused) + - Mean value of distribution (double) ( 16 chars) + - Number of non-zero mass Dirac deltas (uint32_t) ( 8 chars) + - Pairs of: + - Support position (float/double) (8/16 chars) + - Probability mass (uint64_t) ( 16 chars) + +Ux-bytes specification: + - Particle value (double) ( 8 bytes) + - Representation type (uint8_t) ( 1 byte ) + - Number of samples (uint64_t) ( 8 bytes) (unused) + - Mean value of distribution (double) ( 8 bytes) + - Number of non-zero mass Dirac deltas (uint32_t) ( 4 bytes) + - Pairs of: + - Support position (float/double) (4/8 bytes) + - Probability mass (uint64_t) ( 8 bytes) + Requirements: - pip install git+https://github.com/signaloid/signaloid-python """ +from __future__ import annotations import argparse import sys import traceback from typing import Any - -import matplotlib -import matplotlib.pyplot as plt - 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 +from signaloid.distributional_information_plotting.sample_generator import ( + sample_from_distributional_value, +) +import numpy as np + + +def _parse_ux_data(ux_data_raw: str) -> DistributionalValue: + """Validate, parse, and print info for a Ux-data string. + + Args: + ux_data_raw: Raw Ux-string or Ux-bytes from CLI input. + + Returns: + The parsed DistributionalValue. + + Raises: + SystemExit: If the input is empty. + ValueError: If the input cannot be parsed into a DistributionalValue. + """ + ux_data = ux_data_raw.strip() + + if not ux_data: + print("Error: --ux-data cannot be an empty string.") + sys.exit(1) + + print("\nParsing Ux-data...") + + dist_value = DistributionalValue.parse(ux_data) + if dist_value is None: + raise ValueError("Failed to parse Ux-data into DistributionalValue.") + print("Successfully parsed Ux-data!") + print(f"Particle value: {dist_value.particle_value}") + print(f"Mean: {dist_value.mean}") + print(f"Variance: {dist_value.variance}") + print(f"Number of Dirac deltas: {dist_value.UR_order}") + print(f"Double Precision: {dist_value.double_precision}") -def parse_arguments() -> argparse.Namespace: - # Create the parser + return dist_value + + +def _positive_int(value: str) -> int: + """Argparse type that enforces a positive integer (>= 1).""" + try: + ivalue = int(value) + except ValueError: + raise argparse.ArgumentTypeError(f"invalid int value: '{value}'") + if ivalue < 1: + raise argparse.ArgumentTypeError( + f"--num-samples must be a positive integer (>= 1), got {ivalue}" + ) + return ivalue + + +def parse_arguments(argv: list[str] | None = None) -> argparse.Namespace: parser = argparse.ArgumentParser( description="Set of tools for working with Signaloid Ux data." ) @@ -55,11 +122,10 @@ def parse_arguments() -> argparse.Namespace: title="Command", ) - # Register all command parsers command_plot_parser(command_subparsers) + command_sample_parser(command_subparsers) - # Parse the arguments - args = parser.parse_args() + args = parser.parse_args(argv) return args @@ -71,9 +137,9 @@ def command_plot_parser(command_subparsers: Any) -> None: formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Example: - %(prog)s 0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000 - %(prog)s 09168733bf9ad93f000100000000000000c7c72324c19ad93f01000000c7c72324c19ad93f0000000000000080 - %(prog)s -o output.png 0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000 + %(prog)s --ux-data=0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000 + %(prog)s --ux-data=09168733bf9ad93f000100000000000000c7c72324c19ad93f01000000c7c72324c19ad93f0000000000000080 + %(prog)s -o output.png --ux-data=0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000 """, ) @@ -81,90 +147,45 @@ def command_plot_parser(command_subparsers: Any) -> None: "-o", "--output", type=str, - default="plot.png", - help="Output PNG filename (default: plot.png)", + help="Output PNG filename (if not specified, displays plot interactively)", ) plot_parser.add_argument( - "ux_data", + "--ux-data", type=str, + required=True, help='Ux-string or Ux-bytes to plot (e.g., "90.6Ux04000000...")', ) + plot_parser.set_defaults(func=command_plot) + def command_plot(args: argparse.Namespace) -> None: - """ - Ux-Data Plotter - - This script reads a Signaloid Ux-string or Ux-bytes from command-line arguments and plots - the distributional data using the signaloid-python library. - - Ux-string format specification: - - Particle value (double in string format) - - "Ux" ( 2 chars) - - Representation type (uint8_t) ( 2 chars) - - Number of samples (uint64_t) ( 16 chars) (unused) - - Mean value of distribution (double) ( 16 chars) - - Number of non-zero mass Dirac deltas (uint32_t) ( 8 chars) - - Pairs of: - - Support position (float/double) (8/16 chars) - - Probability mass (uint64_t) ( 16 chars) - - Ux-bytes specification: - - Particle value (double) ( 8 bytes) - - Representation type (uint8_t) ( 1 byte ) - - Number of samples (uint64_t) ( 8 bytes) (unused) - - Mean value of distribution (double) ( 8 bytes) - - Number of non-zero mass Dirac deltas (uint32_t) ( 4 bytes) - - Pairs of: - - Support position (float/double) (4/8 bytes) - - Probability mass (uint64_t) ( 8 bytes) + """Plot a Signaloid Ux distributional value. Example usage: - signaloid-uxdata-toolkit plot 0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000 - signaloid-uxdata-toolkit plot 09168733bf9ad93f000100000000000000c7c72324c19ad93f01000000c7c72324c19ad93f0000000000000080 - signaloid-uxdata-toolkit plot -o output.png 0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000 + signaloid-uxdata-toolkit plot --ux-data=0.40007Ux000... + signaloid-uxdata-toolkit plot -o output.png --ux-data=0.40007Ux000... """ - ux_data: str = args.ux_data.strip() - print("Signaloid Ux-data Plotter") print("=" * 50) - # Validate input - if not ux_data: - print("Error: Empty Ux-data provided.") - sys.exit(1) - try: - # Parse the Ux string into a DistributionalValue object - print("\nParsing Ux-data...") - - dist_value = DistributionalValue.parse(ux_data) - if dist_value is None: - raise ValueError("Failed to parse Ux-data into DistributionalValue.") - - # Display basic information about the distribution - print("Successfully parsed Ux-data!") - print(f"Particle value: {dist_value.particle_value}") - print(f"Mean: {dist_value.mean}") - print(f"Variance: {dist_value.variance}") - print(f"Number of Dirac deltas: {dist_value.UR_order}") - print(f"Double Precision: {dist_value.double_precision}") + dist_value = _parse_ux_data(args.ux_data) # Create PlotData object from the distributional value print("\nPreparing plot data...") plot_data = PlotData(dist_value) - # Set matplotlib to non-interactive backend for saving - matplotlib.use("Agg") - # Plot the distribution using the plot wrapper function print("Generating plot...") - plot(plot_data) - # Save the figure to file - plt.savefig(args.output, dpi=300, bbox_inches="tight") - print(f"✓ Plot saved to: {args.output}") + if args.output is None: + # Show the plot interactively + plot(plot_data, save=False) + else: + plot(plot_data, path=args.output, save=True) + except Exception as e: print(f"\nError parsing or plotting Ux-data: {e}") traceback.print_exc() @@ -172,15 +193,86 @@ def command_plot(args: argparse.Namespace) -> None: sys.exit(1) -def main() -> None: +def command_sample_parser(command_subparsers: Any) -> None: + sample_parser = command_subparsers.add_parser( + "sample", + help="Sample from Signaloid Ux distributional data", + description="Sample from Signaloid Ux distributional data", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Example: +%(prog)s --ux-data=0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000 --num-samples 10 +%(prog)s --ux-data=09168733bf9ad93f000100000000000000c7c72324c19ad93f01000000c7c72324c19ad93f0000000000000080 --num-samples 100 +%(prog)s -o samples.txt --ux-data=0.40007Ux0000000000000000013FD99AC12423C7C7000000013FD99AC12423C7C78000000000000000 --num-samples 10 + """, + ) + + sample_parser.add_argument( + "-o", + "--output", + type=str, + help="Output sample filename (example: samples.txt)", + ) + + sample_parser.add_argument( + "--num-samples", + type=_positive_int, + required=True, + help="Number of samples to generate (must be >= 1)", + ) + + sample_parser.add_argument( + "--ux-data", + type=str, + required=True, + help='Ux-string or Ux-bytes to sample from (e.g., "90.6Ux04000000...")', + ) + + sample_parser.set_defaults(func=command_sample) + + +def command_sample(args: argparse.Namespace) -> None: + """Sample from a Signaloid Ux distributional value. + + Example usage: + signaloid-uxdata-toolkit sample --ux-data=0.40007Ux000... --num-samples 10 + signaloid-uxdata-toolkit sample -o samples.txt --ux-data=0.40007Ux000... --num-samples 100 + """ + print("Signaloid Ux-data Sampler") + print("=" * 50) + + try: + dist_value = _parse_ux_data(args.ux_data) + + # Generate the samples + print("Generating samples...") + samples = sample_from_distributional_value(dist_value, args.num_samples) + + # Output the samples + if args.output: + np.savetxt(args.output, samples, delimiter=",") + print(f"Saved samples to: {args.output}") + else: + for sample in samples: + print(sample) + except Exception as e: + print(f"\nError parsing or sampling Ux-data: {e}") + traceback.print_exc() + print("\nPlease ensure you have a valid Ux-data format.") + sys.exit(1) + + +def main(argv: list[str] | None = None) -> None: """ Main function to parse and run the selected command. """ # Parse arguments - args: argparse.Namespace = parse_arguments() + args: argparse.Namespace = parse_arguments(argv) if args.command == "plot": command_plot(args) + elif args.command == "sample": + command_sample(args) else: print("Unknown command. Use -h for help.") sys.exit(1) diff --git a/src/signaloid/uxdata_toolkit_test.py b/src/signaloid/uxdata_toolkit_test.py new file mode 100644 index 0000000..6a970ae --- /dev/null +++ b/src/signaloid/uxdata_toolkit_test.py @@ -0,0 +1,393 @@ +# Copyright (c) 2026, Signaloid. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to +# deal in the Software without restriction, including without limitation the +# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +# sell copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. + + +"""Tests for signaloid-uxdata-toolkit CLI.""" + +import argparse +import tempfile +import unittest +from pathlib import Path + +import numpy as np + +import signaloid.uxdata_toolkit as toolkit +from signaloid.distributional.distributional import DistributionalValue + + +# ============================================================================ +# Constants +# ============================================================================ + +SAMPLE_UX_STRING = "-0.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bf9ad93f000100000000000000c7c72324c19ad93f01000000c7c72324c19ad93f0000000000000080" +SAMPLE_UX_STRING_WITH_SPECIAL_VALUES = "nanUx0400000000000000017FF8000000000000000000063FF0000000000000155555555555550040080000000000001555555555555500000000000000000015555555555555007FF80000000000001555555555555500FFF000000000000015555555555555007FF00000000000001555555555555500" + + +# ============================================================================ +# Unit Tests: Argument Parsing +# ============================================================================ + + +class TestArgumentParsing(unittest.TestCase): + """Tests for CLI argument parsing.""" + + def test_plot_command_basic(self) -> None: + args = toolkit.parse_arguments(["plot", f"--ux-data={SAMPLE_UX_STRING}"]) + self.assertEqual(args.command, "plot") + self.assertEqual(args.ux_data, SAMPLE_UX_STRING) + self.assertIsNone(args.output) + + def test_plot_command_custom_output(self) -> None: + args = toolkit.parse_arguments( + ["plot", "-o", "custom.png", f"--ux-data={SAMPLE_UX_STRING}"] + ) + self.assertEqual(args.output, "custom.png") + + def test_sample_command_basic(self) -> None: + num_samples = 50 + args = toolkit.parse_arguments( + [ + "sample", + "--num-samples", + str(num_samples), + f"--ux-data={SAMPLE_UX_STRING}", + ] + ) + self.assertEqual(args.command, "sample") + self.assertEqual(args.ux_data, SAMPLE_UX_STRING) + self.assertIsNone(args.output) + self.assertEqual(args.num_samples, num_samples) + + def test_sample_command_with_num_samples(self) -> None: + args = toolkit.parse_arguments( + ["sample", "--num-samples", "500", f"--ux-data={SAMPLE_UX_STRING}"] + ) + self.assertEqual(args.num_samples, 500) + + def test_sample_command_custom_output(self) -> None: + args = toolkit.parse_arguments( + [ + "sample", + "-o", + "out.txt", + "--num-samples", + "10", + f"--ux-data={SAMPLE_UX_STRING}", + ] + ) + self.assertEqual(args.output, "out.txt") + + def test_no_command_provided(self) -> None: + args = toolkit.parse_arguments([]) + self.assertIsNone(args.command) + + def test_plot_missing_ux_data(self) -> None: + with self.assertRaises(SystemExit): + toolkit.parse_arguments(["plot"]) + + def test_sample_missing_ux_data(self) -> None: + with self.assertRaises(SystemExit): + toolkit.parse_arguments(["sample", "--num-samples", "10"]) + + def test_sample_missing_num_samples(self) -> None: + with self.assertRaises(SystemExit): + toolkit.parse_arguments(["sample", "--ux-data", SAMPLE_UX_STRING]) + + def test_sample_zero_num_samples_rejected(self) -> None: + with self.assertRaises(SystemExit): + toolkit.parse_arguments( + ["sample", "--num-samples", "0", "--ux-data", SAMPLE_UX_BYTES] + ) + + def test_sample_negative_num_samples_rejected(self) -> None: + with self.assertRaises(SystemExit): + toolkit.parse_arguments( + ["sample", "--num-samples", "-5", "--ux-data", SAMPLE_UX_BYTES] + ) + + +# ============================================================================ +# Unit Tests: command_plot +# ============================================================================ + + +class TestCommandPlot(unittest.TestCase): + """Tests for the plot command handler.""" + + def _make_plot_args(self, tmp_dir: str, **overrides: object) -> argparse.Namespace: + defaults: dict[str, object] = { + "command": "plot", + "ux_data": SAMPLE_UX_STRING, + "output": str(Path(tmp_dir) / "test_plot.png"), + } + defaults.update(overrides) + return argparse.Namespace(**defaults) + + def test_plot_saves_file(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_plot_args(tmp_dir) + toolkit.command_plot(args) + self.assertTrue(Path(args.output).exists()) + + def test_plot_with_ux_bytes(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_plot_args(tmp_dir, ux_data=SAMPLE_UX_BYTES) + toolkit.command_plot(args) + self.assertTrue(Path(args.output).exists()) + + def test_plot_output_is_nonzero_size(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_plot_args(tmp_dir) + toolkit.command_plot(args) + self.assertGreater(Path(args.output).stat().st_size, 0) + + def test_plot_invalid_ux_data(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_plot_args(tmp_dir, ux_data="invalid_data") + with self.assertRaises(SystemExit): + toolkit.command_plot(args) + + def test_plot_empty_ux_data(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_plot_args(tmp_dir, ux_data=" ") + with self.assertRaises(SystemExit): + toolkit.command_plot(args) + + def test_plot_strips_whitespace(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_plot_args(tmp_dir, ux_data=f" {SAMPLE_UX_STRING} ") + toolkit.command_plot(args) + self.assertTrue(Path(args.output).exists()) + + def test_plot_with_special_values(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_plot_args( + tmp_dir, ux_data=SAMPLE_UX_STRING_WITH_SPECIAL_VALUES + ) + toolkit.command_plot(args) + self.assertTrue(Path(args.output).exists()) + self.assertGreater(Path(args.output).stat().st_size, 0) + + +# ============================================================================ +# Unit Tests: command_sample +# ============================================================================ + + +class TestCommandSample(unittest.TestCase): + """Tests for the sample command handler.""" + + def _make_sample_args( + self, tmp_dir: str, **overrides: object + ) -> argparse.Namespace: + defaults = { + "command": "sample", + "ux_data": SAMPLE_UX_STRING, + "output": str(Path(tmp_dir) / "test_samples.txt"), + "num_samples": 100, + } + defaults.update(overrides) + return argparse.Namespace(**defaults) + + def test_sample_saves_file(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_sample_args(tmp_dir) + toolkit.command_sample(args) + self.assertTrue(Path(args.output).exists()) + + def test_sample_correct_count(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_sample_args(tmp_dir) + toolkit.command_sample(args) + data = np.loadtxt(args.output, delimiter=",") + self.assertEqual(data.size, args.num_samples) + + def test_sample_with_ux_bytes(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_sample_args( + tmp_dir, ux_data=SAMPLE_UX_BYTES, num_samples=50 + ) + toolkit.command_sample(args) + data = np.loadtxt(args.output, delimiter=",") + self.assertEqual(data.size, 50) + + def test_sample_values_are_finite(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_sample_args(tmp_dir) + toolkit.command_sample(args) + data = np.loadtxt(args.output, delimiter=",") + self.assertTrue(np.all(np.isfinite(data))) + + def test_sample_invalid_ux_data(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_sample_args( + tmp_dir, ux_data="invalid_data", num_samples=10 + ) + with self.assertRaises(SystemExit): + toolkit.command_sample(args) + + def test_sample_empty_ux_data(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_sample_args(tmp_dir, ux_data=" ", num_samples=10) + with self.assertRaises(SystemExit): + toolkit.command_sample(args) + + def test_sample_strips_whitespace(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_sample_args( + tmp_dir, ux_data=f" {SAMPLE_UX_STRING} ", num_samples=10 + ) + toolkit.command_sample(args) + self.assertTrue(Path(args.output).exists()) + + def test_sample_single_sample(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_sample_args(tmp_dir, num_samples=1) + toolkit.command_sample(args) + data = np.loadtxt(args.output, delimiter=",") + self.assertEqual(data.size, 1) + + def test_sample_with_special_values(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + args = self._make_sample_args( + tmp_dir, + ux_data=SAMPLE_UX_STRING_WITH_SPECIAL_VALUES, + num_samples=100, + ) + toolkit.command_sample(args) + self.assertTrue(Path(args.output).exists()) + data = np.loadtxt(args.output, delimiter=",") + self.assertEqual(data.size, 100) + + +# ============================================================================ +# Unit Tests: main() +# ============================================================================ + + +class TestMain(unittest.TestCase): + """Tests for the main dispatch function.""" + + def test_main_no_command_exits(self) -> None: + with self.assertRaises(SystemExit): + toolkit.main(argv=[]) + + +# ============================================================================ +# Integration Tests +# ============================================================================ + + +class TestIntegrationPlot(unittest.TestCase): + """Integration tests for the full plot pipeline.""" + + def test_plot_end_to_end_ux_string(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + output = str(Path(tmp_dir) / "e2e_plot.png") + args = toolkit.parse_arguments( + ["plot", "-o", output, f"--ux-data={SAMPLE_UX_STRING}"] + ) + toolkit.command_plot(args) + + output_path = Path(output) + self.assertTrue(output_path.exists()) + self.assertGreater(output_path.stat().st_size, 0) + + def test_plot_end_to_end_ux_bytes(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + output = str(Path(tmp_dir) / "e2e_bytes_plot.png") + args = toolkit.parse_arguments( + ["plot", "-o", output, "--ux-data", SAMPLE_UX_BYTES] + ) + toolkit.command_plot(args) + + output_path = Path(output) + self.assertTrue(output_path.exists()) + self.assertGreater(output_path.stat().st_size, 0) + + +class TestIntegrationSample(unittest.TestCase): + """Integration tests for the full sample pipeline.""" + + def test_sample_end_to_end(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + output = str(Path(tmp_dir) / "e2e_samples.txt") + n_samples = 50 + args = toolkit.parse_arguments( + [ + "sample", + "-o", + output, + "--num-samples", + str(n_samples), + f"--ux-data={SAMPLE_UX_STRING}", + ] + ) + toolkit.command_sample(args) + + data = np.loadtxt(output, delimiter=",") + self.assertEqual(data.size, n_samples) + self.assertTrue(np.all(np.isfinite(data))) + + def test_sample_default_output_filename(self) -> None: + args = toolkit.parse_arguments( + ["sample", "--num-samples", "1", f"--ux-data={SAMPLE_UX_STRING}"] + ) + self.assertIsNone(args.output) + + def test_sample_statistical_sanity(self) -> None: + """Verify that samples have the correct statistics.""" + with tempfile.TemporaryDirectory() as tmp_dir: + output = str(Path(tmp_dir) / "stats_samples.txt") + n_samples = 100_000 + args = toolkit.parse_arguments( + [ + "sample", + "-o", + output, + "--num-samples", + str(n_samples), + f"--ux-data={SAMPLE_UX_STRING}", + ] + ) + toolkit.command_sample(args) + + data = np.loadtxt(output, delimiter=",") + self.assertEqual(data.size, n_samples) + + dist_value = DistributionalValue.parse(SAMPLE_UX_STRING) + if dist_value is None: + raise ValueError("Failed to parse Ux-data into DistributionalValue.") + + if dist_value.mean is not None and dist_value.variance is not None: + threshold_mean = 5 * np.sqrt(dist_value.variance) / np.sqrt(n_samples) + threshold_std = ( + 5 * np.sqrt(dist_value.variance) / np.sqrt(2 * n_samples) + ) + + 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 + ) + + +if __name__ == "__main__": + unittest.main()