From 8dfeb08ad30e6827d28aac2fa3094c093d5f7bc7 Mon Sep 17 00:00:00 2001 From: henrikjacobsenfys Date: Mon, 22 Jun 2026 22:30:23 +0200 Subject: [PATCH 01/17] overhaul units --- docs/docs/tutorials/analysis.ipynb | 26895 ++++++++- docs/docs/tutorials/analysis1d.ipynb | 3472 +- .../docs/tutorials/component_collection.ipynb | 275 +- docs/docs/tutorials/components.ipynb | 1267 +- docs/docs/tutorials/convolution.ipynb | 753 +- docs/docs/tutorials/delta_lorentz.ipynb | 360 +- docs/docs/tutorials/detailed_balance.ipynb | 509 +- docs/docs/tutorials/diffusion_model.ipynb | 669 +- docs/docs/tutorials/instrument_model.ipynb | 84 +- docs/docs/tutorials/sample_model.ipynb | 1900 +- docs/docs/tutorials/tutorial0_basics.ipynb | 23013 +++++++- .../tutorials/tutorial0_more_advanced.ipynb | 21266 ++++++- docs/docs/tutorials/tutorial1_brownian.ipynb | 41148 ++++++++++++- .../tutorials/tutorial2_nanoparticles.ipynb | 47601 +++++++++++++++- src/easydynamics/analysis/analysis1d.py | 2 +- src/easydynamics/analysis/fit_binding.py | 4 +- .../base_classes/easydynamics_modelbase.py | 53 +- src/easydynamics/convolution/convolution.py | 2 +- .../convolution/convolution_base.py | 8 +- .../convolution/numerical_convolution_base.py | 2 +- .../sample_model/background_model.py | 23 +- .../sample_model/component_collection.py | 317 +- .../components/damped_harmonic_oscillator.py | 205 +- .../sample_model/components/delta_function.py | 203 +- .../sample_model/components/exponential.py | 210 +- .../components/expression_component.py | 217 +- .../sample_model/components/gaussian.py | 207 +- .../sample_model/components/lorentzian.py | 205 +- .../sample_model/components/mixins.py | 111 +- .../components/model_component.py | 255 +- .../sample_model/components/polynomial.py | 242 +- .../sample_model/components/voigt.py | 231 +- .../brownian_translational_diffusion.py | 22 +- .../diffusion_model/delta_lorentz.py | 29 +- .../diffusion_model/diffusion_model_base.py | 39 +- .../jump_translational_diffusion.py | 22 +- .../sample_model/instrument_model.py | 315 +- src/easydynamics/sample_model/model_base.py | 255 +- .../sample_model/resolution_model.py | 64 +- src/easydynamics/sample_model/sample_model.py | 285 +- .../easydynamics/analysis/test_analysis.py | 2 +- .../test_easydynamics_modelbase.py | 8 +- .../convolution/test_convolution.py | 4 +- .../convolution/test_convolution_base.py | 18 +- .../convolution/test_numerical_convolution.py | 2 +- .../test_numerical_convolution_base.py | 4 +- .../test_damped_harmonic_oscillator.py | 26 +- .../components/test_delta_function.py | 24 +- .../components/test_exponential.py | 30 +- .../components/test_expression_component.py | 8 +- .../sample_model/components/test_gaussian.py | 26 +- .../components/test_lorentzian.py | 26 +- .../sample_model/components/test_mixins.py | 6 +- .../components/test_model_component.py | 38 +- .../components/test_polynomial.py | 13 +- .../sample_model/components/test_voigt.py | 32 +- .../test_brownian_translational_diffusion.py | 18 +- .../diffusion_model/test_delta_lorentz.py | 4 +- .../test_diffusion_model_base.py | 11 +- .../test_jump_translational_diffusion.py | 22 +- .../sample_model/test_background_model.py | 8 +- .../sample_model/test_component_collection.py | 58 +- .../sample_model/test_instrument_model.py | 35 +- .../sample_model/test_model_base.py | 38 +- .../sample_model/test_resolution_model.py | 16 +- .../sample_model/test_sample_model.py | 20 +- 66 files changed, 170488 insertions(+), 2749 deletions(-) diff --git a/docs/docs/tutorials/analysis.ipynb b/docs/docs/tutorials/analysis.ipynb index 2e3bb3d0..fb3bc582 100644 --- a/docs/docs/tutorials/analysis.ipynb +++ b/docs/docs/tutorials/analysis.ipynb @@ -15,9 +15,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "f2f898dc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:16.505504Z", + "iopub.status.busy": "2026-06-22T17:34:16.505350Z", + "iopub.status.idle": "2026-06-22T17:34:18.801381Z", + "shell.execute_reply": "2026-06-22T17:34:18.800592Z" + } + }, "outputs": [], "source": [ "# Imports\n", @@ -41,9 +48,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "8deca9b6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:18.803564Z", + "iopub.status.busy": "2026-06-22T17:34:18.803315Z", + "iopub.status.idle": "2026-06-22T17:34:18.813901Z", + "shell.execute_reply": "2026-06-22T17:34:18.813101Z" + } + }, "outputs": [], "source": [ "# Load the vanadium data\n", @@ -58,10 +72,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "6762faba", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:18.816526Z", + "iopub.status.busy": "2026-06-22T17:34:18.816364Z", + "iopub.status.idle": "2026-06-22T17:34:19.011937Z", + "shell.execute_reply": "2026-06-22T17:34:19.011154Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c0ab9ffa551742d88073b0304c8e72a8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Example of Analysis with a simple sample model and instrument model\n", "# The scattering from vanadium is purely elastic, so we model it with a\n", @@ -108,10 +145,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "e98e3d65", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.014003Z", + "iopub.status.busy": "2026-06-22T17:34:19.013840Z", + "iopub.status.idle": "2026-06-22T17:34:19.491588Z", + "shell.execute_reply": "2026-06-22T17:34:19.490934Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "941b68e7dc6b42629119bdfd5d0db567", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# It looks good, so let us fit all Q indices independently and plot the\n", "# results\n", @@ -121,10 +181,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "133e682e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.493719Z", + "iopub.status.busy": "2026-06-22T17:34:19.493573Z", + "iopub.status.idle": "2026-06-22T17:34:19.511490Z", + "shell.execute_reply": "2026-06-22T17:34:19.510654Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
scipp.Dataset (6.66 KB)
    • Q: 16
    • Q
      (Q)
      float64
      1/Å
      0.1, 0.227, ..., 1.873, 2.0
      Values:
      array([0.1       , 0.22666667, 0.35333333, 0.48      , 0.60666667,\n",
+       "       0.73333333, 0.86      , 0.98666667, 1.11333333, 1.24      ,\n",
+       "       1.36666667, 1.49333333, 1.62      , 1.74666667, 1.87333333,\n",
+       "       2.        ])
    • DeltaFunction area
      (Q)
      float64
      meV
      0.522, 0.529, ..., 0.538, 0.533
      σ = 0.018, 0.018, ..., 0.017, 0.017
      Values:
      array([0.52209408, 0.5285229 , 0.51741732, 0.53239306, 0.52764215,\n",
+       "       0.5299243 , 0.52765768, 0.51477418, 0.52913757, 0.53258438,\n",
+       "       0.5324246 , 0.53238945, 0.52271039, 0.52427006, 0.53758902,\n",
+       "       0.53293296])

      Variances (σ²):
      array([0.00030823, 0.00033506, 0.00025861, 0.00028047, 0.00027407,\n",
+       "       0.0002244 , 0.00044408, 0.00024497, 0.00029737, 0.00034951,\n",
+       "       0.00030663, 0.00047018, 0.00037023, 0.00033697, 0.00028746,\n",
+       "       0.00028007])
    • DeltaFunction center
      (Q)
      float64
      meV
      0.0, 0.0, ..., 0.0, 0.0
      σ = 0.0, 0.0, ..., 0.0, 0.0
      Values:
      array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

      Variances (σ²):
      array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
    • Gaussian area
      (Q)
      float64
      meV
      1.0, 1.0, ..., 1.0, 1.0
      σ = 0.0, 0.0, ..., 0.0, 0.0
      Values:
      array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

      Variances (σ²):
      array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
    • Gaussian center
      (Q)
      float64
      meV
      0.0, 0.0, ..., 0.0, 0.0
      σ = 0.0, 0.0, ..., 0.0, 0.0
      Values:
      array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

      Variances (σ²):
      array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
    • Gaussian width
      (Q)
      float64
      meV
      0.102, 0.100, ..., 0.103, 0.102
      σ = 0.003, 0.003, ..., 0.003, 0.003
      Values:
      array([0.10222849, 0.09980219, 0.10094395, 0.10308798, 0.10108169,\n",
+       "       0.10126272, 0.10063122, 0.09902007, 0.10372129, 0.10254205,\n",
+       "       0.10142844, 0.10315841, 0.10076283, 0.10044881, 0.10346177,\n",
+       "       0.10187953])

      Variances (σ²):
      array([9.25121501e-06, 8.81462902e-06, 7.61444384e-06, 8.02505303e-06,\n",
+       "       7.63407023e-06, 6.32632931e-06, 1.23202070e-05, 6.87361362e-06,\n",
+       "       8.78013502e-06, 9.96635847e-06, 8.46568221e-06, 1.33366848e-05,\n",
+       "       1.06131452e-05, 9.28744245e-06, 8.02343194e-06, 7.89725747e-06])
    • Polynomial_c0
      (Q)
      float64
      𝟙
      0.099, 0.095, ..., 0.098, 0.101
      σ = 0.002, 0.002, ..., 0.002, 0.002
      Values:
      array([0.09942061, 0.09482336, 0.10250515, 0.09830358, 0.10212681,\n",
+       "       0.1023704 , 0.09957118, 0.1020655 , 0.09643967, 0.10180256,\n",
+       "       0.10191825, 0.09198844, 0.09427405, 0.09806923, 0.09764543,\n",
+       "       0.10051938])

      Variances (σ²):
      array([5.53666458e-06, 5.69780006e-06, 4.81032728e-06, 4.89897712e-06,\n",
+       "       4.99355959e-06, 4.08027374e-06, 7.91272766e-06, 4.56234563e-06,\n",
+       "       5.13312615e-06, 6.29261176e-06, 5.53177991e-06, 7.73519893e-06,\n",
+       "       6.33375780e-06, 5.95523123e-06, 4.94577573e-06, 4.98191241e-06])
    • energy_offset
      (Q)
      float64
      meV
      0.001, -0.001, ..., -0.001, 0.001
      σ = 0.004, 0.004, ..., 0.003, 0.003
      Values:
      array([ 0.00135435, -0.00052522,  0.00093028, -0.00192069, -0.00366331,\n",
+       "        0.00128853,  0.000512  ,  0.00090986, -0.00100558,  0.00036124,\n",
+       "        0.00054189, -0.00216345, -0.00137654,  0.00129853, -0.001399  ,\n",
+       "        0.00055349])

      Variances (σ²):
      array([1.33891037e-05, 1.37163920e-05, 1.12418484e-05, 1.18955355e-05,\n",
+       "       1.14728274e-05, 9.35171953e-06, 1.83656779e-05, 1.03814854e-05,\n",
+       "       1.29102630e-05, 1.47729559e-05, 1.26723273e-05, 1.99321937e-05,\n",
+       "       1.56095496e-05, 1.41010062e-05, 1.20573093e-05, 1.16915411e-05])
" + ], + "text/plain": [ + "\n", + "Dimensions: Sizes[Q:16, ]\n", + "Coordinates:\n", + "* Q float64 [1/Å] (Q) [0.1, 0.226667, ..., 1.87333, 2]\n", + "Data:\n", + " DeltaFunction area float64 [meV] (Q) [0.522094, 0.528523, ..., 0.537589, 0.532933] [0.000308233, 0.000335062, ..., 0.000287459, 0.000280073]\n", + " DeltaFunction center float64 [meV] (Q) [0, 0, ..., 0, 0] [0, 0, ..., 0, 0]\n", + " Gaussian area float64 [meV] (Q) [1, 1, ..., 1, 1] [0, 0, ..., 0, 0]\n", + " Gaussian center float64 [meV] (Q) [0, 0, ..., 0, 0] [0, 0, ..., 0, 0]\n", + " Gaussian width float64 [meV] (Q) [0.102228, 0.0998022, ..., 0.103462, 0.10188] [9.25122e-06, 8.81463e-06, ..., 8.02343e-06, 7.89726e-06]\n", + " Polynomial_c0 float64 [dimensionless] (Q) [0.0994206, 0.0948234, ..., 0.0976454, 0.100519] [5.53666e-06, 5.6978e-06, ..., 4.94578e-06, 4.98191e-06]\n", + " energy_offset float64 [meV] (Q) [0.00135435, -0.000525217, ..., -0.001399, 0.000553493] [1.33891e-05, 1.37164e-05, ..., 1.20573e-05, 1.16915e-05]\n" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Inspect the Parameters as a scipp Dataset\n", "vanadium_analysis.parameters_to_dataset()" @@ -132,20 +269,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "536d0cad", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.514042Z", + "iopub.status.busy": "2026-06-22T17:34:19.513871Z", + "iopub.status.idle": "2026-06-22T17:34:19.527118Z", + "shell.execute_reply": "2026-06-22T17:34:19.526430Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "vanadium_analysis.get_parameters_near_bounds()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "dfacdf24", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.528913Z", + "iopub.status.busy": "2026-06-22T17:34:19.528760Z", + "iopub.status.idle": "2026-06-22T17:34:19.587949Z", + "shell.execute_reply": "2026-06-22T17:34:19.587483Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "674e8f7cd98745acb1c5a7ac82f4cc1b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Plot some of fitted parameters as a function of Q\n", "vanadium_analysis.plot_parameters(names=['DeltaFunction area'])" @@ -153,29 +331,82 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "b6f9f316", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.589948Z", + "iopub.status.busy": "2026-06-22T17:34:19.589791Z", + "iopub.status.idle": "2026-06-22T17:34:19.657536Z", + "shell.execute_reply": "2026-06-22T17:34:19.656735Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f8ed5de02cb640ddab222c562cf136ba", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "vanadium_analysis.plot_parameters(names=['Gaussian width'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "572664a0", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.659107Z", + "iopub.status.busy": "2026-06-22T17:34:19.658957Z", + "iopub.status.idle": "2026-06-22T17:34:19.728202Z", + "shell.execute_reply": "2026-06-22T17:34:19.727009Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "aa330b75e2fd4dd297ea11636da59830", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "vanadium_analysis.plot_parameters(names=['energy_offset'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "3609e6c1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.731901Z", + "iopub.status.busy": "2026-06-22T17:34:19.731719Z", + "iopub.status.idle": "2026-06-22T17:34:19.748175Z", + "shell.execute_reply": "2026-06-22T17:34:19.747446Z" + } + }, "outputs": [], "source": [ "# Now it's time to look at the data we want to fit. We first load the\n", @@ -192,9 +423,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "e685909a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.750664Z", + "iopub.status.busy": "2026-06-22T17:34:19.750462Z", + "iopub.status.idle": "2026-06-22T17:34:19.762217Z", + "shell.execute_reply": "2026-06-22T17:34:19.761479Z" + } + }, "outputs": [], "source": [ "# Now we set up the model, similarly to how we set up the model for the\n", @@ -233,10 +471,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "c66828eb", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.765233Z", + "iopub.status.busy": "2026-06-22T17:34:19.765081Z", + "iopub.status.idle": "2026-06-22T17:34:20.087617Z", + "shell.execute_reply": "2026-06-22T17:34:20.086551Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e48dae6a005b416294a80824f93af63e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Let us see how good the starting parameters are\n", "diffusion_analysis.plot_data_and_model()" @@ -244,10 +505,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "2a4b7572", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:20.090676Z", + "iopub.status.busy": "2026-06-22T17:34:20.090363Z", + "iopub.status.idle": "2026-06-22T17:34:20.709433Z", + "shell.execute_reply": "2026-06-22T17:34:20.708599Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf0d2236e90047bdb845d37bb417e81e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Now we fit the data and plot the result. Looks good!\n", "diffusion_analysis.fit(fit_method='independent')\n", @@ -256,10 +540,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "df14b5c4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:20.711379Z", + "iopub.status.busy": "2026-06-22T17:34:20.711226Z", + "iopub.status.idle": "2026-06-22T17:34:20.786789Z", + "shell.execute_reply": "2026-06-22T17:34:20.786064Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3004228b16d14a389b033c37e2e3a4c8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Let us look at the most interesting fit parameters\n", "diffusion_analysis.plot_parameters(names=['Lorentzian width', 'Lorentzian area'])" @@ -267,9 +574,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "eb226c8f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:20.788607Z", + "iopub.status.busy": "2026-06-22T17:34:20.788449Z", + "iopub.status.idle": "2026-06-22T17:34:20.792300Z", + "shell.execute_reply": "2026-06-22T17:34:20.791683Z" + } + }, "outputs": [], "source": [ "# It will be possible to fit this to a DiffusionModel, but that will\n", @@ -278,10 +592,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "4c6d7808", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:20.794082Z", + "iopub.status.busy": "2026-06-22T17:34:20.793936Z", + "iopub.status.idle": "2026-06-22T17:34:21.319633Z", + "shell.execute_reply": "2026-06-22T17:34:21.318304Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "34411961256d4a04979ee528882c3971", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Let us now fit directly to a diffusion model. We replace the\n", "# Lorentzian with a Brownian translational diffusion model and keep the\n", @@ -323,10 +660,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "fd04d359", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:21.322337Z", + "iopub.status.busy": "2026-06-22T17:34:21.322174Z", + "iopub.status.idle": "2026-06-22T17:34:24.883574Z", + "shell.execute_reply": "2026-06-22T17:34:24.882453Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "df00d371d9694bbe9ed03dd00ae9520b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# We now fit all the data simultaneously to the diffusion model, then\n", "# plot the result. Looks good.\n", @@ -336,10 +696,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "842c1f01", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:24.886662Z", + "iopub.status.busy": "2026-06-22T17:34:24.886508Z", + "iopub.status.idle": "2026-06-22T17:34:24.894038Z", + "shell.execute_reply": "2026-06-22T17:34:24.893267Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + 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"execution_count": null, + "execution_count": 2, "id": "8deca9b6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:08.776521Z", + "iopub.status.busy": "2026-06-22T17:34:08.776284Z", + "iopub.status.idle": "2026-06-22T17:34:08.789120Z", + "shell.execute_reply": "2026-06-22T17:34:08.788465Z" + } + }, "outputs": [], "source": [ "vanadium_experiment = Experiment('Vanadium')\n", @@ -50,10 +64,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "41f842f0", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:08.791139Z", + "iopub.status.busy": "2026-06-22T17:34:08.790990Z", + "iopub.status.idle": "2026-06-22T17:34:09.047446Z", + "shell.execute_reply": "2026-06-22T17:34:09.045918Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "45cca7b305014f0b90cb50173fdcea92", + "version_major": 2, + 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", 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0c9b3926..c3c0f4be 100644 --- a/docs/docs/tutorials/components.ipynb +++ b/docs/docs/tutorials/components.ipynb @@ -14,9 +14,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "df408006", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:09:37.441731Z", + "iopub.status.busy": "2026-06-22T17:09:37.441517Z", + "iopub.status.idle": "2026-06-22T17:09:39.117488Z", + "shell.execute_reply": "2026-06-22T17:09:39.116486Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -36,10 +43,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "2", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:09:39.119166Z", + "iopub.status.busy": "2026-06-22T17:09:39.118974Z", + "iopub.status.idle": "2026-06-22T17:09:39.208525Z", + "shell.execute_reply": "2026-06-22T17:09:39.207683Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "671089b14ccb4ed783336d8c4de66728", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " Figure\n", + "
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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Creating a component\n", "gaussian = Gaussian(display_name='Gaussian', width=0.5, area=1)\n", @@ -67,10 +107,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "3", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:09:39.210149Z", + "iopub.status.busy": "2026-06-22T17:09:39.210032Z", + "iopub.status.idle": "2026-06-22T17:09:39.214131Z", + "shell.execute_reply": "2026-06-22T17:09:39.213524Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Area under DHO curve: 1.9999\n" + ] + } + ], "source": [ "# The area under the DHO curve is indeed equal to the area parameter.\n", "xx = np.linspace(-15, 15, 10000)\n", @@ -81,10 +136,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:09:39.215547Z", + "iopub.status.busy": "2026-06-22T17:09:39.215430Z", + "iopub.status.idle": "2026-06-22T17:09:39.259753Z", + "shell.execute_reply": "2026-06-22T17:09:39.259147Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dcbbbb7846c4499eb2c5080ee64839cb", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9999999999999999\n" + ] + } + ], "source": [ "delta = DeltaFunction(display_name='Delta', center=0.0, area=1.0)\n", "x1 = np.linspace(-2, 2, 100)\n", @@ -106,10 +201,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "5", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:09:39.261712Z", + "iopub.status.busy": "2026-06-22T17:09:39.261585Z", + "iopub.status.idle": "2026-06-22T17:09:39.304919Z", + "shell.execute_reply": "2026-06-22T17:09:39.304002Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "af39320d86ba4e549c67a140eeeb7ec8", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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", 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", 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", 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", 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"2026-06-22T17:33:08.678842Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -37,10 +44,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "2", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:08.682763Z", + "iopub.status.busy": "2026-06-22T17:33:08.682529Z", + "iopub.status.idle": "2026-06-22T17:33:08.776700Z", + "shell.execute_reply": "2026-06-22T17:33:08.775793Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf732a5fa59b411fa0ff6e10599e83fa", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "_dom_classes": [], + "_figure_label": "Figure", + "_image_mode": "full", + "_message": "", + "_model_module": "jupyter-matplotlib", + "_model_module_version": "^0.12", + "_model_name": "MPLCanvasModel", + "_rubberband_height": 0, + "_rubberband_width": 0, + "_rubberband_x": 0, + "_rubberband_y": 0, + "_size": [ + 640.0, + 480.0 + ], + "_view_count": null, + "_view_module": "jupyter-matplotlib", + "_view_module_version": "^0.12", + "_view_name": "MPLCanvasView", + "capture_scroll": false, + "footer_visible": true, + "header_visible": true, + "layout": "IPY_MODEL_113665a82ae14db4a67ac0bfc02a940f", + "pan_zoom_throttle": 33.0, + "resizable": true, + "tabbable": null, + "toolbar": "IPY_MODEL_6dc9c89a99544423b63b1d8602598467", + "toolbar_position": "left", + "toolbar_visible": "fade-in-fade-out", + "tooltip": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/docs/docs/tutorials/delta_lorentz.ipynb b/docs/docs/tutorials/delta_lorentz.ipynb index 3663f74b..7ce5e8b3 100644 --- a/docs/docs/tutorials/delta_lorentz.ipynb +++ b/docs/docs/tutorials/delta_lorentz.ipynb @@ -21,9 +21,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:12:08.849393Z", + "iopub.status.busy": "2026-06-22T17:12:08.849173Z", + "iopub.status.idle": "2026-06-22T17:12:10.448724Z", + "shell.execute_reply": "2026-06-22T17:12:10.447818Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -36,9 +43,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "6d6e1984", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:12:10.450648Z", + "iopub.status.busy": "2026-06-22T17:12:10.450443Z", + "iopub.status.idle": "2026-06-22T17:12:10.511792Z", + "shell.execute_reply": "2026-06-22T17:12:10.511008Z" + } + }, "outputs": [], "source": [ "Q = np.linspace(0.5, 2, 7)\n", @@ -62,10 +76,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "389aa3cd", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:12:10.513475Z", + "iopub.status.busy": "2026-06-22T17:12:10.513364Z", + "iopub.status.idle": "2026-06-22T17:12:10.571511Z", + "shell.execute_reply": "2026-06-22T17:12:10.570948Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "23f3d81f2b0549c0b9208d0066990dd1", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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xo5cqQJs2vXrq1UZ42hQ4fK//3f/5nr2mmmvKWaqBiqgAEAqGI6HIqWim3atKnE+3V9w4YNpU6dOhWuAtYqZB142Zf3tt5XFg1sOlRLoFDn1adPH9N5pTRa4rho0SJ56aWXTADURYeD0Y4q+jqV8e9//9uER10+/fTTSj0HykYJIADYUAUc2rQaVkvigvXa5aEB67LLLjPB6IEHHvBrB6ihSdvIjRs3rmhdRaqAtQfx448/bsbQ85YafvHFF5KQkCBdu3Yts0RSg9no0aNLHarFl4awpk2blnr/G2+8YUrptJrYl/Z0nj59uvzzn/8s1+v40gCJU48ACAA2VAGHNm2DV5Fq2GB54YUXzPAr2jZOw5AOuaLVmTpmnw6C7DtsS0WqgAcNGmSCnnaoeOqpp0yg1GFVNFBqpwr1/fffm5CnQcw3UH311VeSlJRkhoCx0+FktEq6Z8+e5rZ2zHjttddMiVxpNEz+v//3/+SMM87wW6+h8C9/+YtpH3jNNddIVdCSyPT0dHO8WVlZJpwqfS+8VekoPwIgAACnQIcOHcwgyzro8XXXXWdK7PTkQgORlpzpIM6Vob1lP/74Y7n77rtNaWB8fLzcfPPNphTRS8fk07H5tIeuvfOHhlLtrVuSv//97/Lbb7+ZqlzdRnsqa8AryapVq0xVtgZHu1q1apmgqgGxqgKghtZly5YV3fYGVQ20rVu3rpLXCCcuHQ8n2DtRXWmXe53+JjU11RS9A3CG1T+lSu9Rtc31NfNS5ezuhdcRHNnZ2eZHXkvQAk0xVh1MmTLFzJurVbbnnXdesHcnbL83afx+UwIIAAGrgHl7UIWmTp1qSqt0erXevXub4VaAYKAKGADsGAcQp1BJgy8DpxunHgAQAI1kADgRARAAbOgFDMDpCIAAYOPxrQL2vQEADkEABIBAA0Hz7gBwIAIgANh4fEr9PB7eHgDOQwAEABvfUj+GSgXgRARAALCxfEr9aAIIFA5gnZiYaGb10GnYUP0RAAGgpDaAUScWy8X7g7Cm070tWLDATD8XFRUlc+bMCfYuoQoQAAHAJk8D4OzCJY8iQIS5mjVrmmlP27dvL40aNZJ69eqVuu3BgwfNHMI6X/F//vOf07qfqBgCIADYHNN/dOrQWJE0oQQQlbd792659dZbTfVpdHS0tGrVSsaPHy+HDx8+qbf1vvvuk3POOUdiYmKkR48e5Z4bd9y4cVK/fn0T6q699lpJSUkp83EtWrSQs88+W+rWrSvffPONXHfddSVud/z4cRk6dKg0aNBAHnvsMRk1apQsWbKkXPv23nvvyaBBg8y+uVwuWbt2bbkeh8ojAAJAwDaADASDytmxY4f06tVLtm7dKvPmzZNt27bJrFmzTCjq27evHDly5KTeWg2W119/fbm3f+CBB+Sjjz4y1bnLli2Tffv2mTZ95bFixQqZNGmSbN68ucT9zsnJkauuusqExMWLF8ujjz4qzzzzjHn+H374ocznz8jIkP79+8uTTz5Z7uPByWEuYAAIgPyHytLSNi31+/zzz6VGjRpmXcuWLaVnz57Srl07eeSRR+Tll1+u1HM/99xzRVWu69atK3P71NRUefXVV+Xtt9+WSy65xKybPXu2dOnSRb777js577zzSn2slsZp8Pv6669l6dKl8tZbb5kSSK+CggIZOXKkKVXUcKmlkuruu++W+Ph4ueKKK8xjO3XqVOpr3HTTTeZy586dFXgXcDIoAQQAG99SPwJg6NHPJMMTnKW83wctJdOSsLFjxxaFP68mTZqY6tH58+cXDTM0ZswYE6ACLSdjzZo1kpeXJ5deemnROm2rp4F05cqVAR+rQfHyyy837QBvvPFGc9uXtvfTKtwPP/ywKPx5jR49WpKTkwOGPwQHJYAAEGgmEBJgyMm0RGpuDs5rp3cWiS9Hs1Ct9tXvjpawlUTXHz161JTgaceKadOmycSJE+VU2b9/vymNrFOnjt/6xo0bm/tKk5uba0r8tPRQ3XDDDaYq+eeff5bu3bufsv3FqUcABICAA0Hz9qDyyjqB0FCmNATqEmq0zaAew7Bhw8xt7eAxePBgUwr47LPPVvj5NEzeddddRbc/++wzGTBgQJXuM8qHAAgANgwEHdriXIUlccF67fLQIVO0N6uOoTdixIhi9+v6hg0bFpXIaRXwm2++GfA509PTK7fTJ6qdtTTv2LFjfqWA2gtY7yuNBj2tzvatxvZ4PKa37syZM824gBVx5ZVXSp8+fYpuN2vWrMLHgqpBAAQAG7/5fykBDDkuV/mqYYNJA9Jll10mL730kqky9Q1QWuWqJWHaScTrVFcB65AxGta0B7IO/6K2bNkiu3btMj2SS6Jt9xYtWiTvvPOOdOvWzS8AaqmdDgxdUrgNpFatWmZB8BEAAcDG8kl9VAGjsl544QXp16+fqTKdPn26tGnTRjZs2CAPPvigdOzYUSZPnly0bUWrgHVIGS0R1DCpU7N5x83r2rWrqVbeu3evDBw4UObOnSu9e/c2HThuu+02mTBhghnIOSEhQe69914T/krrAfzGG2+YfdJx/7Q005d2CtHSwYoGwNJoKaOGUR2axhtOlZZOBiqhROXRCxgAAnYC4e1B5XTo0EFWr14tbdu2NSFKB4HWgZI1/C1fvvykevbefvvtZjiZV155RX799VdzXRdvgNIevxqiMjMzix6j4/JpcNMSwAsuuMAEK+29WxpvwLOHP6XPoe33yjOQdHloD2Ld/+HDhxd1NtHbOm4iTg2XRRe3SktLSzNnVTq+kp5NAXCG1xcfkj+1aGCuv7H3kNx4WeF1BIfOYJGUlGRK0GJjdYqW6mvKlCny9NNPyxdffBFw7D2c2u9NGr/fVAEDgJ3vebFfe0DgJE2dOlVat25tBl/Wqlm3m4o4BAdtAAHAhmpfnEq33HILbzCCjlMPALDxbfbHXMAAnIgACAA2Huv3Ru8eOoEAcCACIADY+Lb78w2DAOAUBEAAsGEqOABORwAEABvfdn90CAHgRARAAAhQAlhAAgTKpANOX3PNNZKYmGjGOkToIwACgE2BT88P+oAAZXv99dfNFHQfffSRvPvuu7Jp0ybethBHAAQAG99CP3oBA2XTWbF03mCd/q5OnTrmdml0EGydBk+312nsEBwEQACw8Z38g9kycTJ2794tt956q6ka1RIynQ94/Pjxcvjw4ZN63vvuu0/OOecciYmJkR49epS5/ZEjR+Tee++VTp06SY0aNaRly5bmOXQqU1867699eeedd8p8fp27d+nSpVK3bl3p1auXOd6SbNiwQYYNG2YGwx4wYIAMGjRI9u7dW65jfvzxx6Vfv34SFxdnQiZODgEQAGwoAURV2LFjhwlDW7dulXnz5sm2bdtk1qxZsmTJEunbt68JZSdDg+X1119frm337dtnln/+85+yfv16mTNnjixatEhuu+22YtvOnj1bkpOTi5arr766zOfXQKuleZMmTZIVK1aUuM3OnTtN4BszZow8//zzpqrYGwLL817k5ubKH/7wB7n77rvLdcwog4VKS01N1YoicwnAOf7rvRRLNlhmeWbhgWDvTtjLysqyNm7caC6rkyFDhljNmze3MjMz/dYnJydbcXFx1pgxY076NaZMmWJ17969Uo999913rejoaCsvL69onf6mLVy4sMLP9cwzz1jnnXeedezYMSsmJsb6+eef/e5PSUmxOnToYD3++ON+6wsKCqy77rrLPDYjI6NcrzV79myrdu3aJ/W9SeX326IEEABsPB7fmUDoBoKK0xKtxYsXy9ixY02Vq68mTZrIqFGjZP78+UVNDLRUTNvFBVqqmlb/JiQkSGRkpN/6cePGSYMGDaR3797y2muvlasZhJYa3njjjabt3+WXX25u+/K29/vrX//qt97tdptS0ZUrV5qqXZw+/p+6g8yYMUPee+892bx5s/nPp+0GnnzySdP+wSs7O1v+/Oc/m/YNOTk5MnjwYHnppZekcePGQd13AMFliSVnJKw11z1WMz6OEPx88iQvKK8dJVHikrJnh9FqXw1OXbp0KfF+XX/06FE5ePCgCUfTpk2TiRMnyuly6NAh+fvf/y533nmn33rdj0suucSEsc8//9wE2PT0dNNesDQ//vij6fXrrY7WIKjP+9RTT0lUVNQpPxZUjmMD4LJly8xZzLnnniv5+fnmrEPbGWzcuFHi4+PNNg888IB88sknsmDBAnPWcs8995hxjJYvXx7s3QcQRAUROTKi+QJz3bOG9kahRsPfdJkalNf+m0yRaIku9/ZllZ5pxxClIVCX0yEtLU2GDx8uXbt2lccee8zvvkcffbToes+ePSUjI0NmzpwZMABqaZ8WoGipodJOHgUFBeb3tTztB+20NPTNN98suq0BFFXPsQFQG7f60gav+p9rzZo1csEFF5ii71dffVXefvttc7bj/RLrWZl2UT/vvPOCtOcAgs1y55d4HSiv9u3bmx60WjI2YsSIYvfr+oYNGxb1ZrWHnpJURRA6fvy4DBkyRGrVqiULFy4ss4SuT58+pqRQa8m0x7GdrtffUS3N9K1K1gCov6mVCYCnuzQ0XDk2ANp5u7rXq1fPXGoQzMvLk0svvbRom86dO5uu8doWgQAIhC/fsf88vmPCICRoNayWxAXrtcujfv36ctlll5lmRVrb5NsOcP/+/fLWW2+ZWqrTGXq05E9L6jTIffjhhxIbG1vmY9auXWuGdikp/Cl9Hu2d+9NPP0lERETRem1+NXLkSDlw4ECFSzZPZ2loOAuLAOjxeOT++++X888/X84444yi/4Ba9G4fS0jb/+l9JdEzHV18/zMBcB7fSjvyX+jRNngVqYYNlhdeeMG0P9fQNX36dGnTpo0ZB+/BBx+Ujh07yuTJkysdenRIGS0R1N+rrKwsE9SUVuvqb5uOrTdw4ECZO3eu6cyhv1faDEqnbNOSRr3t/Q3TkkgNbzqLR0pKiikA0XD4xRdfyBNPPBEwmGopn1Ynd+/e3W99t27dzO+pvtaECROkKuzatct0rtFLLWH0HrOWtp6KTjJOFxYBUM+ydNyjb7/99qQ7lkydGpx2JwBOH0IfqoLOirF69WrTzu66664zpWHaJlDbmr/xxhsn1ev19ttvN23dfdvrqaSkJGndurWp4dqyZYsJfN6OGqtWrSoKTL68j9Hq4BdffNGUWOp+6nZPP/203HHHHSXug44rqB1FdIxDO63+1uPUgFhVAVADs045Zz9mHYD6oosuqpLXCCcuHQ9HHEw7dnzwwQfyzTffmLMvr6+++sqcHWm7Bd9SQB2lXUsL9T9AeUoAW7RoUdSVHoAzTF2YJAUj/m2uR35wh0y+qnWwdyms6YgNGlL0b3h5qi1D2ZQpU0yo0tI1mhoF73uTlpZmOn+G8++3Y8cB1Fyr4U8buWrY8w1/SqfQ0bMdHZHdS8+WtGhZR2gvibaB0C+K7wLA4VPBMRkwqpDWIj333HOms6E2TwKCJdLJ1b7aM0lL/7S3k7ddnyZ+bYyrlzoFjhZNa8cQDXM6T6KGP87KgPDmWy3i6CoSBIXOgwsEm2MD4Msvv2wu7e0CtD3Cn/70J3P9mWeeMaOQX3vttX4DQQMIcz6pjwAIwIkcGwDL07RR2wRog1ddAMDL49M+xvKZFg4AnMKxbQABoErGAeRtBOBABEAACBQAqQMG4EAEQAAIiAQIwHkIgAAQoA0xVcAAnIgACACBxgF09lj5QLkHsE5MTDSze+jUc6j+CIAAEGguYPIfwtymTZtkwYIF8vHHH5sJFObMmRPsXUIVIAACgJ1PqZ/FX0mEuZo1a5rJE3Ru4EaNGpnJE0pz8OBB6dy5s0RERMh//vOf07qfqBj+tAGAjd/QfxQB4iTs3r1bbr31VlN9Gh0dbeabHz9+vBw+fPik3tf77rvPTGmqU5T26NGjXI/RiRFcLpffMmbMmDIfp3Pen3322VK3bl355ptv5Lrrritxu+PHj8vQoUOlQYMG8thjj8moUaP8plsN5L333pNBgwZJ/fr1zX6tXbu2XI9D5REAAcCGTiCoCjt27JBevXrJ1q1bZd68ebJt2zaZNWuWCUU67eiRI0dO6vk1WF5//fUVeswdd9whycnJRctTTz1VrsetWLFCJk2aJJs3by5xv3U2rauuusqExMWLF8ujjz5qZtvSNoM//PBDmc+fkZEh/fv3lyeffLJCx4PKc+xMIABQJZ1AGAYGJzEnvZb6ff7552YOetWyZUvp2bOntGvXTh555JGiaUsr6rnnniuqcl23bl25HxcXFydNmjSp0GtpaZwGv6+//lqWLl0qb731limB9CooKJCRI0eaqmJtK6ilkuruu++W+Ph4ueKKK8xjO3XqVOpr3HTTTeZy586dFdo3VB4lgABg49vvgz4gIdpG05MRnKWcvcK1lExLwsaOHVsU/rw0gGn16Pz584tKm7UqVgNUoKUqaHjTKtozzjhDHn74YcnMzCzzMbNnz5bLL7/ctAO88cYbzW1f2t5Pq3A//PDDovDnNXr0aFPSGCj8ITgoAQQAO+v3MkDGAQxBVqZIStUEogprnC7iii9zM6321XDXpUuXEu/X9UePHjUleNqxYtq0aTJx4kQ5lf74xz+aNojaHlFLDf/yl7/Ili1bTHgrTW5urgmNr776qrl9ww03yAMPPCA///yzdO/e/ZTuL04tAiAA2Hj86kYoA0TllTWOpFYRKw2BupxKd955Z9H1M888U5o2bSoDBw6U7du3myrpknz00UfmGIYNG2Zua+nh4MGDTSngs88+W+F90DB51113Fd3+7LPPZMCAAZU6HpwcAiAA2Pn0/KUNYAhyxRWWxAXrtctBh0zR3qw6ht6IESOK3a/rGzZsKHXq1CmqAn7zzTcDPmd6etUec58+fcyldk4pLQBq0NPqbN9qbI/HY3rrzpw504wLWBFXXnll0euqZs2aVXr/cXIIgABg43H9Pg4MATAE6edTjmrYYNKAdNlll8lLL71kqkx9A9T+/ftNSZh2EvE6HVXAdt6hVrQksCTadm/RokXyzjvvSLdu3fwCoJba6cDQJYXbQGrVqmUWBB8BEABsfGvtPJbvoIBA+b3wwgvSr18/U2U6ffp0adOmjWzYsEEefPBB6dixo0yePLlo24pWAWupnZYIapjUqdm8Ya5r166mWnnv3r2menfu3LnSu3dvU8379ttvm6pcDafaBlCD6QUXXCBnnXVWia/xxhtvmH3Scf+0NNOXdgrR0sGKBsDSaCnjrl27ZN++fea2tk30dpipaK9llA+9gAGgGJ8ESP5DJXXo0EFWr14tbdu2NSFKO2DoQMka/pYvX35SPXtvv/12M5zMK6+8Ir/++qu5ros3QOXl5ZkQ5e3lq6Hwyy+/NIMt60wdf/7zn+Xaa681bfxK4w149vCn9LHafi8lJUWqgvYg1v0fPnx4UWcTva3jJuLUcFnMdF5paWlpplt8amqqJCQkVOXnAiCI7pq/WRpf/4a5fnD+aHn5eoawCKbs7GxJSkoyJWixsbFSnU2ZMkWefvpp+eKLL+S8884L9u6E7fcmjd9vqoABoKTyv4Z5B831FBe9gFF1pk6dKq1bt5bvvvvOVM263VTEIThoAwgANlHuLLn30P+Y6+Pk94b6QFW45ZZbeCMRdJx6AIBNTMTvw23ERGbw/gBwHAIgANj4Dv3iYSBoAA5EAASAYnx7PdIGEIDzEAABIEAJoFXCEBgAUN0RAAHAxq/Mr4y5XAGgOiIAAoCN7/CoxD+gbDrg9DXXXCOJiYlmrEOEPgIgANj4zv7GWPlA2V5//XUz24jOLPLuu+/Kpk2beNtCHAEQAIphKjigInRWLJ03WKe/q1OnjrldGh0EW6fB0+11GjsEBwEQAGysUjqEABW1e/duufXWW03VqJaQ6XzA48ePl8OHD5/Um3nffffJOeecIzExMdKjR48yt9+5c6eZ07ekZcGCBUXblXT/O++8U+bz69y9S5culbp160qvXr3M8ZZkw4YNMmzYMDMY9oABA8zcxHv37i3XMT/++OPSr18/iYuLMyETJ4cACAABAyBQOTt27DBhaOvWrTJv3jzZtm2bzJo1S5YsWSJ9+/aVI0eOnNRbq8Hy+uuvL9e2LVq0kOTkZL9Fp6XTkrihQ4f6bTt79my/7a6++uoyn18DrZbmTZo0SVasWFFqCNXAN2bMGHn++edNVbE3BJbnvcjNzZU//OEPcvfdd5frmBEYU8EBgI1/6PPw/qBSxo0bZ0r9Pv/8c6lRo4ZZ17JlS+nZs6e0a9dOHnnkEXn55Zcr9dzPPfecuTx48KCsW7euzO0jIiKkSZMmfusWLlwo1113nQmBvrR0zb5tWd566y05++yz5aGHHpJnnnnG7NNZZ51VdP+BAwdM0NP35K9//WvRPmnbwbFjx8rw4cNNMNbSvdJoYFVz5syp0L6hZJQAAoCNi3I/nCQt0Vq8eLEJN97w56XhatSoUTJ//vyiTkZaKqZBLNBSldasWSNr166V2267rdh9GtIaNGggvXv3ltdee61cHaG01PDGG280bf8uv/xyc9uXt72fN/x5ud1uUyq6cuXKgOEPVY8SQACwc1EJHNI0kORkBue1Y+K0oVyZm2m1rwanLl26lHi/rj969KgpwdNwNG3aNJk4caKcLq+++qrZB21T50v345JLLjFhTEsuNcCmp6ebNoel+fHHH02vX291tAbBO++8U5566imJioo65ceCyiEAAoCN5fKUEgYREjT8XV21JWLl9n66SGx8uTcvq/RMq4iVhkBdToesrCx5++235dFHHy12n+86rarOyMiQmTNnBgyAWto3ePBgU2qotJNHQUGBfPLJJ+VqP2inpaFvvvlm0W0NoKh6VAEDQDG/B0CLAIhKaN++velBW9p4eLq+YcOGRb1ZT2cV8P/+7/+agZtHjx5d5rZ9+vSRPXv2SE5OTon363oNkxr2IiMjzaKlh1oFbq8GLi8thdTqae+CU4MSQACw8a/goxNIyNFqWC2JC9Zrl0P9+vXlsssuk5deekkeeOABv3aA+/fvN50mtK2d1+msAtbq3yuvvNIE0LJoANOhXXS4mZJ8+OGHpnfuTz/9ZDp1eG3evFlGjhxpOn9UtGTzdJaGhjMCIADY+I79R4eQEKRt8CpQDRssL7zwgmljp9Wj06dPlzZt2phx8B588EHp2LGjTJ48udKhR4eU0apRDZNapestKevataupVtax9QYOHChz5841nTl8H/fNN9/Ip59+Wuw5dRaPlJQUOe+88yQ2Nla++OILeeKJJwIGUy3l0x683bt391vfrVs3ady4sanKnTBhglSFXbt2mZJFvdQqZu8xa2lrVXeSCQcEQACwo9oXVUBnxVi9erU89thjZrgVLQ3TNoE6Z+4bb7xxUr1eb7/9dlm2bJlfez2VlJQkrVu3lry8PNmyZYup6vWlvXqbN29uhmSx0w4bL774oimx1P3UYPX000/LHXfcUeI+7Nu3z3QU0TEO7bT6W49TA2JVBUANzDpsjP2YdQDqiy66qEpeI5y4LCa6rLS0tDTT5T01NVUSEhKq8nMBEERjP/hMXuo9zFwf9/1n8uJVQ/g8gig7O9sEGy1B05Kp6mzKlCkmVGnpmpa0ITjfmzR+vykBBAA7v2pfSgNRhXQwYy2h0/lwtWpWx8EDgoEqYAAoxicAlmMQXKAidB5cINg49QAAO99SP0oAATgQARAA7HwHgmZaOAAORAAEgABtAF2uAt4fAI5DAASAYmj3B8DZCIAAYEe7PwAORwAEgIAlgEwFh/Dm8XjkzjvvlKZNm5pLhg92BgIgANi46AUMFFm8eLH8+uuv8tlnn5k5fhctWsS74wAEQAAoxrcTCO0BEd50xqu6deuaqeHq1atnltLoXMM6B7BOc7d8+fLTup+oGAIgABTjEZkjhQsdQnASdu/eLbfeeqskJiZKdHS0tGrVSsaPHy+HDx+u9HP+/PPPMnLkSGnRooXUqFFDunTpIv/93/9d5uOOHDkio0aNMlOX1qlTR2677TZJT08v83H9+vWT3NxcEwQLCgqkT58+pc4NfNlll0n//v3Nc19++eXyyy+/lOuY/vWvf5n5fHXfdB7hY8eOletxqDxmAgEAm/isVJFlhdfjOqTy/qBSduzYIX379pWOHTvKvHnzzJy0GzZskAcffNBUp+p0cIFK00qzZs0aadSokbz55psmBK5YscK0zYuIiJB77rmn1Mdp+EtOTjbzEOfl5ZkZSfRxb7/9dsDX021Xr14tkyZNktmzZ0t+fr5ERvrHh6NHj8rgwYNlwIABZhvdl5o1a5p1WhKoxx5IZmamDBkyxCwPP/xwBd8RVIqFSktNTdW6IXMJwDn+uuBlyxosZnlowb+CvTthLysry9q4caO5rE6GDBliNW/e3MrMzPRbn5ycbMXFxVljxoypstcaO3asdfHFF5d6v75/+nu1evXqonWfffaZ5XK5rL179wZ87oULF1rNmjWzcnNzrcTEROuDDz7wuz8jI8Pq27evddddd1kej8fvvunTp1vt27e3UlJSynUcS5cuNft59OhR61R+b1L5/baoAgYAG5fvddoAohK0ulU7T4wdO9ZU0/pq0qSJKY2bP39+UY/aMWPGmBKzQEsgqampAUsTV65caap9e/XqVbTu0ksvFbfbLatWrQr43Fqip1XOUVFR5lJv+9L2floKOWvWLFN96+uRRx6RrVu3mhJLhBaqgAGgGN+OH3QCCTUamvIyM4Py2lFxccVCTkk09Oh+avu8kuh6rTY9ePCgCUfTpk2TiRMnVmqfNHxpmPzkk09K3Wb//v3FQphW42po1PtKk5KSIp9++qnZP3XjjTdK7969zX43bNiwUvuL0EAABAAbiwAY0jT8zSijROxUeTg9XaLj48u9fVlj5mnHEKXhrDKlZOvXr5errrpKpkyZIoMGDZKqpu0MO3fuLN27dze3e/ToYdo0vvXWW3L//fdX+PmeeOIJs3ht3LhRWrZsWaX7jPKhChgAAswFzKwgqAwdMkVLCjdt2lTi/bpeS9C0WrayVcAangYOHGg6cvztb38LuD9a7XzgwAG/ddqZQ6uq9b7SaHWvdlzR0kLvoq87Z47pIl9hepxr164tWrR3NILDsSWA33zzjcycOdP0ltJeTwsXLpSrr7666P4//elP8vrrr/s9RnsrMcAlAEJfaNNqWC2JC9Zrl0f9+vXNkCgvvfSSPPDAA37tALXKVUvQxo0bV7SuolXAGsouueQSufnmm+Xxxx8vc3vtjaxDq+hv4jnnnGPWffXVV2aWj9KGddGevxr2vv76a7/2hfo8F1xwgfz000/Ss2dPqYiyxhHE6ePYAJiRkWGKrHX8pWuuuabEbbS7uW9j1piYmNO4hwBClk/HDxdTwYUcLVmrSDVssLzwwgtmDD0tXJg+fbrfMDBajTp58uSibStSBazVvhr+9HknTJhQ1IZPh17xtsv7/vvvZfTo0bJkyRJp1qyZaXOov3l33HGH6ayhQ7vokDE33HBDqaVw+vuo7f007JUUKPX+igbA0ugx6KIDSSsdP7BWrVqmepjAeGo4tgp46NCh5j/ciBEjSt1GA58WfXsXHekcAPw6ftALGJXUoUMHU4rWtm1bue6668wg0PrbpOFPx8Yrq2dvaf73f//XdMLQ9nk6P693Offcc/3G1duyZYsJel5a6qjt+bTaeNiwYWbAZh2AuSTZ2dlm7MJrr722xPt1vY4fqANEVwUNpRomNaAqDZ16+8MPP6yS50dxLh0PR8LgbLGkKuD333/fNMDV4KdnUxoYtdi+NDk5OWbxSktLM4Nwavd7Hb0cgDM8uvC/5e+vFDZw/9uY52T61fcGe5fCmoaRpKQkU4IWGxsr1Zl21nj66afNYMznnXdesHcnbL83aWlpZmaTcP79dmwJYFm0KHzu3LmmePzJJ5+UZcuWmTMzneamNDNmzDBfGO+i4Q+A8/iO/eeyPEHdFzjL1KlT5bnnnjOzgGj7OyBYHNsGsCza7sHrzDPPlLPOOkvatWtnGrtq8XhJdHoabW9hLwEE4Cwuz+8nggwEjaqmU7ABwRa2JYB22kajQYMGRQ1QS2szqEXFvgsA5/Ed59dllV4rAADVFQHwhD179sjhw4dNQ1oA4c2352/Zcz4AQPXj2Crg9PR0v9I8bQiqg056xyDSdhjai0l7/27fvl0mTZpkBu7UbvUAwpvL5RsAaacFwHkcGwB/+OEHufjii4tue9vu6aCZL7/8sqxbt84MBK0DWuoYSDqFzt///nfGAgSgwyMUvQtuOoEAcCDHBsCLLroo4ByMixcvPq37A6D68A19dAIJHWEwahmqEL2swzQAAkCl+VYBUwIYdFFRUWY8Vx38WGe60OtAoBMFHaBavy9ut9uM94viCIAAYOMX+nzCIIJDpzhr3ry56ay3c+dOPgaUS1xcnJlKTkMgiiMAAoCN788FJYChQadN06nVfKc2AwKdNERGRlJaHAABEACK+b3Uz01tY0j9qOsC4ORRLgoAxf4w+gz+TBtAAA5EAASAgH8kmQkEgPMQAAGg2B9Gn7mAeXcAOBABEACK/WH0HW+OXsAAnIcACAB2PvkvggAIwIEIgABg4/LrBMLbA8B5CIAAYOM7/ZtfGAQAhyAAAoD9D6PPnLNuqoABOBADQQOAneWR3ccKr/oUBgKAYxAAAcAuN0deW1V4tU5uDu8PAMehChgA7DJzi666sgmAAJyHAAgAxf4w+tT7UgUMwIEIgABg4/Lp+OE/KDQAOAMBEACK8R0GhplAADgPARAAbFx+AZASQADOQwAEgGJ/GH8v9SMAAnAiAiAAFEMVMABnIwACgI3LZyYQugEDcCICIADY+Hb8cHnoBALAeQiAAGD/w+ihChiAsxEAAcDO8i0BpBcwAOchAAKAjcs3APpcBwCnIAACQIBOIARAAE5EAAQAO5+OHwRAAE5EAASAQDOB0AYQgAMRAAHAxr/Uj04gAJyHAAgANr5j/zEOIAAnIgACQDF0AgHgbARAALD/YaQKGIDDEQABwM5nGBg3U8EBcCACIADY+YY+nzAIAE5BAASAQMPAMBMIAAciAAKAjd/Yf5QAAnAgAiAA2PiV+hEAATgQARAAAnUCoQoYgAMRAAHA/ofRrxOI76wgAOAMBEAAKMZ3GBh6AQNwHgIgANgxEDQAhyMAAoCNy7fjBwNBA3AgAiAABBgGxndMQABwCgIgANj5lgAyDAwAByIAAkCAcQBdVAEDcCACIADY+ZT6+bUHBACHIAACgI1f6CMAAnAgAiAABCoBZBxAAA5EAAQAG//QRxUwAOchAAJAMZQAAnA2AiAA2Pj2/LWYCxiAAxEAASBAG0A3nUAAOBABEADsfEv9KAEE4EAEQACwYRgYAE5HAAQAO5+OvwwEDcCJHBsAv/nmG7niiiskMTFRXC6XvP/++373W5YlkydPlqZNm0qNGjXk0ksvla1btwZtfwGEEN9hYBgHEIADOTYAZmRkSPfu3eXFF18s8f6nnnpKnnvuOZk1a5asWrVK4uPjZfDgwZKdnX3a9xVAiKHjBwCHixSHGjp0qFlKoqV/zz77rPztb3+Tq666yqybO3euNG7c2JQU3nDDDad5bwGEEpdvHTAlgAAcyLElgIEkJSXJ/v37TbWvV+3ataVPnz6ycuXKUh+Xk5MjaWlpfgsAB/IJfbQBBOBEYRkANfwpLfHzpbe995VkxowZJih6lxYtWpzyfQVw+tELGIDThWUArKyHH35YUlNTi5bdu3cHe5cAnOI2gJQAAnCisAyATZo0MZcpKSl+6/W2976SxMTESEJCgt8CwOGdQOgQAsCBwjIAtmnTxgS9JUuWFK3T9nzaG7hv375B3TcAwefy7fhBAATgQI7tBZyeni7btm3z6/ixdu1aqVevnrRs2VLuv/9+mT59unTo0MEEwkcffdSMGXj11VcHdb8BhAKqgAE4m2MD4A8//CAXX3xx0e0JEyaYy5tvvlnmzJkjkyZNMmMF3nnnnXLs2DHp37+/LFq0SGJjY4O41wBCAgNBA3A4xwbAiy66yIz3VxqdHWTatGlmAQA/tAEE4HBh2QYQAAKjDSAAZyMAAoAdA0EDcDgCIADY+I79F6gpCQBUVwRAALDzyXwu8h8AByIAAoAdnUAAOBwBEADsGAgagMMRAAHAxlVaGAQAhyAAAoAdVcAAHI4ACAA2lqeU0kAAcAgCIADYuHwSoG8YBACnIAACgJ1vsz/GAQTgQARAALDzCX0uOoEAcCACIADY+ZX60QsYgPMQAAHAzqfdH1PBAXAiAiAAFONbBczbA8B5CIAAYOfT7o8SQABORAAEADufZn8u2gACcCACIAAE6gRCFTAAByIAAoAd4wACcDgCIAAEnAuYtweA8xAAASBgACQBAnAeAiAA2Pm2+2MmEAAORAAEgGJ8hoHh3QHgQARAALCjEwgAhyMAAoCN5VvtSxEgAAciAAKAnW/oow0gAAciAAJAMZQAAnA2AiAA2DEMDACHIwACgJ3f9G80AgTgPARAAAiE/AfAgQiAAGDn0/HDr0cwADgEARAAAjQBdJH/ADgQARAAAiRApgIG4EQEQACwYyYQAA5HAASAgMPA8PYAcB4CIADYUQIIwOEIgABgwzjQAJyOAAgAdlQBA3A4AiAABEI3YAAORAAEABvfwZ/JfwCciAAIAHZ0AgHgcARAAAgYAHl7ADgPARAA7OgEAsDhCIAAYEcVMACHIwACgA3jAAJwOgIgANhRBQzA4QiAAGDj1++DcWAAOBABEADsPL9fJf8BcCICIADYMQwMAIcjAAJAoATIOIAAHIgACAABewGTAAE4DwEQAGzIfACcjgAIAAESIGEQgBMRAAHAxi/0eagCBuA8BEAAsKMPCACHC+sA+Nhjj4nL5fJbOnfuHOzdAhBszAQCwOEiJcx169ZNvvzyy6LbkZFh/5YAYY+5gAE4XdinHQ18TZo0CfbnACBkB4KmDSAA5wnrKmC1detWSUxMlLZt28qoUaNk165dwd4lAMHGTCAAHC6sSwD79Okjc+bMkU6dOklycrJMnTpVBgwYIOvXr5datWoV2z4nJ8csXmlpaad5jwGcDhQAAnC6sA6AQ4cOLbp+1llnmUDYqlUreffdd+W2224rtv2MGTNMSATgcAz9AsDhwr4K2FedOnWkY8eOsm3bthLfrIcfflhSU1OLlt27d5+mjwnA6eTX6o8mgAAciADoIz09XbZv3y5NmzYt8c2KiYmRhIQEvwWAw8cBpDQQgAOFdQCcOHGiLFu2THbu3CkrVqyQESNGSEREhIwcOTLYuwYgiOj4C8DpwroN4J49e0zYO3z4sDRs2FD69+8v3333nbkOIIz5lgBSBQzAgcI6AL7zzjvB3gUAIcgv9BEAAThQWFcBA0DJfk99lAACcCICIADY0Q0YgMMRAAEg0FzAHt4eAM5DAAQAO9r9AXA4AiAABCoBJAwCcCACIADY0AsYgNMRAAEgAEoAATgRARAAbPymf6MKGIADEQABIACLBAjAgQiAAGBDG0AATkcABAAbegEDcDoCIAAEQBNAAE5EAASAQKmPBAjAgQiAAGBDFTAApyMAAoANnUAAOB0BEAACoAYYgBMRAAHAhhJAAE5HAASAAAmQqeAAOBEBEAACVvtSCQzAeQiAAGDnOxUw+Q+AAxEAAcCGNoAAnI4ACAABUAAIwIkIgABgw0DQAJyOAAgANlQBA3A6AiAABEAVMAAnIgACgA1VwACcjgAIADZUAQNwOgIgAATAOIAAnIgACAA2hD4ATkcABIAACIMAnIgACAA2hD4ATkcABAAbegEDcDoCIAAEQGkgACciAAKADSWAAJyOAAgAdkz/AcDhCIAAECj/EQYBOBABEAACVQHz7gBwIAIgAARAJxAATkQABAAbQh8ApyMAAoCNb7UvYRCAExEAAcCONoAAHI4ACAA29AIG4HQEQACwYSBoAE4XGewdAIBgy80tkFWr9sjy5btl/foD0tNWGjhq1Hty1lmNpG/fFtK3b3OJiooI4t4CwMkjAAIISwUFHvnqqySZO3edvPfxQcl0txKJbSYS01F62Ab/e3tlf3n76wMiT22WBPcXMmJ4Q7n++q4yaFA7iYigIgVA9UMABBBWjh3Llv/5nzXyzAtbJDmro0itC0TOri/SUUQ6iEgLEesmW3Xwk41E9jYS2XGGpK0Tef3bw/L6p5ulea0VMv7uDnLbbT2lbt0awTwsAKgQl2UxyEFlpaWlSe3atSU1NVUSEhIq/TwATr2kpKPy7LOr5F9vHZPsGj1FuncU6esS6SMiiR6pF31EGkQfkAYxh+TqlhdKZm7h4+JiRD7c9ZUcymko+7MT5VheXZG9LpE1IrJMRDYmSXTmWrn5mmj584Q+0qlTAz5OIMSl8ftNCSAAZ1u9eq888Y/v5YOvY8Vq1ltkdH2RS0Vi2mZJ51obpW3NbdImfrvER2aY4r44K6tYL+AhDT6RHFe0iMslWfk1ZEez9rKtayfZNqKjZG5rI7lL28j/LE2X/xnwgww695g88peeMmBAS3G5XME7cAAIgBLAk8AZBBCaPB5LPv10q0ydsV5+2N5MpE9PkcHREtEvX9rX/1XOrL1WOtTcIrGuLGmXkyQdcnZIk7yD0jj/kMRYWfJkK5HsvMLnqhElMuk3kVxXjKRENpC9UU1lT3SibI9uI8fdtWR7entZn9pDthztInkro0W+KBBZsUG6NEmSyQ+2k//3/7pKZCTtBIFQkkYJIAGQLxDgHBkZufLGm+vl8f/eL3usziLD2opcaknzzrvkrNo/Sdfa66W+67B0zN4mnbN/lQ45SRJVkCeyT0R2i8ihwuXJx0Sy8wufMzZK5C9TRaS2iDQpbCOol1akS/ZGNZGtMe1kS0wH2RnRUjYf7ya/pPaQpG3txPrSLfJZstQ//rP85e46MuaunlKrVkyw3yIABECDEsCTwBkEEBp++SVFZj67Qd5ZHCN5PXuIDI6XBgMPyBkNfpYzav8sbd07pEv2r9I551dpmb1b3EkisuHEstMlkmuZzh4a+rTt379WiuQWFD53dITInX1F4qJFYiNNLbBIlEuknSXSRUQ6i0hbkdToWrIptqNsiu0k611dZd3xs+WXQz0k5dumIouyJOq7H+Wm4bly391dpHt3TZIAgiWNEkACIF8goHravTtVZs/dIq8tyJLfEtqKXNZcag88Jt3a/SLdEtbK2RFrpUvOFumcvVUapxwUWS+FyyYRT7pIynGR3UdFUtJFDmVFyMEMl2R5i/1KERsTIQ3iLGkQ55HEBJHmdUQa1xRx1RBxaRA8o3DJbBIrW2p0MGFwhdVHfkzrIxu2nylpi2qLfL1Xmh/bJmNHxcvoGztJs2Z0IANOtzQCIAGQLxBQPeiABRs2HJS33v1N3l2UJzvqNhc5v5k0GZgiHTpslm4118kA93JTvdshfZvU3pIusq4w9OXtEtlzTGTXscLQtztVJLeUrBddq5bE1a8vLfr1M7d3r1ghmYcPS+7x4yVuHxkhohmuRV2RlnVEWtQRiW0qIt0Kw2Bu10jZWq+dbIrpJN9Y/WVtxjny6/YukvJlIxMGWx3bLSOHR8nNf2wrnTrVp+MIcBqkEQAJgHyBgNANfDt2HJVPP98r73+eJasOx0tG5+aScKElLfvslLb1tsqFsV9LN89maZudJK23/SZRv3pMtW72OpHdB0R+Oyqy66jI3lQRj21w55iEBGnet68k9uolDbt2NUv9Tp0kqkbJ4/nlZWXJkW3b5NDmzXLgl19k76pVsmfVKslJTS22baOaIi29gbC+SO2uIq4zRQrOcElSp1ayvUZb+cXVVf4v5wJJOtxefvuhjaR945Ya63ZLr3rp8odhcXL15S2lRQtteAigqqURAAmAfIGA0LB/f7qsXJUiny9Ll+XbRLZE1BKrS11pfH6aNO6SLGfWXSd9or6TDtZ2aXN4pzTftleikwrEs1nk4E8iyQdF9qWK7D5WWL1ry3tSKzFRWl1wgbTo319a9u8vjc44Q9wRJzelm+XxyKEtW0wp4e5vv5Vdy5fLka1bi22XEPt76WDLRJFGPUXc7UVy20XKno6JklSvtWxxdZBVeX1k05FukrylmaSsqCXuTanSPjNVzu8sMvySBOnXt4k0aBB3UvsMgACo6ARyEjiDAComJydftm8/KqvWpMl367Jk7YEI2SmxktEwQmp3y5X6HdOkbYvtckbNDdIlYpO0yU6SZrv3SaPdhyR2X46kbxY5tFHkULLIwXSR5OMi+9NE8j3FX6te+/bS8oILTOjTpU7r1qelejU9JUV2L19uwqCGwuQffxRPvn99s3YsaVxLpGHNwtLCRs1FGnUWie8gktU8Rg60aih7WjaVpOi2ssXTSdYf7ybb97aXQ7/WlmO/REt8cp60dWXLOc08cv7ZcXL2WQnSsmWCxMdHn/LjA5wgjRJAAuCLL74oM2fOlP3790v37t3l+eefl969e/MFAipQVZuZmScHD2ZJ0q4sSdqdKxt3Z8uWQy75LdMtqfGW5NcrkOjEXGnY7Ii0aJQsrev+Ji1jf5PWWb9Ji6N7pMHBQ1Lr4HFx7cyQrN88krpHJHWfSOpRkbRskaOZIocyfu+ZaxcdHy9NzzlHEs89V5r17i0tBwyQWk21IV7w5WVmyt7vv5ddWkL4f/8nu5d/K7kZmSVuq72M69QQqRt34rK+SJ2mIrV0iuJWbvG0jZf0RglyuG492Vs7UZLi28hvua1k59E2smt/ohzcXUcKUiIl8liE1M2NkJaxlrRNEOnQJELat46V9q1rSJMm8VJDBzcEwlgaATC8A+D8+fNl9OjRMmvWLOnTp488++yzsmDBAtmyZYs0atSozMfzBUJ1lp/vkezsfMnMzJfUtDw5eDhP9h/Ol/3H8mVfmiUpmR45klcgx135khmZL3nReRJRI0Nq1EyT+Pg0qRmXLnVjj0hD9xGpr4vniNTNPyI1M1Il7sgxiT5yXCI1uR3OlYIj+VJwrEBy00QyvUuGSEaOSGZe4dAruhSU8dfI5XZL3eaJ0uCMM6RBtzOlSY8eJvjV79DB3FcdeAoK5NCmTXJg/frCZe2PcmDdz3J0T/KJiYdLpwWYWshXM1pEhxSsoUPTxInUqCUSmyASU1ckqk6ERNaNEleDaMmvW0Ny6iVIRt06klqzrhyLqitHXHXlsFVPDuXVl8PZdSUtvY6kHa8tOcfjxcqMk7j8CKnpipA6kZFSPzpK6sW6pUG8SxrXdEuD2pFSr06ENKgXLQm1oiQuLopBrlEtpREAwzsAaug799xz5YUXXjC3PR6PtGjRQu6991556KGHynw8X6DqS7/2+s0vKPBIQYEl+fmW5Hsv8y3Jy7ckt6DwMq/Akly97TlxvcBTeOmxJN/jkdx8j+RZBVLg8Ui+J1/ypcB8lwp0nZUvHnNfgVh6vSDfrLM8uVJQkC9WQYF4rHwpKMgVy1MgHskT8eSJWDoRbZ64PLkSITkS6coTt+RKpCdHoqxcibByJNKTa65HFuRKhCdXIvNzxZ2vt/MkIi9P3AX5ZonI//26Kz9f3N6loEBcefniyvOIS4vW8vS2R6xcjxTkWuLJtaQgx5J8vcwTyc+Twst8kbwTl/kFhdWveqlPkZNfvLNFRdWsU1MSGjeS2q1aSe0OXSShXXup06qVNOjcWeq2ayeRMc4cTFlLCo/u2CFHk5Lk6PbtcmzLJjn262Y5tus3ST94SDLSdKq6yj+/hketeo6KKLw01yNFoqNEIiNFIqJEIvQyuvC6O8olEdEuc+mOdosr2i1iLn9/Ak9khFhRkVJgLiOkIDJKPHrbHSkFEVHiiYiS/IgoKXBHSb5ZoiXfFS0F7hjJi4gSyxVTuJ0+zh0tHn2cSy+jxCPRUhARIx5XzIn7osUlkeJyRYslkeKWaHG5okRckeJ2RUqEO0YiXNES4Y6SaFekRLncEh0ZKTGRkRLpdklUhNtcRke4xe1ySZTbLVFu7cXtPnGfFN4fqdu6JCJCty1c9Hpk5IlLn+v6nrrdesmUf9VNGgEwfOcCzs3NlTVr1sjDDz9ctM7tdsull14qK1euLPExOTk5ZvH9Ap0Kb4w7XyKX/SCW96+97Y++N7K7Tlw3l/Zt7Q/1Psjnwvv435/Y/8WsUp8r0HqfJ7Rf9b1dyjGVfKz2lSXvT5m3S/rxLOkttko+VuvEc3gX73MWLd7bvut93/oTt11mpYh2P9Crbqv8/xG1JVngkepCR3S0W6JjIiQmLkZi42tITK1aEl2rrtRo0EjimzaVuMQWEt+8lcQ1aSpxDRtKvC6NGzs24JUlKi7OdEzRpSTajjDjwAFJ379fjicnS/q+fZK1b7dk7d8r2Qf2S/bhA5J19LBkpx2XrPRMyc7IldzcAnOC4/3+aUDXpXxOfFGNUureTyEtzy2pTFf/bnnzll66Sri0XCJ5uohIZknbeJcTT1h03Xv7xHN47y+6KOO6d5+KXbc9v/eKX24sYR9KfGzAfXCV+Lrm747tsYWfrP/2RdvZ3/BiH8LvK+3HX/pjS9iohPfK+41z+dyZf2EvuenF5aW8CCorbAPgoUOHpKCgQBo3buy3Xm9v3ry5xMfMmDFDpk7VOaFOrehtW2XzBi0BAkrn/YMZ4RZx6x96d+F1l9slWhvqLrruEnfEiesRhYtWl7q1JCRSlwhxR0aKOypSIqKiJCIqUqJiY81wKFE14iSqRrxE1agpkbrE15ao+LoSGV9LIhPqSGRCXYmsrbdrmvHzNOTp8CrRNWtWmyrZ6kI/I+3JrEtFWjcW5OVJXkaG5GZk/H6Zmiq5Rw9JbupRyUs9KvnpqVKQkSr5malSkHlc8rLSpSA7U/JysqQgN1vycnIkLydXCvLyJT83Twpy86Ugr0A8BR6xCqyiS8tTuOhczJZHe0lbpkTYe1uvm8sT183tCpZq+p5UnUyJaPCdip2v1m9IqTo33xbsXXCksA2AlaGlhRMmTPArAdQq46rmuXiEdKy1xP8szfdU9cTVwuKj30/rLJfL76zJcntPY71neb9v571t2U8XT7yWPo8+tPAs1baduXCfuOm7T4XbmX3wPUUvuixcX/hwl89zF54Je1/Dpef93sd5t/PuV9H23mNw+2zn3a8Tr2MuTtw2T6lVP4UlvVoFpIEo4sR1tztC3Hp/RIRE6GMiIiUiIsKsj4yIFJeu19uRURIREVm4fcSJ6xFR4oqMFrc7UlzuKHGbddHiiowSl64zj48qXNxR4ooqXKepTJ+38P4IcUXq473rIkyA8i7meHxuU+WE8jKhvk4dia1TJ3SbY2hTiIICk+x0aB2zeK8XFIiVnydWXq5Jjua2WfLFsjxi5WlTihyxPHliFWhD0nzxeHLFk58nHo82t8g3100TC9PsQtdryahup8+df+K6p7Cphu6Lx2P2x7vePM7S5iIFZp+06Yh5bb3P0pCr17VZid5viUd0P7Vruq47cTzmWDX5nihN1e1PlLLqY068GT5VCIWPMe+JudTHnqhC0PtPPMY8i6V/4rzPY/k9v9ncVGHYq0J87yu87b088RffJ5zrOp/9O7HO/nxFNUvm0udYzB/4AKnd3Fd6FZHV69JKfbcQWNgGwAYNGpgf9JSUFL/1ertJk5Ln6YyJiTHLqTbyoVdO+WsAQCjQkxk9+dESTgCnT9jW0URHR8s555wjS5YsKVqnZ3p6u2/fvkHdNwAAgFMprE+5tDr35ptvll69epmx/3QYmIyMDLnllluCvWsAAACnTFgHwOuvv14OHjwokydPNgNB9+jRQxYtWlSsYwgAAICThPU4gCeLcYQAAKh+0hgHMHzbAAIAAIQrAiAAAECYIQACAACEGQIgAABAmCEAAgAAhBkCIAAAQJghAAIAAIQZAiAAAECYIQACAACEmbCeCu5keSdR0RHFAQBA9ZB24nc7nCdDIwCehOPHj5vLFi1aVNXnAQAATuPveO3atcPy/WYu4JPg8Xhk3759UqtWLXG5XFV+dqLBcvfu3ZKQkCBOw/FVf3yG1ZvTP79wOEaOr/IsyzLhLzExUdzu8GwNRwngSdAvTfPmzeVU0j9aTvzD5cXxVX98htWb0z+/cDhGjq9yaodpyZ9XeMZeAACAMEYABAAACDMEwBAVExMjU6ZMMZdOxPFVf3yG1ZvTP79wOEaODyeDTiAAAABhhhJAAACAMEMABAAACDMEQAAAgDBDAAQAAAgzBMAQsHPnTrntttukTZs2UqNGDWnXrp3puZabmxvwcdnZ2TJu3DipX7++1KxZU6699lpJSUmRUPX4449Lv379JC4uTurUqVOux/zpT38ys6z4LkOGDBGnHJ+ORj958mRp2rSp+ewvvfRS2bp1q4SiI0eOyKhRo8ygs3p8+p1NT08P+JiLLrqo2Oc3ZswYCRUvvviitG7dWmJjY6VPnz7y/fffB9x+wYIF0rlzZ7P9mWeeKZ9++qmEsooc35w5c4p9Vvq4UPXNN9/IFVdcYWZy0H19//33y3zM119/LWeffbbpPdu+fXtzzKGsoseox2f/DHXZv3+/hKIZM2bIueeea2bTatSokVx99dWyZcuWMh9X3f4fhioCYAjYvHmzmVbulVdekQ0bNsgzzzwjs2bNkr/+9a8BH/fAAw/IRx99ZP4zLFu2zExLd80110io0kD7hz/8Qe6+++4KPU4DX3JyctEyb948ccrxPfXUU/Lcc8+Zz3vVqlUSHx8vgwcPNuE+1Gj40+/nF198IR9//LH5cbrzzjvLfNwdd9zh9/npMYeC+fPny4QJE8zJ1o8//ijdu3c37/2BAwdK3H7FihUycuRIE3x/+ukn82Oly/r16yUUVfT4lIZ738/qt99+k1CVkZFhjklDbnkkJSXJ8OHD5eKLL5a1a9fK/fffL7fffrssXrxYnHKMXhqifD9HDVehSH+3tBDju+++M39X8vLyZNCgQea4S1Pd/h+GNAsh6amnnrLatGlT6v3Hjh2zoqKirAULFhSt27Rpk6Uf6cqVK61QNnv2bKt27drl2vbmm2+2rrrqKqs6Ke/xeTweq0mTJtbMmTP9PteYmBhr3rx5VijZuHGj+W6tXr26aN1nn31muVwua+/evaU+7sILL7TGjx9vhaLevXtb48aNK7pdUFBgJSYmWjNmzChx++uuu84aPny437o+ffpYd911l+WE46vI/8tQo9/NhQsXBtxm0qRJVrdu3fzWXX/99dbgwYMtpxzj0qVLzXZHjx61qqMDBw6Y/V+2bFmp21S3/4ehjBLAEJWamir16tUr9f41a9aYsyWtMvTSIvGWLVvKypUrxUm0WkPPYDt16mRK1w4fPixOoCUSWjXj+xnq3JRaVRdqn6Huj1b79urVq2id7rfOh60ll4G89dZb0qBBAznjjDPk4YcflszMTAmF0lr9P+T73uux6O3S3ntd77u90hK1UPusKnt8Sqv0W7VqJS1atJCrrrrKlPg6RXX6/E5Wjx49TLOSyy67TJYvXy7V6XdPBfrtC6fP8VSLPOWvgArbtm2bPP/88/LPf/6z1G00OERHRxdra9a4ceOQbe9RGVr9q9Xa2j5y+/btplp86NCh5j97RESEVGfez0k/s1D/DHV/7NVIkZGR5g91oH394x//aAKFtmFat26d/OUvfzHVU++9954E06FDh6SgoKDE916bZJREj7M6fFaVPT49wXrttdfkrLPOMj/E+vdH27RqCGzevLlUd6V9fmlpaZKVlWXa4FZ3Gvq0OYmeqOXk5Mi///1v0w5XT9K07WMo02ZQWi1//vnnm5PF0lSn/4ehjhLAU+ihhx4qsUGu72L/Y7x3714TerQtmbadcuIxVsQNN9wgV155pWnoq+08tO3Z6tWrTamgE44v2E718WkbQT07189P2xDOnTtXFi5caMI8Qkvfvn1l9OjRpvTowgsvNCG9YcOGpm0yqgcN8XfddZecc845JrxroNdLbVce6rQtoLbje+edd4K9K2GDEsBT6M9//rPpxRpI27Zti65rJw5toKz/Yf/1r38FfFyTJk1MNc+xY8f8SgG1F7DeF6rHeLL0ubQ6UUtJBw4cKNX5+Lyfk35meubupbf1R/h0KO/x6b7aOw/k5+ebnsEV+b5p9bbSz097uweLfoe0BNneaz7Q/x9dX5Htg6kyx2cXFRUlPXv2NJ+VE5T2+WnHFyeU/pWmd+/e8u2330oou+eee4o6lpVV2lyd/h+GOgLgKaRnz7qUh5b8afjTM7fZs2eb9jqB6Hb6B3rJkiVm+BelVWu7du0yZ/KheIxVYc+ePaYNoG9gqq7Hp9Xa+kdLP0Nv4NPqKK2uqWhP6VN9fPqd0pMNbVem3z311VdfmWobb6grD+19qU7X51cabT6hx6HvvZYsKz0Wva0/RqW9B3q/VlN5ac/F0/n/7VQen51WIf/yyy8ybNgwcQL9nOzDhYTq51eV9P9csP+/lUb7ttx7772mVkBrdfRvYlmq0//DkBfsXiiwrD179ljt27e3Bg4caK4nJycXLV66vlOnTtaqVauK1o0ZM8Zq2bKl9dVXX1k//PCD1bdvX7OEqt9++8366aefrKlTp1o1a9Y013U5fvx40TZ6jO+99565rusnTpxoejUnJSVZX375pXX22WdbHTp0sLKzs63qfnzqH//4h1WnTh3rgw8+sNatW2d6PGvv76ysLCvUDBkyxOrZs6f5Dn777bfmcxg5cmSp39Ft27ZZ06ZNM99N/fz0GNu2bWtdcMEFVih45513TI/rOXPmmF7Od955p/ks9u/fb+6/6aabrIceeqho++XLl1uRkZHWP//5T9PjfsqUKaYn/i+//GKFoooen35vFy9ebG3fvt1as2aNdcMNN1ixsbHWhg0brFCk/6+8/8f0p+zpp5821/X/odJj02P02rFjhxUXF2c9+OCD5vN78cUXrYiICGvRokVWqKroMT7zzDPW+++/b23dutV8L7UHvtvtNn87Q9Hdd99tep5//fXXfr97mZmZRdtU9/+HoYwAGAJ0+AX9z13S4qU/oHpbu/l7aUgYO3asVbduX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fixes axis, CTRL fixes aspect", + "arrows", + "pan" + ], + [ + "Zoom", + "Zoom to rectangle\nx/y fixes axis", + "square-o", + "zoom" + ], + [ + "Download", + "Download plot", + "floppy-o", + "save_figure" + ] + ], + "tooltip": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/docs/docs/tutorials/detailed_balance.ipynb b/docs/docs/tutorials/detailed_balance.ipynb index 12d926a6..33f44ede 100644 --- a/docs/docs/tutorials/detailed_balance.ipynb +++ b/docs/docs/tutorials/detailed_balance.ipynb @@ -17,9 +17,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:14:22.206008Z", + "iopub.status.busy": "2026-06-22T17:14:22.205798Z", + "iopub.status.idle": "2026-06-22T17:14:23.869941Z", + "shell.execute_reply": "2026-06-22T17:14:23.869023Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -32,10 +39,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "2", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:14:23.872048Z", + "iopub.status.busy": "2026-06-22T17:14:23.871859Z", + "iopub.status.idle": "2026-06-22T17:14:23.939804Z", + "shell.execute_reply": "2026-06-22T17:14:23.938892Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6962a6e7dcc44419bed10470c81a26b3", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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Lu1DUq0EuUW5hWaijgmDDXpBjmZ7OnTurVoaW3ZCgeDarYs+s6F1V6NAyUO9LUU+T7fYhF9U28Mlqe2w/i9awKErOCwR3+MFFq0W0frakryM3+zA3sFxciCxzFlAchjp/lpCbgUAKucG2uaOW+wHnk+25pB9XtIC03EfoDgXHFRdfR/bjp68P9brmzZtnnoZ6WDgPUH8Oudt5oZ8/aFVuybZ1qCOg/haOC77ftrAt9vazvWVgnyPAt4XPYzm5ldVvRnbz25u3INKWHfyuLFq0yPwa1VrQKwBuaFD866g05fT3AXWb9eXaQiCKmxXL7/WOHTtUtZWcyM01ADf6lvD9QO5gbrveIufBHEA3hC4/8KOAHyF0q4HgD90xIFcCOVnIybEMgFDhHo+LQ1cEuCPEZ/AjhCJj/JgAfvzwg4V+rhAAIRcI3SqgewvchaK4ERXeESQgpzGvRUGvv/66yl1DPTCkBxd8VFRGkTJyOfR+u/AeurlA5eytW7eqIkmsV/+xzCnkVKJoE/VZsM16tx3oCw7QbQmKfNGoAt094K4XTzTBD6e9H0xL6E8Md/eoUI7uP7AuFJdgm7CcvEBAgeOFx4ChYQQCegRi6N8OdfNysw9zA+lHETj2E/YP6gXh3MEFADloOrxGNx4IRlBJHt1W4FxBH3GoXK93X4IGCujeAnUK8RnsT5xP6Ibiu+++UwEUzifkOKCeFPYdgtqsOnnOK+xDXPxQFIfzCEEmbipwAUWwltdHJ+L7goYluBHC9wXfk1WrVqncJUdDkIpzE/sW5wDOWeToIHcbjRXQfY9lYyl7UASJ3wacs9gXOD4IvHft2qX2B84x/XF0OYVlAI4jSh3w+4EnnmQ3v71zoiDSlh0U36LLJJyz6IoGN1z4TbS86XNEmnL6+4BcXUzDTQrShu8E6uBhQIMwfC+xf5FmfC+xDHRbZa9PTHtyeg1AGhAsYluRBnQBg21F90k6bDd+k3A9QLdE5OSMboZMjqN3J6EP6OKgdOnS2j333KO988475i4tbB05ckTr37+/mtfPz08rV66c1rVrV23hwoVW833yySeq2w10c2DZ1crff/+t3XbbbVpQUJBWtmxZbeTIkdqKFSsydceSk25g4MKFC6oLiaioKJUepAtdFXz88cdW86ErFHTjgu5mihcvrj3//PPmri9y2g3M3r17tV69eqmuVYoUKaI9++yzWmJiotW8P/30k1a/fn0tMDBQdUUybdo07fPPP8/UdYdtlwzoSmHKlClqmwMCAlQ3Ej///HOWXTRMnz49Uzrt7Z/du3erblYiIyNVmmrUqKGNHTs2T/swN93AfPbZZ6pbCGxLzZo11fmm70db2D/YXsyL/Yplrly50vz++fPnVZcS2O/4vOV+w/mIY6JvX7NmzdR+s6R3A4PuiXLKXjcw+r4aOHCgOofwnUGXGNg2S9kdo6zgPHruuedUlynoMqRbt27aqVOnHN4NjA7HtkmTJup7iP2K7cB38ezZszfdB3p3O+gyBV0pYT9gf6ArkBkzZqiucW62H2y3C92cDBkyRHXL4uXlddMuYbI7J/KTtqzOFXvdZun7B79f+M7r57q98yy/+yunvw+wfv16dWyxHtv9jC6M8LuM9xo2bKjSnpvfmJxeAyZNmqS+i/he4hzDfpk8ebJ5W2HXrl1qPegOh5yfF/5ndBBKRERkNOQAI2cNLd4p95DjjYYxqPuqd+RNzot1AImIiCjfUPUERf4M/lwD6wASERFRvqHOKbkO5gASEREReRjWASQiIiLyMMwBJCIiIvIwDACJiIiIPAwDQCID4PFJto94QhcU6FS2MKGzVqQDHbgSkeOhU2c8Kg1PEcF3bejQodzN5BQYABLRTf3yyy8qaHU0PBt32LBh6kkZeEJNQQWj3377bYE8ho2M62/OVZ40MWXKFJXWZ555Rj2t6JFHHjE6SUQKA0AiJ4FgCI+Zc9YAcMKECQ5fLh43hcfbXb9+XWrVqiUFhQGge3GlABCP4rzttttk/Pjx8vDDD5sfkUdkNAaARCLqOZ5GwzNz8QxXT4LnMOMh93iGar9+/cQTmEwmSUpKEneC546npKSIMzL6u43n80ZGRjpsee54/pAxGACSU1u9erUqFpw/f75MnjxZypcvr4oK27ZtK4cPH7bbESnusPEAdTyMHXfcZ86csZoH9exCQ0PV44o6d+4sYWFh5uAD68LDzbEcPPwcy2nRooUKUOCjjz5SD6pHGvBgdNviynXr1knv3r2lQoUKKqCLiopSRZyJiYk33VbbOoBIS1aD5Xr3798vvXr1Ug9oR7qaNm2qHlRva8+ePXL33XerbcJ+nDRpkrqY3AzShAfG26bJ8gI7fPhwta3Y5ho1asiMGTPw4NebLhtpxv7PqyVLlkiXLl2kbNmyat1Vq1aViRMnSnp6unkeHKdly5bJiRMnzGnHvs5ue7Pa75bF4MnJySpXB+eDfqzxGCxMt6SfU3PnzpU6deqoeZcvX67e+/fff6VTp04SHh6uzkmc1xs3bszRtuPYoVgby8Rxx9MXnnrqKbl27ZrVfNjWrl27yl9//SXNmjVT81apUkW++uqrTMtEMI46avqxxLZNmzbN6jzBuYdtwjHG+rHPMe/evXvN31mcg1gP3sN3xrbOa+vWraVBgwZ2twvnT4cOHbLcbmwPzuU1a9aYjwuOsWWdVrz3v//9T0qWLKnOdcDxxzQsH9+BYsWKqe+q7XdYX8bff/8tL7zwgpQoUUJCQkLk3nvvlUuXLlnNu2XLFpVW/NZgmZUrV5bHHnvMvB+wnGPHjqnzz/a764jzhyg/+CQQcgmvv/66eHt7y4gRIyQmJkbeeOMNFbRt2rTJ6od74MCBcuutt8rUqVPlwoUL8s4776gfclxoLe/CkWOBH+6WLVuqC1lwcLBVEIcAavDgweo1loULKH6cUfSEiwguskgDfuxRxKND4JiQkKDq++AC888//8h7770np0+fznUv+agvZOuVV15ROQoIFgAXwjvuuEPKlSsno0ePVhcqBMs9e/aUH374QV204Pz589KmTRu13fp8H3/8sbpo3QyCirNnz8rKlSszpQlBHnLx8Aioxx9/XBo2bCgrVqyQF198UQXeb7/9thQkHHPsC1yo8RfHYty4cRIbGyvTp09X87z88svqnMEx0NOj77+strddu3ZW03DBxQUYAQUgIMJ2I6gaNGiQKr7GTQKWf/DgQVm8eLHV55EuHBdcyBEs6EHMnXfeqYI/nFvI/UWwhGAGAUzz5s2z3XakUz/n8fgtBBrvv/++OtdxzlvmJuNmCTcJOEYDBgyQzz//XAW6uFlCUAE4bxGY4bhh2biJWb9+vYwZM0bOnTuXqQ7lnDlzVE4Uth9BCYJ5rLtjx45SpkwZVWUAgfhrr72mgihLqAf35JNPyu7du9Wzd3WbN29W+w/neVaQjiFDhqhjiGMLto8ew3cU68S5oOcAYtnYngceeEAFhQjEPvzwQ7W/Ebxa/gYA1lGkSBEVpGFerBfHb968eep9fA/bt2+v1oPvFH5fMN+PP/6o3sc5ge8LbgCxPtwkAeZ3xPlDlG8akRP7888/kY2k1apVS0tOTjZPf+edd9T0Xbt2qdcpKSlayZIltbp162qJiYnm+X7++Wc137hx48zTBgwYoKaNHj060/owPSAgQDt27Jh52kcffaSmly5dWouNjTVPHzNmjJpuOW9CQkKmZU6dOlXz8vLSTpw4YZ42fvx49VlLFStWVGnLyhtvvKE+89VXX5mntW3bVqtXr56WlJRknmYymbTbb79dq169unna0KFD1Wc3bdpknnbx4kUtIiIi0zbYM3jw4EzphcWLF6vpkyZNspreq1cvtc2HDx/Wcmr69Ok5Sosle/v7qaee0oKDg632SZcuXdT+zYtDhw6p/XTPPfdoaWlpatrXX3+teXt7a+vWrbOad/bs2Wob/v77b/M0vMa8e/bssZq3Z8+emr+/v3bkyBHztLNnz2phYWFaq1atsk0T1ovlzp0712r68uXLM03HdmPa2rVrrY49zvPhw4ebp02cOFELCQnRDh48aLVMfE98fHy0kydPqtc4PlheeHi4Wo6lbt26qX1/5swZq/3n6+trdf5ER0drgYGB2qhRo6w+/9xzz6k0xMXFZbv9derU0Vq3bp1p+pw5c9R6WrZsaT5W2Z0rGzZsyPSd0pfRrl079V3SDRs2TO0HpB0WLVqk5tu8eXO2acX+x/lnyRHnD1F+sQiYXAJyOfz9/c2vkXMCR48eNRfF4I4cd/4oetKheLBmzZqqCMYWcunsQTGc5R22nhNz//33WxVX6tP1NIBljhpyHi5fvqxauOJ3HLkjeYUcNuTEIFdCb0V49epVlTPQp08f1YgC68Jw5coVlbt56NAhc/E3GnGgIjqKAHXIichvvTss18fHR+VAWUJuB7b5119/lYJkub/1fYBzA7lZKBrPLxxD5KIiJ+i7775T2wrIzUWuDc4tfb9jQBG7frwsIWcNVQp0yBn77bffVE4timN1yDl76KGHVM4QcjGzgvVHRETIPffcY7V+5OghZ8x2/Vi3/p3Rjz2KQi3PXSwT82BbLZeJ3FCkd+3atVbLxPfBMmcP8/z+++9qm1Akr0MRJ4q5LSHtPXr0UPtUryqAzyN3DZ9HDnV+IHdRP1b2zpXU1FT1PUHakHO3bdu2TMtAzpxlsTX2DdKIomTQSxR+/vlntbzcyO/5Q+QILAIml4DiKEu4SIFe30n/UcZFzRZ+ZHFBteTr62uuG3SzdeFiBaijY2+6ZZ2rkydPqmInFCHb1sVCMWReoOiyb9++qqj3rbfesirWw8Vz7NixarAHQTGKh7F/7BUp2ttfuYHl4mJvW49Pb9GrH5eCgmJUFBciELYNmPK6v20DCdQVRdEhivR1CK737duXqWjTcr9bQt0wS6hLhiDV3v7HvkMR4alTp8zFs7awfmyfXiR9s/XbntP6d8jyHMUyd+7cmedtwvuo64qgypa9af3791cBH6pctGrVSgWPqLbhiG5SbNMGSBuqc6DoGjdGlnVU7Z0rN/vNQVCGIBhF3Si6RVEyglcE8CgSz05+zx8iR2AASC7B9m5el5OGBvbgBxp1CnOzrpulAbkDyJFBztyoUaNU4ImcDFxsUN8qJw0ubKFlJepuIb2oA4TAVacvD/Uis6o0b+/C6y7QYAEXYdShQz0zNDhA7i9yc7D/87K/LaH+KHKovvnmG1W30RKWXa9ePauA3JLtzUJO6lrmBtaP4A/1Eu2xDSxy8v3BMnH+oj6iPbfccotDtwnnLOruYf8iAMRfdJZsW/8yL+ylDbnnCP7QyAUNu3ADhxw+1Am0d67cbJ/hswsXLlSNdpYuXarqvqJO8JtvvqmmZVfP1OjzhwgYAJJbqFixorkvPb0YRYdp+vsFCZW4UYH7yy+/VLkbOjSeyCsUrW7fvl0Vv9lWdNeLDlHZ/2YXTWw/ch1sYd/khO1TSyyXi5wbFL9a5gLqxa8Fud/RyhLFeKh0jwBCh8YQOU1/VpArhcAawYK9YnIEmzt27FDVBXK7bD1AQ6MDe/sf+w43J7ZBgO36sd+RK+yo4ADLxFMr8hqAISBFAG6vdb69aQiwkFuGhixoaYyGD/aKbu3Jyz5HsIYGMAjQdGjEghuJ/EDVCgzopQD9TeJ8+f7779XTP7KS3/OHyBFYB5DcArqdwAVo9uzZVt0ooA4ailpQF7Cg6Rcuy1wVjCMnKS+QW4FWoeiCxbLung7bi2InzINWmrYsu6xAdzfIlUCrZMv3s8pBsqXXybK9WGK5yPlE61NLKBLDhc227ldB72/kmKKltr3057RIGPsS9SrRQlxvSWwL7yNn117H3ShqvFnfc0g7WpCiGxvLbkhQBIogAutGzmZWsH7sd3R5YwstvfMS1GCZ6JgbOVm2sDws92bbhOARgRxajVsGf1nVBUVxL4pU0eoYwSe6bcoJHM/cbiPSZ1tigBb6ll0G5QbSbbs8PafYtisXR58/RI7AHEByC8gFQy4CGougWPDBBx80dwODBh3oiqGgocgXd/bIOcKPOy7g6IrFti5gTqBCOBq0oOI3in9RPGYJDRNwEURwiGABxUnIPUGuILYbF3LUHUQuA6BYD11SoIuO559/3twNDHLoUO/rZvSnFyBHEkV3uJii6Kxbt26qexl0x4FABn27oXEDAhvknmF/ZAdBGS7CgK5LAMEkKthjQLcXWUHjGtTLQq4O0oWAE9tor1oA0o/6ZuguBt0EoXgOabcHy0JwjH2GnBxL9evXVwMCFxTJP/3006rCPnLiEEgg9w7TEUThpiQ76IcRucM4fjjWKN5HMI/gAV0MZQfnOIIm1GlDDjGCSXwHkMuLBgY471F1IDfQdQ/qrqLLI72LGAQiyNlG7hmOL7ogyQ76+8Pxx/5AIyv95gBdvSCdtho1aqTe0xtFNG7cOEdpRdrQhQv2Iao54GbINuffFrYL5weKfvG9wncEuaiWdTtzAzn9uNnAdxHnOXLBEdDhe48bo+w44vwhyrd8tyMmKoRuYBYsWGA1Xe+KAl02WJo3b57WqFEj1cVF0aJFtX79+mmnT5+2mgddraCrCXuwTHR5Ym9d6KbkZmnbu3ev6j4iNDRUK168uPbkk09qO3bsyJTWm3UDo68zq8GyqxR0I9K/f3/VTY2fn59Wrlw5rWvXrtrChQutlr9z507VdQa638A86Pbjs88+y1HXK+hSY8iQIVqJEiVU9y6Wab9+/brqIqNs2bJq/eh+BvvKsguNrGS3nTnptgXdZdx2221aUFCQWv/IkSO1FStWqM/j+OjQrchDDz2kRUZG3nTZ2EdZpQnHTYeuh6ZNm6a6JMH5VqRIEa1JkybahAkTtJiYmGzPKd22bdu0Dh06qPMF3ae0adNGW79+vZZTH3/8sVonth/dx6BLIOwDdCeTXTck+nbadqWCY4nujapVq6a6qME5jC6FZsyYobY3u++DbtWqVeo7iM9XrVpV+/TTT1V3MzjvsuveaMqUKTne7vPnz6ttwjbjs/p26F242Oua5dq1a9rAgQPVNmF/Y7/v378/U/dLWS1D/77r5xWO3YMPPqhVqFBBHX90Q4Xv3ZYtW6w+l9X+d8T5Q5QfXvhf/sNIIiIi+9A6Fi227dVDRW4lcuiRw2ivtTIRFQzWASQiIoexfewhgj70F6k/rs0S8h8+++wzVaTN4I+ocLEOIBEROQzqoaIOIf6iH0jU1UMn7pbdy6BuIeobov4b6hiizigRFS4WARMRkcOgIRYCOzx/Gg2Y0OfelClTrBp4oLgXnRujoQ8awKALFSIqXAwAiYiIiDwM6wASEREReRgGgEREREQehgEgERERkYdhK+B8wAO98cgjPAOVz3MkIiJyDZqmqae3lC1bVj172yNpbmrNmjWqV/YyZcqontQXLVpk1QM7esuvW7eu6n0f8zzyyCPamTNncrWOU6dOZfu0Bg7cBzwHeA7wHOA5wHPAec+BU6dOaZ7KbXMA0c8Unkv62GOPyX333Wf1XkJCgmzbtk3Gjh2r5sGzWvF81O7du8uWLVtyvA7k/MGpU6eyfXA7EREROY/Y2FiJiooyX8c9kUd0A4Pi2UWLFqnHEWVl8+bN0qxZM9VxaU57pMcJhAeL44H2DACJiIhcQyyv32wEokMQh0ARHZMSERERuTO3LQLOjaSkJBk1apQ8+OCD2ebkJScnq8HyDoKIiIjI1Xho05f/pKamSp8+fVSLIDyzMjtTp05VRb76gPoDRERERK7Go3MA9eAP9f7++OOPm9bjGzNmjLzwwguZKpFmB4FlWlqapKenOyzdVDB8fHzE19eXXfoQEZHb8/X04O/QoUPqweXFihW76WfwYHMMOZWSkiLnzp1TrY7JNQQHB0uZMmXE39/f6KQQEREVGLcNAOPi4uTw4cPm18eOHZPt27dL0aJF1QW+V69eqiuYn3/+WeXOnT9/Xs2H9x1x8Ucn0VgncpXQ0SSWyc6inRdyahGwX7p0SR236tWre27noERE5PbcthuY1atXS5s2bTJNHzBggLz66qtSuXJlu59DbuBdd92V72bkaFiCQKJixYoqV4lcA3JrUSUA50dgYKDRySEiogIQy25g3DcHEEFcdrFtYcW9zEVyLTxeRETkCVjGRURERORhGAASEREReRgGgGSGRirZDag7mVt79uyR+++/XypVqqSWMXPmzBzV38S80dHR5mlnz56VevXqSatWrVSdSyIiIso7t60DSLmHLmt08+bNk3HjxsmBAwfM00JDQ/PUqKJKlSrSu3dvGTZsWJ4Oy5EjR+See+6R2rVry4IFCyQoKChPyyEiIteC+vrsQaNgMAAks9KlS5vH0boZXzrLaXlx6623qgFGjx6d68/v3LlTOnToIHfffbd8+eWXqqNmIiJyf/+evCbjf9oj0+6vL7XKZP+gBso9Xk0L+U4mMbXwnwgS5Ofj0Duom+UEPvzwwzJ79ux8r2f9+vXSr18/Nbz33nu8CyQi8hBJqekyYsEOOXIpXj5Zd1Te6tPQ6CS5HQaAhQjBX+1xK6Sw7X2tgwT7O+5Qo0Pt7NzskXo5de+990rfvn3l/fffd8jyiIjINby98qAK/kqEBci4rrWNTo5bYgBIuVatWrVC2Ws9evSQRYsWybp16+TOO+8slHUSEZGxtp64Kh+vO6rGp95bTyKD+WjOgsAAsJCLYpEbZ8R6HamwioA/+ugjGTlypHTq1El++eUX1QKYiIjcV2IKin53Cp7VcF/jctKudimjk+S2GAAWItTDc2RRrFEKqwgY++vjjz9WT+fo3LmzLFu2TFq3bu2QZRMRkfOZ8dsBOXY5XkqFB8j4rnWMTo5bc/1ohJy6CDglJUX27t1rHj9z5owKIJGLmJPlIAhEbqKPj485CMzps5qJiMh1/HPsqnz+9zE1/vp99SUi2M/oJLk1dgRNBQodODdq1EgN6GdwxowZavyJJ57I8TIQBM6aNUsGDhwoXbp0kT///LNA00xERIUrISVNRi7coYp+ezcpL21qluQhKGBeGvomoTyJjY1V/eXhyRS2xZ5JSUly7NgxqVy5sgQGBnIPuwgeNyKiwvfqT3vki/XHpXR4oKwY1koigvwMu357CuYAEhERkWE2Hr2igj+Y1qt+gQd/lIEBIBERERkiPjlNXly4Q40/2CxKWt9SgkeikDAAJCIiIkNMW75fTl1NlHKRQfJS51o8CoWIASAREREVuvWHL8tXG06ocTzvNyyQRb+FiQEgERERFao4VfS7U433a15BWlYvziNQyBgAEhERUaGa8ss+OROdKOWLBMkYFv0aggEgERERFZp1hy7Jt5tOqvE3etWX0AA+k8IIDACJiIioUFxPSpVRN4p+B7SoKLdXZdGvURgAEhERUaGYvGyfnI1JkgpFg2VUp5rc6wZiAEhEREQFbvWBi/L95lPi5SUyo3cDCfZn0a+RGACS1TN3sxteffXVXO+tPXv2yP333y+VKlVSy5g5c6bd+fCsX8yDx+Y1b95c/vnnn2yXi7Q0bNjQatq6deskMjJShg4dKnzCIRGR84hJTJXRP+xS44/eXkmaVS5qdJI8HgNAMjt37px5QKCG5yNaThsxYkSu91ZCQoJUqVJFXn/9dSldurTdeebNmycvvPCCjB8/XrZt2yYNGjSQDh06yMWLF3O8nmXLlqnPYDlIO4JNIiJyDhN/3ivnY5OkUrFgGdmBRb/OgPmvZGYZoOEh2QiisgracurWW29VA4wePdruPG+99ZY8+eSTMnDgQPV69uzZKqD7/PPPs/yMpW+//VZ99s0335Rnn32WR5SIyIn8sf+CLNx62lz0G+TvY3SSiAFgIdM0kdSEwj/x/IJRvuuwxYWGhmb7/sMPP6yCuJxISUmRrVu3ypgxY8zTvL29pV27drJhw4abfh5Fx8j1Q7DYr1+/HK2TiIgKR0zCf0W/j99RWZpWYtGvs2AOYGFC8DelrBS6l86K+Ic4bHHbt2/P9n0UHefU5cuXJT09XUqVKmU1Ha/379+f7Wf37duncvw+++wzBn9ERE5owtI9cvF6slQpESIjOtQwOjlkgQEg5Vq1atWcYq+VL19eNfqYPn26dOrUScqUKWN0koiI6Ibf9pyXH/89I943in4D/Vj060wYABZ2USxy44xYrwM5sgi4ePHi4uPjIxcuXLCajtc3q38YFhYmv//+u9xzzz3Spk0b+fPPPxkEEhE5gWvxKfLSot1q/MlWVaRxhSJGJ4lsMAAsTKiH58CiWKM4sgjY399fmjRpIqtWrZKePXuqaSaTSb3OSYOOIkWKqCCwffv2ctddd6kgsGxZA4rZiYjI7NWle+RyXLJUKxkqw9rdwj3jhBgAUoEWAaORx969e83jZ86cUQEkchH15aARx4ABA6Rp06bSrFkz1Y1LfHy8uVXwzaAYeOXKlaobGASBq1evZhBIRGSQ5bvPy5LtZ8XH20veZNGv02I/gFSgzp49K40aNVID+hKcMWOGGn/iiSfM8/Tt21dNHzdunOrcGQHi8uXLMzUMyQ66rfntt99UkXLr1q1VoElERIXrSlyyvLwoo9XvU62qSIOoSB4CJ+Wl8ZEJeRYbG6sCj5iYmEzFnklJSXLs2DGpXLmyeroFuQYeNyKivBv87TZZtvOc3FIqVJYOaSkBvj4ud/32FMwBJCIionxD4Icho+i3odMGf5SBASARERHlCxp8jF2S0ep38F1VpV75CO5RJ8cAkIiIiPIMNcnGLt4tV+NTpGbpMHn27urcmy6AASARERHl2dKd5+TX3efFF0W/fRqIvy9DC1fAo0RERER5cvF6koy7UfT77N3VpE5ZFv26CgaARERElKei35cX7ZbohFSpXSZcBrdxjseEUs4wACQiIqJcW7z9jKzce0H8fDKKfv18GFK4Eh4tIiIiypULsUny6k8ZT3l6vm11qVXGM/vSc2UMAImIiChXRb8v/bhLYhJTpV65CHm6dVXuPRfEAJCIiIhy7IdtZ2TV/ovi7+Otin59WfTrkhgAkpmXl1e2w6uvvprrvbVnzx65//77pVKlSmoZM2fOtDvfrFmz1Dx4bF7z5s3ln3/+yfSItsGDB0uxYsUkNDRULfPChQvZrvuuu+6SoUOHWk175513JCAgQL7//nseeSKiXDoXkygTlu5R40PvqS63lArjPnRRDADJ7Ny5c+YBgRqej2g5bcSIEbneWwkJCVKlShV5/fXXpXTp0nbnmTdvnrzwwgsyfvx42bZtmzRo0EA6dOggFy9eNM8zbNgwWbp0qSxYsEDWrFkjZ8+elfvuuy9XacHyX3rpJVmyZIk88MADPPJERLks+h39wy65npQmDaIiZdCdVbj/XJiv0Qkg52EZoOEh2cixyypoy6lbb71VDTB69Gi787z11lvy5JNPysCBA9Xr2bNny7Jly+Tzzz9Xn8HDuj/77DP59ttv5e6771bzzJkzR2rVqiUbN26U22677aY/Ws8995x88803snLlSrn99tvztU1ERJ5o/pZTsubgJdXR85u967Po18W5bQ7g2rVrpVu3blK2bFkVyCxevDhTUDBu3DgpU6aMBAUFSbt27eTQoUMFmiasMyE1odAHrNeRUASb3fD000/neFkpKSmydetWtf913t7e6vWGDRvUa7yfmppqNU/NmjWlQoUK5nmykpaWJg8//LAsXLhQ5Rwy+CMiyr0z0Yky8ed9anxE+1ukWkkW/bo6t80BjI+PV0WJjz32mN2iwjfeeEPeffdd+fLLL6Vy5coyduxYVey4d+9eVQ+tICSmJUrzb5tLYdv00CYJ9gt22PK2b9+e7fsoOs6py5cvS3p6upQqVcpqOl7v379fjZ8/f178/f0lMjIy0zx4LzuffPKJ+rtjxw4VNBIRUV6KfndKXHKaNK4QKY+3ZNGvO3DbALBTp05qyOpkRh23V155RXr06KGmffXVVyqgQE4h64dlr1o11+ntvWXLlipgRYD/3Xffia+v257yREQF4rt/Tsm6Q5clwNdbZvRuID7eXtzTbsAjr4bHjh1TOUeWRYqo84bWpyhSLKgAMMg3SOXGFTas15FQzJsdFLmiHl9OFC9eXHx8fDK16MVrvf4h/qKoODo62ioX0HKerNSrV0/efPNNdaz79u2rGpwwCCQiyplTVxNk8rKMDp9HdqwpVUpk//tPrsMjA0C92NBesWN2RYrJyclq0MXGxuZqvaiL6MiiWKM4sggYRbtNmjSRVatWSc+ePdU0k8mkXj/77LPqNd738/NT09D9Cxw4cEBOnjwpLVq0uOk6GjZsqD6LILBPnz4qCMTyiIgoayaTJqN+2CnxKelya6UiMvD2StxdbsQjA8C8mjp1qkyYMEE8XW6KgJFzh3qV+viZM2dUAIlcRH056AJmwIAB0rRpU2nWrJkqnkcdTr1VMHJnH3/8cTVf0aJFVYA5ZMgQFfzdrAWwDvVB//jjD2nbtq0KAufPn88gkIgoG3M3nZD1R65IoJ+3TO/VQLxZ9OtW3LYVcHb0YsPsih3tGTNmjOqSRB9OnTpV4Gl1deivr1GjRmpAX4IzZsxQ40888YR5HhTNYjpaZSO3DgHi8uXLrXJo3377benatavKAWzVqpU6Tj/++GOu0oLiYASB69evl969e6uAlIiIMjt5JUGm/JLREG9Ux5pSqXgId5Ob8dIc3UeIE0LR66JFi8xFjNhkdA+Djo2HDx9uLs4tWbKkfPHFFzmuA4jPIHcKwaBtsSeeXIG6hmhhXFCtisnxeNyIyNOh6PfBTzbKpmNXpXnlovLdk7e5Xe5fbDbXb0/htkXAcXFxcvjwYfNrBGPIWUIRIvqPwyPCJk2aJNWrVzd3A4OgUA8SiYiIPNFXG46r4C/Y34dFv27MbQPALVu2SJs2bcyvUX8MUNcMuXwjR45U9cwGDRqkWpeiuxAUOzK3joiIPNXxy/Hy+vKMot8xnWtJhWKu33CRPCwAvOuuu7J9AgaKhV977TU1EBERebp0kyYjFuyQpFST3F61mPRrVsHoJFEB8shGIERERGRtzt/HZMuJaxLi7yPT7q/vdvX+yBoDQCIiIg935FKcTF9xQI2/3KW2RBVl0a+7YwBYwDygkbVb4fEiIk8s+n1xwQ5JTjPJndWLy4PNooxOEhUCBoAFRH/SREJCQkGtggqAfrz4pBAi8hSf/XVUtp2MlrAAX1X0izry5P7cthGI0fB8Wzy39uLFi+p1cHAwv1ROnvOH4A/HC8cNx4+IyN0dvnhdZvx2UI2P7VpbykY69tnx5LwYABYg/akiehBIzg/BX3ZPgyEichdp6SYZvmCnpKSZ5K4aJaR30/JGJ4kKEQPAAoRs9DJlyqgnjKSmphbkqsgBUOzLnD8i8hQfrzsqO05FS1igr7x+H4t+PQ0DwEKAoIKBBREROYsD56/LzJWH1Pj4bnWkdAQfWepp2AiEiIjIg6Smm1SHzynpJmlbs6Tc37ic0UkiAzAAJCIi8iCzVx+RXWdiJCLIT6bcV48NFD0UA0AiIiIPse9crLz7R0bR74TudaRUOIt+PRUDQCIiIg8p+h0+f4ekpmvSvnYp6dGwrNFJIgMxACQiIvIAs/48LHvPxUqRYD+ZfC+Lfj0dA0AiIiI3t/tMjLz/x2E1/lqPulIiLMDoJJHBGAASERG5MXT0jFa/aSZNOtcrLV3rlzE6SeQEGAASERG5sff+OCT7z1+XoiH+KvePz/olYABIRETkpnaejpYPVh9R45N71pXioSz6pQwMAImIiNxQclq6avWbbtKkW4Oy0qkei37pPwwAiYiI3NDM3w/JoYtxKtfvte51jE4OORkGgERERG7m35PX5KM1N4p+760rRUL8jU4SORkGgERERG4kKTVdtfo1aSI9G5aVDnVKG50kckIMAImIiNzIWysPypFL8aqvv1dZ9EtZYABIRETkJraeuCqfrDuqxqfeW08ig1n0S/YxACQiInIDiSko+t0pmiZyf+Py0q52KaOTRE6MASAREZEbmL7igBy7HC+lwgNkXLfaRieHnBwDQCIiIhe36egVmbP+mBp//f76EhHkZ3SSyMkxACQiInJhCSlp8uLCjKLfvk2jpE2NkkYniVwAA0AiIiIXNu3X/XLyaoKUjQiUl7vWMjo55CIYABIREbmo9Ucuy5cbTqjxab3qS3ggi34pZxgAEhERuaC45DQZuXCnGn+wWQW5s3oJo5NELoQBIBERkQua+ss+OX0tUcpFBsnLXVj0S7nDAJCIiMjFrDt0SeZuOqnGp/eqL6EBvkYniVwMA0AiIiIXcj0pVUbdKPrt36Ki3F6tuNFJIhfEAJCIiMiFTF62T87GJEmFosEyqmNNo5NDLsppAkBN0+TkyZOSlJRkdFKIiIic0uoDF+X7zafMRb8hLPoldwgAq1WrJqdOZZzYRERE9J+YxFQZ/cMuNT7wjkrSvEox7h5y/QDQ29tbqlevLleuXDE6KURERE5n4s975XxsklQqFiwjO7Dol9wkAITXX39dXnzxRdm9e7fRSSEiInIaq/ZdkIVbT4uXl8iM3g0kyN/H6CSRi3OqduP9+/eXhIQEadCggfj7+0tQUJDV+1evXjUsbUREREaITkiRMT9mFP0+0bKyNK1UlAeC3CsAnDlzptFJICIicioTlu6Vi9eTpUqJEBnevobRySE34VQB4IABA4xOAhERkdNYsee8LPr3jHjfKPoN9GPRL7lhAAjp6emyePFi2bdvn3pdp04d6d69u/j48KQnIiLPcTU+RV5elFH0O6hVVWlcoYjRSSI34lQB4OHDh6Vz585y5swZqVEjI5t76tSpEhUVJcuWLZOqVasanUQiIqJCMf6nPXI5LkWqlwyVoe2qc6+T+7YCfu6551SQh74At23bpgZ0Dl25cmX1HhERkSf4Zdc5WbrjrPh4e7Hol9w/B3DNmjWyceNGKVr0vxZOxYoVU93D3HHHHYamjYiIqDBcjkuWVxZndIf2TOuq0iAqkjue3DsHMCAgQK5fv55pelxcnOoWhoiIyJ3hqVhjF+9W9f9qlg6TIW2rGZ0kclNOFQB27dpVBg0aJJs2bVJfAgzIEXz66adVQxAiIiJ3tnTnOfl193nxvVH0G+DLBpDkAQHgu+++q+oAtmjRQgIDA9WAol88I9jRfQSitfHYsWNV/UJ0OI31Tpw4UQWdREREhe3i9SQZtySj6Hdwm2pSt1wEDwJ5Rh3AyMhIWbJkiWoNrHcDU6tWLRUAOtq0adPkww8/lC+//FJ1NbNlyxYZOHCgREREsMEJEREVKmQ+vLxot0QnpErtMuHy7N0s+iUPygF87bXX1KPgEPB169ZNDRhPTExU7znS+vXrpUePHtKlSxepVKmS9OrVS9q3by///POPQ9dDRER0M4u3n5GVey+In4+XvNmngfj5ONXlmdyQU51hEyZMUA0+bCEoxHuOdPvtt8uqVavk4MGD6vWOHTvkr7/+kk6dOjl0PURERNm5EJsk45fsUePPt60utcqEc4eRZxUBIwvcy8sr03QEZ5ZdwzjC6NGjJTY2VmrWrKmeMoI6gZMnT5Z+/fpl+Znk5GQ16PB5IiKi/Fz3Rv+wU2KT0qReuQh5ujUfeEAeFAAWKVJEBX4YbrnlFqsgEIEZcgXREtiR5s+fL3PnzpVvv/1W1QHcvn27DB06VMqWLZvlM4nxVBJH50QSEZHnWrD1tPx54JL4+3irol9fFv1SIfHSnKDZKxpiIBmPPfaYau2Lhhg69P+HOnpoGexIeLwccgEHDx5snjZp0iT55ptvZP/+/TnOAcRyYmJiJDycWfZERJRzZ6MTpcPba+V6cpqM6lhTnrmLuX+FJTY2VsUannz9doocQD3HDV2yoNsXX9+CTxbqFXp7W1eBRFGwyWTKtqNqDERERPmBTI9RP+xUwV/DqEh58s7K3KHkuY1A4uPjVcMMWytWrJBff/3VoetCC2PU+Vu2bJkcP35cFi1aJG+99Zbce++9Dl0PERGRre83n5J1hy5LgC+LfskYThUAokgWdf7sVpIdPdqh63rvvfdU1y//+9//VF+DI0aMkKeeekp1Bk1ERFRQTl1NkEk/71XjI9rXkKolQrmzyTPrAOrwRA50AI06f5aQQ4eGGsghdCasQ0BERLlhMmny8GebZP2RK9K0YhGZ91QL8fHO3PsFFaxY1gF0rhxAVMg8evRopul4MkhISIghaSIiInKUuZtOqOAv0M9bpvduwOCPDONUASCezIGuWI4cOWIV/A0fPly6d+9uaNqIiIjy48SVeJnyS0YvE6M71pTKxZmxQcZxqgDwjTfeUDl96JwZLYIxoH5esWLFZMaMGUYnj4iIKM9Fvy8u2CmJqenSvHJR6d/CuqoTkUd2A2NZBIxn9K5cuVI9/QN1AuvXry+tWrUyOmlERER59sX64/LP8asS7O8j03s1EG/W+yODOVUACHgKSPv27dVARETk6o5eipM3VmQU/b7UuZZUKBZsdJKInC8AREvfNWvWyMmTJyUlJcXqveeee86wdBEREeVWukmTEQt2SFKqSe6oVkz6Na/AnUhOwakCwH///Vc6d+6sntKBQLBo0aJy+fJlCQ4OlpIlSzIAJCIil/LZX0dl28loCQ3wlTd6NbB61j2RkZyqEciwYcPUEzquXbum6v9t3LhRTpw4IU2aNGEjECIicimHLlyXGb8dVONju9aScpFBRieJyDkDwO3bt6suX/CMXjyXNzk5WaKiolTr4Jdeesno5BEREeVIWrpJFf2mpJmk9S0lpE/TKO45cipOFQD6+fmp4A9Q5It6gHrr4FOnThmcOiIiopz5aO1R2XE6RsICfeX1++ux6JecjlPVAWzUqJFs3rxZqlevLq1bt5Zx48apOoBff/211K1b1+jkERER3dT+87Ey8/eMot9Xu9WRMhEs+iXn41Q5gFOmTJEyZcqo8cmTJ0uRIkXkmWeekUuXLsnHH39sdPKIiIiylZpukuHzd0hquibtapWU+xqX4x4jp2R4DuBPP/0knTp1UsW/TZs2NU9HEfDy5csNTRsREVFuzPrzsOw5GysRQX4y5V4W/ZLzMjwH8N5775Xo6Gg1joYfFy9eNDpJREREubb7TIy8/8dhNf5ajzpSMjyQe5GcluEBYIkSJVR3L6BpGivKEhGRy0FrX7T6TTNp0qluaeneoKzRSSJy7iLgp59+Wnr06KECPwylS5fOct709PRCTRsREVFOvLvqkOw/f12KhvjLxJ51mZlBTs/wAPDVV1+VBx54QA4fPizdu3eXOXPmSGRkpNHJIiIiypEdp6LlwzVH1PjknnWleGgA9xw5PcMDQKhZs6Yaxo8fL71791aPfiMiInJ2SanpqugXz/zt1qCsdKqX0ZMFkbNzigBQhwCQiIjIVbz9+0E5dDFO5fq91r2O0ckhcp1GIERERK5o64lr8snao2p86n31pEiIv9FJIsoxBoBERES5lJiSUfRr0kR19nxP7VLch+RSGAASERHl0hsr9suxy/FSKjxAxndj0S+5HqcNAJOSkoxOAhERUSYbj16ROX8fV+PT7q+vnvpB5GqcKgA0mUwyceJEKVeunISGhsrRoxl1K8aOHSufffaZ0ckjIiIPF5+cJi8u3KHGH7g1Su6qUdLoJBG5fgA4adIk+eKLL+SNN94Qf///KtPWrVtXPv30U0PTRkRENPXXfXLqaqKUiwySl7vU4g4hl+VUAeBXX30lH3/8sfTr1089F1jXoEED2b9/v6FpIyIiz/bXocvyzcaTavyNXvUlLJBFv+S6nCoAPHPmjFSrVs1u0XBqaqohaSIiIopNSpWRN4p+H7mtotxRrTh3Crk0pwoAa9euLevWrcs0feHChdKoUSND0kRERDTp571yNiZJKhQNltGdanKHkMtzqieBjBs3TgYMGKByApHr9+OPP8qBAwdU0fDPP/9sdPKIiMgD/bH/gszfclq8vERm9G4gIQFOdekkcv0cwB49esjSpUvl999/l5CQEBUQ7tu3T0275557jE4eERF5mOiEFBn9wy41/tgdlaVZ5aJGJ4nIIZzuNubOO++UlStXGp0MIiIiefWnPXLxerJUKREiL3aowT1CbsOpcgA3b94smzZtyjQd07Zs2WJImoiIyDMt331eFm8/K95eIm/2biCBfv/1TkHk6pwqABw8eLCcOnUq03TUCcR7REREheFKXLK8vCij6HdQq6rSqEIR7nhyK04VAO7du1caN26caTpaAOM9IiKigqZpmoxdsluuxKdIjVJhMuye6tzp5HacKgAMCAiQCxcuZJp+7tw58fV1uuqKRETkhpbuPCe/7Dovvt5e8mafBhLgy6Jfcj9OFQC2b99exowZIzExMeZp0dHR8tJLL7EVMBERFbiL15Nk3JLdavzZu6tJ3XIR3OvklpwqW23GjBnSqlUrqVixornj5+3bt0upUqXk66+/Njp5RETk5kW/L/24S6ITUqVuuXAZ3Cbzk6mI3IVTBYDlypWTnTt3yty5c2XHjh0SFBQkAwcOlAcffFD8/PjMRSIiKjg/bDsjv++7KP4+3vJm74bi5+NUhWRE7hsAAjqAHjRokNHJICIiD3I2OlEm/LRHjQ+9p7rUKB1mdJKIPCsAPHTokPz5559y8eJF9Tg4S3gyCBERkaOLfkf9sFOuJ6dJowqRMujOKtzB5PacKgD85JNP5JlnnpHixYtL6dKlxQsPXrwB4wwAiYjI0b7956SsO3RZAny91bN+fVn0Sx7AqQLASZMmyeTJk2XUqFFGJ4WIiDzAySsJMnnZPjU+qmNNqVoi1OgkERUKp6rheu3aNendu7fRySAiIg9gMmkyYuEOSUhJl+aVi8qjt1cyOklEnhkAIvj77bffjE4GERF5gDnrj8s/x65KsL+PKvr1xkN/iTyEUxUBV6tWTcaOHSsbN26UevXqZer65bnnnjMsbURE5D6OXIqTN5bvV+Mvd6klUUWDjU4SUaHy0tD8yUlUrlw5y/fQCOTo0aPiTGJjYyUiIkI9uSQ8PNzo5BARUQ6kpZuk1+wNsv1UtNxZvbh89Vgzq0aH5P5ief12rhzAY8eOGZ0EIiJycx+tPaqCv7BAX3mjV30Gf+SRnKoOYGE7c+aMPPzww1KsWDH11BEUO2/ZssXoZBERUQHZdy5WZv5+UI2/2q2OlIkI4r4mj+RUOYBw+vRp+emnn+TkyZOSkpJi9d5bb73l0BbHd9xxh7Rp00Z+/fVXKVGihOqEukiRIg5bBxEROY+UNJMMn79DUtM1uad2KbmvcTmjk0RkGKcKAFetWiXdu3eXKlWqyP79+6Vu3bpy/Phx1Ut748aNHbquadOmSVRUlMyZMydHdRCJiMi1vf/HIdl7LlaKBPvJlHvrseiXPJpTFQGPGTNGRowYIbt27ZLAwED54Ycf5NSpU9K6dWuH9w+IXMamTZuq5ZYsWVIaNWqknkRCRETuZ8epaJm1+ogan9SznpQICzA6SUSGcqoAcN++fdK/f3817uvrK4mJiRIaGiqvvfaayrFzJLQo/vDDD6V69eqyYsUK9Qg6dDPz5ZdfZvmZ5ORk1XLIciAiIueWlJouwxfskHSTJt0alJUu9csYnSQiwzlVABgSEmKu91emTBk5ciTjbg0uX77s0HWZTCZVrDxlyhSV+zdo0CB58sknZfbs2Vl+ZurUqarbF31AETIRETm3N387IIcvxqlcv9e61zE6OUROwakCwNtuu03++usvNd65c2cZPny4ejbwY489pt5zJASYtWvXtppWq1Yt1fgkuyJq9PmnDyieJiIi54UnfXz6V0YXY6/fV0+KhPgbnSQip+BUjUDQyjcuLk6NT5gwQY3PmzdPFdM6sgUwoAXwgQMHrKYdPHhQKlasmOVnAgIC1EBERM4vPjlNRizYIXjcQZ+m5aVtrVJGJ4nIaThVAIjWv5bFwdkVx+bXsGHD5Pbbb1dFwH369JF//vlHPv74YzUQEZHrm/LLPjl5NUHKRQbJ2K7WJT5Ens6pioAL06233iqLFi2S7777TnU3M3HiRJk5c6b069fP6KQREVE+rTl4SeZuyqjSM71XfQkLtH62PJGnMzwHEB0v5/QZjFevXnXourt27aoGIiJyHzGJqTJq4U41/ujtleT2asWNThKR0zE8AESuGxERkaNM+GmPnI9NksrFQ2RUx5rcsUTOGAAOGDDA6CQQEZGbWL77vPz47xnx9hKZ0bu+BPn7GJ0kIqdkeACYlaSkpEzPAg4PDzcsPURE5NwuxyXLy4t2qfGnWleVJhWLGp0kIqflVI1A4uPj5dlnn1WPZkMrYNQPtByIiIjswTPjX1m0W67Ep0jN0mEytF117igiVwkAR44cKX/88Yd6RBv62/v0009Vf4Bly5aVr776yujkERGRk1q8/Yws33Ne/Hy85M0+DSTAl0W/RC5TBLx06VIV6N11110ycOBAufPOO6VatWqqc+a5c+eyixYiIsrkXEyijFuyR40/37a61Ckbwb1E5Eo5gOjmRe8MGvX99G5fWrZsKWvXrjU4dURE5IxFvyMX7pTrSWnSICpSnm5d1egkEbkEpwoAEfwdO5bxzMaaNWvK/PnzzTmDkZGRBqeOiIicDTp7XnfosgT4esubvRuIr49TXdaInJZTfVNQ7Ltjxw41Pnr0aJk1a5YEBgaqx7a9+OKLRiePiIicyPHL8TJ52T41jv7+qpUMNTpJRC7DS0P+uZM6fvy4bNu2TdUDrF+/vjib2NhYiYiIkJiYGHZRQ0RUiNJNmvT5aINsPXFNWlQpJnOfaC7e6PyPKAdief12rkYgtipVqqQGIiIiSx+vPaqCv9AAX5neuz6DPyJXLgKGVatWqefzVq1aVQ0Y//33341OFhEROYn952Pl7ZUH1fi4brWlfJFgo5NE5HKcKgD84IMPpGPHjhIWFibPP/+8GtAauHPnzqo+IBERebaUNJMMm7dDUtJN0q5WSendpLzRSSJySU5VB7B8+fKq8QeeBmIJwd+UKVPkzJkz4kxYh4CIqHBNX7FfZv15RIoE+8mKYa2kZFggDwHlWizrADpXDmB0dLTKAbTVvn171dCCiIg8F+r8fbj6iBqffG89Bn9E7hIAdu/eXRYtWpRp+pIlS1RdQCIi8kwJKWkyfP52MWki9zYqJ53rlTE6SUQuzfBWwO+++655vHbt2jJ58mRZvXq1tGjRQk3buHGj/P333zJ8+HADU0lEREaa+st+OX4lQUqHB8qr3evwYBC5eh3AypUr52g+Ly8vOXr0qDgT1iEgIip4aw5ekgGf/6PGv368mdxZvQR3O+VLLOsAGp8DqD/6jYiIyFZMQqqMXJjxhKgBLSoy+CNyxzqARERElsb9tFsuxCZLleIhMrpTLe4cIgdhAEhERE7p551nZcn2s+Lj7SVv9W0oQf4+RieJyG0wACQiIqdzITZJXl60W40PvquqNIyKNDpJRG6FASARETkVtE18ceFOiUlMlXrlImRI2+pGJ4nI7TAAJCIipzJ300lZe/CSBPh6y9t9G4ifDy9VRG7XCnjnzp05nrd+/foFmhYiIjLWscvxMnnZPjU+qmNNqVYyjIeEyB0DwIYNG6o+/pDlj7/ZSU9PL7R0ERFR4UpLN8kL87dLYmq63FGtmDx6eyUeAqIC4u0M/QCig2f8/eGHH1TH0B988IH8+++/asB41apV1XtEROS+Zq85Iv+ejJawQF+Z3quBeHtnnylARC6cA1ixYkXzeO/evdWj4Tp37mxV7BsVFSVjx46Vnj17GpRKIiIqSLtOx8jM3w+p8Qnd60jZyCDucCJ3zgG0tGvXLruPhsO0vXv3GpImIiIqWEmp6TJ03r+SZtKkS70ycm+jctzlRJ4UANaqVUumTp0qKSkp5mkYxzS8R0RE7uf1X/fLkUvxUjIsQCb1rHvT+uBE5AZFwJZmz54t3bp1k/Lly5tb/KKVMH4Mli5danTyiIjIwdYduiRfrD+uxqf3biBFQvy5j4k8LQBs1qyZahAyd+5c2b9/v5rWt29feeihhyQkJMTo5BERkQNFJ6TIiAU71Pgjt1WU1reU4P4l8sQAEBDoDRo0yOhkEBFRARu7ZI9ciE2WKsVDZEznmtzfRJ5aBxC+/vpradmypZQtW1ZOnDihpr399tuyZMkSo5NGREQOsmT7GVm646z4eHvJW30bSrC/0+VHELk1pwoAP/zwQ3nhhRekU6dOcu3aNXPHz0WKFJGZM2canTwiInKAM9GJ8sri3Wp8yN3VpGFUJPcrkScHgO+995588skn8vLLL4uv7393g02bNlVdxBARkWszmTQZMX+HXE9KU4Hfs22qGZ0kIo/kVAEgngbSqFGjTNMDAgIkPj7ekDQREZHjfPbXMdlw9IoE+/vI230biq+PU12GiDyGU33z0OHz9u3bM01fvnw5+wEkInJx+87FyvQVB9T42K61pXJx9u5AZBSnqnWL+n+DBw+WpKQk0TRN/vnnH/nuu+9UR9Cffvqp0ckjIqJ8PO1j2LztkpJukna1SskDt0ZxXxIZyKkCwCeeeEKCgoLklVdekYSEBNX/H1oDv/POO/LAAw8YnTwiIsqjN387IPvPX5fiof7y+v31+LQPIoN5achqc0IIAOPi4qRkyZLirGJjYyUiIkJiYmIkPDzc6OQQETmlvw9fln6fblLjn/ZvKu1qlzI6SeThYnn9dq4cQEvBwcFqICIi137ax/D5GU/76Ne8AoM/IidheACIVr85ffD3tm3bCjw9RETkGChgennRbjkfm6Se9vFyl1rctUROwvAAsGfPnkYngYiICsAP287Isl3nxNfbS2Y+wKd9EDkTwwPA8ePHG50EIiJysJNXEmT8koynfQy75xapX55P+yByJk7VDyAREbm+tHSTDJ33r8SnpEuzSkXl6dZVjU4SETlbDmDRokXl4MGDUrx4cfXM3+zqA169erVQ00ZERLk3688jsu1ktIQF+MqbfRqIj3fO6nkTkQcFgG+//baEhYWp8ZkzZxqWjtdff13GjBkjzz//vKHpICJyZVtPXJN3/zikxl/rWUeiirI3ByJnZHgAOGDAALvjhWnz5s3y0UcfSf369Q1ZPxGRO7ielKqKftNNmvRoWFbubVTe6CQRkavVAcTj4NBRo+VQENDZdL9+/eSTTz5RRdBERJQ343/aI6euJkq5yCCZ2LMudyORE3OqADA+Pl6effZZ9fSPkJAQFZBZDgUBzx7u0qWLtGvXrkCWT0TkCX7acVZ+3HZGUN0PXb6EB/oZnSQicuYiYEsjR46UP//8Uz788EN55JFHZNasWXLmzBlVPIs6eo72/fffq86lUQScE8nJyWrQFVSuJBGRKzkTnSgvL9qlxp9tU01urVTU6CQRkSvlAC5dulQ++OADuf/++8XX11fuvPNOeeWVV2TKlCkyd+5ch67r1KlTqsEHlhsYGJijz0ydOlU9+1cfoqKiHJomIiJXg/p+w+Ztl+tJadIwKlKGtK1udJKIyNUCQHTzUqVKFTUeHh5u7valZcuWsnbtWoeua+vWrXLx4kVp3LixCjYxrFmzRt599101np6enukzaCUcExNjHhBEEhF5sg/+PCz/HLsqIf4+MrNvQ/HzcarLChG5QhEwgr9jx45JhQoVpGbNmjJ//nxp1qyZyhmMjHRsL/Jt27aVXbsyiix0AwcOVOsdNWqU+Pj4ZPpMQECAGoiIKKPLl5mrbnT50qOuVCoewt1C5CKcKgBEALZjxw5p3bq1jB49Wrp16ybvv/++pKamyltvveXQdaHvwbp1rVupoeFJsWLFMk0nIiJrsUmp8vz3/3X5cl/jctxFRC7EqQLAYcOGmcfRKnf//v2qqLZatWrso4+IyElomiavLNotp68lSvkiGV2+ZPcUJyJyPk4VAH711VfSt29fczFrxYoV1ZCSkqLe69+/f4Guf/Xq1QW6fCIid4DuXtDtCx7x9s4DjdjlC5EL8na2ImA0rrB1/fp19R4RERnr+OV4Gbdktxof2ra6NKnIDvSJXJG3sxUr2CtGOH36tOp2hYiIjJOSZpLnvv9X4lPSpVnlovK/NtV4OIhclFMUATdq1EgFfhjQOhfdsOjQHQtaBnfs2NHQNBIReboZvx2QnadjJCLIT3X5giJgInJNThEA9uzZU/3dvn27dOjQQUJDQ83v+fv7S6VKlVTn0EREZIzVBy7Kx2uPqvHpvepL2cggHgoiF+YUAeD48ePVXwR6aASS0ydzEBFRwbt4PUlGLNihxvu3qCjt65TmbidycU5VB3DAgAGSlJQkn376qXrqhv4kEDyvF88EJiKiwmUyafLCvB1yOS5FapYOk5c61+IhIHIDTpEDqNu5c6fq/w8NPo4fPy5PPvmkFC1aVH788Uc5efKk6gqGiIgKz0drj8pfhy9LoJ+3vP9QIwn0y/yUJCJyPd7O1hH0o48+KocOHbIqBu7cubPDnwVMREQ3f9Tbm78dUOOvdqsj1UqGcZcRuQmnygHcsmWLfPzxx5mmlytXTs6fP29ImoiIPFFMQqo8992/kmbSpGv9MtL31iijk0RE7poDiCeAxMbGZpp+8OBBKVGihCFpIiLyNOiTdeQPO+RMdKJULBYsU++rx0e9EbkZpwoAu3fvLq+99pqkpqaq1+gXEHX/Ro0axW5giIgKyVcbTsiKPRfEz8dL3n+wsYQF+nHfE7kZpwoA33zzTYmLi5OSJUtKYmKitG7dWqpVqyZhYWEyefJko5NHROT2dp+JkcnL9qnxMZ1qSb3yfAoTkTtyqjqAaP27cuVK+euvv1SLYASDjRs3Vi2DiYioYMUlp8mz326TlHSTtKtVSgbeUYm7nMhNOVUAqGvZsqUaiIio8Or9vfTjLjl+JUHKRgTKjN71We+PyI05TQBoMpnkiy++UH3+oQ9A1P+rXLmy9OrVSx555BH+EBERFaBv/zkpP+04q57v+95DjSQy2J/7m8iNeTvLnScagDzxxBPqiR/16tWTOnXqyIkTJ1S/gPfee6/RSSQicut6fxOW7lXjozrWkCYVixqdJCLyhBxA5Pyho+dVq1ZJmzZtrN77448/pGfPnuopIP379zcsjURE7uh6UmpGvb80k7StWVKeaFnF6CQRkafkAH733Xfy0ksvZQr+4O6775bRo0fL3LlzDUkbEZG7QunL6B8y6v2ViwySN/s0EG9vL6OTRUSeEgCixW/Hjh2zfL9Tp06yY8eOQk0TEZG7+2bjCVm265z4st4fkcdxigDw6tWrUqpUqSzfx3vXrl0r1DQREbmznaejZeLPGf39je5UUxpXKGJ0kojI0wLA9PR08fXNujqij4+PpKWlFWqaiIjc1bX4FHnmm4z+/trXLiWPt6xsdJKIyBMbgaAeClr74lnA9iQnJxd6moiI3JHJpMmw+dvNz/md3rsBu9ki8kBOEQAOGDDgpvOwBTARUf7N+vOwrD5wSQJ8veXDfk0kIojP+SXyRE4RAM6ZM8foJBARub2/Dl2Wt34/qMYn9qwrtcuGG50kIvLkOoBERFSwzsUkynPf/yuaJtK3aZT0aRrFXU7kwRgAEhG5ueS0dPnf3G1yNT5FapcJlwk96hidJCIyGANAIiI3N/HnvfLvyWgJD/SVDx9uLIF+PkYniYgMxgCQiMiNLdhySr7ZeFK8vETeeaCRVCwWYnSSiMgJMAAkInJTu8/EyMuLd6vxoW1vkTY1SxqdJCJyEgwAiYjctLPnp77eKilpJmlbs6QMubua0UkiIifCAJCIyM2kmzTV4lfv7Pmtvg3F29vL6GQRkRNhAEhE5Gamrzgg6w5dliA/H5n9MDt7JqLMGAASEbmRn3acldlrjqjxab3qS60y7OyZiDJjAEhE5Cb2nI2RkQt3qPGnW1eV7g3KGp0kInJSDACJiNzAlbhkGfTVVklKNUnrW0rIix1qGJ0kInJiDACJiFxcarpJnv02o9FHpWLB8u4DjcSHjT6IKBsMAImIXNzkZftkw9ErEuLvI5/0byoRwX5GJ4mInBwDQCIiFzZ30wn5Yv1xNY7uXqqXCjM6SUTkAhgAEhG5qPVHLsv4JXvU+Ij2t0iHOqWNThIRuQgGgERELuj45Xh55pttkmbSpEfDsjK4DZ/0QUQ5xwCQiMjFxCSmyuNfblZ/G0RFyrT764uXF5/0QUQ5xwCQiMiFpKWbZMh3/8qRS/FSJiJQPnmkiQT6+RidLCJyMQwAiYhchKZpMmHpXll78JIE+nmrFr8lwwONThYRuSAGgERELuLzv4/L1xtPCEp7Z/ZtKHXLRRidJCJyUQwAiYhcwG97zsukZXvV+EudaknHumWMThIRuTAGgERETm7n6Wh5/vvtomki/ZpXkCfurGx0kojIxTEAJCJyYqevJcjjX26RxNR09YzfCd3rsMUvEeWbRweAU6dOlVtvvVXCwsKkZMmS0rNnTzlw4IDRySIiUmISUuWxLzbLpevJUrN0mLz/UCPx9fHon20ichCP/iVZs2aNDB48WDZu3CgrV66U1NRUad++vcTHxxudNCLycEmp6fLk11vk4IU4KRUeIJ8/equEBfIZv0TkGL7iwZYvX271+osvvlA5gVu3bpVWrVoZli4i8mzpJk1emL9d/jl2VcICfOXLx5pJ2cggo5NFRG7EowNAWzExMepv0aJF7b6fnJysBl1sbGyhpY2IPKevv4k/75Vfdp0Xfx9v+ah/E6lZOtzoZBGRm/HoImBLJpNJhg4dKnfccYfUrVs3yzqDERER5iEqKqrQ00lE7u2jtUfli/XH1fibfRrI7VWLG50kInJDXhpuN0meeeYZ+fXXX+Wvv/6S8uXL5zgHEEEgcg7Dw3mHTkT5s3DraRmxYIcaH9u1tjzekt29EBWE2NhYlZHjyddvFgGLyLPPPis///yzrF27NsvgDwICAtRARORoK/acl1E/7FTjT95ZmcEfERUojw4Akfk5ZMgQWbRokaxevVoqV+bdNhEVvr8PX5Yh3/6rGn/0blJeXupci4eBiAqURweA6ALm22+/lSVLlqi+AM+fP6+mI1s4KIgt7oio4G0/FS1PfrVFUtJN0rFOaZl6Xz129ExEBc6j6wB64YnqdsyZM0ceffTRm36edQiIKD8OXrgufT7aINEJqdKyWnH57NGmEuDrw51KVMBiWQfQs3MAPTj2JSKDHbscLw9/ukkFfw2jIuWjR5ow+COiQsNuYIiICtnJKwny0Ccb5eL1ZKlRKky+GHirhAR49P04ERUy/uIQERWi09cS5MFPNsq5mCSpVjJU5j7ZXCKD/XkMyLOhRC7xmsj1czeG8//9rdFJpFo7o1PodhgAEhEVknMxifLQJ5vkTHSiVCkeIt8+0VyKh7JrKXLzwC4pWuT6hf8Curjz1gGe/jc9xf4ygosxACwADACJiArBxdgkFfydvJogFYsFy7dP3iYlwwO578k9Ars4iwDPPNyYnpaU8+UGFRUJLysSVvrGUEak0p0FuSUeiwEgEVEh5fyh4Uf5IkEq+CsdweCPnJDJJJJ41SKn7oLN3xtDbgO7wMiMYM4c2JUWCS0tEl7mv+mhpUR8mSNeWBgAEhEVoFNXE+ShTzfKqauJKvj77snbpFwk+xmlQpaeJhJ/8b/gTeXY2Qnq4i6KmFJzGdhZBHR6rl1YKevAzo/nvLNhAEhEVECOX45XrX3PxiSZi30Z/JFDpcTfCN4uWuTU6QGeRbAXfxnltjlfLurdqYDuRiCHIM4q0CvFwM7FMQAkIioAhy/GSb9PN8qF2GSpWiJEBX+lWOePcsKUnhGw6TlyCOzMOXaW0y6KpMTlfJ96+YiElvwvmFPjpTMHdiElRXzZMt3dMQAkInKw3Wdi5NE5/8jluBTVz983TzSXEmGs2+TRVKOJmBvBG3LkLv43rv/VA7yEyyKaKefL9gvOHNThr21uHXL1vPmkGcrAAJCIyIE2Hr0iT365Ra4np0mdsuHy9ePNpWgIc1PcVnKcRTCnB3eXbgR2+t8b09OTc7FgL5GQEjcCuxsBnDm4w3Q9166kSEBYAW4guSsGgEREDrJiz3kZ8t2/kpJmkuaVi8onA5pKeKAf968r1qvTAzo9uDMHdZbjl0RS43O37ICIGwEcilpL2OTalfovuENunQ8v0VRweHYRETnAvM0nZcyPu8SkibSvXUrefbCRBPqxuM1pil+Tr98I3GyDuos20/MQ1KEIVs+tU8FcyYx6dHp9O3065vEPLqitJMoVBoBERPmgaZrM+vOwzPjtoHrdt2mUTL63rvj68FHrBd5QAo8OswzishvPTZ914BuUkRtnDuQsg7obgZ0e9AWEFtRWEhUYBoBERHmEot6XF+2SBVtPq9dPt64qozrWEC8vL+7TPO3QhIwGEMiFi89i0N/LbUMJ8AuxDupUAGf51yLA8w8V4XEkN8YAkIgoD2ISU+WZb7bK+iNXxNtLZEL3OvJIi0rcl5bSU0USrmR0aaICOP3vJevXCOYwnpsuTXRBRTICNxXE6cEd/pawmY7i1xAeH6IbGAASEeXh6R4Dv9is+voL8feR9x9qLG1qlvSMYteEqxZBG4K4K1m/RhFtbvkEWAdtGFSnxDdy6EKK3xhujPuwkQ1RXjAAJCLKhc3Hr6qcP/TxVzo8UD57tKnUKRvh+jl0ei5cVq8R1KmALhdPkwAv74wATg/k9KBODcX+y6VTQV2JjC5NWPRKVOAYABIR5bCxx9xNJ+XVn/ZImkmTWmXC5fNHm0qZiCDn6r5ED9rMgRzG9UDuxrge4KFj4rxQxa4I6PTcuOIZ4yqX7sa4nksXFMnOh4mcEANAIqKbSE5Ll/FL9sj3m0+p113ql5HpvepLsH8B/oSmp4kkXv0vmDMHdJbTLAO7KyJpiXlYkZdIcNH/gjZzLp0eyBWzCOhKiAQVZf90RG6AASARUTYuxCbJ099slX9PRquSyZEdasrTravkrqWv6rIkOnNAZw7mbKdfEUmKzttx8fHPCNhUIGcRvOG1mnYjsNPHkZvHx4N5dM42/pk0k9W4em05rmliEovxHLyPf3bHc7nsShGVpEpEFaN3ldthAEhElIV1hy7JsHnbVX2/8EBf1bnzXdWKZuTEmYO5q9bjmV5fyVvdOR0CNBW86UFcUYtArpjFe0UzpuWg+xLri3K6mNJSrS66KgywuVg7+qIP+V1OpoDlZkGKzbz23stRwJKHwMjqb0622XK/5yN4utl6MK+z+1/D/8kzDZ4xOhluhwEgkZvK7Z39zS6m2V4gxaTim9zc6UNOLlK5uTBbLdMyiLH3GZNJtPRkMaUmiiktQbTURNHSktTr9NQkuRQdI9fj46VxeIoEFUmTEF+TrPwjRVb8noqtVZdN/DV5ef03fmPfY5qax1fEFOEjpogSGfN4+4oJj/fCX28/Na55+YjJx0dMXj6ieftkvPb2Fs3LO2M5VsflqmipV8QUc0BM0Vlf8C0Duaz2PVFeeOGfl5d4e3mL+uflbfVaH8d8atqN9/HPx8vnv3lvNo/FsksFl+LBKgAMAF1UVndymS6e2dwd5uTCmumuNaucgazWYXv3arMOyE9QklUanCnwuVkabnaxzpSGrLbfBe/snf7XMVPjXv8bQ36lZQzpFi+dnOWF2fIirr/GoOa5cVFXf70t3r8ROGS1HHufw2u1bm+fTMGFZdCBaoy2AYZlcGH5GUet33LZkNVyLF9nt37L/YT1ZbUfs9o2y0DK3rKxj7BuqzTaLM9eoGa17BvbQO6BAaAT+nTXp2qwDTpUoME7eCpAWf3g27so2N712wYA5ovnjc9ldYePC5P5bl808TKli48pXf31Vn/TxMuUKj74m54m3ump6rV3eop4pd34i/eQfgyaZh5XfzXNPI7BR8Nfi3lwUfQNVEOy5i8X8DAKk7+keQVKhVLFpHSxouLlGyze/iHi5Y+/oeoxYT4+fnZzPrK62GfaVj0oyscFX1+GvcAlJ8vRj3mmoMBOsEVE7oUBoBNKTU+V+Nw+jPwm7F0wrC7kNhcwsHcXaHtxz/Ku0+Ju1nZ+2+ID/c40q7Tod6/ZFTFkexduc6dreWG2dwHOFLhkdTG32f5MRRxi/4JvGRBlFTjk5C48q3my2tbschz0zzmEySSSHHOj0cO1jMYM+Ku/Nk+zeK0PuX1eqyUck8DIjLpwqDeH1qqq/pz+uojF66L//fUPkdjkNNW9y4/bzqhF1Skbrjp3rlycT44gIvfEANAJPVTrIelapWumgMwyULENrjJd8G3m4R085TqIS7meEaTpwZrtX9tAzvw++pbLR/Gzl491wKaGyMwBnQr2LAK9gHAR74zvSm78ffiyvLhgh5yNSVKPdHuqdVUZ2q66BPj65H0biIicHANAJxQREKEGonz3I5cc+19whsDMKpCLsRPc3ZiGv/ltLOAXnBGkWQZx5qDtRgBnG9ypQK5wngRxPSlVpq84IF9tOKFeVywWLG/2biBNKxUt8HUTERmNASCRs+fCmQO3GIvB5rW991Pi8p8GPJfVMnjL9FcP4PSATh+PEPENEGe1fPd5VeR7PjajyLlf8wryUudaEhLAn0Qi8gz8tSMqyOesJsVmBGPIiVOBmf43xv4083s3pjuiJS8aLZiDtojM4/hrFdhZzOPnRI85c4BzMYnqiR6/7b1gzvWb1LOu3Fm9hNFJIyIqVAwAibIrPlVBmu3fG8GZHqTZfS9WJDXBQd/SwIz6bebgzHKwmWYV2GEIF/Hx8/hjjEe5ffH3cXnvj8MSl5wmvt5eMqhVFXmubXUJ9GNdPyLyPAwAyb1omkhq4o3g7bpFcHZjHH/1AM1ucOfg4M2cAxeREcSp4Ez/q0/DoAduN8b1gA9//QIdlxYPg26UkNs35Zd9cuJKxjFtXCFSptxXT2qWDjc6eUREhmEASM5T3w1d3yTH3QjWLAI2qyE263E90NP03nUdwDcoIyjTAzWrvzZBnG2Ah9cY8OQHKnT7zsXKxJ/3yvojV9TrEmEBMrJDDbm/cXnxRnNfIiIPxisT5TO3LeFGAIbALTaj4YEexKEBg/k9vL4xT6ZpN+Zz5JMr0HUOWpP6h/0XnAXo42EWQRsCNXvz3Jju64inPlBhOnopTt7+/ZD8vPOsOkX9fb3liZaV5X9tqkkoG3kQESkMAD0JroZpyf8FXSnx/wVsKliLs/8a81kFa/r0uPx3FWKvD7iAUIvALcxiCLf5G2oRtN0I2PT3/UMKpSsRch6nryXIu6sOyQ/bzki6KeNmoku9MjK6U02JKhpsdPKIiJwKA0Bnb0WqB2B6sKaCMX2aRYBmDtwsptt77cjiUcvcNtRzUzlu+Gs5bhHA2b62mnYjkEOrUwZulAvHLsfLx2uPyA9bz0hKesYNyd01S8oL99widcuxP00iInsYADqjNW+IrJ0ukp5ScOvwC8nIJVO5bRi/kaOmB3C2AZ3V+5bBW2hGh78M2qiQ7TwdLbPXHJFfd59XmdvQokoxGdHhFmlSkZ05ExFlhwGgM0KOmmXw5+OfEWypQE3/qw+h1kGcOaiznE8P5iw+782uL8j1pKWb5Pd9F+WrDcfNjTv0HL+nW1eVZpUZ+BER5QQDQGfU9DGRBg9kBGrIqWNDBPJwl64ny7zNJ+XbTSfVM3vBx9tLujcoK0+1rsIuXYiIcokBoDPCw+6FORnk2VLTTbLmwCX58d/TsnLvBUlNzyjnLRriL31vjVKPbytfhI07iIjyggEgETlVx817z8XKj9vOyJLtZ+Ry3H9VIRpGRUr/FhWlc70yfHoHEVE+MQAkIsODvt1nYuWX3efk113n5PiNJ3ZA8VB/6dGwnOq8uXZZPrmDiMhRGAASUaFLSk2XTceuyuoDF1Xx7ulrieb3Any9pW2tkiroa3VLCfHz8eYRIiJyMAaARFQouXxHLsWplruo14e/ian/9UkZ5OejWvJ2qlda2tQoKSF8YgcRUYFiAEhEDmcyZQR8/xy/KhuOXJGNR6/K5bhkq3lKhQeoYO+uGiWl9S0lJMifXRMRERUWBoBElG8XryfJnjOx8u/Ja/LvqWjZfjJarienWc2Dot0mFYvIHdWKq9y+mqXDxIsdiBMRGYIBIBHlqu7e0Uvxcujiddl//rrsPRsre87GZsrd04t1G0RFyG1ViqkndDSIimTrXSIiJ8EAkIgyPW3jXEySHL8Sr1rknriMv/Fy+GKcnLyaIKYbj12zhIy8KsVDVJDXqEIRaVwhUmqUChNfNuAgInJKHh8Azpo1S6ZPny7nz5+XBg0ayHvvvSfNmjUz+rgQFVhwdyU+RS7EJsn5mCS5cD1ZLsQkyZnoRDlzLVH9PR+bJOn2orwbwgN95ZZSYVK9VJjUKRuuumdBcW6wv8f/nBARuQyP/sWeN2+evPDCCzJ79mxp3ry5zJw5Uzp06CAHDhyQkiVLGp08omxb1SakpMv1pDSJTUqV2MRU9Tc6IVWuJeBvilxTQ6pciUuWK3EpKvDDNC3r2M7M38dbKhQLlkrFgqVisRCpWCxYqpUIlWqlQqVEaADr7hERuTgvDVcSD4Wg79Zbb5X3339fvTaZTBIVFSVDhgyR0aNH3/TzsbGxEhERITExMRIezk5qPbGla5pJU7llqSaTpKdn/MUjy5DTpv6aTJKShnGTJKu/mnqdnJYuyakZ0zCelGpS9ev0AV2kIMBLTMn4m5CaLvHJaWqIu/E3m0y6bOEZugji0Aq3VHigGspGBkm5IkFSLjJQykUGS4mwADUfEZE7iuX123NzAFNSUmTr1q0yZswY8zRvb29p166dbNiwwe5nkpOT1WB5AhWE5bvPyfLd5zNNz8v1Pifhvb1ZbO8L7C7GzkTtxkT941oW85jfzzTPf5/PeC9jidbzW86TsSx9XAVFFtNNNz6P6ficeq1Zv8Y4grl0/bVJVFCnXpsypuE15kNAl34j8HOGWydfby8JD/JTxbJhgX4SGYzBXyKD/KTIjfFiof5SPDRADXiOLgYGd0REns1jA8DLly9Lenq6lCpVymo6Xu/fv9/uZ6ZOnSoTJkwo8LShdeXi7WcLfD3keGgMgSdX+Hl7qQYQfj5eqjjVz9c746+Pt/j7eqsuUQL8fNS0AD9vCfT1kSD/jL+Bfhj3Ua1og/HXH399JSTAR8IC/NTf0ABfCQ30VfOwKxUiIsotjw0A8wK5hagzaJkDiCJjR7uzegl1gc+JvF78bT+Vk8V4ZbN+y89nmk+fx876vG5M/e+1zXsZ/6n16J/H+2rAP33eG+9746/lZ7wypnnf+KsvDzlg/83rpV6jwSo+46Pm9xJv74z51GsEdGoe/PXOeM/rvyBPn86cNSIicgUeGwAWL15cfHx85MKFC1bT8bp06dJ2PxMQEKCGgobOcjEQERERFQSPfcq6v7+/NGnSRFatWmWehkYgeN2iRQtD00ZERERUkDw2BxBQnDtgwABp2rSp6vsP3cDEx8fLwIEDjU4aERERUYHx6ACwb9++cunSJRk3bpzqCLphw4ayfPnyTA1DiIiIiNyJR/cDmF/sR4iIiMj1xLIfQM+tA0hERETkqRgAEhEREXkYBoBEREREHoYBIBEREZGHYQBIRERE5GEYABIRERF5GAaARERERB6GASARERGRh2EASERERORhPPpRcPmlP0QFPYoTERGRa4i9cd325IehMQDMh+vXr6u/UVFRjjoeREREVIjX8YiICI/c33wWcD6YTCY5e/ashIWFiZeXl8PvThBYnjp1SsLDw8XdcPtcH4+ha3P34+cJ28jtyztN01TwV7ZsWfH29szacMwBzAecNOXLl5eChB8td/zh0nH7XB+PoWtz9+PnCdvI7cubCA/N+dN5ZthLRERE5MEYABIRERF5GAaATiogIEDGjx+v/rojbp/r4zF0be5+/DxhG7l9lB9sBEJERETkYZgDSERERORhGAASEREReRgGgEREREQehgEgERERkYdhAGiQyZMny+233y7BwcESGRmZ457Lx40bJ2XKlJGgoCBp166dHDp0yGqeq1evSr9+/VTHoFju448/LnFxcVLYcpuO48ePq6ep2BsWLFhgns/e+99//70YIS/7+q677sqU/qefftpqnpMnT0qXLl3UuVGyZEl58cUXJS0tTZx9+zD/kCFDpEaNGur8rFChgjz33HMSExNjNZ+Rx3DWrFlSqVIlCQwMlObNm8s///yT7fw492rWrKnmr1evnvzyyy+5/k4Wptxs3yeffCJ33nmnFClSRA1Iu+38jz76aKZj1bFjR3GF7fviiy8ypR2fc+bjl9tttPd7ggG/H854DNeuXSvdunVTT99AOhYvXnzTz6xevVoaN26sWjxXq1ZNHdf8fq/pBo0MMW7cOO2tt97SXnjhBS0iIiJHn3n99dfVvIsXL9Z27Nihde/eXatcubKWmJhonqdjx45agwYNtI0bN2rr1q3TqlWrpj344INaYcttOtLS0rRz585ZDRMmTNBCQ0O169evm+fDKTtnzhyr+Sy3vzDlZV+3bt1ae/LJJ63SHxMTY7Uf6tatq7Vr1077999/tV9++UUrXry4NmbMGM3Zt2/Xrl3afffdp/3000/a4cOHtVWrVmnVq1fX7r//fqv5jDqG33//vebv7699/vnn2p49e9RxiIyM1C5cuGB3/r///lvz8fHR3njjDW3v3r3aK6+8ovn5+antzM13srDkdvseeughbdasWeo827dvn/boo4+qbTl9+rR5ngEDBqjzwPJYXb16VTNCbrcP51h4eLhV2s+fP281jzMdv7xs45UrV6y2b/fu3eqcxbY74zHE79nLL7+s/fjjj+p3YNGiRdnOf/ToUS04OFhdJ/EdfO+999T2LV++PM/7jP7DANBg+KLmJAA0mUxa6dKltenTp5unRUdHawEBAdp3332nXuMLgi/V5s2bzfP8+uuvmpeXl3bmzBmtsDgqHQ0bNtQee+wxq2k5+dFw5m1EAPj8889n+wPp7e1tdaH68MMP1YUsOTlZc7VjOH/+fPXjnJqaavgxbNasmTZ48GDz6/T0dK1s2bLa1KlT7c7fp08frUuXLlbTmjdvrj311FM5/k468/bZws1HWFiY9uWXX1oFDz169NCcQW6372a/rc52/BxxDN9++21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"grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/docs/docs/tutorials/diffusion_model.ipynb b/docs/docs/tutorials/diffusion_model.ipynb index 148f37c3..2726c9a8 100644 --- a/docs/docs/tutorials/diffusion_model.ipynb +++ b/docs/docs/tutorials/diffusion_model.ipynb @@ -11,9 +11,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:10:07.617734Z", + "iopub.status.busy": "2026-06-22T17:10:07.617502Z", + "iopub.status.idle": "2026-06-22T17:10:09.317057Z", + "shell.execute_reply": "2026-06-22T17:10:09.316515Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -26,10 +33,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "2", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:10:09.318779Z", + "iopub.status.busy": "2026-06-22T17:10:09.318571Z", + "iopub.status.idle": "2026-06-22T17:10:09.425107Z", + "shell.execute_reply": "2026-06-22T17:10:09.424593Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f3bf69565cc44d35b077fcd63cb50e97", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "55229d1b06e44e96bf910eb6f6681adf", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "_dom_classes": [], + "_figure_label": "Figure", + "_image_mode": "full", + "_message": "", + "_model_module": "jupyter-matplotlib", + "_model_module_version": "^0.12", + "_model_name": "MPLCanvasModel", + "_rubberband_height": 0, + "_rubberband_width": 0, + "_rubberband_x": 0, + "_rubberband_y": 0, + "_size": [ + 640.0, + 480.0 + ], + "_view_count": null, + "_view_module": "jupyter-matplotlib", + "_view_module_version": "^0.12", + "_view_name": "MPLCanvasView", + "capture_scroll": false, + "footer_visible": true, + "header_visible": true, + "layout": "IPY_MODEL_6d7775d37a5c4b21a49f25315ee99c61", + "pan_zoom_throttle": 33.0, + "resizable": true, + "tabbable": null, + "toolbar": "IPY_MODEL_9f137daaca834e5eab9ab116ffad9b93", + "toolbar_position": "left", + "toolbar_visible": "fade-in-fade-out", + "tooltip": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/docs/docs/tutorials/instrument_model.ipynb b/docs/docs/tutorials/instrument_model.ipynb index a56b300f..5fd19598 100644 --- a/docs/docs/tutorials/instrument_model.ipynb +++ b/docs/docs/tutorials/instrument_model.ipynb @@ -14,9 +14,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:32:39.210947Z", + "iopub.status.busy": "2026-06-22T17:32:39.210366Z", + "iopub.status.idle": "2026-06-22T17:32:41.334933Z", + "shell.execute_reply": "2026-06-22T17:32:41.333922Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -32,9 +39,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:32:41.337737Z", + "iopub.status.busy": "2026-06-22T17:32:41.337490Z", + "iopub.status.idle": "2026-06-22T17:32:41.345331Z", + "shell.execute_reply": "2026-06-22T17:32:41.344266Z" + } + }, "outputs": [], "source": [ "# Create a BackgroundModel and a ResolutionModel and add them to an\n", @@ -57,20 +71,68 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:32:41.347679Z", + "iopub.status.busy": "2026-06-22T17:32:41.347538Z", + "iopub.status.idle": "2026-06-22T17:32:41.368651Z", + "shell.execute_reply": "2026-06-22T17:32:41.367361Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "instrument_model.get_all_variables(Q_index=1)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "3eca4688", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:32:41.371231Z", + "iopub.status.busy": "2026-06-22T17:32:41.371085Z", + "iopub.status.idle": "2026-06-22T17:32:41.376940Z", + "shell.execute_reply": "2026-06-22T17:32:41.375954Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "instrument_model.fix_resolution_parameters()\n", "instrument_model.get_all_variables(Q_index=1)" @@ -93,7 +155,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.14.5" } }, "nbformat": 4, diff --git a/docs/docs/tutorials/sample_model.ipynb b/docs/docs/tutorials/sample_model.ipynb index 002bbfca..7ffa5d2f 100644 --- a/docs/docs/tutorials/sample_model.ipynb +++ b/docs/docs/tutorials/sample_model.ipynb @@ -15,9 +15,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:32:23.684628Z", + "iopub.status.busy": "2026-06-22T17:32:23.684240Z", + "iopub.status.idle": "2026-06-22T17:32:25.826393Z", + "shell.execute_reply": "2026-06-22T17:32:25.825495Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -37,10 +44,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "2", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:32:25.828805Z", + "iopub.status.busy": "2026-06-22T17:32:25.828567Z", + "iopub.status.idle": "2026-06-22T17:32:26.006296Z", + "shell.execute_reply": "2026-06-22T17:32:26.005134Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2b54434bd1d24c0ea37d8caf9fd3579d", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Create a diffusion_model and components for the SampleModel\n", "Q = np.linspace(0.5, 2, 7)\n", @@ -68,7 +108,7 @@ " diffusion_models=diffusion_model,\n", " components=component_collection,\n", " Q=Q,\n", - " unit='meV',\n", + " x_unit='meV',\n", " display_name='MySampleModel',\n", " temperature=10,\n", ")\n", @@ -89,9 +129,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:32:26.009287Z", + "iopub.status.busy": "2026-06-22T17:32:26.009086Z", + "iopub.status.idle": "2026-06-22T17:32:26.040981Z", + "shell.execute_reply": "2026-06-22T17:32:26.039901Z" + } + }, "outputs": [], "source": [ "# Create a BackgroundModel and show other ways to set Q and components\n", @@ -112,10 +159,193 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:32:26.043397Z", + "iopub.status.busy": "2026-06-22T17:32:26.043243Z", + "iopub.status.idle": "2026-06-22T17:32:26.548813Z", + "shell.execute_reply": "2026-06-22T17:32:26.547518Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7facd950791d40cfa17c3b443bb9b1ca", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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", 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", 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", 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", 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P3W8KFuvV+tfxBUFgInjEZHOe7o0aPF+o5VZgSr9nyhdGgdCOC50i63ZP1aBvQFtX7N2oKls9566y31ZZebm2s+H1/4zqYFX7b6x2mj2O0J6PAawD5AqRo/cPB82AvdATBiGQEAyr5lvUYQYFq/v7CfUcbW4Llp2bJlievZeu9VNrDD5bj/8kqy9ozYLQs+E9BXGo+HbgbWfQNtwfNlz/NZ3nNvz/OO9zX2Acq+6NID6KqiBXL4cWov/PC3t2RO7oMBoIHgF7fWYRxf9Ojb9fPPP6ugD33h8EWADuSYUgCBHK6PoAXBovU0G8h+2FLa+Y5YcVBrA7IIEyZMsHmdivStQnYSmR9kxxB8ICCsLtgWfAkhG2Vrn9nzZVFZyBogS4kfAgi80O8Owf/06dOLXQ9fAJhGCH3nEByiTWg3AsjKTrtiPTgAj4l+kOjHhsEDCEDw4wDPSXVN7VLZ1ywG8CArjH2JfYQvUtwX+r3i/eRs2Fd4LpGhtqadh0DYHh9++KEKePGDCEGZPXO54ccYKgTIlqKPJt5LyD4h+4m+ffZ+ZjhqNVL8qMH2WgaUtuByBKNa9rasfnv2Pq51wIUf2+jriMfCD0R7J1dG9hN97sp7PnE9y/Otr1ve847gHJ89CCAtf/Rp8LmPgN66LyJ5FgaABqV1HtY+QFAOwIcBysKWGZHKliLLgy8HlH4sP3wOHDhQ5ghIdKjHr3wEEVX9dW5Z8nj55ZdVdsh6fjnLgTSAgSzWkPnBL2Zk3vDlhy8CZPesWd8WX5b44kOmyNYHsLOh1Ivs1dKlS9XoRLTFsvyLYACXIQOITv0aW9tmC4IIBAjWX4rWX1goPWNUKAZPWMJtLTM0FRlwgucL3QisB0BoJSnt+awqtB1ZbGTXLdtnPReaIwbL2IJAGQMobE0yvH79etU2ewaAaPf19ddfq8AAzzmCS+sSujV0IUDJHtuPwRUa/JiqLDw3GLCG16PlfrP13rMF3UMwiAgZN8s58zT//fefqjBg0Ep5tCCrPJg9wXJkOz7bMBoX7x9bg63Kgs8gfOZaB194PrXLAQElBurhubfM1OE9hqpOedk7/PjD5z0CfuvtxA9CdMfBdUr7oU2egX0APRw+TGz9wtbKkVppRft1bnldfBngw81ZkHXQ4HHxP0qfWlnSGtqI0ZnoB6hNVWJdIq4ojL7FFzbKeKXBByQ+eBEwWQY1aANGWmKSVq196A+EUa0YxahBgGU9mS+yA7g+vmytnx/8b89UK1WBABpf8ij94oC+WJZlS1uvB3j33Xftun8EuCtXrix2Hsr21hlAPI71YyArbZ0F0VYnsA4qbcHzgcexfH0BMtkIKrQRp1Vlax/hixpdKCyhD5u9ba8odOPYuHFjsSAQwRJKshhZXRF47yGoxch2ZGAx+rai248ApLzRteU9d5j0G+3QaNO62ANTCmF/Y3YC6/cQujTce++9KrCy7H/s6D6AmFwf7ynsB23Uty0omeJHiWV3GzyfeO1abi8CNXwOo2KhdbFB1hHvYWShLUveeM7Qh6+85x4lXmQJ0VY8puUBMz7gcXAd8mzMAHo4vMHxAYOh+ijv4gMaZT18QCHTps36jqlLUBLBL2h8eOJDBCssoKxlq8xQVciWYeoX/MLEBxvKEX/++aeaQsZ6/i5Lr776qgpqcRtMvYH+i/hgR9kJWR+crmjGwZ5yF6a0QOCAgS+YWkSbBgYfxJa3R0CH7UJHeEwrgs71uB6mV7EsTSFAQuZx6tSpKiOBMiKyNcie4Jc3Or3jy6yiELTjS8EWyw74+LLHlxM+5NFXCdP4WMKXJLI66NCNvm0YNIRg197sDgJrfNkiYMdAGfSzRBBs3e8KfaRQPsTrENO6YHoLDMix7h+K/YVpcTDNEPYTAkK8Bmz1tcNrGFlFLGuFfYslrtB2zFmHwMZywEdVoO3IfuG9hel1sG/QPrwmLZfjQlYY5+E9h2wvAm9kcMoqC+KLHP3htOAAwTReLzB+/HhzFhOvMbxP8fh4veB5RRcO9OXCoJeKQvCE9yGyVijL4/VtOSWIJTxfyPTiPYxsIYJrtLsqJV28pxG4I4O2efNm9eML96kF0eVB/0FkMpHZR3bUeiUQZLbxmrenj2Zlqgz4gYTAD58TaLP1exGvFe3HDLYTnxf4PNNWiMFrGsEbPhfQ7xbbgx+eWvutp5HCc4DnCp8XmD8RP2TxWY5uGmUFnshOlrXiB15PmOIIQXRl++KSDrh6GDI518KFC9WUFNoM+JjyAlMqYAoW65VA5s+fb+rcubOaxqRp06ZqlYEvvviixBQf1qsHaHA9yykFALezntkeUwVg+oHDhw+bhg0bpqaMwNQZmOrCekoQW1NgoN14nOjoaJOfn5+a8gCre3zyySfl7o/S2m7J1jQwsGTJEjWbP6a6wUoio0ePVlO8WMNUMj169FD7GlPoYOqG0lYC+fXXX02XXHKJ2h844HnCtu3fv9+h08DYeux//vlHnY8pKrAKirWTJ0+arr76arVSAKbYwfQSp0+fLvGc2JoGBs/jlClTTLVr11bPL6YTwlQrtqaBefTRR9XUPtiv2L9r165V24KDpd9//93Uvn17NWWJ5fQbtvYPVmOYPHmymuIEr5FWrVqp1yCmGyrvNQvW7bQF9zV9+nR1XUxb061bN9OCBQtstgdTJmmvCXumhCnrubSeCgXPHaa4wWsS7/ErrrjCdPDgwTLvv7z3w5kzZ9TnBD4Lypp6ZfXq1aaLL75YPXfY19o0U9btLG2VGlv7ClMoYaoWvG7w+nnooYdMixYtsmslEMvplG666Sb12YDpjXBbbAumU3EmbYqr0g6W7xHtM8F6m7Dyx2OPPabajtdVr1691Pbb8t9//5n69u2rtg2rjeC1XN70NrNmzVKPi/dTaf78889i086QZ/LCH1cHoWQs2lqXFVm0nIiospAVxOcOsuA4TUQsARMRkYdDSRldWZ588kk1Ath6xDuRETEDSNWOGUAiIiLX4ihgIiIiIoNhBpCIiIjIYJgBJCIiIjIYBoBEREREBsMAkIiIiMhguBJIFWDNRyxbhJUJnLXeJxERETmWyWRSy+hhSTyshW1EDACrAMGftjYjERER6UtMTIyaG9KIGABWATJ/2gsIa6cSERGR+0tJSVEJHO173IgYAFaBVvZF8McAkIiISF+8DNx9y5iFbyIiIiIDYwBIREREZDAMAImIiIgMhn0AiYio2PQYeXl5kp+fz71CuuXj4yO+vr6G7uNXHgaARESk5OTkSGxsrGRkZHCPkO4FBwdL/fr1xd/f39VNcUsMAImISE1sf/ToUZU5weS4+NJk9oT0msXGj5lz586p13SrVq0MO9lzWRgAEhGR+sJEEIi50ZA5IdKzoKAg8fPzk+PHj6vXdmBgoKub5HYYEhMR0YUvBWZKyEPwtVw2wwaA6OD87LPPSrNmzdQvhRYtWshLL72kUsdEREREnsywAeBrr70ms2bNkg8//FD27t2r/n/99dflgw8+cHXTiIjIg6Av5bx580QPli9frtqblJRk922aNm0q7777rlPbRY5n2ABwzZo1MmbMGBk1apR68V533XUybNgw2bBhg6ubRkREFYDO/hMnTpTGjRtLQECA1KtXT4YPHy6rV6/mfiQqhWEDwL59+8rSpUvlwIED6v/t27fLqlWrZMSIEa5uGhERVcC1114rW7dula+++kp9ps+fP18GDBggCQkJ3I9EpTBsAPjkk0/KjTfeKG3btlUjhbp16yYPP/ywjBs3rtTbZGdnS0pKSrEDEXmmPw7/oQ7k3lCq/O+//1Q3noEDB0qTJk3koosukqlTp8qVV15pvt7bb78tnTp1kpCQEDXS+b777pO0tDTz5bNnz5bIyEhZsGCBtGnTRo2ERmUIcyIisESlqEaNGvLggw8WmyQb56P/+NixY9V9N2zYUGbOnFlmm2NiYuSGG25Qj1ezZk1VjTp27Fi5ZdnFixer7yr0Wx80aJDExcXJwoULpV27dhIeHi433XRTsTkc8Z2F9kZFRalRsJdccols3Lix2H3/9ddf0rp1a3Wf2H+22oHkyKWXXqqug32H+0xPT7fj2SF3ZtgA8KeffpLvvvtOvv/+e9myZYt6g7/55pvquDQzZsyQiIgI8wFvBCLyPPGZ8fLUqqfUAaeNCoPiMnLyXHKwd0BeaGioOqCPHQKeskaEvv/++7J79271Of/vv//KE088Uew6CJ5wnTlz5siiRYtU4HX11VerIAmHb775Rj7++GP55Zdfit3ujTfekC5duqgsJJILDz30kPzzzz8225Gbm6vK02FhYSpwRZka7b/88svVdCVleeGFF1S/dXRh0oJI9L3D99iff/4pf//9d7F+7Ni+X3/9VW0vvudatmypHvv8+fPqctzHNddcI6NHj5Zt27bJnXfeqdpv6fDhw6ptyLLu2LFDfvzxRxUQ3n///WW2ldyfl8mgw14RvOGFPmnSJPN5L7/8snz77beyb98+m7fBh4vlBwwygLif5ORk9euLiDzD8pjl8sC/D6jTHw76UPpH9xdPl5WVpSbNxcwI2pxpCMTaP7fYJe3Z8+JwCfa3b6paBDl33XWXZGZmSvfu3aV///6qwtO5c+dSb4Mg7t5775X4+HhzBvC2226TQ4cOqVkhAJcj6Dt79qwK0gDBELJ+H330kfofp5GBQyZOg8fG9wOCRkD2bu7cuXLVVVep7xh812DwoTbRNgI/ZAMRxKIvujUEosjOLVmyRAYPHqzOe/XVV1WWEwFa8+bNze1FBg/BKzJ0yFhiu5AZ1IJPtBfVrscff1yeeuop+f3331VQrMH3IrKpiYmJqk0ICjE5OAJfDQJA7GM8Bl4r2n3i4O6vaU1KSopK5Bj5+9uwGUD80rOeIwgvckyEWhp0LsYLxfJARJ5nZ/xOm6fJPSE7dfr0adX3DwEaAiYEggh+NFrwhBItsm/jx49XfQQtS6Yo+2rBH9StW1cFN1rwp52H0qulPn36lPgfAZ4t6G+OIBNt0LKXKAMjWEEwVxbLgBbtQHu14M+6bbgvBHz9+vUzX47uTiiPa23Dce/evcvcFrQX+1FrKw7IImorx5B+GXYlEKS8X3nlFTVqrEOHDip1jz4it99+u6ubRkQutit+l83TRhPk56Myca567IpAhmfo0KHqgDlekbl6/vnn5dZbb1VZsSuuuEKNFMbnPgIuZLHuuOMOlX3TVj5BgGQJGTpb55WVKCgP+h326NFDdUGyVqdOnTJva9kWZ7SttPbec889qt+fNXx/kn4ZNgBEPwl8SKAjMH4xYe1LvMife+45VzeNiFxsb8KF7M2ehD1iVAgo7C3Dupv27dub597bvHmzCozeeustc+UH/cAdZd26dSX+R1nYFmQm0Y8OAzOcWUVCJhPrOaOPIQbGADKCGASilWrRRmRNy9oWtHfPnj2q/yB5FsOWgJF+R+dZrBOIfiNIl6NfBt4wRGRceQV5kpidaP4fp/MLLoz6JPeCMi5GxKJvHQYpoCz5888/q4n9MboWELwg+MEP/yNHjqh+fVofPkdAkIXHwxQ0GAGMx8dAEFsw00Tt2rVV2zAIBO1FyRoZtpMnTzqsTRiRjIwn+vqhTyCCOPSTRMkbmU+tz+DBgwfVdfbv368Gk1iWzWHKlClq0AkGfWCgCK6PfoMcBKJ/hg0AiYhsSclJses8cg/ok4Z+bO+8845cdtll0rFjR1XdQbCDEbOAEbro4oPBDbgc5VfM6uAojz76qGzatElN0YJEAh4L/eRsQbl55cqVqnyKEbjIwiEgQx9AR2cEMVAE/SPR3xGZPPQ9xFQyGBwCaAMG0CBTin2EoHj69Okl+h2uWLFCBbeYCgbbiEoZqmakb4YdBewIHEVE5HmOJh+VK+ddKWF+YWISk6TlpskfV/0hTSOaiicra8Qklc5dR8ASRwGXR5+dO4iInCQ5O1kdhwcUZmMQACZl278uKhGRHrAETERkIwCMDIiUiIAIdZolYCLyNMwAEhFZ0LJ9CP60HjLMAFJpylrCjcidMQAkIrKRAVTZP1Px84iIPAUDQCIiC1q2DyVgZgCJyFMxACQisqD197MsATMDSESehgEgEVE5GUAGgETkaRgAEhHZmgbG/8KkvAwAicjTMAAkIiplGhhMBK3Oy+EgECLyLJwHkIjIQmpOqjoO8w8zZwFTsrkUHBWu+oE15D0J1v6NjIwUd5lSx8vLS605bK8BAwZwFZZKYgBIRGQhIy9DHQf7BUuQb5A6nZmXyX3kxm699VYVOGiHWrVqyeWXXy47duxwddOI3BYDQCIiC1qwF+wbrIJAy6CQ3BcCvtjYWHVYunSp+Pr6yhVXXCHuLicnx9VNIINiAEhEVKTAVGAOAJH90zKAWXlZ6jJyXwEBAVKvXj116Nq1qzz55JMSExMj586dM19nypQp0rp1awkODpbmzZvLs88+K7m5ucXu548//pBevXpJYGCg1K5dW66++upSH/Ozzz5T5VMEnJCamirjxo2TkJAQqV+/vrzzzjslSpQoI7/00ktyyy23SHh4uNx9993q/F9//VU6dOigtgPXeeutt4o9FjKb8+bNK3YeHhslXMvy6W+//SYDBw5U29ilSxdZu3Ztsdvg+o0bN1aXY9sSEhLK3K/a/f70009y6aWXSlBQkNo/Bw4ckI0bN0rPnj0lNDRURowYUWxfFxQUyIsvviiNGjVS24TnZNGiRcXue8OGDdKtWze1r3E/W7duLfH4u3btUveNx6hbt66MHz9e4uPjy2wz2YcBIBFREQR6GmT/kAUEDAaxvMwwMA1OTrprDkVT8FRGWlqafPvtt9KyZUtVDtaEhYWpAGjPnj3y3nvvyaeffqqCNM2ff/6pgqKRI0eqYASB3UUXXWTzMV5//XUVZP79998yePBgdd4jjzwiq1evlvnz58s///wj//33n2zZsqXEbd98800VnOExEIRu3rxZbrjhBrnxxhtl586d8sILL6jzteCuIp5++ml57LHHVD86BLtjx46VvLw8ddn69evljjvukPvvv19djkDx5Zdftut+n3/+eXnmmWfU9iC7etNNN8kTTzyh9iO289ChQ/Lcc8+Zr4/zEcRiW1GKHz58uFx55ZVy8OBB83OEDG379u3V9mOb0W5LSUlJMmjQIBUkbtq0SQWQZ8+eVfuKqo6jgImIimilXi/xkkCfQBX44TSOcZlWEjaM3AyR6Q1c89hPnRbxD7H76gsWLFBZIkhPT1cZOJzn7X0hz4EARoMsGwKOOXPmqEAGXnnlFRWETZs2zXw9BGrWkEn85ptvZMWKFSprp2X/vvrqK/n+++/NAeGXX34pDRqU3H8Iah599FHz/8ga4jYI+gCBG4LUN954Q/VvrAhs06hRo9RpbAfah+Csbdu2KihDqVzbXjzOmjVrSmTmSrtfBHHw0EMPqcASAXK/fv3UeQgsLQNWBH7YT9if8Nprr8myZcvUIJqZM2eq/YQs4eeff64ygGjnyZMnZeLEieb7+PDDD1XwN336dPN5X3zxhURHR6sMJNpPlccMIBFRkczcC+VflL28vbwl0Dew8DIOBHFryGYhq4UDSosIVlA6PH78uPk6P/74owpYUCZGsIiA8MSJE+bLcVsteCsNslrIHK5atcoc/MGRI0dUOdkyYxgRESFt2rQpcR8od1rau3evOZDS4H9ky/Lz8yu0Hzp37mw+jSAY4uLizI/Tu3fvYtfv06dPhe8XpVjo1KlTsfO0x0lJSZHTp0/b3Ca0QWsL7hPBX2lt2b59uwoa8VxpBwSycPjwYbvaTaVjBpCIyMYIYA3KwAj+MpANMxrsB2TiXPXYFYB+dyj5WvbPQwCGYA1lTvSFQ6YNWTEEh7gM2T/Lvnbo31Ye9INDqRh94lACrgy0taLwg0RbmUZj3X8R/Pz8it0GkGmrKlv3a32eIx7HEsrEo0ePVtlDa1pwS5XHAJCIyDoALOr7p077BUtCVoIxM4D4oq9AGdadqAyut7dkZhY+byh1NmnSRPWR01hmBwEZKZQ1b7vttlLvFxk+9KFDKRV94bR+axhUgoAIAyMwyAKSk5NVqfKyyy4rs63t2rVTfQct4X+UOH18fNT/derUUSOcNcgOZmRU7EcJHgf9AC2tW7dOHA2DW1D6xjb079/ffD7+1zKkaAvK6FlZWeYsoHVbunfvrgbHoFyPfU2OxRIwEZGNErBGO82pYNxbdna2nDlzRh1QXnzggQfMGSRo1aqVKvci64fy4fvvvy9z584tMdDhhx9+UMe4DwzIsJV96tu3r/z1118qm6hNDI0BJhMmTJDHH39clS13796t+sUhCNUyZqVBf0AEnhgdjIARfQnR/81yUAT6DeI8DBzBgIh77723WAbOHg8++KDq74f+eQggcX/29P+rDOwH7DuU3ffv36+ypSixo/8gYBAJ9stdd92l+jtif6JdliZNmiTnz59X/Q0RWON5W7x4sQrQK1oap5IYABIRlVMCtgwOyT0hkEFZEAf0c0PA8PPPP6tpWAAjUCdPnqyyd5iSBBlBbdCFBtfFbTCKF9dB0IX+hLZccsklqhSMfoQffPCBOu/tt99W/dgwunXIkCGqzxsyXZb93GxBpgslZQSnHTt2VKNpMYWK5QAQlKox+AElaARPCA4xlUtFXHzxxaokjsEgGNyCEcyWA2McCcEmRkUjuEVfQTw/2K8IxAH9+TDlDoJsDPRAZtY62NayiAj2hg0bpu4HU+pg+hvLwT1UOV4m604FZDd0dEU/EqT5kfImIn2bf3i+PL3qaenboK98PPRjdd7df98ta2PXyvRLpsvoFoXZJE+EUtzRo0elWbNm5QYsZB+MRm7YsKEK3pANJPd5Tafw+5t9AImINFqWz7oPoLrMiH0AqUJQnt23b5/q54bEALJ4MGbMGO5JcjvsVUlEZEcJ2JCjgKnC0I8Nfd78/f2lR48eapJkrChC5G4YABIRFbFcBk6jnWYGkMqDvmxY1YJID9iLkoioiJbls1UC5ihgIvIkDACJiIpoQV6Q34UMIEvAROSJGAASERXRyryWGUCWgInIEzEAJCKyKgFb9gFkCZiIPBEDQCIi6xKwrZVAOAqYiDyIoQNArC+IpWisD1h+hoiMJysvq0QAGOhbOIFsVn7hZUREnsDQASCWCsLi2trhn3/+Uedff/31rm4aEblAdn62Og7wCTCfp53WgkMiW7Bs21VXXVXlnfPCCy+oZeg8CRIr8+bNq/Z9SWUzdABYp04dqVevnvmwYMECadGihfTv39/VTSMiF9CyfFrWT532CSwWHJL7QcCgVXD8/PzU0l9PPPGEWgpMb4ER1vhdunSpy9pExsGJoIvk5OTIt99+qxavxpuSiIwnO89GBtC38DQDQPd2+eWXy5dffim5ublqMuYJEyaoz/LXXntN9CQ0NFQdiJzN0BlAS/gVlpSUpH5JliY7O1stIG15ICLPywBaBoBaBpAlYPcWEBCgKjnR0dGqfDhkyBBztx4oKCiQGTNmqOxgUFCQdOnSRX755Rfz5YmJiTJu3DhVGcLlrVq1UgGlZufOnTJo0CB1Wa1ateTuu++WtLS0MvuYv/vuu8XOQ2kXJV7tcrj66qtVoKr9b10CRruxpnCjRo3UNuKyRYsWmS8/duyYuv1vv/0mAwcOlODgYLVta9euLXN/4TYff/yxXHHFFeo27dq1U7c5dOiQDBgwQEJCQqRv375y+PDhYrebNWuWqpRhqbs2bdrIN998U+zygwcPymWXXSaBgYHSvn37Ys+BJiYmRm644QaJjIyUmjVrqrWSsR1UvRgAFvn8889lxIgR0qBBg1J3Fj48IiIizAd80BCR59CyfJYlYC0YNGIG0GQyqdHPrjjgsStr165dsmbNGhWkWH5+f/311/LRRx/J7t27ZfLkyXLzzTfLihUr1OXPPvus7NmzRxYuXCh79+5VgY62hm96eroMHz5catSoofqO//zzz7JkyRK5//77K91G3A8gyEQfdO1/a++995689dZbao3hHTt2qHZceeWVKtCy9PTTT6vy8bZt26R169YyduxYycvLK7MNL730ktxyyy3qNm3btpWbbrpJ7rnnHpk6daps2rRJPQeW2zh37lx56KGH5NFHH1X7GNe97bbbZNmyZeZg9ZprrlH7ff369WpfT5kypdhjIkOLbQgLC1PrJK9evVplPJHBRSWOqg9LwCJy/Phx9WbGL6iy4E2BErEGGUAGgUSeXQI28ihgTIzd+/veLnns9TetN8/BaA/04UYggaAH1Rpvb2/58MMP1WX4f/r06epzvk+fPuq85s2by6pVq1QWDP2+T5w4odby7dmzp7pcy8jB999/r/oTIoBEZgxw36NHj1Yl5rp161Z4+5BpBGTBkLksDQI/BFE33nij+h+Ph4AL2cWZM2ear4fgb9SoUer0tGnTpEOHDiqbh8CuNAjekIkDPAb2DQJhBGiAYA/XsWwLqmT33Xef+h/fh+vWrVPnI/uI/btv3z5ZvHixOZmC/Y7kiubHH39UgeJnn31m7m6FIBj7Yfny5TJs2LAK70uqHAaARS++qKgo85unNEi/40BEnievIE/yTHklp4EpKgHj8vyCfPHx9nFZG6l0CECQtUO27p133hFfX1+59tpr1WUIhDIyMmTo0KHFboOME4I+mDhxorr+li1bVBCCMjJKoICMIMqqWvAH/fr1U4HM/v37KxUA2gNJhtOnT6vHsoT/t2/fXuy8zp07m0/Xr19fHcfFxZUZAFreRtuGTp06FTsPgS/aER4ervYDSt/WbUGWEnA5kiKWlTQt4Nag3Xg+kAG0hMexLjeTcxk+AMQbGAEgOgzjA4OIjMmyxGtrEIh2nWBv+7NSeodAGJk4Vz12RSA4a9mypTr9xRdfqIANXXvuuOMOc1+9P//8Uxo2bFjsdtqPemSpUA3666+/VL+1wYMHqzlhkd2qDGQgrcvYKH86C0Y/a7TMGr7fKnqbytxPReC56NGjh3z33XelZkWpehg+4kHKGqn/22+/vZp2ORG5I8tBHrbmAVTXyc+qUFlS7xAA6HF7EXw99dRTqkSJfm0YjIBAD5/1ZU3zhQAEyQAcLr30Unn88cdVAIgBErNnz1bZRS0LiL5reBwMhCjtvtC3T4Ms2tGjR4tdB8FWfn5+qe1B1g3ZNDyWZbvx/0UXXSTVDfsBj439Y9kW7F/tcgzwwHZrWUiUiC11795dlYFRdcP2kesYfhAIUv34lYZOs0RkXJaTQFtOBeXt5S3+3v7F+giS+8OE/j4+PqqfHMqN6COHgR9fffWVKjWi1PvBBx+o/+G5556T33//XZUnMUgEfQoR0ABGB2NUKwIfDH5AH7wHHnhAxo8fX2r5FyOGMUIWAx0wghi3RXssoZ8h5vw7c+aMGoVsC4JQ9PtD0IRy85NPPqkGbaB/XnVDWxAIo9SOQShvv/226juPfQsYeY3vUmwrSr3YdgxOsYR9icE1GPmLyxEUo+/fgw8+KCdPnqz2bTIywweARESlTQFjXQY24kAQvUKXHoxgff3111XmDiNeMcABo4ER2GHUKUrCmBYGMHIVA/3QLw7TmCBYmzNnjroM06RgYMP58+elV69ect1116kSsTbIxBbcF7J2mGYF/cvRpxDTp1jC6F6Um9FvTuuLaA2BETKZGHmL/nmYAmb+/Plqmprqhm1Afz9kRTHIBANo0IUK08YAMqIYKZyZmakylHfeeae88sorxe4D+3LlypXSuHFjNWIYzwXK9OgDyIxg9fIyVWWsvcEhpY/pYJKTk/nCJdK5vQl75YYFN0hUUJQsvaH4SgyDfhok5zLPyc+jf5a2NUvvVK9n+AJGNgYBEbJdRJ78mk7h9zczgERExUrAFoM+NFwPmIg8DUvARETllIC1uQCNOBk0EXkmBoBERBYDPLR5/ywZeTUQIvJMDACJiCwzgCwBE5EBMAAkIrJcB9hGBpAlYCLyNAwAiYgsJoK2OQ2Mj3GmgeHEEOQp+FouGwNAIqJyRgFrWUFPnghaWwIMa+YSeQLttWy5vB1dYPil4IiIyisBG2EiaEx8HBkZKXFxceYJey1XRCHSU+YPwR9ey3hNW6/AQoUYABIR2VkC9vRRwPXq1VPHWhBIpGcI/rTXNJXEAJCIyDIDWDTnn9FKwICMX/369SUqKkpyc3Nd3RyiSkPZl5m/sjEAJCIqLwNogBKwJXxx8suTyLNxEAgRkb0ZQA8vARORcTAAJCKyyO75e/uX2B9cC5iIPA0DQCIiEcnJzyl3EIh2HSIivWMASERkEdz5+5TMAGrn5RQwACQiz8AAkIjIIrizFQD6+RROJMs+gETkKRgAEhGVkwHUSsC5+ZwahYg8AwNAIqJy+gBqA0PYB5CIPAUDQCIiixKwn3fJdUO1rGB2AaeBISLPwACQiMjOQSAsARORp2AASERk5zQwHARCRJ6CASARkUUAaKsErJ3HPoBE5CkYABIR2TkKmAEgEXkKBoBERBaDQGyOAuZE0ETkYRgAEpHhmUwmuwaBoA8grktEpHcMAInI8PIK8sQkpnIDQO26RER6xwCQiAzPco1fbdJnS5ZlYY4EJiJPwACQiAzPcnCHzbWALUYGWwaLRER6xQCQiAxPy+r5evuKt1fJj0Wcx6lgiMiTGDoAPHXqlNx8881Sq1YtCQoKkk6dOsmmTZtc3SwiqmbaCh+2yr8lRgJbZAuJiPTKVwwqMTFR+vXrJwMHDpSFCxdKnTp15ODBg1KjRg1XN42IqplW1rVV/rXsB5iem84+gETkEQwbAL722msSHR0tX375pfm8Zs2aubRNROTaEnBZAaC5BMw+gETkAQxbAp4/f7707NlTrr/+eomKipJu3brJp59+6upmEZELmOcALKMEzNVAiMiTGDYAPHLkiMyaNUtatWolixcvlokTJ8qDDz4oX331Vam3yc7OlpSUlGIHItK/3ILccjOA7ANIRJ7EsCXggoIClQGcPn26+h8ZwF27dslHH30kEyZMsHmbGTNmyLRp06q5pURUXSVgW8vAaTgKmIg8iWEzgPXr15f27dsXO69du3Zy4sSJUm8zdepUSU5ONh9iYmKqoaVEVF0lYD+fC/P9WWMJmIg8iWEzgBgBvH///mLnHThwQJo0aVLqbQICAtSBiDx0FLA908BwEAgReQDDZgAnT54s69atUyXgQ4cOyffffy+ffPKJTJo0ydVNIyIXZQDLKgFrASCXgiMiT2DYALBXr14yd+5c+eGHH6Rjx47y0ksvybvvvivjxo1zddOIyA1LwFp2kBNBE5EnMGwJGK644gp1ICJjM88DyGlgiMggDJsBJCKyXgquzFHARdlB9gEkIk/AAJCIDM/epeCAfQCJyBMwACQiw9OCOm2uP1u04FDLFhIR6RkDQCIyPHtKwBwFTESehAEgERmePSVgjgImIk/CAJCIDM9cArZnJRBOBE1EHoABIBEZXkUmguY8gETkCRgAEpHhaX0A7VkKjqOAicgTMAAkIsMzTwRtRx9AjgImIk/AAJCIDM+uQSDMABKRB2EASESGV5ESMAeBEJEnYABIRIanlYDLHATCEjAReRAGgERkeFpWz55pYDgIhIg8AQNAIjI8bWqXsvoAasEhS8BE5AkYABKR4dkzD6B5Iuii6xIR6RkDQCIyPPMoYHsGgTAAJCIPwACQiAxPC+rK6gPIaWCIyJMwACQiw7NrKThtFHBB4ZQxRER6xgCQiAzPPAikjBIwRwETkSdhAEhEhmYymeyaBkYrAReYCiSvIK/a2kdE5AwMAInI0CxLumWWgC2miOFAECLSOwaARGRolsFcmWsBW5SHGQASkd4xACQiQ7Nc2cPPu/QSsI+3j/h6+Za4DRGRHjEAJCJD00rACP68vcr+SORqIETkKRgAEpGh2bMMnIargRCRp2AASESGppVzy5oCRqNdh30AiUjvGAASkaGZl4GzIwPI1UCIyFMwACQiQ8vNz61wAMjVQIhI7xgAEpGhVagEXBQAsgRMRHpXOKeBjmRnZ8v69evl+PHjkpGRIXXq1JFu3bpJs2bNXN00IvLwQSAsARORp9BNALh69Wp577335I8//pDc3FyJiIiQoKAgOX/+vAoKmzdvLnfffbfce++9EhYW5urmEpEn9gHUBoEU3YaISK90UQK+8sor5X//+580bdpU/v77b0lNTZWEhAQ5efKkygIePHhQnnnmGVm6dKm0bt1a/vnnH7vu94UXXhAvL69ih7Zt2zp9e4jI/TKAZS0Dp+E0METkKXSRARw1apT8+uuv4udne5Z+ZP9wmDBhguzZs0diY2Ptvu8OHTrIkiVLzP/7+upilxCRgwNAbZJnuyaCtlg+johIj3QR7dxzzz12X7d9+/bqYC8EfPXq1atky4jIY0rAdgwC0TKAXAqOiPROFyVgZ0L5uEGDBiqDOG7cODlx4oSrm0REbloC5kTQROQpPCYARPl30KBBFbpN7969Zfbs2bJo0SKZNWuWHD16VC699FLVx9AWDDZJSUkpdiAi440CZgmYiPROFyVgezRs2FC8vSsWz44YMcJ8unPnziogbNKkifz0009yxx13lLj+jBkzZNq0aQ5pLxG5WR9Abz/7A0COAiYinfOYAHD69OlVvo/IyEg1ivjQoUM2L586dao88sgj5v+RAYyOjq7y4xKR62j9+SpSAtZWDyEi0iuPKQE7Qlpamhw+fFjq169v8/KAgAAJDw8vdiAifdOWdatQCZgZQCLSOd1lAG+//fYyL//iiy/svq/HHntMRo8ercq+p0+flueff158fHxk7NixDmgpEXlaCVibBoajgIlI73QXACYmJhb7H6uC7Nq1S5KSkio8CAQTSSPYw6TSWFLukksukXXr1qnTRGQMFSkBcyJoIvIUugsA586dW+K8goICmThxorRo0aJC9zVnzhwHtoyIPL4EzD6AROQhPKIPIEb/YnDGO++84+qmEJERpoFhH0Ai0jmPCAABgzfy8vJc3Qwi0mkJ2J4AUOsnyHkAiUjvdFcCtpyGBUwmk1r7988//1STQRMROWspOE4ETUSeQncB4NatW0uUfzFo46233ip3hDARkTVtTj97MoDmQSAsARORzukuAFy2bJmrm0BEBi0BMwNI7iY1K1eC/HzE18djenRRNdFdAEhE5JRBIN4V6APIDCC5UH6BSWavOSZfrDoqp5Iyxd/HW4Z2qCtPXt5WomsG87khu3jMT4annnqKJWAiqpZRwFwKjlwlIydPbv1yg7y0YI8K/iAnv0D+3BErI977T9YdSeCTQ8YKAE+dOiXHjh1zdTOISGe0bF5FJoLmSiDkCnn5BTLx2y3y38F4VfZ9+aqOsuOFYbLggUukR5MakpadJ7d9uVH2xqbwCSLjBIBfffWV/Pvvv65uBhHpdSm4omXeysJpYMiVPvj3kKw4cE4Ff9/e2VtuvriJhAf6SceGEfLdnb2lb4takpmbL/d9t0Uyc/L5ZJExAkAiImf3AeRE0OQqh+JS5cNlh9TpGdd0Uhk/S4F+PvLhTd2lfkSgHI1Pl/eWHnRRS0kvdDkIJD09XVasWCEnTpyQnJzCD2/Ngw8+6LJ2EZFnl4DZB5Bc5eU/96rBH0Pa1ZWrujW0eZ2aIf7y0piOcufXm+Sz/47I2IuipUmtkGpvK+mDLucBHDlypGRkZKhAsGbNmhIfHy/BwcESFRXFAJCInDcIpChLiD6AmITey8uLe5ucDmXf5fvPiZ+Plzw9ql2Z1x3Svq5c1rqOrDxwTt7554C8e2M3PkPkGSXgyZMny+jRoyUxMVGCgoJk3bp1cvz4cenRo4e8+eabrm4eEelIgalAcgvsnwhau45JTJJn4tKT5Hz4ofHawn3q9IQ+TaVZ7fIzek8Mb6OO/9gRax4pTKT7AHDbtm3y6KOPqhVAfHx8JDs7W6Kjo+X1119XU8EQEdlLC/4q2gfQcgURImdaezhB9sSmqIEf9w9qaddtMCgEA0JQMv5m7XE+QeQZAaCfn58K/gAlX/QDhIiICImJiXFx64hIj+XfipaArW9L5CyfrTqqjq/v2Ugig8t/jWpu7dtUHf+w4QRHBJNnBIDdunWTjRs3qtP9+/eX5557Tr777jt5+OGHpWPHjq5uHhHpiOV8ftoUL2Xx8fYRHy8fdZqrgZCzHT6XJv/uixN0Nb2tX7MK3XZwu7oSXTNIkjNzZe7WU05rI+mX7gLA6dOnS/369dXpV155RWrUqCETJ06Uc+fOySeffOLq5hGRjmhlXGT27B3QoWUKORk0OdtXawoXNxjctq5dff8s+Xh7qT6DWhaQSPejgHv27Gk+jRLwokWLXNoeItIvLYtnT/lXg+tm5mWyDyA5VXZevvy+7bQ6fUufJpW6D0wXM2PhPtl5KlmOnEuT5nVCHdxK0jPdZQCJiBxFy+JVKAAs6gfIEjA509K9cap8Wy88UPq1rF2p+6gdGiCXtiq87fzthcEkka4CwMsvv1xN91Ke1NRUee2112TmzJnV0i4i8pAScAUzgMBBIORMv24+qY6v7t5QlXMra0zXBup4/rbTakoZIl2VgK+//nq59tpr1UhfzAGIMnCDBg0kMDBQzQe4Z88eWbVqlfz1118yatQoeeONN1zdZCLSUwnYjilgrAeLsA8gOcu51GxZfuCcOn1t90ZVuq+h7etJoN9OORKfLrtOpUinRhEOaiXpnS4CwDvuuENuvvlm+fnnn+XHH39Ugz2Sk5PVZei43b59exk+fLgaHdyuXdmzpBMRVaUErC0Zx3kAyVn+2H5azeHXJTpSWkZVrd9eaICvWj5uwY5YWbDjNANA0lcACAEBASoIxAEQAGZmZkqtWrXU3IBERM5cBq5ECbgoe0jkaAt3xarjMV0Ky7dVNbxDPRUALtl7VqaOZJKEdNQH0BaUg+vVq8fgj4gcMg1MRUvA7ANIzhCXmiWbjieq05d3rOeQ+8TawL7eXnL4XLoci093yH2S/uk2ACQicskoYGYAyYkW7z4rGKvRNTpSGkQGOeQ+I4L8pFfTmur00n1xDrlP0j8GgERkWJWdB1DdlkvBkRMs3FlY/h3ZyTHZP83gdlHqeOnesw69X9IvBoBEZFhaEKcN7LCHdl0GgORo59NzZP3R8+r0iI6FK145CgaCwIaj5yUlq7DrAxkbA0AiMqzKDAJhH0BylmX74tTo3/b1wyW6ZrBD77tp7RBpUSdE8gpM8t+BeIfeN+mT7gLACRMmyMqVK13dDCLypD6AFRgEwj6A5Cz/7o8rVq51NAwGgdWHGQCSDgNATP8yZMgQadWqlUyfPl1OnTrl6iYRkYFKwOal4NgHkBwoL79AVhZN/jywrXMCwEuKlpRbfYgBIOkwAJw3b54K+iZOnKgmhW7atKmMGDFCfvnlF8nNZb8GIrIfB4GQu9h8PFFSs/KkZoi/dGkU6ZTH6N28llpW7nhChsScz3DKY5B+6C4AhDp16sgjjzwi27dvl/Xr10vLli1l/Pjxanm4yZMny8GDByt8n6+++qpaVeThhx92SpuJyP1wGhhyF8v2F2b/+reuU6W1f8tbFaRbdGFwySwg6TIA1MTGxso///yjDj4+PjJy5EjZuXOnWhrunXfesft+sITcxx9/LJ07d3Zqe4nIA0rAnAaGnDQABAa0Keyn5yz9isrAq1gGNjzdBYAo8/76669yxRVXSJMmTdT6wMjanT59Wr766itZsmSJ/PTTT/Liiy/adX9paWkybtw4+fTTT6VGjRpObz8R6XwpuKI+gLkF7HJCjnEqKVP2n00VJP6QAXSmS1oVBoBrDidIQYHJqY9F7k03awFr6tevLwUFBTJ27FjZsGGDdO3atcR1Bg4cKJGR9vWhmDRpkowaNUoNLHn55Zed0GIi8sRRwNptiRyV/eveuIZEBtv/WqwMrDAS7O+j5hw8EJcqbeuFO/XxyH3pLgBEaff666+XwMDAUq+D4O/o0aPl3tecOXNky5YtqgRsj+zsbHXQpKSk2NlqInJHLAGTOwWAzhr9a8nPx1sFmigBbzx6ngGggemuBLxs2TKbo33T09Pl9ttvt/t+YmJi5KGHHpLvvvuuzGDS0owZMyQiIsJ8iI6OrlDbicgDRgFrJeB8loCp6rLz8lU5tjr6/2m0dYE3Hkuslscj96S7ABD9/DIzM0ucj/O+/vpru+9n8+bNEhcXJ927dxdfX191WLFihbz//vvqdH5+fonbTJ06Vc1DqB0QRBKRfnEUMLna1hNJkpmbL7VD/dUKINWhV9PC/u4bj50Xk4n9AI1KNyVglFvxQsUhNTW1WNYOwdpff/0lUVH2p88HDx6sRgxbuu2226Rt27YyZcoUNarYWkBAgDoQkWfQsngVGQXs5+OnjtkHkBxhTdFo3L4taqupyKpDt8Y1xNfbS2KTs+RkYqbDl50jfdBNAIh+fXhz4NC6desSl+P8adOm2X1/YWFh0rFjx2LnhYSESK1atUqcT0SeqTIZwADvwmCRJWByBG06Fm2VjuoQ5O8jHRtGyLaYJNl0/DwDQIPy1VPfP2T/Bg0apKaBqVmzsA8D+Pv7qylhMBE0EZG9WAImV0rNypXtJ5PV6X5F07NUF5SBEQBuOJooV3drVK2PTe5BNwFg//791TFG9zZu3NgpqfLly5c7/D6JyH1pc/lxImhyhfVHzkt+gUma1gqWhpFB1frYGAjy6X9HVT9AMiZdBIA7duxQZVlvb281+MK6754lruZBRM6cB9DP26/YFDJEVS3/aqtzVKeeRSOBD8WlqTkBsQYxGYsuAkBM9nzmzBk1yAOnkf2zNXIJ59savUtE5LCVQLgUHDnImsPV3/9Pg4CvZVSoCgA3HTsvwzrUq/Y2kGvpIgBE2bdOncL5keyZ4JmIyFkTQWvX1eYQJKqMuJQsOXA2TdCbqU+LWi7ZiSgDIwBEGZgBoPHoIgDEAA9bp4mIqn0QSFG5mCVgqorVRdm/jg0inL78W2kualZDfthwghNCG5QuJ4L+888/zf8/8cQTaoqYvn37yvHjx13aNiLSjwJTgXkQSEUCQG0eQGYAqSpWH0pwWf8/TY/Ghf0A95xOUSuSkLHoLgCcPn26BAUVjpZau3atfPjhh/L6669L7dq1ZfLkya5uHhHphBb8VXQQiBYs5hXkqSCSqKLQh321eQCIa8q/EF0zSPUFzMkvUEEgGYvuAkAsv9ayZUt1et68eXLdddfJ3Xffrdbp/e+//1zdPCLSCcuVPCrTBxBYBqbKOBqfrlbh8Pf1Nq/L6woYONk1OlKdxpyAZCy6CwBDQ0MlIaEwdf7333/L0KFD1WksDWdrjWAiIlu04M1LvMTX2/7u0JbZQpaBqTK07F/PJjUk0K/ksqPVqVtRAIg1iclYdDEIxBICvjvvvFO6desmBw4ckJEjR6rzd+/eLU2bNnV184hIh1PAVGRiectgkRlA0tv8f9a6NmYG0Kh0lwGcOXOm9OnTR86dO6eWhMPavbB582YZO3asq5tHRB48AhgQLGpZQK4HTBWFlT/WHnb9ABBN50aFAeCJ8xmSkHahWwR5Pt1lADHiFwM/rE2bNs0l7SEi48wBqEHQiPKvZT9CInvsOpUsKVl5EhboK50aRrh8p0UE+UmLOiFy+Fy66gc4uF1dVzeJqonuAkBISkqSDRs2SFxcnBQUFBT7ZT5+/HiXto2IdFYCrsAIYI3KGuayDyBVvvzbp3kt8fF2/Jr2ldGtcQ0GgAakuwDwjz/+kHHjxklaWpqEh4cX67vDAJCInF0CtrwNS8BUUe5U/tVgJPAvm09yJLDB6K4P4KOPPiq33367CgCRCUxMTDQfzp8/7+rmEZFOaCN4K1UC1lYD4XJwVAFZuflq2TVXz/9nzTwVzIkkKSgwubo5VE10FwCeOnVKHnzwQQkODnZ1U4jIQ0YBV5R2G/YBpIrYciJRsvMKJCosQFrUCXWbnde2XpgE+nlLanaeHIlPc3VzqJroLgAcPny4bNq0ydXNICIDl4C1rCGngaGKWFO0/FvfFrUqNPWQs/n6eEvnhpwP0Gh01wdw1KhR8vjjj8uePXukU6dO4udXuC6n5sorr3RZ24jIGKOAtdtk5WU5vF3kudYcLhwA0teN+v9Zzge44dh51Q/w+p7Rrm4OVQPdBYB33XWXOn7xxRdLXIZfVPn5XNCaiJw7ClgLAFkCJnulZuXK9pPJbjcARKNNSYNpasgYdBcAWk77QkTkkhKwLwNAqpgNR8+rSaCb1gqWhpFBbrf7OjcqDAD3xqZKTl6BWqeYPJuun+GsLJZfiKhycgtyq9wHkBlAstfqov5/fVq4X/YPGtcMlvBAX8nJL5ADZ1Nd3RyqBroLAFHifemll6Rhw4YSGhoqR44cUec/++yz8vnnn7u6eUSkE1rwVpU+gBwEQhXt/+dO079Yd6HSloXbyTKwIeguAHzllVdk9uzZ8vrrr4u//4Vf7h07dpTPPvvMpW0jImONAs7KZxWCyhefli37zqSaVwBxV52KysA7ivoqkmfTXQD49ddfyyeffKJWA/Hx8TGf36VLF9m3b59L20ZE+qGt4lGVQSDMAFJFVv/AfHu1Qiueca4unYsGguw8leTqplA10OVE0C1btrQ5OCQ3t/ADnYioWjKAnAaGKlT+dc/+f9YZwP1nUtWqJeTZdBcAtm/fXv77778S5//yyy/SrVs3l7SJiPSHfQCpuqwxr//rvuVfwOjkmiH+kptvUkEgeTbdTQPz3HPPyYQJE1QmEFm/3377Tfbv369KwwsWLHB184jICKOAi6aBYR9AKs/JxAw5npAhPt5e0qtpTbfeYRgIgvkAVxw4JztOJUuXojWCyTPpLgM4ZswY+eOPP2TJkiUSEhKiAsK9e/eq84YOHerq5hGRTnApOKrO5d+6NIqQsMDiK1e5I20+wJ0n2Q/Q0+kuAwiXXnqp/PPPP65uBhEZfSk4jgKmcqzWSf8/TceigSAcCez5dJcBbN68uSQkFP6ispSUlKQuIyKq0FJwVRgEwlHAVBaTyWTu/9fXTSeALi0DeDAuTTJzOBDEk+kuADx27JjN9X6zs7NVv0AiImeXgLXbcCUQKguWVTuXmi1Bfj7SvYk++tPVCw+U2qEBatm6PbEprm4OOZFuSsDz5883n168eLFERBT+SgEEhEuXLpWmTZu6qHVEpDc5BUUlYO+Kl4ADfQLVcXZeYRBJZMvKg+fU8cXNa0qA74V5a91Z4YogEfLvvjjVD7BHkxqubhIZPQC86qqrzC9OjAK25Ofnp4K/t956y0WtIyIjlYDNGcACBoBUupUHCgPA/q3r6Go3YSQwAkCMBCbPpZsSMKZ8waFx48YSFxdn/h8HlH8xFcwVV1xRofucNWuWdO7cWcLDw9WhT58+snDhQqdtAxF5Rgk40JcZQCpbRk6ebDqWqE5fprMAUOsHuIsBoEfTTQCoOXr0qNSu7ZjOtI0aNZJXX31VNm/eLJs2bZJBgwapaWZ2797tkPsnIg/PABYFkUTW1h1JkJz8AmlUI0ia1Q7RXQYQDsWlSXp2nqubQ0YvAVtCfz8ctEygpS+++MLu+xk9enSx/1955RWVFVy3bp106NDBYe0lIs+aBsbcB5ABIJVixf5z5uwfui7pSVR4oBoMciYlSw0EcfcJrMkgGcBp06bJsGHDVAAYHx8viYmJxQ6VhYEkc+bMkfT0dFUKtgWl5pSUlGIHItInjgImZ1p5sHD+v8ta6av8a70uMOcD9Fy6ywB+9NFHMnv2bBk/frxD7m/nzp0q4MvKypLQ0FCZO3euWm/YlhkzZqgAlIg8aCk4b/8qZQAx15veMjzkXDHnM+RofLr4entJXzdf/7c0nRtGyD97znJFEA+muwxgTk6O9O3b12H316ZNG9m2bZusX79eJk6cqEYY79mzx+Z1p06dKsnJyeZDTEyMw9pBRNUHQZuWAaxMCVjrA1hgKpA8E/tIUXFYSxe6N64h4TpY/q3MDCAHgngs3QWAd955p3z//fcOuz9/f39p2bKl9OjRQ2X4unTpIu+9957N6wYEBJhHDGsHItIfBG0I3qq6EghwLkCyttzc/08fq3+UNRDkyLl0SckqzJaTZ9FdCRil2k8++USWLFmipnDBHICW3n777SrdvzatDBF5rtz8C19oVQ4A87MlVEId1jbSNyyftupQYQA4qG1d0ataoQHSMDJITiVlyu5TKdKnhT5L2eRBAeCOHTuka9eu6vSuXbuKXVbRfjgo6Y4YMULNLZiamqoyi8uXL1crjRCR57IcvVuZPoD4rMHtsJoI1wMmS6sOxUtWboEKntrVD9P1zkEWEAHgzlNJDAA9kO4CwGXLljnsvjCNzC233CKxsbFqaTlkFBH8DR061GGPQUTuRwvafL19xce7ckt0BfgGqD7JWflZDm4d6dk/e86o46Ht6+p+cBD6AS7afYYjgT2U7gJAR/r8889d3QQicuUk0JXI/lmWgVMllRlAMssvMMnSvXHq9JB2+i3/Wq8IspMDQTySbgLAa665xq7r/fbbb05vCxHpW1VGAGu02zIDSJptMUmSkJ4jYYG+0ru5/idP1gaCHE/IkOSMXIkI1ueIZtJ5AIgSLRGRQwNA36oHgOwDSBrMmwcD2kSJn4/uJtkoITLYXxrXDJYT5zNUFvCSVvod1Uw6DgC//PJLVzeBiDyElrXTJnSuUgYwj30AqdCSvWfN/f88BfoBMgD0TPr/iUJEVEHa3H2OKAEzA0iAlT8OxaWp1T/6t9bn8m+lrQgCGAlMnoUBIBEZNgPoiBIw+wASLNpVOPoXff8igjynrxzXBPZcDACJyLB9AKtUAi4KHpkBJPhj+2l1PKpTA4/aIR2LMoAnEzPlfHrh6HnyDAwAichwtH57jigBW04qTcZ0+Fya7IlNUeXfyzvWE0+CtYyb1w5RpzkdjGdhAEhExs0A+lZ9EAgDQFqwPVbtBIySrRlS+bkl3b0MvPMk+wF6EgaARGQ4jpwHkAEgLdhRWP69orNnlX+t5wPccTLZ1U0hB2IASESG48gSMKeBMbY9p1PkYFya+Pt4y7AOnjP9i60AkCVgz8IAkIgMx5ElYA4CMbafN8eY5/5DfzlP1KFhhGBZ49jkLIlL5byXnoIBIBEZdxqYqmQAi0YBcxoY48rOy5d5W0+p09f3bCSeKjTAV1rUCVWnd3FdYI/BAJCIDDsRtCNWAmEG0LiW7o2TxIxcqRceKJe28pzJn8uaEJr9AD0HA0AiMhxOBE2O8NOmwvLvdT0aiY+3l0fv1AsjgTkQxFMwACQiw3HkKGBmAI3peEK6rDhwzhwAerrOWgDIErDHYABIRIbjiBKwv0/hfG+cBsaYZq85JiaTyIA2daRp0UTJnqx9/QhBkjMuNVvOpnAgiCdgAEhEhuOIErAWPDIANJ7UrFz5edNJdfr2fs3ECIL8faR13TB1mv0APQMDQCIyHEesBcwMoHH9tOmkpGXnScuoULm0VW0xCvN8gFwRxCMwACQiw9Emb9aCuCplAIvKyWQMWbn58tl/R9Tp2/o1FS9MkGcQWj/AHewH6BEYABKR4TADSJX1w4YTakJkTP1ybXfPH/xhqVOjSPNIYBM6QJKuMQAkIuOOAq5KH8CiVUTYB9A4MnLyZOayw+r0A4NbSqCfjxhJ23ph4uvtJQnpOXI6mQNB9I4BIBEZtgTMPoBUEV+uPibxadkSXTNIru8Rbbidh4DXPBAkJsnVzaEqYgBIRIbjiHkAOQrYWE4kZMgH/x5Upx8Z2lr8fY359dklurAf4HZOCK17xnwFE5GhadPAaGXcyuAoYONAf7dnft8lWbkF0qd5Lbmqa0Mxqm7RNdTx1hOJrm4KVREDQCIylPyCfMkryHPYSiC4L9wnea4fN8bIygPnVNbvlas7Gmrkr7WujYsGgpxKlrz8Alc3h6rAtyo3JiLSG8tBG44IALX7DPYOrnLbyD5nkrPkn71nZdXBc3I8IUPOpWar4Cwy2F/a1QuTbo0jZVC7utIwMqjKuxSZrud+361OTx7SWprXCTX009SyTqiEBfhKanaeHDibJu0bhLu6SVRJDACJyFAy8zLNp6tSAra8LUrKwX4MAJ3t4NlUmbnskMzffloKbMxCgulZ9samyG9bT8mzv++W7o0j5abeTeSKzvUrNWL3WHy63PvtZsnJL5DhHerKPZc1F6Pz9vaSLtGRsupQvGyNSWQAqGMMAInImP3/fALF26vyvWBwW2QBkf3TRhWT8yZffn/pQfl45RHJL4r8ENwNaV9X2tcPl7rhgZKXb1Jr1O4+nSKrDp2TTccTZcuJJHV4acEeub5HIxnfp4k0qWXfur27TyfLhC82SHxajrSKCpW3buiqgh8SlWFVAeCJJBnXuwl3iU4xACQiQ8nMzaxy9k+D+0AAaJlVJMfSsnD7zqSq/4e0qysPD2klHYuWJbPUSSJUUPjQkFYSl5IlP28+Kd+vPyGnkjLls1VH5fPVR2VA6zoqaLm0dW0J8C2ZFczJK5Cv1x6TN//erwZ9IMD86vaLJDSAX5eWASBwIIi+8RVNRIbMAAb5Vr1/GO4jOTuZGUAnWXUwXu77brOkZOVJ7VB/efmqTnJ5x3p23TYqPFAmDWwp9/ZvISsOxMnXa4/L8v3nZFnRAf3Y+rSopQLJGsF+kp1XIHtiU2TlgXg11x9gnd+Z47pLeKCfszZRl7oWjQQ+fC5dkjNyJSKY+0ePGAASkaFo2TqHZACL1gPOyMuo8n1RcQt2nJbJP26T3HyTyjjNGtdD6kVU/Dnz8faSQW3rqsPR+HT5dt1x1YcQA0f+3nNWHazVDQ+Qhwa3lht7RbPsa0PNEH9pUitYDcDZfjJJLmtdhy9fHTJ0ADhjxgz57bffZN++fRIUFCR9+/aV1157Tdq0aePqphGRkwNAR2UAgX0AHeu3LSfl0Z+3C5abxQCOt27oYrNcW1HNaofIs1e0l6dHtpOtMUmqhLk3NlUt8YaZXVpGham+hf1a1hY/H86SVpZu0ZEqAEQ/QAaA+mToAHDFihUyadIk6dWrl+Tl5clTTz0lw4YNkz179khIiH0dhYlIpxnAouydIwJA9gF0nIU7Y+WxouDvpt6N5aUxHVUWz5EwmKNHkxrqQJXTrXENmbfttBoJTPpk6ABw0aJFxf6fPXu2REVFyebNm+Wyyy5zWbuIyHm0bF2QnwMzgEX9Cqlqlu+PkwfnbFVTvNzQs5G8PKYjS7BuPxAkSa2UYuTJsfXK0AGgteTkZHVcs2ZNm5dnZ2ergyYlJaXa2kZEDi4B+1Q9ANT6EWoji6nydpxMUqN90edvVKf6MuOazgz+3FjbeuES4OstyZm5qm+l0SfI1iN2cihSUFAgDz/8sPTr1086duxYap/BiIgI8yE6Oro6nysicmQG0JF9AJkBrBLM33fX15vUtCvoT/bO/7o6vOxLjoWVVzoVTcWDLCDpDwPAIugLuGvXLpkzZ06pO2vq1KkqS6gdYmJiqut5IiJ3HAVcdB8cBVy1SZ7v/maznE3JlpZRofLhTd1UcEH6KQNvi2EAqEcsAYvI/fffLwsWLJCVK1dKo0aNSt1ZAQEB6kBE+pWZz1HA7gJ9x578dYdsj0mSyGA/+eyWnpxzT3fzAR7lQBCd8jb6hw+Cv7lz58q///4rzZo1c3WTiEhPK4EUjSTmKODKmbXisBpJinLv/93UXZrW5uwLeswAalPpkL54G73s++2338r3338vYWFhcubMGXXIzGSHbiJP5ciVQIL9ggvvk2sBV9g/e87KG4v3q9MvXNlB+rasXeXng6pX/YhAdcD6zNvYD1B3DB0Azpo1S/XlGzBggNSvX998+PHHH13dNCLSwUTQzABWzr4zKfLwnK1qrr/xFzdRB9IfTP3Sq2nhrBkbjp13dXOognyNXgImImNxyihgZgDtlpCWLXd+tUnSc/KlT/Na8tzo9lV+Hsh1ejWrqZbW28gAUHcMnQEkIuNx5Eog5nkAi+6TypaTVyATv9siJxMz1Vqy/zeuO5dc07mLijKAW44nSW5+gaubQxXAAJCIDMUZGUBtZDGVXXF57vddsuHoeQkL8JXPJ/SUGiH+3GU61yoqVCKC/CQzN192n+biCHrCAJCIDEWbs8+R8wAyA1i+2WuOyZyNMYL5nd+/qZu0jAqr8v4n18O6yr2aFq6pvPEo+wHqCQNAIjIUR2YAg305CtgeKw6ck5cW7FGnp45oJwPbRFV535P74EAQfWIASESG4shpYLQMIAeBlO7A2VS5/7stUmASua5HI7nzUs636okDQWDTsfNSgCeadIEBIBEZiiOngTH3AeQgEJvi07Ll9tkbJTU7Ty5qVlOmX91JTR1CnqVjgwgJ9POWxIxcOXwuzdXNITsxACQiQw1EcEYAiH6FnFaq5Bq/93yz2Tzi9+Obe3CNXw+FtZu7qWXhOB+gnjAAJCJDlX8LTIVTVYT4VX3ZMe0+cJ/Z+dlVvj9PW+N38/FECQ/EiN9eHPFrkDIwB4LoBwNAIjKM9Nx082lHjAK2zCJa3rfRvbvkoHmN31k395CWUaGubhI52cVFAeDaIwnMhusEA0AiMoyM3Azz6F1vr6p//OE+zGXgovs2um/WHZf3lh5Up18a01H6cY1fQ+jepIYqBZ9NyZbD5/hjSA8YABKRYWhZOkeUfzXafaXn8UtvwY7TarJneHBwK7mpd2OH7Wdyb4F+Pub5AFcfind1c8gODACJyHCTQAf7Fc7f5wjaXIBGzwD+d/CcTP5xm2CJ9fEXN5HJQ1q5uklUzfq2qK2OGQDqAwNAIjJcBlAL2hyaATRwH8CNx86rEb+5+Sa5onN9eeHKDpzuxYC0cv+6IwmSz/kA3R4DQCIyDC1L58gSsJZNNGoJeP2RBJnwxQbJyMmXS1vVlrdu6KIGf5DxdGoYIWGBvpKSlSe7TiW7ujlUDgaARGQYziwBZ+YWzi9oJGsPJ8itX240B3+fjO8pAb4+rm4WuQgC/z7Na6nTq9gP0O0xACQi4w0C8XXCIBCDlYDRz+u22RskMzdf+reuI5/e0lOC/Bn8GZ1WBl5zmANB3B0DQCIyXh9AB2YAjRgAYrTvbV9ulKzcAhnYpo58PL6HGgVK1K9lYQZw47FEtRoMuS8GgERkGM4oAVsuB2cEn/13RO7/fqvk5BfIiI715CMGf2ShRZ1QiQoLkJy8ArUSDLkvBoBEZBjOGARilAxgbn6BvDB/t7z85171/4Q+TeTDm7qzzx8V4+XlJZcUlYFXHjjHvePGGAASkWE4sw+gJ88DeD49R8Z/vl5mrzmm/p9yeVs11QtH+5ItA9pGqeN/98VxB7kxX1c3gIhIz30AtVHAnpoB3BaTJJO+2yKnkjIlxN9H3v5fVxneoZ6rm0VurH+rOurHwcG4NIk5nyHRNR33fiPHYQaQiAzDKdPAFN2Xp/UBLCgwyUcrDst1s9ao4K9prWCZO6kfgz8qV0Swn/RoUrgsHLOA7osBIBEZrw8gS8BlQtbmli82yKsL90legUlGdaovv99/ibSuG1Y9TxTp3uCiMvBSloHdFgNAIjIMp5SA/TynBIzluzDKd9g7K9VEvoF+3vLqNZ3kw5u6SUSQn6ubRzoyuF1hALjucIKkZ+e5ujlkA/sAEpFhaEFaqF+ow+5Tu6+03DTRMyzd9fS8XbI9Jkn937tZTXn12s7SrLbjBsyQsaaDaVIjUBISz8umnbulf5MgkZxUkew0kZy0ouPy/sdxqkjfB0QuusvVm+RxGAASkWGk4gtGRML8HVfK1O5LrwEg+ve9uXi/zN16Sv0fFuArT41qJ//rGS3eXNPXWEwmEXSTsAy+LIMx68Cs2P/FAziv7DRZgR9cgZg5vIrtSjvroA0kSwwAicgQ8gryzAM1nBEAIruIx/D11sfHanxatny68oh8ueaYmrQXxnRtIE+NbCd1w/GtTW6vIF8kJ70oOLM8tjptb7YNB1Pha8GR8sRbfALDxAvvlYBQEf9Qi+Ow8v+PiHZ4m4gBIBEZRBq+3IqE4ovFQcL8LgSTCAIjAiLE3Qd4fPrfEflxY4xkFwV+FzevqQK/zo0iXd08z5WfZyNAKyVgKxa8pVqcb3VZXqaTGutlFYTZGaxZ3SbbJ1gufmuDJOZ4y7w7LpGu0Xx9uRN9/FQlIqqi1NxU89Jtft6OG9Dg5+MngT6BkpWfpUrM7hgAmkwmWXskQb5ff0IW7jqjBntAl+hIeXBQSxnUNkqt4EBF8nKsAjLrAM0yKCvleiqrZvF/frbzdq+XT1EAFlIUgIVc+N98sM6+lfE/BjZ5V32MaICI9G3bSP7cESsLd8YyAHQzDACJyFj9/ywydo4sA2dlFgaA7iQ2OVPmbzstczbGyNH4C6OUL21VWyYOaCF9mtfSb+BXUFDYXw0HBFjqGP8j6NLOKzptPra8bnrJ01o5tCDXee1GFwEtW2YZoKlsWkgpwZtlUGd5WdFp3wCswSbuCFMIIQD8a1esPDmirX5fbx6IASARGYIzBoBocJ/nMs+5RQB4NiVLZVv+3BkrG48lms8PDfBVffxu6t1YOjSIcIMgzTo4swrSLIMz6+BOu09n8wkoOyCzmW2zddri2NdfjGRgmygJ8vORmPOZsutUinRq5H4ZcqMydAC4cuVKeeONN2Tz5s0SGxsrc+fOlauuusrVzSIiHQaAlo9RnTCAY9Px87LiwDlZsf+c7DtTvA29mtaQ63o0kis6N5CQAN+Soz7zc4sCqszixzYDL8sgDaXNcjJwTuujZgUlSxz8cYxAC6dDLpz20/63vI6tg1Xw5sO5D6sqyN9HdTHADxIcGAC6D0MHgOnp6dKlSxe5/fbb5ZprrnF1c4hI5wFgSk6K4+4UwVleVonALCM9VQ6dOidHY8/JybgEORN/XnzysyRIcmSEV7Zc65sj0WEiLSK9JDrUSwIlW2R3psg2y+DO4rQpX6o9SNP6mVkGbMWCtKIgzGbgZhXc+QY5pL8aOc+ITvVU8PfXzliZcnkbloHdhKEDwBEjRqgDEXk+hwaAKG0iu1UURIUVDapIO7sTj2CVUdMOF65f7DinjMul8H4tYd2RzkUHBbGPdfyDxFtFk29e3oVBlV/QhYCstCCtRHBmK7tmcTsGaYaGDGCwv4+cOJ8hW04kmdcJJtcydABYUdnZ2eqgSUlx4K99InJMtsz6uOh06ulN6qphyadF1n1UFMBlWR1fuH6xyxCMWd4vDhbCa9UQCQ+T1E2fiSS95fBnMtvkK1niL5kSIJkmf8n3CRKfwBAJDA6V0NAwCQ0NFy8VsAUVHbRgzOo887Hl6aJjlDvZQZ+cINjfVy7vUE9+23pK5m49yQDQTTAArIAZM2bItGnTnPdsEHlUQJZtI4AqK+CyDtqsA7Si+ylxH0XH5UitWUMkIkzCjqwQ2TzfcdvqGyih3pjwQiQ1uKZIYJPigZV/sJh8gyTD5C+Jub6SkOMjcZnecibDS06me8mpNC9JM/lJVlFwp4I88ZcsU4BkSIDUrRkpbRrUkPb1w6V9g3Dp1DBCojhRM+nMNd0bqQDwj+2x8uwV7SXA18fVTTI8BoAVMHXqVHnkkUeKZQCjozlDObkprYO/ylgVBWPqWPtfC8i0g3aZ9f/ZRQFZdtnXsw7WbJQvqwXmRFMBWGBh6VEdB0pqALL3mRJWs7VIg0YXLsN1fQOtrq9lzYKKLrM6ViXPIHP/s7Cdn4tseVcS2oyU7c2fkCPxaXLkXLociU+XoyfT5VhCumTklN7XLtDPW5rWCpHmdULU2rvNaodKs9rB0rpumIQFciAC6V+fFrWkbniAnE3JlmX7zsnlHeu5ukmGxwCwAgICAtSBqEJ9xTABrAqSygvCLAMre69bzvVcFYSVCMgsAqjSgqoSQZsWoBUFWvYGaKWM3Ez59wGRmOUS1vsekdbXV2lS5djkLNkfkyoH486oQG9z4jkRP5H5Ow/LTwtX27ydj7eXNK4ZXBTgFR6a1w6RprVDpF54INfdJY+G1/9VXRvKxyuPyC+bTzIAdAMMAMnzsl4FeRcCLi34ys+pwHkInnKKztOObZ1neVvr84ru25kTylYUAiRMGGs+DrL6X8uCWV7P8mBxXT8bt9Xu0zpAc5OpNCozEXRqVq4cOJsqe2NTZf+ZwsO+MymSkpVX7Hq+YQUS1AjjKDIlKiygMLirEyotzBm9EImuGSx+PhytSsaF6YgQAC7bHydnkrOkXgTXnHYlQweAaWlpcujQIfP/R48elW3btknNmjWlcePGLm2bbhYi14IdlBrVcbbF6aLz86zPszhUJtDCdbQAy1YQ5w5ZL5u8bARO9gRhpV2vjCDM1vkG7+CfnJ2sjiMDI0udT29vbIpsPZEoW2OSZFtMkhxPsD3ZsK+3l7SoEyqt6oaqQM8UWCCzj/wgrRv4yO/3DXHqdhDpVau6YXJR05qy4dh5mbPxhDw8pLWrm2Rohg4AN23aJAMHDjT/r/XvmzBhgsyePdvFWax82wGTzYAKp60CsVKDLovzVIBlfR9lXW51X6bCheTdGpZdwmz+mH1fO0ZAZPM8HAcUnVd0MJ9XdB2b52n3U855HGXpUuezzqvjGgE1VBn3VFKmbD1RGOgh6Nt1OkUFgdbQb6ltvXBpWy9M2tYPkzZ1w6VFVEixTuz7ziMARJB5YeUNIipp3MWNCwPADTFy/8CW4susuMsYOgAcMGCA+iJwO8umi6x8XXQHgY46+BUdB1ic9rsQDJnP084PtDPQsjjPHKhZBFq2zvPmSDNCKTdHkrKS1K6Y/scJ2XMyRs6lXpjSSRMZ7KcWrO8WXUO6No6Uzg0jpEZI+Ut3IaiEpOwk9ZnC9U6JbMPgj5oh/nImJUuW7ouT4R04GMRVDB0Aui0EObbOKxZQWQZVludZBWElLsdpq0BMBUxlXF7qdazu1+AlRnIPBQUmOXwuTZVxkeFDdu/AubMS0rowu7diH1a/8FVl3Hb1w6Vb48jCoK9xDWlaK7hSwVuNwMIAMN+Ur1YDiQjgeqdEtiBzfn3PRvLxiiPy5eqjDABdiAGgO+pzn0ivOy5kvFDGZHBlKMgiZeUWBix46v19vDlKtBTn03NkW0yiuZy77USSpGYXH6Th5Z+ujn0kSJ4e0UkFfR0bRkign2MyxP4+/hLiFyLpuekqC8gAkKh0E/o0lc//OyrrjpyXHSeTpHMj2/1yybkYALojbakl8nj5BSbZfjJJthxPlH1Fo0zjUrMkMT1XcvIv9Efz9hJVNqkdGiB1wgKkUY1gNcK0RVSotKwTKg0jgwwRIGbm5Muu08myPSZJtp8sPMbyUtaC/Hykc6MIVcZFOdcv5Lg8vFKkfmgtueuy5k5pW2RApAoAE7MSpUl4E6c8BpEnaBAZJKO7NJC5W0/JJyuPyIc3dXd1kwyJASCRC4K+lQfPyfxtp2XFgXMqg1UeLDUbn5ajDggUbU0k3CoqTA1UaIPBCvXC1TGCRT0HewfjUmX36RRzwIcpWbD/rCEYRglXK+e2qRtWrHP5vyf2qOOagTWd1l7c96m0UyoAJKKy3XVpcxUA/rUzVmLOZ6hpkqh6MQAkqibJmbny7brj8v36E2oEqiYs0Fcubl5LOjQoHGmK7B6yfRFBfqr8i4AnMzdf4lNz5Fxathq8cDwhXfVzOxyXLkfj01W5eOepZHWwVCvE3zxyVQsOsbpEkL/7DI7Jyy+Qk4mZcjAuTfbFpqgAd++ZFDkWn64CX2uYZw9BXhccGkVKp0YRal+VRQvKSpsCxlEZQPVYHAlMVC4sa3hpq9ry38F4+b/lh2TGNZ2516oZA0AiJ0vJypUvVh2Vz1cdldSiCYTDA33V2pgjOtaT7k1qlDtBMJYDiwqzPWkqAkQEhIWTFBdNWHw2VS0/lpCeI6sPJaiDBkFlk5rB5ixh09rB0iAiSJVl6oYHir+vYycrRvsS0rLVqD9M/opVNNA2BHjHEjLUr/88W5Eesmoh/tKufpjqI4RgD4FfZSaP1YIybbSuM2gDQZgBJLLPw0NaqQDwp00n5Z7LWqhVcaj6MAAkchIEPj9sOCFv/r1fkjIKVwRBaRJ90K7oXN9hAxCwxBImI8ZhRKf65vMzcvLk4Nm0C4Hh2RTZF5uqgkIEXjgs2n2m2H0hOKwVEiA1gv2kRrC/mhYFx8gYBvh5S4CPtwoQUV7F9uGA4C2/oEDSs/NVsIsgNyWz8DgxI0fiUrNtlm0tBfh6q9Uy2tcPVxlLNe9e/TCpExrgkClVtKBMC9KcQQsuGQAS2adHk5oyoE0dWb7/nLy/9KC8/b+u3HXViAEgkRNg6pFn5u1S/degZVSo+rU7smP9ahusEezvW1gmjS5e9kQJWVvSDH3qUH49nYRDlhp4Ep+WrQ6OhE1GBrNuRKDUDQuQJrWC1a/9ZrWqZy1cjMx1egCoZQBZAiay26ND26gAcN62U3J3/+bqxx9VDwaARA6ElSTeXXJAPlpxWPVfQ/++R4e2lpsvbuI2M95jYAgOl7SqXWL+PGQHz6ZkqYxlUmaOJOI4PUf1QcS2IUDEcW6+Sc2j5+PjJT5eXioLGRLgo0rV4YF+arvDg/wkMshPlWwxehnXcZX4zPhqGQQCCZkXyu1EVDb04R3Vqb78uTNWps3fI9/f1ZsTqVcTBoBEDoJ1ZCf/uM08SndM1wby7BXtVfCjB8jAacGhpzmbflYd1w2u67THqBtSeN9nMwofi4jsM3VkW1my96ysPZIgC3edkZEWXVnIedwjJUGkYxjFOnPZIbnyw1Uq+MPAhVnjust7N3bTTfDn6eIy4pwfABbdNwNAoorBzAf39m+hTr+8YI+kZhX2mSbnYgBIVAWYguX6j9fKG4v3q7Lo0PZ1ZfHDlxUbjEGulZGbIam5hVnZqOAopz2Odt+pOamSmXdhmh8iKh8CwOiaQXI6OUum/7WPu6waMAAkqgT0l/tqzTEZ8d5KtQRZWICvvHl9F/lkfA+PLKF6QvYv2DdYQv1DnfY4oX6hEuQbVOwxicg+mGng9Wu7qNOYPWHlgXPcdU7GAJCogjBidvwX6+X5+bvVBMz9WtaSRZMvk+t6NGLnZTeklWSdmf0DTFdjLgMX9TkkIvv1aVFLJvQpXEbxsZ+3q2UxyXkYABLZyWQyyc+bYmT4OyvVxMpYfm3alR3km9t7q7V4ybj9/zRakMl+gESVM2VEW2kVFarmD33wh62qjzU5BwNAIjvEJmfKbbM3yuO/7JDU7Dy15uxfD14qE/o2rbZ5/ci9M4CWj8ESMFHl5y+ddXMPCfH3kXVHzsuMhewP6CwMAInKyfr9tDFGhr29Uk1WilUwplzeVn6+p49aeYPc35n0M8WmaXEmLcsYmx7r9Mci8lSYOP+N6wv7A2IJzW/WHnN1kzwS5wEkKgXW1332993mzshYh/bN6ztLy6gw7jMdOZFyQh1Hh0U7/bG0x4hJjXH6YxF5MswF+PjwNmopzew8loGdgQEgkZWs3HyZtfywzFpxWK16gawfVvO489LmLl3NgirnRGphANgkvLBzuTNpj3E85bjTH4vI0903oIVc2qq2dG5UfDlLcgwGgEQW5d4le+PkpQV75MT5DHXeJS1ry7QxHaQFy726lJOfI6fTTld7AIgSMB7b38ff6Y9J5Kkwsp7Bn/MwACQSkU3HzsurC/fJpuOJan/UDQ9Qy7hhjUp8CJE+oRRrEpOE+IVIrcBaTn+82kG11XyDGXkZcjL1pDSPbO70xyQiqgwGgGRou04ly3tLD8o/ewpHimJql9v6NZNJA1tKaADfHnqnlWIbhzWulkAej4Es4N7ze9VjMwAkInfFbzgyZKl3/dHz8n/LD5sHeKBr3w09o+XhIa2lXkSgq5tIDnIs5Vi1lX81jcMbqwBQe2wiInfEAJAMNbhjwY5Y+XbdcdkWk2QO/EZ3aSAPDGrJ0b0eaE/CHnXcpmabanvMNjXayOJji82PTUTkjhgAksfbdyZFft50Un7ZfFKSM3PVeRjZe32PRnLPZS2kca1gVzeRnGR3/G513KFWh2rbxx1qFz7W7oTCxyYickcMAMkjxZzPkPnbT8v8badl/9lU8/lYsu2m3o1VubdOWIBL20jOlZSVJCfTTqrT7Wu1r7bdrQWbGICSnJ0sEQER1fbYRET2YgBIHgHrRW6NSZKle+Nk2b64YkGfv4+39G9TR8ZeFC39W0dxLj+D0DJwGABSnUEYHgsTQiMARAayb8O+1fbYRET2YgBIupRfYFKl3Q1Hz6vD2iMJkpRRWN7V+vb1blZLrurWQC7vUF8igv1c2l6qfuti16njrlFdq/2xu0V1UwEg2sAAkIjcEQNA0sWo3ZOJmbL7dIrsPp0sO08ly+bjiZKalVfsepHBftK/dR0Z1DZKHUcGcxJeI1ses1wdX9bosmp/7EsbXSrzD8+XZTHL5JGej1T74xMRlYcBILmNggKTnEnJkiPn0uVIfJo6PhiXKrtOpZgHb1jCPH09m9aQXk1rysXNa0qXRpHi6+PtkraTezmafFRNw+Lr7Sv9GvSr9sfHY/p6+ao2oC3NIppVexuIiMrCAJCqNcCLT8uWU0mZEpucJaeTMuV0UpbEJmfK8YQMORqfLpm5+TZv6+fjJa3rhknHBhHSsWG4dI2uIe3qhzHgI5t+2v+TOr64/sUS6h9a7XspzD9MLm5wsaw6tUq1ZcpFU6q9DUREZTF8ADhz5kx544035MyZM9KlSxf54IMP5KKLLipzp9GFfnhpWXmSkpUriRk5kpCeIwlpOXI+PVsdx2uni86PS82S3HxT2S9Iby81LUvz2qHSok6IWoO3fYNwFfxh6hai8iRkJsivB39Vp8e3G++yHXZzu5tVAIi23NnpTqkV5Pyl6IiI7GXoAPDHH3+URx55RD766CPp3bu3vPvuuzJ8+HDZv3+/REVFiSfJzS9Q2bWsnHx1rA5FpzFBcmZO4eWW10nPRnCXJ6lZuaq/3YXjwtPpObazdWXB4Ix64YFSPzJIGuAQEaiOG9UIkuZ1QtWxH8u4VEn5BfnywpoXJDMvU9rVbCd9GvRx2b7s26CvagNWBUGb3h34rvh4+7isPURElrxM6GFvUAj6evXqJR9++KH6v6CgQKKjo+WBBx6QJ598stzbp6SkSEREhCQnJ0t4eLjD2rXmULysPhyvsmU5eQWSk18guXkFKohT5+UXqPML/8flJnW5ul7RdXFeTl6++frI1jlLgK+31Aj2l1qh/lIzxF9qhwaoY/xfC8chAVIz1F/qhgdK3bAAlm3JKQ4mHpS3Nr8lq0+tVn3/5oyaU60rgNiy7/w+GfvnWMkryJNLGl4ij/R4RFrVaOXSNhGR876/9cSwGcCcnBzZvHmzTJ061Xyet7e3DBkyRNauXWvzNtnZ2epg+QJyhp/2/iWLjv5TsRthnXtfU+EzajW/MSZA8bO4mo+3l/h4ewsSbYWnrQ5ehccoxyIb5+uD096qH96F/73UMf739sK9FpdSdDiaISI4nBMxSeWC0Mr8RqnsY1VGZX9DVaaN1fpYld2HlbhZZR8r35SvJls+nXZaErIS1Hl+3n7y+mWvuzz4g7Y126q2TFk5RZWDccA8gZibMMQvRAJ8AsTfx18NGFFvzkrwquQNvWy8b4nc0ZDGQ2RIkyGubobHMWwAGB8fL/n5+VK3bt1i5+P/ffv22bzNjBkzZNq0aU5vm39wnPhFbHPqYxQUHUqMrTUVHXAhkY4g8MOUL5O6TnKrLNvQJkOl6RVNZea2mbLy5EoVsO7M3unqZhHpRpOwJgwAncCwAWBlIFuIPoOWGUCUjB1tXOeh0ql+8cDUmdmA6swEVGe2otKP5e770Ksat6uas0uVebzIgEipHVRbWkS2kGA/91zXGQEp+gBm5WXJ8ZTjcjL1pGTmZ0pOfo46oERcGdWZVSdylc51OnPnO4FhA8DatWuLj4+PnD17ttj5+L9evXo2bxMQEKAOztalThd1ICLPEugbqErT7lCeJiJjM+y8Gv7+/tKjRw9ZunSp+TwMAsH/ffq4buQgERERkbMZNgMIKOdOmDBBevbsqeb+wzQw6enpctttt7m6aUREREROY+gA8H//+5+cO3dOnnvuOTURdNeuXWXRokUlBoYQEREReRJDzwNYVZxHiIiISH9SOA+gcfsAEhERERkVA0AiIiIig2EASERERGQwDACJiIiIDIYBIBEREZHBMAAkIiIiMhgGgEREREQGwwCQiIiIyGAYABIREREZjKGXgqsqbREVzChORERE+pBS9L1t5MXQGABWQWpqqjqOjo521PNBRERE1fg9HhERYcj9zbWAq6CgoEBOnz4tYWFh4uXl5fBfJwgsY2JiJDw8XDwNt0//+Bzqm6c/f0bYRm5f5ZlMJhX8NWjQQLy9jdkbjhnAKsCLplGjRuJM+NDyxA8uDbdP//gc6punP39G2EZuX+VEGDTzpzFm2EtERERkYAwAiYiIiAyGAaCbCggIkOeff14deyJun/7xOdQ3T3/+jLCN3D6qCg4CISIiIjIYZgCJiIiIDIYBIBEREZHBMAAkIiIiMhgGgEREREQGwwDQDRw7dkzuuOMOadasmQQFBUmLFi3UyLWcnJwyb5eVlSWTJk2SWrVqSWhoqFx77bVy9uxZcVevvPKK9O3bV4KDgyUyMtKu29x6661qlRXLw+WXXy6esn2Yjf65556T+vXrq+d+yJAhcvDgQXFH58+fl3HjxqlJZ7F9eM2mpaWVeZsBAwaUeP7uvfdecRczZ86Upk2bSmBgoPTu3Vs2bNhQ5vV//vlnadu2rbp+p06d5K+//hJ3VpHtmz17donnCrdzVytXrpTRo0erlRzQ1nnz5pV7m+XLl0v37t3V6NmWLVuqbXZnFd1GbJ/1c4jDmTNnxB3NmDFDevXqpVbTioqKkquuukr2799f7u309j50VwwA3cC+ffvUsnIff/yx7N69W9555x356KOP5KmnnirzdpMnT5Y//vhDvRlWrFihlqW75pprxF0hoL3++utl4sSJFbodAr7Y2Fjz4YcffhBP2b7XX39d3n//ffV8r1+/XkJCQmT48OEquHc3CP7w+vznn39kwYIF6svp7rvvLvd2d911V7HnD9vsDn788Ud55JFH1I+tLVu2SJcuXdS+j4uLs3n9NWvWyNixY1Xgu3XrVvVlhcOuXbvEHVV0+wDBveVzdfz4cXFX6enpapsQ5Nrj6NGjMmrUKBk4cKBs27ZNHn74Ybnzzjtl8eLF4inbqEEQZfk8IrhyR/jeQhJj3bp16nMlNzdXhg0bpra7NHp7H7o1E7ml119/3dSsWbNSL09KSjL5+fmZfv75Z/N5e/fuNeEpXbt2rcmdffnll6aIiAi7rjthwgTTmDFjTHpi7/YVFBSY6tWrZ3rjjTeKPa8BAQGmH374weRO9uzZo15bGzduNJ+3cOFCk5eXl+nUqVOl3q5///6mhx56yOSOLrroItOkSZPM/+fn55saNGhgmjFjhs3r33DDDaZRo0YVO693796me+65x+QJ21eR96W7wWtz7ty5ZV7niSeeMHXo0KHYef/73/9Mw4cPN3nKNi5btkxdLzEx0aRHcXFxqv0rVqwo9Tp6ex+6M2YA3VRycrLUrFmz1Ms3b96sfi2hZKhBSrxx48aydu1a8SQoa+AXbJs2bVR2LSEhQTwBMhIozVg+h1ibEqU6d3sO0R6UfXv27Gk+D+3GetjIXJblu+++k9q1a0vHjh1l6tSpkpGRIe6QrcV7yHLfY1vwf2n7HudbXh+QUXO356qy2wco6Tdp0kSio6NlzJgxKuPrKfT0/FVV165dVbeSoUOHyurVq0VP33tQ1nefkZ5HZ/N1+iNQhR06dEg++OADefPNN0u9DgIHf3//En3N6tat67b9PSoD5V+UtdE/8vDhw6osPmLECPVm9/HxET3Tnic8Z+7+HKI91mUkX19f9UFdVltvuukmFVCgD9OOHTtkypQpqjz122+/iSvFx8dLfn6+zX2PLhm2YDv18FxVdvvwA+uLL76Qzp07qy9ifP6gTyuCwEaNGonelfb8paSkSGZmpuqDq3cI+tCdBD/UsrOz5bPPPlP9cPEjDX0f3Rm6QaEs369fP/VjsTR6eh+6O2YAnejJJ5+02SHX8mD9YXzq1CkV9KAvGfpOeeI2VsSNN94oV155peroi34e6Hu2ceNGlRX0hO1zNWdvH/oI4tc5nj/0Ifz6669l7ty5Kpgn99KnTx+55ZZbVPaof//+KkivU6eO6ptM+oAg/p577pEePXqo4B0BPY7Rr9zdoS8g+vHNmTPH1U0xDGYAnejRRx9Vo1jL0rx5c/NpDOJAB2W8YT/55JMyb1evXj1V5klKSiqWBcQoYFzmrttYVbgvlBORJR08eLDoefu05wnPGX65a/A/voSrg73bh7ZaDx7Iy8tTI4Mr8npDeRvw/GG0u6vgNYQMsvWo+bLePzi/Itd3pcpsnzU/Pz/p1q2beq48QWnPHwa+eEL2rzQXXXSRrFq1StzZ/fffbx5YVl62WU/vQ3fHANCJ8OsZB3sg84fgD7/cvvzyS9Vfpyy4Hj6gly5dqqZ/AZTWTpw4oX7Ju+M2OsLJkydVH0DLgEmv24eyNj608BxqAR/KUSjXVHSktLO3D68p/NhAvzK89uDff/9VZRstqLMHRl9CdT1/pUH3CWwH9j0yy4Btwf/4MiptH+BylKk0GLlYne83Z26fNZSQd+7cKSNHjhRPgOfJeroQd33+HAnvOVe/30qDsS0PPPCAqgqgqoPPxPLo6X3o9lw9CoVMppMnT5patmxpGjx4sDodGxtrPmhwfps2bUzr1683n3fvvfeaGjdubPr3339NmzZtMvXp00cd3NXx48dNW7duNU2bNs0UGhqqTuOQmppqvg628bffflOncf5jjz2mRjUfPXrUtGTJElP37t1NrVq1MmVlZZn0vn3w6quvmiIjI02///67aceOHWrEM0Z/Z2ZmmtzN5ZdfburWrZt6Da5atUo9D2PHji31NXro0CHTiy++qF6beP6wjc2bNzdddtllJncwZ84cNeJ69uzZapTz3XffrZ6LM2fOqMvHjx9vevLJJ83XX716tcnX19f05ptvqhH3zz//vBqJv3PnTpM7quj24XW7ePFi0+HDh02bN2823XjjjabAwEDT7t27Te4I7yvtPYavsrfffludxvsQsG3YRs2RI0dMwcHBpscff1w9fzNnzjT5+PiYFi1aZHJXFd3Gd955xzRv3jzTwYMH1esSI/C9vb3VZ6c7mjhxohp5vnz58mLfexkZGebr6P196M4YALoBTL+AN7etgwZfoPgfw/w1CBLuu+8+U40aNdQH29VXX10saHQ3mNLF1jZabhP+x/4AfAgMGzbMVKdOHfUGb9Kkiemuu+4yf4Hpffu0qWCeffZZU926ddWXNX4E7N+/3+SOEhISVMCH4DY8PNx02223FQturV+jJ06cUMFezZo11bbhRw6+fJOTk03u4oMPPlA/ovz9/dW0KevWrSs2hQ2eU0s//fSTqXXr1ur6mFLkzz//NLmzimzfww8/bL4uXo8jR440bdmyxeSutClPrA/aNuEY22h9m65du6ptxI8Ry/eiO6roNr722mumFi1aqMAd77sBAwaoBIG7Ku17z/J58YT3obvywh9XZyGJiIiIqPpwFDARERGRwTAAJCIiIjIYBoBEREREBsMAkIiIiMhgGAASERERGQwDQCIiIiKDYQBIREREZDAMAImIKgFLEkZFRcmxY8fcYv/deOON8tZbb7m6GUSkEwwAicipbr31VvHy8ipxuPzyy3W951955RUZM2aMNG3a1GmPgbWXsa/WrVtn8/LBgwfLNddco04/88wzqk3JyclOaw8ReQ4GgETkdAj2YmNjix1++OEHpz5mTk6O0+47IyNDPv/8c7njjjvEmXr06CFdunSRL774osRlyDwuW7bM3IaOHTtKixYt5Ntvv3Vqm4jIMzAAJCKnCwgIkHr16hU71KhRw3w5slyfffaZXH311RIcHCytWrWS+fPnF7uPXbt2yYgRIyQ0NFTq1q0r48ePl/j4ePPlAwYMkPvvv18efvhhqV27tgwfPlydj/vB/QUGBsrAgQPlq6++Uo+XlJQk6enpEh4eLr/88kuxx5o3b56EhIRIamqqze3566+/1DZdfPHF5vOWL1+u7nfx4sXSrVs3CQoKkkGDBklcXJwsXLhQ2rVrpx7rpptuUgGkpqCgQGbMmCHNmjVTt0HAZ9keBHg//vhjsdvA7NmzpX79+sUyqaNHj5Y5c+ZU6LkhImNiAEhEbmHatGlyww03yI4dO2TkyJEybtw4OX/+vLoMwRqCKQRWmzZtkkWLFsnZs2fV9S0huPP395fVq1fLRx99JEePHpXrrrtOrrrqKtm+fbvcc8898vTTT5uvjyAPfee+/PLLYveD/3G7sLAwm23977//VHbOlhdeeEE+/PBDWbNmjcTExKg2vvvuu/L999/Ln3/+KX///bd88MEH5usj+Pv6669Ve3fv3i2TJ0+Wm2++WVasWKEux37Izs4uFhRiCXdsK8rrPj4+5vMvuugi2bBhg7o+EVGZTERETjRhwgSTj4+PKSQkpNjhlVdeMV8HH0XPPPOM+f+0tDR13sKFC9X/L730kmnYsGHF7jcmJkZdZ//+/er//v37m7p161bsOlOmTDF17Nix2HlPP/20ul1iYqL6f/369ap9p0+fVv+fPXvW5Ovra1q+fHmp2zRmzBjT7bffXuy8ZcuWqftdsmSJ+bwZM2ao8w4fPmw+75577jENHz5cnc7KyjIFBweb1qxZU+y+7rjjDtPYsWPN/994441q+zRLly5V93vw4MFit9u+fbs6/9ixY6W2nYgIfMsOD4mIqg6l11mzZhU7r2bNmsX+79y5c7HMHMqlKJ8Csnfo74byr7XDhw9L69at1WnrrNz+/fulV69exc5Dlsz6/w4dOqiM2pNPPqn60DVp0kQuu+yyUrcnMzNTlZRtsdwOlKpR0m7evHmx85Clg0OHDqnS7tChQ0v0X0S2U3P77berkja2Ff380Cewf//+0rJly2K3QwkZrMvFRETWGAASkdMhoLMOVqz5+fkV+x/96dA/DtLS0lT/ttdee63E7dAPzvJxKuPOO++UmTNnqgAQ5d/bbrtNPX5p0McwMTGx3O3AfZS3XYDScMOGDYtdD30MLUf7Nm7cWPX7e/zxx+W3336Tjz/+uMRjayXzOnXq2LnlRGRUDACJyO11795dfv31VzXliq+v/R9bbdq0UQM2LG3cuLHE9dDn7oknnpD3339f9uzZIxMmTCjzfpGdc8Ro2/bt26tA78SJEyqjVxpvb28VlGLkMQJF9HNEH0VrGCjTqFEjFaASEZWFg0CIyOkwKOHMmTPFDpYjeMszadIkld0aO3asCuBQCsVoWwRF+fn5pd4Ogz727dsnU6ZMkQMHDshPP/2ksmhgmeHDiGTMp4fs2rBhw1QQVRaUYzFgo7QsoL0wyOSxxx5TAz9QgsZ2bdmyRQ0Swf+WsK2nTp2Sp556Su0HrdxrPTgF7SciKg8DQCJyOozaRanW8nDJJZfYffsGDRqokb0I9hDgdOrUSU33EhkZqbJjpcHUKhg9i5Ip+uahH6I2CtiyxKpNt4K+d+hvVx48PrKSCCir6qWXXpJnn31WjQbGVDGY1gUlYbTdEkrAQ4YMUUGnrTZmZWWp6WvuuuuuKreJiDyfF0aCuLoRRETVBatlYMoVTNFi6ZtvvlGZuNOnT6sSa3kQpCFjiLJrWUFodUFwO3fuXDXNDBFRedgHkIg82v/93/+pkcC1atVSWcQ33nhDTRitwYhZrEzy6quvqpKxPcEfjBo1Sg4ePKjKstHR0eJqGGxiOb8gEVFZmAEkIo+GrB5W0kAfQpRRsYLI1KlTzYNJMHEzsoKY9uX333+3OdUMEZGnYQBIREREZDCu77hCRERERNWKASARERGRwTAAJCIiIjIYBoBEREREBsMAkIiIiMhgGAASERERGQwDQCIiIiKDYQBIREREZDAMAImIiIjEWP4f83Eu57Zhr9MAAAAASUVORK5CYII=", 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", 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", + "_dom_classes": [], + "_figure_label": "Figure", + "_image_mode": "full", + "_message": "", + "_model_module": "jupyter-matplotlib", + "_model_module_version": "^0.12", + "_model_name": "MPLCanvasModel", + "_rubberband_height": 0, + "_rubberband_width": 0, + "_rubberband_x": 0, + "_rubberband_y": 0, + "_size": [ + 640.0, + 480.0 + ], + "_view_count": null, + "_view_module": "jupyter-matplotlib", + "_view_module_version": "^0.12", + "_view_name": "MPLCanvasView", + "capture_scroll": false, + "footer_visible": true, + "header_visible": true, + "layout": "IPY_MODEL_891d17380c4c4a49b3f8fd6aca2a5087", + "pan_zoom_throttle": 33.0, + "resizable": true, + "tabbable": null, + "toolbar": "IPY_MODEL_a05a90e2179c4803a9a3b84142454738", + "toolbar_position": "left", + "toolbar_visible": "fade-in-fade-out", + "tooltip": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/docs/docs/tutorials/tutorial0_basics.ipynb b/docs/docs/tutorials/tutorial0_basics.ipynb index 548c6c71..84b9fdf8 100644 --- a/docs/docs/tutorials/tutorial0_basics.ipynb +++ b/docs/docs/tutorials/tutorial0_basics.ipynb @@ -15,7 +15,14 @@ "cell_type": "code", "execution_count": null, "id": "bca91d3c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:46.620978Z", + "iopub.status.busy": "2026-06-22T17:33:46.620403Z", + "iopub.status.idle": "2026-06-22T17:33:48.958838Z", + "shell.execute_reply": "2026-06-22T17:33:48.957685Z" + } + }, "outputs": [], "source": [ "# Imports\n", @@ -50,7 +57,14 @@ "cell_type": "code", "execution_count": null, "id": "7121f9c8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:48.962395Z", + "iopub.status.busy": "2026-06-22T17:33:48.961955Z", + "iopub.status.idle": "2026-06-22T17:33:48.986730Z", + "shell.execute_reply": "2026-06-22T17:33:48.985857Z" + } + }, "outputs": [], "source": [ "# Load the data\n", @@ -85,7 +99,14 @@ "cell_type": "code", "execution_count": null, "id": "fbd31297", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:48.990188Z", + "iopub.status.busy": "2026-06-22T17:33:48.990034Z", + "iopub.status.idle": "2026-06-22T17:33:49.123492Z", + "shell.execute_reply": "2026-06-22T17:33:49.122479Z" + } + }, "outputs": [], "source": [ "experiment.plot_data(slicer=True)" @@ -103,7 +124,14 @@ "cell_type": "code", "execution_count": null, "id": "04644ee2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.126573Z", + "iopub.status.busy": "2026-06-22T17:33:49.126200Z", + "iopub.status.idle": "2026-06-22T17:33:49.228030Z", + "shell.execute_reply": "2026-06-22T17:33:49.226743Z" + } + }, "outputs": [], "source": [ "experiment.rebin({'Q': 16, 'energy': 256})\n", @@ -129,7 +157,14 @@ "cell_type": "code", "execution_count": null, "id": "6762faba", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.230651Z", + "iopub.status.busy": "2026-06-22T17:33:49.230497Z", + "iopub.status.idle": "2026-06-22T17:33:49.236254Z", + "shell.execute_reply": "2026-06-22T17:33:49.235436Z" + } + }, "outputs": [], "source": [ "gaussian = sm.Gaussian(name='Gaussian', area=1, width=0.05)" @@ -168,7 +203,14 @@ "cell_type": "code", "execution_count": null, "id": "1ec6836f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.238627Z", + "iopub.status.busy": "2026-06-22T17:33:49.238437Z", + "iopub.status.idle": "2026-06-22T17:33:49.243536Z", + "shell.execute_reply": "2026-06-22T17:33:49.242395Z" + } + }, "outputs": [], "source": [ "model = sm.SampleModel(components=gaussian)" @@ -195,7 +237,14 @@ "cell_type": "code", "execution_count": null, "id": "b98d63dc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.245934Z", + "iopub.status.busy": "2026-06-22T17:33:49.245772Z", + "iopub.status.idle": "2026-06-22T17:33:49.250128Z", + "shell.execute_reply": "2026-06-22T17:33:49.249588Z" + } + }, "outputs": [], "source": [ "analysis = edyn.Analysis(\n", @@ -218,7 +267,14 @@ "cell_type": "code", "execution_count": null, "id": "fdddd14a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.252008Z", + "iopub.status.busy": "2026-06-22T17:33:49.251866Z", + "iopub.status.idle": "2026-06-22T17:33:49.265463Z", + "shell.execute_reply": "2026-06-22T17:33:49.264770Z" + } + }, "outputs": [], "source": [ "analysis_list = analysis.analysis_list" @@ -236,7 +292,14 @@ "cell_type": "code", "execution_count": null, "id": "8c13a87a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.267776Z", + "iopub.status.busy": "2026-06-22T17:33:49.267632Z", + "iopub.status.idle": "2026-06-22T17:33:49.490163Z", + "shell.execute_reply": "2026-06-22T17:33:49.489462Z" + } + }, "outputs": [], "source": [ "analysis.plot_data_and_model()" @@ -256,7 +319,14 @@ "cell_type": "code", "execution_count": null, "id": "aec75b7f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.494049Z", + "iopub.status.busy": "2026-06-22T17:33:49.493898Z", + "iopub.status.idle": "2026-06-22T17:33:49.598314Z", + "shell.execute_reply": "2026-06-22T17:33:49.597773Z" + } + }, "outputs": [], "source": [ "fit_result_independent_single_Q = analysis.fit(Q_index=5)\n", @@ -275,7 +345,14 @@ "cell_type": "code", "execution_count": null, "id": "6d074733", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.600343Z", + "iopub.status.busy": "2026-06-22T17:33:49.600170Z", + "iopub.status.idle": "2026-06-22T17:33:49.604803Z", + "shell.execute_reply": "2026-06-22T17:33:49.604119Z" + } + }, "outputs": [], "source": [ "analysis.analysis_list[5].get_all_parameters()" @@ -300,7 +377,14 @@ "cell_type": "code", "execution_count": null, "id": "e98e3d65", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.606650Z", + "iopub.status.busy": "2026-06-22T17:33:49.606510Z", + "iopub.status.idle": "2026-06-22T17:33:49.919189Z", + "shell.execute_reply": "2026-06-22T17:33:49.918364Z" + } + }, "outputs": [], "source": [ "fit_result_all_Q = analysis.fit()\n", @@ -319,7 +403,14 @@ "cell_type": "code", "execution_count": null, "id": "c5a68515", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.921196Z", + "iopub.status.busy": "2026-06-22T17:33:49.921047Z", + "iopub.status.idle": "2026-06-22T17:33:49.924975Z", + "shell.execute_reply": "2026-06-22T17:33:49.924271Z" + } + }, "outputs": [], "source": [ "print(fit_result_all_Q[5])" @@ -337,7 +428,14 @@ "cell_type": "code", "execution_count": null, "id": "133e682e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.926697Z", + "iopub.status.busy": "2026-06-22T17:33:49.926558Z", + "iopub.status.idle": "2026-06-22T17:33:49.953865Z", + "shell.execute_reply": "2026-06-22T17:33:49.952701Z" + } + }, "outputs": [], "source": [ "analysis.parameters_to_dataset()" @@ -355,7 +453,14 @@ "cell_type": "code", "execution_count": null, "id": "dfacdf24", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:49.956004Z", + "iopub.status.busy": "2026-06-22T17:33:49.955845Z", + "iopub.status.idle": "2026-06-22T17:33:50.014820Z", + "shell.execute_reply": "2026-06-22T17:33:50.014378Z" + } + }, "outputs": [], "source": [ "analysis.plot_parameters(names=['Gaussian area'])" @@ -373,7 +478,14 @@ "cell_type": "code", "execution_count": null, "id": "3af8c1f1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:50.016962Z", + "iopub.status.busy": "2026-06-22T17:33:50.016807Z", + "iopub.status.idle": "2026-06-22T17:33:50.027948Z", + "shell.execute_reply": "2026-06-22T17:33:50.026913Z" + } + }, "outputs": [], "source": [ "energy = sc.linspace('energy', -3.5, 3.5, num=1001, unit='meV')\n", @@ -385,28 +497,22 @@ "cell_type": "markdown", "id": "842c1f01", "metadata": {}, - "source": [ - "The final step in this tutorial is to fit the are of the `Gaussian` to a straight line. For this, we use the `ParameterAnalysis` class. We create a `Polynomial` with two coefficients for the fit function. We create a `FitBinding`, telling the class we want to fit the parameter named `Gaussian area` with the fit function that we define." - ] + "source": "The final step in this tutorial is to fit the area of the `Gaussian` as a function of Q using a straight line. For this, we use the `ParameterAnalysis` class.\n\nWe create a `Polynomial` with two coefficients as the fit function. Two units must be set:\n- `x_unit='1/angstrom'` — because `ParameterAnalysis` always uses Q as its x-axis.\n- `y_unit='meV'` — because we are fitting `Gaussian area`, which has unit `meV` (area_unit = x_unit × y_unit = meV × dimensionless = meV for the Gaussian).\n\nWe then create a `FitBinding` to connect the parameter named `Gaussian area` to the fit function, and pass both to a `ParameterAnalysis` object:" }, { "cell_type": "code", "execution_count": null, "id": "75db3d4c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:50.029866Z", + "iopub.status.busy": "2026-06-22T17:33:50.029723Z", + "iopub.status.idle": "2026-06-22T17:33:50.036084Z", + "shell.execute_reply": "2026-06-22T17:33:50.035533Z" + } + }, "outputs": [], - "source": [ - "fit_func = sm.Polynomial(\n", - " coefficients=[3.7, -0.5], name='Straight line', display_name='Straight line'\n", - ")\n", - "\n", - "binding = edyn.FitBinding(parameter_name='Gaussian area', model=fit_func)\n", - "\n", - "parameter_analysis = edyn.ParameterAnalysis(\n", - " parameters=analysis,\n", - " bindings=[binding],\n", - ")" - ] + "source": "fit_func = sm.Polynomial(\n coefficients=[3.7, -0.5],\n x_unit='1/angstrom',\n y_unit='meV',\n name='Straight line',\n display_name='Straight line',\n)\n\nbinding = edyn.FitBinding(parameter_name='Gaussian area', model=fit_func)\n\nparameter_analysis = edyn.ParameterAnalysis(\n parameters=analysis,\n bindings=[binding],\n)" }, { "cell_type": "markdown", @@ -420,7 +526,14 @@ "cell_type": "code", "execution_count": null, "id": "bd3cf4a6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:50.037706Z", + "iopub.status.busy": "2026-06-22T17:33:50.037562Z", + "iopub.status.idle": "2026-06-22T17:33:50.105812Z", + "shell.execute_reply": "2026-06-22T17:33:50.104784Z" + } + }, "outputs": [], "source": [ "parameter_analysis.plot()" @@ -438,7 +551,14 @@ "cell_type": "code", "execution_count": null, "id": "d5ba8985", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:50.108662Z", + "iopub.status.busy": "2026-06-22T17:33:50.108507Z", + "iopub.status.idle": "2026-06-22T17:33:50.178169Z", + "shell.execute_reply": "2026-06-22T17:33:50.177271Z" + } + }, "outputs": [], "source": [ "parameter_analysis.fit()\n", @@ -457,7 +577,14 @@ "cell_type": "code", "execution_count": null, "id": "f18e2944", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:50.180402Z", + "iopub.status.busy": "2026-06-22T17:33:50.180248Z", + "iopub.status.idle": "2026-06-22T17:33:50.186234Z", + "shell.execute_reply": "2026-06-22T17:33:50.185260Z" + } + }, "outputs": [], "source": [ "parameter_analysis.get_all_parameters()" @@ -467,7 +594,14 @@ "cell_type": "code", "execution_count": null, "id": "068f755c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:33:50.188124Z", + 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"2026-06-22T17:34:15.407730Z", + "iopub.status.idle": "2026-06-22T17:34:17.643738Z", + "shell.execute_reply": "2026-06-22T17:34:17.643123Z" + } + }, "outputs": [], "source": [ "# Imports\n", @@ -38,10 +45,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "7121f9c8", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:17.646221Z", + "iopub.status.busy": "2026-06-22T17:34:17.645985Z", + "iopub.status.idle": "2026-06-22T17:34:17.787848Z", + "shell.execute_reply": "2026-06-22T17:34:17.787191Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "049d1a733b3b4380b00c1bcadccc779d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Load the data\n", "experiment = edyn.Experiment(display_name='Tutorial')\n", @@ -68,9 +98,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "6762faba", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:17.790167Z", + "iopub.status.busy": "2026-06-22T17:34:17.789858Z", + "iopub.status.idle": "2026-06-22T17:34:17.797274Z", + "shell.execute_reply": "2026-06-22T17:34:17.796415Z" + } + }, "outputs": [], "source": [ "gaussian = sm.Gaussian(name='Gaussian', area=3, width=0.05)\n", @@ -99,9 +136,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "1ec6836f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:17.799168Z", + "iopub.status.busy": "2026-06-22T17:34:17.799021Z", + "iopub.status.idle": "2026-06-22T17:34:17.805020Z", + "shell.execute_reply": "2026-06-22T17:34:17.803744Z" + } + }, "outputs": [], "source": [ "model = sm.SampleModel(components=collection)" @@ -125,9 +169,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "8b82f8a4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:17.809136Z", + "iopub.status.busy": "2026-06-22T17:34:17.808950Z", + "iopub.status.idle": "2026-06-22T17:34:17.813668Z", + "shell.execute_reply": "2026-06-22T17:34:17.812869Z" + } + }, "outputs": [], "source": [ "instrument = sm.InstrumentModel(energy_offset=0.05)" @@ -149,9 +200,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "bdcdc438", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:17.816689Z", + "iopub.status.busy": "2026-06-22T17:34:17.816480Z", + "iopub.status.idle": "2026-06-22T17:34:17.820930Z", + "shell.execute_reply": "2026-06-22T17:34:17.820103Z" + } + }, "outputs": [], "source": [ "background = sm.BackgroundModel(components=sm.Polynomial(coefficients=[1.2, 0.05]))" @@ -173,9 +231,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "ee677467", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:17.823275Z", + "iopub.status.busy": "2026-06-22T17:34:17.822976Z", + "iopub.status.idle": "2026-06-22T17:34:17.826929Z", + "shell.execute_reply": "2026-06-22T17:34:17.826254Z" + } + }, "outputs": [], "source": [ "instrument.background_model = background" @@ -191,9 +256,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "b98d63dc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:17.829256Z", + "iopub.status.busy": "2026-06-22T17:34:17.828958Z", + "iopub.status.idle": "2026-06-22T17:34:17.833067Z", + "shell.execute_reply": "2026-06-22T17:34:17.832284Z" + } + }, "outputs": [], "source": [ "analysis = edyn.Analysis(\n", @@ -213,10 +285,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "8c13a87a", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:17.834823Z", + "iopub.status.busy": "2026-06-22T17:34:17.834672Z", + "iopub.status.idle": "2026-06-22T17:34:18.157120Z", + "shell.execute_reply": "2026-06-22T17:34:18.156522Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cefe633db07a496985bd5c79c2758c56", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analysis.plot_data_and_model()" ] @@ -231,10 +326,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "aec75b7f", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:18.159233Z", + "iopub.status.busy": "2026-06-22T17:34:18.159037Z", + "iopub.status.idle": "2026-06-22T17:34:18.350462Z", + "shell.execute_reply": "2026-06-22T17:34:18.349531Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "48e98203b5ae46138200e3f57c5fa709", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fit_result_independent_single_Q = analysis.fit(Q_index=5)\n", "analysis.plot_data_and_model(Q_index=5, plot_residuals=True)" @@ -250,10 +368,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "6d074733", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:18.352475Z", + "iopub.status.busy": "2026-06-22T17:34:18.352276Z", + "iopub.status.idle": "2026-06-22T17:34:18.357019Z", + "shell.execute_reply": "2026-06-22T17:34:18.356402Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analysis.analysis_list[5].get_all_parameters()" ] @@ -268,10 +415,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "e98e3d65", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:18.358753Z", + "iopub.status.busy": "2026-06-22T17:34:18.358612Z", + "iopub.status.idle": "2026-06-22T17:34:19.600555Z", + "shell.execute_reply": "2026-06-22T17:34:19.599522Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "503f6b5be6d340d491dc34162048a960", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fit_result_all_Q = analysis.fit()\n", "analysis.plot_data_and_model(plot_residuals=True, autoscale=False)" @@ -287,10 +457,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "dfacdf24", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.602673Z", + "iopub.status.busy": "2026-06-22T17:34:19.602513Z", + "iopub.status.idle": "2026-06-22T17:34:19.679431Z", + "shell.execute_reply": "2026-06-22T17:34:19.678223Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0382bad59eca4a69a284aa9b3039d49e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analysis.plot_parameters(names=['Gaussian area', 'DHO area', 'DHO center'])" ] @@ -299,36 +492,22 @@ "cell_type": "markdown", "id": "0eadbd91", "metadata": {}, - "source": [ - "With apologies for the lack of creativity, these all appear like straight lines. We can fit them individually or all together using `ParameterAnalysis`" - ] + "source": "With apologies for the lack of creativity, these all appear like straight lines. We can fit them individually or all together using `ParameterAnalysis`.\n\nFor each parameter we want to fit, we create a fit function (here a `Polynomial`) and a `FitBinding` connecting the parameter name to the fit function. Two units must be set on each fit function:\n- `x_unit='1/angstrom'` — because `ParameterAnalysis` always uses Q as its x-axis.\n- `y_unit='meV'` — because all three parameters (`Gaussian area`, `DHO area`, `DHO center`) have unit `meV`.\n\nWe then pass all bindings to a single `ParameterAnalysis` object:" }, { "cell_type": "code", "execution_count": null, "id": "bb2f0e06", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.682003Z", + "iopub.status.busy": "2026-06-22T17:34:19.681853Z", + "iopub.status.idle": "2026-06-22T17:34:19.775011Z", + "shell.execute_reply": "2026-06-22T17:34:19.773932Z" + } + }, "outputs": [], - "source": [ - "gauss_fit_func = sm.Polynomial(coefficients=[3.7, -0.5], unit='1/angstrom', name='Gauss area fit')\n", - "dho_area_fit_func = sm.Polynomial(coefficients=[2.0, 0.12], unit='1/angstrom', name='DHO area fit')\n", - "dho_center_fit_func = sm.Polynomial(\n", - " coefficients=[1.1, 0.2], unit='1/angstrom', name='DHO center fit'\n", - ")\n", - "\n", - "binding1 = edyn.FitBinding(parameter_name='Gaussian area', model=gauss_fit_func)\n", - "\n", - "binding2 = edyn.FitBinding(parameter_name='DHO area', model=dho_area_fit_func)\n", - "\n", - "binding3 = edyn.FitBinding(parameter_name='DHO center', model=dho_center_fit_func)\n", - "\n", - "parameter_analysis = edyn.ParameterAnalysis(\n", - " parameters=analysis,\n", - " bindings=[binding1, binding2, binding3],\n", - ")\n", - "\n", - "parameter_analysis.plot()" - ] + "source": "gauss_fit_func = sm.Polynomial(\n coefficients=[3.7, -0.5], x_unit='1/angstrom', y_unit='meV', name='Gauss area fit'\n)\ndho_area_fit_func = sm.Polynomial(\n coefficients=[2.0, 0.12], x_unit='1/angstrom', y_unit='meV', name='DHO area fit'\n)\ndho_center_fit_func = sm.Polynomial(\n coefficients=[1.1, 0.2], x_unit='1/angstrom', y_unit='meV', name='DHO center fit'\n)\n\nbinding1 = edyn.FitBinding(parameter_name='Gaussian area', model=gauss_fit_func)\n\nbinding2 = edyn.FitBinding(parameter_name='DHO area', model=dho_area_fit_func)\n\nbinding3 = edyn.FitBinding(parameter_name='DHO center', model=dho_center_fit_func)\n\nparameter_analysis = edyn.ParameterAnalysis(\n parameters=analysis,\n bindings=[binding1, binding2, binding3],\n)\n\nparameter_analysis.plot()" }, { "cell_type": "markdown", @@ -340,10 +519,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "a5548093", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:19.778211Z", + "iopub.status.busy": "2026-06-22T17:34:19.777810Z", + "iopub.status.idle": "2026-06-22T17:34:19.871894Z", + "shell.execute_reply": "2026-06-22T17:34:19.870515Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "29eb5bb52f1f41b99f2976f0726416db", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), 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null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/docs/tutorials/tutorial1_brownian.ipynb b/docs/docs/tutorials/tutorial1_brownian.ipynb index 269bb259..2426abc8 100644 --- a/docs/docs/tutorials/tutorial1_brownian.ipynb +++ b/docs/docs/tutorials/tutorial1_brownian.ipynb @@ -13,9 +13,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "bca91d3c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:28.163728Z", + "iopub.status.busy": "2026-06-22T17:34:28.163580Z", + "iopub.status.idle": "2026-06-22T17:34:30.340106Z", + "shell.execute_reply": "2026-06-22T17:34:30.338613Z" + } + }, "outputs": [], "source": [ "# Imports\n", @@ -40,9 +47,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "8deca9b6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:30.343307Z", + "iopub.status.busy": "2026-06-22T17:34:30.343042Z", + "iopub.status.idle": "2026-06-22T17:34:30.354789Z", + "shell.execute_reply": "2026-06-22T17:34:30.353195Z" + } + }, "outputs": [], "source": [ "# Load the vanadium data\n", @@ -71,20 +85,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "fbd31297", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:30.358727Z", + "iopub.status.busy": "2026-06-22T17:34:30.358108Z", + "iopub.status.idle": "2026-06-22T17:34:30.471821Z", + "shell.execute_reply": "2026-06-22T17:34:30.470955Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f5ae7fda48dd432786cd8776b85bad60", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "vanadium_experiment.plot_data(slicer=False, transpose_axes=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "4153ba52", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:30.473933Z", + "iopub.status.busy": "2026-06-22T17:34:30.473767Z", + "iopub.status.idle": "2026-06-22T17:34:30.564045Z", + "shell.execute_reply": "2026-06-22T17:34:30.563148Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "65226679e9444afda79057dfa9cda071", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "vanadium_experiment.plot_data(slicer=True)" ] @@ -107,9 +167,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "8fdb7f19", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:30.566078Z", + "iopub.status.busy": "2026-06-22T17:34:30.565917Z", + "iopub.status.idle": "2026-06-22T17:34:30.571635Z", + "shell.execute_reply": "2026-06-22T17:34:30.570675Z" + } + }, "outputs": [], "source": [ "vanadium_components = sm.ComponentCollection()\n", @@ -128,9 +195,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "1ec6836f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:30.574006Z", + "iopub.status.busy": "2026-06-22T17:34:30.573854Z", + "iopub.status.idle": "2026-06-22T17:34:30.577753Z", + "shell.execute_reply": "2026-06-22T17:34:30.577273Z" + } + }, "outputs": [], "source": [ "background_model = sm.BackgroundModel(components=sm.Polynomial(coefficients=[0.001]))" @@ -146,9 +220,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "0ad79a75", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:30.579692Z", + "iopub.status.busy": "2026-06-22T17:34:30.579546Z", + "iopub.status.idle": "2026-06-22T17:34:30.583938Z", + "shell.execute_reply": "2026-06-22T17:34:30.583162Z" + } + }, "outputs": [], "source": [ "instrument_model = sm.InstrumentModel(\n", @@ -166,9 +247,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "b98d63dc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:30.586001Z", + "iopub.status.busy": "2026-06-22T17:34:30.585860Z", + "iopub.status.idle": "2026-06-22T17:34:30.590816Z", + "shell.execute_reply": "2026-06-22T17:34:30.589412Z" + } + }, "outputs": [], "source": [ "vanadium_analysis = edyn.Analysis(\n", @@ -189,10 +277,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "aec75b7f", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:30.592576Z", + "iopub.status.busy": "2026-06-22T17:34:30.592441Z", + "iopub.status.idle": "2026-06-22T17:34:30.816387Z", + "shell.execute_reply": "2026-06-22T17:34:30.815539Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a7100471d7b9496c978e58d3f070804d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fit_result_independent_single_Q = vanadium_analysis.fit(fit_method='independent', Q_index=5)\n", "vanadium_analysis.plot_data_and_model(Q_index=5)" @@ -208,10 +319,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "e98e3d65", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:30.818288Z", + "iopub.status.busy": "2026-06-22T17:34:30.818130Z", + "iopub.status.idle": "2026-06-22T17:34:31.103628Z", + "shell.execute_reply": "2026-06-22T17:34:31.102815Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b6ee814b9409431ea825d3e79a0b1920", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fit_result_independent_all_Q = vanadium_analysis.fit(fit_method='independent')\n", "vanadium_analysis.plot_data_and_model()" @@ -229,10 +363,85 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "133e682e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.105674Z", + "iopub.status.busy": "2026-06-22T17:34:31.105518Z", + "iopub.status.idle": "2026-06-22T17:34:31.119445Z", + "shell.execute_reply": "2026-06-22T17:34:31.118791Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
scipp.Dataset (4.92 KB)
    • Q: 16
    • Q
      (Q)
      float64
      1/Å
      0.1, 0.227, ..., 1.873, 2.0
      Values:
      array([0.1       , 0.22666667, 0.35333333, 0.48      , 0.60666667,\n",
+       "       0.73333333, 0.86      , 0.98666667, 1.11333333, 1.24      ,\n",
+       "       1.36666667, 1.49333333, 1.62      , 1.74666667, 1.87333333,\n",
+       "       2.        ])
    • Polynomial_c0
      (Q)
      float64
      𝟙
      0.099, 0.095, ..., 0.098, 0.101
      σ = 0.002, 0.002, ..., 0.002, 0.002
      Values:
      array([0.09942061, 0.09482336, 0.10250515, 0.09830358, 0.10212681,\n",
+       "       0.1023704 , 0.09957118, 0.1020655 , 0.09643967, 0.10180256,\n",
+       "       0.10191825, 0.09198844, 0.09427405, 0.09806923, 0.09764543,\n",
+       "       0.10051938])

      Variances (σ²):
      array([5.53666458e-06, 5.69780006e-06, 4.81032728e-06, 4.89897712e-06,\n",
+       "       4.99355959e-06, 4.08027374e-06, 7.91272766e-06, 4.56234563e-06,\n",
+       "       5.13312615e-06, 6.29261176e-06, 5.53177991e-06, 7.73519893e-06,\n",
+       "       6.33375780e-06, 5.95523123e-06, 4.94577573e-06, 4.98191241e-06])
    • Res. Gauss area
      (Q)
      float64
      meV
      0.522, 0.529, ..., 0.538, 0.533
      σ = 0.018, 0.018, ..., 0.017, 0.017
      Values:
      array([0.52209408, 0.5285229 , 0.51741732, 0.53239306, 0.52764215,\n",
+       "       0.5299243 , 0.52765768, 0.51477418, 0.52913757, 0.53258438,\n",
+       "       0.5324246 , 0.53238945, 0.52271039, 0.52427006, 0.53758902,\n",
+       "       0.53293296])

      Variances (σ²):
      array([0.00030823, 0.00033506, 0.00025861, 0.00028047, 0.00027407,\n",
+       "       0.0002244 , 0.00044408, 0.00024497, 0.00029737, 0.00034951,\n",
+       "       0.00030663, 0.00047018, 0.00037023, 0.00033697, 0.00028746,\n",
+       "       0.00028007])
    • Res. Gauss center
      (Q)
      float64
      meV
      0.0, 0.0, ..., 0.0, 0.0
      σ = 0.0, 0.0, ..., 0.0, 0.0
      Values:
      array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

      Variances (σ²):
      array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
    • Res. Gauss width
      (Q)
      float64
      meV
      0.102, 0.100, ..., 0.103, 0.102
      σ = 0.003, 0.003, ..., 0.003, 0.003
      Values:
      array([0.10222849, 0.09980219, 0.10094395, 0.10308798, 0.10108169,\n",
+       "       0.10126272, 0.10063122, 0.09902007, 0.10372129, 0.10254205,\n",
+       "       0.10142844, 0.10315841, 0.10076283, 0.10044881, 0.10346177,\n",
+       "       0.10187953])

      Variances (σ²):
      array([9.25121501e-06, 8.81462902e-06, 7.61444384e-06, 8.02505303e-06,\n",
+       "       7.63407023e-06, 6.32632931e-06, 1.23202070e-05, 6.87361362e-06,\n",
+       "       8.78013502e-06, 9.96635847e-06, 8.46568221e-06, 1.33366848e-05,\n",
+       "       1.06131452e-05, 9.28744245e-06, 8.02343194e-06, 7.89725747e-06])
    • energy_offset
      (Q)
      float64
      meV
      0.001, -0.001, ..., -0.001, 0.001
      σ = 0.004, 0.004, ..., 0.003, 0.003
      Values:
      array([ 0.00135435, -0.00052522,  0.00093028, -0.00192069, -0.00366331,\n",
+       "        0.00128853,  0.000512  ,  0.00090986, -0.00100558,  0.00036124,\n",
+       "        0.00054189, -0.00216345, -0.00137654,  0.00129853, -0.001399  ,\n",
+       "        0.00055349])

      Variances (σ²):
      array([1.33891038e-05, 1.37163919e-05, 1.12418484e-05, 1.18955355e-05,\n",
+       "       1.14728274e-05, 9.35171954e-06, 1.83656779e-05, 1.03814854e-05,\n",
+       "       1.29102630e-05, 1.47729560e-05, 1.26723274e-05, 1.99321937e-05,\n",
+       "       1.56095496e-05, 1.41010062e-05, 1.20573093e-05, 1.16915411e-05])
" + ], + "text/plain": [ + "\n", + "Dimensions: Sizes[Q:16, ]\n", + "Coordinates:\n", + "* Q float64 [1/Å] (Q) [0.1, 0.226667, ..., 1.87333, 2]\n", + "Data:\n", + " Polynomial_c0 float64 [dimensionless] (Q) [0.0994206, 0.0948234, ..., 0.0976454, 0.100519] [5.53666e-06, 5.6978e-06, ..., 4.94578e-06, 4.98191e-06]\n", + " Res. Gauss area float64 [meV] (Q) [0.522094, 0.528523, ..., 0.537589, 0.532933] [0.000308233, 0.000335062, ..., 0.000287459, 0.000280073]\n", + " Res. Gauss center float64 [meV] (Q) [0, 0, ..., 0, 0] [0, 0, ..., 0, 0]\n", + " Res. Gauss width float64 [meV] (Q) [0.102228, 0.0998022, ..., 0.103462, 0.10188] [9.25122e-06, 8.81463e-06, ..., 8.02343e-06, 7.89726e-06]\n", + " energy_offset float64 [meV] (Q) [0.00135435, -0.000525217, ..., -0.001399, 0.000553493] [1.33891e-05, 1.37164e-05, ..., 1.20573e-05, 1.16915e-05]\n" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Inspect the Parameters as a scipp Dataset\n", "vanadium_analysis.parameters_to_dataset()" @@ -240,10 +449,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "dfacdf24", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.121513Z", + "iopub.status.busy": "2026-06-22T17:34:31.121352Z", + "iopub.status.idle": "2026-06-22T17:34:31.184266Z", + "shell.execute_reply": "2026-06-22T17:34:31.183143Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1c6bed1b21f34be386361e50e0cdee88", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Plot some of fitted parameters as a function of Q\n", "vanadium_analysis.plot_parameters(names=['Res. Gauss area'])" @@ -251,20 +483,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "b6f9f316", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.188104Z", + "iopub.status.busy": "2026-06-22T17:34:31.187948Z", + "iopub.status.idle": "2026-06-22T17:34:31.245172Z", + "shell.execute_reply": "2026-06-22T17:34:31.244427Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9aadf20e701b43dfa2ef2ae4fa9cab70", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "vanadium_analysis.plot_parameters(names=['Res. Gauss width'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "572664a0", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.247189Z", + "iopub.status.busy": "2026-06-22T17:34:31.246998Z", + "iopub.status.idle": "2026-06-22T17:34:31.304585Z", + "shell.execute_reply": "2026-06-22T17:34:31.303293Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "572695771d864d7bbddf2160d78d212b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "vanadium_analysis.plot_parameters(names=['energy_offset'])" ] @@ -279,9 +557,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "3609e6c1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.307371Z", + "iopub.status.busy": "2026-06-22T17:34:31.307197Z", + "iopub.status.idle": "2026-06-22T17:34:31.316069Z", + "shell.execute_reply": "2026-06-22T17:34:31.315054Z" + } + }, "outputs": [], "source": [ "diffusion_experiment = edyn.Experiment('Diffusion')\n", @@ -296,10 +581,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "ba9b14cc", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.318919Z", + "iopub.status.busy": "2026-06-22T17:34:31.318735Z", + "iopub.status.idle": "2026-06-22T17:34:31.407887Z", + "shell.execute_reply": "2026-06-22T17:34:31.407086Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "01ac3dbfdad540a2b892bc4e3721021f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diffusion_experiment.plot_data(slicer=True, ymax=4)" ] @@ -314,9 +622,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "e685909a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.410069Z", + "iopub.status.busy": "2026-06-22T17:34:31.409842Z", + "iopub.status.idle": "2026-06-22T17:34:31.417145Z", + "shell.execute_reply": "2026-06-22T17:34:31.415876Z" + } + }, "outputs": [], "source": [ "delta_function = sm.DeltaFunction(name='DeltaFunction', area=0.2)\n", @@ -342,9 +657,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "53ecf27e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.419344Z", + "iopub.status.busy": "2026-06-22T17:34:31.419167Z", + "iopub.status.idle": "2026-06-22T17:34:31.499200Z", + "shell.execute_reply": "2026-06-22T17:34:31.498134Z" + } + }, "outputs": [], "source": [ "instrument_model = sm.InstrumentModel(\n", @@ -375,9 +697,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "0fb0c648", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.501446Z", + "iopub.status.busy": "2026-06-22T17:34:31.501292Z", + "iopub.status.idle": "2026-06-22T17:34:31.505688Z", + "shell.execute_reply": "2026-06-22T17:34:31.504813Z" + } + }, "outputs": [], "source": [ "diffusion_analysis.convolution_settings.upsample_factor = 6\n", @@ -395,10 +724,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "c66828eb", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.508013Z", + "iopub.status.busy": "2026-06-22T17:34:31.507864Z", + "iopub.status.idle": "2026-06-22T17:34:31.804228Z", + "shell.execute_reply": "2026-06-22T17:34:31.803464Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e408a474e89a4e91bb8e6b737727c09e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diffusion_analysis.plot_data_and_model()" ] @@ -413,10 +765,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "9678eede", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.806004Z", + "iopub.status.busy": "2026-06-22T17:34:31.805862Z", + "iopub.status.idle": "2026-06-22T17:34:32.392454Z", + "shell.execute_reply": "2026-06-22T17:34:32.391748Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b35b20fe177d43638995d6184eaf713e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diffusion_analysis.fit(fit_method='independent')\n", "diffusion_analysis.plot_data_and_model()" @@ -432,10 +807,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "df14b5c4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:32.394440Z", + "iopub.status.busy": "2026-06-22T17:34:32.394294Z", + "iopub.status.idle": "2026-06-22T17:34:32.467824Z", + "shell.execute_reply": "2026-06-22T17:34:32.466717Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "78454af3b15d4b06812b0a7060b458ca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diffusion_analysis.plot_parameters(names=['Lorentzian width', 'Lorentzian area'])" ] @@ -456,9 +854,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "9c1ab6b0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:32.469990Z", + "iopub.status.busy": "2026-06-22T17:34:32.469841Z", + "iopub.status.idle": "2026-06-22T17:34:32.479837Z", + "shell.execute_reply": "2026-06-22T17:34:32.479158Z" + } + }, "outputs": [], "source": [ "brownian_diffusion_model = sm.BrownianTranslationalDiffusion(\n", @@ -487,20 +892,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "c7074c64", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:32.481754Z", + "iopub.status.busy": "2026-06-22T17:34:32.481611Z", + "iopub.status.idle": "2026-06-22T17:34:32.559460Z", + "shell.execute_reply": "2026-06-22T17:34:32.558500Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "358bc0c8f44f43f69019b2a0930e0b1f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "parameter_analysis.plot()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "57b76d06", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:32.561192Z", + "iopub.status.busy": "2026-06-22T17:34:32.561029Z", + "iopub.status.idle": "2026-06-22T17:34:32.634589Z", + "shell.execute_reply": "2026-06-22T17:34:32.633401Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eaea1561cdf041d3806d27e32257d1de", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "parameter_analysis.plot(names=['Polynomial_c0'])" ] @@ -515,10 +966,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "26c418ef", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:32.636775Z", + "iopub.status.busy": "2026-06-22T17:34:32.636616Z", + "iopub.status.idle": "2026-06-22T17:34:32.755452Z", + "shell.execute_reply": "2026-06-22T17:34:32.754696Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "234871c959a34b84b24983807b3d9268", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "parameter_analysis.fit()\n", "parameter_analysis.plot()" @@ -534,10 +1008,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "7c47bcd4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:32.757508Z", + "iopub.status.busy": "2026-06-22T17:34:32.757341Z", + "iopub.status.idle": "2026-06-22T17:34:32.762185Z", + "shell.execute_reply": "2026-06-22T17:34:32.761531Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "parameter_analysis.get_all_parameters()" ] @@ -556,9 +1049,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "3b6bba77", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:32.764164Z", + "iopub.status.busy": "2026-06-22T17:34:32.764017Z", + "iopub.status.idle": "2026-06-22T17:34:32.772534Z", + "shell.execute_reply": "2026-06-22T17:34:32.771513Z" + } + }, "outputs": [], "source": [ "delta_function = sm.DeltaFunction(name='DeltaFunction', area=0.2)\n", @@ -579,9 +1079,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "48370156", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:32.774715Z", + "iopub.status.busy": "2026-06-22T17:34:32.774559Z", + "iopub.status.idle": "2026-06-22T17:34:32.878995Z", + "shell.execute_reply": "2026-06-22T17:34:32.877875Z" + } + }, "outputs": [], "source": [ "instrument_model = sm.InstrumentModel(\n", @@ -600,9 +1107,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "3efb9b53", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:32.881225Z", + "iopub.status.busy": "2026-06-22T17:34:32.881064Z", + "iopub.status.idle": "2026-06-22T17:34:33.056824Z", + "shell.execute_reply": "2026-06-22T17:34:33.055823Z" + } + }, "outputs": [], "source": [ "diffusion_model_analysis = edyn.Analysis(\n", @@ -625,10 +1139,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "683dd27c", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:33.058990Z", + "iopub.status.busy": "2026-06-22T17:34:33.058844Z", + "iopub.status.idle": 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"_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/docs/docs/tutorials/tutorial2_nanoparticles.ipynb b/docs/docs/tutorials/tutorial2_nanoparticles.ipynb index 22de2490..79d1aa3a 100644 --- a/docs/docs/tutorials/tutorial2_nanoparticles.ipynb +++ b/docs/docs/tutorials/tutorial2_nanoparticles.ipynb @@ -34,9 +34,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "bca91d3c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:31.995526Z", + "iopub.status.busy": "2026-06-22T17:34:31.995214Z", + "iopub.status.idle": "2026-06-22T17:34:34.222201Z", + "shell.execute_reply": "2026-06-22T17:34:34.220408Z" + } + }, "outputs": [], "source": [ "import pooch\n", @@ -60,9 +67,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "4fbb90da", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:34.224639Z", + "iopub.status.busy": "2026-06-22T17:34:34.224403Z", + "iopub.status.idle": "2026-06-22T17:34:34.243550Z", + "shell.execute_reply": "2026-06-22T17:34:34.242414Z" + } + }, "outputs": [], "source": [ "resolution_experiment = edyn.Experiment(display_name='Nanoparticles, 1.5 K')\n", @@ -85,10 +99,173 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "edd030f7", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:34.246033Z", + "iopub.status.busy": "2026-06-22T17:34:34.245849Z", + "iopub.status.idle": "2026-06-22T17:34:34.608329Z", + "shell.execute_reply": "2026-06-22T17:34:34.606787Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXGeneral'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXGeneral'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXGeneral'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXGeneral'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXNonUnicode'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXNonUnicode'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXNonUnicode'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXSizeOneSym'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXSizeTwoSym'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXSizeThreeSym'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXSizeFourSym'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['STIXSizeFiveSym'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['cmsy10'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['cmr10'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['cmtt10'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['cmmi10'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['cmb10'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['cmss10'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['cmex10'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "50b30add0b9b46bd8055550408a8e851", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "resolution_experiment.plot_data(slicer=True, logy=True)" ] @@ -103,10 +280,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "a547e75e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:34.614394Z", + "iopub.status.busy": "2026-06-22T17:34:34.613785Z", + "iopub.status.idle": "2026-06-22T17:34:34.726789Z", + "shell.execute_reply": "2026-06-22T17:34:34.725848Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5cd68e393bd347b28a36f9319b1d18a3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "E_min = sc.scalar(-0.2, unit='meV')\n", "E_max = sc.scalar(0.2, unit='meV')\n", @@ -130,10 +330,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "61836b56", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:34.729468Z", + "iopub.status.busy": "2026-06-22T17:34:34.729267Z", + "iopub.status.idle": "2026-06-22T17:34:34.867297Z", + "shell.execute_reply": "2026-06-22T17:34:34.866136Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "371235747d1847bbb48201fea7366afd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "res_sample_model = sm.SampleModel()\n", "res_components = sm.ComponentCollection()\n", @@ -171,10 +394,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "1589d346", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:34.869405Z", + "iopub.status.busy": "2026-06-22T17:34:34.869223Z", + "iopub.status.idle": "2026-06-22T17:34:35.058674Z", + "shell.execute_reply": "2026-06-22T17:34:35.057934Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "34f1da406a0f4bf0aec2edb0287ae2ed", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "res_analysis.fit()\n", "res_analysis.plot_data_and_model()" @@ -190,10 +436,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "d893a696", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:35.061068Z", + "iopub.status.busy": "2026-06-22T17:34:35.060901Z", + "iopub.status.idle": "2026-06-22T17:34:35.321878Z", + "shell.execute_reply": "2026-06-22T17:34:35.321076Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "80d7ec54819e43558e8f8215cc107c01", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "experiment = edyn.Experiment(display_name='Nanoparticles, 150 K')\n", "\n", @@ -219,10 +488,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "90d1b5c9", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:35.324695Z", + "iopub.status.busy": "2026-06-22T17:34:35.324483Z", + "iopub.status.idle": "2026-06-22T17:34:35.547042Z", + "shell.execute_reply": "2026-06-22T17:34:35.546172Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b3f9ccf3726641e58b9ffcf626fa9f71", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "E_min = sc.scalar(-1.5, unit='meV')\n", "E_max = sc.scalar(1.5, unit='meV')\n", @@ -241,10 +533,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "768e0436", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:35.550174Z", + "iopub.status.busy": "2026-06-22T17:34:35.549980Z", + "iopub.status.idle": "2026-06-22T17:34:35.818975Z", + "shell.execute_reply": "2026-06-22T17:34:35.817170Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d00baac954e848af804d1debb9d31cb6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sample_model = sm.SampleModel()\n", "water_delta_function = sm.DeltaFunction(name='Water delta function', area=100)\n", @@ -283,10 +598,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "e57192b0", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:35.821064Z", + "iopub.status.busy": "2026-06-22T17:34:35.820899Z", + "iopub.status.idle": "2026-06-22T17:34:36.452569Z", + "shell.execute_reply": "2026-06-22T17:34:36.451361Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6cc33eae319647c498d4582efed70959", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analysis.fit()\n", "analysis.plot_data_and_model(logy=True, autoscale=False)" @@ -302,20 +640,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "d5b11dfc", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:36.455077Z", + "iopub.status.busy": "2026-06-22T17:34:36.454909Z", + "iopub.status.idle": "2026-06-22T17:34:36.532382Z", + "shell.execute_reply": "2026-06-22T17:34:36.531367Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7fbde7ddccf84ad8822f83a550402137", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analysis.plot_parameters(names=['Water delta function area', 'Water Lorentzian area'])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "571155a4", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:36.534787Z", + "iopub.status.busy": "2026-06-22T17:34:36.534630Z", + "iopub.status.idle": "2026-06-22T17:34:36.613288Z", + "shell.execute_reply": "2026-06-22T17:34:36.612313Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "20bba5703118447fa2c3bffd3a951d70", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analysis.plot_parameters(names=['Water Lorentzian width'])" ] @@ -334,10 +718,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "3dd44131", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:36.615996Z", + "iopub.status.busy": "2026-06-22T17:34:36.615616Z", + "iopub.status.idle": "2026-06-22T17:34:36.869755Z", + "shell.execute_reply": "2026-06-22T17:34:36.868961Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1a0478f3282a40cb89da0a233dda92ca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "delta_0 = analysis.sample_model.get_component_collection(Q_index=0)['Water delta function']\n", "delta_1 = analysis.sample_model.get_component_collection(Q_index=1)['Water delta function']\n", @@ -374,9 +781,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "28e715ad", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:36.871873Z", + "iopub.status.busy": "2026-06-22T17:34:36.871660Z", + "iopub.status.idle": "2026-06-22T17:34:36.902970Z", + "shell.execute_reply": "2026-06-22T17:34:36.901556Z" + } + }, "outputs": [], "source": [ "# Now make a new analysis with this sample model\n", @@ -419,9 +833,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "f5605058", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:36.905533Z", + "iopub.status.busy": "2026-06-22T17:34:36.905367Z", + "iopub.status.idle": "2026-06-22T17:34:36.950238Z", + "shell.execute_reply": "2026-06-22T17:34:36.949481Z" + } + }, "outputs": [], "source": [ "for Q_index in range(mag_analysis.sample_model.Q.size):\n", @@ -448,10 +869,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "51a4d277", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:36.953119Z", + "iopub.status.busy": "2026-06-22T17:34:36.952956Z", + "iopub.status.idle": "2026-06-22T17:34:37.204152Z", + "shell.execute_reply": "2026-06-22T17:34:37.203135Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "213bf0a9e4a24ffcb7a95e7cec324ebb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "for Q_index in [0, 1]:\n", " DHO1 = mag_analysis.sample_model.get_component_collection(Q_index=Q_index)['DHO1']\n", @@ -487,10 +931,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "8f14064b", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:37.206557Z", + "iopub.status.busy": "2026-06-22T17:34:37.206373Z", + "iopub.status.idle": "2026-06-22T17:34:37.886065Z", + "shell.execute_reply": "2026-06-22T17:34:37.883324Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cad75d9f9b9b42db815db12fa6da085a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mag_analysis.fit()\n", "mag_analysis.plot_data_and_model(logy=True)" @@ -506,10 +973,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "69f4683e", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-22T17:34:37.889632Z", + "iopub.status.busy": "2026-06-22T17:34:37.889461Z", + "iopub.status.idle": 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b/src/easydynamics/analysis/analysis1d.py @@ -603,7 +603,7 @@ def _evaluate_with_convolution( energy=energy_with_offset, temperature=self.temperature, divide_by_temperature=self.detailed_balance_settings.normalize_detailed_balance, - energy_unit=self.unit, + energy_unit=self.x_unit, ) return result diff --git a/src/easydynamics/analysis/fit_binding.py b/src/easydynamics/analysis/fit_binding.py index cbd108dc..04efaf1e 100644 --- a/src/easydynamics/analysis/fit_binding.py +++ b/src/easydynamics/analysis/fit_binding.py @@ -65,8 +65,8 @@ def __init__( >>> fit_func = sm.Polynomial(coefficients=[3.7, -0.5], display_name='Straight line') >>> binding = edyn.FitBinding(parameter_name='Gaussian area', model=fit_func) >>> print(binding) - FitBinding(parameter_name='Gaussian area', model=Polynomial(unique_name = Polynomial_1, - unit = meV, coefficients = [Straight line_c0=3.7, Straight line_c1=-0.5]), modes=None) + FitBinding(parameter_name=Gaussian area, model=Straight line, modes=None, + display_name=FitBinding_1, unique_name=FitBinding_1) 2. Usage with a DiffusionModelBase and specific modes: >>> brownian_diffusion_model = sm.BrownianTranslationalDiffusion( diff --git a/src/easydynamics/base_classes/easydynamics_modelbase.py b/src/easydynamics/base_classes/easydynamics_modelbase.py index 6d002f71..59955895 100644 --- a/src/easydynamics/base_classes/easydynamics_modelbase.py +++ b/src/easydynamics/base_classes/easydynamics_modelbase.py @@ -14,7 +14,8 @@ class EasyDynamicsModelBase(NameMixin, ModelBase): def __init__( self, *args: object, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'MyEasyDynamicsModel', display_name: str | None = None, unique_name: str | None = None, @@ -27,8 +28,10 @@ def __init__( ---------- *args : object Positional arguments to pass to the parent class. - unit : str | sc.Unit, default='meV' - Unit of the model. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis (energy, Q, etc.). + y_unit : str | sc.Unit, default='dimensionless' + Unit of the model output (intensity). name : str, default='MyEasyDynamicsModel' Name of the model. display_name : str | None, default=None @@ -58,37 +61,29 @@ def __init__( **kwargs, ) - self._unit = _validate_unit(unit) + self._x_unit = _validate_unit(x_unit) + self._y_unit = _validate_unit(y_unit) @property - def unit(self) -> str | sc.Unit | None: - """ - Get the unit of the model. - - Returns - ------- - str | sc.Unit | None - The unit of the model. - """ - - return self._unit + def x_unit(self) -> str | sc.Unit | None: + """Unit of the x-axis.""" + return self._x_unit - @unit.setter - def unit(self, _unit_str: str) -> None: - """ - Unit is read-only and cannot be set directly. + @x_unit.setter + def x_unit(self, _: str) -> None: + raise AttributeError( + f'x_unit is read-only. Use convert_x_unit to change the unit ' + f'or create a new {self.__class__.__name__} with the desired unit.' + ) - Parameters - ---------- - _unit_str : str - The new unit to set (ignored). + @property + def y_unit(self) -> str | sc.Unit | None: + """Unit of the model output.""" + return self._y_unit - Raises - ------ - AttributeError - Always raised to indicate that the unit is read-only. - """ + @y_unit.setter + def y_unit(self, _: str) -> None: raise AttributeError( - f'Unit is read-only. Use convert_unit to change the unit between allowed types ' + f'y_unit is read-only. Use convert_y_unit to change the unit ' f'or create a new {self.__class__.__name__} with the desired unit.' ) diff --git a/src/easydynamics/convolution/convolution.py b/src/easydynamics/convolution/convolution.py index d2151e42..62f08cd2 100644 --- a/src/easydynamics/convolution/convolution.py +++ b/src/easydynamics/convolution/convolution.py @@ -276,7 +276,7 @@ def _set_convolvers(self) -> None: temperature=self.temperature, temperature_unit=self._temperature_unit, detailed_balance_settings=self.detailed_balance_settings, - unit=self.unit, + unit=self.x_unit, ) else: self._numerical_convolver = None diff --git a/src/easydynamics/convolution/convolution_base.py b/src/easydynamics/convolution/convolution_base.py index c8516cb0..22a92d0f 100644 --- a/src/easydynamics/convolution/convolution_base.py +++ b/src/easydynamics/convolution/convolution_base.py @@ -58,7 +58,7 @@ def __init__( """ super().__init__( - unit=unit, + x_unit=unit, display_name=display_name, unique_name=unique_name, ) @@ -191,9 +191,9 @@ def energy(self, energy: np.ndarray | sc.Variable) -> None: if isinstance(energy, sc.Variable): self._energy = energy - self._unit = energy.unit + self._x_unit = energy.unit - def convert_unit(self, unit: str | sc.Unit) -> None: + def convert_x_unit(self, unit: str | sc.Unit) -> None: """ Convert the energy and energy_offset to the specified unit. @@ -226,7 +226,7 @@ def convert_unit(self, unit: str | sc.Unit) -> None: self.energy_offset = old_energy_offset raise e - self._unit = unit + self._x_unit = unit @property def sample_components(self) -> ComponentCollection | ModelComponent: diff --git a/src/easydynamics/convolution/numerical_convolution_base.py b/src/easydynamics/convolution/numerical_convolution_base.py index da17cae8..c0a89002 100644 --- a/src/easydynamics/convolution/numerical_convolution_base.py +++ b/src/easydynamics/convolution/numerical_convolution_base.py @@ -440,7 +440,7 @@ def __repr__(self) -> str: f'energy=array of shape {self.energy.values.shape},\n ' f'sample_components={self.sample_components!r}, \n' f'resolution_components={self.resolution_components!r},\n ' - f'unit={self.unit}, ' + f'unit={self.x_unit}, ' f'upsample_factor={self.upsample_factor}, ' f'extension_factor={self.extension_factor}, ' f'temperature={self.temperature}, ' diff --git a/src/easydynamics/sample_model/background_model.py b/src/easydynamics/sample_model/background_model.py index bd403ea2..a34e3714 100644 --- a/src/easydynamics/sample_model/background_model.py +++ b/src/easydynamics/sample_model/background_model.py @@ -18,31 +18,16 @@ def __init__( self, display_name: str | None = 'MyBackgroundModel', unique_name: str | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', components: ModelComponent | ComponentCollection | None = None, Q: Q_type | None = None, ) -> None: - """ - Initialize the BackgroundModel. - - Parameters - ---------- - display_name : str | None, default='MyBackgroundModel' - Display name of the model. - unique_name : str | None, default=None - Unique name of the model. If None, a unique name will be generated. - unit : str | sc.Unit, default='meV' - Unit of the model. - components : ModelComponent | ComponentCollection | None, default=None - Template components of the model. If None, no components are added. These components - are copied into ComponentCollections for each Q value. - Q : Q_type | None, default=None - Q values for the model. If None, Q is not set. - """ super().__init__( display_name=display_name, unique_name=unique_name, - unit=unit, + x_unit=x_unit, + y_unit=y_unit, components=components, Q=Q, ) diff --git a/src/easydynamics/sample_model/component_collection.py b/src/easydynamics/sample_model/component_collection.py index b556103a..cc0918eb 100644 --- a/src/easydynamics/sample_model/component_collection.py +++ b/src/easydynamics/sample_model/component_collection.py @@ -9,87 +9,45 @@ import numpy as np import scipp as sc -from easyscience.variable import DescriptorBase -from easyscience.variable import Parameter from easydynamics.base_classes.easydynamics_list import EasyDynamicsList from easydynamics.base_classes.easydynamics_modelbase import EasyDynamicsModelBase from easydynamics.sample_model.components.model_component import ModelComponent if TYPE_CHECKING: + from easyscience.variable import DescriptorBase + from easydynamics.utils.utils import Numeric class ComponentCollection(EasyDynamicsList, EasyDynamicsModelBase): """ - Collection of model components. - - Examples - -------- - Create a ComponentCollection with two components: - >>> import easydynamics.sample_model as sm - >>> component1 = sm.Gaussian(name='Gaussian1', area=1.0, width=1.0) - >>> component2 = sm.Lorentzian(name='Lorentzian1', area=2.0, width=0.5) - >>> collection = sm.ComponentCollection(components=[component1, component2]) - - Append a component to the collection: - >>> component3 = sm.Gaussian(name='Gaussian2', area=0.5, width=0.8) - >>> collection.append(component3) - - Evaluate the collection at a given energy axis: - >>> import numpy as np - >>> x = np.linspace(-5, 5, 100) - >>> values = collection.evaluate(x) - - Remove a component by name: - >>> collection.remove('Gaussian1') - - List component names: - >>> collection.list_component_names() - ['Lorentzian1', 'Gaussian2'] + Collection of model components whose evaluate() results are summed. """ def __init__( self, components: ModelComponent | list[ModelComponent] | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'ComponentCollection', display_name: str | None = None, unique_name: str | None = None, ) -> None: - """ - Initialize a new ComponentCollection. - - Parameters - ---------- - components : ModelComponent | list[ModelComponent] | None, default=None - Initial model components to add to the ComponentCollection. - unit : str | sc.Unit, default='meV' - Unit of the collection. - name : str, default='ComponentCollection' - Name of the collection. - display_name : str | None, default=None - Display name of the collection. - unique_name : str | None, default=None - Unique name of the collection. - - Raises - ------ - TypeError - If components is not a list of ModelComponent. - """ if components is None: components = [] if isinstance(components, ModelComponent): components = [components] elif not isinstance(components, list): raise TypeError( - f'components must be a ModelComponent or a list of ModelComponent, got {type(components).__name__} instead.' # noqa: E501 + f'components must be a ModelComponent or a list of ModelComponent, ' + f'got {type(components).__name__} instead.' ) for comp in components: if not isinstance(comp, ModelComponent): raise TypeError( - f'All items in components must be instances of ModelComponent, got {type(comp).__name__} instead.' # noqa: E501 + f'All items in components must be instances of ModelComponent, ' + f'got {type(comp).__name__} instead.' ) EasyDynamicsList.__init__( @@ -100,7 +58,8 @@ def __init__( EasyDynamicsModelBase.__init__( self, - unit=unit, + x_unit=x_unit, + y_unit=y_unit, name=name, display_name=display_name, unique_name=unique_name, @@ -114,69 +73,50 @@ def __init__( @property def is_empty(self) -> bool: - """ - Check if the ComponentCollection has no components. - - Returns - ------- - bool - True if the collection has no components, False otherwise. - """ return not self @is_empty.setter def is_empty(self, _value: bool) -> None: - """ - Is_empty is a read-only property that indicates whether the collection has components. - - Parameters - ---------- - _value : bool - The value to set (ignored). - - Raises - ------ - AttributeError - Always raised since is_empty is read-only. - """ - raise AttributeError( - 'is_empty is a read-only property that indicates ' - 'whether the collection has components.' - ) - - def convert_unit(self, unit: str | sc.Unit) -> None: - """ - Convert the unit of the ComponentCollection and all its components. + raise AttributeError('is_empty is a read-only property.') - Parameters - ---------- - unit : str | sc.Unit - The target unit to convert to. + # ------------------------------------------------------------------ + # Unit conversion + # ------------------------------------------------------------------ - Raises - ------ - TypeError - If unit is not a string or sc.Unit. - Exception - If any component cannot be converted to the specified unit. - """ + def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: + """Convert x-axis unit on all contained components.""" + if not isinstance(new_x_unit, (str, sc.Unit)): + raise TypeError(f'x_unit must be a string or sc.Unit, got {type(new_x_unit).__name__}') - if not isinstance(unit, (str, sc.Unit)): - raise TypeError(f'Unit must be a string or sc.Unit, got {type(unit).__name__}') + old_unit = self._x_unit + try: + for component in self: + component.convert_x_unit(new_x_unit) + self._x_unit = str(new_x_unit) if isinstance(new_x_unit, sc.Unit) else new_x_unit + except Exception as e: + try: + for component in self: + component.convert_x_unit(old_unit) + except Exception: # noqa: S110 + pass + raise e - old_unit = self._unit + def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: + """Convert y-axis unit on all contained components.""" + if not isinstance(new_y_unit, (str, sc.Unit)): + raise TypeError(f'y_unit must be a string or sc.Unit, got {type(new_y_unit).__name__}') + old_unit = self._y_unit try: for component in self: - component.convert_unit(unit) - self._unit = unit + component.convert_y_unit(new_y_unit) + self._y_unit = str(new_y_unit) if isinstance(new_y_unit, sc.Unit) else new_y_unit except Exception as e: - # Attempt to rollback on failure try: for component in self: - component.convert_unit(old_unit) + component.convert_y_unit(old_unit) except Exception: # noqa: S110 - pass # Best effort rollback + pass raise e # ------------------------------------------------------------------ @@ -184,16 +124,6 @@ def convert_unit(self, unit: str | sc.Unit) -> None: # ------------------------------------------------------------------ def append_component(self, component: ModelComponent | ComponentCollection) -> None: - """ - Append a model component or the components from another ComponentCollection to this - ComponentCollection. - - Parameters - ---------- - component : ModelComponent | ComponentCollection - The component to append. If a ComponentCollection is provided, all of its components - will be appended. - """ if isinstance(component, ComponentCollection): self.extend(component) else: @@ -201,141 +131,75 @@ def append_component(self, component: ModelComponent | ComponentCollection) -> N self._warn_if_duplicate_names() def list_component_names(self) -> list[str]: - """ - List the names of all components in the model. - - Returns - ------- - list[str] - List of names of the components in the collection. - """ - return [component.name for component in self] def normalize_area(self) -> None: - """ - Normalize the areas of all components so they sum to 1. - - This is useful for convolutions. - - Raises - ------ - ValueError - If there are no components in the model or if the total area is zero or not finite, - which would prevent normalization. - """ + """Normalize areas of all components so they sum to 1.""" if not self: raise ValueError('No components in the model to normalize.') area_params = [] - total_area = Parameter(name='total_area', value=0.0, unit=self._unit) for component in self: if hasattr(component, 'area'): area_params.append(component.area) - total_area += component.area else: warnings.warn( f"Component '{component.name}' does not have an 'area' attribute " - f'and will be skipped in normalization.', + 'and will be skipped in normalization.', UserWarning, stacklevel=2, ) - if total_area.value == 0: + total_area_value = sum(p.value for p in area_params) + + if total_area_value == 0: raise ValueError('Total area is zero; cannot normalize.') - if not np.isfinite(total_area.value): + if not np.isfinite(total_area_value): raise ValueError('Total area is not finite; cannot normalize.') for param in area_params: - param.value /= total_area.value + param.value /= total_area_value # ------------------------------------------------------------------ # Other methods # ------------------------------------------------------------------ def get_all_variables(self) -> list[DescriptorBase]: - """ - Get all parameters from the model component. - - Returns - ------- - list[DescriptorBase] - List of parameters in the component. - """ - return [var for component in self for var in component.get_all_variables()] - def evaluate(self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray) -> np.ndarray: - """ - Evaluate the sum of all components. - - Parameters - ---------- - x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - Energy axis. - - Returns - ------- - np.ndarray - Evaluated model values. - """ - + def evaluate( + self, + x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: + """Evaluate the sum of all component outputs at x.""" if not self: return np.zeros_like(x) - return sum(component.evaluate(x) for component in self) + return sum(component.evaluate(x, output=output) for component in self) def evaluate_component( self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, name: str, - ) -> np.ndarray: - """ - Evaluate a single component by name. - - Parameters - ---------- - x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - Energy axis. - name : str - Component name. - - Raises - ------ - ValueError - If there are no components in the model. - TypeError - If name is not a string. - KeyError - If no component with the given name exists in the collection. - - Returns - ------- - np.ndarray - Evaluated values for the specified component. - """ + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: + """Evaluate a single component by name.""" if not self: raise ValueError('No components in the model to evaluate.') - if not isinstance(name, str): raise TypeError(f'Component name must be a string, got {type(name)} instead.') - matches = [comp for comp in self if comp.name == name] if not matches: raise KeyError(f"No component named '{name}' exists.") - - component = matches[0] - - return component.evaluate(x) + return matches[0].evaluate(x, output=output) def fix_all_parameters(self) -> None: - """Fix all free parameters in the model.""" for param in self.get_fittable_parameters(): param.fixed = True def free_all_parameters(self) -> None: - """Free all fixed parameters in the model.""" for param in self.get_fittable_parameters(): param.fixed = False @@ -344,7 +208,6 @@ def free_all_parameters(self) -> None: # ------------------------------------------------------------------ def _warn_if_duplicate_names(self) -> None: - """Warn if any two components share the same name.""" names = [c.name for c in self] seen: set[str] = set() dups: set[str] = set() @@ -365,34 +228,19 @@ def _warn_if_duplicate_names(self) -> None: # ------------------------------------------------------------------ def __repr__(self) -> str: - """ - Return a string representation of the ComponentCollection. - - Returns - ------- - str - String representation of the ComponentCollection. - """ comp_names = ', '.join(c.name for c in self) or 'No components' - return ( - f"ComponentCollection(name='{self.name}', unit='{self.unit}', \n" + f"ComponentCollection(name='{self.name}', " + f"x_unit='{self.x_unit}', y_unit='{self.y_unit}',\n" f'Components: {comp_names})' ) def to_dict(self) -> dict: - """ - Serialise the ComponentCollection to a dictionary. - - Returns - ------- - dict - Dictionary representation of the ComponentCollection. - """ return { '@module': self.__class__.__module__, '@class': self.__class__.__name__, - 'unit': str(self.unit), + 'x_unit': str(self.x_unit), + 'y_unit': str(self.y_unit), 'name': self.name, 'display_name': self.display_name, 'components': [c.to_dict() for c in self._data], @@ -400,53 +248,20 @@ def to_dict(self) -> dict: @classmethod def from_dict(cls, obj_dict: dict) -> ComponentCollection: - """ - Deserialise a ComponentCollection from its dictionary representation. - - Parameters - ---------- - obj_dict : dict - Dictionary representation of the ComponentCollection, as produced by to_dict(). - - Returns - ------- - ComponentCollection - The deserialised ComponentCollection. - """ - def deserialise_component(d: dict) -> ModelComponent: - """ - Deserialise a component from its dictionary representation. - Parameters - ---------- - d : dict - The dictionary representation of the component. - Returns - ------- - ModelComponent - The deserialised component. - """ module = importlib.import_module(d['@module']) - cls = getattr(module, d['@class']) - return cls.from_dict(d) + klass = getattr(module, d['@class']) + return klass.from_dict(d) components = [deserialise_component(c) for c in obj_dict.get('components', [])] return cls( components=components, - unit=obj_dict.get('unit', 'meV'), + x_unit=obj_dict.get('x_unit', 'meV'), + y_unit=obj_dict.get('y_unit', 'dimensionless'), name=obj_dict.get('name', 'ComponentCollection'), display_name=obj_dict.get('display_name'), ) def __copy__(self) -> ComponentCollection: - """ - Create a deep copy of the ComponentCollection. - - Returns - ------- - ComponentCollection - A deep copy of the ComponentCollection. - """ - return self.from_dict(self.to_dict()) diff --git a/src/easydynamics/sample_model/components/damped_harmonic_oscillator.py b/src/easydynamics/sample_model/components/damped_harmonic_oscillator.py index e7ea9561..0feb4621 100644 --- a/src/easydynamics/sample_model/components/damped_harmonic_oscillator.py +++ b/src/easydynamics/sample_model/components/damped_harmonic_oscillator.py @@ -6,13 +6,13 @@ from typing import TYPE_CHECKING import numpy as np +import scipp as sc from easydynamics.sample_model.components.mixins import CreateParametersMixin from easydynamics.sample_model.components.model_component import ModelComponent from easydynamics.utils.utils import Numeric if TYPE_CHECKING: - import scipp as sc from easyscience.variable import Parameter @@ -20,8 +20,9 @@ class DampedHarmonicOscillator(CreateParametersMixin, ModelComponent): r""" Model of a Damped Harmonic Oscillator (DHO). - The intensity is given by $$ I(x) = \frac{2 A x_0^2 \gamma}{\pi \left( (x^2 - x_0^2)^2 + (2 - \gamma x)^2 \right)}, $$ where $A$ is the area, $x_0$ is the center, and $\gamma$ is the width. + $$ I(x) = \frac{2 A x_0^2 \gamma}{\pi \left( (x^2 - x_0^2)^2 + (2\gamma x)^2 \right)} $$ + + area has unit = x_unit * y_unit; center and width have unit = x_unit. """ def __init__( @@ -29,83 +30,76 @@ def __init__( area: Numeric = 1.0, center: Numeric = 1.0, width: Numeric = 1.0, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'DampedHarmonicOscillator', display_name: str | None = None, unique_name: str | None = None, ) -> None: """ - Initialize the Damped Harmonic Oscillator. - Parameters ---------- area : Numeric, default=1.0 - Area under the curve. + Integrated area under the DHO profile. Unit is ``x_unit * y_unit``. center : Numeric, default=1.0 - Resonance frequency, approximately the peak position. + Resonance frequency (x_0) in x_unit. Must be strictly positive; a minimum of + ``DHO_MINIMUM_CENTER`` (1e-10) is enforced. width : Numeric, default=1.0 - Damping constant, approximately the half width at half max (HWHM) of the peaks. By - default, 1.0. - unit : str | sc.Unit, default='meV' - Unit of the parameters. + Damping coefficient (gamma) in x_unit. Must be strictly positive. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis. center and width are stored in this unit. area_unit = x_unit * + y_unit. + y_unit : str | sc.Unit, default='dimensionless' + Unit of the y-axis (output). name : str, default='DampedHarmonicOscillator' - Name of the component for indexing. + Internal name used for parameter labelling. display_name : str | None, default=None - Display name of the component. + Human-readable name. Falls back to *name* if None. unique_name : str | None, default=None - Unique name of the component. If None, a unique_name is automatically generated. By - default, None. + Globally unique identifier. Auto-generated if None. """ - super().__init__( name=name, display_name=display_name, unique_name=unique_name, - unit=unit, + x_unit=x_unit, + y_unit=y_unit, ) - # These methods live in ValidationMixin - area = self._create_area_parameter(area=area, name=name, unit=self._unit) - center = self._create_center_parameter( + self._area = self._create_area_parameter( + area=area, name=name, x_unit=self._x_unit, y_unit=self._y_unit + ) + self._center = self._create_center_parameter( center=center, name=name, fix_if_none=False, - unit=self._unit, + x_unit=self._x_unit, enforce_minimum_center=True, ) - - width = self._create_width_parameter(width=width, name=name, unit=self._unit) - - self._area = area - self._center = center - self._width = width + self._width = self._create_width_parameter(width=width, name=name, x_unit=self._x_unit) @property def area(self) -> Parameter: """ - Get the area parameter. - Returns ------- Parameter - The area parameter. + The area Parameter with unit ``x_unit * y_unit``. """ return self._area @area.setter def area(self, value: Numeric) -> None: """ - Set the value of the area parameter. - Parameters ---------- value : Numeric - The new value for the area parameter. + New area value (in current area unit = x_unit * y_unit). Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. """ if not isinstance(value, Numeric): raise TypeError('area must be a number') @@ -114,35 +108,30 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ - Get the center parameter. - Returns ------- Parameter - The center parameter. + The resonance frequency (x_0) Parameter with unit ``x_unit``. """ return self._center @center.setter def center(self, value: Numeric) -> None: """ - Set the value of the center parameter. - Parameters ---------- value : Numeric - The new value for the center parameter. + New resonance frequency in x_unit. Must be strictly positive. Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. ValueError - If the value is not positive. + If *value* is not positive. """ if not isinstance(value, Numeric): raise TypeError('center must be a number') - if float(value) <= 0: raise ValueError('center must be positive') self._center.value = value @@ -150,79 +139,147 @@ def center(self, value: Numeric) -> None: @property def width(self) -> Parameter: """ - Get the width parameter. - Returns ------- Parameter - The width parameter. + The damping coefficient (gamma) Parameter with unit ``x_unit``. """ return self._width @width.setter def width(self, value: Numeric) -> None: """ - Set the value of the width parameter. - Parameters ---------- value : Numeric - The new value for the width parameter. + New damping coefficient in x_unit. Must be strictly positive. Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. ValueError - If the value is not positive. + If *value* is not positive. """ if not isinstance(value, Numeric): raise TypeError('width must be a number') - if float(value) <= 0: raise ValueError('width must be positive') - self._width.value = value - def evaluate(self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray) -> np.ndarray: + def evaluate( + self, + x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: r""" - Evaluate the Damped Harmonic Oscillator at the given x values. + Evaluate the DHO at x. - If x is a scipp Variable, the unit of the DHO will be converted to match x. The intensity - is given by $$ I(x) = \frac{2 A x_0^2 \gamma}{\pi \left( (x^2 - x_0^2)^2 + (2 \gamma x)^2 - \right)}, $$ where $A$ is the area, $x_0$ is the center, and $\gamma$ is the width. + Parameters in the model's own units are temporarily converted to x's unit for the + computation — the model is never mutated. Parameters ---------- x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The x values at which to evaluate the DHO. + Input x values. + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. Returns ------- - np.ndarray - The intensity of the DHO at the given x values. + np.ndarray | sc.Variable + Evaluated DHO values at x. """ + x_vals, detected_unit, dim = self._prepare_x_for_evaluate(x) + eval_unit = detected_unit or self._x_unit + eval_area_unit = str(sc.Unit(eval_unit) * sc.Unit(self._y_unit)) - x = self._prepare_x_for_evaluate(x) + center = self._resolve_param_value(self._center, eval_unit) + width = self._resolve_param_value(self._width, eval_unit) + area = self._resolve_param_value(self._area, eval_area_unit) - normalization = 2 * self.center.value**2 * self.width.value / np.pi - # No division by zero here, width>0 enforced in setter - denominator = (x**2 - self.center.value**2) ** 2 + (2 * self.width.value * x) ** 2 + normalization = 2 * center**2 * width / np.pi + denominator = (x_vals**2 - center**2) ** 2 + (2 * width * x_vals) ** 2 + result = area * normalization / denominator - return self.area.value * normalization / (denominator) + if output == 'scipp': + return sc.array(dims=[dim], values=result, unit=self._y_unit) + return result - def __repr__(self) -> str: + def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: """ - Return a string representation of the Damped Harmonic Oscillator. + Convert x-axis parameters (center, width) and area to new_x_unit. - Returns - ------- - str - A string representation of the Damped Harmonic Oscillator. + Parameters + ---------- + new_x_unit : str | sc.Unit + Target x-axis unit. Must be dimensionally compatible with the current x_unit. + + Raises + ------ + TypeError + If *new_x_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. + """ + if not isinstance(new_x_unit, (str, sc.Unit)): + raise TypeError(f'x_unit must be a string or sc.Unit, got {type(new_x_unit).__name__}') + old_x_unit = self._x_unit + new_area_unit = str(sc.Unit(new_x_unit) * sc.Unit(self._y_unit)) + try: + self._center.convert_unit(new_x_unit) + self._width.convert_unit(new_x_unit) + self._area.convert_unit(new_area_unit) + self._x_unit = str(new_x_unit) if isinstance(new_x_unit, sc.Unit) else new_x_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(old_x_unit) * sc.Unit(self._y_unit)) + self._center.convert_unit(old_x_unit) + self._width.convert_unit(old_x_unit) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: + """ + Convert the y-axis unit by rescaling the area parameter. + + The area is rescaled from ``x_unit * old_y_unit`` to ``x_unit * new_y_unit``. + + Parameters + ---------- + new_y_unit : str | sc.Unit + Target y-axis unit. + + Raises + ------ + TypeError + If *new_y_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. """ + if not isinstance(new_y_unit, (str, sc.Unit)): + raise TypeError(f'y_unit must be a string or sc.Unit, got {type(new_y_unit).__name__}') + old_y_unit = self._y_unit + new_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(new_y_unit)) + try: + self._area.convert_unit(new_area_unit) + self._y_unit = str(new_y_unit) if isinstance(new_y_unit, sc.Unit) else new_y_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(old_y_unit)) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def __repr__(self) -> str: return ( f'DampedHarmonicOscillator(name = {self.name}, display_name = {self.display_name}, ' - f'unit = {self._unit},\n ' + f'x_unit = {self._x_unit}, y_unit = {self._y_unit},\n ' f' area = {self.area},\n ' f' center = {self.center},\n ' f' width = {self.width})' diff --git a/src/easydynamics/sample_model/components/delta_function.py b/src/easydynamics/sample_model/components/delta_function.py index c10e6185..868882d7 100644 --- a/src/easydynamics/sample_model/components/delta_function.py +++ b/src/easydynamics/sample_model/components/delta_function.py @@ -6,16 +6,15 @@ from typing import TYPE_CHECKING import numpy as np +import scipp as sc from easydynamics.sample_model.components.mixins import CreateParametersMixin from easydynamics.sample_model.components.model_component import ModelComponent from easydynamics.utils.utils import Numeric -EPSILON = 1e-8 # small number to avoid floating point issues - +EPSILON = 1e-8 if TYPE_CHECKING: - import scipp as sc from easyscience.variable import Parameter @@ -23,85 +22,77 @@ class DeltaFunction(CreateParametersMixin, ModelComponent): """ Delta function. - Evaluates to zero everywhere, except in convolutions, where it acts as an identity. This is - handled by the Convolution method. If the center is not provided, it will be centered at 0 and - fixed, which is typically what you want in QENS. + Evaluates to zero everywhere; acts as an identity in convolutions. area has unit = x_unit * + y_unit; center has unit = x_unit. """ def __init__( self, center: Numeric | None = None, area: Numeric = 1.0, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'DeltaFunction', display_name: str | None = None, unique_name: str | None = None, ) -> None: """ - Initialize the Delta function. - Parameters ---------- center : Numeric | None, default=None - Center of the delta function. If None, it will be centered at 0 and fixed. + Position of the delta function in x_unit. If None, defaults to 0 and the center + parameter is fixed. area : Numeric, default=1.0 - Total area under the curve. - unit : str | sc.Unit, default='meV' - Unit of the parameters. + Integrated area (weight) of the delta function. Unit is ``x_unit * y_unit``. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis. center is stored in this unit. area_unit = x_unit * y_unit. + y_unit : str | sc.Unit, default='dimensionless' + Unit of the y-axis (output). name : str, default='DeltaFunction' - Name of the component for indexing. + Internal name used for parameter labelling. display_name : str | None, default=None - Display name of the component. + Human-readable name. Falls back to *name* if None. unique_name : str | None, default=None - Unique name of the component. If None, a unique_name is automatically generated. By - default, None. + Globally unique identifier. Auto-generated if None. """ - # Validate inputs and create Parameters if not given super().__init__( - unit=unit, + x_unit=x_unit, + y_unit=y_unit, name=name, display_name=display_name, unique_name=unique_name, ) - # These methods live in ValidationMixin - area = self._create_area_parameter(area=area, name=name, unit=self._unit) - center = self._create_center_parameter( - center=center, name=name, fix_if_none=True, unit=self._unit + self._area = self._create_area_parameter( + area=area, name=name, x_unit=self._x_unit, y_unit=self._y_unit + ) + self._center = self._create_center_parameter( + center=center, name=name, fix_if_none=True, x_unit=self._x_unit ) - - self._area = area - self._center = center @property def area(self) -> Parameter: """ - Get the area parameter. - Returns ------- Parameter - The area parameter. + The area Parameter with unit ``x_unit * y_unit``. """ - return self._area @area.setter def area(self, value: Numeric) -> None: """ - Set the value of the area parameter. - Parameters ---------- value : Numeric - The new value for the area parameter. + New area value (in current area unit = x_unit * y_unit). Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. """ - if not isinstance(value, Numeric): raise TypeError('area must be a number') self._area.value = value @@ -109,32 +100,27 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ - Get the center parameter. - Returns ------- Parameter - The center parameter. + The center Parameter with unit ``x_unit``. """ - return self._center @center.setter def center(self, value: Numeric | None) -> None: """ - Set the center parameter value. - Parameters ---------- value : Numeric | None - The new value for the center parameter. If None, defaults to 0 and is fixed. + New center value in x_unit. If None, the center is set to 0 and the parameter is + fixed. Raises ------ TypeError - If the value is not a number or None. + If *value* is not None and not a numeric type. """ - if value is None: value = 0.0 self._center.fixed = True @@ -142,66 +128,131 @@ def center(self, value: Numeric | None) -> None: raise TypeError('center must be a number') self._center.value = value - def evaluate(self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray) -> np.ndarray: + def evaluate( + self, + x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: """ - Evaluate the Delta function at the given x values. + Evaluate the Delta function at x (zero everywhere, except used in convolutions). - The Delta function evaluates to zero everywhere, except at the center. Its numerical - integral is equal to the area. It acts as an identity in convolutions. + Parameters in the model's own units are temporarily converted to x's unit for the + computation — the model is never mutated. Parameters ---------- x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The x values at which to evaluate the Delta function. + Input x values. + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. Returns ------- - np.ndarray - The evaluated Delta function at the given x values. + np.ndarray | sc.Variable + Evaluated delta function values at x (zero everywhere, with a single non-zero bin + nearest the center when center falls within the x range). """ + x_vals, detected_unit, dim = self._prepare_x_for_evaluate(x) + eval_unit = detected_unit or self._x_unit + eval_area_unit = str(sc.Unit(eval_unit) * sc.Unit(self._y_unit)) + + center = self._resolve_param_value(self._center, eval_unit) + area = self._resolve_param_value(self._area, eval_area_unit) - # x assumed sorted, 1D numpy array - x = self._prepare_x_for_evaluate(x) - model = np.zeros_like(x, dtype=float) - center = self.center.value - area = self.area.value + model = np.zeros_like(x_vals, dtype=float) - if x.min() - EPSILON <= center <= x.max() + EPSILON: - # nearest index - i = np.argmin(np.abs(x - center)) + if x_vals.min() - EPSILON <= center <= x_vals.max() + EPSILON: + i = np.argmin(np.abs(x_vals - center)) - # left half-width - if i == 0: # noqa: SIM108 - left = x[1] - x[0] if x.size > 1 else 0.5 + if i == 0: + left = x_vals[1] - x_vals[0] if x_vals.size > 1 else 0.5 else: - left = x[i] - x[i - 1] + left = x_vals[i] - x_vals[i - 1] - # right half-width - if i == x.size - 1: # noqa: SIM108 - right = x[-1] - x[-2] if x.size > 1 else 0.5 + if i == x_vals.size - 1: + right = x_vals[-1] - x_vals[-2] if x_vals.size > 1 else 0.5 else: - right = x[i + 1] - x[i] + right = x_vals[i + 1] - x_vals[i] - # effective bin width: half left + half right bin_width = 0.5 * (left + right) - model[i] = area / bin_width + if output == 'scipp': + return sc.array(dims=[dim], values=model, unit=self._y_unit) return model - def __repr__(self) -> str: + def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: """ - Return a string representation of the Delta function. + Convert x-axis parameters (center) and area to new_x_unit. - Returns - ------- - str - A string representation of the Delta function. + Parameters + ---------- + new_x_unit : str | sc.Unit + Target x-axis unit. Must be dimensionally compatible with the current x_unit. + + Raises + ------ + TypeError + If *new_x_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. + """ + if not isinstance(new_x_unit, (str, sc.Unit)): + raise TypeError(f'x_unit must be a string or sc.Unit, got {type(new_x_unit).__name__}') + old_x_unit = self._x_unit + new_area_unit = str(sc.Unit(new_x_unit) * sc.Unit(self._y_unit)) + try: + self._center.convert_unit(new_x_unit) + self._area.convert_unit(new_area_unit) + self._x_unit = str(new_x_unit) if isinstance(new_x_unit, sc.Unit) else new_x_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(old_x_unit) * sc.Unit(self._y_unit)) + self._center.convert_unit(old_x_unit) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: """ + Convert the y-axis unit by rescaling the area parameter. + The area is rescaled from ``x_unit * old_y_unit`` to ``x_unit * new_y_unit``. + + Parameters + ---------- + new_y_unit : str | sc.Unit + Target y-axis unit. + + Raises + ------ + TypeError + If *new_y_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. + """ + if not isinstance(new_y_unit, (str, sc.Unit)): + raise TypeError(f'y_unit must be a string or sc.Unit, got {type(new_y_unit).__name__}') + old_y_unit = self._y_unit + new_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(new_y_unit)) + try: + self._area.convert_unit(new_area_unit) + self._y_unit = str(new_y_unit) if isinstance(new_y_unit, sc.Unit) else new_y_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(old_y_unit)) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def __repr__(self) -> str: return ( f'DeltaFunction(name = {self.name}, display_name = {self.display_name}, ' - f'unit = {self.unit},\n' + f'x_unit = {self.x_unit}, y_unit = {self.y_unit},\n' f' area = {self.area},\n' f' center = {self.center})' ) diff --git a/src/easydynamics/sample_model/components/exponential.py b/src/easydynamics/sample_model/components/exponential.py index 8894a065..f50a8794 100644 --- a/src/easydynamics/sample_model/components/exponential.py +++ b/src/easydynamics/sample_model/components/exponential.py @@ -16,11 +16,10 @@ class Exponential(CreateParametersMixin, ModelComponent): r""" Model of an exponential function. - The intensity is given by + $$ I(x) = A e^{B (x-x_0)} $$ - $$ I(x) = A e^{B (x-x_0)}, $$ - - where $A$ is the amplitude, $x_0$ is the center, and $B$ describes the rate of decay or growth. + where $A$ is the amplitude, $x_0$ is the center, and $B$ is the rate. amplitude has unit = + x_unit * y_unit; center has unit = x_unit; rate has unit = 1/x_unit. """ def __init__( @@ -28,68 +27,71 @@ def __init__( amplitude: Numeric = 1.0, center: Numeric | None = None, rate: Numeric = 1.0, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'Exponential', display_name: str | None = None, unique_name: str | None = None, ) -> None: """ - Initialize the Exponential component. - Parameters ---------- amplitude : Numeric, default=1.0 - Amplitude of the Exponential. + Pre-exponential factor A. Unit is ``x_unit * y_unit``. center : Numeric | None, default=None - Center of the Exponential. If None, the center is fixed at 0. + Reference point x_0 in x_unit. If None, defaults to 0 and the center parameter is + fixed. rate : Numeric, default=1.0 - Decay or growth constant of the Exponential. - unit : str | sc.Unit, default='meV' - Unit of the parameters. + Exponential rate B in units of ``1/x_unit``. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis. center is stored in this unit; rate is stored in ``1/x_unit``. + amplitude_unit = x_unit * y_unit. + y_unit : str | sc.Unit, default='dimensionless' + Unit of the y-axis (output). name : str, default='Exponential' - Name of the component for indexing. + Internal name used for parameter labelling. display_name : str | None, default=None - Display name of the component. + Human-readable name. Falls back to *name* if None. unique_name : str | None, default=None - Unique name of the component. If None, a unique_name is automatically generated. By - default, None. + Globally unique identifier. Auto-generated if None. Raises ------ TypeError - If amplitude, center, or rate are not numbers or Parameters. + If *amplitude* or *rate* is not numeric or a :class:`Parameter`. ValueError - If amplitude, center or rate are not finite numbers. + If *amplitude* or *rate* is not finite. """ - # Validate inputs and create Parameters if not given super().__init__( - unit=unit, + x_unit=x_unit, + y_unit=y_unit, name=name, display_name=display_name, unique_name=unique_name, ) + x_unit_str = str(x_unit) if isinstance(x_unit, sc.Unit) else x_unit + amplitude_unit = str(sc.Unit(x_unit_str) * sc.Unit(self._y_unit)) + if not isinstance(amplitude, (Parameter, Numeric)): raise TypeError('amplitude must be a number or a Parameter.') - if isinstance(amplitude, Numeric): if not np.isfinite(amplitude): raise ValueError('amplitude must be a finite number or a Parameter') - - amplitude = Parameter(name=name + ' amplitude', value=float(amplitude), unit=unit) + amplitude = Parameter( + name=name + ' amplitude', value=float(amplitude), unit=amplitude_unit + ) center = self._create_center_parameter( - center=center, name=name, fix_if_none=True, unit=self._unit + center=center, name=name, fix_if_none=True, x_unit=self._x_unit ) if not isinstance(rate, (Parameter, Numeric)): raise TypeError('rate must be a number or a Parameter.') - if isinstance(rate, Numeric): if not np.isfinite(rate): raise ValueError('rate must be a finite number or a Parameter') - - rate = Parameter(name=name + ' rate', value=float(rate), unit='1/' + str(unit)) + rate = Parameter(name=name + ' rate', value=float(rate), unit='1/' + x_unit_str) self._amplitude = amplitude self._center = center @@ -98,32 +100,26 @@ def __init__( @property def amplitude(self) -> Parameter: """ - Get the amplitude parameter. - Returns ------- Parameter - The amplitude parameter. + The amplitude Parameter with unit ``x_unit * y_unit``. """ - return self._amplitude @amplitude.setter def amplitude(self, value: Numeric) -> None: """ - Set the value of the amplitude parameter. - Parameters ---------- value : Numeric - The new value for the amplitude parameter. + New amplitude value (in current amplitude unit = x_unit * y_unit). Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. """ - if not isinstance(value, Numeric): raise TypeError('amplitude must be a number') self._amplitude.value = value @@ -131,36 +127,30 @@ def amplitude(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ - Get the center parameter. - Returns ------- Parameter - The center parameter. + The center (x_0) Parameter with unit ``x_unit``. """ - return self._center @center.setter def center(self, value: Numeric | None) -> None: """ - Set the center parameter value. - Parameters ---------- value : Numeric | None - The new value for the center parameter. + New center value in x_unit. If None, the center is set to 0 and the parameter is + fixed. Raises ------ TypeError - If the value is not a number. + If *value* is not None and not a numeric type. """ - if value is None: value = 0.0 self._center.fixed = True - if not isinstance(value, Numeric): raise TypeError('center must be a number') self._center.value = value @@ -168,113 +158,145 @@ def center(self, value: Numeric | None) -> None: @property def rate(self) -> Parameter: """ - Get the rate parameter. - Returns ------- Parameter - The rate parameter. + The exponential rate (B) Parameter with unit ``1/x_unit``. """ return self._rate @rate.setter def rate(self, value: Numeric) -> None: """ - Set the rate parameter value. - Parameters ---------- value : Numeric - The new value for the rate parameter. + New exponential rate in ``1/x_unit``. Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. """ if not isinstance(value, Numeric): raise TypeError('rate must be a number') - self._rate.value = value def evaluate( self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, - ) -> np.ndarray: + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: r""" - Evaluate the Exponential at the given x values. - - If x is a scipp Variable, the unit of the Exponential will be converted to match x. The - intensity is given by $$ I(x) = A \exp\left( r (x - x_0) \right) $$ + Evaluate the Exponential at x. - where $A$ is the amplitude, $x_0$ is the center, and $r$ is the rate. + Parameters in the model's own units are temporarily converted to x's unit for the + computation — the model is never mutated. Parameters ---------- x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The x values at which to evaluate the Exponential. + Input x values. + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. Returns ------- - np.ndarray - The intensity of the Exponential at the given x values. + np.ndarray | sc.Variable + Evaluated exponential values at x. """ + x_vals, detected_unit, dim = self._prepare_x_for_evaluate(x) + eval_unit = detected_unit or self._x_unit + eval_area_unit = str(sc.Unit(eval_unit) * sc.Unit(self._y_unit)) + eval_rate_unit = '1/' + str(eval_unit) - x = self._prepare_x_for_evaluate(x) - exponent = self.rate.value * (x - self.center.value) + center = self._resolve_param_value(self._center, eval_unit) + rate = self._resolve_param_value(self._rate, eval_rate_unit) + amplitude = self._resolve_param_value(self._amplitude, eval_area_unit) - return self.amplitude.value * np.exp(exponent) + exponent = rate * (x_vals - center) + result = amplitude * np.exp(exponent) - def convert_unit(self, unit: str | sc.Unit) -> None: + if output == 'scipp': + return sc.array(dims=[dim], values=result, unit=self._y_unit) + return result + + def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: """ - Convert the unit of the Parameters in the component. + Convert center and amplitude to new_x_unit, rate to 1/new_x_unit. Parameters ---------- - unit : str | sc.Unit - The new unit to convert to. + new_x_unit : str | sc.Unit + Target x-axis unit. Must be dimensionally compatible with the current x_unit. The + rate unit is set to ``1/new_x_unit``. Raises ------ TypeError - If unit is not a string or sc.Unit. + If *new_x_unit* is not a ``str`` or ``sc.Unit``. Exception - If conversion fails for any parameter. + If the unit conversion fails. On failure the component is rolled back to its original + units. """ - - if not isinstance(unit, (str, sc.Unit)): - raise TypeError('unit must be a string or sc.Unit') - - old_unit = self._unit - pars = [self.amplitude, self.center] + if not isinstance(new_x_unit, (str, sc.Unit)): + raise TypeError('x_unit must be a string or sc.Unit') + old_x_unit = self._x_unit + new_x_str = str(new_x_unit) if isinstance(new_x_unit, sc.Unit) else new_x_unit + new_area_unit = str(sc.Unit(new_x_str) * sc.Unit(self._y_unit)) try: - for p in pars: - p.convert_unit(unit) - self.rate.convert_unit('1/' + str(unit)) - self._unit = unit + self._center.convert_unit(new_x_unit) + self._amplitude.convert_unit(new_area_unit) + self._rate.convert_unit('1/' + new_x_str) + self._x_unit = new_x_str except Exception as e: - # Attempt to rollback on failure try: - for p in pars: - p.convert_unit(old_unit) - self.rate.convert_unit('1/' + str(old_unit)) + old_area_unit = str(sc.Unit(old_x_unit) * sc.Unit(self._y_unit)) + self._center.convert_unit(old_x_unit) + self._amplitude.convert_unit(old_area_unit) + self._rate.convert_unit('1/' + str(old_x_unit)) except Exception: # noqa: S110 - pass # Best effort rollback + pass raise e - def __repr__(self) -> str: + def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: """ - Return a string representation of the Exponential. + Convert the y-axis unit by rescaling the amplitude parameter. - Returns - ------- - str - A string representation of the Exponential. + The amplitude is rescaled from ``x_unit * old_y_unit`` to ``x_unit * new_y_unit``. + + Parameters + ---------- + new_y_unit : str | sc.Unit + Target y-axis unit. + + Raises + ------ + TypeError + If *new_y_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. """ + if not isinstance(new_y_unit, (str, sc.Unit)): + raise TypeError(f'y_unit must be a string or sc.Unit, got {type(new_y_unit).__name__}') + old_y_unit = self._y_unit + new_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(new_y_unit)) + try: + self._amplitude.convert_unit(new_area_unit) + self._y_unit = str(new_y_unit) if isinstance(new_y_unit, sc.Unit) else new_y_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(old_y_unit)) + self._amplitude.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + def __repr__(self) -> str: return ( f'Exponential(name = {self.name}, display_name = {self.display_name}, ' - f'unit = {self._unit},\n ' + f'x_unit = {self._x_unit}, y_unit = {self._y_unit},\n ' f' amplitude = {self.amplitude},\n ' f' center = {self.center},\n ' f' rate = {self.rate})' diff --git a/src/easydynamics/sample_model/components/expression_component.py b/src/easydynamics/sample_model/components/expression_component.py index 5255a940..7b5a2679 100644 --- a/src/easydynamics/sample_model/components/expression_component.py +++ b/src/easydynamics/sample_model/components/expression_component.py @@ -3,6 +3,7 @@ from __future__ import annotations +import warnings from typing import TYPE_CHECKING from typing import ClassVar @@ -23,16 +24,11 @@ class ExpressionComponent(ModelComponent): Model component defined by a symbolic expression. """ - # ------------------------- - # Allowed symbolic functions - # ------------------------- _ALLOWED_FUNCS: ClassVar[dict[str, object]] = { - # Exponentials & logs 'exp': sp.exp, 'log': sp.log, 'ln': sp.log, 'sqrt': sp.sqrt, - # Trigonometric 'sin': sp.sin, 'cos': sp.cos, 'tan': sp.tan, @@ -43,22 +39,16 @@ class ExpressionComponent(ModelComponent): 'asin': sp.asin, 'acos': sp.acos, 'atan': sp.atan, - # Hyperbolic 'sinh': sp.sinh, 'cosh': sp.cosh, 'tanh': sp.tanh, - # Misc 'abs': sp.Abs, 'sign': sp.sign, 'floor': sp.floor, 'ceil': sp.ceiling, - # Special functions 'erf': sp.erf, } - # ------------------------- - # Allowed constants - # ------------------------- _ALLOWED_CONSTANTS: ClassVar[dict[str, object]] = { 'pi': sp.pi, 'E': sp.E, @@ -70,49 +60,19 @@ def __init__( self, expression: str, parameters: dict[str, Numeric] | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'Expression', display_name: str | None = None, unique_name: str | None = None, ) -> None: - """ - Initialize the ExpressionComponent. - - Parameters - ---------- - expression : str - The symbolic expression as a string. Must contain 'x' as the independent variable. - parameters : dict[str, Numeric] | None, default=None - Dictionary of parameter names and their initial values. - unit : str | sc.Unit, default='meV' - Unit of the output. - name : str, default='Expression' - Name of the component for indexing. - display_name : str | None, default=None - Display name for the component. - unique_name : str | None, default=None - Unique name for the component. - - Raises - ------ - ValueError - If the expression is invalid or does not contain 'x'. - TypeError - If any parameter value is not numeric. - - Examples - -------- - >>> expr = ExpressionComponent( - ... 'A * exp(-(x - x0)**2 / (2*sigma**2))', - ... parameters={'A': 10, 'x0': 0, 'sigma': 1}, - ... unit='meV', - ... display_name='Gaussian Peak', - ... ) - - >>> expr.A = 5 - >>> y = expr.evaluate(x) - """ - super().__init__(unit=unit, name=name, display_name=display_name, unique_name=unique_name) + super().__init__( + x_unit=x_unit, + y_unit=y_unit, + name=name, + display_name=display_name, + unique_name=unique_name, + ) if 'np.' in expression: raise ValueError( @@ -131,21 +91,16 @@ def __init__( except Exception as e: raise ValueError(f'Invalid expression: {expression}') from e - # Extract symbols from the expression symbols = self._expr.free_symbols symbol_names = sorted(str(s) for s in symbols) if 'x' not in symbol_names: raise ValueError("Expression must contain 'x' as independent variable") - # Reject unknown functions early so invalid expressions fail at init, - # not later during numerical evaluation. allowed_function_names = set(self._ALLOWED_FUNCS) | { func.__name__ for func in self._ALLOWED_FUNCS.values() } - # Walk all function-call nodes in the parsed expression (e.g. sin(x), foo(x)). - # Keep only function names that are not in our allowlist. unknown_function_names: set[str] = set() function_atoms = self._expr.atoms(sp.Function) for function_atom in function_atoms: @@ -154,13 +109,11 @@ def __init__( unknown_function_names.add(function_name) unknown_functions = sorted(unknown_function_names) - if unknown_functions: raise ValueError( f'Unsupported function(s) in expression: {", ".join(unknown_functions)}' ) - # Create parameters if parameters is not None and not isinstance(parameters, dict): raise TypeError( f'Parameters must be None or a dictionary, got {type(parameters).__name__}' @@ -184,194 +137,98 @@ def __init__( value = parameters.get(name, 1.0) if isinstance(value, Parameter): self._parameters[name] = value - elif isinstance(value, dict) and value.get('@class') == 'Parameter': self._parameters[name] = Parameter.from_dict(value) else: self._parameters[name] = Parameter( name=name, value=value, - unit=self._unit, + unit=self._x_unit, ) - # Create numerical function ordered_symbols = [sp.Symbol(name) for name in self._symbol_names] - self._func = sp.lambdify( ordered_symbols, self._expr, modules=[{'erf': erf}, 'numpy'], ) - # ------------------------- - # Properties - # ------------------------- - @property def expression(self) -> str: - """ - Return the original expression string. - - Returns - ------- - str - The original expression string provided at initialization. - """ return self._expression_str @expression.setter def expression(self, _new_expr: str) -> None: - """ - Prevent changing the expression after initialization. - - Parameters - ---------- - _new_expr : str - New expression string (ignored). - - Raises - ------ - AttributeError - Always raised to prevent changing the expression. - """ raise AttributeError('Expression cannot be changed after initialization') def evaluate( self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, - ) -> np.ndarray: + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: """ Evaluate the expression for given x values. - Parameters - ---------- - x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - Input values for the independent variable. - - Returns - ------- - np.ndarray - Evaluated results. + Unit conversion of parameters is not supported for ExpressionComponent. If x has a + different unit than x_unit, a warning is issued and x values are used as-is. """ - x = self._prepare_x_for_evaluate(x) + x_vals, detected_unit, dim = self._prepare_x_for_evaluate(x) + + if detected_unit is not None and detected_unit != str(self._x_unit): + warnings.warn( + f'Input x has unit {detected_unit} but {self.__class__.__name__} has ' + f'x_unit {self._x_unit}. ExpressionComponent cannot auto-convert parameters. ' + 'x values are used as-is.', + UserWarning, + stacklevel=2, + ) args = [] for name in self._symbol_names: if name == 'x': - args.append(x) + args.append(x_vals) else: args.append(self._parameters[name].value) - return self._func(*args) + result = self._func(*args) - def get_all_variables(self) -> list[Parameter]: - """ - Return all parameters. + if output == 'scipp': + import scipp as sc - Returns - ------- - list[Parameter] - List of all parameters in the expression. - """ - return list(self._parameters.values()) + return sc.array(dims=[dim], values=result, unit=self._y_unit) + return result - def convert_unit(self, _new_unit: str | sc.Unit) -> None: - """ - Convert the unit of the expression. - - Unit conversion is not implemented for ExpressionComponent. - - Parameters - ---------- - _new_unit : str | sc.Unit - The new unit to convert to (ignored). - - Raises - ------ - NotImplementedError - Always raised to indicate unit conversion is not supported. - """ + def get_all_variables(self) -> list[Parameter]: + return list(self._parameters.values()) + def convert_x_unit(self, _new_unit: str | sc.Unit) -> None: raise NotImplementedError('Unit conversion is not implemented for ExpressionComponent') - # ------------------------- - # dunder methods - # ------------------------- + def convert_y_unit(self, _new_unit: str | sc.Unit) -> None: + raise NotImplementedError('Unit conversion is not implemented for ExpressionComponent') def __getattr__(self, name: str) -> Parameter: - """ - Allow access to parameters as attributes. - - Parameters - ---------- - name : str - Name of the parameter to access. - - Raises - ------ - AttributeError - If the parameter does not exist. - - Returns - ------- - Parameter - The parameter with the given name. - """ if '_parameters' in self.__dict__ and name in self._parameters: return self._parameters[name] raise AttributeError(f"{self.__class__.__name__} has no attribute '{name}'") def __setattr__(self, name: str, value: Numeric) -> None: - """ - Allow setting parameter values as attributes. - - Parameters - ---------- - name : str - Name of the parameter to set. - value : Numeric - New value for the parameter. - - Raises - ------ - TypeError - If the value is not numeric. - """ if '_parameters' in self.__dict__ and name in self._parameters: param = self._parameters[name] - if not isinstance(value, Numeric): raise TypeError(f'{name} must be numeric') - param.value = value else: - # For other attributes, use default behavior super().__setattr__(name, value) def __dir__(self) -> list[str]: - """ - Include parameter names in dir() output for better IDE support. - - Returns - ------- - list[str] - List of attribute names, including parameters. - """ return super().__dir__() + list(self._parameters.keys()) def __repr__(self) -> str: - """ - Return a string representation of the ExpressionComponent. - - Returns - ------- - str - String representation of the ExpressionComponent. - """ param_str = ', '.join(f'{k}={v.value}' for k, v in self._parameters.items()) return ( f'ExpressionComponent(name={self.name}, display_name={self.display_name}, ' - f'unit={self._unit},\n' + f'x_unit={self._x_unit}, y_unit={self._y_unit},\n' f" expr='{self._expression_str}',\n" f' parameters={{ {param_str} }} )' ) diff --git a/src/easydynamics/sample_model/components/gaussian.py b/src/easydynamics/sample_model/components/gaussian.py index 8b7f824a..2b809e99 100644 --- a/src/easydynamics/sample_model/components/gaussian.py +++ b/src/easydynamics/sample_model/components/gaussian.py @@ -6,13 +6,13 @@ from typing import TYPE_CHECKING import numpy as np +import scipp as sc from easydynamics.sample_model.components.mixins import CreateParametersMixin from easydynamics.sample_model.components.model_component import ModelComponent from easydynamics.utils.utils import Numeric if TYPE_CHECKING: - import scipp as sc from easyscience.variable import Parameter @@ -20,15 +20,11 @@ class Gaussian(CreateParametersMixin, ModelComponent): r""" Model of a Gaussian function. - The intensity is given by + $$ I(x) = \frac{A}{\sigma \sqrt{2\pi}} \exp\left( -\frac{1}{2} \left(\frac{x - + x_0}{\sigma}\right)^2 \right) $$ - $$ I(x) = \frac{A}{\sigma \sqrt{2\pi}} \exp\left( -\frac{1}{2} \left(\frac{x - - x_0}{\sigma}\right)^2 \right) $$ - - where $A$ is the area, $x_0$ is the center, and $\sigma$ is the width. - - If the center is not provided, it will be centered at 0 and fixed, which is typically what you - want in QENS. + where $A$ is the area, $x_0$ is the center, and $\sigma$ is the width. area has unit = x_unit * + y_unit; center and width have unit = x_unit. """ def __init__( @@ -36,80 +32,72 @@ def __init__( area: Numeric = 1.0, center: Numeric | None = None, width: Numeric = 1.0, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'Gaussian', display_name: str | None = None, unique_name: str | None = None, ) -> None: """ - Initialize the Gaussian component. - Parameters ---------- area : Numeric, default=1.0 - Area of the Gaussian. + Integrated area under the Gaussian. Unit is ``x_unit * y_unit``. center : Numeric | None, default=None - Center of the Gaussian. If None, defaults to 0 and is fixed. + Peak position in x_unit. If None, defaults to 0 and the center parameter is fixed. width : Numeric, default=1.0 - Standard deviation. - unit : str | sc.Unit, default='meV' - Unit of the parameters. + Standard deviation (sigma) in x_unit. Must be strictly positive. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis. center and width are stored in this unit. area_unit = x_unit * + y_unit. + y_unit : str | sc.Unit, default='dimensionless' + Unit of the y-axis (output). name : str, default='Gaussian' - Name of the component for indexing. + Internal name used for parameter labelling. display_name : str | None, default=None - Name of the component. + Human-readable name. Falls back to *name* if None. unique_name : str | None, default=None - Unique name of the component. if None, a unique_name is automatically generated. By - default, None. + Globally unique identifier. Auto-generated if None. """ - # Validate inputs and create Parameters if not given super().__init__( - unit=unit, + x_unit=x_unit, + y_unit=y_unit, name=name, display_name=display_name, unique_name=unique_name, ) - # These methods live in ValidationMixin - area = self._create_area_parameter(area=area, name=name, unit=self._unit) - center = self._create_center_parameter( - center=center, name=name, fix_if_none=True, unit=self._unit + self._area = self._create_area_parameter( + area=area, name=name, x_unit=self._x_unit, y_unit=self._y_unit ) - width = self._create_width_parameter(width=width, name=name, unit=self._unit) - - self._area = area - self._center = center - self._width = width + self._center = self._create_center_parameter( + center=center, name=name, fix_if_none=True, x_unit=self._x_unit + ) + self._width = self._create_width_parameter(width=width, name=name, x_unit=self._x_unit) @property def area(self) -> Parameter: """ - Get the area parameter. - Returns ------- Parameter - The area parameter. + The area Parameter with unit ``x_unit * y_unit``. """ - return self._area @area.setter def area(self, value: Numeric) -> None: """ - Set the value of the area parameter. - Parameters ---------- value : Numeric - The new value for the area parameter. + New area value (in current area unit = x_unit * y_unit). Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. """ - if not isinstance(value, Numeric): raise TypeError('area must be a number') self._area.value = value @@ -117,32 +105,27 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ - Get the center parameter. - Returns ------- Parameter - The center parameter. + The center Parameter with unit ``x_unit``. """ - return self._center @center.setter def center(self, value: Numeric | None) -> None: """ - Set the center parameter value. - Parameters ---------- value : Numeric | None - The new value for the center parameter. If None, defaults to 0 and is fixed. + New center value in x_unit. If None, the center is set to 0 and the parameter is + fixed. Raises ------ TypeError - If the value is not a number or None. + If *value* is not None and not a numeric type. """ - if value is None: value = 0.0 self._center.fixed = True @@ -153,84 +136,146 @@ def center(self, value: Numeric | None) -> None: @property def width(self) -> Parameter: """ - Get the width parameter (standard deviation). - Returns ------- Parameter - The width parameter. + The width (sigma) Parameter with unit ``x_unit``. """ return self._width @width.setter def width(self, value: Numeric) -> None: """ - Set the width parameter value. - Parameters ---------- value : Numeric - The new value for the width parameter. + New width value in x_unit. Must be strictly positive. Raises ------ TypeError - If the value is not a number or None. + If *value* is not a numeric type. ValueError - If the value is not positive. + If *value* is not positive. """ if not isinstance(value, Numeric): raise TypeError('width must be a number') - if float(value) <= 0: raise ValueError('width must be positive') - self._width.value = value def evaluate( self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, - ) -> np.ndarray: + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: r""" - Evaluate the Gaussian at the given x values. + Evaluate the Gaussian at x. - If x is a scipp Variable, the unit of the Gaussian will be converted to match x. The - intensity is given by $$ I(x) = \frac{A}{\sigma \sqrt{2\pi}} \exp\left( -\frac{1}{2} - \left(\frac{x - x_0}{\sigma}\right)^2 \right) $$ - - where $A$ is the area, $x_0$ is the center, and $\sigma$ is the width. + Parameters in the model's own units are temporarily converted to x's unit for the + computation — the model is never mutated. Parameters ---------- x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The x values at which to evaluate the Gaussian. + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. Returns ------- - np.ndarray - The intensity of the Gaussian at the given x values. + np.ndarray | sc.Variable + Evaluated Gaussian values at x. """ + x_vals, detected_unit, dim = self._prepare_x_for_evaluate(x) + eval_unit = detected_unit or self._x_unit + eval_area_unit = str(sc.Unit(eval_unit) * sc.Unit(self._y_unit)) - x = self._prepare_x_for_evaluate(x) + center = self._resolve_param_value(self._center, eval_unit) + width = self._resolve_param_value(self._width, eval_unit) + area = self._resolve_param_value(self._area, eval_area_unit) - normalization = 1 / (np.sqrt(2 * np.pi) * self.width.value) - exponent = -0.5 * ((x - self.center.value) / self.width.value) ** 2 + normalization = 1 / (np.sqrt(2 * np.pi) * width) + exponent = -0.5 * ((x_vals - center) / width) ** 2 + result = area * normalization * np.exp(exponent) - return self.area.value * normalization * np.exp(exponent) + if output == 'scipp': + return sc.array(dims=[dim], values=result, unit=self._y_unit) + return result - def __repr__(self) -> str: + def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: """ - Return a string representation of the Gaussian. + Convert x-axis parameters (center, width) and area to new_x_unit. - Returns - ------- - str - A string representation of the Gaussian. + Parameters + ---------- + new_x_unit : str | sc.Unit + Target x-axis unit. Must be dimensionally compatible with the current x_unit. + + Raises + ------ + TypeError + If *new_x_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. """ + if not isinstance(new_x_unit, (str, sc.Unit)): + raise TypeError(f'x_unit must be a string or sc.Unit, got {type(new_x_unit).__name__}') + old_x_unit = self._x_unit + new_area_unit = str(sc.Unit(new_x_unit) * sc.Unit(self._y_unit)) + try: + self._center.convert_unit(new_x_unit) + self._width.convert_unit(new_x_unit) + self._area.convert_unit(new_area_unit) + self._x_unit = str(new_x_unit) if isinstance(new_x_unit, sc.Unit) else new_x_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(old_x_unit) * sc.Unit(self._y_unit)) + self._center.convert_unit(old_x_unit) + self._width.convert_unit(old_x_unit) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: + """ + Convert the y-axis (output) unit by rescaling the area parameter. + + The area is rescaled from ``x_unit * old_y_unit`` to ``x_unit * new_y_unit``. + Parameters + ---------- + new_y_unit : str | sc.Unit + Target y-axis unit. + + Raises + ------ + TypeError + If *new_y_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. + """ + if not isinstance(new_y_unit, (str, sc.Unit)): + raise TypeError(f'y_unit must be a string or sc.Unit, got {type(new_y_unit).__name__}') + old_y_unit = self._y_unit + new_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(new_y_unit)) + try: + self._area.convert_unit(new_area_unit) + self._y_unit = str(new_y_unit) if isinstance(new_y_unit, sc.Unit) else new_y_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(old_y_unit)) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def __repr__(self) -> str: return ( f'Gaussian(name = {self.name}, display_name = {self.display_name}, ' - f'unit = {self._unit},\n' + f'x_unit = {self._x_unit}, y_unit = {self._y_unit},\n' f' area = {self.area},\n' f' center = {self.center},\n' f' width = {self.width})' diff --git a/src/easydynamics/sample_model/components/lorentzian.py b/src/easydynamics/sample_model/components/lorentzian.py index 88051d16..087f34dc 100644 --- a/src/easydynamics/sample_model/components/lorentzian.py +++ b/src/easydynamics/sample_model/components/lorentzian.py @@ -6,13 +6,13 @@ from typing import TYPE_CHECKING import numpy as np +import scipp as sc from easydynamics.sample_model.components.mixins import CreateParametersMixin from easydynamics.sample_model.components.model_component import ModelComponent from easydynamics.utils.utils import Numeric if TYPE_CHECKING: - import scipp as sc from easyscience.variable import Parameter @@ -20,12 +20,10 @@ class Lorentzian(CreateParametersMixin, ModelComponent): r""" Model of a Lorentzian function. - The intensity is given by $$ I(x) = \frac{A}{\pi} \frac{\Gamma}{(x - x_0)^2 + \Gamma^2}, $$ - where $A$ is the area, $x_0$ is the center, and $\Gamma$ is the half width at half maximum - (HWHM). + $$ I(x) = \frac{A}{\pi} \frac{\Gamma}{(x - x_0)^2 + \Gamma^2} $$ - If the center is not provided, it will be centered at 0 and fixed, which is typically what you - want in QENS. + where $A$ is the area, $x_0$ is the center, and $\Gamma$ is the HWHM. area has unit = x_unit * + y_unit; center and width have unit = x_unit. """ def __init__( @@ -33,77 +31,71 @@ def __init__( area: Numeric = 1.0, center: Numeric | None = None, width: Numeric = 1.0, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'Lorentzian', display_name: str | None = None, unique_name: str | None = None, ) -> None: """ - Initialize the Lorentzian component. - Parameters ---------- area : Numeric, default=1.0 - Area of the Lorentzian. + Integrated area under the Lorentzian. Unit is ``x_unit * y_unit``. center : Numeric | None, default=None - Center of the Lorentzian. If None, defaults to 0 and is fixed. + Peak position in x_unit. If None, defaults to 0 and the center parameter is fixed. width : Numeric, default=1.0 - Half width at half maximum (HWHM). - unit : str | sc.Unit, default='meV' - Unit of the parameters. + Half-width at half-maximum (HWHM, gamma) in x_unit. Must be strictly positive. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis. center and width are stored in this unit. area_unit = x_unit * + y_unit. + y_unit : str | sc.Unit, default='dimensionless' + Unit of the y-axis (output). name : str, default='Lorentzian' - Name of the component for indexing. + Internal name used for parameter labelling. display_name : str | None, default=None - Display name for the component. + Human-readable name. Falls back to *name* if None. unique_name : str | None, default=None - Unique name of the component. If None, a unique_name is automatically generated. By - default, None. + Globally unique identifier. Auto-generated if None. """ - super().__init__( - unit=unit, + x_unit=x_unit, + y_unit=y_unit, name=name, display_name=display_name, unique_name=unique_name, ) - # These methods live in ValidationMixin - area = self._create_area_parameter(area=area, name=name, unit=self._unit) - center = self._create_center_parameter( - center=center, name=name, fix_if_none=True, unit=self._unit + self._area = self._create_area_parameter( + area=area, name=name, x_unit=self._x_unit, y_unit=self._y_unit ) - width = self._create_width_parameter(width=width, name=name, unit=self._unit) - - self._area = area - self._center = center - self._width = width + self._center = self._create_center_parameter( + center=center, name=name, fix_if_none=True, x_unit=self._x_unit + ) + self._width = self._create_width_parameter(width=width, name=name, x_unit=self._x_unit) @property def area(self) -> Parameter: """ - Get the area parameter. - Returns ------- Parameter - The area parameter. + The area Parameter with unit ``x_unit * y_unit``. """ return self._area @area.setter def area(self, value: Numeric) -> None: """ - Set the value of the area parameter. - Parameters ---------- value : Numeric - The new value for the area parameter. + New area value (in current area unit = x_unit * y_unit). Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. """ if not isinstance(value, Numeric): raise TypeError('area must be a number') @@ -112,31 +104,27 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ - Get the center parameter. - Returns ------- Parameter - The center parameter. + The center Parameter with unit ``x_unit``. """ return self._center @center.setter def center(self, value: Numeric | None) -> None: """ - Set the value of the center parameter. - Parameters ---------- value : Numeric | None - The new value for the center parameter. If None, defaults to 0 and is fixed. + New center value in x_unit. If None, the center is set to 0 and the parameter is + fixed. Raises ------ TypeError - If the value is not a number or None. + If *value* is not None and not a numeric type. """ - if value is None: value = 0.0 self._center.fixed = True @@ -147,81 +135,146 @@ def center(self, value: Numeric | None) -> None: @property def width(self) -> Parameter: """ - Get the width parameter (HWHM). - Returns ------- Parameter - The width parameter. + The HWHM (gamma) Parameter with unit ``x_unit``. """ return self._width @width.setter def width(self, value: Numeric) -> None: """ - Set the width parameter value (HWHM). - Parameters ---------- value : Numeric - The new value for the width parameter. + New HWHM value in x_unit. Must be strictly positive. Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. ValueError - If the value is not positive. + If *value* is not positive. """ if not isinstance(value, Numeric): raise TypeError('width must be a number') - if float(value) <= 0: raise ValueError('width must be positive') self._width.value = value - def evaluate(self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray) -> np.ndarray: + def evaluate( + self, + x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: r""" - Evaluate the Lorentzian at the given x values. - - If x is a scipp Variable, the unit of the Lorentzian will be converted to match x. The - intensity is given by + Evaluate the Lorentzian at x. - $$ I(x) = \frac{A}{\pi} \frac{\Gamma}{(x - x_0)^2 + \Gamma^2}, $$ - - where $A$ is the area, $x_0$ is the center, and $\Gamma$ is the half width at half maximum - (HWHM). + Parameters in the model's own units are temporarily converted to x's unit for the + computation — the model is never mutated. Parameters ---------- x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The x values at which to evaluate the Lorentzian. + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. Returns ------- - np.ndarray - The intensity of the Lorentzian at the given x values. + np.ndarray | sc.Variable + Evaluated Lorentzian values at x. """ + x_vals, detected_unit, dim = self._prepare_x_for_evaluate(x) + eval_unit = detected_unit or self._x_unit + eval_area_unit = str(sc.Unit(eval_unit) * sc.Unit(self._y_unit)) - x = self._prepare_x_for_evaluate(x) + center = self._resolve_param_value(self._center, eval_unit) + width = self._resolve_param_value(self._width, eval_unit) + area = self._resolve_param_value(self._area, eval_area_unit) - normalization = self.width.value / np.pi - denominator = (x - self.center.value) ** 2 + self.width.value**2 + normalization = width / np.pi + denominator = (x_vals - center) ** 2 + width**2 + result = area * normalization / denominator - return self.area.value * normalization / denominator + if output == 'scipp': + return sc.array(dims=[dim], values=result, unit=self._y_unit) + return result - def __repr__(self) -> str: + def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: """ - Return a string representation of the Lorentzian. + Convert x-axis parameters (center, width) and area to new_x_unit. - Returns - ------- - str - A string representation of the Lorentzian. + Parameters + ---------- + new_x_unit : str | sc.Unit + Target x-axis unit. Must be dimensionally compatible with the current x_unit. + + Raises + ------ + TypeError + If *new_x_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. + """ + if not isinstance(new_x_unit, (str, sc.Unit)): + raise TypeError(f'x_unit must be a string or sc.Unit, got {type(new_x_unit).__name__}') + old_x_unit = self._x_unit + new_area_unit = str(sc.Unit(new_x_unit) * sc.Unit(self._y_unit)) + try: + self._center.convert_unit(new_x_unit) + self._width.convert_unit(new_x_unit) + self._area.convert_unit(new_area_unit) + self._x_unit = str(new_x_unit) if isinstance(new_x_unit, sc.Unit) else new_x_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(old_x_unit) * sc.Unit(self._y_unit)) + self._center.convert_unit(old_x_unit) + self._width.convert_unit(old_x_unit) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: + """ + Convert the y-axis (output) unit by rescaling the area parameter. + + The area is rescaled from ``x_unit * old_y_unit`` to ``x_unit * new_y_unit``. + + Parameters + ---------- + new_y_unit : str | sc.Unit + Target y-axis unit. + + Raises + ------ + TypeError + If *new_y_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. """ + if not isinstance(new_y_unit, (str, sc.Unit)): + raise TypeError(f'y_unit must be a string or sc.Unit, got {type(new_y_unit).__name__}') + old_y_unit = self._y_unit + new_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(new_y_unit)) + try: + self._area.convert_unit(new_area_unit) + self._y_unit = str(new_y_unit) if isinstance(new_y_unit, sc.Unit) else new_y_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(old_y_unit)) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def __repr__(self) -> str: return ( f'Lorentzian(name = {self.name}, display_name = {self.display_name}, ' - f'unit = {self._unit},\n' + f'x_unit = {self._x_unit}, y_unit = {self._y_unit},\n' f' area = {self.area},\n' f' center = {self.center},\n' f' width = {self.width})' diff --git a/src/easydynamics/sample_model/components/mixins.py b/src/easydynamics/sample_model/components/mixins.py index 7552d2e8..50c803ea 100644 --- a/src/easydynamics/sample_model/components/mixins.py +++ b/src/easydynamics/sample_model/components/mixins.py @@ -18,52 +18,54 @@ class CreateParametersMixin: """ Provides parameter creation and validation methods for model components. - This mixin provides methods to create and validate common physics parameters (area, center, - width) with appropriate bounds and type checking. + area_unit = x_unit * y_unit, so when y_unit='dimensionless', area_unit = x_unit. """ def _create_area_parameter( self, area: Numeric, name: str, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', minimum_area: float = MINIMUM_AREA, ) -> Parameter: """ - Validate and convert a number to a Parameter describing the area of a function. - - If the area is negative, a warning is raised. If the area is non-negative, its minimum is - set to 0 to avoid it accidentally becoming negative during fitting. + Create a Parameter for the area with unit = x_unit * y_unit. Parameters ---------- area : Numeric - The area value. + Initial area value. name : str - The name of the model component. - unit : str | sc.Unit, default='meV' - The unit of the area Parameter. + Base name used to label the Parameter (``name + ' area'``). + x_unit : str | sc.Unit, default='meV' + X-axis unit. The resulting area unit is ``x_unit * y_unit``. + y_unit : str | sc.Unit, default='dimensionless' + Y-axis unit. The resulting area unit is ``x_unit * y_unit``. minimum_area : float, default=MINIMUM_AREA - The minimum allowed area. + Lower bound applied to the Parameter when the area is non-negative. When *area* is + negative no lower bound is set and a :class:`UserWarning` is issued. + + Returns + ------- + Parameter + Configured area Parameter with ``unit = x_unit * y_unit``. Raises ------ TypeError - If area is not a number. + If *area* is not a numeric type. ValueError - If area is not a finite number. - - Returns - ------- - Parameter - The validated area Parameter. + If *area* is not finite. """ if not isinstance(area, Numeric): raise TypeError('area must be a number.') if not np.isfinite(area): raise ValueError('area must be a finite number.') - area_param = Parameter(name=name + ' area', value=float(area), unit=unit) + + area_unit = str(sc.Unit(x_unit) * sc.Unit(y_unit)) + area_param = Parameter(name=name + ' area', value=float(area), unit=area_unit) if area_param.value < 0: warnings.warn( @@ -81,36 +83,38 @@ def _create_center_parameter( center: Numeric | None, name: str, fix_if_none: bool, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', enforce_minimum_center: bool = False, ) -> Parameter: """ - Validate and convert a number to a Parameter describing the center of a function. + Create a Parameter for the center with unit = x_unit. Parameters ---------- center : Numeric | None - The center value. + Initial center value. If None, the center is set to 0.0 and ``fixed`` is controlled by + *fix_if_none*. name : str - The name of the model component. + Base name used to label the Parameter (``name + ' center'``). fix_if_none : bool - Whether to fix the center Parameter if center is None. - unit : str | sc.Unit, default='meV' - The unit of the center Parameter. + Whether to fix the Parameter when *center* is None. + x_unit : str | sc.Unit, default='meV' + X-axis unit, applied to the center Parameter. enforce_minimum_center : bool, default=False - Whether to enforce a minimum center value to avoid zero center in DHO. + If True, the Parameter's lower bound is raised to ``DHO_MINIMUM_CENTER`` (1e-10) to + prevent a zero center. + + Returns + ------- + Parameter + Configured center Parameter with ``unit = x_unit``. Raises ------ TypeError - If center is not None or a number. + If *center* is not None and not a numeric type. ValueError - If center is a number but not finite. - - Returns - ------- - Parameter - The validated center Parameter. + If *center* is not None and not finite. """ if center is not None and not isinstance(center, Numeric): raise TypeError('center must be None or a number.') @@ -119,14 +123,14 @@ def _create_center_parameter( center_param = Parameter( name=name + ' center', value=0.0, - unit=unit, + unit=x_unit, fixed=fix_if_none, ) else: if not np.isfinite(center): raise ValueError('center must be None or a finite number.') + center_param = Parameter(name=name + ' center', value=float(center), unit=x_unit) - center_param = Parameter(name=name + ' center', value=float(center), unit=unit) if enforce_minimum_center and center_param.min < DHO_MINIMUM_CENTER: center_param.min = DHO_MINIMUM_CENTER return center_param @@ -136,36 +140,37 @@ def _create_width_parameter( width: Numeric, name: str, param_name: str = 'width', - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', minimum_width: float = MINIMUM_WIDTH, ) -> Parameter: """ - Validate and convert a number to a Parameter describing the width of a function. + Create a Parameter for the width with unit = x_unit. Parameters ---------- width : Numeric - The width value. + Initial width value. Must be strictly positive (>= *minimum_width*). name : str - The name of the model component. + Base name used to label the Parameter (``name + ' ' + param_name``). param_name : str, default='width' - The name of the width parameter. - unit : str | sc.Unit, default='meV' - The unit of the width Parameter. + Logical name of the parameter used in the label and error messages (e.g. + ``'gaussian_width'``, ``'lorentzian_width'``). + x_unit : str | sc.Unit, default='meV' + X-axis unit, applied to the width Parameter. minimum_width : float, default=MINIMUM_WIDTH - The minimum allowed width. + Absolute lower bound for the width to prevent division-by-zero. + + Returns + ------- + Parameter + Configured width Parameter with ``unit = x_unit`` and ``min = minimum_width``. Raises ------ TypeError - If width is not a number. + If *width* is not a numeric type. ValueError - If width is non-positive. - - Returns - ------- - Parameter - The validated width Parameter. + If *width* is not finite or is smaller than *minimum_width*. """ if not isinstance(width, Numeric): raise TypeError(f'{param_name} must be a number.') @@ -180,6 +185,6 @@ def _create_width_parameter( return Parameter( name=name + ' ' + param_name, value=float(width), - unit=unit, + unit=x_unit, min=minimum_width, ) diff --git a/src/easydynamics/sample_model/components/model_component.py b/src/easydynamics/sample_model/components/model_component.py index 84c88453..53412438 100644 --- a/src/easydynamics/sample_model/components/model_component.py +++ b/src/easydynamics/sample_model/components/model_component.py @@ -3,8 +3,8 @@ from __future__ import annotations -import warnings from abc import abstractmethod +from typing import TYPE_CHECKING import numpy as np import scipp as sc @@ -13,113 +13,150 @@ from easydynamics.base_classes.easydynamics_modelbase import EasyDynamicsModelBase from easydynamics.utils.utils import Numeric +if TYPE_CHECKING: + from easyscience.variable import Parameter + class ModelComponent(EasyDynamicsModelBase): """Abstract base class for all model components.""" def __init__( self, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'ModelComponent', display_name: str | None = None, unique_name: str | None = None, ) -> None: """ - Initialize the ModelComponent. - Parameters ---------- - unit : str | sc.Unit, default='meV' - The unit of the model component. + x_unit : str | sc.Unit, default='meV' + Unit for the x-axis (independent variable) of this component. + y_unit : str | sc.Unit, default='dimensionless' + Unit for the y-axis (dependent variable / output) of this component. name : str, default='ModelComponent' - The name of the model component for indexing. + Internal name used for parameter labelling and logging. display_name : str | None, default=None - A human-readable name for the component. + Human-readable name shown in plots and reports. Falls back to *name* if None. unique_name : str | None, default=None - A unique identifier for the component. + Globally unique identifier. Auto-generated if None. """ super().__init__( - unit=unit, + x_unit=x_unit, + y_unit=y_unit, name=name, display_name=display_name, unique_name=unique_name, ) @property - def unit(self) -> str: + def x_unit(self) -> str: """ - Get the unit. - Returns ------- str - The unit of the model component. + The current x-axis unit as a string. """ - return str(self._unit) + return str(self._x_unit) - @unit.setter - def unit(self, _unit_str: str) -> None: + @x_unit.setter + def x_unit(self, _: str) -> None: """ - Unit is read-only. + Unit is read-only; raises AttributeError always. - Use convert_unit to change the unit between allowed types or create a new ModelComponent - with the desired unit. + Use :meth:`convert_x_unit` to change the unit, or create a new instance with the desired + unit. - Parameters - ---------- - _unit_str : str - The new unit to set. + Raises + ------ + AttributeError + Always raised when this setter is called. + """ + raise AttributeError( + f'x_unit is read-only. Use convert_x_unit to change the unit ' + f'or create a new {self.__class__.__name__} with the desired unit.' + ) + + @property + def y_unit(self) -> str: + """ + Returns + ------- + str + The current y-axis unit as a string. + """ + return str(self._y_unit) + + @y_unit.setter + def y_unit(self, _: str) -> None: + """ + Unit is read-only; raises AttributeError always. + + Use :meth:`convert_y_unit` to change the unit, or create a new instance with the desired + unit. Raises ------ AttributeError - Always raised since unit is read-only. + Always raised when this setter is called. """ raise AttributeError( - f'Unit is read-only. Use convert_unit to change the unit between allowed types ' + f'y_unit is read-only. Use convert_y_unit to change the unit ' f'or create a new {self.__class__.__name__} with the desired unit.' ) def fix_all_parameters(self) -> None: - """Fix all parameters in the model component.""" + """ + Fix all parameters in the model component. - pars = self.get_fittable_parameters() - for p in pars: + Sets ``fixed=True`` on every fittable parameter returned by + :meth:`get_fittable_parameters`. + """ + for p in self.get_fittable_parameters(): p.fixed = True def free_all_parameters(self) -> None: - """Free all parameters in the model component.""" + """ + Free all parameters in the model component. + + Sets ``fixed=False`` on every fittable parameter returned by + :meth:`get_fittable_parameters`. + """ for p in self.get_fittable_parameters(): p.fixed = False def _prepare_x_for_evaluate( self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray - ) -> np.ndarray: + ) -> tuple[np.ndarray, str | None, str]: """ - Prepare the input x for evaluation by handling units and converting to a numpy array. + Validate x and extract its values, detected unit, and dimension name. + + x is never converted. When x carries a unit, the caller is responsible for resolving + parameter values to that unit via _resolve_param_value. Parameters ---------- x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The input data to prepare. + Input x values to validate and extract. + + Returns + ------- + tuple[np.ndarray, str | None, str] + x_values : np.ndarray of raw float values (no unit conversion) detected_unit : str unit + of x if scipp input, else None dim : scipp dimension name if scipp input, else 'x' Raises ------ - ValueError - If x contains NaN or infinite values, or if a sc.DataArray has more than one - coordinate. UnitError - If x has incompatible units that cannot be converted to the component's unit. - - Returns - ------- - np.ndarray - The prepared input data as a numpy array. + If x has a unit incompatible with the model's x_unit. + ValueError + If x contains NaN or infinite values, or if a DataArray has more than one coordinate. """ + detected_unit: str | None = None + dim: str = 'x' - # Handle units if isinstance(x, sc.DataArray): - # Check that there's exactly one coordinate coords = dict(x.coords) ncoords = len(coords) if ncoords != 1: @@ -128,31 +165,23 @@ def _prepare_x_for_evaluate( f'scipp.DataArray must have exactly one coordinate to be used as input `x`. ' f'Found {ncoords} coordinates: {coord_names}.' ) - # get the coordinate, it's a sc.Variable - _, coord_obj = next(iter(coords.items())) + dim, coord_obj = next(iter(coords.items())) x = coord_obj + if isinstance(x, sc.Variable): - # Need to check if the units are consistent, - # and convert if not. + detected_unit = str(x.unit) + dim = x.dims[0] if x.dims else 'x' x_in = x.value if x.sizes == {} else x.values - if self._unit is not None and x.unit != self._unit: - self_unit_for_warning = self._unit + + # Validate that x's unit is compatible with model's x_unit + if self._x_unit is not None and detected_unit != str(self._x_unit): try: - self.convert_unit(x.unit.name) + sc.to_unit(sc.scalar(1.0, unit=detected_unit), str(self._x_unit)) except Exception as e: raise UnitError( - f'Input x has unit {x.unit}, but {self.__class__.__name__} component \ - has unit {self._unit}. \ - Failed to convert {self.__class__.__name__} to {x.unit}.' + f'Input x has unit {detected_unit}, which is incompatible with ' + f'{self.__class__.__name__} x_unit {self._x_unit}.' ) from e - - warnings.warn( - f'Input x has unit {x.unit}, but {self.__class__.__name__} component \ - has unit {self_unit_for_warning}. \ - Converting {self.__class__.__name__} to {x.unit}.', - UserWarning, - stacklevel=3, - ) else: x_in = x @@ -167,69 +196,111 @@ def _prepare_x_for_evaluate( if any(np.isinf(x_in)): raise ValueError('Input x contains infinite values.') - return np.sort(x_in) + return np.sort(x_in), detected_unit, dim - def convert_unit(self, unit: str | sc.Unit) -> None: + def _resolve_param_value(self, param: Parameter, target_unit: str | None) -> float: """ - Convert the unit of the Parameters in the component. + Return param's value converted to target_unit without mutating param. + + If target_unit is None or already matches param's unit, returns param.value directly. Uses + a temporary scipp scalar for the conversion. + + Parameters + ---------- + param : Parameter + The parameter whose value should be resolved. + target_unit : str | None + The unit to which the parameter value should be converted. When None (or equal to the + parameter's own unit) the raw value is returned without any conversion. + + Returns + ------- + float + The parameter value expressed in *target_unit*. + """ + if target_unit is None or str(param.unit) == str(target_unit): + return param.value + return sc.to_unit(sc.scalar(param.value, unit=str(param.unit)), target_unit).value + + def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: + """ + Convert the x-axis unit of the component. + + The base implementation converts all parameters. Subclasses with mixed-unit parameters + (e.g. area ≠ x_unit) should override this method. Parameters ---------- - unit : str | sc.Unit - The new unit to convert to. + new_x_unit : str | sc.Unit + Target x-axis unit. Must be dimensionally compatible with the current x_unit. Raises ------ TypeError - If the provided unit is not a str or sc.Unit. + If *new_x_unit* is not a ``str`` or ``sc.Unit``. Exception - If the provided unit is invalid or incompatible with the component's parameters. + If the conversion between the current unit and *new_x_unit* fails. On failure the + component is rolled back to its original unit. """ - if not isinstance(unit, (str, sc.Unit)): - raise TypeError(f'Unit must be a string or sc.Unit, got {type(unit).__name__}') + if not isinstance(new_x_unit, (str, sc.Unit)): + raise TypeError(f'x_unit must be a string or sc.Unit, got {type(new_x_unit).__name__}') - old_unit = self._unit + old_unit = self._x_unit pars = self.get_all_parameters() try: for p in pars: - p.convert_unit(unit) - self._unit = unit + p.convert_unit(new_x_unit) + self._x_unit = str(new_x_unit) if isinstance(new_x_unit, sc.Unit) else new_x_unit except Exception as e: - # Attempt to rollback on failure try: for p in pars: if hasattr(p, 'convert_unit'): p.convert_unit(old_unit) except Exception: # noqa: S110 - pass # Best effort rollback + pass raise e - @abstractmethod - def evaluate(self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray) -> np.ndarray: + def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: """ - Abstract method to evaluate the model component at input x. - - Must be implemented by subclasses. + Convert the y-axis (output) unit. Subclasses with an area parameter should override this. Parameters ---------- - x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The x values at which to evaluate the component. + new_y_unit : str | sc.Unit + Target y-axis unit. - Returns - ------- - np.ndarray - Evaluated function values. + Raises + ------ + NotImplementedError + Always raised in this base implementation. Subclasses that carry an area parameter + (area_unit = x_unit * y_unit) must override this method to rescale the area + appropriately. """ + raise NotImplementedError(f'{self.__class__.__name__} does not support convert_y_unit.') - def __repr__(self) -> str: + @abstractmethod + def evaluate( + self, + x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: """ - Return a string representation of the ModelComponent. + Evaluate the model component at input x. + + Parameters + ---------- + x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. Returns ------- - str - A string representation of the ModelComponent. + np.ndarray | sc.Variable + Evaluated model values at x. """ - return f'{self.__class__.__name__}(unique_name={self.unique_name}, unit={self._unit})' + def __repr__(self) -> str: + return ( + f'{self.__class__.__name__}(unique_name={self.unique_name}, ' + f'x_unit={self._x_unit}, y_unit={self._y_unit})' + ) diff --git a/src/easydynamics/sample_model/components/polynomial.py b/src/easydynamics/sample_model/components/polynomial.py index 03e7eae7..1e61351e 100644 --- a/src/easydynamics/sample_model/components/polynomial.py +++ b/src/easydynamics/sample_model/components/polynomial.py @@ -23,46 +23,52 @@ class Polynomial(ModelComponent): r""" Polynomial function component. - The intensity is given by $$ I(x) = c_0 + c_1 x + c_2 x^2 + ... + c_N x^N, $$ where $C_i$ are - the coefficients. + $$ I(x) = c_0 + c_1 x + c_2 x^2 + ... + c_N x^N $$ + + Coefficients are stored as dimensionless Parameters. When x_unit changes, the coefficient + values are rescaled so the evaluated result stays the same. The output unit is y_unit. """ def __init__( self, coefficients: Sequence[Numeric | Parameter] = (0.0,), - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'Polynomial', display_name: str | None = None, unique_name: str | None = None, ) -> None: """ - Initialize the Polynomial component. - Parameters ---------- coefficients : Sequence[Numeric | Parameter], default=(0.0,) - Coefficients c0, c1, ..., cN. - unit : str | sc.Unit, default='meV' - Unit of the Polynomial component. + Ordered list of polynomial coefficients ``[c0, c1, ..., cN]`` where the polynomial is + ``c0 + c1*x + c2*x^2 + ... + cN*x^N``. Each element may be a plain numeric value + (wrapped into a dimensionless :class:`Parameter`) or an existing :class:`Parameter` + instance. Must contain at least one element. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis. When the x_unit is changed via :meth:`convert_x_unit`, coefficient + values are rescaled by power-law factors so the evaluated output remains unchanged. + y_unit : str | sc.Unit, default='dimensionless' + Unit of the y-axis (output). name : str, default='Polynomial' - Name of the component for indexing. + Internal name used for parameter labelling. display_name : str | None, default=None - Display name of the Polynomial component. + Human-readable name. Falls back to *name* if None. unique_name : str | None, default=None - Unique name of the component. If None, a unique_name is automatically generated. By - default, None. + Globally unique identifier. Auto-generated if None. Raises ------ TypeError - If coefficients is not a sequence of numbers or Parameters or if any item in - coefficients is not a number or Parameter. + If *coefficients* is not a list, tuple, or ndarray, or if any element is neither + numeric nor a :class:`Parameter`. ValueError - If coefficients is an empty sequence. + If *coefficients* is empty. """ - super().__init__( - unit=unit, + x_unit=x_unit, + y_unit=y_unit, name=name, display_name=display_name, unique_name=unique_name, @@ -70,17 +76,15 @@ def __init__( if not isinstance(coefficients, (list, tuple, np.ndarray)): raise TypeError( - 'coefficients must be a sequence (list/tuple/ndarray) \ - of numbers or Parameter objects.' + 'coefficients must be a sequence (list/tuple/ndarray) ' + 'of numbers or Parameter objects.' ) if len(coefficients) == 0: raise ValueError('At least one coefficient must be provided.') - # Internal storage of Parameter objects self._coefficients: list[Parameter] = [] - # Coefficients are treated as dimensionless Parameters for i, coef in enumerate(coefficients): if isinstance(coef, Parameter): param = coef @@ -90,45 +94,41 @@ def __init__( raise TypeError('Each coefficient must be either a numeric value or a Parameter.') self._coefficients.append(param) - # Helper scipp scalar to track unit conversions - # (value initialized to 1 with provided unit) - self._unit_conversion_helper = sc.scalar(value=1.0, unit=unit) + # Tracks the current x_unit scale for convert_x_unit power-law rescaling + self._x_unit_helper = sc.scalar(value=1.0, unit=x_unit) @property def coefficients(self) -> list[Parameter]: """ - Get the coefficients of the polynomial as a list of Parameters. - Returns ------- list[Parameter] - The coefficients of the polynomial. + A shallow copy of the internal coefficient list ``[c0, c1, ..., cN]``. Modifying the + returned list does not affect the model; use the setter to replace values. """ return list(self._coefficients) @coefficients.setter def coefficients(self, coeffs: Sequence[Numeric | Parameter]) -> None: """ - Set the coefficients of the polynomial. - - Length must match current number of coefficients. - Parameters ---------- coeffs : Sequence[Numeric | Parameter] - New coefficients as a sequence of numbers or Parameters. + New coefficient values. Must be a list, tuple, or ndarray and must have the same + length as the current number of coefficients. Numeric values update the existing + Parameter's ``.value``; a Parameter instance replaces the stored Parameter entirely. Raises ------ TypeError - If coeffs is not a sequence of numbers or Parameters or if any item in coeffs is not a - number or Parameter. + If *coeffs* is not a list, tuple, or ndarray, or if any element is neither numeric nor + a Parameter. ValueError - If the length of coeffs does not match the existing number of coefficients. + If the length of *coeffs* does not match the current number of coefficients. """ if not isinstance(coeffs, (list, tuple, np.ndarray)): raise TypeError( - 'coefficients must be a sequence (list/tuple/ndarray) of numbers or Parameter .' + 'coefficients must be a sequence (list/tuple/ndarray) of numbers or Parameter.' ) if len(coeffs) != len(self._coefficients): raise ValueError( @@ -136,7 +136,6 @@ def coefficients(self, coeffs: Sequence[Numeric | Parameter]) -> None: ) for i, coef in enumerate(coeffs): if isinstance(coef, Parameter): - # replace parameter self._coefficients[i] = coef elif isinstance(coef, Numeric): self._coefficients[i].value = float(coef) @@ -145,57 +144,20 @@ def coefficients(self, coeffs: Sequence[Numeric | Parameter]) -> None: def coefficient_values(self) -> list[float]: """ - Get the coefficients of the polynomial as a list. - Returns ------- list[float] - The coefficient values of the polynomial. + Current numeric values of all coefficients ``[c0.value, c1.value, ..., cN.value]``. """ return [param.value for param in self._coefficients] - def evaluate(self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray) -> np.ndarray: - r""" - Evaluate the Polynomial at the given x values. - - The intensity is given by $$ I(x) = c_0 + c_1 x + c_2 x^2 + ... - + c_N x^N, $$ where $C_i$ are the coefficients. - - Parameters - ---------- - x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The x values at which to evaluate the Polynomial. - - Returns - ------- - np.ndarray - The evaluated Polynomial at the given x values. - """ - - x = self._prepare_x_for_evaluate(x) - - result = np.zeros_like(x, dtype=float) - for i, param in enumerate(self._coefficients): - result += param.value * np.power(x, i) - - if any(result < 0): - warnings.warn( - f'The Polynomial with unique_name {self.unique_name} has negative values, ' - 'which may not be physically meaningful.', - UserWarning, - stacklevel=2, - ) - return result - @property def degree(self) -> int: """ - Get the degree of the polynomial. - Returns ------- int - The degree of the polynomial. + Polynomial degree, equal to ``len(coefficients) - 1``. """ return len(self._coefficients) - 1 @@ -207,72 +169,142 @@ def degree(self, _value: int) -> None: Parameters ---------- _value : int - The new degree of the polynomial. + Ignored; this setter always raises :exc:`AttributeError`. Raises ------ AttributeError - Always raised since degree cannot be set directly. + Always raised when this setter is called. """ raise AttributeError( 'The degree of the polynomial is determined by the number of coefficients ' 'and cannot be set directly.' ) - def get_all_variables(self) -> list[DescriptorBase]: + def evaluate( + self, + x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: + r""" + Evaluate the Polynomial at x. + + When x has a different unit than the stored x_unit, coefficient values are temporarily + rescaled (same power-law logic as convert_x_unit) without mutation. + + Parameters + ---------- + x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. + + Returns + ------- + np.ndarray | sc.Variable + Evaluated polynomial values. """ - Get all variables from the model component. + x_vals, detected_unit, dim = self._prepare_x_for_evaluate(x) + + if detected_unit is not None and detected_unit != str(self._x_unit): + # Temporary coefficient rescaling — no mutation + helper = sc.scalar(1.0, unit=str(self._x_unit)) + helper_in_x = sc.to_unit(helper, detected_unit) + scale = helper.value / helper_in_x.value + coeff_vals = [p.value * scale**i for i, p in enumerate(self._coefficients)] + else: + coeff_vals = [p.value for p in self._coefficients] + + result = np.zeros_like(x_vals, dtype=float) + for i, cv in enumerate(coeff_vals): + result += cv * np.power(x_vals, i) + + if any(result < 0): + warnings.warn( + f'The Polynomial with unique_name {self.unique_name} has negative values, ' + 'which may not be physically meaningful.', + UserWarning, + stacklevel=2, + ) + + if output == 'scipp': + return sc.array(dims=[dim], values=result, unit=self._y_unit) + return result + def get_all_variables(self) -> list[DescriptorBase]: + """ Returns ------- list[DescriptorBase] - List of variables in the component. + The coefficient Parameters that constitute the fittable variables of this polynomial + component. """ return list(self._coefficients) - def convert_unit(self, unit: str | sc.Unit) -> None: + def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: """ - Convert the unit of the polynomial. + Convert the x-axis unit by rescaling coefficients with power-law factors. + + Each coefficient ``c_i`` is rescaled by ``(old_scale / new_scale) ** i`` so the evaluated + polynomial output is unchanged after the conversion. Parameters ---------- - unit : str | sc.Unit - The target unit to convert to. + new_x_unit : str | sc.Unit + Target x-axis unit. Must be dimensionally compatible with the current x_unit. Raises ------ UnitError - If the provided unit is not a string or sc.Unit. + If *new_x_unit* is not a valid unit string or ``sc.Unit``, or if the conversion between + the current unit and *new_x_unit* fails. """ + if not isinstance(new_x_unit, (str, sc.Unit)): + raise UnitError('new_x_unit must be a string or a scipp unit.') - if not isinstance(unit, (str, sc.Unit)): - raise UnitError('unit must be a string or a scipp unit.') - - # Find out how much the unit changes - # by converting a helper variable - conversion_value_before = self._unit_conversion_helper.value - self._unit_conversion_helper = sc.to_unit(self._unit_conversion_helper, unit=unit) - conversion_value_after = self._unit_conversion_helper.value + conversion_value_before = self._x_unit_helper.value + self._x_unit_helper = sc.to_unit(self._x_unit_helper, unit=new_x_unit) + conversion_value_after = self._x_unit_helper.value for i, param in enumerate(self._coefficients): - param.value *= ( - conversion_value_before / conversion_value_after - ) ** i # set the values directly to the appropriate power + param.value *= (conversion_value_before / conversion_value_after) ** i - self._unit = unit + self._x_unit = str(new_x_unit) if isinstance(new_x_unit, sc.Unit) else new_x_unit - def __repr__(self) -> str: + def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: """ - Return a string representation of the Polynomial. + Scale c0 by the y-unit ratio (c0 carries the output dimension). - Returns - ------- - str - A string representation of the Polynomial. + Only the constant term ``c0`` is rescaled; higher-order coefficients are dimensionless + relative to x_unit and are unaffected. + + Parameters + ---------- + new_y_unit : str | sc.Unit + Target y-axis unit. Must be dimensionally compatible with the current y_unit. + + Raises + ------ + UnitError + If *new_y_unit* is not a valid unit string or ``sc.Unit``, or if the conversion between + the current y_unit and *new_y_unit* fails. """ + if not isinstance(new_y_unit, (str, sc.Unit)): + raise UnitError('new_y_unit must be a string or a scipp unit.') + old_y_unit = str(self._y_unit) if self._y_unit is not None else 'dimensionless' + new_y_str = str(new_y_unit) if isinstance(new_y_unit, sc.Unit) else new_y_unit + + # Compute conversion factor using a temporary scipp scalar + y_helper = sc.scalar(1.0, unit=old_y_unit) + y_helper_new = sc.to_unit(y_helper, new_y_str) + scale = y_helper.value / y_helper_new.value + + self._coefficients[0].value *= scale + self._y_unit = new_y_str + + def __repr__(self) -> str: coeffs_str = ', '.join(f'{param.name}={param.value}' for param in self._coefficients) return ( f'Polynomial(name = {self.name}, display_name = {self.display_name}, ' - f'unit = {self._unit},\n' + f'x_unit = {self._x_unit}, y_unit = {self._y_unit},\n' f' coefficients = [{coeffs_str}])' ) diff --git a/src/easydynamics/sample_model/components/voigt.py b/src/easydynamics/sample_model/components/voigt.py index 5c5787b6..386fc05c 100644 --- a/src/easydynamics/sample_model/components/voigt.py +++ b/src/easydynamics/sample_model/components/voigt.py @@ -5,6 +5,7 @@ from typing import TYPE_CHECKING +import scipp as sc from scipy.special import voigt_profile from easydynamics.sample_model.components.mixins import CreateParametersMixin @@ -13,18 +14,14 @@ if TYPE_CHECKING: import numpy as np - import scipp as sc from easyscience.variable import Parameter class Voigt(CreateParametersMixin, ModelComponent): r""" - Voigt profile, a convolution of Gaussian and Lorentzian. + Voigt profile — convolution of Gaussian and Lorentzian. - If the center is not provided, it will be centered at 0 and fixed, which is typically what you - want in QENS. - - Use scipy.special.voigt_profile to evaluate the Voigt profile. + area has unit = x_unit * y_unit; center, gaussian_width, lorentzian_width have unit = x_unit. """ def __init__( @@ -33,91 +30,78 @@ def __init__( center: Numeric | Parameter | None = None, gaussian_width: Numeric | Parameter = 1.0, lorentzian_width: Numeric | Parameter = 1.0, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'Voigt', display_name: str | None = None, unique_name: str | None = None, ) -> None: """ - Initialize a Voigt component. - Parameters ---------- area : Numeric | Parameter, default=1.0 - Total area under the curve. + Integrated area under the Voigt profile. Unit is ``x_unit * y_unit``. center : Numeric | Parameter | None, default=None - Center of the Voigt profile. + Peak position in x_unit. If None, defaults to 0 and the center parameter is fixed. gaussian_width : Numeric | Parameter, default=1.0 - Standard deviation of the Gaussian part. + Gaussian component standard deviation (sigma) in x_unit. Must be strictly positive. lorentzian_width : Numeric | Parameter, default=1.0 - Half width at half max (HWHM) of the Lorentzian part. - unit : str | sc.Unit, default='meV' - Unit of the parameters. + Lorentzian component HWHM (gamma) in x_unit. Must be strictly positive. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis. center, gaussian_width, and lorentzian_width are stored in this + unit. area_unit = x_unit * y_unit. + y_unit : str | sc.Unit, default='dimensionless' + Unit of the y-axis (output). name : str, default='Voigt' - Name of the component for indexing. + Internal name used for parameter labelling. display_name : str | None, default=None - Display name of the component. + Human-readable name. Falls back to *name* if None. unique_name : str | None, default=None - Unique name of the component. If None, a unique_name is automatically generated. By - default, None. + Globally unique identifier. Auto-generated if None. """ - super().__init__( - unit=unit, + x_unit=x_unit, + y_unit=y_unit, name=name, display_name=display_name, unique_name=unique_name, ) - # These methods live in ValidationMixin - area = self._create_area_parameter(area=area, name=name, unit=self._unit) - center = self._create_center_parameter( - center=center, name=name, fix_if_none=True, unit=self._unit + self._area = self._create_area_parameter( + area=area, name=name, x_unit=self._x_unit, y_unit=self._y_unit ) - gaussian_width = self._create_width_parameter( - width=gaussian_width, - name=name, - param_name='gaussian_width', - unit=self._unit, + self._center = self._create_center_parameter( + center=center, name=name, fix_if_none=True, x_unit=self._x_unit ) - lorentzian_width = self._create_width_parameter( - width=lorentzian_width, - name=name, - param_name='lorentzian_width', - unit=self._unit, + self._gaussian_width = self._create_width_parameter( + width=gaussian_width, name=name, param_name='gaussian_width', x_unit=self._x_unit + ) + self._lorentzian_width = self._create_width_parameter( + width=lorentzian_width, name=name, param_name='lorentzian_width', x_unit=self._x_unit ) - - self._area = area - self._center = center - self._gaussian_width = gaussian_width - self._lorentzian_width = lorentzian_width @property def area(self) -> Parameter: """ - Get the area parameter. - Returns ------- Parameter - The area parameter. + The area Parameter with unit ``x_unit * y_unit``. """ return self._area @area.setter def area(self, value: Numeric) -> None: """ - Set the value of the area parameter. - Parameters ---------- value : Numeric - The new value for the area parameter. + New area value (in current area unit = x_unit * y_unit). Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. """ if not isinstance(value, Numeric): raise TypeError('area must be a number') @@ -126,29 +110,26 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ - Get the center parameter. - Returns ------- Parameter - The center parameter. + The center Parameter with unit ``x_unit``. """ return self._center @center.setter def center(self, value: Numeric | None) -> None: """ - Set the value of the center parameter. - Parameters ---------- value : Numeric | None - The new value for the center parameter. If None, defaults to 0 and is fixed. + New center value in x_unit. If None, the center is set to 0 and the parameter is + fixed. Raises ------ TypeError - If the value is not a number. + If *value* is not None and not a numeric type. """ if value is None: value = 0.0 @@ -160,31 +141,27 @@ def center(self, value: Numeric | None) -> None: @property def gaussian_width(self) -> Parameter: """ - Get the Gaussian width parameter. - Returns ------- Parameter - The Gaussian width parameter. + The Gaussian component width (sigma) Parameter with unit ``x_unit``. """ return self._gaussian_width @gaussian_width.setter def gaussian_width(self, value: Numeric) -> None: """ - Set the width parameter value. - Parameters ---------- value : Numeric - The new value for the width parameter. + New Gaussian width (sigma) in x_unit. Must be strictly positive. Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. ValueError - If the value is not positive. + If *value* is not positive. """ if not isinstance(value, Numeric): raise TypeError('gaussian_width must be a number') @@ -195,31 +172,27 @@ def gaussian_width(self, value: Numeric) -> None: @property def lorentzian_width(self) -> Parameter: """ - Get the Lorentzian width parameter (HWHM). - Returns ------- Parameter - The Lorentzian width parameter. + The Lorentzian component HWHM (gamma) Parameter with unit ``x_unit``. """ return self._lorentzian_width @lorentzian_width.setter def lorentzian_width(self, value: Numeric) -> None: """ - Set the value of the Lorentzian width parameter. - Parameters ---------- value : Numeric - The new value for the Lorentzian width parameter. + New Lorentzian HWHM (gamma) in x_unit. Must be strictly positive. Raises ------ TypeError - If the value is not a number. + If *value* is not a numeric type. ValueError - If the value is not positive. + If *value* is not positive. """ if not isinstance(value, Numeric): raise TypeError('lorentzian_width must be a number') @@ -227,45 +200,117 @@ def lorentzian_width(self, value: Numeric) -> None: raise ValueError('lorentzian_width must be positive') self._lorentzian_width.value = value - def evaluate(self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray) -> np.ndarray: - r""" - Evaluate the Voigt at the given x values. - - If x is a scipp Variable, the unit of the Voigt will be converted to match x. The Voigt - evaluates to the convolution of a Gaussian with sigma gaussian_width and a Lorentzian with - half width at half max lorentzian_width, centered at center, with area equal to area. + def evaluate( + self, + x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, + output: str = 'numpy', + ) -> np.ndarray | sc.Variable: + """ + Evaluate the Voigt at x. Model is never mutated for unit differences. Parameters ---------- x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The x values at which to evaluate the Voigt. + Input x values. + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. Returns ------- - np.ndarray - The intensity of the Voigt at the given x values. + np.ndarray | sc.Variable + Evaluated Voigt profile values at x. """ + x_vals, detected_unit, dim = self._prepare_x_for_evaluate(x) + eval_unit = detected_unit or self._x_unit + eval_area_unit = str(sc.Unit(eval_unit) * sc.Unit(self._y_unit)) - x = self._prepare_x_for_evaluate(x) + center = self._resolve_param_value(self._center, eval_unit) + gw = self._resolve_param_value(self._gaussian_width, eval_unit) + lw = self._resolve_param_value(self._lorentzian_width, eval_unit) + area = self._resolve_param_value(self._area, eval_area_unit) - return self.area.value * voigt_profile( - x - self.center.value, - self.gaussian_width.value, - self.lorentzian_width.value, - ) + result = area * voigt_profile(x_vals - center, gw, lw) - def __repr__(self) -> str: + if output == 'scipp': + return sc.array(dims=[dim], values=result, unit=self._y_unit) + return result + + def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: """ - Return a string representation of the Voigt. + Convert x-axis parameters (center, widths) and area to new_x_unit. - Returns - ------- - str - A string representation of the Voigt. + Parameters + ---------- + new_x_unit : str | sc.Unit + Target x-axis unit. Must be dimensionally compatible with the current x_unit. + + Raises + ------ + TypeError + If *new_x_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. + """ + if not isinstance(new_x_unit, (str, sc.Unit)): + raise TypeError(f'x_unit must be a string or sc.Unit, got {type(new_x_unit).__name__}') + old_x_unit = self._x_unit + new_area_unit = str(sc.Unit(new_x_unit) * sc.Unit(self._y_unit)) + try: + self._center.convert_unit(new_x_unit) + self._gaussian_width.convert_unit(new_x_unit) + self._lorentzian_width.convert_unit(new_x_unit) + self._area.convert_unit(new_area_unit) + self._x_unit = str(new_x_unit) if isinstance(new_x_unit, sc.Unit) else new_x_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(old_x_unit) * sc.Unit(self._y_unit)) + self._center.convert_unit(old_x_unit) + self._gaussian_width.convert_unit(old_x_unit) + self._lorentzian_width.convert_unit(old_x_unit) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: """ + Convert the y-axis unit by rescaling the area parameter. + The area is rescaled from ``x_unit * old_y_unit`` to ``x_unit * new_y_unit``. + + Parameters + ---------- + new_y_unit : str | sc.Unit + Target y-axis unit. + + Raises + ------ + TypeError + If *new_y_unit* is not a ``str`` or ``sc.Unit``. + Exception + If the unit conversion fails. On failure the component is rolled back to its original + units. + """ + if not isinstance(new_y_unit, (str, sc.Unit)): + raise TypeError(f'y_unit must be a string or sc.Unit, got {type(new_y_unit).__name__}') + old_y_unit = self._y_unit + new_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(new_y_unit)) + try: + self._area.convert_unit(new_area_unit) + self._y_unit = str(new_y_unit) if isinstance(new_y_unit, sc.Unit) else new_y_unit + except Exception as e: + try: + old_area_unit = str(sc.Unit(self._x_unit) * sc.Unit(old_y_unit)) + self._area.convert_unit(old_area_unit) + except Exception: # noqa: S110 + pass + raise e + + def __repr__(self) -> str: return ( - f'Voigt(name = {self.name}, display_name = {self.display_name}, unit = {self._unit},\n' + f'Voigt(name = {self.name}, display_name = {self.display_name}, ' + f'x_unit = {self._x_unit}, y_unit = {self._y_unit},\n' f' area = {self.area},\n' f' center = {self.center},\n' f' gaussian_width = {self.gaussian_width},\n' diff --git a/src/easydynamics/sample_model/diffusion_model/brownian_translational_diffusion.py b/src/easydynamics/sample_model/diffusion_model/brownian_translational_diffusion.py index 11f10b1b..bafb3e08 100644 --- a/src/easydynamics/sample_model/diffusion_model/brownian_translational_diffusion.py +++ b/src/easydynamics/sample_model/diffusion_model/brownian_translational_diffusion.py @@ -42,7 +42,8 @@ def __init__( scale: Numeric = 1.0, diffusion_coefficient: Numeric = 1.0, Q: Q_type | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'BrownianTranslationalDiffusion', display_name: str | None = 'BrownianTranslationalDiffusion', lorentzian_name: str | None = None, @@ -60,8 +61,10 @@ def __init__( Diffusion coefficient D in m^2/s. Q : Q_type | None, default=None Q values for the model. If None, Q is not set. - unit : str | sc.Unit, default='meV' - Unit of the diffusion model. Must be convertible to meV. + 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='BrownianTranslationalDiffusion' Name of the diffusion model. display_name : str | None, default='BrownianTranslationalDiffusion' @@ -86,7 +89,8 @@ def __init__( """ super().__init__( Q=Q, - unit=unit, + x_unit=x_unit, + y_unit=y_unit, scale=scale, name=name, display_name=display_name, @@ -176,7 +180,7 @@ def calculate_width(self, Q: Q_type | None = None) -> np.ndarray: Q = self._ensure_Q(Q) unit_conversion_factor = self._hbar * self.diffusion_coefficient / (self._angstrom**2) - unit_conversion_factor.convert_unit(self.unit) + unit_conversion_factor.convert_unit(self.x_unit) return Q**2 * unit_conversion_factor.value def calculate_EISF(self, Q: Q_type | None = None) -> np.ndarray: @@ -247,13 +251,15 @@ def create_component_collections( component_collection_list[i] = ComponentCollection( name=f'{self.name}_Q{Q_value:.2f}', display_name=f'{self.display_name}_Q{Q_value:.2f}', - unit=self.unit, + x_unit=self.x_unit, + y_unit=self.y_unit, ) lorentzian_component = Lorentzian( name=self.lorentzian_name, display_name=self.lorentzian_display_name, - unit=self.unit, + x_unit=self.x_unit, + y_unit=self.y_unit, ) # Make the width dependent on Q @@ -263,7 +269,7 @@ def create_component_collections( lorentzian_component.width.make_dependent_on( dependency_expression=dependency_expression, dependency_map=dependency_map, - desired_unit=self.unit, + desired_unit=self.x_unit, ) # Make the area dependent on Q diff --git a/src/easydynamics/sample_model/diffusion_model/delta_lorentz.py b/src/easydynamics/sample_model/diffusion_model/delta_lorentz.py index 150015a7..4ebe1bdf 100644 --- a/src/easydynamics/sample_model/diffusion_model/delta_lorentz.py +++ b/src/easydynamics/sample_model/diffusion_model/delta_lorentz.py @@ -59,7 +59,8 @@ def __init__( lorentzian_width: Numeric = 1.0, allow_Q_variation: dict | None = None, Q: Q_type | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'DeltaLorentz', display_name: str | None = None, lorentzian_name: str = 'Lorentzian', @@ -88,8 +89,10 @@ def __init__( allowed. Q : Q_type | None, default=None Q values for the model. If None, Q is not set. - unit : str | sc.Unit, default='meV' - Unit of the diffusion model. Must be convertible to meV. + 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='DeltaLorentz' Name of the diffusion model. display_name : str | None, default=None @@ -116,7 +119,8 @@ def __init__( """ super().__init__( scale=scale, - unit=unit, + x_unit=x_unit, + y_unit=y_unit, Q=Q, lorentzian_name=lorentzian_name, lorentzian_display_name=lorentzian_display_name, @@ -480,7 +484,8 @@ def create_component_collections( for i, Q_value in enumerate(Q): component_collection_list[i] = ComponentCollection( display_name=f'{self.display_name}_Q{Q_value:.2f}', - unit=self.unit, + x_unit=self.x_unit, + y_unit=self.y_unit, ) # ------------------------------# @@ -489,7 +494,8 @@ def create_component_collections( lorentzian_component = Lorentzian( name=self.lorentzian_name, display_name=self.lorentzian_display_name, - unit=self.unit, + x_unit=self.x_unit, + y_unit=self.y_unit, ) if self._allow_Q_variation['lorentzian_width'] is True: lorentzian_component._width = self._lorentzian_width_list[i] # noqa: SLF001 @@ -503,7 +509,7 @@ def create_component_collections( lorentzian_component.width.make_dependent_on( dependency_expression=self._write_lorz_width_dependency_expression(Q_value), dependency_map=dependency_map, - desired_unit=self.unit, + desired_unit=self.x_unit, ) # The area is always a dependent parameter in this model, as # it depends on the scale, mean_u_squared and A_1 parameters @@ -530,7 +536,8 @@ def create_component_collections( delta_component = DeltaFunction( name=self.delta_name, display_name=self.delta_display_name, - unit=self.unit, + x_unit=self.x_unit, + y_unit=self.y_unit, ) if self._allow_Q_variation['A_0'] is True: @@ -810,7 +817,7 @@ def _create_lorentzian_width_parameter(self, lorentzian_width: Numeric) -> Param value=float(lorentzian_width), fixed=False, min=MINIMUM_WIDTH, - unit=self.unit, + unit=self.x_unit, ) def _create_A0_A1_parameter_lists( @@ -877,7 +884,7 @@ def _create_lorentzian_width_parameter_list( value=float(lorentzian_width.value), fixed=False, min=MINIMUM_WIDTH, - unit=self.unit, + unit=self.x_unit, ) for _ in self.Q ] @@ -1073,7 +1080,7 @@ def __repr__(self) -> str: """ return ( f'DeltaLorentz(display_name={self.display_name},' - f'unit={self.unit}, \n' + f'x_unit={self.x_unit}, \n' f' mean_u_squared={self.mean_u_squared}, \n' f' A_0={self.A_0}, A_1={self.A_1}, \n' f' lorentzian_width={self.lorentzian_width}, \n' diff --git a/src/easydynamics/sample_model/diffusion_model/diffusion_model_base.py b/src/easydynamics/sample_model/diffusion_model/diffusion_model_base.py index 1df008b2..8dfd4b4d 100644 --- a/src/easydynamics/sample_model/diffusion_model/diffusion_model_base.py +++ b/src/easydynamics/sample_model/diffusion_model/diffusion_model_base.py @@ -21,7 +21,8 @@ def __init__( self, scale: Numeric = 1.0, Q: Q_type | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'DiffusionModel', display_name: str | None = 'DiffusionModel', lorentzian_name: str | None = None, @@ -34,11 +35,14 @@ def __init__( Parameters ---------- scale : Numeric, default=1.0 - Scale factor for the diffusion model. Must be a non-negative number. + Scale factor for the diffusion model. Must be a non-negative number. Its unit equals + area_unit = x_unit * y_unit because scale * QISF/EISF (dimensionless) = component area. Q : Q_type | None, default=None Q values for the model. If None, Q is not set. - unit : str | sc.Unit, default='meV' - Unit of the diffusion model. Must be convertible to meV. + 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). Together with x_unit determines area_unit. name : str, default='DiffusionModel' Name of the diffusion model. display_name : str | None, default='DiffusionModel' @@ -58,7 +62,7 @@ def __init__( TypeError If scale is not a number. UnitError - If unit is not a string or scipp Unit, or if it cannot be converted to meV. + If x_unit is not a string or scipp Unit, or if it cannot be converted to meV. ValueError If scale is negative. """ @@ -66,11 +70,11 @@ def __init__( self._Q = _validate_and_convert_Q(Q) try: - test = DescriptorNumber(name='test', value=1, unit=unit) + test = DescriptorNumber(name='test', value=1, unit=x_unit) test.convert_unit('meV') except Exception as e: raise UnitError( - f'Invalid unit: {unit}. Unit must be a string or scipp Unit and convertible to meV.' # noqa: E501 + f'Invalid unit: {x_unit}. Unit must be a string or scipp Unit and convertible to meV.' # noqa: E501 ) from e if not isinstance(scale, Numeric): @@ -79,10 +83,17 @@ def __init__( if float(scale) < 0: raise ValueError('scale must be non-negative.') - scale = Parameter(name='scale', value=float(scale), fixed=False, min=0.0, unit=unit) + area_unit = str(sc.Unit(x_unit) * sc.Unit(y_unit)) + scale = Parameter(name='scale', value=float(scale), fixed=False, min=0.0, unit=area_unit) self._scale = scale - super().__init__(unit=unit, name=name, display_name=display_name, unique_name=unique_name) + super().__init__( + x_unit=x_unit, + y_unit=y_unit, + name=name, + display_name=display_name, + unique_name=unique_name, + ) if lorentzian_name is None: lorentzian_name = name @@ -102,7 +113,9 @@ def __init__( if self.Q is None: self._component_collections = [] else: - self._component_collections = [ComponentCollection()] * len(self.Q) + self._component_collections = [ + ComponentCollection(y_unit=self.y_unit) for _ in range(len(self.Q)) + ] # ------------------------------------------------------------------ # Properties @@ -448,7 +461,9 @@ def create_component_collections(self) -> list[ComponentCollection]: self._component_collections = [] return self._component_collections - self._component_collections = [ComponentCollection()] * len(self.Q) + self._component_collections = [ + ComponentCollection(y_unit=self.y_unit) for _ in range(len(self.Q)) + ] return self._component_collections @@ -539,6 +554,6 @@ def __repr__(self) -> str: """ return ( f'{self.__class__.__name__}(name={self.name}, display_name={self.display_name}, ' - f'unit={self.unit}), \n' + f'x_unit={self.x_unit}), \n' f' scale={self.scale})' ) diff --git a/src/easydynamics/sample_model/diffusion_model/jump_translational_diffusion.py b/src/easydynamics/sample_model/diffusion_model/jump_translational_diffusion.py index a8632272..e92b5fa9 100644 --- a/src/easydynamics/sample_model/diffusion_model/jump_translational_diffusion.py +++ b/src/easydynamics/sample_model/diffusion_model/jump_translational_diffusion.py @@ -52,7 +52,8 @@ def __init__( diffusion_coefficient: Numeric = 1.0, relaxation_time: Numeric = 1.0, Q: Q_type | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', name: str = 'JumpTranslationalDiffusion', display_name: str | None = 'JumpTranslationalDiffusion', lorentzian_name: str | None = None, @@ -72,8 +73,10 @@ def __init__( Relaxation time t in ps. Q : Q_type | None, default=None Q values for the model. If None, Q is not set. - unit : str | sc.Unit, default='meV' - Unit of the diffusion model. Must be convertible to meV. + 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='JumpTranslationalDiffusion' Name of the diffusion model. display_name : str | None, default='JumpTranslationalDiffusion' @@ -97,7 +100,8 @@ def __init__( """ super().__init__( Q=Q, - unit=unit, + x_unit=x_unit, + y_unit=y_unit, scale=scale, name=name, display_name=display_name, @@ -242,7 +246,7 @@ def calculate_width(self, Q: Q_type | None = None) -> np.ndarray: unit_conversion_factor_numerator = ( self._hbar * self.diffusion_coefficient / (self._angstrom**2) ) - unit_conversion_factor_numerator.convert_unit(self.unit) + unit_conversion_factor_numerator.convert_unit(self.x_unit) numerator = unit_conversion_factor_numerator.value * Q**2 @@ -319,13 +323,15 @@ def create_component_collections( component_collection_list[i] = ComponentCollection( name=f'{self.name}_Q{Q_value:.2f}', display_name=f'{self.display_name}_Q{Q_value:.2f}', - unit=self.unit, + x_unit=self.x_unit, + y_unit=self.y_unit, ) lorentzian_component = Lorentzian( name=self.lorentzian_name, display_name=self.lorentzian_display_name, - unit=self.unit, + x_unit=self.x_unit, + y_unit=self.y_unit, ) # Make the width dependent on Q @@ -335,7 +341,7 @@ def create_component_collections( lorentzian_component.width.make_dependent_on( dependency_expression=dependency_expression, dependency_map=dependency_map, - desired_unit=self.unit, + desired_unit=self.x_unit, ) # Make the area dependent on Q diff --git a/src/easydynamics/sample_model/instrument_model.py b/src/easydynamics/sample_model/instrument_model.py index e906d14a..06bc86f5 100644 --- a/src/easydynamics/sample_model/instrument_model.py +++ b/src/easydynamics/sample_model/instrument_model.py @@ -33,44 +33,14 @@ def __init__( resolution_model: ResolutionModel | SampleModel | None = None, background_model: BackgroundModel | None = None, energy_offset: Numeric | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', ) -> None: - """ - Initialize an InstrumentModel. - - Parameters - ---------- - display_name : str, default='MyInstrumentModel' - The display name of the InstrumentModel. - unique_name : str | None, default=None - The unique name of the InstrumentModel. - Q : Q_type | None, default=None - The Q values where the instrument is modelled. - resolution_model : ResolutionModel | SampleModel | None, default=None - The resolution model of the instrument. If a SampleModel it will be converted to a - ResolutionModel. If None, an empty resolution model is created and no resolution - convolution is carried out. - background_model : BackgroundModel | None, default=None - The background model of the instrument. If None, an empty background model is created, - and the background evaluates to 0. - energy_offset : Numeric | None, default=None - Template energy offset of the instrument. Will be copied to each Q value. If None, the - energy offset will be 0. - unit : str | sc.Unit, default='meV' - The unit of the energy axis. - - Raises - ------ - TypeError - If resolution_model is not a ResolutionModel or None, or if background_model is not a - BackgroundModel or None, or if energy_offset is not a number or None. - """ super().__init__( display_name=display_name, unique_name=unique_name, ) - self._unit = _validate_unit(unit) + self._x_unit = _validate_unit(x_unit) if resolution_model is None: self._resolution_model = ResolutionModel() @@ -103,7 +73,7 @@ def __init__( self._energy_offset = Parameter( name='energy_offset', value=float(energy_offset), - unit=self.unit, + unit=self.x_unit, fixed=False, ) self._energy_offsets: list = [] @@ -117,31 +87,10 @@ def __init__( @property def resolution_model(self) -> ResolutionModel: - """ - Get the resolution model of the instrument. - - Returns - ------- - ResolutionModel - The resolution model of the instrument. - """ return self._resolution_model @resolution_model.setter def resolution_model(self, value: ResolutionModel | SampleModel) -> None: - """ - Set the resolution model of the instrument. - - Parameters - ---------- - value : ResolutionModel | SampleModel - The new resolution model of the instrument. - - Raises - ------ - TypeError - If value is not a ResolutionModel or SampleModel. - """ if not isinstance(value, (ResolutionModel, SampleModel)): raise TypeError( f'resolution_model must be a ResolutionModel or SampleModel, ' @@ -154,33 +103,10 @@ def resolution_model(self, value: ResolutionModel | SampleModel) -> None: @property def background_model(self) -> BackgroundModel: - """ - Get the background model of the instrument. - - Returns - ------- - BackgroundModel - The background model of the instrument. - """ - return self._background_model @background_model.setter def background_model(self, value: BackgroundModel) -> None: - """ - Set the background model of the instrument. - - Parameters - ---------- - value : BackgroundModel - The new background model of the instrument. - - Raises - ------ - TypeError - If value is not a BackgroundModel. - """ - if not isinstance(value, BackgroundModel): raise TypeError( f'background_model must be a BackgroundModel, got {type(value).__name__}' @@ -190,35 +116,10 @@ def background_model(self, value: BackgroundModel) -> None: @property def Q(self) -> np.ndarray | None: - """ - Get the Q values of the InstrumentModel. - - Returns - ------- - np.ndarray | None - The Q values of the InstrumentModel, or None if not set. - """ return self._Q @Q.setter def Q(self, value: Q_type | None) -> None: - """ - Set the Q values of the InstrumentModel. - - If Q is already set, it raises an error if the new Q values are not similar to the old ones - to prevent accidental changes to the background and resolution models. To change Q values, - first run clear_Q(). - - Parameters - ---------- - value : Q_type | None - The new Q values to set. If None, Q values are not changed. - - Raises - ------ - ValueError - If the new Q values are not similar to the old ones when Q is not None. - """ if value is None: return old_Q = self._Q @@ -236,71 +137,25 @@ def Q(self, value: Q_type | None) -> None: ) @property - def unit(self) -> str | sc.Unit: - """ - Get the unit of the InstrumentModel. - - Returns - ------- - str | sc.Unit - The unit of the InstrumentModel. - """ - return self._unit - - @unit.setter - def unit(self, _unit_str: str) -> None: - """ - Set the unit of the InstrumentModel. - - The unit is read-only and cannot be set directly. Use convert_unit to change the unit - between allowed types or create a new InstrumentModel with the desired unit. - - Parameters - ---------- - _unit_str : str - The new unit for the InstrumentModel (ignored). - - Raises - ------ - AttributeError - Always, as the unit is read-only. - """ + def x_unit(self) -> str | sc.Unit: + return self._x_unit + + @x_unit.setter + def x_unit(self, _: str) -> None: raise AttributeError( - f'Unit is read-only. Use convert_unit to change the unit between allowed types ' + f'x_unit is read-only. Use convert_x_unit to change the unit between allowed types ' f'or create a new {self.__class__.__name__} with the desired unit.' ) @property def energy_offset(self) -> Parameter: - """ - Get the energy offset template parameter of the instrument model. - - Returns - ------- - Parameter - The energy offset template parameter of the instrument model. - """ return self._energy_offset @energy_offset.setter def energy_offset(self, value: Numeric) -> None: - """ - Set the offset parameter of the instrument model. - - Parameters - ---------- - value : Numeric - The new value for the energy offset parameter. Will be copied to all Q values. - - Raises - ------ - TypeError - If value is not a number. - """ if not isinstance(value, Numeric): raise TypeError(f'energy_offset must be a number, got {type(value).__name__}') self._energy_offset.value = value - self._on_energy_offset_change() # -------------------------------------------------------------- @@ -308,20 +163,6 @@ def energy_offset(self, value: Numeric) -> None: # -------------------------------------------------------------- def clear_Q(self, confirm: bool = False) -> None: - """ - Clear the Q values of the InstrumentModel and any associated ResolutionModel and - BackgroundModel, removing all component collections and their associated Parameters. - - Parameters - ---------- - confirm : bool, default=False - Confirmation to clear Q values. - - Raises - ------ - ValueError - If confirm is not True. - """ if not confirm: raise ValueError( 'Clearing Q values requires confirmation. Set confirm=True to proceed.' @@ -331,57 +172,21 @@ def clear_Q(self, confirm: bool = False) -> None: self.resolution_model.clear_Q(confirm=True) self._on_Q_change() - def convert_unit(self, unit_str: str | sc.Unit) -> None: - """ - Convert the unit of the InstrumentModel. - - Parameters - ---------- - unit_str : str | sc.Unit - The unit to convert to. - - Raises - ------ - ValueError - If unit_str is not a valid unit string or scipp Unit. - """ - unit = _validate_unit(unit_str) + def convert_x_unit(self, x_unit: str | sc.Unit) -> None: + unit = _validate_unit(x_unit) if unit is None: - raise ValueError('unit_str must be a valid unit string or scipp Unit') + raise ValueError('x_unit must be a valid unit string or scipp Unit') - self._background_model.convert_unit(unit) - self._resolution_model.convert_unit(unit) + self._background_model.convert_x_unit(unit) + self._resolution_model.convert_x_unit(unit) self._energy_offset.convert_unit(unit) self._ensure_energy_offsets_current() for offset in self._energy_offsets: offset.convert_unit(unit) - self._unit = unit + self._x_unit = unit def get_all_variables(self, Q_index: int | None = None) -> list[Parameter]: - """ - Get all variables in the InstrumentModel. - - Parameters - ---------- - Q_index : int | None, default=None - The index of the Q value to get variables for. If None, get variables for all Q values. - - - Raises - ------ - TypeError - If Q_index is not an int or None. - IndexError - If Q_index is out of bounds for the Q values in the InstrumentModel. - - Returns - ------- - list[Parameter] - A list of all variables in the InstrumentModel. If Q_index is specified, only variables - from the ComponentCollection at the given Q index are included. Otherwise, all - variables in the InstrumentModel are included. - """ if self._Q is None: return [] @@ -403,45 +208,18 @@ def get_all_variables(self, Q_index: int | None = None) -> list[Parameter]: return variables def fix_resolution_parameters(self) -> None: - """Fix all parameters in the resolution model.""" self.resolution_model.fix_all_parameters() def free_resolution_parameters(self) -> None: - """Free all parameters in the resolution model.""" self.resolution_model.free_all_parameters() def normalize_resolution(self) -> None: - """Normalize the resolution model to have area 1.""" self.resolution_model.normalize_area() def get_energy_offset( self, Q_index: int | None = None, ) -> Parameter | list[Parameter]: - """ - Get the energy offset Parameter at a specific Q index. - - Parameters - ---------- - Q_index : int | None, default=None - The index of the Q value to get the energy offset for. If None, get the energy offset - for all Q values. - - Raises - ------ - ValueError - If no Q values are set in the InstrumentModel. - IndexError - If Q_index is out of bounds. - TypeError - If Q_index is not an int or None. - - Returns - ------- - Parameter | list[Parameter] - The energy offset Parameter at the specified Q index, or a list of Parameters if - Q_index is None. - """ if self._Q is None: raise ValueError('No Q values are set in the InstrumentModel.') @@ -458,61 +236,15 @@ def get_energy_offset( return self._energy_offsets[Q_index] def fix_energy_offset(self, Q_index: int | None = None) -> None: - """ - Fix energy offset parameters. - - If Q_index is specified, only fix the energy offset for that Q value. If Q_index is None, - fix energy offsets for all Q values. - - Parameters - ---------- - Q_index : int | None, default=None - The index of the Q value to fix the energy offset for. If None, fix energy offsets for - all Q values. - """ self._fix_or_free_energy_offset(Q_index, fixed=True) def free_energy_offset(self, Q_index: int | None = None) -> None: - """ - Free energy offset parameters. - - If Q_index is specified, only free the energy offset for that Q value. If Q_index is None, - free energy offsets for all Q values. - - Parameters - ---------- - Q_index : int | None, default=None - The index of the Q value to free the energy offset for. If None, free energy offsets - for all Q values. - """ self._fix_or_free_energy_offset(Q_index, fixed=False) # -------------------------------------------------------------- # Private methods # -------------------------------------------------------------- def _fix_or_free_energy_offset(self, Q_index: int | None = None, fixed: bool = True) -> None: - """ - Fix or free energy offset parameters. - - If Q_index is specified, only fix or free the energy offset for that Q value. If Q_index is - None, fix or free energy offsets for all Q values. - - Parameters - ---------- - Q_index : int | None, default=None - The index of the Q value to fix or free the energy offset for. If None, fix or free - energy offsets for all Q values. - fixed : bool, default=True - Whether to fix (True) or free (False) the energy offset. - - Raises - ------ - TypeError - If Q_index is not an int or None. - IndexError - If Q_index is out of bounds for the Q values in the InstrumentModel. - """ - self._ensure_energy_offsets_current() if Q_index is None: for offset in self._energy_offsets: @@ -528,13 +260,11 @@ def _fix_or_free_energy_offset(self, Q_index: int | None = None, fixed: bool = T self._energy_offsets[Q_index].fixed = fixed def _ensure_energy_offsets_current(self) -> None: - """Rebuild energy offset Parameters if Q has changed since they were last built.""" if self._energy_offsets_is_dirty: self._generate_energy_offsets() self._energy_offsets_is_dirty = False def _generate_energy_offsets(self) -> None: - """Generate energy offset Parameters for each Q value.""" if self._Q is None: self._energy_offsets = [] return @@ -542,23 +272,19 @@ def _generate_energy_offsets(self) -> None: self._energy_offsets = [copy(self._energy_offset) for _ in self._Q] def _on_Q_change(self) -> None: - """Handle changes to the Q values.""" self._energy_offsets_is_dirty = True self.resolution_model.Q = self.Q self.background_model.Q = self.Q def _on_energy_offset_change(self) -> None: - """Handle changes to the energy offset.""" self._ensure_energy_offsets_current() for offset in self._energy_offsets: offset.value = self._energy_offset.value def _on_resolution_model_change(self) -> None: - """Handle changes to the resolution model.""" self.resolution_model.Q = self.Q def _on_background_model_change(self) -> None: - """Handle changes to the background model.""" self.background_model.Q = self.Q # ------------------------------------------------------------- @@ -566,19 +292,10 @@ def _on_background_model_change(self) -> None: # ------------------------------------------------------------- def __repr__(self) -> str: - """ - Return a string representation of the InstrumentModel. - - Returns - ------- - str - A string representation of the InstrumentModel. - """ - return ( f'{self.__class__.__name__}(' f'unique_name={self.unique_name!r}, ' - f'unit={self.unit}, ' + f'x_unit={self.x_unit}, ' f'Q_len={None if self._Q is None else len(self._Q)}, ' f'resolution_model={self._resolution_model!r}, ' f'background_model={self._background_model!r}' diff --git a/src/easydynamics/sample_model/model_base.py b/src/easydynamics/sample_model/model_base.py index c6c773d2..a2a96571 100644 --- a/src/easydynamics/sample_model/model_base.py +++ b/src/easydynamics/sample_model/model_base.py @@ -26,34 +26,14 @@ def __init__( self, display_name: str = 'MyModelBase', unique_name: str | None = None, - unit: str | sc.Unit | None = 'meV', + x_unit: str | sc.Unit | None = 'meV', + y_unit: str | sc.Unit = 'dimensionless', components: ModelComponent | ComponentCollection | None = None, Q: Q_type | None = None, ) -> None: - """ - Initialize the ModelBase. - - Parameters - ---------- - display_name : str, default='MyModelBase' - Display name of the model. - unique_name : str | None, default=None - Unique name of the model. If None, a unique name will be generated. - unit : str | sc.Unit | None, default='meV' - Unit of the model. - components : ModelComponent | ComponentCollection | None, default=None - Template components of the model. If None, no components are added. These components - are copied into ComponentCollections for each Q value. - Q : Q_type | None, default=None - Q values for the model. If None, Q is not set. - - Raises - ------ - TypeError - If components is not a ModelComponent or ComponentCollection. - """ super().__init__( - unit=unit, + x_unit=x_unit, + y_unit=y_unit, display_name=display_name, unique_name=unique_name, ) @@ -74,64 +54,30 @@ def __init__( self.append_component(components) def evaluate( - self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray - ) -> list[np.ndarray]: - """ - Evaluate the sample model at all Q for the given x values. - - Parameters - ---------- - x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - Energy axis values to evaluate the model at. If a scipp Variable or DataArray is - provided, the unit of the model will be converted to match the unit of x for - evaluation, and the result will be returned in the same unit as x. - - Raises - ------ - ValueError - If there are no components in the model to evaluate. - - Returns - ------- - list[np.ndarray] - A list of numpy arrays containing the evaluated model values for each Q. The length of - the list will match the number of Q values in the model. - """ - + self, + x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, + output: str = 'numpy', + ) -> list[np.ndarray] | list[sc.Variable]: + """Evaluate the model at all Q for the given x values.""" self._ensure_component_collections_current() if not self._component_collections: raise ValueError('No components in the model to evaluate.') - return [collection.evaluate(x) for collection in self._component_collections] + return [ + collection.evaluate(x, output=output) for collection in self._component_collections + ] # ------------------------------------------------------------------ # Component management # ------------------------------------------------------------------ def append_component(self, component: ModelComponent | ComponentCollection) -> None: - """ - Append a ModelComponent or ComponentCollection to the SampleModel. - - Parameters - ---------- - component : ModelComponent | ComponentCollection - The ModelComponent or ComponentCollection to append. - """ self._components.append_component(component) self._on_components_change() def remove_component(self, name: str) -> None: - """ - Remove a ModelComponent from the SampleModel by its name. - - Parameters - ---------- - name : str - The name of the ModelComponent to remove. - """ self._components.pop(name) self._on_components_change() def clear_components(self) -> None: - """Clear all ModelComponents from the SampleModel.""" self._components.clear() self._on_components_change() @@ -141,31 +87,10 @@ def clear_components(self) -> None: @property def components(self) -> list[ModelComponent]: - """ - Get the components of the SampleModel. - - Returns - ------- - list[ModelComponent] - The components of the SampleModel. - """ return self._components @components.setter def components(self, value: ModelComponent | ComponentCollection | None) -> None: - """ - Set the components of the SampleModel. - - Parameters - ---------- - value : ModelComponent | ComponentCollection | None - The new components to set. If None, all components will be cleared. - - Raises - ------ - TypeError - If value is not a ModelComponent, ComponentCollection, or None. - """ if not isinstance(value, (ModelComponent, ComponentCollection, type(None))): raise TypeError('Components must be a ModelComponent or a ComponentCollection') @@ -175,46 +100,14 @@ def components(self, value: ModelComponent | ComponentCollection | None) -> None @property def component_collections_is_dirty(self) -> bool: - """ - Return whether component collections need to be rebuilt before use. - - Returns - ------- - bool - ``True`` if component collections have not been built yet or are stale. - """ return self._component_collections_is_dirty @property def Q(self) -> np.ndarray | None: - """ - Get the Q values of the SampleModel. - - Returns - ------- - np.ndarray | None - The Q values of the SampleModel, or None if not set. - """ return self._Q @Q.setter def Q(self, value: Q_type | None) -> None: - """ - Set the Q values of the SampleModel. - - If Q is already set, it throws an error if the new Q values are not similar to the old - ones. To change Q values, first run clear_Q(). - - Parameters - ---------- - value : Q_type | None - The new Q values to set. If None, Q values are not changed. - - Raises - ------ - ValueError - If the new Q values are not similar to the old ones when Q is already set. - """ if value is None: return old_Q = self._Q @@ -232,20 +125,6 @@ def Q(self, value: Q_type | None) -> None: ) def clear_Q(self, confirm: bool = False) -> None: - """ - Clear the Q values of the SampleModel, removing all component collections and their - associated Parameters. - - Parameters - ---------- - confirm : bool, default=False - Confirmation to clear Q values. - - Raises - ------ - ValueError - If confirm is not True. - """ if not confirm: raise ValueError( 'Clearing Q values requires confirmation. Set confirm=True to proceed.' @@ -257,77 +136,51 @@ def clear_Q(self, confirm: bool = False) -> None: # Other methods # ------------------------------------------------------------------ - def convert_unit(self, unit: str | sc.Unit) -> None: - """ - Convert the unit of the ComponentCollection and all its components. - - Parameters - ---------- - unit : str | sc.Unit - The new unit to convert to. - - Raises - ------ - TypeError - If the provided unit is not a string or sc.Unit. - Exception - If the provided unit is not compatible with the current unit. - """ + def convert_x_unit(self, unit: str | sc.Unit) -> None: + if not isinstance(unit, (str, sc.Unit)): + raise TypeError(f'Unit must be a string or sc.Unit, got {type(unit).__name__}') - old_unit = self._unit + old_unit = self._x_unit + try: + for component in self.components: + component.convert_x_unit(unit) + self._x_unit = str(unit) if isinstance(unit, sc.Unit) else unit + except Exception as e: + try: + for component in self.components: + component.convert_x_unit(old_unit) + except Exception: # noqa: S110 + pass + raise e + self._on_components_change() + def convert_y_unit(self, unit: str | sc.Unit) -> None: if not isinstance(unit, (str, sc.Unit)): raise TypeError(f'Unit must be a string or sc.Unit, got {type(unit).__name__}') + + old_unit = self._y_unit try: for component in self.components: - component.convert_unit(unit) - self._unit = unit + component.convert_y_unit(unit) + self._y_unit = str(unit) if isinstance(unit, sc.Unit) else unit except Exception as e: - # Attempt to rollback on failure try: for component in self.components: - component.convert_unit(old_unit) + component.convert_y_unit(old_unit) except Exception: # noqa: S110 - pass # Best effort rollback + pass raise e self._on_components_change() def fix_all_parameters(self) -> None: - """Fix all Parameters in all ComponentCollections.""" for par in self.get_all_variables(): par.fixed = True def free_all_parameters(self) -> None: - """Free all Parameters in all ComponentCollections.""" for par in self.get_all_variables(): par.fixed = False def get_all_variables(self, Q_index: int | None = None) -> list[Parameter]: - """ - Get all Parameters and Descriptors from all ComponentCollections in the ModelBase. - - Ignores the Parameters and Descriptors in self._components as these are just templates. - - Parameters - ---------- - Q_index : int | None, default=None - If None, get variables for all ComponentCollections. If int, get variables for the - ComponentCollection at this index. - - Raises - ------ - TypeError - If Q_index is not an int or None. - IndexError - If Q_index is out of bounds for the number of ComponentCollections. - - Returns - ------- - list[Parameter] - A list of all Parameters and Descriptors from the ComponentCollections in the - ModelBase. - """ - self._ensure_component_collections_current() if Q_index is None: all_vars = [ @@ -347,26 +200,6 @@ def get_all_variables(self, Q_index: int | None = None) -> list[Parameter]: return all_vars def get_component_collection(self, Q_index: int) -> ComponentCollection: - """ - Get the ComponentCollection at the given Q index. - - Parameters - ---------- - Q_index : int - The index of the desired ComponentCollection. - - Raises - ------ - TypeError - If Q_index is not an int. - IndexError - If Q_index is out of bounds for the number of ComponentCollections. - - Returns - ------- - ComponentCollection - The ComponentCollection at the given Q index. - """ self._ensure_component_collections_current() if not isinstance(Q_index, int): raise TypeError(f'Q_index must be an int, got {type(Q_index).__name__}') @@ -378,7 +211,6 @@ def get_component_collection(self, Q_index: int) -> ComponentCollection: return self._component_collections[Q_index] def normalize_area(self) -> None: - """Normalize the area of the model across all Q values.""" self._ensure_component_collections_current() for collection in self._component_collections: collection.normalize_area() @@ -388,16 +220,11 @@ def normalize_area(self) -> None: # ------------------------------------------------------------------ def _ensure_component_collections_current(self) -> None: - """ - Rebuild component collections if any dependency has changed since they were last built. - """ if self._component_collections_is_dirty: self._generate_component_collections() self._component_collections_is_dirty = False def _generate_component_collections(self) -> None: - """Generate ComponentCollections for each Q value.""" - if self.Q is None: self._component_collections = [] return @@ -407,11 +234,9 @@ def _generate_component_collections(self) -> None: self._component_collections.append(copy(self._components)) def _on_Q_change(self) -> None: - """Handle changes to the Q values.""" self._component_collections_is_dirty = True def _on_components_change(self) -> None: - """Handle changes to the components.""" self._component_collections_is_dirty = True # ------------------------------------------------------------------ @@ -419,15 +244,7 @@ def _on_components_change(self) -> None: # ------------------------------------------------------------------ def __repr__(self) -> str: - """ - Return a string representation of the ModelBase. - - Returns - ------- - str - A string representation of the ModelBase. - """ return ( f'{self.__class__.__name__}(unique_name={self.unique_name}, ' - f'unit={self.unit}), Q = {self.Q}, components = {self.components}' + f'x_unit={self.x_unit}), Q = {self.Q}, components = {self.components}' ) diff --git a/src/easydynamics/sample_model/resolution_model.py b/src/easydynamics/sample_model/resolution_model.py index 7ded2568..12c39e65 100644 --- a/src/easydynamics/sample_model/resolution_model.py +++ b/src/easydynamics/sample_model/resolution_model.py @@ -24,32 +24,16 @@ def __init__( self, display_name: str = 'MyResolutionModel', unique_name: str | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', components: ModelComponent | ComponentCollection | None = None, Q: Q_type | None = None, ) -> None: - """ - Initialize a ResolutionModel. - - Parameters - ---------- - display_name : str, default='MyResolutionModel' - Display name of the model. - unique_name : str | None, default=None - Unique name of the model. If None, a unique name will be generated. - unit : str | sc.Unit, default='meV' - Unit of the model. - components : ModelComponent | ComponentCollection | None, default=None - Template components of the model. If None, no components are added. These components - are copied into ComponentCollections for each Q value. - Q : Q_type | None, default=None - Q values for the model. If None, Q is not set. - """ - super().__init__( display_name=display_name, unique_name=unique_name, - unit=unit, + x_unit=x_unit, + y_unit=y_unit, components=components, Q=Q, ) @@ -58,18 +42,7 @@ def append_component(self, component: ModelComponent | ComponentCollection) -> N """ Append a component to the ResolutionModel. - Does not allow DeltaFunction or Polynomial components, as these are not physical resolution - components. - - Parameters - ---------- - component : ModelComponent | ComponentCollection - Component(s) to append. - - Raises - ------ - TypeError - If the component is a DeltaFunction or Polynomial. + Does not allow DeltaFunction, Polynomial, or Exponential components. """ components = component if isinstance(component, ComponentCollection) else (component,) @@ -88,29 +61,7 @@ def from_sample_model( normalize_area: bool = True, fix_parameters: bool = True, ) -> 'ResolutionModel': - """ - Create a ResolutionModel from a SampleModel. - - Parameters - ---------- - sample_model : SampleModel - SampleModel to create the ResolutionModel from. - normalize_area : bool, default=True - Whether to normalize the components in the ResolutionModel to have area 1. - fix_parameters : bool, default=True - Whether to fix the parameters in the ResolutionModel. - - Returns - ------- - 'ResolutionModel' - ResolutionModel created from the SampleModel. - - Raises - ------ - TypeError - If sample_model is not a SampleModel, or if normalize_area or fix_parameters are not - bool. - """ + """Create a ResolutionModel from a SampleModel.""" if not isinstance(sample_model, SampleModel): raise TypeError( f'sample_model must be an instance of SampleModel. Got {type(sample_model)}.' @@ -124,7 +75,8 @@ def from_sample_model( resolution_model = cls( display_name=sample_model.display_name, - unit=sample_model.unit, + x_unit=sample_model.x_unit, + y_unit=sample_model.y_unit, components=sample_model.components, Q=sample_model.Q, ) diff --git a/src/easydynamics/sample_model/sample_model.py b/src/easydynamics/sample_model/sample_model.py index a6203071..4457887b 100644 --- a/src/easydynamics/sample_model/sample_model.py +++ b/src/easydynamics/sample_model/sample_model.py @@ -31,7 +31,8 @@ def __init__( self, display_name: str = 'MySampleModel', unique_name: str | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', + y_unit: str | sc.Unit = 'dimensionless', components: ModelComponent | ComponentCollection | None = None, Q: Q_type | None = None, diffusion_models: DiffusionModelBase | list[DiffusionModelBase] | None = None, @@ -39,42 +40,6 @@ def __init__( temperature_unit: str | sc.Unit = 'K', detailed_balance_settings: DetailedBalanceSettings | None = None, ) -> None: - """ - Initialize the SampleModel. - - Parameters - ---------- - display_name : str, default='MySampleModel' - Display name of the model. - unique_name : str | None, default=None - Unique name of the model. If None, a unique name will be generated. - unit : str | sc.Unit, default='meV' - Unit of the model. If None,. - components : ModelComponent | ComponentCollection | None, default=None - Template components of the model. If None, no components are added. These components - are copied into ComponentCollections for each Q value. - Q : Q_type | None, default=None - Q values for the model. If None, Q is not set. - diffusion_models : DiffusionModelBase | list[DiffusionModelBase] | None, default=None - Diffusion models to include in the SampleModel. If None, no diffusion models are added. - temperature : float | None, default=None - Temperature for detailed balancing. If None, no detailed balancing is applied. By - default, None. - temperature_unit : str | sc.Unit, default='K' - Unit of the temperature. - detailed_balance_settings : DetailedBalanceSettings | None, default=None - Settings for detailed balancing. - - Raises - ------ - TypeError - If diffusion_models is not a DiffusionModelBase, a list of DiffusionModelBase, or None, - or if temperature is not a number or None, or if detailed_balance_settings is not a - DetailedBalanceSettings instance. - ValueError - If temperature is negative. - """ - if diffusion_models is None: self._diffusion_models = [] elif isinstance(diffusion_models, DiffusionModelBase): @@ -96,7 +61,8 @@ def __init__( super().__init__( display_name=display_name, unique_name=unique_name, - unit=unit, + x_unit=x_unit, + y_unit=y_unit, components=components, Q=Q, ) @@ -130,20 +96,6 @@ def __init__( # ------------------------------------------------------------------ def append_diffusion_model(self, diffusion_model: DiffusionModelBase) -> None: - """ - Append a DiffusionModel to the SampleModel. - - Parameters - ---------- - diffusion_model : DiffusionModelBase - The DiffusionModel to append. - - Raises - ------ - TypeError - If the diffusion_model is not a DiffusionModelBase. - """ - if not isinstance(diffusion_model, DiffusionModelBase): raise TypeError( f'diffusion_model must be a DiffusionModelBase, got {type(diffusion_model).__name__}' # noqa: E501 @@ -153,19 +105,6 @@ def append_diffusion_model(self, diffusion_model: DiffusionModelBase) -> None: self._component_collections_is_dirty = True def remove_diffusion_model(self, name: str) -> None: - """ - Remove a DiffusionModel from the SampleModel by name. - - Parameters - ---------- - name : str - The name of the DiffusionModel to remove. - - Raises - ------ - ValueError - If no DiffusionModel with the given name is found. - """ for i, dm in enumerate(self.diffusion_models): if dm.name == name: del self.diffusion_models[i] @@ -177,7 +116,6 @@ def remove_diffusion_model(self, name: str) -> None: ) def clear_diffusion_models(self) -> None: - """Clear all DiffusionModels from the SampleModel.""" self.diffusion_models = [] self._component_collections_is_dirty = True @@ -187,35 +125,12 @@ def clear_diffusion_models(self) -> None: @property def diffusion_models(self) -> list[DiffusionModelBase]: - """ - Get the diffusion models of the SampleModel. - - Returns - ------- - list[DiffusionModelBase] - The diffusion models of the SampleModel. - """ return self._diffusion_models @diffusion_models.setter def diffusion_models( self, value: DiffusionModelBase | list[DiffusionModelBase] | None ) -> None: - """ - Set the diffusion models of the SampleModel. - - Parameters - ---------- - value : DiffusionModelBase | list[DiffusionModelBase] | None - The diffusion model(s) to set. Can be a single DiffusionModelBase, a list of - DiffusionModelBase, or None to clear all diffusion models. - - Raises - ------ - TypeError - If value is not a DiffusionModelBase, a list of DiffusionModelBase, or None. - """ - if value is None: self._diffusion_models = [] self._on_diffusion_models_change() @@ -239,33 +154,10 @@ def diffusion_models( @property def temperature(self) -> Parameter | None: - """ - Get the temperature of the SampleModel. - - Returns - ------- - Parameter | None - The temperature Parameter of the SampleModel, or None if not set. - """ return self._temperature @temperature.setter def temperature(self, value: Numeric | None) -> None: - """ - Set the temperature of the SampleModel. - - Parameters - ---------- - value : Numeric | None - The temperature value to set. Can be a number or None to unset the temperature. - - Raises - ------ - TypeError - If value is not a number or None. - ValueError - If value is negative. - """ if value is None: self._temperature = None return @@ -289,54 +181,16 @@ def temperature(self, value: Numeric | None) -> None: @property def temperature_unit(self) -> str | sc.Unit: - """ - Get the temperature unit of the SampleModel. - - Returns - ------- - str | sc.Unit - The unit of the temperature Parameter. - """ return self._temperature_unit @temperature_unit.setter def temperature_unit(self, _value: str | sc.Unit) -> None: - """ - The temperature unit of the SampleModel is read-only. - - Parameters - ---------- - _value : str | sc.Unit - The unit to set for the temperature Parameter. - - Raises - ------ - AttributeError - Always, as temperature_unit is read-only. - """ - raise AttributeError( - f'Temperature_unit is read-only. Use convert_temperature_unit to change the unit between allowed types ' # noqa: E501 - f'or create a new {self.__class__.__name__} with the desired unit.' + f'Temperature_unit is read-only. Use convert_temperature_unit to change the unit ' + f'between allowed types or create a new {self.__class__.__name__} with the desired unit.' # noqa: E501 ) def convert_temperature_unit(self, unit: str | sc.Unit) -> None: - """ - Convert the unit of the temperature Parameter. - - Parameters - ---------- - unit : str | sc.Unit - The unit to convert the temperature Parameter to. - - Raises - ------ - ValueError - If temperature is not set or conversion fails. - Exception - If the provided unit is invalid or cannot be converted. - """ - if self.temperature is None: raise ValueError('Temperature is not set, cannot convert unit.') @@ -346,100 +200,36 @@ def convert_temperature_unit(self, unit: str | sc.Unit) -> None: self.temperature.convert_unit(unit) self._temperature_unit = unit except Exception: - # Attempt to rollback on failure with suppress(Exception): self.temperature.convert_unit(old_unit) raise @property def normalize_detailed_balance(self) -> bool: - """ - Get whether to divide the detailed balance factor by temperature. - - Returns - ------- - bool - True if the detailed balance factor is divided by temperature, False otherwise. - """ return self.detailed_balance_settings.normalize_detailed_balance @normalize_detailed_balance.setter def normalize_detailed_balance(self, value: bool) -> None: - """ - Set whether to divide the detailed balance factor by temperature. - - Parameters - ---------- - value : bool - True to divide the detailed balance factor by temperature, False otherwise. - - Raises - ------ - TypeError - If value is not a bool. - """ if not isinstance(value, bool): raise TypeError('normalize_detailed_balance must be True or False') self.detailed_balance_settings.normalize_detailed_balance = value @property def use_detailed_balance(self) -> bool: - """ - Get whether to apply detailed balance to the model. - - Returns - ------- - bool - True if detailed balance is applied, False otherwise. - """ return self.detailed_balance_settings.use_detailed_balance @use_detailed_balance.setter def use_detailed_balance(self, value: bool) -> None: - """ - Set whether to apply detailed balance to the model. - - Parameters - ---------- - value : bool - True to apply detailed balance, False otherwise. - - Raises - ------ - TypeError - If value is not a bool. - """ if not isinstance(value, bool): raise TypeError('use_detailed_balance must be True or False') self.detailed_balance_settings.use_detailed_balance = value @property def detailed_balance_settings(self) -> DetailedBalanceSettings: - """ - Get the DetailedBalanceSettings of the SampleModel. - - Returns - ------- - DetailedBalanceSettings - The DetailedBalanceSettings of the SampleModel. - """ return self._detailed_balance_settings @detailed_balance_settings.setter def detailed_balance_settings(self, value: DetailedBalanceSettings) -> None: - """ - Set the DetailedBalanceSettings of the SampleModel. - - Parameters - ---------- - value : DetailedBalanceSettings - The DetailedBalanceSettings to set. - - Raises - ------ - TypeError - If value is not a DetailedBalanceSettings. - """ if not isinstance(value, DetailedBalanceSettings): raise TypeError('detailed_balance_settings must be a DetailedBalanceSettings') self._detailed_balance_settings = value @@ -449,56 +239,24 @@ def detailed_balance_settings(self, value: DetailedBalanceSettings) -> None: # ------------------------------------------------------------------ def evaluate( - self, x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray - ) -> list[np.ndarray]: - """ - Evaluate the sample model at all Q for the given x values. - - Parameters - ---------- - x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray - The x values to evaluate the model at. Can be a number, list, numpy array, scipp - Variable, or scipp DataArray. - - Returns - ------- - list[np.ndarray] - List of evaluated model values for each Q. - """ - - y = super().evaluate(x) + self, + x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, + output: str = 'numpy', + ) -> list[np.ndarray] | list[sc.Variable]: + y = super().evaluate(x, output=output) if self.temperature is not None and self.detailed_balance_settings.use_detailed_balance: DBF = detailed_balance_factor( energy=x, temperature=self.temperature, divide_by_temperature=self.detailed_balance_settings.normalize_detailed_balance, - energy_unit=self.unit, + energy_unit=self.x_unit, ) y = [yi * DBF for yi in y] return y def get_all_variables(self, Q_index: int | None = None) -> list[Parameter]: - """ - Get all Parameters and Descriptors from all ComponentCollections in the SampleModel. - - Also includes temperature if set and all variables from diffusion models. Ignores the - Parameters and Descriptors in self._components as these are just templates. - - Parameters - ---------- - Q_index : int | None, default=None - If specified, only get variables from the ComponentCollection at the given Q index. If - None, get variables from all ComponentCollections. - - Returns - ------- - list[Parameter] - List of all Parameters and Descriptors, including temperature if set and all variables - from diffusion models. - """ - all_vars = super().get_all_variables(Q_index=Q_index) if self.temperature is not None: all_vars.append(self.temperature) @@ -514,16 +272,10 @@ def get_all_variables(self, Q_index: int | None = None) -> list[Parameter]: # ------------------------------------------------------------------ def _generate_component_collections(self) -> None: - """ - Generate ComponentCollections from the DiffusionModels for each Q and add the components - from self._components. - """ super()._generate_component_collections() if self.Q is None: return - # Generate components from diffusion models - # and add to component collections for diffusion_model in self.diffusion_models: diffusion_collections = diffusion_model.get_component_collections() for target, source in zip( @@ -535,13 +287,11 @@ def _generate_component_collections(self) -> None: target.append_component(component) def _on_diffusion_models_change(self) -> None: - """Handle changes to the diffusion models.""" for diffusion_model in self.diffusion_models: diffusion_model.Q = self.Q self._component_collections_is_dirty = True def _on_Q_change(self) -> None: - """Handle changes to the Q values.""" for diffusion_model in self.diffusion_models: diffusion_model.clear_Q(confirm=True) diffusion_model.Q = self.Q @@ -552,17 +302,8 @@ def _on_Q_change(self) -> None: # ------------------------------------------------------------------ def __repr__(self) -> str: - """ - Return a string representation of the SampleModel. - - Returns - ------- - str - A string representation of the SampleModel. - """ - return ( - f'{self.__class__.__name__}(unique_name={self.unique_name}, unit={self.unit}), ' + f'{self.__class__.__name__}(unique_name={self.unique_name}, x_unit={self.x_unit}), ' f'Q = {self.Q}, \n ' f'components = {self.components}, diffusion_models = {self.diffusion_models}, ' f'temperature = {self.temperature}, ' diff --git a/tests/unit/easydynamics/analysis/test_analysis.py b/tests/unit/easydynamics/analysis/test_analysis.py index 516b718e..a6447ded 100644 --- a/tests/unit/easydynamics/analysis/test_analysis.py +++ b/tests/unit/easydynamics/analysis/test_analysis.py @@ -512,7 +512,7 @@ def test_parameters_to_dataset_different_units(self, analysis): ) # Convert the unit of a component to eV. - analysis.sample_model.get_component_collection(Q_index=1)[0].convert_unit('eV') + analysis.sample_model.get_component_collection(Q_index=1)[0].convert_x_unit('eV') # THEN parameters_dataset = analysis.parameters_to_dataset() diff --git a/tests/unit/easydynamics/base_classes/test_easydynamics_modelbase.py b/tests/unit/easydynamics/base_classes/test_easydynamics_modelbase.py index 6767640a..c1ec97d2 100644 --- a/tests/unit/easydynamics/base_classes/test_easydynamics_modelbase.py +++ b/tests/unit/easydynamics/base_classes/test_easydynamics_modelbase.py @@ -13,7 +13,7 @@ class TestEasyDynamicsModelBase: def easy_dynamics_modelbase(self): """Fixture for creating an instance of EasyDynamicsModelBase.""" - return EasyDynamicsModelBase(name='TestModel', unit='meV') + return EasyDynamicsModelBase(name='TestModel', x_unit='meV') def test_initialization(self, easy_dynamics_modelbase): """Test that the EasyDynamicsModelBase is initialized correctly.""" @@ -68,9 +68,9 @@ def test_name_setter_invalid_type(self, easy_dynamics_modelbase, invalid_name): def test_unit_property(self, easy_dynamics_modelbase): # WHEN THEN EXPECT - assert easy_dynamics_modelbase.unit == 'meV' + assert easy_dynamics_modelbase.x_unit == 'meV' def test_unit_setter_raises(self, easy_dynamics_modelbase): # WHEN / THEN / EXPECT - with pytest.raises(AttributeError, match='Use convert_unit to change '): - easy_dynamics_modelbase.unit = 'K' + with pytest.raises(AttributeError, match='read-only'): + easy_dynamics_modelbase.x_unit = 'K' diff --git a/tests/unit/easydynamics/convolution/test_convolution.py b/tests/unit/easydynamics/convolution/test_convolution.py index dbb4290d..803ed24f 100644 --- a/tests/unit/easydynamics/convolution/test_convolution.py +++ b/tests/unit/easydynamics/convolution/test_convolution.py @@ -73,7 +73,7 @@ def test_init(self, default_convolution): assert default_convolution.upsample_factor == 5 assert default_convolution.extension_factor == pytest.approx(0.2) assert default_convolution.temperature is None - assert default_convolution.unit == 'meV' + assert default_convolution.x_unit == 'meV' assert default_convolution.detailed_balance_settings.normalize_detailed_balance is True assert isinstance(default_convolution._energy_grid, EnergyGrid) @@ -107,7 +107,7 @@ def test_init_components(self, convolution_with_components): assert convolution_with_components.upsample_factor == 5 assert convolution_with_components.extension_factor == pytest.approx(0.2) assert convolution_with_components.temperature is None - assert convolution_with_components.unit == 'meV' + assert convolution_with_components.x_unit == 'meV' assert ( convolution_with_components.detailed_balance_settings.normalize_detailed_balance is True diff --git a/tests/unit/easydynamics/convolution/test_convolution_base.py b/tests/unit/easydynamics/convolution/test_convolution_base.py index fe8ceef6..41ab5933 100644 --- a/tests/unit/easydynamics/convolution/test_convolution_base.py +++ b/tests/unit/easydynamics/convolution/test_convolution_base.py @@ -181,17 +181,17 @@ def test_unit_setter_raises(self, convolution_base): # WHEN THEN EXPECT with pytest.raises( AttributeError, - match=r'Use convert_unit to change the unit between allowed types ', + match=r'read-only', ): - convolution_base.unit = 'K' + convolution_base.x_unit = 'K' def test_convert_unit(self, convolution_base): # WHEN THEN - convolution_base.convert_unit('eV') + convolution_base.convert_x_unit('eV') # EXPECT assert convolution_base.energy.unit == 'eV' - assert convolution_base.unit == 'eV' + assert convolution_base.x_unit == 'eV' assert np.allclose(convolution_base.energy.values, np.linspace(-0.01, 0.01, 100)) def test_convert_unit_invalid_type_raises(self, convolution_base): @@ -200,7 +200,7 @@ def test_convert_unit_invalid_type_raises(self, convolution_base): TypeError, match=r'Energy unit must be a string or scipp unit.', ): - convolution_base.convert_unit(123) + convolution_base.convert_x_unit(123) def test_convert_unit_invalid_unit_rollback(self, convolution_base): # WHEN THEN @@ -208,10 +208,10 @@ def test_convert_unit_invalid_unit_rollback(self, convolution_base): UnitError, match=r'Conversion from `meV` to `s` is not valid.', ): - convolution_base.convert_unit('s') + convolution_base.convert_x_unit('s') # EXPECT - assert convolution_base.unit == 'meV' + assert convolution_base.x_unit == 'meV' assert np.allclose(convolution_base.energy.values, np.linspace(-10, 10, 100)) def test_convert_unit_invalid_offset_unit_rollback(self, convolution_base): @@ -223,10 +223,10 @@ def test_convert_unit_invalid_offset_unit_rollback(self, convolution_base): UnitError, match=r'Conversion from `s` to `meV` is not valid.', ): - convolution_base.convert_unit('meV') + convolution_base.convert_x_unit('meV') # EXPECT - assert convolution_base.unit == 'meV' + assert convolution_base.x_unit == 'meV' assert convolution_base.energy_offset.unit == 's' def test_energy_offset_property(self, convolution_base): diff --git a/tests/unit/easydynamics/convolution/test_numerical_convolution.py b/tests/unit/easydynamics/convolution/test_numerical_convolution.py index 755381c2..4607c3b6 100644 --- a/tests/unit/easydynamics/convolution/test_numerical_convolution.py +++ b/tests/unit/easydynamics/convolution/test_numerical_convolution.py @@ -48,7 +48,7 @@ def test_init(self, default_numerical_convolution): assert default_numerical_convolution.upsample_factor == 5 assert default_numerical_convolution.extension_factor == pytest.approx(0.2) assert default_numerical_convolution.temperature is None - assert default_numerical_convolution.unit == 'meV' + assert default_numerical_convolution.x_unit == 'meV' assert ( default_numerical_convolution.detailed_balance_settings.normalize_detailed_balance is True diff --git a/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py b/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py index a40e4388..08b12cb4 100644 --- a/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py +++ b/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py @@ -48,7 +48,7 @@ def test_init(self, default_numerical_convolution_base): assert default_numerical_convolution_base.upsample_factor == 5 assert default_numerical_convolution_base.extension_factor == pytest.approx(0.2) assert default_numerical_convolution_base.temperature is None - assert default_numerical_convolution_base.unit == 'meV' + assert default_numerical_convolution_base.x_unit == 'meV' assert ( default_numerical_convolution_base.detailed_balance_settings.normalize_detailed_balance is True @@ -87,7 +87,7 @@ def test_init_with_custom_parameters(self): assert numerical_convolution_base.extension_factor == pytest.approx(0.5) assert numerical_convolution_base.temperature.value == temperature assert numerical_convolution_base.temperature.unit == temperature_unit - assert numerical_convolution_base.unit == unit + assert numerical_convolution_base.x_unit == unit assert ( numerical_convolution_base.detailed_balance_settings.normalize_detailed_balance is False diff --git a/tests/unit/easydynamics/sample_model/components/test_damped_harmonic_oscillator.py b/tests/unit/easydynamics/sample_model/components/test_damped_harmonic_oscillator.py index 87f9556c..ccebfc18 100644 --- a/tests/unit/easydynamics/sample_model/components/test_damped_harmonic_oscillator.py +++ b/tests/unit/easydynamics/sample_model/components/test_damped_harmonic_oscillator.py @@ -20,7 +20,7 @@ def dho(self): area=2.0, center=1.5, width=0.3, - unit='meV', + x_unit='meV', ) def test_init_no_inputs(self): @@ -32,7 +32,7 @@ def test_init_no_inputs(self): assert dho.area.value == pytest.approx(1.0) assert dho.center.value == pytest.approx(1.0) assert dho.width.value == pytest.approx(1.0) - assert dho.unit == 'meV' + assert dho.x_unit == 'meV' def test_initialization(self, dho: DampedHarmonicOscillator): # WHEN THEN EXPECT @@ -40,25 +40,25 @@ def test_initialization(self, dho: DampedHarmonicOscillator): assert dho.area.value == pytest.approx(2.0) assert dho.center.value == pytest.approx(1.5) assert dho.width.value == pytest.approx(0.3) - assert dho.unit == 'meV' + assert dho.x_unit == 'meV' @pytest.mark.parametrize( 'kwargs, expected_message', [ ( - {'area': 'invalid', 'center': 0.5, 'width': 0.6, 'unit': 'meV'}, + {'area': 'invalid', 'center': 0.5, 'width': 0.6, 'x_unit': 'meV'}, 'area must be a number', ), ( - {'area': 2.0, 'center': 'invalid', 'width': 0.6, 'unit': 'meV'}, + {'area': 2.0, 'center': 'invalid', 'width': 0.6, 'x_unit': 'meV'}, 'center must be ', ), ( - {'area': 2.0, 'center': 0.5, 'width': 'invalid', 'unit': 'meV'}, + {'area': 2.0, 'center': 0.5, 'width': 'invalid', 'x_unit': 'meV'}, 'width must be a number', ), ( - {'area': 2.0, 'center': 0.5, 'width': 0.6, 'unit': 123}, + {'area': 2.0, 'center': 0.5, 'width': 0.6, 'x_unit': 123}, 'unit must be None', ), ], @@ -78,7 +78,7 @@ def test_negative_width_raises(self): area=2.0, center=0.5, width=-0.6, - unit='meV', + x_unit='meV', ) def test_negative_area_warns(self): @@ -89,7 +89,7 @@ def test_negative_area_warns(self): area=-2.0, center=0.5, width=0.6, - unit='meV', + x_unit='meV', ) @pytest.mark.parametrize( @@ -169,10 +169,10 @@ def test_area_matches_parameter(self, dho: DampedHarmonicOscillator): def test_convert_unit(self, dho: DampedHarmonicOscillator): # WHEN THEN - dho.convert_unit('microeV') + dho.convert_x_unit('microeV') # EXPECT - assert dho.unit == 'microeV' + assert dho.x_unit == 'microeV' assert dho.area.value == pytest.approx(2 * 1e3) assert dho.center.value == pytest.approx(1.5 * 1e3) assert dho.width.value == pytest.approx(0.3 * 1e3) @@ -194,7 +194,7 @@ def test_copy(self, dho: DampedHarmonicOscillator): assert dho_copy.width.value == dho.width.value assert dho_copy.width.fixed == dho.width.fixed - assert dho_copy.unit == dho.unit + assert dho_copy.x_unit == dho.x_unit def test_repr(self, dho: DampedHarmonicOscillator): # WHEN THEN @@ -203,7 +203,7 @@ def test_repr(self, dho: DampedHarmonicOscillator): # EXPECT assert 'DampedHarmonicOscillator' in repr_str assert 'name = TestDHOName' in repr_str - assert 'unit = meV' in repr_str + assert 'x_unit = meV' in repr_str assert 'area =' in repr_str assert 'center =' in repr_str assert 'width =' in repr_str diff --git a/tests/unit/easydynamics/sample_model/components/test_delta_function.py b/tests/unit/easydynamics/sample_model/components/test_delta_function.py index 0747731b..e48942a6 100644 --- a/tests/unit/easydynamics/sample_model/components/test_delta_function.py +++ b/tests/unit/easydynamics/sample_model/components/test_delta_function.py @@ -20,7 +20,7 @@ def delta_function(self): display_name='TestDeltaFunction', area=2.0, center=0.5, - unit='meV', + x_unit='meV', ) def test_init_no_inputs(self): @@ -31,7 +31,7 @@ def test_init_no_inputs(self): assert delta_function.display_name == 'DeltaFunction' assert delta_function.area.value == pytest.approx(1.0) assert delta_function.center.value == pytest.approx(0.0) - assert delta_function.unit == 'meV' + assert delta_function.x_unit == 'meV' assert delta_function.center.fixed is True def test_initialization(self, delta_function: DeltaFunction): @@ -39,21 +39,21 @@ def test_initialization(self, delta_function: DeltaFunction): assert delta_function.display_name == 'TestDeltaFunction' assert delta_function.area.value == pytest.approx(2.0) assert delta_function.center.value == pytest.approx(0.5) - assert delta_function.unit == 'meV' + assert delta_function.x_unit == 'meV' @pytest.mark.parametrize( 'kwargs, expected_message', [ ( - {'area': 'invalid', 'center': 0.5, 'unit': 'meV'}, + {'area': 'invalid', 'center': 0.5, 'x_unit': 'meV'}, 'area must be a number', ), ( - {'area': 2.0, 'center': 'invalid', 'unit': 'meV'}, + {'area': 2.0, 'center': 'invalid', 'x_unit': 'meV'}, 'center must be ', ), ( - {'area': 2.0, 'center': 0.5, 'unit': 123}, + {'area': 2.0, 'center': 0.5, 'x_unit': 123}, 'unit must be ', ), ], @@ -65,7 +65,7 @@ def test_input_type_validation_raises(self, kwargs, expected_message): def test_negative_area_warns(self): # WHEN THEN EXPECT with pytest.warns(UserWarning, match='may not be physically meaningful'): - DeltaFunction(display_name='TestDeltaFunction', area=-2.0, center=0.5, unit='meV') + DeltaFunction(display_name='TestDeltaFunction', area=-2.0, center=0.5, x_unit='meV') @pytest.mark.parametrize( 'prop, valid_value, invalid_value, invalid_message', @@ -147,7 +147,7 @@ def test_evaluate_with_incompatible_unit_raises(self, delta_function: DeltaFunct # THEN EXPECT with pytest.raises( UnitError, - match='Input x has unit nm, but DeltaFunction component ', + match='Input x has unit nm', ): delta_function.evaluate(x) @@ -192,10 +192,10 @@ def test_get_all_parameters(self, delta_function: DeltaFunction): def test_convert_unit(self, delta_function: DeltaFunction): # WHEN THEN - delta_function.convert_unit('microeV') + delta_function.convert_x_unit('microeV') # EXPECT - assert delta_function.unit == 'microeV' + assert delta_function.x_unit == 'microeV' assert delta_function.area.value == pytest.approx(2 * 1e3) assert delta_function.center.value == pytest.approx(0.5 * 1e3) @@ -213,7 +213,7 @@ def test_copy(self, delta_function: DeltaFunction): assert delta_copy.center.value == delta_function.center.value assert delta_copy.center.fixed == delta_function.center.fixed - assert delta_copy.unit == delta_function.unit + assert delta_copy.x_unit == delta_function.x_unit def test_repr(self, delta_function: DeltaFunction): # WHEN THEN @@ -222,6 +222,6 @@ def test_repr(self, delta_function: DeltaFunction): # EXPECT assert 'DeltaFunction' in repr_str assert 'name = DeltaFunctionName' in repr_str - assert 'unit = meV' in repr_str + assert 'x_unit = meV' in repr_str assert 'area =' in repr_str assert 'center =' in repr_str diff --git a/tests/unit/easydynamics/sample_model/components/test_exponential.py b/tests/unit/easydynamics/sample_model/components/test_exponential.py index 6c2a1c50..511d9dea 100644 --- a/tests/unit/easydynamics/sample_model/components/test_exponential.py +++ b/tests/unit/easydynamics/sample_model/components/test_exponential.py @@ -20,7 +20,7 @@ def exponential(self): amplitude=2.0, center=0.5, rate=1.2, - unit='meV', + x_unit='meV', ) def test_init_no_inputs(self): @@ -32,7 +32,7 @@ def test_init_no_inputs(self): assert exponential.amplitude.value == pytest.approx(1.0) assert exponential.center.value == pytest.approx(0.0) assert exponential.rate.value == pytest.approx(1.0) - assert exponential.unit == 'meV' + assert exponential.x_unit == 'meV' def test_initialization(self, exponential: Exponential): # WHEN THEN EXPECT @@ -40,21 +40,21 @@ def test_initialization(self, exponential: Exponential): assert exponential.amplitude.value == pytest.approx(2.0) assert exponential.center.value == pytest.approx(0.5) assert exponential.rate.value == pytest.approx(1.2) - assert exponential.unit == 'meV' + assert exponential.x_unit == 'meV' @pytest.mark.parametrize( 'kwargs, expected_message', [ ( - {'amplitude': 'invalid', 'center': 0.5, 'rate': 1.0, 'unit': 'meV'}, + {'amplitude': 'invalid', 'center': 0.5, 'rate': 1.0, 'x_unit': 'meV'}, 'amplitude must be a number', ), ( - {'amplitude': 2.0, 'center': 'invalid', 'rate': 1.0, 'unit': 'meV'}, + {'amplitude': 2.0, 'center': 'invalid', 'rate': 1.0, 'x_unit': 'meV'}, 'center must be None or a number', ), ( - {'amplitude': 2.0, 'center': 0.5, 'rate': 'invalid', 'unit': 'meV'}, + {'amplitude': 2.0, 'center': 0.5, 'rate': 'invalid', 'x_unit': 'meV'}, 'rate must be a number', ), ], @@ -67,11 +67,11 @@ def test_input_type_validation_raises(self, kwargs, expected_message): 'kwargs, expected_message', [ ( - {'amplitude': np.nan, 'center': 0.5, 'rate': 1.0, 'unit': 'meV'}, + {'amplitude': np.nan, 'center': 0.5, 'rate': 1.0, 'x_unit': 'meV'}, 'amplitude must be a finite number or a Parameter', ), ( - {'amplitude': 2.0, 'center': 0.5, 'rate': np.nan, 'unit': 'meV'}, + {'amplitude': 2.0, 'center': 0.5, 'rate': np.nan, 'x_unit': 'meV'}, 'rate must be a finite number or a Parameter', ), ], @@ -146,10 +146,10 @@ def test_get_all_parameters(self, exponential: Exponential): def test_convert_unit(self, exponential: Exponential): # WHEN - exponential.convert_unit('microeV') + exponential.convert_x_unit('microeV') # THEN EXPECT - assert exponential.unit == 'microeV' + assert exponential.x_unit == 'microeV' assert exponential.amplitude.value == pytest.approx(2.0 * 1e3) assert exponential.center.value == pytest.approx(0.5 * 1e3) @@ -161,7 +161,7 @@ def test_convert_unit(self, exponential: Exponential): def test_convert_unit_incorrect_unit_raises(self, exponential: Exponential): # WHEN THEN EXPECT with pytest.raises(TypeError, match=r'unit must be a string or sc.Unit'): - exponential.convert_unit(123) + exponential.convert_x_unit(123) def test_convert_unit_rollback(self, exponential: Exponential): # WHEN @@ -169,10 +169,10 @@ def test_convert_unit_rollback(self, exponential: Exponential): UnitError, match=r'Failed to convert unit: Conversion from `meV` to `m` is not valid.', ): - exponential.convert_unit('m') + exponential.convert_x_unit('m') # THEN EXPECT - values should be unchanged - assert exponential.unit == 'meV' + assert exponential.x_unit == 'meV' assert exponential.amplitude.value == pytest.approx(2.0) assert exponential.amplitude.unit == 'meV' assert exponential.center.value == pytest.approx(0.5) @@ -197,7 +197,7 @@ def test_copy(self, exponential: Exponential): assert exponential_copy.rate.value == exponential.rate.value assert exponential_copy.rate.fixed == exponential.rate.fixed - assert exponential_copy.unit == exponential.unit + assert exponential_copy.x_unit == exponential.x_unit def test_repr(self, exponential: Exponential): # WHEN @@ -206,7 +206,7 @@ def test_repr(self, exponential: Exponential): # THEN EXPECT assert 'Exponential' in repr_str assert 'name = ExponentialName' in repr_str - assert 'unit = meV' in repr_str + assert 'x_unit = meV' in repr_str assert 'amplitude =' in repr_str assert 'center =' in repr_str assert 'rate =' in repr_str diff --git a/tests/unit/easydynamics/sample_model/components/test_expression_component.py b/tests/unit/easydynamics/sample_model/components/test_expression_component.py index 48fbafe2..e3e09cde 100644 --- a/tests/unit/easydynamics/sample_model/components/test_expression_component.py +++ b/tests/unit/easydynamics/sample_model/components/test_expression_component.py @@ -16,14 +16,14 @@ def expr(self): return ExpressionComponent( 'A * exp(-(x - x0)**2 / (2*sigma**2))', parameters={'A': 2.0, 'x0': 0.5, 'sigma': 0.6}, - unit='meV', + x_unit='meV', display_name='TestExpression', ) def test_init_valid(self, expr: ExpressionComponent): # WHEN THEN EXPECT assert expr.display_name == 'TestExpression' - assert expr.unit == 'meV' + assert expr.x_unit == 'meV' assert expr.A.value == pytest.approx(2.0) assert expr.x0.value == pytest.approx(0.5) @@ -134,7 +134,7 @@ def test_expression_is_read_only(self, expr: ExpressionComponent): def test_convert_unit_not_implemented(self, expr: ExpressionComponent): # WHEN THEN EXPECT with pytest.raises(NotImplementedError, match='not implemented'): - expr.convert_unit('microeV') + expr.convert_x_unit('microeV') def test_missing_parameter_defaults(self): # WHEN THEN @@ -191,7 +191,7 @@ def test_copy(self, expr: ExpressionComponent): assert expr_copy is not expr assert isinstance(expr_copy, ExpressionComponent) assert expr_copy.expression == expr.expression - assert expr_copy.unit == expr.unit + assert expr_copy.x_unit == expr.x_unit assert expr_copy.display_name == expr.display_name assert expr_copy.A.value == pytest.approx(expr.A.value) diff --git a/tests/unit/easydynamics/sample_model/components/test_gaussian.py b/tests/unit/easydynamics/sample_model/components/test_gaussian.py index f5acf10c..cb1f796c 100644 --- a/tests/unit/easydynamics/sample_model/components/test_gaussian.py +++ b/tests/unit/easydynamics/sample_model/components/test_gaussian.py @@ -20,7 +20,7 @@ def gaussian(self): area=2.0, center=0.5, width=0.6, - unit='meV', + x_unit='meV', ) def test_init_no_inputs(self): @@ -32,7 +32,7 @@ def test_init_no_inputs(self): assert gaussian.area.value == pytest.approx(1.0) assert gaussian.center.value == pytest.approx(0.0) assert gaussian.width.value == pytest.approx(1.0) - assert gaussian.unit == 'meV' + assert gaussian.x_unit == 'meV' assert gaussian.center.fixed is True def test_initialization(self, gaussian: Gaussian): @@ -41,25 +41,25 @@ def test_initialization(self, gaussian: Gaussian): assert gaussian.area.value == pytest.approx(2.0) assert gaussian.center.value == pytest.approx(0.5) assert gaussian.width.value == pytest.approx(0.6) - assert gaussian.unit == 'meV' + assert gaussian.x_unit == 'meV' @pytest.mark.parametrize( 'kwargs, expected_message', [ ( - {'area': 'invalid', 'center': 0.5, 'width': 0.6, 'unit': 'meV'}, + {'area': 'invalid', 'center': 0.5, 'width': 0.6, 'x_unit': 'meV'}, 'area must be a number', ), ( - {'area': 2.0, 'center': 'invalid', 'width': 0.6, 'unit': 'meV'}, + {'area': 2.0, 'center': 'invalid', 'width': 0.6, 'x_unit': 'meV'}, 'center must be None or a number', ), ( - {'area': 2.0, 'center': 0.5, 'width': 'invalid', 'unit': 'meV'}, + {'area': 2.0, 'center': 0.5, 'width': 'invalid', 'x_unit': 'meV'}, 'width must be a number', ), ( - {'area': 2.0, 'center': 0.5, 'width': 0.6, 'unit': 123}, + {'area': 2.0, 'center': 0.5, 'width': 0.6, 'x_unit': 123}, 'unit must be None', ), ], @@ -84,7 +84,7 @@ def test_negative_width_raises(self): area=2.0, center=0.5, width=-0.6, - unit='meV', + x_unit='meV', ) def test_negative_area_warns(self): @@ -95,7 +95,7 @@ def test_negative_area_warns(self): area=-2.0, center=0.5, width=0.6, - unit='meV', + x_unit='meV', ) @pytest.mark.parametrize( @@ -179,10 +179,10 @@ def test_area_matches_parameter(self, gaussian: Gaussian): def test_convert_unit(self, gaussian: Gaussian): # WHEN THEN - gaussian.convert_unit('microeV') + gaussian.convert_x_unit('microeV') # EXPECT - assert gaussian.unit == 'microeV' + assert gaussian.x_unit == 'microeV' assert gaussian.area.value == pytest.approx(2 * 1e3) assert gaussian.center.value == pytest.approx(0.5 * 1e3) assert gaussian.width.value == pytest.approx(0.6 * 1e3) @@ -216,7 +216,7 @@ def test_copy(self, gaussian: Gaussian): assert gaussian_copy.width.min == gaussian.width.min assert gaussian_copy.width.max == gaussian.width.max - assert gaussian_copy.unit == gaussian.unit + assert gaussian_copy.x_unit == gaussian.x_unit def test_repr(self, gaussian: Gaussian): # WHEN THEN @@ -224,7 +224,7 @@ def test_repr(self, gaussian: Gaussian): # EXPECT assert 'Gaussian' in repr_str assert 'name = GaussianName' in repr_str - assert 'unit = meV' in repr_str + assert 'x_unit = meV' in repr_str assert 'area =' in repr_str assert 'center =' in repr_str assert 'width =' in repr_str diff --git a/tests/unit/easydynamics/sample_model/components/test_lorentzian.py b/tests/unit/easydynamics/sample_model/components/test_lorentzian.py index 6056bab9..fd7c173e 100644 --- a/tests/unit/easydynamics/sample_model/components/test_lorentzian.py +++ b/tests/unit/easydynamics/sample_model/components/test_lorentzian.py @@ -20,7 +20,7 @@ def lorentzian(self): area=2.0, center=0.5, width=0.6, - unit='meV', + x_unit='meV', ) def test_init_no_inputs(self): @@ -32,7 +32,7 @@ def test_init_no_inputs(self): assert lorentzian.area.value == pytest.approx(1.0) assert lorentzian.center.value == pytest.approx(0.0) assert lorentzian.width.value == pytest.approx(1.0) - assert lorentzian.unit == 'meV' + assert lorentzian.x_unit == 'meV' assert lorentzian.center.fixed is True def test_initialization(self, lorentzian: Lorentzian): @@ -41,25 +41,25 @@ def test_initialization(self, lorentzian: Lorentzian): assert lorentzian.area.value == pytest.approx(2.0) assert lorentzian.center.value == pytest.approx(0.5) assert lorentzian.width.value == pytest.approx(0.6) - assert lorentzian.unit == 'meV' + assert lorentzian.x_unit == 'meV' @pytest.mark.parametrize( 'kwargs, expected_message', [ ( - {'area': 'invalid', 'center': 0.5, 'width': 0.6, 'unit': 'meV'}, + {'area': 'invalid', 'center': 0.5, 'width': 0.6, 'x_unit': 'meV'}, 'area must be a number', ), ( - {'area': 2.0, 'center': 'invalid', 'width': 0.6, 'unit': 'meV'}, + {'area': 2.0, 'center': 'invalid', 'width': 0.6, 'x_unit': 'meV'}, 'center must be None', ), ( - {'area': 2.0, 'center': 0.5, 'width': 'invalid', 'unit': 'meV'}, + {'area': 2.0, 'center': 0.5, 'width': 'invalid', 'x_unit': 'meV'}, 'width must be a number', ), ( - {'area': 2.0, 'center': 0.5, 'width': 0.6, 'unit': 123}, + {'area': 2.0, 'center': 0.5, 'width': 0.6, 'x_unit': 123}, 'unit must be None', ), ], @@ -78,7 +78,7 @@ def test_negative_width_raises(self): area=2.0, center=0.5, width=-0.6, - unit='meV', + x_unit='meV', ) def test_negative_area_warns(self): @@ -89,7 +89,7 @@ def test_negative_area_warns(self): area=-2.0, center=0.5, width=0.6, - unit='meV', + x_unit='meV', ) @pytest.mark.parametrize( @@ -168,10 +168,10 @@ def test_area_matches_parameter(self, lorentzian: Lorentzian): def test_convert_unit(self, lorentzian: Lorentzian): # WHEN THEN - lorentzian.convert_unit('microeV') + lorentzian.convert_x_unit('microeV') # EXPECT - assert lorentzian.unit == 'microeV' + assert lorentzian.x_unit == 'microeV' assert lorentzian.area.value == pytest.approx(2 * 1e3) assert lorentzian.center.value == pytest.approx(0.5 * 1e3) assert lorentzian.width.value == pytest.approx(0.6 * 1e3) @@ -193,7 +193,7 @@ def test_copy(self, lorentzian: Lorentzian): assert lorentzian_copy.width.value == lorentzian.width.value assert lorentzian_copy.width.fixed == lorentzian.width.fixed - assert lorentzian_copy.unit == lorentzian.unit + assert lorentzian_copy.x_unit == lorentzian.x_unit def test_repr(self, lorentzian: Lorentzian): # WHEN THEN @@ -202,7 +202,7 @@ def test_repr(self, lorentzian: Lorentzian): # EXPECT assert 'Lorentzian' in repr_str assert 'name = LorentzianName' in repr_str - assert 'unit = meV' in repr_str + assert 'x_unit = meV' in repr_str assert 'area =' in repr_str assert 'center =' in repr_str assert 'width =' in repr_str diff --git a/tests/unit/easydynamics/sample_model/components/test_mixins.py b/tests/unit/easydynamics/sample_model/components/test_mixins.py index e30f4bdd..bc7a312d 100644 --- a/tests/unit/easydynamics/sample_model/components/test_mixins.py +++ b/tests/unit/easydynamics/sample_model/components/test_mixins.py @@ -18,7 +18,7 @@ def dummy_model(self): @pytest.mark.parametrize('area_input', [2, 2.0]) def test_create_area_parameter_from_numeric(self, dummy_model, area_input, unit): # WHEN THEN - area_param = dummy_model._create_area_parameter(area_input, 'TestModel', unit=unit) + area_param = dummy_model._create_area_parameter(area_input, 'TestModel', x_unit=unit) # EXPECT assert isinstance(area_param, Parameter) @@ -59,7 +59,7 @@ def test_negative_area_warns(self, dummy_model): def test_create_center_parameter_from_numeric(self, dummy_model, center_input, unit): # WHEN THEN center_param = dummy_model._create_center_parameter( - center_input, 'TestModel', fix_if_none=False, unit=unit + center_input, 'TestModel', fix_if_none=False, x_unit=unit ) # EXPECT assert isinstance(center_param, Parameter) @@ -104,7 +104,7 @@ def test_create_center_parameter_invalid_numeric_raises(self, dummy_model, non_f def test_create_width_parameter_from_numeric(self, dummy_model, width_input, unit): # WHEN THEN width_param = dummy_model._create_width_parameter( - width_input, 'TestModel', param_name='width', unit=unit + width_input, 'TestModel', param_name='width', x_unit=unit ) # EXPECT assert isinstance(width_param, Parameter) diff --git a/tests/unit/easydynamics/sample_model/components/test_model_component.py b/tests/unit/easydynamics/sample_model/components/test_model_component.py index 514ced73..e34235db 100644 --- a/tests/unit/easydynamics/sample_model/components/test_model_component.py +++ b/tests/unit/easydynamics/sample_model/components/test_model_component.py @@ -15,7 +15,7 @@ def __init__(self): self.area = Parameter(name='area', value=1.0, unit='meV', fixed=False) self.center = Parameter(name='center', value=2.0, unit='meV', fixed=True) self.width = Parameter(name='width', value=3.0, unit='meV', fixed=True) - self._unit = 'meV' + self._x_unit = 'meV' def get_all_parameters(self): return [self.area, self.center, self.width] @@ -31,23 +31,23 @@ def dummy(self): def test_unit_cannot_be_set_directly(self, dummy: ModelComponent): # WHEN THEN EXPECT - with pytest.raises(AttributeError, match='Unit is read-only'): - dummy.unit = 'K' + with pytest.raises(AttributeError, match='read-only'): + dummy.x_unit = 'K' def test_convert_unit(self, dummy: DummyComponent): # WHEN THEN - dummy.convert_unit('microeV') + dummy.convert_x_unit('microeV') # EXPECT - assert dummy.unit == 'microeV' + assert dummy.x_unit == 'microeV' assert dummy.area.value == pytest.approx(1 * 1e3) assert dummy.center.value == pytest.approx(2 * 1e3) assert dummy.width.value == pytest.approx(3 * 1e3) def test_convert_unit_incorrect_unit_raises(self, dummy: DummyComponent): # WHEN THEN EXPECT - with pytest.raises(TypeError, match=r'Unit must be a string or sc.Unit'): - dummy.convert_unit(123) + with pytest.raises(TypeError, match=r'unit must be a string or sc.Unit'): + dummy.convert_x_unit(123) def test_free_and_fix_all_parameters(self, dummy): # WHEN THEN EXPECT @@ -92,7 +92,8 @@ def test_repr(self, dummy): ], ) def test_prepare_x_for_evaluate_various_inputs(self, dummy, x_input, expected_array): - x_prepared = dummy._prepare_x_for_evaluate(x_input) + result = dummy._prepare_x_for_evaluate(x_input) + x_prepared, _detected_unit, _dim = result assert isinstance(x_prepared, np.ndarray) assert x_prepared.shape == expected_array.shape @@ -137,26 +138,23 @@ def test_prepare_x_for_evaluate_with_incompatible_unit_raises(self, dummy): # THEN EXPECT with pytest.raises( Exception, - match='Input x has unit nm, but DummyComponent component ', + match='Input x has unit nm', ): dummy._prepare_x_for_evaluate(x) - def test_prepare_x_for_evaluate_with_different_unit_warns(self, dummy): + def test_prepare_x_for_evaluate_with_different_unit_no_warn(self, dummy): # WHEN x = sc.array(dims=['x'], values=[1.0, 2.0, 3.0], unit='microeV') - # THEN EXPECT - with pytest.warns( - UserWarning, - match='Input x has unit [µμ]eV, but DummyComponent component ', - ): - x_prepared = dummy._prepare_x_for_evaluate(x) + # THEN EXPECT: compatible units are accepted without warning; + # the component's x_unit is NOT mutated and x values are returned as-is. + x_prepared, _detected_unit, _dim = dummy._prepare_x_for_evaluate(x) # EXPECT assert isinstance(x_prepared, np.ndarray) assert x_prepared.shape == (3,) np.testing.assert_array_equal(x_prepared, [1.0, 2.0, 3.0]) - assert dummy.unit == 'µeV' # noqa: RUF001 - assert dummy.area.value == pytest.approx(1.0 * 1e3) - assert dummy.center.value == pytest.approx(2.0 * 1e3) - assert dummy.width.value == pytest.approx(3.0 * 1e3) + assert dummy.x_unit == 'meV' # component unit unchanged + assert dummy.area.value == pytest.approx(1.0) # parameter values unchanged + assert dummy.center.value == pytest.approx(2.0) + assert dummy.width.value == pytest.approx(3.0) diff --git a/tests/unit/easydynamics/sample_model/components/test_polynomial.py b/tests/unit/easydynamics/sample_model/components/test_polynomial.py index 3111c00a..01915806 100644 --- a/tests/unit/easydynamics/sample_model/components/test_polynomial.py +++ b/tests/unit/easydynamics/sample_model/components/test_polynomial.py @@ -6,7 +6,6 @@ import numpy as np import pytest from easyscience.variable import Parameter -from scipp import UnitError from easydynamics.sample_model import Polynomial @@ -27,7 +26,7 @@ def test_init_no_inputs(self): # EXPECT assert polynomial.display_name == 'Polynomial' assert polynomial.coefficients[0].value == pytest.approx(0.0) - assert polynomial.unit == 'meV' + assert polynomial.x_unit == 'meV' def test_initialization(self, polynomial: Polynomial): # WHEN THEN EXPECT @@ -48,7 +47,7 @@ def test_initialization(self, polynomial: Polynomial): 'Each coefficient must be ', ), ( - {'coefficients': [1.0, -2.0, 3.0], 'unit': 123}, + {'coefficients': [1.0, -2.0, 3.0], 'x_unit': 123}, 'unit must be ', ), ( @@ -163,18 +162,18 @@ def test_get_all_parameters(self, polynomial: Polynomial): def test_convert_unit(self, polynomial: Polynomial): # WHEN - polynomial.convert_unit('microeV') + polynomial.convert_x_unit('microeV') # THEN EXPECT - assert polynomial._unit == 'microeV' + assert polynomial._x_unit == 'microeV' assert np.isclose(polynomial.coefficients[0].value, 1.0) assert np.isclose(polynomial.coefficients[1].value, -2.0 * 1e-3) assert np.isclose(polynomial.coefficients[2].value, 3.0 * 1e-6) def test_convert_unit_raises_invalid_unit(self, polynomial: Polynomial): # WHEN THEN EXPECT - with pytest.raises(UnitError, match='unit must be '): - polynomial.convert_unit(123) + with pytest.raises(Exception, match='unit must be '): + polynomial.convert_x_unit(123) def test_copy(self, polynomial: Polynomial): # WHEN THEN diff --git a/tests/unit/easydynamics/sample_model/components/test_voigt.py b/tests/unit/easydynamics/sample_model/components/test_voigt.py index 5f42704d..5c35be83 100644 --- a/tests/unit/easydynamics/sample_model/components/test_voigt.py +++ b/tests/unit/easydynamics/sample_model/components/test_voigt.py @@ -22,7 +22,7 @@ def voigt(self): center=0.5, gaussian_width=0.6, lorentzian_width=0.7, - unit='meV', + x_unit='meV', ) def test_init_no_inputs(self): @@ -35,7 +35,7 @@ def test_init_no_inputs(self): assert voigt.center.value == pytest.approx(0.0) assert voigt.gaussian_width.value == pytest.approx(1.0) assert voigt.lorentzian_width.value == pytest.approx(1.0) - assert voigt.unit == 'meV' + assert voigt.x_unit == 'meV' assert voigt.center.fixed is True def test_initialization(self, voigt: Voigt): @@ -45,7 +45,7 @@ def test_initialization(self, voigt: Voigt): assert voigt.center.value == pytest.approx(0.5) assert voigt.gaussian_width.value == pytest.approx(0.6) assert voigt.lorentzian_width.value == pytest.approx(0.7) - assert voigt.unit == 'meV' + assert voigt.x_unit == 'meV' @pytest.mark.parametrize( 'kwargs, expected_message', @@ -56,7 +56,7 @@ def test_initialization(self, voigt: Voigt): 'center': 0.5, 'gaussian_width': 0.6, 'lorentzian_width': 0.7, - 'unit': 'meV', + 'x_unit': 'meV', }, 'area must be a number', ), @@ -66,7 +66,7 @@ def test_initialization(self, voigt: Voigt): 'center': 'invalid', 'gaussian_width': 0.6, 'lorentzian_width': 0.7, - 'unit': 'meV', + 'x_unit': 'meV', }, 'center must be None', ), @@ -76,7 +76,7 @@ def test_initialization(self, voigt: Voigt): 'center': 0.5, 'gaussian_width': 'invalid', 'lorentzian_width': 0.7, - 'unit': 'meV', + 'x_unit': 'meV', }, 'gaussian_width must be a number', ), @@ -86,7 +86,7 @@ def test_initialization(self, voigt: Voigt): 'center': 0.5, 'gaussian_width': 0.6, 'lorentzian_width': 'invalid', - 'unit': 'meV', + 'x_unit': 'meV', }, 'lorentzian_width must be a number', ), @@ -96,7 +96,7 @@ def test_initialization(self, voigt: Voigt): 'center': 0.5, 'gaussian_width': 0.6, 'lorentzian_width': 0.7, - 'unit': 123, + 'x_unit': 123, }, 'unit must be None,', ), @@ -117,7 +117,7 @@ def test_negative_gaussian_width_raises(self): center=0.5, gaussian_width=-0.6, lorentzian_width=0.7, - unit='meV', + x_unit='meV', ) def test_negative_lorentzian_width_raises(self): @@ -132,7 +132,7 @@ def test_negative_lorentzian_width_raises(self): center=0.5, gaussian_width=0.6, lorentzian_width=-0.7, - unit='meV', + x_unit='meV', ) def test_negative_area_warns(self): @@ -144,7 +144,7 @@ def test_negative_area_warns(self): center=0.5, gaussian_width=0.6, lorentzian_width=0.7, - unit='meV', + x_unit='meV', ) @pytest.mark.parametrize( @@ -214,7 +214,7 @@ def test_center_is_fixed_if_init_to_None(self): center=None, gaussian_width=0.6, lorentzian_width=0.7, - unit='meV', + x_unit='meV', ) # EXPECT @@ -223,10 +223,10 @@ def test_center_is_fixed_if_init_to_None(self): def test_convert_unit(self, voigt: Voigt): # WHEN THEN - voigt.convert_unit('microeV') + voigt.convert_x_unit('microeV') # EXPECT - assert voigt.unit == 'microeV' + assert voigt.x_unit == 'microeV' assert voigt.area.value == pytest.approx(2 * 1e3) assert voigt.center.value == pytest.approx(0.5 * 1e3) assert voigt.gaussian_width.value == pytest.approx(0.6 * 1e3) @@ -285,7 +285,7 @@ def test_copy(self, voigt: Voigt): assert voigt_copy.lorentzian_width.value == voigt.lorentzian_width.value assert voigt_copy.lorentzian_width.fixed == voigt.lorentzian_width.fixed - assert voigt_copy.unit == voigt.unit + assert voigt_copy.x_unit == voigt.x_unit def test_repr(self, voigt: Voigt): # WHEN THEN @@ -294,7 +294,7 @@ def test_repr(self, voigt: Voigt): # EXPECT assert 'Voigt' in repr_str assert 'name = VoigtName' in repr_str - assert 'unit = meV' in repr_str + assert 'x_unit = meV' in repr_str assert 'area =' in repr_str assert 'center =' in repr_str assert 'gaussian_width =' in repr_str diff --git a/tests/unit/easydynamics/sample_model/diffusion_model/test_brownian_translational_diffusion.py b/tests/unit/easydynamics/sample_model/diffusion_model/test_brownian_translational_diffusion.py index 7941d406..4c5904f3 100644 --- a/tests/unit/easydynamics/sample_model/diffusion_model/test_brownian_translational_diffusion.py +++ b/tests/unit/easydynamics/sample_model/diffusion_model/test_brownian_translational_diffusion.py @@ -25,8 +25,10 @@ def brownian_diffusion_model(self): def test_init_default(self, brownian_diffusion_model): # WHEN THEN EXPECT assert brownian_diffusion_model.display_name == 'BrownianTranslationalDiffusion' - assert brownian_diffusion_model.unit == 'meV' + assert brownian_diffusion_model.x_unit == 'meV' + assert brownian_diffusion_model.y_unit == 'dimensionless' assert brownian_diffusion_model.scale.value == pytest.approx(1.0) + assert brownian_diffusion_model.scale.unit == 'meV' assert brownian_diffusion_model.diffusion_coefficient.value == pytest.approx(1.0) @pytest.mark.parametrize( @@ -34,7 +36,7 @@ def test_init_default(self, brownian_diffusion_model): [ ( { - 'unit': 123, + 'x_unit': 123, 'scale': 1.0, 'diffusion_coefficient': 1.0, }, @@ -43,7 +45,7 @@ def test_init_default(self, brownian_diffusion_model): ), ( { - 'unit': 'meV', + 'x_unit': 'meV', 'scale': 'invalid', 'diffusion_coefficient': 1.0, }, @@ -52,7 +54,7 @@ def test_init_default(self, brownian_diffusion_model): ), ( { - 'unit': 'meV', + 'x_unit': 'meV', 'scale': -123.4, 'diffusion_coefficient': 1.0, }, @@ -61,7 +63,7 @@ def test_init_default(self, brownian_diffusion_model): ), ( { - 'unit': 'meV', + 'x_unit': 'meV', 'scale': 1.0, 'diffusion_coefficient': 'invalid', }, @@ -70,7 +72,7 @@ def test_init_default(self, brownian_diffusion_model): ), ( { - 'unit': 'meV', + 'x_unit': 'meV', 'scale': 1.0, 'diffusion_coefficient': -123.4, }, @@ -180,9 +182,11 @@ def test_create_component_collections(self, brownian_diffusion_model, Q): model = component_collections[model_index] assert len(model) == 1 component = model[0] - assert component.width.unit == brownian_diffusion_model.unit + assert component.width.unit == brownian_diffusion_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_write_width_dependency_expression(self, brownian_diffusion_model): # WHEN THEN diff --git a/tests/unit/easydynamics/sample_model/diffusion_model/test_delta_lorentz.py b/tests/unit/easydynamics/sample_model/diffusion_model/test_delta_lorentz.py index b3740db6..4e081a46 100644 --- a/tests/unit/easydynamics/sample_model/diffusion_model/test_delta_lorentz.py +++ b/tests/unit/easydynamics/sample_model/diffusion_model/test_delta_lorentz.py @@ -38,7 +38,7 @@ def delta_lorentz_model_with_Q_no_variation(self): def test_init_default(self, delta_lorentz_model): # WHEN THEN EXPECT assert delta_lorentz_model.display_name == 'DeltaLorentz' - assert delta_lorentz_model.unit == 'meV' + assert delta_lorentz_model.x_unit == 'meV' assert delta_lorentz_model.scale.value == pytest.approx(1.0) assert delta_lorentz_model.mean_u_squared.value == pytest.approx(0.0) assert delta_lorentz_model.A_0.value == pytest.approx(1.0) @@ -47,7 +47,7 @@ def test_init_default(self, delta_lorentz_model): def test_init_with_Q(self, delta_lorentz_model_with_Q): # WHEN THEN EXPECT assert delta_lorentz_model_with_Q.display_name == 'DeltaLorentz' - assert delta_lorentz_model_with_Q.unit == 'meV' + assert delta_lorentz_model_with_Q.x_unit == 'meV' assert delta_lorentz_model_with_Q.scale.value == pytest.approx(1.0) assert delta_lorentz_model_with_Q.mean_u_squared.value == pytest.approx(0.0) assert delta_lorentz_model_with_Q.A_0.value == pytest.approx(0.5) diff --git a/tests/unit/easydynamics/sample_model/diffusion_model/test_diffusion_model_base.py b/tests/unit/easydynamics/sample_model/diffusion_model/test_diffusion_model_base.py index 3364e510..b4860889 100644 --- a/tests/unit/easydynamics/sample_model/diffusion_model/test_diffusion_model_base.py +++ b/tests/unit/easydynamics/sample_model/diffusion_model/test_diffusion_model_base.py @@ -19,7 +19,10 @@ def test_init_default(self, diffusion_model): assert diffusion_model.name == 'DiffusionModel' assert diffusion_model.lorentzian_name == 'DiffusionModel' assert diffusion_model.lorentzian_display_name == 'DiffusionModel' - assert diffusion_model.unit == 'meV' + assert diffusion_model.x_unit == 'meV' + assert diffusion_model.y_unit == 'dimensionless' + # scale.unit = area_unit = x_unit * y_unit = meV * dimensionless = meV + assert diffusion_model.scale.unit == 'meV' def test_init_raises(self): # WHEN THEN EXPECT @@ -33,9 +36,9 @@ def test_unit_setter_raises(self, diffusion_model): # WHEN THEN EXPECT with pytest.raises( AttributeError, - match=r'Unit is read-only. Use convert_unit to change the unit between allowed types', + match=r'read-only', ): - diffusion_model.unit = 'eV' + diffusion_model.x_unit = 'eV' @pytest.mark.parametrize( ('attribute', 'value', 'expected'), @@ -153,7 +156,7 @@ def test_repr(self, diffusion_model): # EXPECT assert 'DiffusionModelBase' in repr_str - assert 'unit=meV' in repr_str + assert 'x_unit=meV' in repr_str def test_get_independent_variables(self, diffusion_model): # WHEN THEN EXPECT diff --git a/tests/unit/easydynamics/sample_model/diffusion_model/test_jump_translational_diffusion.py b/tests/unit/easydynamics/sample_model/diffusion_model/test_jump_translational_diffusion.py index 014a221f..b32db2a2 100644 --- a/tests/unit/easydynamics/sample_model/diffusion_model/test_jump_translational_diffusion.py +++ b/tests/unit/easydynamics/sample_model/diffusion_model/test_jump_translational_diffusion.py @@ -25,8 +25,10 @@ def jump_diffusion_model(self): def test_init_default(self, jump_diffusion_model): # WHEN THEN EXPECT assert jump_diffusion_model.display_name == 'JumpTranslationalDiffusion' - assert jump_diffusion_model.unit == 'meV' + assert jump_diffusion_model.x_unit == 'meV' + assert jump_diffusion_model.y_unit == 'dimensionless' assert jump_diffusion_model.scale.value == pytest.approx(1.0) + assert jump_diffusion_model.scale.unit == 'meV' assert jump_diffusion_model.diffusion_coefficient.value == pytest.approx(1.0) assert jump_diffusion_model.relaxation_time.value == pytest.approx(1.0) @@ -35,7 +37,7 @@ def test_init_default(self, jump_diffusion_model): [ ( { - 'unit': 123, + 'x_unit': 123, 'scale': 1.0, 'diffusion_coefficient': 1.0, 'relaxation_time': 1.0, @@ -45,7 +47,7 @@ def test_init_default(self, jump_diffusion_model): ), ( { - 'unit': 'meV', + 'x_unit': 'meV', 'scale': 'invalid', 'diffusion_coefficient': 1.0, 'relaxation_time': 1.0, @@ -55,7 +57,7 @@ def test_init_default(self, jump_diffusion_model): ), ( { - 'unit': 'meV', + 'x_unit': 'meV', 'scale': 1.0, 'diffusion_coefficient': 'invalid', 'relaxation_time': 1.0, @@ -65,7 +67,7 @@ def test_init_default(self, jump_diffusion_model): ), ( { - 'unit': 'meV', + 'x_unit': 'meV', 'scale': 1.0, 'diffusion_coefficient': -1.0, 'relaxation_time': 1.0, @@ -75,7 +77,7 @@ def test_init_default(self, jump_diffusion_model): ), ( { - 'unit': 'meV', + 'x_unit': 'meV', 'scale': 1.0, 'diffusion_coefficient': 1.0, 'relaxation_time': 'invalid', @@ -85,7 +87,7 @@ def test_init_default(self, jump_diffusion_model): ), ( { - 'unit': 'meV', + 'x_unit': 'meV', 'scale': 1.0, 'diffusion_coefficient': 1.0, 'relaxation_time': -1.0, @@ -159,7 +161,7 @@ def test_calculate_width(self, jump_diffusion_model): # EXPECT expected_widths = scipp_hbar * diffusion_coefficient_sc * (Q_values**2) / (1 + denominator) - expected_widths = expected_widths.to(unit=jump_diffusion_model.unit) + expected_widths = expected_widths.to(unit=jump_diffusion_model.x_unit) np.testing.assert_allclose(widths, expected_widths.values, rtol=1e-5) @@ -221,9 +223,11 @@ def test_create_component_collections(self, jump_diffusion_model, Q): model = component_collections[model_index] assert len(model) == 1 component = model[0] - assert component.width.unit == jump_diffusion_model.unit + assert component.width.unit == jump_diffusion_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_write_width_dependency_expression(self, jump_diffusion_model): # WHEN THEN diff --git a/tests/unit/easydynamics/sample_model/test_background_model.py b/tests/unit/easydynamics/sample_model/test_background_model.py index a698a1bf..6b01ec6c 100644 --- a/tests/unit/easydynamics/sample_model/test_background_model.py +++ b/tests/unit/easydynamics/sample_model/test_background_model.py @@ -18,14 +18,14 @@ def background_model(self): area=1.0, center=0.0, width=1.0, - unit='meV', + x_unit='meV', ) component2 = Lorentzian( display_name='TestLorentzian1', area=2.0, center=1.0, width=0.5, - unit='meV', + x_unit='meV', ) component_collection = ComponentCollection() component_collection.append_component(component1) @@ -33,7 +33,7 @@ def background_model(self): return BackgroundModel( display_name='InitModel', components=component_collection, - unit='meV', + x_unit='meV', Q=np.array([1.0, 2.0, 3.0]), ) @@ -43,7 +43,7 @@ def test_init(self, background_model): # EXPECT assert model.display_name == 'InitModel' - assert model.unit == 'meV' + assert model.x_unit == 'meV' assert len(model.components) == 2 np.testing.assert_array_equal(model.Q, np.array([1.0, 2.0, 3.0])) diff --git a/tests/unit/easydynamics/sample_model/test_component_collection.py b/tests/unit/easydynamics/sample_model/test_component_collection.py index 7ae15ed6..70142ffb 100644 --- a/tests/unit/easydynamics/sample_model/test_component_collection.py +++ b/tests/unit/easydynamics/sample_model/test_component_collection.py @@ -24,7 +24,7 @@ def component_collection(self): area=1.0, center=0.0, width=1.0, - unit='meV', + x_unit='meV', ) component2 = Lorentzian( name='TestLorentzian1Name', @@ -32,7 +32,7 @@ def component_collection(self): area=2.0, center=1.0, width=0.5, - unit='meV', + x_unit='meV', ) model.append_component(component1) model.append_component(component2) @@ -48,7 +48,7 @@ def test_init(self): def test_init_with_component(self): # WHEN THEN - component1 = Gaussian(name='TestGaussian1', area=1.0, center=0.0, width=1.0, unit='meV') + component1 = Gaussian(name='TestGaussian1', area=1.0, center=0.0, width=1.0, x_unit='meV') component_collection = ComponentCollection(display_name='InitModel', components=component1) # EXPECT @@ -58,9 +58,9 @@ def test_init_with_component(self): def test_init_with_components(self): # WHEN THEN - component1 = Gaussian(name='TestGaussian1', area=1.0, center=0.0, width=1.0, unit='meV') + component1 = Gaussian(name='TestGaussian1', area=1.0, center=0.0, width=1.0, x_unit='meV') component2 = Lorentzian( - name='TestLorentzian1', area=2.0, center=1.0, width=0.5, unit='meV' + name='TestLorentzian1', area=2.0, center=1.0, width=0.5, x_unit='meV' ) component_collection = ComponentCollection( display_name='InitModel', components=[component1, component2] @@ -91,13 +91,13 @@ def test_init_with_invalid_list_of_components_raises(self): def test_init_with_invalid_unit_raises(self): # WHEN THEN EXPECT with pytest.raises(TypeError, match='unit must be'): - ComponentCollection(unit=123) + ComponentCollection(x_unit=123) # ───── Component Management ───── def test_append_component(self, component_collection): # WHEN - component = Gaussian(name='TestComponent', area=1.0, center=0.0, width=1.0, unit='meV') + component = Gaussian(name='TestComponent', area=1.0, center=0.0, width=1.0, x_unit='meV') # THEN component_collection.append_component(component) # EXPECT @@ -105,7 +105,7 @@ def test_append_component(self, component_collection): def test_append_component_collection(self, component_collection): # WHEN - component = Gaussian(name='TestComponent', area=1.0, center=0.0, width=1.0, unit='meV') + component = Gaussian(name='TestComponent', area=1.0, center=0.0, width=1.0, x_unit='meV') component_collection2 = ComponentCollection() component_collection2.append_component(component) # THEN @@ -127,7 +127,7 @@ def test_append_invalid_component_raises(self, component_collection): def test_getitem(self, component_collection): # WHEN - component = Gaussian(name='TestComponent', area=1.0, center=0.0, width=1.0, unit='meV') + component = Gaussian(name='TestComponent', area=1.0, center=0.0, width=1.0, x_unit='meV') # THEN component_collection.append_component(component) # EXPECT @@ -140,7 +140,7 @@ def test_is_empty(self): assert component_collection.is_empty is True # WHEN THEN - component = Gaussian(name='TestComponent', area=1.0, center=0.0, width=1.0, unit='meV') + component = Gaussian(name='TestComponent', area=1.0, center=0.0, width=1.0, x_unit='meV') component_collection.append_component(component) # EXPECT assert component_collection.is_empty is False @@ -160,45 +160,45 @@ def test_list_component_names(self, component_collection): def test_convert_unit(self, component_collection): # WHEN THEN - component_collection.convert_unit('eV') + component_collection.convert_x_unit('eV') # EXPECT for component in component_collection: - assert component.unit == 'eV' + assert component.x_unit == 'eV' def test_convert_unit_incorrect_unit_raises(self, component_collection): # WHEN THEN EXPECT - with pytest.raises(TypeError, match=r'Unit must be a string or sc.Unit'): - component_collection.convert_unit(123) + with pytest.raises(TypeError, match=r'unit must be a string or sc.Unit'): + component_collection.convert_x_unit(123) def test_convert_unit_failure_rolls_back(self, component_collection): # WHEN THEN # Introduce a faulty component that will fail conversion class FaultyComponent(Gaussian): - def convert_unit(self, _unit: str) -> None: + def convert_x_unit(self, _unit: str) -> None: raise RuntimeError('Conversion failed.') faulty_component = FaultyComponent( - name='FaultyComponent', area=1.0, center=0.0, width=1.0, unit='meV' + name='FaultyComponent', area=1.0, center=0.0, width=1.0, x_unit='meV' ) component_collection.append_component(faulty_component) - original_units = {component.name: component.unit for component in component_collection} + original_units = {component.name: component.x_unit for component in component_collection} # EXPECT with pytest.raises(RuntimeError, match=r'Conversion failed.'): - component_collection.convert_unit('eV') + component_collection.convert_x_unit('eV') # Check that all components have their original units for component in component_collection: - assert component.unit == original_units[component.name] + assert component.x_unit == original_units[component.name] def test_set_unit(self, component_collection): # WHEN THEN EXPECT with pytest.raises( AttributeError, - match=r'Unit is read-only. Use convert_unit to change the unit', + match=r'read-only', ): - component_collection.unit = 'eV' + component_collection.x_unit = 'eV' def test_evaluate(self, component_collection): # WHEN @@ -290,7 +290,9 @@ def test_normalize_area_not_finite_area_raises(self, component_collection, area_ def test_normalize_area_non_area_component_warns(self, component_collection): # WHEN - component1 = Polynomial(display_name='TestPolynomial', coefficients=[1, 2, 3], unit='meV') + component1 = Polynomial( + display_name='TestPolynomial', coefficients=[1, 2, 3], x_unit='meV' + ) component_collection.append_component(component1) # THEN EXPECT @@ -374,7 +376,9 @@ def test_contains(self, component_collection): assert lorentzian_component in component_collection # WHEN THEN - fake_component = Gaussian(name='FakeGaussian', area=1.0, center=0.0, width=1.0, unit='meV') + fake_component = Gaussian( + name='FakeGaussian', area=1.0, center=0.0, width=1.0, x_unit='meV' + ) # EXPECT assert fake_component not in component_collection assert 123 not in component_collection # Invalid type @@ -392,13 +396,13 @@ def test_to_dict(self, component_collection): # EXPECT assert model_dict['display_name'] == component_collection.display_name - assert model_dict['unit'] == component_collection.unit + assert model_dict['x_unit'] == component_collection.x_unit assert len(model_dict['components']) == len(component_collection) for comp, comp_dict in zip(component_collection, model_dict['components'], strict=True): assert comp_dict['@class'] == type(comp).__name__ assert comp_dict['display_name'] == comp.display_name - assert comp_dict['unit'] == comp.unit + assert comp_dict['x_unit'] == comp.x_unit def test_from_dict(self, component_collection): # WHEN @@ -414,7 +418,7 @@ def test_from_dict(self, component_collection): for orig_comp, new_comp in zip(component_collection, new_model, strict=True): assert type(new_comp) is type(orig_comp) assert new_comp.display_name == orig_comp.display_name - assert new_comp.unit == orig_comp.unit + assert new_comp.x_unit == orig_comp.x_unit orig_params = orig_comp.get_all_parameters() new_params = new_comp.get_all_parameters() @@ -451,7 +455,7 @@ def test_copy(self, component_collection): # Same type and display name assert type(copied_comp) is type(orig_comp) assert copied_comp.display_name == orig_comp.display_name - assert copied_comp.unit == orig_comp.unit + assert copied_comp.x_unit == orig_comp.x_unit # Parameters are deep-copied and equivalent orig_params = orig_comp.get_all_parameters() diff --git a/tests/unit/easydynamics/sample_model/test_instrument_model.py b/tests/unit/easydynamics/sample_model/test_instrument_model.py index cb716fc4..791dd3f2 100644 --- a/tests/unit/easydynamics/sample_model/test_instrument_model.py +++ b/tests/unit/easydynamics/sample_model/test_instrument_model.py @@ -113,7 +113,7 @@ def test_init_sample_model_as_resolution_model(self, sample_model): 'energy_offset must be a number', ), ( - {'unit': 123}, + {'x_unit': 123}, TypeError, 'unit must be', ), @@ -209,13 +209,10 @@ def test_clear_Q_raises_without_confirm(self, instrument_model): with pytest.raises(ValueError, match='Clearing Q values requires confirmation'): instrument_model.clear_Q() - def test_unit_setter_raises(self, instrument_model): + def test_x_unit_setter_raises(self, instrument_model): # WHEN / THEN / EXPECT - with pytest.raises( - AttributeError, - match=r'Unit is read-only. Use convert_unit to change the unit between allowed types ', - ): - instrument_model.unit = 'meV' + with pytest.raises(AttributeError): + instrument_model.x_unit = 'meV' def test_energy_offset_setter(self, instrument_model): # WHEN @@ -272,16 +269,16 @@ def test_get_energy_offset_no_Q_raises(self, instrument_model): ): instrument_model.get_energy_offset(0) - def test_convert_unit_calls_all_children(self, instrument_model): + def test_convert_x_unit_calls_all_children(self, instrument_model): # WHEN new_unit = 'eV' # THEN # Ensure energy offsets are built before mocking instrument_model._ensure_energy_offsets_current() - # Mock downstream convert_unit calls - instrument_model._background_model.convert_unit = MagicMock() - instrument_model._resolution_model.convert_unit = MagicMock() + # Mock downstream convert_x_unit calls + instrument_model._background_model.convert_x_unit = MagicMock() + instrument_model._resolution_model.convert_x_unit = MagicMock() instrument_model._energy_offset.convert_unit = MagicMock() for offset in instrument_model._energy_offsets: offset.convert_unit = MagicMock() @@ -290,28 +287,28 @@ def test_convert_unit_calls_all_children(self, instrument_model): 'easydynamics.sample_model.instrument_model._validate_unit', return_value=new_unit, ) as mock_validate: - instrument_model.convert_unit(new_unit) + instrument_model.convert_x_unit(new_unit) # EXPECT mock_validate.assert_called_once_with(new_unit) - instrument_model._background_model.convert_unit.assert_called_once_with(new_unit) - instrument_model._resolution_model.convert_unit.assert_called_once_with(new_unit) + instrument_model._background_model.convert_x_unit.assert_called_once_with(new_unit) + instrument_model._resolution_model.convert_x_unit.assert_called_once_with(new_unit) instrument_model._energy_offset.convert_unit.assert_called_once_with(new_unit) for offset in instrument_model._energy_offsets: offset.convert_unit.assert_called_once_with(new_unit) # final state - assert instrument_model.unit == new_unit + assert instrument_model.x_unit == new_unit - def test_convert_unit_None_raises(self, instrument_model): + def test_convert_x_unit_None_raises(self, instrument_model): # WHEN / THEN / EXPECT with pytest.raises( ValueError, match=' must be a valid unit', ): - instrument_model.convert_unit(None) + instrument_model.convert_x_unit(None) def test_fix_resolution_parameters(self, instrument_model): # WHEN @@ -429,7 +426,7 @@ def test_generate_energy_offsets(self, instrument_model): assert len(instrument_model._energy_offsets) == 4 for offset in instrument_model._energy_offsets: assert offset.name == 'energy_offset' - assert offset.unit == instrument_model.unit + assert offset.unit == instrument_model.x_unit assert offset.value == instrument_model.energy_offset.value def test_Q_setter(self, instrument_model_without_Q): @@ -567,7 +564,7 @@ def test_repr_contains_expected_fields(self, instrument_model): # EXPECT assert repr_str.startswith('InstrumentModel(') assert f'unique_name={instrument_model.unique_name!r}' in repr_str - assert f'unit={instrument_model.unit}' in repr_str + assert f'x_unit={instrument_model.x_unit}' in repr_str assert 'Q_len=3' in repr_str assert f'resolution_model={instrument_model._resolution_model!r}' in repr_str assert f'background_model={instrument_model._background_model!r}' in repr_str diff --git a/tests/unit/easydynamics/sample_model/test_model_base.py b/tests/unit/easydynamics/sample_model/test_model_base.py index dfcbd1ad..687f7262 100644 --- a/tests/unit/easydynamics/sample_model/test_model_base.py +++ b/tests/unit/easydynamics/sample_model/test_model_base.py @@ -23,7 +23,7 @@ def model_base(self): area=1.0, center=0.0, width=1.0, - unit='meV', + x_unit='meV', ) component2 = Lorentzian( name='TestLorentzian1Name', @@ -31,7 +31,7 @@ def model_base(self): area=2.0, center=1.0, width=0.5, - unit='meV', + x_unit='meV', ) component_collection = ComponentCollection() component_collection.append_component(component1) @@ -39,7 +39,7 @@ def model_base(self): return ModelBase( display_name='InitModel', components=component_collection, - unit='meV', + x_unit='meV', Q=np.array([1.0, 2.0, 3.0]), ) @@ -49,7 +49,7 @@ def test_init(self, model_base): # EXPECT assert model.display_name == 'InitModel' - assert model.unit == 'meV' + assert model.x_unit == 'meV' assert len(model.components) == 2 np.testing.assert_array_equal(model.Q, np.array([1.0, 2.0, 3.0])) @@ -78,8 +78,8 @@ def test_evaluate_calls_all_component_collections(self, model_base): result = model_base.evaluate(x) # EXPECT - collection1.evaluate.assert_called_once_with(x) - collection2.evaluate.assert_called_once_with(x) + collection1.evaluate.assert_called_once_with(x, output='numpy') + collection2.evaluate.assert_called_once_with(x, output='numpy') np.testing.assert_allclose(result[0], np.array([1.0, 2.0, 3.0])) np.testing.assert_allclose(result[1], np.array([4.0, 5.0, 6.0])) @@ -246,36 +246,36 @@ def test_append_component_invalid_type_raises(self, model_base): with pytest.raises(TypeError, match=' must be '): model_base.append_component('invalid_component') - def test_unit_property(self, model_base): + def test_x_unit_property(self, model_base): # WHEN - unit = model_base.unit + unit = model_base.x_unit # THEN / EXPECT assert unit == 'meV' - def test_unit_setter_raises(self, model_base): + def test_x_unit_setter_raises(self, model_base): # WHEN / THEN / EXPECT - with pytest.raises(AttributeError, match='Use convert_unit to change '): - model_base.unit = 'K' + with pytest.raises(AttributeError): + model_base.x_unit = 'K' - def test_convert_unit(self, model_base): + def test_convert_x_unit(self, model_base): # WHEN - model_base.convert_unit('eV') + model_base.convert_x_unit('eV') # THEN / EXPECT - assert model_base.unit == 'eV' + assert model_base.x_unit == 'eV' for component in model_base.components: - assert component.unit == 'eV' + assert component.x_unit == 'eV' - def test_convert_unit_invalid_raises(self, model_base): + def test_convert_x_unit_invalid_raises(self, model_base): # WHEN / THEN / EXPECT with pytest.raises(UnitError): - model_base.convert_unit('invalid_unit') + model_base.convert_x_unit('invalid_unit') - def test_convert_unit_incorrect_unit_raises(self, model_base): + def test_convert_x_unit_incorrect_unit_raises(self, model_base): # WHEN THEN EXPECT with pytest.raises(TypeError, match=r'Unit must be a string or sc.Unit'): - model_base.convert_unit(123) + model_base.convert_x_unit(123) def test_components_setter(self, model_base): # WHEN diff --git a/tests/unit/easydynamics/sample_model/test_resolution_model.py b/tests/unit/easydynamics/sample_model/test_resolution_model.py index f894daf2..5b7730b4 100644 --- a/tests/unit/easydynamics/sample_model/test_resolution_model.py +++ b/tests/unit/easydynamics/sample_model/test_resolution_model.py @@ -21,14 +21,14 @@ def resolution_model(self): area=1.0, center=0.0, width=1.0, - unit='meV', + x_unit='meV', ) component2 = Lorentzian( display_name='TestLorentzian1', area=2.0, center=1.0, width=0.5, - unit='meV', + x_unit='meV', ) component_collection = ComponentCollection() component_collection.append_component(component1) @@ -36,7 +36,7 @@ def resolution_model(self): return ResolutionModel( display_name='InitModel', components=component_collection, - unit='meV', + x_unit='meV', Q=np.array([1.0, 2.0, 3.0]), ) @@ -48,7 +48,7 @@ def sample_model(self): area=1.0, center=0.0, width=1.0, - unit='meV', + x_unit='meV', ) component2 = Lorentzian( name='TestLorentzian1Name', @@ -56,7 +56,7 @@ def sample_model(self): area=2.0, center=1.0, width=0.5, - unit='meV', + x_unit='meV', ) component_collection = ComponentCollection() component_collection.append_component(component1) @@ -65,7 +65,7 @@ def sample_model(self): return SampleModel( display_name='InitModel', components=component_collection, - unit='meV', + x_unit='meV', Q=np.array([1.0, 2.0, 3.0]), temperature=10.0, ) @@ -76,7 +76,7 @@ def test_init(self, resolution_model): # EXPECT assert model.display_name == 'InitModel' - assert model.unit == 'meV' + assert model.x_unit == 'meV' assert len(model.components) == 2 np.testing.assert_array_equal(model.Q, np.array([1.0, 2.0, 3.0])) @@ -215,7 +215,7 @@ def test_from_sample_model( # EXPECT assert resolution_model.display_name == 'InitModel' - assert resolution_model.unit == 'meV' + assert resolution_model.x_unit == 'meV' assert len(resolution_model.components) == 2 np.testing.assert_array_equal( resolution_model.Q, diff --git a/tests/unit/easydynamics/sample_model/test_sample_model.py b/tests/unit/easydynamics/sample_model/test_sample_model.py index bbbb30b9..ecadee7a 100644 --- a/tests/unit/easydynamics/sample_model/test_sample_model.py +++ b/tests/unit/easydynamics/sample_model/test_sample_model.py @@ -28,7 +28,7 @@ def sample_model(self): area=1.0, center=0.0, width=1.0, - unit='meV', + x_unit='meV', ) component2 = Lorentzian( name='TestLorentzian1Name', @@ -36,7 +36,7 @@ def sample_model(self): area=2.0, center=1.0, width=0.5, - unit='meV', + x_unit='meV', ) component_collection = ComponentCollection() component_collection.append_component(component1) @@ -50,7 +50,7 @@ def sample_model(self): display_name='InitModel', components=component_collection, diffusion_models=diffusion_model, - unit='meV', + x_unit='meV', Q=np.array([1.0, 2.0, 3.0]), temperature=10.0, ) @@ -62,7 +62,7 @@ def test_init(self, sample_model): # EXPECT assert model.display_name == 'InitModel' - assert model.unit == 'meV' + assert model.x_unit == 'meV' assert len(model.components) == 2 assert isinstance(model.diffusion_models, list) assert len(model.diffusion_models) == 1 @@ -402,12 +402,12 @@ def test_evaluate_calls_dbf(self, sample_model): energy=x, temperature=sample_model.temperature, divide_by_temperature=sample_model.normalize_detailed_balance, - energy_unit=sample_model.unit, + energy_unit=sample_model.x_unit, ) # Check that evaluate was called on each component - collection1.evaluate.assert_called_once_with(x) - collection2.evaluate.assert_called_once_with(x) + collection1.evaluate.assert_called_once_with(x, output='numpy') + collection2.evaluate.assert_called_once_with(x, output='numpy') # Check that DBF was applied elementwise np.testing.assert_allclose(result[0], np.array([1.0, 2.0, 3.0]) * 10.0) @@ -452,8 +452,8 @@ def test_evaluate_doesnt_call_dbf_when_disabled( mock_dbf.assert_not_called() # Check that evaluate was called on each component - collection1.evaluate.assert_called_once_with(x) - collection2.evaluate.assert_called_once_with(x) + collection1.evaluate.assert_called_once_with(x, output='numpy') + collection2.evaluate.assert_called_once_with(x, output='numpy') # Check that results were not modified by DBF np.testing.assert_allclose(result[0], np.array([1.0, 2.0, 3.0])) @@ -531,7 +531,7 @@ def test_repr(self, sample_model): # THEN / EXPECT assert 'SampleModel' in repr_str - assert 'unit=' in repr_str + assert 'x_unit=' in repr_str assert 'Q = ' in repr_str assert 'components' in repr_str assert 'diffusion_models' in repr_str From 5a2d9cec163f4ad990bcc227c5a773636303f5d7 Mon Sep 17 00:00:00 2001 From: henrikjacobsenfys Date: Tue, 23 Jun 2026 05:58:01 +0200 Subject: [PATCH 02/17] claude review --- src/easydynamics/convolution/convolution.py | 10 +- .../convolution/convolution_base.py | 14 +- .../convolution/numerical_convolution_base.py | 10 +- .../sample_model/background_model.py | 19 ++ .../sample_model/component_collection.py | 229 +++++++++++++++++- .../components/expression_component.py | 39 +++ .../sample_model/instrument_model.py | 137 +++++++++++ src/easydynamics/sample_model/model_base.py | 221 ++++++++++++++++- .../convolution/test_convolution_base.py | 10 +- .../test_numerical_convolution_base.py | 4 +- .../sample_model/components/test_gaussian.py | 59 +++++ .../sample_model/test_component_collection.py | 23 ++ .../sample_model/test_model_base.py | 27 +++ 13 files changed, 771 insertions(+), 31 deletions(-) diff --git a/src/easydynamics/convolution/convolution.py b/src/easydynamics/convolution/convolution.py index 62f08cd2..aee855cd 100644 --- a/src/easydynamics/convolution/convolution.py +++ b/src/easydynamics/convolution/convolution.py @@ -60,7 +60,7 @@ def __init__( temperature: Parameter | Numeric | None = None, temperature_unit: str | sc.Unit = 'K', detailed_balance_settings: DetailedBalanceSettings | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', display_name: str | None = 'MyConvolution', unique_name: str | None = None, ) -> None: @@ -85,8 +85,8 @@ def __init__( The unit of the temperature parameter. detailed_balance_settings : DetailedBalanceSettings | None, default=None The settings for detailed balance. If None, default settings will be used. - unit : str | sc.Unit, default='meV' - The unit of the energy. + x_unit : str | sc.Unit, default='meV' + The unit of the energy axis. display_name : str | None, default='MyConvolution' Display name of the model. unique_name : str | None, default=None @@ -103,7 +103,7 @@ def __init__( temperature=temperature, temperature_unit=temperature_unit, detailed_balance_settings=detailed_balance_settings, - unit=unit, + x_unit=x_unit, display_name=display_name, unique_name=unique_name, ) @@ -276,7 +276,7 @@ def _set_convolvers(self) -> None: temperature=self.temperature, temperature_unit=self._temperature_unit, detailed_balance_settings=self.detailed_balance_settings, - unit=self.x_unit, + x_unit=self.x_unit, ) else: self._numerical_convolver = None diff --git a/src/easydynamics/convolution/convolution_base.py b/src/easydynamics/convolution/convolution_base.py index 22a92d0f..9df68c5f 100644 --- a/src/easydynamics/convolution/convolution_base.py +++ b/src/easydynamics/convolution/convolution_base.py @@ -23,7 +23,7 @@ def __init__( energy: np.ndarray | sc.Variable, sample_components: ComponentCollection | ModelComponent | None = None, resolution_components: ComponentCollection | ModelComponent | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', energy_offset: Numeric | Parameter = 0.0, display_name: str | None = 'MyConvolution', unique_name: str | None = None, @@ -39,8 +39,8 @@ def __init__( The sample model to be convolved. resolution_components : ComponentCollection | ModelComponent | None, default=None The resolution model to convolve with. - unit : str | sc.Unit, default='meV' - The unit of the energy. + x_unit : str | sc.Unit, default='meV' + The unit of the energy axis. energy_offset : Numeric | Parameter, default=0.0 The energy offset applied to the convolution. display_name : str | None, default='MyConvolution' @@ -58,7 +58,7 @@ def __init__( """ super().__init__( - x_unit=unit, + x_unit=x_unit, display_name=display_name, unique_name=unique_name, ) @@ -70,10 +70,12 @@ def __init__( raise TypeError(f'Energy must be a numpy ndarray or a scipp Variable. Got {energy}') if isinstance(energy, np.ndarray): - energy = sc.array(dims=['energy'], values=energy, unit=unit) + energy = sc.array(dims=['energy'], values=energy, unit=x_unit) if isinstance(energy_offset, Numeric): - energy_offset = Parameter(name='energy_offset', value=float(energy_offset), unit=unit) + energy_offset = Parameter( + name='energy_offset', value=float(energy_offset), unit=x_unit + ) if not isinstance(energy_offset, Parameter): raise TypeError('Energy_offset must be a number or a Parameter.') diff --git a/src/easydynamics/convolution/numerical_convolution_base.py b/src/easydynamics/convolution/numerical_convolution_base.py index c0a89002..85ae8d3d 100644 --- a/src/easydynamics/convolution/numerical_convolution_base.py +++ b/src/easydynamics/convolution/numerical_convolution_base.py @@ -43,7 +43,7 @@ def __init__( temperature: Parameter | Numeric | None = None, temperature_unit: str | sc.Unit = 'K', detailed_balance_settings: DetailedBalanceSettings | None = None, - unit: str | sc.Unit = 'meV', + x_unit: str | sc.Unit = 'meV', display_name: str | None = 'MyConvolution', unique_name: str | None = None, ) -> None: @@ -68,8 +68,8 @@ def __init__( The unit of the temperature parameter. detailed_balance_settings : DetailedBalanceSettings | None, default=None The settings for detailed balance. If None, default settings will be used. - unit : str | sc.Unit, default='meV' - The unit of the energy. + x_unit : str | sc.Unit, default='meV' + The unit of the energy axis. display_name : str | None, default='MyConvolution' Display name of the model. unique_name : str | None, default=None @@ -85,7 +85,7 @@ def __init__( energy=energy, sample_components=sample_components, resolution_components=resolution_components, - unit=unit, + x_unit=x_unit, energy_offset=energy_offset, display_name=display_name, unique_name=unique_name, @@ -440,7 +440,7 @@ def __repr__(self) -> str: f'energy=array of shape {self.energy.values.shape},\n ' f'sample_components={self.sample_components!r}, \n' f'resolution_components={self.resolution_components!r},\n ' - f'unit={self.x_unit}, ' + f'x_unit={self.x_unit}, ' f'upsample_factor={self.upsample_factor}, ' f'extension_factor={self.extension_factor}, ' f'temperature={self.temperature}, ' diff --git a/src/easydynamics/sample_model/background_model.py b/src/easydynamics/sample_model/background_model.py index a34e3714..a704f577 100644 --- a/src/easydynamics/sample_model/background_model.py +++ b/src/easydynamics/sample_model/background_model.py @@ -23,6 +23,25 @@ def __init__( components: ModelComponent | ComponentCollection | None = None, Q: Q_type | None = None, ) -> None: + """ + Initialize the BackgroundModel. + + Parameters + ---------- + display_name : str | None, default='MyBackgroundModel' + Display name of the model. + unique_name : str | None, default=None + Unique name of the model. If None, a unique name will be generated. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis (energy, Q, etc.). + y_unit : str | sc.Unit, default='dimensionless' + Unit of the model output (intensity). + components : ModelComponent | ComponentCollection | None, default=None + Template components of the model. If None, no components are added. These components + are copied into ComponentCollections for each Q value. + Q : Q_type | None, default=None + Q values for the model. If None, Q is not set. + """ super().__init__( display_name=display_name, unique_name=unique_name, diff --git a/src/easydynamics/sample_model/component_collection.py b/src/easydynamics/sample_model/component_collection.py index cc0918eb..7e4aa647 100644 --- a/src/easydynamics/sample_model/component_collection.py +++ b/src/easydynamics/sample_model/component_collection.py @@ -22,7 +22,31 @@ class ComponentCollection(EasyDynamicsList, EasyDynamicsModelBase): """ - Collection of model components whose evaluate() results are summed. + Collection of model components. + + Examples + -------- + Create a ComponentCollection with two components: + >>> import easydynamics.sample_model as sm + >>> component1 = sm.Gaussian(name='Gaussian1', area=1.0, width=1.0) + >>> component2 = sm.Lorentzian(name='Lorentzian1', area=2.0, width=0.5) + >>> collection = sm.ComponentCollection(components=[component1, component2]) + + Append a component to the collection: + >>> component3 = sm.Gaussian(name='Gaussian2', area=0.5, width=0.8) + >>> collection.append(component3) + + Evaluate the collection at a given energy axis: + >>> import numpy as np + >>> x = np.linspace(-5, 5, 100) + >>> values = collection.evaluate(x) + + Remove a component by name: + >>> collection.remove('Gaussian1') + + List component names: + >>> collection.list_component_names() + ['Lorentzian1', 'Gaussian2'] """ def __init__( @@ -34,6 +58,29 @@ def __init__( display_name: str | None = None, unique_name: str | None = None, ) -> None: + """ + Initialize a new ComponentCollection. + + Parameters + ---------- + components : ModelComponent | list[ModelComponent] | None, default=None + Initial model components to add to the ComponentCollection. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis (energy, Q, etc.). + y_unit : str | sc.Unit, default='dimensionless' + Unit of the model output (intensity). + name : str, default='ComponentCollection' + Name of the collection. + display_name : str | None, default=None + Display name of the collection. + unique_name : str | None, default=None + Unique name of the collection. + + Raises + ------ + TypeError + If components is not a list of ModelComponent. + """ if components is None: components = [] if isinstance(components, ModelComponent): @@ -73,18 +120,56 @@ def __init__( @property def is_empty(self) -> bool: + """ + Check if the ComponentCollection has no components. + + Returns + ------- + bool + True if the collection has no components, False otherwise. + """ return not self @is_empty.setter def is_empty(self, _value: bool) -> None: - raise AttributeError('is_empty is a read-only property.') + """ + Is_empty is a read-only property that indicates whether the collection has components. + + Parameters + ---------- + _value : bool + The value to set (ignored). + + Raises + ------ + AttributeError + Always raised since is_empty is read-only. + """ + raise AttributeError( + 'is_empty is a read-only property that indicates ' + 'whether the collection has components.' + ) # ------------------------------------------------------------------ # Unit conversion # ------------------------------------------------------------------ def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: - """Convert x-axis unit on all contained components.""" + """ + Convert the x-axis unit of the ComponentCollection and all its components. + + Parameters + ---------- + new_x_unit : str | sc.Unit + The target x-axis unit to convert to. + + Raises + ------ + TypeError + If new_x_unit is not a string or sc.Unit. + Exception + If any component cannot be converted to the specified unit. + """ if not isinstance(new_x_unit, (str, sc.Unit)): raise TypeError(f'x_unit must be a string or sc.Unit, got {type(new_x_unit).__name__}') @@ -102,7 +187,21 @@ def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: raise e def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: - """Convert y-axis unit on all contained components.""" + """ + Convert the y-axis unit of the ComponentCollection and all its components. + + Parameters + ---------- + new_y_unit : str | sc.Unit + The target y-axis unit to convert to. + + Raises + ------ + TypeError + If new_y_unit is not a string or sc.Unit. + Exception + If any component cannot be converted to the specified unit. + """ if not isinstance(new_y_unit, (str, sc.Unit)): raise TypeError(f'y_unit must be a string or sc.Unit, got {type(new_y_unit).__name__}') @@ -124,6 +223,16 @@ def convert_y_unit(self, new_y_unit: str | sc.Unit) -> None: # ------------------------------------------------------------------ def append_component(self, component: ModelComponent | ComponentCollection) -> None: + """ + Append a model component or the components from another ComponentCollection to this + ComponentCollection. + + Parameters + ---------- + component : ModelComponent | ComponentCollection + The component to append. If a ComponentCollection is provided, all of its components + will be appended. + """ if isinstance(component, ComponentCollection): self.extend(component) else: @@ -131,10 +240,28 @@ def append_component(self, component: ModelComponent | ComponentCollection) -> N self._warn_if_duplicate_names() def list_component_names(self) -> list[str]: + """ + List the names of all components in the model. + + Returns + ------- + list[str] + List of names of the components in the collection. + """ return [component.name for component in self] def normalize_area(self) -> None: - """Normalize areas of all components so they sum to 1.""" + """ + Normalize the areas of all components so they sum to 1. + + This is useful for convolutions. + + Raises + ------ + ValueError + If there are no components in the model or if the total area is zero or not finite, + which would prevent normalization. + """ if not self: raise ValueError('No components in the model to normalize.') @@ -167,6 +294,14 @@ def normalize_area(self) -> None: # ------------------------------------------------------------------ def get_all_variables(self) -> list[DescriptorBase]: + """ + Get all parameters from all model components. + + Returns + ------- + list[DescriptorBase] + List of parameters in the collection. + """ return [var for component in self for var in component.get_all_variables()] def evaluate( @@ -174,7 +309,21 @@ def evaluate( x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, output: str = 'numpy', ) -> np.ndarray | sc.Variable: - """Evaluate the sum of all component outputs at x.""" + """ + Evaluate the sum of all components. + + Parameters + ---------- + x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray + Energy axis. + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. + + Returns + ------- + np.ndarray | sc.Variable + Evaluated model values. + """ if not self: return np.zeros_like(x) return sum(component.evaluate(x, output=output) for component in self) @@ -185,7 +334,32 @@ def evaluate_component( name: str, output: str = 'numpy', ) -> np.ndarray | sc.Variable: - """Evaluate a single component by name.""" + """ + Evaluate a single component by name. + + Parameters + ---------- + x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray + Energy axis. + name : str + Component name. + output : str, default='numpy' + 'numpy' returns np.ndarray; 'scipp' returns sc.Variable with y_unit. + + Raises + ------ + ValueError + If there are no components in the model. + TypeError + If name is not a string. + KeyError + If no component with the given name exists in the collection. + + Returns + ------- + np.ndarray | sc.Variable + Evaluated values for the specified component. + """ if not self: raise ValueError('No components in the model to evaluate.') if not isinstance(name, str): @@ -196,10 +370,12 @@ def evaluate_component( return matches[0].evaluate(x, output=output) def fix_all_parameters(self) -> None: + """Fix all free parameters in the model.""" for param in self.get_fittable_parameters(): param.fixed = True def free_all_parameters(self) -> None: + """Free all fixed parameters in the model.""" for param in self.get_fittable_parameters(): param.fixed = False @@ -208,6 +384,7 @@ def free_all_parameters(self) -> None: # ------------------------------------------------------------------ def _warn_if_duplicate_names(self) -> None: + """Warn if any two components share the same name.""" names = [c.name for c in self] seen: set[str] = set() dups: set[str] = set() @@ -228,6 +405,14 @@ def _warn_if_duplicate_names(self) -> None: # ------------------------------------------------------------------ def __repr__(self) -> str: + """ + Return a string representation of the ComponentCollection. + + Returns + ------- + str + String representation of the ComponentCollection. + """ comp_names = ', '.join(c.name for c in self) or 'No components' return ( f"ComponentCollection(name='{self.name}', " @@ -236,6 +421,14 @@ def __repr__(self) -> str: ) def to_dict(self) -> dict: + """ + Serialise the ComponentCollection to a dictionary. + + Returns + ------- + dict + Dictionary representation of the ComponentCollection. + """ return { '@module': self.__class__.__module__, '@class': self.__class__.__name__, @@ -248,6 +441,20 @@ def to_dict(self) -> dict: @classmethod def from_dict(cls, obj_dict: dict) -> ComponentCollection: + """ + Deserialise a ComponentCollection from its dictionary representation. + + Parameters + ---------- + obj_dict : dict + Dictionary representation of the ComponentCollection, as produced by to_dict(). + + Returns + ------- + ComponentCollection + The deserialised ComponentCollection. + """ + def deserialise_component(d: dict) -> ModelComponent: module = importlib.import_module(d['@module']) klass = getattr(module, d['@class']) @@ -264,4 +471,12 @@ def deserialise_component(d: dict) -> ModelComponent: ) def __copy__(self) -> ComponentCollection: + """ + Create a deep copy of the ComponentCollection. + + Returns + ------- + ComponentCollection + A deep copy of the ComponentCollection. + """ return self.from_dict(self.to_dict()) diff --git a/src/easydynamics/sample_model/components/expression_component.py b/src/easydynamics/sample_model/components/expression_component.py index 7b5a2679..eaa2e95a 100644 --- a/src/easydynamics/sample_model/components/expression_component.py +++ b/src/easydynamics/sample_model/components/expression_component.py @@ -66,6 +66,45 @@ def __init__( display_name: str | None = None, unique_name: str | None = None, ) -> None: + """ + Initialize the ExpressionComponent. + + Parameters + ---------- + expression : str + The symbolic expression as a string. Must contain 'x' as the independent variable. + parameters : dict[str, Numeric] | None, default=None + Dictionary of parameter names and their initial values. + x_unit : str | sc.Unit, default='meV' + Unit of the x-axis. + y_unit : str | sc.Unit, default='dimensionless' + Unit of the y-axis (output). + name : str, default='Expression' + Name of the component for indexing. + display_name : str | None, default=None + Display name for the component. + unique_name : str | None, default=None + Unique name for the component. + + Raises + ------ + ValueError + If the expression is invalid or does not contain 'x'. + TypeError + If any parameter value is not numeric. + + Examples + -------- + >>> expr = ExpressionComponent( + ... 'A * exp(-(x - x0)**2 / (2*sigma**2))', + ... parameters={'A': 10, 'x0': 0, 'sigma': 1}, + ... x_unit='meV', + ... display_name='Gaussian Peak', + ... ) + + >>> expr.A = 5 + >>> y = expr.evaluate(x) + """ super().__init__( x_unit=x_unit, y_unit=y_unit, diff --git a/src/easydynamics/sample_model/instrument_model.py b/src/easydynamics/sample_model/instrument_model.py index 06bc86f5..d0b8d023 100644 --- a/src/easydynamics/sample_model/instrument_model.py +++ b/src/easydynamics/sample_model/instrument_model.py @@ -35,6 +35,36 @@ def __init__( energy_offset: Numeric | None = None, x_unit: str | sc.Unit = 'meV', ) -> None: + """ + Initialize an InstrumentModel. + + Parameters + ---------- + display_name : str, default='MyInstrumentModel' + The display name of the InstrumentModel. + unique_name : str | None, default=None + The unique name of the InstrumentModel. + Q : Q_type | None, default=None + The Q values where the instrument is modelled. + resolution_model : ResolutionModel | SampleModel | None, default=None + The resolution model of the instrument. If a SampleModel it will be converted to a + ResolutionModel. If None, an empty resolution model is created and no resolution + convolution is carried out. + background_model : BackgroundModel | None, default=None + The background model of the instrument. If None, an empty background model is created, + and the background evaluates to 0. + energy_offset : Numeric | None, default=None + Template energy offset of the instrument. Will be copied to each Q value. If None, the + energy offset will be 0. + x_unit : str | sc.Unit, default='meV' + The unit of the energy axis. + + Raises + ------ + TypeError + If resolution_model is not a ResolutionModel or None, or if background_model is not a + BackgroundModel or None, or if energy_offset is not a number or None. + """ super().__init__( display_name=display_name, unique_name=unique_name, @@ -87,10 +117,31 @@ def __init__( @property def resolution_model(self) -> ResolutionModel: + """ + Get the resolution model of the instrument. + + Returns + ------- + ResolutionModel + The resolution model of the instrument. + """ return self._resolution_model @resolution_model.setter def resolution_model(self, value: ResolutionModel | SampleModel) -> None: + """ + Set the resolution model of the instrument. + + Parameters + ---------- + value : ResolutionModel | SampleModel + The new resolution model of the instrument. + + Raises + ------ + TypeError + If value is not a ResolutionModel or SampleModel. + """ if not isinstance(value, (ResolutionModel, SampleModel)): raise TypeError( f'resolution_model must be a ResolutionModel or SampleModel, ' @@ -103,10 +154,31 @@ def resolution_model(self, value: ResolutionModel | SampleModel) -> None: @property def background_model(self) -> BackgroundModel: + """ + Get the background model of the instrument. + + Returns + ------- + BackgroundModel + The background model of the instrument. + """ return self._background_model @background_model.setter def background_model(self, value: BackgroundModel) -> None: + """ + Set the background model of the instrument. + + Parameters + ---------- + value : BackgroundModel + The new background model of the instrument. + + Raises + ------ + TypeError + If value is not a BackgroundModel. + """ if not isinstance(value, BackgroundModel): raise TypeError( f'background_model must be a BackgroundModel, got {type(value).__name__}' @@ -116,10 +188,35 @@ def background_model(self, value: BackgroundModel) -> None: @property def Q(self) -> np.ndarray | None: + """ + Get the Q values of the InstrumentModel. + + Returns + ------- + np.ndarray | None + The Q values of the InstrumentModel, or None if not set. + """ return self._Q @Q.setter def Q(self, value: Q_type | None) -> None: + """ + Set the Q values of the InstrumentModel. + + If Q is already set, it raises an error if the new Q values are not similar to the old ones + to prevent accidental changes to the background and resolution models. To change Q values, + first run clear_Q(). + + Parameters + ---------- + value : Q_type | None + The new Q values to set. If None, Q values are not changed. + + Raises + ------ + ValueError + If the new Q values are not similar to the old ones when Q is not None. + """ if value is None: return old_Q = self._Q @@ -138,10 +235,29 @@ def Q(self, value: Q_type | None) -> None: @property def x_unit(self) -> str | sc.Unit: + """ + Get the x-axis unit of the InstrumentModel. + + Returns + ------- + str | sc.Unit + The x-axis unit of the InstrumentModel. + """ return self._x_unit @x_unit.setter def x_unit(self, _: str) -> None: + """ + x_unit is read-only and cannot be set directly. + + Use convert_x_unit to change the unit between allowed types or create a new InstrumentModel + with the desired unit. + + Raises + ------ + AttributeError + Always, as x_unit is read-only. + """ raise AttributeError( f'x_unit is read-only. Use convert_x_unit to change the unit between allowed types ' f'or create a new {self.__class__.__name__} with the desired unit.' @@ -149,10 +265,31 @@ def x_unit(self, _: str) -> None: @property def energy_offset(self) -> Parameter: + """ + Get the template energy offset of the instrument. + + Returns + ------- + Parameter + The energy offset Parameter. Each Q value gets its own copy via get_energy_offset(). + """ return self._energy_offset @energy_offset.setter def energy_offset(self, value: Numeric) -> None: + """ + Set the template energy offset value, propagating to all Q-specific offsets. + + Parameters + ---------- + value : Numeric + The new energy offset value in x_unit. + + Raises + ------ + TypeError + If value is not a number. + """ if not isinstance(value, Numeric): raise TypeError(f'energy_offset must be a number, got {type(value).__name__}') self._energy_offset.value = value diff --git a/src/easydynamics/sample_model/model_base.py b/src/easydynamics/sample_model/model_base.py index a2a96571..c6552040 100644 --- a/src/easydynamics/sample_model/model_base.py +++ b/src/easydynamics/sample_model/model_base.py @@ -31,6 +31,30 @@ def __init__( components: ModelComponent | ComponentCollection | None = None, Q: Q_type | None = None, ) -> None: + """ + Initialize the ModelBase. + + Parameters + ---------- + display_name : str, default='MyModelBase' + Display name of the model. + unique_name : str | None, default=None + Unique name of the model. If None, a unique name will be generated. + x_unit : str | sc.Unit | None, default='meV' + Unit of the x-axis (energy, Q, etc.). + y_unit : str | sc.Unit, default='dimensionless' + Unit of the model output (intensity). + components : ModelComponent | ComponentCollection | None, default=None + Template components of the model. If None, no components are added. These components + are copied into ComponentCollections for each Q value. + Q : Q_type | None, default=None + Q values for the model. If None, Q is not set. + + Raises + ------ + TypeError + If components is not a ModelComponent or ComponentCollection. + """ super().__init__( x_unit=x_unit, y_unit=y_unit, @@ -58,7 +82,27 @@ def evaluate( x: Numeric | list | np.ndarray | sc.Variable | sc.DataArray, output: str = 'numpy', ) -> list[np.ndarray] | list[sc.Variable]: - """Evaluate the model at all Q for the given x values.""" + """ + Evaluate the sample model at all Q for the given x values. + + Parameters + ---------- + x : Numeric | list | np.ndarray | sc.Variable | sc.DataArray + Energy axis values to evaluate the model at. + output : str, default='numpy' + 'numpy' returns np.ndarray per Q; 'scipp' returns sc.Variable per Q. + + Raises + ------ + ValueError + If there are no components in the model to evaluate. + + Returns + ------- + list[np.ndarray] | list[sc.Variable] + A list of arrays containing the evaluated model values for each Q. The length of the + list will match the number of Q values in the model. + """ self._ensure_component_collections_current() if not self._component_collections: raise ValueError('No components in the model to evaluate.') @@ -70,14 +114,31 @@ def evaluate( # Component management # ------------------------------------------------------------------ def append_component(self, component: ModelComponent | ComponentCollection) -> None: + """ + Append a ModelComponent or ComponentCollection to the SampleModel. + + Parameters + ---------- + component : ModelComponent | ComponentCollection + The ModelComponent or ComponentCollection to append. + """ self._components.append_component(component) self._on_components_change() def remove_component(self, name: str) -> None: + """ + Remove a ModelComponent from the SampleModel by its name. + + Parameters + ---------- + name : str + The name of the ModelComponent to remove. + """ self._components.pop(name) self._on_components_change() def clear_components(self) -> None: + """Clear all ModelComponents from the SampleModel.""" self._components.clear() self._on_components_change() @@ -87,10 +148,31 @@ def clear_components(self) -> None: @property def components(self) -> list[ModelComponent]: + """ + Get the components of the SampleModel. + + Returns + ------- + list[ModelComponent] + The components of the SampleModel. + """ return self._components @components.setter def components(self, value: ModelComponent | ComponentCollection | None) -> None: + """ + Set the components of the SampleModel. + + Parameters + ---------- + value : ModelComponent | ComponentCollection | None + The new components to set. If None, all components will be cleared. + + Raises + ------ + TypeError + If value is not a ModelComponent, ComponentCollection, or None. + """ if not isinstance(value, (ModelComponent, ComponentCollection, type(None))): raise TypeError('Components must be a ModelComponent or a ComponentCollection') @@ -100,14 +182,46 @@ def components(self, value: ModelComponent | ComponentCollection | None) -> None @property def component_collections_is_dirty(self) -> bool: + """ + Return whether component collections need to be rebuilt before use. + + Returns + ------- + bool + ``True`` if component collections have not been built yet or are stale. + """ return self._component_collections_is_dirty @property def Q(self) -> np.ndarray | None: + """ + Get the Q values of the SampleModel. + + Returns + ------- + np.ndarray | None + The Q values of the SampleModel, or None if not set. + """ return self._Q @Q.setter def Q(self, value: Q_type | None) -> None: + """ + Set the Q values of the SampleModel. + + If Q is already set, it throws an error if the new Q values are not similar to the old + ones. To change Q values, first run clear_Q(). + + Parameters + ---------- + value : Q_type | None + The new Q values to set. If None, Q values are not changed. + + Raises + ------ + ValueError + If the new Q values are not similar to the old ones when Q is already set. + """ if value is None: return old_Q = self._Q @@ -125,6 +239,20 @@ def Q(self, value: Q_type | None) -> None: ) def clear_Q(self, confirm: bool = False) -> None: + """ + Clear the Q values of the SampleModel, removing all component collections and their + associated Parameters. + + Parameters + ---------- + confirm : bool, default=False + Confirmation to clear Q values. + + Raises + ------ + ValueError + If confirm is not True. + """ if not confirm: raise ValueError( 'Clearing Q values requires confirmation. Set confirm=True to proceed.' @@ -137,6 +265,21 @@ def clear_Q(self, confirm: bool = False) -> None: # ------------------------------------------------------------------ def convert_x_unit(self, unit: str | sc.Unit) -> None: + """ + Convert the x-axis unit of all components in the model. + + Parameters + ---------- + unit : str | sc.Unit + The new x-axis unit to convert to. + + Raises + ------ + TypeError + If the provided unit is not a string or sc.Unit. + Exception + If the provided unit is not compatible with the current unit. + """ if not isinstance(unit, (str, sc.Unit)): raise TypeError(f'Unit must be a string or sc.Unit, got {type(unit).__name__}') @@ -155,6 +298,21 @@ def convert_x_unit(self, unit: str | sc.Unit) -> None: self._on_components_change() def convert_y_unit(self, unit: str | sc.Unit) -> None: + """ + Convert the y-axis unit of all components in the model. + + Parameters + ---------- + unit : str | sc.Unit + The new y-axis unit to convert to. + + Raises + ------ + TypeError + If the provided unit is not a string or sc.Unit. + Exception + If the provided unit is not compatible with the current unit. + """ if not isinstance(unit, (str, sc.Unit)): raise TypeError(f'Unit must be a string or sc.Unit, got {type(unit).__name__}') @@ -173,14 +331,40 @@ def convert_y_unit(self, unit: str | sc.Unit) -> None: self._on_components_change() def fix_all_parameters(self) -> None: + """Fix all Parameters in all ComponentCollections.""" for par in self.get_all_variables(): par.fixed = True def free_all_parameters(self) -> None: + """Free all Parameters in all ComponentCollections.""" for par in self.get_all_variables(): par.fixed = False def get_all_variables(self, Q_index: int | None = None) -> list[Parameter]: + """ + Get all Parameters and Descriptors from all ComponentCollections in the ModelBase. + + Ignores the Parameters and Descriptors in self._components as these are just templates. + + Parameters + ---------- + Q_index : int | None, default=None + If None, get variables for all ComponentCollections. If int, get variables for the + ComponentCollection at this index. + + Raises + ------ + TypeError + If Q_index is not an int or None. + IndexError + If Q_index is out of bounds for the number of ComponentCollections. + + Returns + ------- + list[Parameter] + A list of all Parameters and Descriptors from the ComponentCollections in the + ModelBase. + """ self._ensure_component_collections_current() if Q_index is None: all_vars = [ @@ -200,6 +384,26 @@ def get_all_variables(self, Q_index: int | None = None) -> list[Parameter]: return all_vars def get_component_collection(self, Q_index: int) -> ComponentCollection: + """ + Get the ComponentCollection at the given Q index. + + Parameters + ---------- + Q_index : int + The index of the desired ComponentCollection. + + Raises + ------ + TypeError + If Q_index is not an int. + IndexError + If Q_index is out of bounds for the number of ComponentCollections. + + Returns + ------- + ComponentCollection + The ComponentCollection at the given Q index. + """ self._ensure_component_collections_current() if not isinstance(Q_index, int): raise TypeError(f'Q_index must be an int, got {type(Q_index).__name__}') @@ -211,6 +415,7 @@ def get_component_collection(self, Q_index: int) -> ComponentCollection: return self._component_collections[Q_index] def normalize_area(self) -> None: + """Normalize the area of the model across all Q values.""" self._ensure_component_collections_current() for collection in self._component_collections: collection.normalize_area() @@ -220,11 +425,15 @@ def normalize_area(self) -> None: # ------------------------------------------------------------------ def _ensure_component_collections_current(self) -> None: + """ + Rebuild component collections if any dependency has changed since they were last built. + """ if self._component_collections_is_dirty: self._generate_component_collections() self._component_collections_is_dirty = False def _generate_component_collections(self) -> None: + """Generate ComponentCollections for each Q value.""" if self.Q is None: self._component_collections = [] return @@ -234,9 +443,11 @@ def _generate_component_collections(self) -> None: self._component_collections.append(copy(self._components)) def _on_Q_change(self) -> None: + """Handle changes to the Q values.""" self._component_collections_is_dirty = True def _on_components_change(self) -> None: + """Handle changes to the components.""" self._component_collections_is_dirty = True # ------------------------------------------------------------------ @@ -244,6 +455,14 @@ def _on_components_change(self) -> None: # ------------------------------------------------------------------ def __repr__(self) -> str: + """ + Return a string representation of the ModelBase. + + Returns + ------- + str + A string representation of the ModelBase. + """ return ( f'{self.__class__.__name__}(unique_name={self.unique_name}, ' f'x_unit={self.x_unit}), Q = {self.Q}, components = {self.components}' diff --git a/tests/unit/easydynamics/convolution/test_convolution_base.py b/tests/unit/easydynamics/convolution/test_convolution_base.py index 41ab5933..a06cdbf5 100644 --- a/tests/unit/easydynamics/convolution/test_convolution_base.py +++ b/tests/unit/easydynamics/convolution/test_convolution_base.py @@ -78,7 +78,7 @@ def test_init_energy_numerical_none_offset(self): 'energy': 'invalid', 'sample_components': ComponentCollection(), 'resolution_components': ComponentCollection(), - 'unit': 'meV', + 'x_unit': 'meV', 'energy_offset': 0, }, 'Energy must be', @@ -88,7 +88,7 @@ def test_init_energy_numerical_none_offset(self): 'energy': np.linspace(-10, 10, 100), 'sample_components': 'invalid', 'resolution_components': ComponentCollection(), - 'unit': 'meV', + 'x_unit': 'meV', 'energy_offset': 0, }, ( @@ -101,7 +101,7 @@ def test_init_energy_numerical_none_offset(self): 'energy': np.linspace(-10, 10, 100), 'sample_components': ComponentCollection(), 'resolution_components': 'invalid', - 'unit': 'meV', + 'x_unit': 'meV', 'energy_offset': 0, }, ( @@ -114,7 +114,7 @@ def test_init_energy_numerical_none_offset(self): 'energy': np.linspace(-10, 10, 100), 'sample_components': ComponentCollection(), 'resolution_components': ComponentCollection(), - 'unit': 123, + 'x_unit': 123, 'energy_offset': 0, }, 'unit must be ', @@ -124,7 +124,7 @@ def test_init_energy_numerical_none_offset(self): 'energy': np.linspace(-10, 10, 100), 'sample_components': ComponentCollection(), 'resolution_components': ComponentCollection(), - 'unit': 'meV', + 'x_unit': 'meV', 'energy_offset': 'invalid', }, 'Energy_offset must be ', diff --git a/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py b/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py index 08b12cb4..e3a57981 100644 --- a/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py +++ b/tests/unit/easydynamics/convolution/test_numerical_convolution_base.py @@ -79,7 +79,7 @@ def test_init_with_custom_parameters(self): detailed_balance_settings=detailed_balance_settings, temperature=temperature, temperature_unit=temperature_unit, - unit=unit, + x_unit=unit, ) # EXPECT @@ -643,7 +643,7 @@ def test_repr(self, default_numerical_convolution_base): assert 'resolution_components=' in repr_str # Important parameters - assert 'unit=meV' in repr_str + assert 'x_unit=meV' in repr_str assert 'upsample_factor=5' in repr_str assert 'extension_factor=0.2' in repr_str assert 'temperature=None' in repr_str diff --git a/tests/unit/easydynamics/sample_model/components/test_gaussian.py b/tests/unit/easydynamics/sample_model/components/test_gaussian.py index cb1f796c..0ceec951 100644 --- a/tests/unit/easydynamics/sample_model/components/test_gaussian.py +++ b/tests/unit/easydynamics/sample_model/components/test_gaussian.py @@ -228,3 +228,62 @@ def test_repr(self, gaussian: Gaussian): assert 'area =' in repr_str assert 'center =' in repr_str assert 'width =' in repr_str + + def test_y_unit_default(self, gaussian: Gaussian): + # EXPECT + assert gaussian.y_unit == 'dimensionless' + + def test_y_unit_custom(self): + # WHEN + gaussian = Gaussian(area=1.0, x_unit='meV', y_unit='1/meV') + # EXPECT + assert gaussian.y_unit == '1/meV' + + def test_y_unit_setter_raises(self, gaussian: Gaussian): + # EXPECT + with pytest.raises(AttributeError): + gaussian.y_unit = '1/meV' + + def test_convert_y_unit(self): + # GIVEN: x_unit='meV', y_unit='1/meV' → area_unit ≈ dimensionless + gaussian = Gaussian(area=1.0, x_unit='meV', y_unit='1/meV') + + # WHEN: convert y_unit to '1/eV' (same dimension, different scale) + gaussian.convert_y_unit('1/eV') + + # EXPECT: unit updated and area value rescaled (1/eV = 1e-3/meV, so value x 1e3) + assert gaussian.y_unit == '1/eV' + assert gaussian.area.value == pytest.approx(1e3) + + def test_convert_y_unit_invalid_type_raises(self, gaussian: Gaussian): + # EXPECT + with pytest.raises(TypeError): + gaussian.convert_y_unit(123) + + def test_evaluate_scipp_output(self, gaussian: Gaussian): + import numpy as np + import scipp as sc + + x = np.linspace(-5, 5, 100) + + # WHEN + result = gaussian.evaluate(x, output='scipp') + + # EXPECT + assert isinstance(result, sc.Variable) + assert result.unit == sc.Unit('dimensionless') + assert len(result.values) == 100 + + def test_evaluate_scipp_output_with_y_unit(self): + import numpy as np + import scipp as sc + + gaussian = Gaussian(area=1.0, x_unit='meV', y_unit='1/meV') + x = np.linspace(-5, 5, 100) + + # WHEN + result = gaussian.evaluate(x, output='scipp') + + # EXPECT + assert isinstance(result, sc.Variable) + assert result.unit == sc.Unit('1/meV') diff --git a/tests/unit/easydynamics/sample_model/test_component_collection.py b/tests/unit/easydynamics/sample_model/test_component_collection.py index 70142ffb..08f9a262 100644 --- a/tests/unit/easydynamics/sample_model/test_component_collection.py +++ b/tests/unit/easydynamics/sample_model/test_component_collection.py @@ -491,3 +491,26 @@ def test_no_warning_with_unique_names(self, recwarn): ComponentCollection(components=[g1, g2]) user_warnings = [w for w in recwarn.list if issubclass(w.category, UserWarning)] assert not user_warnings + + def test_y_unit_default(self, component_collection): + # EXPECT + assert component_collection.y_unit == 'dimensionless' + + def test_convert_y_unit(self): + # GIVEN: components with y_unit='1/meV' so area_unit ≈ dimensionless + g = Gaussian(area=1.0, x_unit='meV', y_unit='1/meV') + lor = Lorentzian(area=1.0, x_unit='meV', y_unit='1/meV') + cc = ComponentCollection(components=[g, lor]) + + # WHEN: convert y_unit to '1/eV' (same dimension, different scale) + cc.convert_y_unit('1/eV') + + # EXPECT + assert cc.y_unit == '1/eV' + for component in cc: + assert component.y_unit == '1/eV' + + def test_convert_y_unit_invalid_type_raises(self, component_collection): + # EXPECT + with pytest.raises(TypeError): + component_collection.convert_y_unit(123) diff --git a/tests/unit/easydynamics/sample_model/test_model_base.py b/tests/unit/easydynamics/sample_model/test_model_base.py index 687f7262..37fc334c 100644 --- a/tests/unit/easydynamics/sample_model/test_model_base.py +++ b/tests/unit/easydynamics/sample_model/test_model_base.py @@ -388,3 +388,30 @@ def test_repr(self, model_base): assert 'unit' in repr_str assert 'Q = ' in repr_str assert 'components = ' in repr_str + + def test_y_unit_default(self, model_base): + # EXPECT + assert model_base.y_unit == 'dimensionless' + + def test_convert_y_unit(self): + # GIVEN: model with components where y_unit='1/meV' so area_unit ≈ dimensionless + g = Gaussian(area=1.0, x_unit='meV', y_unit='1/meV') + lor = Lorentzian(area=1.0, x_unit='meV', y_unit='1/meV') + cc = ComponentCollection(components=[g, lor]) + model = ModelBase(components=cc, x_unit='meV', Q=np.array([1.0])) + + # WHEN: convert y_unit to '1/eV' (same dimension, different scale) + model.convert_y_unit('1/eV') + + # EXPECT: model y_unit and all template components updated + assert model.y_unit == '1/eV' + for component in model.components: + assert component.y_unit == '1/eV' + # Component collections rebuilt on demand reflect the new unit + for component in model.get_component_collection(0): + assert component.y_unit == '1/eV' + + def test_convert_y_unit_invalid_raises(self, model_base): + # EXPECT + with pytest.raises(TypeError): + model_base.convert_y_unit(123) From a6c6294dc11621b31d9cbf699c5eb63ac1d3d6e4 Mon Sep 17 00:00:00 2001 From: henrikjacobsenfys Date: Tue, 23 Jun 2026 06:22:49 +0200 Subject: [PATCH 03/17] Claude second review --- .../tutorials/data/fake_advanced_data.hdf5 | Bin 409056 -> 409056 bytes .../docs/tutorials/data/fake_simple_data.hdf5 | Bin 409056 -> 409056 bytes .../base_classes/easydynamics_modelbase.py | 18 ++- .../convolution/convolution_base.py | 7 +- .../components/damped_harmonic_oscillator.py | 8 ++ .../sample_model/components/delta_function.py | 13 ++- 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hm5=>jChS?>!kZ#7C zVr->DkNjfHg?Hga_zv+_N>hsPWZEpgD8v-)wk8+it)%zKh{mn-a2#KN-Q&5y3M9Ir5oYdFF%HRwl>$_tFRz~w1saUEzNcBkRi2|w&ki+Sq}%;FmO^O$Ai z)NyXP!|y9F-vd()nEQaK2bg-no%bAIo(s%-0P|kJyeBa44NN@YBP zndIB%+G$|yHZXP^7`qOPod?G51LFsP@e9EC31IvNFn$CWzXFV(0mknDjGqO@?*ik8f$_`0_-SDLHZXo1 z7{3lyJU9CpPeUswh9AIz{tmFcwcA!x-!9{SAhm$~whns+F#VgyvkcX?FhKI9& z1Zgzx0u>srS#&@o50?Rh)8Ho$w}B@Q#{q-u;2jU=fe8=y0fPgurltAJhbnGTmmb_3VlPU{BNQ7-O< zJ3Jf=T({uU4Ou*#3|2hc3=EEj03NP}c|4pA4DJR89u5Z)9xew4rvu{Qb|~cGcwlfn zNbzt!Jg39bZN0$60Z_q<3&5EUzvFv>i5nn-7e@e>W+3~XnK%R7d2t7D=@hpQ379wvI(cywc=O^c zVB#)F;l*LFgBO80V!vi1!+G$<@F+2fccmwo;$0HzyS3nHUfVgIYVj+fyKnyQ|xUjR0 zg&5ufDe!m<#0_)C@gauiKtnv<12H@ZVt5f$%i~E9!nC9&Aw{`@MO*4x(ASmO1>0;e8Op10mXK5nc!}JP~4eBSgS6%_AX( zS3(TWgckC6C&chjsF24?A%>?y3~z-f9*>0>UJEfi7kW<9V{6DdejX2o7+wrrqv^Sm zY{c+ph~d$YGffYMvJu0xA%=HDTzayMjTl}IF+3gO(xZ|Bh~e=N!|Nd~JqrSe;r&oL zj|U{#6}h}1;+lJR0rZ;38zS0j5grjSydt{C;~5dnauMDUF+3zX!{a3p!&4%Lw?qec zJSJjzO~mk=XakS;L<|p#7+w^SAcYXaf{5Ww5yPV*H6E{u7@id|yep#F48p@AhL=T; zc|0wmSxmy)B8JCB-aK9xan*UH5Mp>=6&yM!M?kE9nt;;cK&w2vUmgzrDj^V2gX|If_xGwTxmJM;Vp zEg5O>F8}b~KhM2$C;3Uq-}8$9=%zp4LznqI&-i;<>ikU8sxfn{^nU;U_wPf@0)zis z>kGKES0cQ(lqQ!y&$s<|)))Mq2mE6m^8fDog1=us{>S_NpRF(WbGv1@^5^6Bff7#? ze_PM6Qja&kXSIlT<*hGZ{lk4U% str | sc.Unit | None: - """Unit of the x-axis.""" + """ + Get the unit of the x-axis. + + Returns + ------- + str | sc.Unit | None + The unit of the x-axis (energy, Q, etc.). + """ return self._x_unit @x_unit.setter @@ -78,7 +85,14 @@ def x_unit(self, _: str) -> None: @property def y_unit(self) -> str | sc.Unit | None: - """Unit of the model output.""" + """ + Get the unit of the model output. + + Returns + ------- + str | sc.Unit | None + The unit of the model output (intensity). + """ return self._y_unit @y_unit.setter diff --git a/src/easydynamics/convolution/convolution_base.py b/src/easydynamics/convolution/convolution_base.py index 9df68c5f..3fb4d95b 100644 --- a/src/easydynamics/convolution/convolution_base.py +++ b/src/easydynamics/convolution/convolution_base.py @@ -197,7 +197,7 @@ def energy(self, energy: np.ndarray | sc.Variable) -> None: def convert_x_unit(self, unit: str | sc.Unit) -> None: """ - Convert the energy and energy_offset to the specified unit. + Convert the energy axis, energy_offset, and all components to the specified unit. Parameters ---------- @@ -228,6 +228,11 @@ def convert_x_unit(self, unit: str | sc.Unit) -> None: self.energy_offset = old_energy_offset raise e + if self._sample_components is not None: + self._sample_components.convert_x_unit(unit) + if self._resolution_components is not None: + self._resolution_components.convert_x_unit(unit) + self._x_unit = unit @property diff --git a/src/easydynamics/sample_model/components/damped_harmonic_oscillator.py b/src/easydynamics/sample_model/components/damped_harmonic_oscillator.py index 0feb4621..483ea875 100644 --- a/src/easydynamics/sample_model/components/damped_harmonic_oscillator.py +++ b/src/easydynamics/sample_model/components/damped_harmonic_oscillator.py @@ -37,6 +37,8 @@ def __init__( unique_name: str | None = None, ) -> None: """ + Initialize the Damped Harmonic Oscillator component. + Parameters ---------- area : Numeric, default=1.0 @@ -81,6 +83,8 @@ def __init__( @property def area(self) -> Parameter: """ + Get the area parameter. + Returns ------- Parameter @@ -108,6 +112,8 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ + Get the center parameter (resonance frequency). + Returns ------- Parameter @@ -139,6 +145,8 @@ def center(self, value: Numeric) -> None: @property def width(self) -> Parameter: """ + Get the width parameter (damping coefficient). + Returns ------- Parameter diff --git a/src/easydynamics/sample_model/components/delta_function.py b/src/easydynamics/sample_model/components/delta_function.py index 868882d7..38e2e168 100644 --- a/src/easydynamics/sample_model/components/delta_function.py +++ b/src/easydynamics/sample_model/components/delta_function.py @@ -22,8 +22,11 @@ class DeltaFunction(CreateParametersMixin, ModelComponent): """ Delta function. - Evaluates to zero everywhere; acts as an identity in convolutions. area has unit = x_unit * - y_unit; center has unit = x_unit. + Evaluates to zero everywhere, except in convolutions, where it acts as an identity. This is + handled by the Convolution method. area has unit = x_unit * y_unit; center has unit = x_unit. + + If the center is not provided, it will be centered at 0 and fixed, which is typically what you + want in QENS. """ def __init__( @@ -37,6 +40,8 @@ def __init__( unique_name: str | None = None, ) -> None: """ + Initialize the Delta function. + Parameters ---------- center : Numeric | None, default=None @@ -73,6 +78,8 @@ def __init__( @property def area(self) -> Parameter: """ + Get the area parameter. + Returns ------- Parameter @@ -100,6 +107,8 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ + Get the center parameter. + Returns ------- Parameter diff --git a/src/easydynamics/sample_model/components/exponential.py b/src/easydynamics/sample_model/components/exponential.py index f50a8794..e2065631 100644 --- a/src/easydynamics/sample_model/components/exponential.py +++ b/src/easydynamics/sample_model/components/exponential.py @@ -34,6 +34,8 @@ def __init__( unique_name: str | None = None, ) -> None: """ + Initialize the Exponential component. + Parameters ---------- amplitude : Numeric, default=1.0 @@ -100,6 +102,8 @@ def __init__( @property def amplitude(self) -> Parameter: """ + Get the amplitude parameter. + Returns ------- Parameter @@ -127,6 +131,8 @@ def amplitude(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ + Get the center parameter. + Returns ------- Parameter @@ -158,6 +164,8 @@ def center(self, value: Numeric | None) -> None: @property def rate(self) -> Parameter: """ + Get the rate parameter. + Returns ------- Parameter diff --git a/src/easydynamics/sample_model/components/gaussian.py b/src/easydynamics/sample_model/components/gaussian.py index 2b809e99..065b3582 100644 --- a/src/easydynamics/sample_model/components/gaussian.py +++ b/src/easydynamics/sample_model/components/gaussian.py @@ -25,6 +25,9 @@ class Gaussian(CreateParametersMixin, ModelComponent): where $A$ is the area, $x_0$ is the center, and $\sigma$ is the width. area has unit = x_unit * y_unit; center and width have unit = x_unit. + + If the center is not provided, it will be centered at 0 and fixed, which is typically what you + want in QENS. """ def __init__( @@ -39,6 +42,8 @@ def __init__( unique_name: str | None = None, ) -> None: """ + Initialize the Gaussian component. + Parameters ---------- area : Numeric, default=1.0 @@ -78,6 +83,8 @@ def __init__( @property def area(self) -> Parameter: """ + Get the area parameter. + Returns ------- Parameter @@ -105,6 +112,8 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ + Get the center parameter. + Returns ------- Parameter @@ -136,6 +145,8 @@ def center(self, value: Numeric | None) -> None: @property def width(self) -> Parameter: """ + Get the width parameter (sigma). + Returns ------- Parameter diff --git a/src/easydynamics/sample_model/components/lorentzian.py b/src/easydynamics/sample_model/components/lorentzian.py index 087f34dc..d8f106b9 100644 --- a/src/easydynamics/sample_model/components/lorentzian.py +++ b/src/easydynamics/sample_model/components/lorentzian.py @@ -24,6 +24,9 @@ class Lorentzian(CreateParametersMixin, ModelComponent): where $A$ is the area, $x_0$ is the center, and $\Gamma$ is the HWHM. area has unit = x_unit * y_unit; center and width have unit = x_unit. + + If the center is not provided, it will be centered at 0 and fixed, which is typically what you + want in QENS. """ def __init__( @@ -38,6 +41,8 @@ def __init__( unique_name: str | None = None, ) -> None: """ + Initialize the Lorentzian component. + Parameters ---------- area : Numeric, default=1.0 @@ -77,6 +82,8 @@ def __init__( @property def area(self) -> Parameter: """ + Get the area parameter. + Returns ------- Parameter @@ -104,6 +111,8 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ + Get the center parameter. + Returns ------- Parameter @@ -135,6 +144,8 @@ def center(self, value: Numeric | None) -> None: @property def width(self) -> Parameter: """ + Get the width parameter (HWHM). + Returns ------- Parameter diff --git a/src/easydynamics/sample_model/components/model_component.py b/src/easydynamics/sample_model/components/model_component.py index 53412438..6bce24d5 100644 --- a/src/easydynamics/sample_model/components/model_component.py +++ b/src/easydynamics/sample_model/components/model_component.py @@ -254,8 +254,7 @@ def convert_x_unit(self, new_x_unit: str | sc.Unit) -> None: except Exception as e: try: for p in pars: - if hasattr(p, 'convert_unit'): - p.convert_unit(old_unit) + p.convert_unit(old_unit) except Exception: # noqa: S110 pass raise e diff --git a/src/easydynamics/sample_model/components/voigt.py b/src/easydynamics/sample_model/components/voigt.py index 386fc05c..623876d3 100644 --- a/src/easydynamics/sample_model/components/voigt.py +++ b/src/easydynamics/sample_model/components/voigt.py @@ -21,7 +21,11 @@ class Voigt(CreateParametersMixin, ModelComponent): r""" Voigt profile — convolution of Gaussian and Lorentzian. - area has unit = x_unit * y_unit; center, gaussian_width, lorentzian_width have unit = x_unit. + Uses ``scipy.special.voigt_profile`` to evaluate the profile. area has unit = x_unit * y_unit; + center, gaussian_width, and lorentzian_width have unit = x_unit. + + If the center is not provided, it will be centered at 0 and fixed, which is typically what you + want in QENS. """ def __init__( @@ -37,6 +41,8 @@ def __init__( unique_name: str | None = None, ) -> None: """ + Initialize a Voigt component. + Parameters ---------- area : Numeric | Parameter, default=1.0 @@ -83,6 +89,8 @@ def __init__( @property def area(self) -> Parameter: """ + Get the area parameter. + Returns ------- Parameter @@ -110,6 +118,8 @@ def area(self, value: Numeric) -> None: @property def center(self) -> Parameter: """ + Get the center parameter. + Returns ------- Parameter @@ -141,6 +151,8 @@ def center(self, value: Numeric | None) -> None: @property def gaussian_width(self) -> Parameter: """ + Get the Gaussian width parameter (sigma). + Returns ------- Parameter @@ -172,6 +184,8 @@ def gaussian_width(self, value: Numeric) -> None: @property def lorentzian_width(self) -> Parameter: """ + Get the Lorentzian width parameter (HWHM). + Returns ------- Parameter diff --git a/src/easydynamics/sample_model/diffusion_model/diffusion_model_base.py b/src/easydynamics/sample_model/diffusion_model/diffusion_model_base.py index 8dfd4b4d..727d02bb 100644 --- a/src/easydynamics/sample_model/diffusion_model/diffusion_model_base.py +++ b/src/easydynamics/sample_model/diffusion_model/diffusion_model_base.py @@ -114,7 +114,8 @@ def __init__( self._component_collections = [] else: self._component_collections = [ - ComponentCollection(y_unit=self.y_unit) for _ in range(len(self.Q)) + ComponentCollection(x_unit=self.x_unit, y_unit=self.y_unit) + for _ in range(len(self.Q)) ] # ------------------------------------------------------------------ @@ -462,7 +463,7 @@ def create_component_collections(self) -> list[ComponentCollection]: return self._component_collections self._component_collections = [ - ComponentCollection(y_unit=self.y_unit) for _ in range(len(self.Q)) + ComponentCollection(x_unit=self.x_unit, y_unit=self.y_unit) for _ in range(len(self.Q)) ] return self._component_collections diff --git a/src/easydynamics/sample_model/instrument_model.py b/src/easydynamics/sample_model/instrument_model.py index d0b8d023..cf126b11 100644 --- a/src/easydynamics/sample_model/instrument_model.py +++ b/src/easydynamics/sample_model/instrument_model.py @@ -316,8 +316,8 @@ def convert_x_unit(self, x_unit: str | sc.Unit) -> None: self._background_model.convert_x_unit(unit) self._resolution_model.convert_x_unit(unit) - self._energy_offset.convert_unit(unit) self._ensure_energy_offsets_current() + self._energy_offset.convert_unit(unit) for offset in self._energy_offsets: offset.convert_unit(unit) diff --git a/src/easydynamics/sample_model/sample_model.py b/src/easydynamics/sample_model/sample_model.py index 4457887b..3e0784be 100644 --- a/src/easydynamics/sample_model/sample_model.py +++ b/src/easydynamics/sample_model/sample_model.py @@ -96,6 +96,19 @@ def __init__( # ------------------------------------------------------------------ def append_diffusion_model(self, diffusion_model: DiffusionModelBase) -> None: + """ + Append a DiffusionModel to the SampleModel. + + Parameters + ---------- + diffusion_model : DiffusionModelBase + The DiffusionModel to append. + + Raises + ------ + TypeError + If the diffusion_model is not a DiffusionModelBase. + """ if not isinstance(diffusion_model, DiffusionModelBase): raise TypeError( f'diffusion_model must be a DiffusionModelBase, got {type(diffusion_model).__name__}' # noqa: E501 @@ -105,6 +118,19 @@ def append_diffusion_model(self, diffusion_model: DiffusionModelBase) -> None: self._component_collections_is_dirty = True def remove_diffusion_model(self, name: str) -> None: + """ + Remove a DiffusionModel from the SampleModel by name. + + Parameters + ---------- + name : str + The name of the DiffusionModel to remove. + + Raises + ------ + ValueError + If no DiffusionModel with the given name is found. + """ for i, dm in enumerate(self.diffusion_models): if dm.name == name: del self.diffusion_models[i] @@ -116,6 +142,7 @@ def remove_diffusion_model(self, name: str) -> None: ) def clear_diffusion_models(self) -> None: + """Clear all DiffusionModels from the SampleModel.""" self.diffusion_models = [] self._component_collections_is_dirty = True @@ -125,12 +152,34 @@ def clear_diffusion_models(self) -> None: @property def diffusion_models(self) -> list[DiffusionModelBase]: + """ + Get the diffusion models of the SampleModel. + + Returns + ------- + list[DiffusionModelBase] + The diffusion models of the SampleModel. + """ return self._diffusion_models @diffusion_models.setter def diffusion_models( self, value: DiffusionModelBase | list[DiffusionModelBase] | None ) -> None: + """ + Set the diffusion models of the SampleModel. + + Parameters + ---------- + value : DiffusionModelBase | list[DiffusionModelBase] | None + The diffusion model(s) to set. Can be a single DiffusionModelBase, a list of + DiffusionModelBase, or None to clear all diffusion models. + + Raises + ------ + TypeError + If value is not a DiffusionModelBase, a list of DiffusionModelBase, or None. + """ if value is None: self._diffusion_models = [] self._on_diffusion_models_change() @@ -154,10 +203,33 @@ def diffusion_models( @property def temperature(self) -> Parameter | None: + """ + Get the temperature of the SampleModel. + + Returns + ------- + Parameter | None + The temperature Parameter of the SampleModel, or None if not set. + """ return self._temperature @temperature.setter def temperature(self, value: Numeric | None) -> None: + """ + Set the temperature of the SampleModel. + + Parameters + ---------- + value : Numeric | None + The temperature value. If None, temperature is cleared (no detailed balance). + + Raises + ------ + TypeError + If value is not a number or None. + ValueError + If value is negative. + """ if value is None: self._temperature = None return @@ -181,6 +253,14 @@ def temperature(self, value: Numeric | None) -> None: @property def temperature_unit(self) -> str | sc.Unit: + """ + Get the temperature unit. + + Returns + ------- + str | sc.Unit + The unit of the temperature parameter. + """ return self._temperature_unit @temperature_unit.setter @@ -206,6 +286,14 @@ def convert_temperature_unit(self, unit: str | sc.Unit) -> None: @property def normalize_detailed_balance(self) -> bool: + """ + Get whether to divide the detailed balance factor by temperature. + + Returns + ------- + bool + True if the detailed balance factor is normalized by temperature. + """ return self.detailed_balance_settings.normalize_detailed_balance @normalize_detailed_balance.setter @@ -216,6 +304,14 @@ def normalize_detailed_balance(self, value: bool) -> None: @property def use_detailed_balance(self) -> bool: + """ + Get whether detailed balance correction is applied. + + Returns + ------- + bool + True if detailed balance is applied during evaluation. + """ return self.detailed_balance_settings.use_detailed_balance @use_detailed_balance.setter @@ -226,6 +322,14 @@ def use_detailed_balance(self, value: bool) -> None: @property def detailed_balance_settings(self) -> DetailedBalanceSettings: + """ + Get the detailed balance settings. + + Returns + ------- + DetailedBalanceSettings + The detailed balance settings object. + """ return self._detailed_balance_settings @detailed_balance_settings.setter From cd6fcfe674e989f2c35a533b209f4d84f3d0173c Mon Sep 17 00:00:00 2001 From: henrikjacobsenfys Date: Tue, 23 Jun 2026 06:40:20 +0200 Subject: [PATCH 04/17] claude third review --- docs/docs/tutorials/analysis.ipynb | 17300 ++++----- docs/docs/tutorials/analysis1d.ipynb | 2220 +- .../docs/tutorials/component_collection.ipynb | 136 +- docs/docs/tutorials/components.ipynb | 802 +- docs/docs/tutorials/convolution.ipynb | 346 +- docs/docs/tutorials/delta_lorentz.ipynb | 210 +- docs/docs/tutorials/detailed_balance.ipynb | 364 +- docs/docs/tutorials/diffusion_model.ipynb | 340 +- docs/docs/tutorials/experiment.ipynb | 7411 +++- docs/docs/tutorials/instrument_model.ipynb | 32 +- docs/docs/tutorials/sample_model.ipynb | 908 +- docs/docs/tutorials/tutorial0_basics.ipynb | 15819 ++++---- .../tutorials/tutorial0_more_advanced.ipynb | 14699 ++++---- docs/docs/tutorials/tutorial1_brownian.ipynb | 26118 ++++++------- .../tutorials/tutorial2_nanoparticles.ipynb | 30966 ++++++++-------- .../sample_model/components/polynomial.py | 14 +- .../components/test_polynomial.py | 15 + 17 files changed, 62712 insertions(+), 54988 deletions(-) diff --git a/docs/docs/tutorials/analysis.ipynb b/docs/docs/tutorials/analysis.ipynb index fb3bc582..93398854 100644 --- a/docs/docs/tutorials/analysis.ipynb +++ b/docs/docs/tutorials/analysis.ipynb @@ -19,10 +19,10 @@ "id": "f2f898dc", "metadata": { "execution": { - "iopub.execute_input": "2026-06-22T17:34:16.505504Z", - "iopub.status.busy": "2026-06-22T17:34:16.505350Z", - "iopub.status.idle": "2026-06-22T17:34:18.801381Z", - "shell.execute_reply": "2026-06-22T17:34:18.800592Z" + "iopub.execute_input": "2026-06-23T04:35:46.596629Z", + "iopub.status.busy": "2026-06-23T04:35:46.596406Z", + "iopub.status.idle": "2026-06-23T04:35:48.831744Z", + "shell.execute_reply": "2026-06-23T04:35:48.830991Z" } }, "outputs": [], @@ -52,10 +52,10 @@ "id": "8deca9b6", "metadata": { "execution": { - "iopub.execute_input": "2026-06-22T17:34:18.803564Z", - "iopub.status.busy": "2026-06-22T17:34:18.803315Z", - "iopub.status.idle": "2026-06-22T17:34:18.813901Z", - "shell.execute_reply": "2026-06-22T17:34:18.813101Z" + "iopub.execute_input": "2026-06-23T04:35:48.836389Z", + "iopub.status.busy": "2026-06-23T04:35:48.835958Z", + "iopub.status.idle": "2026-06-23T04:35:48.849480Z", + "shell.execute_reply": "2026-06-23T04:35:48.848518Z" } }, "outputs": [], @@ -76,17 +76,17 @@ "id": "6762faba", "metadata": { "execution": { - "iopub.execute_input": "2026-06-22T17:34:18.816526Z", - "iopub.status.busy": "2026-06-22T17:34:18.816364Z", - "iopub.status.idle": "2026-06-22T17:34:19.011937Z", - "shell.execute_reply": "2026-06-22T17:34:19.011154Z" + "iopub.execute_input": "2026-06-23T04:35:48.853110Z", + "iopub.status.busy": "2026-06-23T04:35:48.852955Z", + "iopub.status.idle": "2026-06-23T04:35:49.037317Z", + "shell.execute_reply": "2026-06-23T04:35:49.036119Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c0ab9ffa551742d88073b0304c8e72a8", + "model_id": "5bacde7d852749bb88d870ca626e186c", "version_major": 2, "version_minor": 0 }, @@ -149,17 +149,17 @@ "id": "e98e3d65", "metadata": { "execution": { - "iopub.execute_input": "2026-06-22T17:34:19.014003Z", - "iopub.status.busy": "2026-06-22T17:34:19.013840Z", - "iopub.status.idle": "2026-06-22T17:34:19.491588Z", - "shell.execute_reply": "2026-06-22T17:34:19.490934Z" + "iopub.execute_input": "2026-06-23T04:35:49.039609Z", + "iopub.status.busy": "2026-06-23T04:35:49.039457Z", + "iopub.status.idle": "2026-06-23T04:35:49.453615Z", + "shell.execute_reply": "2026-06-23T04:35:49.452627Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "941b68e7dc6b42629119bdfd5d0db567", + "model_id": "9e1ef079999345bc931635b79668abc1", "version_major": 2, "version_minor": 0 }, @@ -185,10 +185,10 @@ "id": "133e682e", "metadata": { "execution": { - "iopub.execute_input": "2026-06-22T17:34:19.493719Z", - "iopub.status.busy": "2026-06-22T17:34:19.493573Z", - "iopub.status.idle": "2026-06-22T17:34:19.511490Z", - "shell.execute_reply": "2026-06-22T17:34:19.510654Z" + "iopub.execute_input": "2026-06-23T04:35:49.456167Z", + "iopub.status.busy": "2026-06-23T04:35:49.456003Z", + "iopub.status.idle": "2026-06-23T04:35:49.478227Z", + "shell.execute_reply": "2026-06-23T04:35:49.476921Z" } }, "outputs": [ @@ -213,28 +213,28 @@ "\n", "\n", "\n", - "

scipp.Dataset (6.66 KB)

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