diff --git a/docs/docs/tutorials/diffusion_model.ipynb b/docs/docs/tutorials/diffusion_model.ipynb index e3d613284..fa264b753 100644 --- a/docs/docs/tutorials/diffusion_model.ipynb +++ b/docs/docs/tutorials/diffusion_model.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Diffusion Model\n", - "We support several standard models of diffusion. Here we show an example of Brownian Translational Diffusion, where the scattering is a Lorentzian with width ($\\Gamma$) given by $\\Gamma = D Q^2$, where $D$ is the diffusion coefficient (in m$^2$/s) and $Q$ is the momentum transfer." + "We support several standard models of diffusion. Here we show examples of Brownian Translational Diffusion, Jump Translational Diffusion and Chudley-Elliott Jump Diffusion. In each model the scattering is a Lorentzian for every $Q$, and the models differ in how the width $\\Gamma(Q)$ depends on $Q$. For Brownian Translational Diffusion, the width is given by $\\Gamma = D Q^2$, where $D$ is the diffusion coefficient (in m$^2$/s) and $Q$ is the momentum transfer." ] }, { @@ -24,10 +24,26 @@ "%matplotlib widget" ] }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "## Brownian Translational Diffusion" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "### Model for different Q values" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "2", + "id": "4", "metadata": {}, "outputs": [], "source": [ @@ -62,10 +78,18 @@ "plt.title('Brownian Translational Diffusion Model')" ] }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "### Width as a function of Q" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "3", + "id": "6", "metadata": {}, "outputs": [], "source": [ @@ -81,6 +105,196 @@ "plt.ylim(0, max(HWHM) * 1.1)\n", "plt.title('HWHM vs Q for Brownian Translational Diffusion')" ] + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": [ + "## Jump Translational Diffusion\n", + "In Jump Translational Diffusion, the particle waits for a relaxation time $t$ between jumps. At low $Q$ the width follows the Brownian result, $\\Gamma \\approx \\hbar D Q^2$, but at high $Q$ it levels off:\n", + "\n", + "$$\\Gamma(Q) = \\frac{\\hbar D Q^2}{1 + D t Q^2},$$\n", + "\n", + "where $D$ is the diffusion coefficient (in m$^2$/s) and $t$ is the relaxation time (in ps)." + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "### Model for different Q values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "# Create Jump Translational Diffusion model\n", + "# and plot the model for different Q values.\n", + "# Q is in Angstrom^-1 and energy in meV.\n", + "\n", + "Q = np.linspace(0.5, 2, 7)\n", + "energy = np.linspace(-2, 2, 501)\n", + "scale = 1.0\n", + "diffusion_coefficient = 2.4e-9 # m^2/s\n", + "relaxation_time = 1.0 # ps\n", + "\n", + "jump_diffusion_model = edyn.JumpTranslationalDiffusion(\n", + " display_name='JumpDiffusionModel',\n", + " scale=scale,\n", + " diffusion_coefficient=diffusion_coefficient,\n", + " relaxation_time=relaxation_time,\n", + " Q=Q,\n", + ")\n", + "\n", + "component_collections = jump_diffusion_model.get_component_collections()\n", + "\n", + "cmap = plt.cm.jet\n", + "nQ = len(component_collections)\n", + "plt.figure()\n", + "for Q_index in range(len(component_collections)):\n", + " color = cmap(Q_index / (nQ - 1))\n", + " y = component_collections[Q_index].evaluate(energy)\n", + " plt.plot(energy, y, label=f'Q={Q[Q_index]} Å^-1', color=color)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Energy (meV)')\n", + "plt.ylabel('Intensity (arb. units)')\n", + "plt.title('Jump Translational Diffusion Model')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "### Width as a function of Q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate and plot the half width at half maximum (HWHM) as function\n", + "# of Q\n", + "Q = np.linspace(0.1, 2, 101)\n", + "HWHM = jump_diffusion_model.calculate_width(Q)\n", + "plt.figure()\n", + "plt.plot(Q, HWHM)\n", + "plt.xlabel('Q (Å$^{-1}$)')\n", + "plt.ylabel('HWHM (meV)')\n", + "plt.xlim(0, 2.5)\n", + "plt.ylim(0, max(HWHM) * 1.1)\n", + "plt.title('HWHM vs Q for Jump Translational Diffusion')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "## Chudley-Elliott Jump Diffusion\n", + "The Chudley-Elliott model of\n", + "\n", + "> C. T. Chudley and R. J. Elliott, *Neutron Scattering from a Liquid on a Jump Diffusion Model*,\n", + "> Proc. Phys. Soc. **77**, 353 (1961),\n", + "> [doi:10.1088/0370-1328/77/2/319](https://doi.org/10.1088/0370-1328/77/2/319).\n", + "\n", + "describes a particle that sits on a site for a residence time $\\tau$ and then jumps a fixed distance $l$. The width is\n", + "\n", + "$$\\Gamma(Q) = \\frac{\\hbar}{\\tau}\\left(1 - \\frac{\\sin(Ql)}{Ql}\\right),$$\n", + "\n", + "where $\\tau$ is the residence time (in ps) and $l$ is the jump length (in Å). The width oscillates with $Q$ and approaches $\\hbar/\\tau$ at high $Q$." + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "### Model for different Q values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "# Create Chudley-Elliott Jump Diffusion model\n", + "# and plot the model for different Q values.\n", + "# Q is in Angstrom^-1 and energy in meV.\n", + "\n", + "Q = np.linspace(0.5, 2, 7)\n", + "energy = np.linspace(-2, 2, 501)\n", + "scale = 1.0\n", + "residence_time = 1.0 # ps\n", + "jump_length = 1.5 # Å\n", + "\n", + "chudley_elliott_model = edyn.ChudleyElliottJumpDiffusion(\n", + " display_name='ChudleyElliottModel',\n", + " scale=scale,\n", + " residence_time=residence_time,\n", + " jump_length=jump_length,\n", + " Q=Q,\n", + ")\n", + "\n", + "component_collections = chudley_elliott_model.get_component_collections()\n", + "\n", + "cmap = plt.cm.jet\n", + "nQ = len(component_collections)\n", + "plt.figure()\n", + "for Q_index in range(len(component_collections)):\n", + " color = cmap(Q_index / (nQ - 1))\n", + " y = component_collections[Q_index].evaluate(energy)\n", + " plt.plot(energy, y, label=f'Q={Q[Q_index]} Å^-1', color=color)\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Energy (meV)')\n", + "plt.ylabel('Intensity (arb. units)')\n", + "plt.title('Chudley-Elliott Jump Diffusion Model')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "### Width as a function of Q" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate and plot the half width at half maximum (HWHM) as function\n", + "# of Q\n", + "Q = np.linspace(0.1, 4, 201)\n", + "HWHM = chudley_elliott_model.calculate_width(Q)\n", + "plt.figure()\n", + "plt.plot(Q, HWHM)\n", + "plt.xlabel('Q (Å$^{-1}$)')\n", + "plt.ylabel('HWHM (meV)')\n", + "plt.xlim(0, 4.2)\n", + "plt.ylim(0, max(HWHM) * 1.1)\n", + "plt.title('HWHM vs Q for Chudley-Elliott Jump Diffusion')\n", + "plt.show()" + ] } ], "metadata": { diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md index 10e87adb5..a622ec531 100644 --- a/docs/docs/tutorials/index.md +++ b/docs/docs/tutorials/index.md @@ -49,7 +49,8 @@ tutorials. - 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\frac{\sin(Q l)}{Q l} \right) $$ + + where $\tau$ is the residence time and $l$ is the jump length. $Q$ is assumed to have units of + 1/angstrom. Creates ComponentCollections with Lorentzian components for given Q-values. + + References + ---------- + C. T. Chudley and R. J. Elliott, *Neutron Scattering from a Liquid on a Jump Diffusion Model*, + Proc. Phys. Soc. **77**, 353 (1961), + [doi:10.1088/0370-1328/77/2/319](https://doi.org/10.1088/0370-1328/77/2/319). + + Examples + -------- + **Creating a ChudleyElliottJumpDiffusion model** + + Pass the residence time (in ps) and jump length (in angstroms) along with Q values: + ```python + import numpy as np + import easydynamics as edyn + + Q = np.linspace(0.5, 2, 7) + diffusion_model = edyn.ChudleyElliottJumpDiffusion( + scale=1.0, + residence_time=1.0, + jump_length=1.5, + Q=Q, + ) + component_collections = diffusion_model.create_component_collections() + ``` + + See also the tutorials. + """ + + def __init__( + self, + scale: Numeric = 1.0, + residence_time: Numeric = 1.0, + jump_length: Numeric = 1.0, + Q: Q_type | None = None, + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', + name: str = 'ChudleyElliottJumpDiffusion', + display_name: str | None = 'ChudleyElliottJumpDiffusion', + lorentzian_name: str | None = None, + lorentzian_display_name: str | None = None, + unique_name: str | None = None, + ) -> None: + """ + Initialize a new ChudleyElliottJumpDiffusion model. + + Parameters + ---------- + scale : Numeric, default=1.0 + Scale factor for the diffusion model. Must be a non-negative number. + residence_time : Numeric, default=1.0 + Residence time parameter tau in ps. + jump_length : Numeric, default=1.0 + Jump length parameter l in angstrom. + Q : Q_type | None, default=None + Q values for the model. If None, Q is not set. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis (energy/frequency). Must be convertible to meV. + y_unit : str | sc.Unit, default='dimensionless' + Unit of the model output (intensity). Determines scale.unit = x_unit * y_unit. + name : str, default='ChudleyElliottJumpDiffusion' + Name of the diffusion model. + display_name : str | None, default='ChudleyElliottJumpDiffusion' + Display name of the diffusion model. + lorentzian_name : str | None, default=None + Name of the Lorentzian component. If None, it will be set to the name of the diffusion + model. By default, None. + lorentzian_display_name : str | None, default=None + Display name of the Lorentzian component. If None, it will be set to the + lorentzian_name. By default, None + unique_name : str | None, default=None + Unique name of the diffusion model. If None, a unique name will be generated. By + default, None. + + Raises + ------ + TypeError + If ``residence_time`` or ``jump_length`` is not a number. + ValueError + If ``jump_length`` is negative or ``residence_time`` is below ``MINIMUM_WIDTH``. + """ + super().__init__( + Q=Q, + x_unit=x_unit, + y_unit=y_unit, + scale=scale, + name=name, + display_name=display_name, + lorentzian_name=lorentzian_name, + lorentzian_display_name=lorentzian_display_name, + unique_name=unique_name, + ) + + if not isinstance(residence_time, Numeric): + raise TypeError('residence_time must be a number.') + + if float(residence_time) < MINIMUM_WIDTH: + raise ValueError(f'residence_time must be at least {MINIMUM_WIDTH}.') + + if not isinstance(jump_length, Numeric): + raise TypeError('jump_length must be a number.') + + if float(jump_length) < 0: + raise ValueError('jump_length must be non-negative.') + + # Relaxation time tau + residence_time = Parameter( + name='residence_time', + value=float(residence_time), + fixed=False, + unit='ps', + min=MINIMUM_WIDTH, + ) + + jump_length = Parameter( + name='jump_length', + value=float(jump_length), + fixed=False, + unit='angstrom', + min=0.0, + ) + + self._hbar = hbar + self._angstrom = angstrom + self._residence_time = residence_time + self._jump_length = jump_length + + self._component_collections = self.create_component_collections() + + ################################ + # Properties + ################################ + + @property + def residence_time(self) -> Parameter: + """ + Get the residence time parameter tau. + + Returns + ------- + Parameter + Residence time tau. + """ + return self._residence_time + + @residence_time.setter + def residence_time(self, residence_time: Numeric) -> None: + """ + Set the residence time parameter tau. + + Parameters + ---------- + residence_time : Numeric + Residence time tau in ps. + + Raises + ------ + TypeError + If residence_time is not a number. + ValueError + If residence_time is non-positive. + """ + if not isinstance(residence_time, Numeric): + raise TypeError('residence_time must be a number.') + if float(residence_time) < MINIMUM_WIDTH: + raise ValueError(f'residence_time must be at least {MINIMUM_WIDTH}.') + + self._residence_time.value = float(residence_time) + + @property + def jump_length(self) -> Parameter: + """ + Get the jump length parameter l. + + Returns + ------- + Parameter + Jump length l in angstrom. + """ + return self._jump_length + + @jump_length.setter + def jump_length(self, jump_length: Numeric) -> None: + """ + Set the jump length parameter l . + + Parameters + ---------- + jump_length : Numeric + Jump length l in angstrom. + + Raises + ------ + TypeError + If jump_length is not a number. + ValueError + If jump_length is negative. + """ + if not isinstance(jump_length, Numeric): + raise TypeError('jump_length must be a number.') + + if float(jump_length) < 0: + raise ValueError('jump_length must be non-negative.') + self._jump_length.value = float(jump_length) + + ################################ + # Other methods + ################################ + + def calculate_width(self, Q: Q_type | None = None) -> np.ndarray: + r""" + Calculate the half-width at half-maximum (HWHM) for the diffusion model. $\Gamma(Q) = \hbar + / \tau * ( 1 - \sin(Q * l) / Q * l )$, where $tau$ is the residence time and $l$ is the + jump length. + + Parameters + ---------- + Q : Q_type | None, default=None + Scattering vector in 1/angstrom. Can be a single value or an array of values. If None, + Q values stored in the model are used. + + Returns + ------- + np.ndarray + HWHM values in the unit of the model (e.g., meV). + """ + + Q = self._ensure_Q(Q) + + conversion_factor = self._jump_length / self._angstrom + conversion_factor.convert_unit('dimensionless') + + prefactor = self._hbar / self._residence_time + prefactor.convert_unit(self.x_unit) + + argument = Q * conversion_factor.value + + return prefactor.value * (1 - np.sinc(argument / np.pi)) + + def calculate_EISF(self, Q: Q_type) -> np.ndarray: + """ + Calculate the Elastic Incoherent Structure Factor (EISF). + + Parameters + ---------- + Q : Q_type + Scattering vector in 1/angstrom. Can be a single value or an array of values. + + Returns + ------- + np.ndarray + EISF values (dimensionless). + """ + Q = self._ensure_Q(Q) + + return np.zeros_like(Q) + + def calculate_QISF(self, Q: Q_type) -> np.ndarray: + """ + Calculate the Quasi-Elastic Incoherent Structure Factor (QISF). + + Parameters + ---------- + Q : Q_type + Scattering vector in 1/angstrom. Can be a single value or an array of values. + + Returns + ------- + np.ndarray + QISF values (dimensionless). + """ + Q = self._ensure_Q(Q) + + return np.ones_like(Q) + + def create_component_collections( + self, + ) -> list[ComponentCollection]: + """ + Create ComponentCollection components for the diffusion model at given Q values. + + The created collections are installed on the model (they become the collections returned by + ``get_component_collections``), so the returned list is the live one. + + Returns + ------- + list[ComponentCollection] + List of ComponentCollections with Jump Diffusion Lorentzian components. + """ + if self.Q is None: + self._component_collections = [] + return self._component_collections + + Q = self.Q.values + component_collection_list = [None] * len(Q) + # In more complex models, this is used to scale the area of the + # Lorentzians and the delta function. + QISF = self.calculate_QISF(Q) + + # Create a Lorentzian component for each Q-value, with width + # D*Q^2 and area equal to scale. No delta function, as the EISF + # is 0. + for i, Q_value in enumerate(Q): + component_collection_list[i] = ComponentCollection( + name=f'{self.name}_Q{Q_value:.2f}', + display_name=f'{self.display_name}_Q{Q_value:.2f}', + x_unit=self.x_unit, + y_unit=self.y_unit, + ) + + lorentzian_component = Lorentzian( + name=self.lorentzian_name, + display_name=self.lorentzian_display_name, + x_unit=self.x_unit, + y_unit=self.y_unit, + ) + + # Make the width dependent on Q + dependency_expression = self._write_width_dependency_expression(Q[i]) + dependency_map = self._write_width_dependency_map_expression() + + # easyscience propagates inf bounds through arithmetic, producing inf/inf=nan + # as a transient intermediate. Python's min/max ignore nan so the final bounds + # are correct; suppress the spurious numpy RuntimeWarning. + with np.errstate(invalid='ignore'): + lorentzian_component.width.make_dependent_on( + dependency_expression=dependency_expression, + dependency_map=dependency_map, + desired_unit=self.x_unit, + ) + + # Make the area dependent on Q + area_dependency_map = self._write_area_dependency_map_expression() + lorentzian_component.area.make_dependent_on( + dependency_expression=self._write_area_dependency_expression(QISF[i]), + dependency_map=area_dependency_map, + ) + + component_collection_list[i].append_component(lorentzian_component) + + self._component_collections = component_collection_list + return self._component_collections + + ################################ + # Private methods + ################################ + def _on_Q_change(self) -> None: + """ + Update the component collections when Q changes. This is called automatically when the Q + property is set. It regenerates the component collections based on the new Q values. + """ + self._component_collections = self.create_component_collections() + + def _write_width_dependency_expression(self, Q: float) -> str: + """ + Write the dependency expression for the width as a function of Q to make dependent + Parameters. + + Parameters + ---------- + Q : float + Scattering vector in 1/angstrom. + + Raises + ------ + TypeError + If Q is not a float. + + Returns + ------- + str + Dependency expression for the width. + """ + if not isinstance(Q, (float)): + raise TypeError('Q must be a float.') + + # Q is given as a float, so we need to add the units + # TODO(markbujehein): Replace sin(x)/x with sinc once it is supported for Parameters #272 # ruff: ignore[line-contains-todo] + return f'(hbar / tau) * (1 - sin({Q} * l.value) / ({Q} * l.value) )' + + def _write_width_dependency_map_expression(self) -> dict[str, DescriptorNumber]: + """ + Write the dependency map expression to make dependent Parameters. + + Returns + ------- + dict[str, DescriptorNumber] + Dependency map for the width. + """ + return { + 'tau': self.residence_time, + 'l': self.jump_length, + 'hbar': self._hbar, + 'angstrom': self._angstrom, + } + + ################################ + # dunder methods + ################################ + + def __repr__(self) -> str: + """ + String representation of the JumpTranslationalDiffusion model. + + Returns + ------- + str + String representation of the JumpTranslationalDiffusion model. + """ + return ( + f'{self.__class__.__name__}(' + f'name={self.name!r}, display_name={self.display_name!r},\n' + f'x_unit={self.x_unit}, y_unit={self.y_unit}, \n' + f' residence_time={self.residence_time},\n' + f' jump_length={self.jump_length}),\n' + f' scale={self.scale})' + ) diff --git a/tests/unit/easydynamics/analysis/test_parameter_analysis.py b/tests/unit/easydynamics/analysis/test_parameter_analysis.py index 647c73c22..133cb805b 100644 --- a/tests/unit/easydynamics/analysis/test_parameter_analysis.py +++ b/tests/unit/easydynamics/analysis/test_parameter_analysis.py @@ -21,6 +21,9 @@ from easydynamics.sample_model.diffusion_model.brownian_translational_diffusion import ( BrownianTranslationalDiffusion, ) +from easydynamics.sample_model.diffusion_model.chudley_elliott_jump_diffusion import ( + ChudleyElliottJumpDiffusion, +) from easydynamics.sample_model.diffusion_model.delta_lorentz import DeltaLorentz from easydynamics.sample_model.diffusion_model.jump_translational_diffusion import ( JumpTranslationalDiffusion, @@ -1700,3 +1703,21 @@ def test_diffusion_fit_converts_dataset_units(self): # EXPECT: the diffusion coefficient is recovered despite the unit differences assert fit_model.diffusion_coefficient.value == pytest.approx(2.4e-9, rel=1e-3) + + def test_chudley_elliott_diffusion_width_only_fit(self): + # WHEN: synthetic widths from a known chudley-elliott diffusion model + Q = np.linspace(0.4, 2.0, 9) + truth = ChudleyElliottJumpDiffusion(residence_time=2.0, jump_length=2.0) + dataset = self._dataset_from_targets(truth, Q) + + # THEN: fit only the width prediction + fit_model = ChudleyElliottJumpDiffusion(residence_time=1.0, jump_length=1.0) + fit_model.scale.fixed = True + pa = ParameterAnalysis( + parameters=dataset, bindings=FitBinding(model=fit_model, targets=['width']) + ) + pa.fit() + + # EXPECT + assert fit_model.residence_time.value == pytest.approx(2.0, rel=1e-3) + assert fit_model.jump_length.value == pytest.approx(2.0, rel=1e-3) diff --git a/tests/unit/easydynamics/sample_model/diffusion_model/test_chudley_elliott_diffusion.py b/tests/unit/easydynamics/sample_model/diffusion_model/test_chudley_elliott_diffusion.py new file mode 100644 index 000000000..3959e95d6 --- /dev/null +++ b/tests/unit/easydynamics/sample_model/diffusion_model/test_chudley_elliott_diffusion.py @@ -0,0 +1,351 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +import numpy as np +import pytest +import scipp as sc +from scipp import UnitError +from scipp.constants import hbar as scipp_hbar + +from easydynamics.sample_model.diffusion_model.chudley_elliott_jump_diffusion import MINIMUM_WIDTH +from easydynamics.sample_model.diffusion_model.chudley_elliott_jump_diffusion import ( + ChudleyElliottJumpDiffusion, +) + + +class TestChudleyElliottJumpDiffusion: + @pytest.fixture + def chudley_elliott_model(self): + return ChudleyElliottJumpDiffusion() + + def test_init_default(self, chudley_elliott_model): + # WHEN THEN EXPECT + assert chudley_elliott_model.display_name == 'ChudleyElliottJumpDiffusion' + assert chudley_elliott_model.x_unit == 'meV' + assert chudley_elliott_model.y_unit == 'dimensionless' + assert chudley_elliott_model.scale.value == pytest.approx(1.0) + assert chudley_elliott_model.scale.unit == 'meV' + assert chudley_elliott_model.residence_time.value == pytest.approx(1.0) + assert chudley_elliott_model.jump_length.value == pytest.approx(1.0) + + @pytest.mark.parametrize( + 'kwargs,expected_exception, expected_message', + [ + ( + { + 'x_unit': 123, + 'scale': 1.0, + 'residence_time': 1.0, + 'jump_length': 1.0, + }, + UnitError, + 'Invalid unit', + ), + ( + { + 'y_unit': 123, + 'scale': 1.0, + 'residence_time': 1.0, + 'jump_length': 1.0, + }, + UnitError, + 'unit must be None, a string', + ), + ( + { + 'x_unit': 'meV', + 'scale': 'invalid', + 'residence_time': 1.0, + 'jump_length': 1.0, + }, + TypeError, + 'scale must be a number', + ), + ( + { + 'x_unit': 'meV', + 'scale': 1.0, + 'residence_time': 'invalid', + 'jump_length': 1.0, + }, + TypeError, + 'residence_time must be a number', + ), + ( + { + 'x_unit': 'meV', + 'scale': 1.0, + 'residence_time': -1.0, + 'jump_length': 1.0, + }, + ValueError, + 'residence_time must be at least', + ), + ( + { + 'x_unit': 'meV', + 'scale': 1.0, + 'residence_time': 0.0, + 'jump_length': 1.0, + }, + ValueError, + 'residence_time must be at least', + ), + ( + { + 'x_unit': 'meV', + 'scale': 1.0, + 'residence_time': 1.0, + 'jump_length': 'invalid', + }, + TypeError, + 'jump_length must be a number', + ), + ( + { + 'x_unit': 'meV', + 'scale': 1.0, + 'residence_time': 1.0, + 'jump_length': -1.0, + }, + ValueError, + 'jump_length must be non-negative', + ), + ], + ids=[ + 'invalid_x_unit', + 'invalid_y_unit', + 'invalid_scale_type', + 'invalid_residence_time_type', + 'invalid_residence_time_negative', + 'invalid_residence_time_zero', + 'invalid_jump_length_type', + 'invalid_jump_length_negative', + ], + ) + def test_input_type_validation_raises(self, kwargs, expected_exception, expected_message): + with pytest.raises(expected_exception, match=expected_message): + ChudleyElliottJumpDiffusion(display_name='ChudleyElliottJumpDiffusion', **kwargs) + + def test_residence_time_setter(self, chudley_elliott_model): + # WHEN THEN + chudley_elliott_model.residence_time = 3.0 + + # EXPECT + assert chudley_elliott_model.residence_time.value == pytest.approx(3.0) + + def test_residence_time_setter_raises(self, chudley_elliott_model): + # WHEN THEN EXPECT + with pytest.raises(TypeError, match=r'residence_time must be a number.'): + chudley_elliott_model.residence_time = 'invalid' # Invalid type + + @pytest.mark.parametrize('invalid_value', [-1.0, 0.0, MINIMUM_WIDTH / 10]) + def test_residence_time_setter_below_minimum_raises( + self, chudley_elliott_model, invalid_value + ): + # WHEN THEN EXPECT + with pytest.raises(ValueError, match=r'residence_time must be at least'): + chudley_elliott_model.residence_time = invalid_value + + def test_residence_time_has_minimum_width(self, chudley_elliott_model): + # WHEN THEN EXPECT + assert chudley_elliott_model.residence_time.min == MINIMUM_WIDTH + + def test_jump_length_setter(self, chudley_elliott_model): + # WHEN THEN + chudley_elliott_model.jump_length = 2.5 + + # EXPECT + assert chudley_elliott_model.jump_length.value == pytest.approx(2.5) + + def test_jump_length_setter_raises(self, chudley_elliott_model): + # WHEN THEN EXPECT + with pytest.raises(TypeError, match=r'jump_length must be a number.'): + chudley_elliott_model.jump_length = 'invalid' # Invalid type + + def test_jump_length_setter_negative_raises(self, chudley_elliott_model): + # WHEN THEN EXPECT + with pytest.raises(ValueError, match=r'jump_length must be non-negative.'): + chudley_elliott_model.jump_length = -1.0 # Invalid negative value + + def test_calculate_width_type_error(self, chudley_elliott_model): + # WHEN THEN EXPECT + with pytest.raises(TypeError, match='Q must be '): + chudley_elliott_model.calculate_width(Q='invalid') # Invalid type + + def test_calculate_width(self, chudley_elliott_model): + "Test the calculation relying solely on a scipp implementation" + 'instead of our Parameters' + # WHEN + Q_values = sc.linspace('Q', 0.5, 1.5, num=6, unit='1/angstrom') + # sc.values drops the (zero) variance, which scipp cannot broadcast against Q_values + residence_time_sc = sc.values(chudley_elliott_model.residence_time.full_value) + jump_length_sc = sc.values(chudley_elliott_model.jump_length.full_value) + + # THEN + widths = chudley_elliott_model.calculate_width(Q_values) + + # EXPECT + expected_widths = scipp_hbar / residence_time_sc * (1 - sc.sinc(Q_values * jump_length_sc)) + + expected_widths = expected_widths.to(unit=chudley_elliott_model.x_unit) + + np.testing.assert_allclose(widths, expected_widths.values, rtol=1e-5) + + def test_calculate_width_sinc_stability(self, chudley_elliott_model): + """ + Cross-check the calculate_width result (which uses np.sinc) + against an explicit sin(x)/x calculation to ensure mathematical + equivalence and stability. + """ + # WHEN + Q_values = sc.linspace('Q', 0.5, 1.5, num=6, unit='1/angstrom') + + # sc.values drops the (zero) variance, which scipp cannot broadcast against Q_values + residence_time_sc = sc.values(chudley_elliott_model.residence_time.full_value) + jump_length_sc = sc.values(chudley_elliott_model.jump_length.full_value) + + # Reference: calculate explicitly using sin(x) / x + argument = Q_values * jump_length_sc + prefactor = scipp_hbar / residence_time_sc + + # sc.sin() strictly requires rad or deg unit. + # `argument * sc.Unit('rad')` gives a Variable of dtype Variable, which sc.sin rejects. + # TODO(markbujehein): update once the easyscience maths module is implemented #272 # ruff: ignore[line-contains-todo] + argument_rad = argument.copy() + argument_rad.unit = sc.Unit('rad') + + explicit_sinc = sc.sin(argument_rad) / argument + + expected_widths_sin = prefactor * (1 - explicit_sinc) + expected_widths_sin = expected_widths_sin.to(unit=chudley_elliott_model.x_unit) + + # THEN + # Model uses np.sinc() + model_widths = chudley_elliott_model.calculate_width(Q_values) + + # EXPECT + # Both mathematical approaches to yield the same result + np.testing.assert_allclose(model_widths, expected_widths_sin.values, rtol=1e-5) + + def test_calculate_EISF(self, chudley_elliott_model): + # WHEN + Q_values = np.array([0.1, 0.2, 0.3]) # Example Q values in Å^-1 + + # THEN + EISF = chudley_elliott_model.calculate_EISF(Q_values) + + # EXPECT + expected_EISF = np.zeros_like(Q_values) + np.testing.assert_array_equal(EISF, expected_EISF) + + def test_calculate_EISF_type_error(self, chudley_elliott_model): + # WHEN THEN EXPECT + with pytest.raises(TypeError, match='Q must be '): + chudley_elliott_model.calculate_EISF(Q='invalid') # Invalid type + + def test_calculate_QISF(self, chudley_elliott_model): + # WHEN + Q_values = np.array([0.1, 0.2, 0.3]) # Example Q values in Å^-1 + + # THEN + QISF = chudley_elliott_model.calculate_QISF(Q_values) + + # EXPECT + expected_QISF = np.ones_like(Q_values) + np.testing.assert_array_equal(QISF, expected_QISF) + + def test_calculate_QISF_type_error(self, chudley_elliott_model): + # WHEN THEN EXPECT + with pytest.raises(TypeError, match='Q must be '): + chudley_elliott_model.calculate_QISF(Q='invalid') # Invalid type + + @pytest.mark.parametrize( + 'Q', + [ + (0.5), + ([1.0, 2.0, 3.0]), + (np.array([1.0, 2.0, 3.0])), + ], + ids=[ + 'python_scalar', + 'python_list', + 'numpy_array', + ], + ) + def test_create_component_collections(self, chudley_elliott_model, Q): + # WHEN + chudley_elliott_model.Q = Q + + # THEN + component_collections = chudley_elliott_model.create_component_collections() + + # EXPECT + expected_widths = chudley_elliott_model.calculate_width(Q) + for model_index in range(len(component_collections)): + model = component_collections[model_index] + assert len(model) == 1 + component = model[0] + assert component.width.unit == chudley_elliott_model.x_unit + assert np.isclose(component.width.value, expected_widths[model_index]) + assert component.width.independent is False + # area.unit = area_unit = x_unit * y_unit + assert component.area.unit == 'meV' + + def test_create_component_collections_installs_collections(self): + # WHEN + model = ChudleyElliottJumpDiffusion(Q=np.array([1.0, 2.0])) + + # THEN + collections = model.create_component_collections() + + # EXPECT the returned collections are the installed (live) ones, so callers that + # follow the docstring get the same objects the model itself uses + assert collections is model.get_component_collections() + + def test_write_width_dependency_expression(self, chudley_elliott_model): + # WHEN THEN + expression = chudley_elliott_model._write_width_dependency_expression(0.5) + + # EXPECT + expected_expression = '(hbar / tau) * (1 - sin(0.5 * l.value) / (0.5 * l.value) )' + assert expression == expected_expression + + def test_write_width_dependency_map_expression(self, chudley_elliott_model): + # WHEN THEN + expression_map = chudley_elliott_model._write_width_dependency_map_expression() + + # EXPECT + expected_map = { + 'tau': chudley_elliott_model.residence_time, + 'l': chudley_elliott_model.jump_length, + 'hbar': chudley_elliott_model._hbar, + 'angstrom': chudley_elliott_model._angstrom, + } + + assert expression_map == expected_map + + def test_write_width_dependency_expression_raises(self, chudley_elliott_model): + with pytest.raises(TypeError, match='Q must be a float'): + chudley_elliott_model._write_width_dependency_expression('invalid') + + def test_write_area_dependency_expression_raises(self, chudley_elliott_model): + with pytest.raises(TypeError, match='QISF must be a float'): + chudley_elliott_model._write_area_dependency_expression('invalid') + + def test_y_unit_setter_raises(self, chudley_elliott_model): + # WHEN THEN EXPECT + with pytest.raises(AttributeError, match=r'read-only'): + chudley_elliott_model.y_unit = '1/meV' + + def test_repr(self, chudley_elliott_model): + # WHEN THEN + repr_str = repr(chudley_elliott_model) + + # EXPECT + assert 'ChudleyElliottJumpDiffusion' in repr_str + assert 'residence_time' in repr_str + assert 'jump_length' in repr_str + assert 'scale=' in repr_str + # Regression: a stray ')' used to mangle this into 'x_unit=meV), y_unit=...' + assert 'x_unit=meV, y_unit=dimensionless' in repr_str