diff --git a/README.md b/README.md index 7e9167ec..7bb9d615 100644 --- a/README.md +++ b/README.md @@ -49,77 +49,74 @@ The [`jax-finufft`](https://github.com/dfm/jax-finufft) package is an optional d The following is a basic workflow to simulate an image. -First, instantiate the scattering potential representation and its respective method for computing image projections. +First, instantiate the spatial potential energy distribution representation and its respective method for computing image projections. ```python import jax import jax.numpy as jnp -import cryojax.simulator as cs -from cryojax.io import read_array_with_spacing_from_mrc +import cryojax.simulator as cxs +from cryojax.data import read_array_with_spacing_from_mrc -# Instantiate the scattering potential. +# Instantiate the scattering potential filename = "example_scattering_potential.mrc" real_voxel_grid, voxel_size = read_array_with_spacing_from_mrc(filename) -potential = cs.FourierVoxelGridPotential.from_real_voxel_grid(real_voxel_grid, voxel_size) -# ... now instantiate fourier slice extraction -integrator = cs.FourierSliceExtract(interpolation_order=1) -``` - -Here, the 3D scattering potential array is read from `filename`. Then, the abstraction of the scattering potential is then loaded in fourier-space into a `FourierVoxelGridPotential`, and the fourier-slice projection theorem is initialized with `FourierSliceExtract`. The scattering potential can be generated with an external program, such as the [cisTEM](https://github.com/timothygrant80/cisTEM) simulate tool. - -We can now instantiate the representation of a biological specimen, which also includes a pose. - -```python -# First instantiate the pose. Here, angles are given in degrees -pose = cs.EulerAnglePose( +potential = cxs.FourierVoxelGridPotential.from_real_voxel_grid(real_voxel_grid, voxel_size) +# ... now, instantiate the pose. Angles are given in degrees +pose = cxs.EulerAnglePose( offset_x_in_angstroms=5.0, offset_y_in_angstroms=-3.0, view_phi=20.0, view_theta=80.0, view_psi=-10.0, ) -# ... now, build the biological specimen -specimen = cs.Specimen(potential, integrator, pose) +# ... now, build the ensemble. In this case, the ensemble is just a single structure +structural_ensemble = cxs.SingleStructureEnsemble(potential, pose) ``` -Next, build the model for the electron microscope. Here, we simply include a model for the CTF in the weak-phase approximation (linear image formation theory). +Here, the 3D scattering potential array is read from `filename`. Then, the abstraction of the scattering potential is then loaded in fourier-space into a `FourierVoxelGridPotential`. The scattering potential can be generated with an external program, such as the [cisTEM](https://github.com/timothygrant80/cisTEM) simulate tool. Then, the representation of a biological specimen is instantiated, which also includes a pose and conformational heterogeneity. Here, the `SingleStructureEnsemble` class takes a pose but has no heterogeneity. + +Next, build the *scattering theory*. The simplest `scattering_theory` is the `LinearScatteringTheory`. This represents the usual image formation pipeline in cryo-EM, which forms images by computing projections of the potential and convolving the result with a contrast transfer function. ```python from cryojax.image import operators as op -# First, initialize the CTF and its optics model -ctf = cs.CTF( - defocus_u_in_angstroms=10000.0, - defocus_v_in_angstroms=9800.0, +# Initialize the scattering theory. First, instantiate fourier slice extraction +potential_integrator = cxs.FourierSliceExtraction(interpolation_order=1) +# ... next, the contrast transfer theory +ctf = cxs.ContrastTransferFunction( + defocus_in_angstroms=9800.0, + astigmatism_in_angstroms=200.0, astigmatism_angle=10.0, - amplitude_contrast_ratio=0.1) -optics = cs.WeakPhaseOptics(ctf, envelope=op.FourierGaussian(b_factor=5.0)) # b_factor is given in Angstroms^2 -# ... these are stored in the Instrument -voltage_in_kilovolts = 300.0, -instrument = cs.Instrument(voltage_in_kilovolts, optics) + amplitude_contrast_ratio=0.1 +) +transfer_theory = cxs.ContrastTransferTheory(ctf, envelope=op.FourierGaussian(b_factor=5.0)) +# ... now for the scattering theory +scattering_theory = cxs.LinearScatteringTheory(structural_ensemble, potential_integrator, transfer_theory) ``` -The `CTF` has parameters used in CTFFIND4, which take their default values if not -explicitly configured here. Finally, we can instantiate the `ImagePipeline` and simulate an image. +The `ContrastTransferFunction` has parameters used in CTFFIND4, which take their default values if not +explicitly configured here. Finally, we can instantiate the `imaging_pipeline`--the highest level of imaging abstraction in `cryojax`--and simulate an image. Here, we choose a `ContrastImagingPipeline`, which simulates image contrast from a linear scattering theory. ```python -# Instantiate the image configuration -config = cs.ImageConfig(shape=(320, 320), pixel_size=voxel_size) -# Build the image formation model -pipeline = cs.ImagePipeline(config, specimen, instrument) -# ... simulate an image and return in real-space. -image_without_noise = pipeline.render(get_real=True) +# Finally, build the image formation model +# ... first instantiate the instrument configuration +instrument_config = cxs.InstrumentConfig(shape=(320, 320), pixel_size=voxel_size, voltage_in_kilovolts=300.0) +# ... now the imaging pipeline +imaging_pipeline = cxs.ContrastImagingPipeline(instrument_config, scattering_theory) +# ... finally, simulate an image and return in real-space! +image_without_noise = imaging_pipeline.render(get_real=True) ``` -`cryojax` also defines a library of distributions from which to sample the data. These distributions define the stochastic model from which images are drawn. For example, instantiate an `IndependentFourierGaussian` distribution and either sample from it or compute its log-likelihood. +`cryojax` also defines a library of distributions from which to sample the data. These distributions define the stochastic model from which images are drawn. For example, instantiate an `IndependentGaussianFourierModes` distribution and either sample from it or compute its log-likelihood. ```python -from cryojax.image import rfftn +from cryojax.image import rfftn, operators as op from cryojax.inference import distributions as dist -from cryojax.image import operators as op # Passing the ImagePipeline and a variance function, instantiate the distribution -distribution = dist.IndependentFourierGaussian(pipeline, variance=op.Constant(1.0)) +distribution = dist.IndependentGaussianFourierModes( + imaging_pipeline, variance_function=op.Constant(1.0) +) # ... then, either simulate an image from this distribution key = jax.random.PRNGKey(seed=0) image_with_noise = distribution.sample(key) diff --git a/docs/api/simulator/scattering_potential.md b/docs/api/simulator/scattering_potential.md index 0038039f..58d05239 100644 --- a/docs/api/simulator/scattering_potential.md +++ b/docs/api/simulator/scattering_potential.md @@ -1,9 +1,9 @@ # Scattering potential representations -`cryojax` provides different options for how to represent scattering potentials in cryo-EM. +`cryojax` provides different options for how to represent spatial potential energy distributions in cryo-EM. -???+ abstract "`cryojax.simulator.AbstractScatteringPotential`" - ::: cryojax.simulator.AbstractScatteringPotential +???+ abstract "`cryojax.simulator.AbstractPotentialRepresentation`" + ::: cryojax.simulator.AbstractPotentialRepresentation options: members: - rotate_to_pose diff --git a/docs/examples/cross-correlation-search.ipynb b/docs/examples/cross-correlation-search.ipynb new file mode 100644 index 00000000..ed3eb068 --- /dev/null +++ b/docs/examples/cross-correlation-search.ipynb @@ -0,0 +1,610 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a tutorial that demonstrates a cross-correlation-based grid search for the underlying pose of a known structure. The idea of the search follows `cisTEM`'s 2D template matching program.\n", + "\n", + "This is also a tutorial for using `cryojax`'s grid search API. In `cryojax.inference`, the function `run_grid_search` provides a flexible API for minimizing a loss function with grid search, while the abstract interface `AbstractGridSearchMethod` provides a way to extend the API. See the documentation for more information.\n", + "\n", + "*Reference*:\n", + "- Lucas, Bronwyn A., et al. \"Locating macromolecular assemblies in cells by 2D template matching with cisTEM.\" Elife 10 (2021): e68946.*" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import equinox as eqx\n", + "\n", + "import cryojax.simulator as cxs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Plotting imports and function definitions\n", + "from matplotlib import pyplot as plt\n", + "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", + "\n", + "\n", + "def plot_image(image, fig, ax, cmap=\"gray\", label=None, **kwargs):\n", + " im = ax.imshow(image, cmap=cmap, origin=\"lower\", **kwargs)\n", + " divider = make_axes_locatable(ax)\n", + " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", + " fig.colorbar(im, cax=cax)\n", + " if label is not None:\n", + " ax.set(title=label)\n", + " return fig, ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, load the template. This template happens to be generated from cisTEM.\n", + "\n", + "!!! note \n", + " The cisTEM scattering potential has been modified slightly because at the time of writing this, `cryojax` scattering potentials have different conventions than `cisTEM`'s. In particular, the `cisTEM` scattering potential was multiplied by a factor of $1/(\\textrm{voxel size} \\times \\textrm{wavelength})$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from cryojax.data import read_array_with_spacing_from_mrc\n", + "\n", + "\n", + "# First, load the template. This template happens to be generated from cisTEM\n", + "filename = \"./data/ribosome_4ug0_scattering_potential_from_cistem.mrc\"\n", + "template, voxel_size = read_array_with_spacing_from_mrc(filename)\n", + "potential = cxs.FourierVoxelGridPotential.from_real_voxel_grid(\n", + " template, voxel_size, pad_scale=1.5\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, load a particle stack on which to run the grid search. The particle stack will be loaded from a STAR file with the `RelionDataset` interface. See the \"Read a particle stack\" tutorial for more information." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import jax\n", + "\n", + "from cryojax.data import RelionDataset\n", + "from cryojax.image import normalize_image, rfftn\n", + "\n", + "\n", + "@jax.vmap\n", + "def normalize_image_stack(image):\n", + " \"\"\"Normalize a stack of images to have mean 0 and std 1.\"\"\"\n", + " return normalize_image(image, is_real=True)\n", + "\n", + "\n", + "# Load the dataset and index three particles in this dataset\n", + "make_config_fn = lambda shape, pixel_size, voltage_in_kilovolts: cxs.InstrumentConfig(\n", + " shape, pixel_size, voltage_in_kilovolts, padded_shape=potential.shape[0:2]\n", + ")\n", + "dataset = RelionDataset(\n", + " path_to_starfile=\"./data/ribosome_4ug0_particles.star\",\n", + " path_to_relion_project=\"./\",\n", + " make_instrument_config_fn=make_config_fn,\n", + ")\n", + "particle_stack = dataset[:3]\n", + "# Create a normalized image stack in fourier space\n", + "fourier_image_stack = rfftn(\n", + " normalize_image_stack(particle_stack.image_stack), axes=(1, 2)\n", + ")\n", + "# Plot images\n", + "n_images = particle_stack.image_stack.shape[0]\n", + "fig, axes = plt.subplots(figsize=(3 * n_images, 3), ncols=n_images)\n", + "[\n", + " plot_image(\n", + " particle_stack.image_stack[i],\n", + " fig,\n", + " axes[i],\n", + " label=f\"Picked particle {i+1}\",\n", + " )\n", + " for i in range(n_images)\n", + "]\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, build the grid points in orientational space on which to search. This is done by vmapping over the `SO3.sample_uniform` method, which randomly and uniformly samples quaternions. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial\n", + "\n", + "import equinox.internal as eqxi\n", + "import jax\n", + "\n", + "from cryojax.rotations import SO3\n", + "\n", + "\n", + "@partial(eqx.filter_vmap, out_axes=eqxi.if_mapped(axis=0))\n", + "def make_pose_grid(key):\n", + " \"\"\"Create a grid of poses, where the grid is represented as\n", + " a pytree (here, a `QuaternionPose`).\n", + " \"\"\"\n", + " return cxs.QuaternionPose.from_rotation(SO3.sample_uniform(key))\n", + "\n", + "\n", + "# Create the grid\n", + "number_of_poses = 100_000\n", + "keys = jax.random.split(jax.random.PRNGKey(0), number_of_poses)\n", + "pose_grid = make_pose_grid(keys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, finish building the `cryojax` model." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# ... create the structural ensemble\n", + "structural_ensemble = cxs.SingleStructureEnsemble(\n", + " conformational_space=potential, pose=pose_grid\n", + ")\n", + "# ... now the scattering theory\n", + "transfer_theory = cxs.ContrastTransferTheory(particle_stack.ctf)\n", + "projection_method = cxs.FourierSliceExtraction(interpolation_order=1)\n", + "scattering_theory = cxs.LinearScatteringTheory(\n", + " structural_ensemble, projection_method, transfer_theory\n", + ")\n", + "# ... and finally the imaging pipeline.\n", + "imaging_pipeline = cxs.ContrastImagingPipeline(\n", + " particle_stack.instrument_config, scattering_theory\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice we are doing something that may seem a little odd for those new to JAX---we are building an `imaging_pipeline` that has the grid of poses loaded into it. Hopefully the reason for doing this will become clear, however, note that this is just one possible pattern for writing a script, and people should create a workflow that works best for them!\n", + "\n", + "It's time now to define the cross-correlation loss.\n", + "\n", + "!!! info\n", + "\n", + " Before proceeding, its important to define how exactly the\n", + " `cryojax` grid search tool defines a grid. For the grid search,\n", + " the grid is an arbitrary pytree whose leaves are JAX arrays whose\n", + " leading dimension indexes a set grid points. The entire grid is\n", + " then the cartesian product of the grid points of all of its leaves.\n", + " `cryojax` calls this a `tree_grid`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "\n", + "import cryojax as cx\n", + "from cryojax.image import irfftn\n", + "\n", + "\n", + "# Grab an `equinox` filter specification where the CTF parameters\n", + "# have a batch dimension. These parameters are those not in the RELION\n", + "# optics group.\n", + "per_particle_filter_spec = cx.get_filter_spec(\n", + " imaging_pipeline,\n", + " lambda p: (\n", + " p.scattering_theory.transfer_theory.ctf.defocus_in_angstroms,\n", + " p.scattering_theory.transfer_theory.ctf.astigmatism_in_angstroms,\n", + " p.scattering_theory.transfer_theory.ctf.astigmatism_angle,\n", + " p.scattering_theory.transfer_theory.ctf.phase_shift,\n", + " ),\n", + ")\n", + "\n", + "\n", + "@partial(cx.filter_vmap_with_spec, filter_spec=per_particle_filter_spec, in_axes=(0, 0))\n", + "def cross_correlation(pipeline, fourier_observed_image):\n", + " \"\"\"Compute the cross-correlation, batched over images in the `fourier_image_stack`,\n", + " and per-particle parameters in the `pipeline`.\n", + " \"\"\"\n", + " fourier_simulated_image = pipeline.render(get_real=False)\n", + " return (\n", + " irfftn(\n", + " fourier_observed_image * jnp.conj(fourier_simulated_image),\n", + " s=pipeline.instrument_config.shape,\n", + " )\n", + " / pipeline.instrument_config.n_pixels\n", + " )\n", + "\n", + "\n", + "@eqx.filter_jit\n", + "def objective_function(pipeline_at_grid_point, args):\n", + " \"\"\"The objective function for the grid search.\n", + "\n", + " Because the grid search tries to minimize a loss, the\n", + " the object function is the negative cross correlation.\n", + "\n", + " Also, note the particular form of the function arguments.\n", + " See `cryojax.inference.run_grid_search` for more information.\n", + " \"\"\"\n", + " (\n", + " pipeline_not_at_grid_point_vmap,\n", + " pipeline_not_at_grid_point_no_vmap,\n", + " fourier_observed_image_stack,\n", + " ) = args\n", + " pipeline_not_at_grid_point = eqx.combine(\n", + " pipeline_not_at_grid_point_vmap, pipeline_not_at_grid_point_no_vmap\n", + " )\n", + " pipeline = eqx.combine(pipeline_at_grid_point, pipeline_not_at_grid_point)\n", + "\n", + " return -cross_correlation(pipeline, fourier_observed_image_stack)\n", + "\n", + "\n", + "@partial(eqx.filter_vmap, in_axes=(None, (0, None)))\n", + "def simulate_fourier_image_stack(pipeline_at_grid_point, args):\n", + " \"\"\"Simulate an image given a particular grid point.\"\"\"\n", + " pipeline_not_at_grid_point_vmap, pipeline_not_at_grid_point_no_vmap = args\n", + " pipeline_not_at_grid_point = eqx.combine(\n", + " pipeline_not_at_grid_point_vmap, pipeline_not_at_grid_point_no_vmap\n", + " )\n", + " pipeline = eqx.combine(pipeline_at_grid_point, pipeline_not_at_grid_point)\n", + "\n", + " return pipeline.render(get_real=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we have to break up the `imaging_pipeline` into pieces using `equinox.partition`, so that we may smoothly pass through jit/vmap boundaries bfeore recombining pieces using `equinox.combine`. See the \"Simulate a batch of images\" tutorial for an introduction to pytree manipulation with `equinox`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Get a specification for where the grid points are\n", + "tree_grid_filter_spec = cx.get_filter_spec(\n", + " imaging_pipeline, lambda p: p.scattering_theory.structural_ensemble.pose.wxyz\n", + ")\n", + "# ... split up the `imaging_pipeline` into grid points and non-grid points\n", + "pipeline_tree_grid, pipeline_non_tree_grid = eqx.partition(\n", + " imaging_pipeline, tree_grid_filter_spec\n", + ")\n", + "# ... and again into per-particle parameters and non-per-particle parameters\n", + "per_particle_pipeline, non_per_particle_pipeline = eqx.partition(\n", + " pipeline_non_tree_grid, per_particle_filter_spec\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are almost ready to run the search. Before running, generate an image at a particular grid point to make sure simulated images look okay. This will involve using the grid manipulation utilities in `cryojax`, `tree_grid_take` and `tree_grid_unravel_index` (yes, like `numpy.take` and `numpy.unravel_index`)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cryojax.inference import tree_grid_take, tree_grid_unravel_index\n", + "\n", + "\n", + "# Grab the first grid point\n", + "test_pipeline_grid_point = tree_grid_take(\n", + " pipeline_tree_grid, tree_grid_unravel_index(0, pipeline_tree_grid)\n", + ")\n", + "# ... simulate a stack of images at each particle's parameters at this grid point\n", + "test_simulated_fourier_image_stack = simulate_fourier_image_stack(\n", + " test_pipeline_grid_point, (per_particle_pipeline, non_per_particle_pipeline)\n", + ")\n", + "# ... compute the negative cross-correlation between the simulated and observed images\n", + "neg_cc = objective_function(\n", + " test_pipeline_grid_point,\n", + " (per_particle_pipeline, non_per_particle_pipeline, fourier_image_stack),\n", + ")\n", + "\n", + "fig, axes = plt.subplots(figsize=(9, 3.5), ncols=3)\n", + "plot_image(\n", + " irfftn(\n", + " test_simulated_fourier_image_stack[0],\n", + " s=imaging_pipeline.instrument_config.shape,\n", + " ),\n", + " fig,\n", + " axes[0],\n", + " label=\"Test simiulated image\",\n", + ")\n", + "plot_image(\n", + " irfftn(fourier_image_stack[0], s=imaging_pipeline.instrument_config.shape),\n", + " fig,\n", + " axes[1],\n", + " label=\"Test observed data\",\n", + ")\n", + "plot_image(\n", + " -neg_cc[0],\n", + " fig,\n", + " axes[2],\n", + " label=\"Test cross correlation\",\n", + " cmap=\"plasma\",\n", + ")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, run the grid search.\n", + "\n", + "!!! info\n", + "\n", + " The function `run_grid_search` runs the search loop, while the `AbstractGridSearchMethod`\n", + " tells the search loop what to do. The `AbstractGridSearchMethod` below is the `MinimumSearchMethod`,\n", + " which simply stores the minimum value of the loss function (more specifically, it is an *elementwise* minimum, since the cross-correlation function returns a grid of loss-function evaluations)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/michael/mambaforge/envs/cryojax/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py:2554: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", + " start = asarray(start, dtype=computation_dtype)\n", + "/Users/michael/mambaforge/envs/cryojax/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py:2555: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", + " stop = asarray(stop, dtype=computation_dtype)\n", + "/Users/michael/mambaforge/envs/cryojax/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py:66: UserWarning: Explicitly requested dtype float64 requested in astype is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", + " return lax_numpy.astype(arr, dtype)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ea48fbb51f604460834f5cb6df2ca2ea", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_images = particle_stack.image_stack.shape[0]\n", + "fig, axes = plt.subplots(figsize=(3 * n_images, 3), ncols=n_images)\n", + "fig.suptitle(\"Maximum cross-correlation per pixel\")\n", + "[\n", + " plot_image(\n", + " -result.state.current_minimum_eval[i],\n", + " fig,\n", + " axes[i],\n", + " cmap=\"viridis\",\n", + " label=f\"Picked particle {i+1}\",\n", + " )\n", + " for i in range(n_images)\n", + "]\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not bad! To know if we're right, it would be good to take the peak cross-correlation and plot the image that corresponds to those parameters. To do so, define some functions to extract the parameters from the peak, simulate an image from those parameters, and also compute the corresponding cross-correlation grid." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "@partial(eqx.filter_vmap, in_axes=(0, (None, 0, None)), out_axes=eqxi.if_mapped(0))\n", + "def extract_solution_at_minimum(final_state, args):\n", + " pipeline_grid, per_particle_pipeline, non_per_particle_pipeline = args\n", + " image_index_at_minimum = jnp.argmin(final_state.current_minimum_eval.ravel())\n", + " raveled_grid_index_at_minimum = final_state.current_best_raveled_index.ravel()[\n", + " image_index_at_minimum\n", + " ]\n", + " tree_grid_index_at_minimum = tree_grid_unravel_index(\n", + " raveled_grid_index_at_minimum, pipeline_grid\n", + " )\n", + " solution = tree_grid_take(pipeline_grid, tree_grid_index_at_minimum)\n", + " return eqx.combine(\n", + " solution, eqx.combine(per_particle_pipeline, non_per_particle_pipeline)\n", + " )\n", + "\n", + "\n", + "solution_filter_spec = jax.tree_util.tree_map(\n", + " lambda x, y: x or y, per_particle_filter_spec, tree_grid_filter_spec\n", + ")\n", + "solution_pipeline = extract_solution_at_minimum(\n", + " result.state, (pipeline_tree_grid, per_particle_pipeline, non_per_particle_pipeline)\n", + ")\n", + "\n", + "\n", + "@partial(cx.filter_vmap_with_spec, filter_spec=solution_filter_spec)\n", + "def simulate_solution_image_stack(pipeline):\n", + " return pipeline.render()\n", + "\n", + "\n", + "@partial(cx.filter_vmap_with_spec, filter_spec=solution_filter_spec, in_axes=(0, 0))\n", + "def compute_solution_cross_correlation(pipeline, fourier_observed_image):\n", + " fourier_simulated_image = pipeline.render(get_real=False)\n", + " return (\n", + " irfftn(\n", + " fourier_observed_image * jnp.conj(fourier_simulated_image),\n", + " s=pipeline.instrument_config.shape,\n", + " )\n", + " / pipeline.instrument_config.n_pixels\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Simulate the solution and compute its corresponding cross-correlation\n", + "n_images = particle_stack.image_stack.shape[0]\n", + "solution_image_stack = simulate_solution_image_stack(solution_pipeline)\n", + "solution_cross_correlation = compute_solution_cross_correlation(\n", + " solution_pipeline, fourier_image_stack\n", + ")\n", + "\n", + "fig, axes = plt.subplots(figsize=(3 * n_images, 8), ncols=n_images, nrows=3)\n", + "fig.suptitle(\"Best fit particle vs observed particle\")\n", + "for i in range(n_images):\n", + " observed = particle_stack.image_stack[i]\n", + " simulated = solution_image_stack[i]\n", + " cc = solution_cross_correlation[i]\n", + " plot_image(observed, fig, axes[0, i], label=f\"Picked particle {i+1}\")\n", + " plot_image(simulated, fig, axes[1, i], label=f\"Best fit {i+1}\")\n", + " plot_image(cc, fig, axes[2, i], label=f\"Cross correlation {i+1}\", cmap=\"plasma\")\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cryojax", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data/ribosome_4ug0_scattering_potential_from_cistem.mrc b/docs/examples/data/ribosome_4ug0_scattering_potential_from_cistem.mrc index 43071456..0470713b 100644 --- a/docs/examples/data/ribosome_4ug0_scattering_potential_from_cistem.mrc +++ b/docs/examples/data/ribosome_4ug0_scattering_potential_from_cistem.mrc @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0c4d341baa0683a56dc6e3b514144024c98dd7037af9c40bd16052cba277863a +oid sha256:6904d9890796bbbafb42ea39565d8eb6c115f8155ddf63baf394a316be78c340 size 2049024 diff --git a/docs/examples/read-dataset.ipynb b/docs/examples/read-dataset.ipynb index e812d070..954b8d5e 100644 --- a/docs/examples/read-dataset.ipynb +++ b/docs/examples/read-dataset.ipynb @@ -63,7 +63,7 @@ " RELION STAR file for a particle stack. Upon accessing an image in the particle stack, a `RelionParticleStack`\n", " is returned. Specifically, the `RelionParticleStack` stores the image(s) in the image stack, as well as the metadata\n", " in the STAR file. The metadata is used to instantiate compatible `cryojax` objects. For example, the `RelionParticleStack`\n", - " stores a `cryojax` models for the contrast transfer function (the `CTF` class) and the pose (the `EulerAnglePose` class).\n", + " stores a `cryojax` models for the contrast transfer function (the `ContrastTransferFunction` class) and the pose (the `EulerAnglePose` class).\n", "\n", " More generally, a `RelionDataset` is an `AbstractDataset`, which is complemented by the abstraction of a particle stack: the `AbstractParticleStack`.\n", " These abstract interfaces are part of the `cryojax` public API! " @@ -80,16 +80,13 @@ "text": [ "RelionParticleStack(\n", " image_stack=f32[100,100],\n", - " config=ImageConfig(\n", + " instrument_config=InstrumentConfig(\n", " shape=(100, 100),\n", " pixel_size=f32[],\n", + " voltage_in_kilovolts=f32[],\n", + " electrons_per_angstrom_squared=f32[],\n", " padded_shape=(100, 100),\n", - " pad_mode='constant',\n", - " rescale_method='bicubic',\n", - " wrapped_frequency_grid=FrequencyGrid(array=f32[100,51,2]),\n", - " wrapped_padded_frequency_grid=FrequencyGrid(array=f32[100,51,2]),\n", - " wrapped_coordinate_grid=CoordinateGrid(array=f32[100,100,2]),\n", - " wrapped_padded_coordinate_grid=CoordinateGrid(array=f32[100,100,2])\n", + " pad_mode='constant'\n", " ),\n", " pose=EulerAnglePose(\n", " offset_x_in_angstroms=f32[],\n", @@ -99,11 +96,11 @@ " view_theta=f32[],\n", " view_psi=f32[]\n", " ),\n", - " ctf=CTF(\n", - " defocus_u_in_angstroms=f32[],\n", - " defocus_v_in_angstroms=f32[],\n", + " ctf=ContrastTransferFunction(\n", + " defocus_in_angstroms=f32[],\n", + " astigmatism_in_angstroms=f32[],\n", " astigmatism_angle=f32[],\n", - " voltage_in_kilovolts=f32[],\n", + " voltage_in_kilovolts=300.0,\n", " spherical_aberration_in_mm=f32[],\n", " amplitude_contrast_ratio=f32[],\n", " phase_shift=f32[]\n", @@ -201,7 +198,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we see that the `image_stack` attribute has a leading dimension for each image. We can also inspect the metadata read from the STAR file by printing the `CTF`." + "Now, we see that the `image_stack` attribute has a leading dimension for each image. We can also inspect the metadata read from the STAR file by printing the `ContrastTransferFunction`." ] }, { @@ -213,11 +210,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "CTF(\n", - " defocus_u_in_angstroms=Array([10050.97, 10050.97, 10050.97], dtype=float32),\n", - " defocus_v_in_angstroms=Array([9999.999, 9999.999, 9999.999], dtype=float32),\n", + "ContrastTransferFunction(\n", + " defocus_in_angstroms=Array([10050.97, 10050.97, 10050.97], dtype=float32),\n", + " astigmatism_in_angstroms=Array([-50.970703, -50.970703, -50.970703], dtype=float32),\n", " astigmatism_angle=Array([-54.58706, -54.58706, -54.58706], dtype=float32),\n", - " voltage_in_kilovolts=Array(300., dtype=float32),\n", + " voltage_in_kilovolts=300.0,\n", " spherical_aberration_in_mm=Array(2.7, dtype=float32),\n", " amplitude_contrast_ratio=Array(0.1, dtype=float32),\n", " phase_shift=Array([0., 0., 0.], dtype=float32)\n", @@ -234,7 +231,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that not all attributes of the `CTF` have a leading dimension. For those familiar with RELION STAR format, only the `CTF` parameters stored on a per-particle basis have a leading dimension! Parameters stored in the opticsGroup do not have a leading dimension." + "Notice that not all attributes of the `ContrastTransferFunction` have a leading dimension. For those familiar with RELION STAR format, only the `ContrastTransferFunction` parameters stored on a per-particle basis have a leading dimension! Parameters stored in the opticsGroup do not have a leading dimension." ] }, { @@ -294,14 +291,12 @@ "# ... and the image in fourier space\n", "fourier_image = rfftn(relion_particle.image_stack)\n", "# ... and the cartesian coordinate system\n", - "pixel_size = relion_particle.config.pixel_size\n", + "pixel_size = relion_particle.instrument_config.pixel_size\n", "frequency_grid_in_angstroms = (\n", - " relion_particle.config.wrapped_frequency_grid_in_angstroms.get()\n", + " relion_particle.instrument_config.wrapped_frequency_grid_in_angstroms.get()\n", ")\n", "# ... now, compute a radial coordinate system\n", - "radial_frequency_grid_in_angstroms = jnp.linalg.norm(\n", - " frequency_grid_in_angstroms, axis=-1\n", - ")\n", + "radial_frequency_grid_in_angstroms = jnp.linalg.norm(frequency_grid_in_angstroms, axis=-1)\n", "# ... plot the image in fourier space and the radial frequency grid\n", "fig, axes = plt.subplots(figsize=(5, 4), ncols=2)\n", "plot_image(\n", @@ -361,7 +356,7 @@ "\n", "\n", "fig, ax = plt.subplots(figsize=(4, 4))\n", - "N_pixels = math.prod(relion_particle.config.shape)\n", + "N_pixels = math.prod(relion_particle.instrument_config.shape)\n", "spectrum, wavenumbers = powerspectrum(\n", " fourier_image,\n", " radial_frequency_grid_in_angstroms,\n", diff --git a/docs/examples/simulate-image.ipynb b/docs/examples/simulate-image.ipynb index cef6e744..b3e12cb5 100644 --- a/docs/examples/simulate-image.ipynb +++ b/docs/examples/simulate-image.ipynb @@ -4,9 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This tutorial demonstrates a basic usage of the image formation pipeline in `cryojax`.\n", - "\n", - "It will demonstrate almost all of the modeling components that can be used when simulating a single image. This includes models for the instrument optics, electron dose rate, detector, and solvent. These models are all a work in progress." + "This tutorial demonstrates a basic usage of the image formation pipeline in `cryojax`." ] }, { @@ -41,122 +39,97 @@ ] }, { - "cell_type": "code", - "execution_count": 3, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# CryoJAX imports\n", - "import cryojax.simulator as cs\n", - "from cryojax.image import operators as op\n", - "from cryojax.io import read_array_with_spacing_from_mrc" + "First, import the `cryojax` simulator. We will import this with the import hooks from `jaxtyping`, which will give our functions run-time type checking capability. See [here](https://docs.kidger.site/jaxtyping/api/runtime-type-checking/#runtime-type-checking) to learn more." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# Scattering potential stored in MRC format\n", - "filename = \"./data/ribosome_4ug0_scattering_potential_from_cistem.mrc\"" + "# CryoJAX imports\n", + "from jaxtyping import install_import_hook\n", + "\n", + "\n", + "with install_import_hook(\"cryojax\", \"typeguard.typechecked\"):\n", + " import cryojax.simulator as cxs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "First we must read in our 3D scattering potential into a given voxel-based representation of the `potential`. Here, this is the `FourierVoxelGrid`. Then, we choose an integration method onto the exit plane. Here, we use the fourier-slice projection theorem with the `FourierSliceExtract` integrator. In general, the `integrator` will depend on the scattering potential representation." + "First we must read in our 3D scattering potential into a given voxel-based representation of the `potential`. Here, this is the `FourierVoxelGridPotential`. Then, the representation of a biological specimen is instantiated, which also includes a pose and conformational heterogeneity. Here, the `SingleStructureEnsemble` class is used, which does not model heterogeneity." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# Read template into a FourierVoxelGridPotential and choose an integrator\n", + "from cryojax.data import read_array_with_spacing_from_mrc\n", + "\n", + "\n", + "# Scattering potential stored in MRC format\n", + "filename = \"./data/ribosome_4ug0_scattering_potential_from_cistem.mrc\"\n", + "# Read template into a FourierVoxelGridPotential\n", "real_voxel_grid, voxel_size = read_array_with_spacing_from_mrc(filename)\n", - "potential = cs.FourierVoxelGridPotential.from_real_voxel_grid(\n", + "potential = cxs.FourierVoxelGridPotential.from_real_voxel_grid(\n", " real_voxel_grid, voxel_size, pad_scale=2\n", ")\n", - "integrator = cs.FourierSliceExtract(interpolation_order=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we must instantiate the biological specimen. A `Specimen` takes in the `potential`, `integrator`, and also a `pose`. Here, we represent the `pose` with an `EulerAnglePose`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate the pose and build the specimen\n", - "pose = cs.EulerAnglePose(\n", - " offset_x_in_angstroms=-2.0,\n", - " offset_y_in_angstroms=5.0,\n", + "# ... now, instantiate the pose. Angles are given in degrees\n", + "pose = cxs.EulerAnglePose(\n", + " offset_x_in_angstroms=5.0,\n", + " offset_y_in_angstroms=-3.0,\n", " view_phi=20.0,\n", " view_theta=80.0,\n", " view_psi=-10.0,\n", ")\n", - "specimen = cs.Specimen(potential, integrator, pose)" + "# ... now, build the ensemble. In this case, the ensemble is just one potential and a\n", + "# pose\n", + "structural_ensemble = cxs.SingleStructureEnsemble(potential, pose)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now, it's time to configure the imaging instrument. We can include models for the instrument optics, the electron dose, and the detector. Here, we create an instrument just with an optics model, and one that also includes a a detector model. We will see in a few lines why we have done this." + "Next, build the *scattering theory*. The simplest `scattering_theory` is the `LinearScatteringTheory`. This represents the usual image formation pipeline in cryo-EM, which forms images by integrating the potential and convolving the result with a contrast transfer function." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "# Initialize the instrument\n", - "voltage_in_kilovolts = 300.0\n", - "dose = cs.ElectronDose(electrons_per_angstrom_squared=100.0)\n", - "optics = cs.WeakPhaseOptics(\n", - " ctf=cs.CTF(\n", - " defocus_u_in_angstroms=10000.0,\n", - " defocus_v_in_angstroms=9000.0,\n", - " astigmatism_angle=20.0,\n", - " amplitude_contrast_ratio=0.07,\n", - " )\n", + "from cryojax.image import operators as op\n", + "\n", + "\n", + "# Initialize the scattering theory. First, instantiate fourier slice extraction\n", + "potential_integrator = cxs.FourierSliceExtraction(interpolation_order=1)\n", + "# ... next, the contrast transfer theory\n", + "ctf = cxs.ContrastTransferFunction(\n", + " defocus_in_angstroms=10000.0,\n", + " astigmatism_in_angstroms=-200.0,\n", + " astigmatism_angle=10.0,\n", + " amplitude_contrast_ratio=0.1,\n", ")\n", - "detector = cs.PoissonDetector(dqe=cs.IdealDQE(fraction_detected_electrons=1.0))\n", - "instrument_with_dose = cs.Instrument(voltage_in_kilovolts, dose=dose)\n", - "instrument_with_optics = cs.Instrument(voltage_in_kilovolts, dose=dose, optics=optics)\n", - "instrument_with_detector = cs.Instrument(\n", - " voltage_in_kilovolts, dose=dose, optics=optics, detector=detector\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Optionally, we can choose a model for the solvent. Here, we model the ice as gaussian colored noise with `GaussianIce` and choose an analytical model for the power spectrum taken from the `cryojax.image.operators` module. Here, we choose the `Lorenzian`, whose abstract base class is an `AbstractFourierOperator`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Then, choose a model for the solvent. The amplitude is the\n", - "# (squared) characteristic phase shift of the ice phase shifts, and the length_scale is\n", - "# their characteristic length scale.\n", - "solvent = cs.GaussianIce(\n", + "transfer_theory = cxs.ContrastTransferTheory(\n", + " ctf, envelope=op.FourierGaussian(b_factor=5.0)\n", + ")\n", + "# ... add a non-white noise model for the solvent\n", + "solvent = cxs.GaussianIce(\n", " variance=op.Lorenzian(amplitude=0.005**2, length_scale=2.0 * potential.voxel_size)\n", + ")\n", + "# ... now for the scattering theory\n", + "scattering_theory = cxs.LinearScatteringTheory(\n", + " structural_ensemble, potential_integrator, transfer_theory, solvent\n", ")" ] }, @@ -164,53 +137,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we create an `ImageConfig` and initialize our `ImagePipeline`. In this example, we would like to simulate images at each stage of the image formation process. This is controlled by the modeling complexity in the `Instrument`, which here has three levels.\n", - "\n", - "**1. If the `Instrument` just has an accelerating voltage and a dose:** In this case, the returned \"image\" is the phase shifts in the exit plane.\n", - "\n", - "**2. If the `Instrument` also has an optics model:** The returned \"image\" here is the squared wavefunction in the detector plane.\n", - "\n", - "**3. If the `Instrument` also has a detector model:** Last, the returned \"image\" is the detector readout." + "Finally, we create an `InstrumentConfig` and initialize our `AbstractImagingPipeline`. Here, we select the `ContrastImagingPipeline`, which simulates the image contrast given a `scattering_theory`." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "# Create the image configuration\n", - "config = cs.ImageConfig(\n", - " shape=(80, 80), pixel_size=potential.voxel_size, padded_shape=potential.shape[:2]\n", - ")\n", - "# ... now, build the image formation models\n", - "scattering_pipeline = cs.ImagePipeline(\n", - " config=config, specimen=specimen, instrument=instrument_with_dose, solvent=solvent\n", + "# Create the instrument configuration\n", + "instrument_config = cxs.InstrumentConfig(\n", + " shape=(80, 80),\n", + " pixel_size=potential.voxel_size,\n", + " voltage_in_kilovolts=300.0,\n", + " padded_shape=potential.shape[:2],\n", ")\n", - "optics_pipeline = cs.ImagePipeline(\n", - " config=config,\n", - " specimen=specimen,\n", - " instrument=instrument_with_optics,\n", - " solvent=solvent,\n", - ")\n", - "detector_pipeline = cs.ImagePipeline(\n", - " config=config,\n", - " specimen=specimen,\n", - " instrument=instrument_with_detector,\n", - " solvent=solvent,\n", - ")" + "# ... now, build the image formation model\n", + "imaging_pipeline = cxs.ContrastImagingPipeline(instrument_config, scattering_theory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Before proceeding, we must create jit-compiled functions that simulate our images." + "Before proceeding, we must create jit-compiled functions that simulate our images. We can either simulate the model without noise by calling the `imaging_pipeline.render()` function, or we can simulate an image with noise by passing a random number generator key as `imaging_pipeline.render(rng_key)`." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -219,15 +175,15 @@ "\n", "\n", "@eqx.filter_jit\n", - "def compute_image(pipeline: cs.ImagePipeline):\n", - " \"\"\"Simulate an image without noise from a `pipeline`.\"\"\"\n", - " return pipeline.render()\n", + "def compute_image(imaging_pipeline: cxs.AbstractImagingPipeline):\n", + " \"\"\"Simulate an image without noise from a `imaging_pipeline`.\"\"\"\n", + " return imaging_pipeline.render()\n", "\n", "\n", "@eqx.filter_jit\n", - "def compute_noisy_image(pipeline: cs.ImagePipeline, key: PRNGKeyArray):\n", - " \"\"\"Simulate an image with noise from a `pipeline`.\"\"\"\n", - " return pipeline.sample(key)" + "def compute_noisy_image(imaging_pipeline: cxs.AbstractImagingPipeline, key: PRNGKeyArray):\n", + " \"\"\"Simulate an image with noise from a `imaging_pipeline`.\"\"\"\n", + " return imaging_pipeline.render(key)" ] }, { @@ -236,21 +192,21 @@ "source": [ "**What's with the eqx.filter_jit?**\n", "\n", - "This is an example of an equinox *filtered transformation*. In this case, the `eqx.filter_jit` decorator is a lightweight wrapper around `jax.jit` that treats all of the `pipeline`'s JAX arrays as traced at compile time, and all of its non-JAX arrays as static. Alternatively, we could have used the usual `jax.jit` decorator and explicitly passed traced and static pytrees to our function. It is completely optional to use `equinox` decorators.\n", + "This is an example of an equinox *filtered transformation*. In this case, the `eqx.filter_jit` decorator is a lightweight wrapper around `jax.jit` that treats all of the `imaging_pipeline`'s JAX arrays as traced at compile time, and all of its non-JAX arrays as static. Alternatively, we could have used the usual `jax.jit` decorator and explicitly passed traced and static pytrees to our function. It is completely optional to use `equinox` decorators.\n", "\n", "Filtered transformations are a cornerstone to `equinox` and it is highly recommended to learn about them. See [here](https://docs.kidger.site/equinox/all-of-equinox/#2-filtering) in the equinox documentation for an introduction." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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lF7Rq1QqdOnXCv//9b+TPnx+JiYm49tprsWjRIvTo0QPlypXDunXrMGzYMGzZssUJYU7pHGXgwIF48skn0bp1a7Ru3RqrVq1CixYtcOrUKac8qfVvyRHp2dcwLiqX+q3YhTBz5kwvQ4YMXoYMGbzatWt7DzzwgDdjxgzv1KlTznlbt271oqOjveuuuy7sVz3+9VlXwnie5911111e1qxZnbfabdq0SfIN8fLly5P8NTwxMdErVaqUl5CQEPZ98fHxXvPmzf2/hd7033zzzWHXj7RSKiYmxvv+++/9v3377bceAO+VV17x/9auXTsva9as3u7du/2/bd261cuYMWOKV0oB8P73v//5fzt58qRXpUoVL1++fH6dDx8+3APgvf/++/55p06d8mrXru1lz57dO3LkiOd551YAQFY+hGjatKlXqVIlp84TExO9OnXqeKVKlfL/FnrD36xZM6de77vvPi9DhgzeoUOH/L9VqFAhyV/uk1oplVQ/GDp0qBcVFeXt3LnT/1tKV0ql9Hq9evVK0fU8z/N+//1377LLLvO6d+/u/H3fvn1ezpw5nb8fO3bMK1mypFehQgXvjz/+8Nq0aePFxcU53+154SsJJkyYkKJf60OEVk706dPH/1tiYqLXpk0bLyYmxjt48KD/d237pOpoyZIlHgDvvffe8/+W0jZPTf0Yfz4ptdVr1qzxAHj33HOP8/dbbrnlglZKJdW/EhISvOLFizt/C60WmT59uvP3J554wsuWLZu3ZcsW5+8PPfSQlyFDBn+1xaRJkzwA3nPPPeefc+bMGa9+/frJrpTyvHM2gFdA9e/f32vQoIGXL18+f1XqL7/84kVFRTkrqlJqY2rXru1Vq1bNOW/ZsmXOOEuNvwrZT101ktqVUrlz53ZW7Xz++eceAG/y5Mn+3xo0aODlyJEjzG5xGUOrwfRXX88Lt2/9+vXzAHgLFy70//b777978fHxXrFixfy5Qugey5Ur5508edI/96WXXvIAeOvWrQv7Lialti219rZKlSpewYIFHT83c+bMsF+wFy5c6AHwPvjgA+fz06dPD/t7JD+Z0v4/d+5cD4DXt2/fsGtwO2XLli3sV33P87z27dt7MTEx3rZt2/y/7dmzx8uRI4fXoEED/28hP1CvXj1nJXskQm14xRVX+HMQz/O8jz/+2APgjKWk7Iq24alTp7yKFSt6TZo0cf6e0vnYHXfc4RUsWND7+eefnc936tTJy5kzZ5J9xjD+bJJbKeV5npczZ07v6quv9tMpna9Hsm+pmatdiA8IzS3uvPNO5+///e9/PQDe3Llz/b9FmgdEIvR8NHLkSOfvY8eO9aKjox0f43meN3LkSA+A9/XXX/t/S8kc5cCBA15MTIzXpk0b554ffvjhsBWnKfVvKV0p5XmRn30N42LxlxY6b968OZYsWYJrr70W3377LZ577jkkJCTgiiuuwBdffOGfN2nSJCQmJmLgwIGIjnZvOSoqyj/mX8p+//13/Pzzz6hfvz6OHz+O77777rzLuWbNGmzduhW33HILfvnlF/z888/4+eefcezYMTRt2hQLFixAYmKi85lIK1iSolmzZv4vjcC5FUdxcXH44YcfAJxbvTJ79my0b98ehQoV8s8rWbIkWrVqleLvyZgxI+666y4/HRMTg7vuugsHDhzAypUrAZwT6y5QoABuvvlm/7xMmTKhb9++OHr0KL766qvA7/j1118xd+5c3HjjjX4b/Pzzz/jll1+QkJCArVu3Yvfu3c5nevTo4bRj/fr1cfbsWezcuTPF98ZwPzh27Bh+/vln1KlTB57nYfXq1Zf8egAwa9YsHDp0CDfffLNfRz///DMyZMiAWrVqYd68ef65WbNmxejRo7Fp0yY0aNAAU6ZMwbBhw1CkSJHz+u7k6N27t38c+iX41KlTmD17dsTPcB2dPn0av/zyC0qWLInLLrsMq1atCjs/uTZPTf0Yfz4ptdVTp04FAH+lUIgLXTnA/evw4cP4+eef0bBhQ/zwww84fPiwc258fDwSEhKcv02YMAH169fH5Zdf7vSnZs2a4ezZs1iwYIFf/owZM6Jnz57+ZzNkyOCLxSZH/fr1sX//fmzevBnAuRVRDRo0QP369bFw4UIA51ZieJ7nrJRKqY256aabsHLlSmzbts3/2/jx4xEbG4t//etfAM7PX10oN910kyOQHrq3kA87ePAgFixYgG7duoXZLbYDqWHq1KmoWbMm6tWr5/8te/bs6NGjB3bs2IGNGzc653ft2hUxMTERyxiJ1Nq2lLB3716sWbMGXbp0Qc6cOf2/N2/eHOXLl3fOnTBhAnLmzInmzZs7fbdatWrInj17imxhSvv/xIkTERUVhccffzzsGsm109mzZzFz5ky0b98exYsX9/9esGBB3HLLLVi0aBGOHDnifKZ79+7IkCFDsuUPcdtttyFHjhx++oYbbkDBggV9uxMJbsPffvsNhw8fRv369ZNsv+TmY57nYeLEiWjXrh08z3PqMyEhAYcPHz7vfmEYfzbZs2f3d+E7n/m6ktK52oX6gNAY79+/v/P3//znPwCAKVOmOH9Pah4QRGxsLLp27er8bcKECShXrhzKli3r3FuTJk0AwLG9KZmjzJ49G6dOnUKfPn2ce05qfpRa/2YY6YG/dPgeANSoUQOffvopTp06hW+//RafffYZhg0bhhtuuAFr1qxB+fLlsW3bNkRHR4dN1pQNGzbg0Ucfxdy5c8MmP/rgkhq2bt0KAIEhHIcPH3Ym5fHx8Sm+flIvFy6//HL89ttvAM6J0Z44cQIlS5YMOy+pv0WiUKFCYUKipUuXBgDs2LED11xzDXbu3IlSpUqFvfwLLdNN7kXR999/D8/z8Nhjj+Gxxx5L8pwDBw7giiuu8NN6/6F6DN1/atm1axcGDhyIL774Iuwa59MPLvb1gP/vUyHnpsTFxTnpunXromfPnhgxYgQSEhL8cMuLTXR0tPNAAbh9JBInTpzA0KFDMWrUKOzevdsJWU2qjpJr89TWj/HnkxJbvXPnTkRHRzsPdQCSXTqfHF9//TUef/xxLFmyBMePH3fyDh8+7DzYJ2V7t27dirVr10YMBQsJfu/cuRMFCxYMW9ae0vKHXnQsXLgQV155JVavXo0nn3wSefPmxQsvvODnxcXFoXLlyv7nUmpjOnbsiP79+2P8+PF4+OGH4XkeJkyYgFatWvlj4nz81YWS3HgOPdAnF/qZGnbu3BkWDga4voq/73z9TGptW0rLDgClSpUKyytTpozzUmPr1q04fPgw8uXLl+S1UrLRQEr7/7Zt21CoUCHkypUr2WsqBw8exPHjx5McK+XKlUNiYiJ+/PFHP4QVSN08CQivr6ioKJQsWTLZHcG+/PJLPPnkk1izZg1OnjzpfF5Jbj528OBBHDp0CG+++SbefPPNJL8vPW/+YPyzOXr0qG9Lzme+rqR0rnahPiA0t9BnngIFCuCyyy4LezZJrW254oornB8tgHP3tmnTpmTtJpCyOUoku583b94wf5xa/2YY6YG//EupEDExMahRowZq1KiB0qVLo2vXrpgwYUKSv9glxaFDh9CwYUPExcVhyJAhKFGiBDJnzoxVq1bhwQcfvKBfhkOfff7551GlSpUkz9GHmNTs8hDpl0Ke/P5VCNXVf//734i/UqhTuZj3f/bsWTRv3hy//vorHnzwQZQtWxbZsmXD7t27cfvtt6e6H1zs64UIfW7s2LEoUKBAWL7u0njy5EnMnz8fwLkHh/S2M0ifPn0watQo9OvXD7Vr10bOnDkRFRWFTp06JVlHybV5auvHSDsu1FaHiPTr6NmzZ530tm3b0LRpU5QtWxYvvvgiChcujJiYGEydOhXDhg0L619J2d7ExEQ0b94cDzzwQJLfGXrxeqEUKlQI8fHxWLBgAYoVKwbP81C7dm3kzZsX9957L3bu3ImFCxeiTp06/ov/1NiYQoUKoX79+vj444/x8MMPY+nSpdi1axeeffZZ516B1PkrJSoqKkn7q20T4q/gw863jKm1bRebxMRE5MuXDx988EGS+SnRlUyr/p9a0mIHsIULF+Laa69FgwYN8Nprr6FgwYLIlCkTRo0ahXHjxoWdn1Lf9O9//zvii9+QvqZhpCd++uknHD582J+Dn898XUnruVpKV9am1rZEmjdUqlQJL774YpKfKVy4MIDUz1EuJimdRxlGWvC3fDILbWW6d+9eAOdENBMTE7Fx48aIk+z58+fjl19+waeffooGDRr4f9++fXvYuZEGcaS/h371j4uLQ7NmzVJ8HxeLfPnyIXPmzPj+++/D8pL6WyT27NkTtu3yli1bAMAXIixatCjWrl2LxMREZ7VUKPyxaNGiACLXVWiVTaZMmS5qXaXUEa1btw5btmzBmDFjcNttt/l/nzVr1nl9b2qul5owlFCfypcvX4rq6fHHH8emTZvwwgsv4MEHH8RDDz2El19+OfAz5xMWk5iYiB9++MF5SNE+khSffPIJunTpgv/973/+3/744w8cOnQo1WUAUl8/xqVBbXXRokWRmJiIbdu2OSsmQuFszOWXX55k/9BfPCdPnoyTJ0/iiy++cFYxpCaEs0SJEjh69Giyfalo0aKYM2cOjh496ry4Sar8kahfvz4WLFiA+Ph4VKlSBTly5EDlypWRM2dOTJ8+HatWrcLgwYP981Nrs2666Sbcc8892Lx5M8aPH4+sWbOiXbt2zr0CF+avLr/88iTD2s43pDrkF9avXx94XmpsVtGiRZNsF/VVF0pKbVtqyw78/yoDRu+pRIkSmD17NurWrZvsg1bQHCYl/b9EiRKYMWMGfv3118DVUkl9T968eZE1a9aIbRIdHe0/xJ0vWl+e5+H7778PfAk0ceJEZM6cGTNmzEBsbKz/91GjRp1XGfLmzYscOXLg7Nmz5puMvxShTXhCL6BSM19P7vkoubnahfqA0Nxi69atzgYS+/fvx6FDhy6avWdKlCiBb7/9Fk2bNg207ymdo7Dd54iEgwcPhq3YTal/C62wUn+UlK8+31B5w0gpf2lNqXnz5iX5K2Uodjj0UNO+fXtER0djyJAhYW+cQ58P/brF1zt16hRee+21sOtny5YtyWX3oZc1OrirVauGEiVK4IUXXsDRo0fDPnfw4MGI93gxyJAhA5o1a4ZJkyZhz549/t+///57TJs2LcXXOXPmDN544w0/ferUKbzxxhvImzcvqlWrBgBo3bo19u3b5+yCdubMGbzyyivInj07GjZsCAD+Kh2tq3z58qFRo0Z44403/AdV5nzrKlu2bCl6wZFUP/A8Dy+99NJ5fW9qrhep/yRFQkIC4uLi8PTTT+P06dNh+VxP33zzDV544QX069cP//nPf3D//ffj1VdfTVbfKzXlYV599VX/2PM8vPrqq8iUKROaNm0a8TMZMmQIG8uvvPLKef9ak5r6Mf58UmqrQxp3+sKUd/cKUaJECRw+fBhr1671/7Z3796wXXSSGoOHDx9O1UPljTfeiCVLlmDGjBlheYcOHcKZM2cAnLN/Z86cweuvv+7nnz17Fq+88kqKv6t+/frYsWMHxo8f74fzRUdHo06dOnjxxRdx+vRpR08qtTarQ4cOyJAhAz788ENMmDABbdu2dX5ouBj+qkSJEvjuu++cc7/99lt8/fXXyX42KfLmzYsGDRrg3Xffxa5du5w8vu/U2KzWrVtj2bJlWLJkif+3Y8eO4c0330SxYsWSDfdPKSm1bakpe8GCBVGlShWMGTPGmYvMmjUrTCvkxhtvxNmzZ/HEE0+EXefMmTPO90Xykynt/x06dIDnec5L0xDaTvo9GTJkQIsWLfD555874XT79+/HuHHjUK9evQsOu37vvfd8PRzg3AvDvXv3BmprZsiQAVFRUU577dixw9k5KzVkyJABHTp0wMSJE5N8wDbfZKRH5s6diyeeeALx8fHo3LkzgNTN1yPZt5TO1S7UB7Ru3RpA+FwitIrpQnf4TYobb7wRu3fvxltvvRWWd+LECX/H0JTOUZo1a4ZMmTLhlVdecc5Nan6UUv8WeikY0gUEzs1ZkgotjvTsaxgXi7/0Sqk+ffrg+PHjuO6661C2bFmcOnUKixcvxvjx41GsWDFfdK5kyZJ45JFH8MQTT6B+/fq4/vrrERsbi+XLl6NQoUIYOnQo6tSpg8svvxxdunRB3759ERUVhbFjxyb5IFWtWjWMHz8e/fv3R40aNZA9e3a0a9cOJUqUwGWXXYaRI0ciR44cyJYtG2rVqoX4+Hi8/fbbaNWqFSpUqICuXbviiiuuwO7duzFv3jzExcVh8uTJf2pdDRo0CDNnzvS1hc6ePYtXX30VFStWxJo1a1J0jUKFCuHZZ5/Fjh07ULp0aYwfPx5r1qzBm2++6W/b3aNHD7zxxhu4/fbbsXLlShQrVgyffPIJvv76awwfPtwXGc2SJQvKly+P8ePHo3Tp0siVKxcqVqyIihUrYsSIEahXrx4qVaqE7t27o3jx4ti/fz+WLFmCn376Cd9++22q779atWp4/fXX8eSTT6JkyZLIly9fkjHsZcuWRYkSJfDf//4Xu3fvRlxcHCZOnHje+lSpuV7oxV7fvn2RkJCADBkyoFOnTkleNy4uDq+//jpuvfVWVK1aFZ06dULevHmxa9cuTJkyBXXr1sWrr76KP/74A126dEGpUqXw1FNPAQAGDx6MyZMno2vXrli3bl2YTliIKlWqIEOGDHj22Wdx+PBhxMbGokmTJhG1SQAgc+bMmD59Orp06YJatWph2rRpmDJlCh5++OHAMJG2bdti7NixyJkzJ8qXL48lS5Zg9uzZ/hbaqSWl9WOkDSm11VWqVMHNN9+M1157DYcPH0adOnUwZ86cJFd0durUCQ8++CCuu+469O3b199GunTp0o6mTosWLRATE4N27drhrrvuwtGjR/HWW28hX758SU6kk+L+++/HF198gbZt2+L2229HtWrVcOzYMaxbtw6ffPIJduzYgTx58qBdu3aoW7cuHnroIezYsQPly5fHp59+mqqJXOiF0+bNm/H000/7f2/QoAGmTZuG2NhY1KhRw/97am1Wvnz50LhxY7z44ov4/fffcdNNNzn50dHRF+yvunXrhhdffBEJCQm44447cODAAYwcORIVKlQI02tMKS+//DLq1auHqlWrokePHoiPj8eOHTswZcoU34eFbOgjjzyCTp06IVOmTGjXrl2SNu6hhx7Chx9+iFatWqFv377IlSsXxowZg+3bt2PixIlhuojnS0ptW2rt7dChQ9GmTRvUq1cP3bp1w6+//opXXnkFFSpUcF4mNmzYEHfddReGDh2KNWvWoEWLFsiUKRO2bt2KCRMm4KWXXsINN9zg119SfjKl/b9x48a49dZb8fLLL2Pr1q1o2bIlEhMTsXDhQjRu3NjfBKNatWqYPXs2XnzxRT9ktVatWnjyyScxa9Ys1KtXD/fccw8yZsyIN954AydPnsRzzz13wW2RK1cu1KtXD127dsX+/fsxfPhwlCxZ0t/ePinatGmDF198ES1btsQtt9yCAwcOYMSIEShZsqTzQjw1PPPMM5g3bx5q1aqF7t27o3z58vj111+xatUqzJ49G7/++uv53qJhXDDTpk3Dd999hzNnzmD//v2YO3cuZs2ahaJFi+KLL75A5syZ/XNTOl8Psm8pnatdiA+oXLkyunTpgjfffNOXa1m2bBnGjBmD9u3bo3Hjxhe9Hm+99VZ8/PHHuPvuuzFv3jzUrVsXZ8+exXfffYePP/4YM2bMQPXq1VM8R8mbNy/++9//YujQoWjbti1at26N1atXY9q0aciTJ4/z3Sn1bxUqVMA111yDAQMG+CtcP/roI/+HBibSs69hXDT+tH390oBp06Z53bp188qWLetlz57di4mJ8UqWLOn16dPH279/f9j57777rnf11Vd7sbGx3uWXX+41bNjQmzVrlp//9ddfe9dcc42XJUsWr1ChQv625ZBtMY8ePerdcsst3mWXXRa2/fLnn3/ulS9f3suYMWPYFuCrV6/2rr/+ei937txebGysV7RoUe/GG2/05syZ458T2s784MGDYeVPaqtzAF6vXr3CztXtrz3P8+bMmeNdffXVXkxMjFeiRAnv7bff9v7zn/94mTNnjlTFPg0bNvQqVKjgrVixwqtdu7aXOXNmr2jRot6rr74adu7+/fu9rl27enny5PFiYmK8SpUqJbkV+uLFi71q1ap5MTExYdu9b9u2zbvtttu8AgUKeJkyZfKuuOIKr23btt4nn3zinxNp+9qktjLdt2+f16ZNGy9HjhweAH/b66TO3bhxo9esWTMve/bsXp48ebzu3bv72zrzfSTVHkmR0uudOXPG69Onj5c3b14vKioqRdeeN2+el5CQ4OXMmdPLnDmzV6JECe/222/3VqxY4Xme5913331ehgwZvG+++cb53IoVK7yMGTN6PXv29P+WVJ956623vOLFi3sZMmRIdrvyLl26eNmyZfO2bdvmtWjRwsuaNauXP39+7/HHH/e3nw2h7f3bb7/5fSZ79uxeQkKC991334WVKTVtnpL6MdKG1NjqEydOeH379vVy587tZcuWzWvXrp33448/hvUZz/O8mTNnehUrVvRiYmK8MmXKeO+//36S4/KLL77wrrrqKi9z5sxesWLFvGeffdZ79913w7ZCLlq0qNemTZsk7+H333/3BgwY4JUsWdKLiYnx8uTJ49WpU8d74YUXvFOnTvnn/fLLL96tt97qxcXFeTlz5vRuvfVWb/Xq1WHjPYh8+fJ5AJy6WbRokQfAq1+/ftj5KbUxId566y0PgJcjRw7vxIkTSZYhJf4qNO4mTJgQ9vn333/fK168uBcTE+NVqVLFmzFjhtelSxfHX27fvt0D4D3//PNhn0+qvdevX+9dd9113mWXXeZlzpzZK1OmjPfYY4855zzxxBPeFVdc4UVHRzvtm5R927Ztm3fDDTf416tZs6b35ZdfOudEusdQ2ZNr05TaNs9Lnb31PM+bOHGiV65cOS82NtYrX7689+mnn4bVcYg333zTq1atmpclSxYvR44cXqVKlbwHHnjA27Nnj39OJD/peSnv/2fOnPGef/55r2zZsl5MTIyXN29er1WrVt7KlSv9c7777juvQYMGXpYsWcK2Ml+1apWXkJDgZc+e3cuaNavXuHFjb/Hixc69pGTreibUhh9++KE3YMAAL1++fF6WLFm8Nm3ahG0vn1T9vfPOO16pUqW82NhYr2zZst6oUaMueD62f/9+r1evXl7hwoW9TJkyeQUKFPCaNm3qvfnmmym6J8O42ITGVehfTEyMV6BAAa958+beSy+95B05ciTJz6Vkvu55wfYtpXO1C/EBp0+f9gYPHuzFx8d7mTJl8goXLuwNGDDA++OPP5zPB80DkiL0fJQUp06d8p599lmvQoUK/rNntWrVvMGDB3uHDx/2z0vpHOXs2bPe4MGDvYIFC3pZsmTxGjVq5K1fv/68/VvovGbNmnmxsbFe/vz5vYcfftibNWtWqp59DeNiEOV56UhJ1Ehz2rdvjw0bNiSpTcE0atQIP//8c7Lx3MY/l9tvvx2ffPJJkiE/hnGhhLaaHzRo0KUuimEYfyHmz5+Pxo0bY8KECf6qMMMwDMMw0g9/aU0pI3WcOHHCSW/duhVTp05Fo0aNLk2BDMMwDMMwDMMwDMP4x/KX1pQyUkfx4sVx++23o3jx4ti5cydef/11xMTERNzm2TAMwzAMwzAMwzAM48/CXkr9g2jZsiU+/PBD7Nu3D7GxsahduzaefvpplCpV6lIXzTAMwzAMwzAMwzCMfximKWUYhmEYhmEYhmEYhmGkOaYpZRiGYRiGYRiGYRiGYaQ59lLKMAzDMAzDMAzDMAzDSHPSnaZUYmIi9uzZgxw5ciAqKupSF8cwjH8onufh999/R6FChRAd/f/v7//44w+cOnXqvK4ZExODzJkzX6wi/mMxP2EYRnrgYvsJ8xEXB/MRhmGkB+xZIuWku5dSe/bsQeHChS91MQzDMAAAP/74I6688koA55xIfHw89u3bd17XKlCgALZv3/63dCZpifkJwzDSExfLT5iPuDiYjzAMIz1hzxLJk+5eSuXIkQMAnDeK/GYxW7ZszvnHjh1z0hkyZIh47ZMnT0Y8Vxv2zJkzEa/5yy+/OOkTJ074x5dddpmTFxMTE/HcLFmyOHl6L3zf2bNnd/J+++03Jx0XF+cfq3b92bNnnTTnZ8zodgEtL5+r9Xf48GFEIleuXBGvo79a8X0Cbh1pebhd9FqxsbFO3h9//OGkufzapqF+FyJTpkz+sbY31zXg1oOWL2/evBHztE35Ovz9SX32yJEjEc/V+uR60T6mbcFlSkxMdPK0v54+fTriudpX+NcA/c7LL7/cP/79998RBPcH7Rval48fP+4f//zzz06e1j3XYai+EhMTsWPHDqdvnDp1Cvv27cOuXbvC+kFyHDlyBEWKFMGpU6f+do4krQm1yQMPPOC3V4kSJfx8tiFAuN9Q+8loX2ayZs3qpNm25suXz8n79ddfnTSPPR47QLjNzpkzZ5LHQPjY4ns7evRoxLIDwObNm/3jcuXKOXlaJk6rLdV62L59u3+su7nyeNd613HI5yZn14J8tPoCtuFan9re3G46xvPnz++kt2zZ4h+rPyxYsGDE8qvt4r6h96n9iO2Rlk9/dWU/fOjQISfvp59+ctJsP3ksAcCBAwecNPs1nVRrX+YyqX3XOuJ203Pz5MnjH2s/V79WoEAB/3jHjh2B38mfVT+r1+WXHKFzT5w4gXvvvfei+AnzERePUHv06dPH71fcp3Q8qA2pXLmyf6z2ZOHChU6ax4v22/j4eP9469atTh7bTQC4+uqr/WMdV1WrVnXShQoV8o/VJ2i/ZTvVrl07J2/evHlOulixYv6x2hOdu/F40TpSu8W2XuuIv1Pn4t99952TZlteqVIlJ0/9FJ+rY1vnBDwPVZuxf/9+Jx16sQCE26J169Y56datW/vHa9eudfLYpgFAyZIl/WOto3HjxvnHt912m5On9rlevXr+sfaxxYsXO+ncuXP7x+oLg54P9+zZ4+RxnQCu/+YxAIT7Hh4/c+fOdfK0b/O9aR2tXLnSP1Y/37ZtWyf99ddf+8dcBwDw7bffOukmTZr4x1999ZWTpy++77nnHv/4wQcfBHBuHL377rv2LJEC0t1LqZDRi46OTvKllDoOncBpOqXn6nV5cGqeGmZOJ1ceTl/IuUEvdnRSGJTW6wbVQ3JliFQe/c7kXkqlpo7+rLrnekjNdfXe+Dpq8IP6clrdd3JtEVRefdmZ0jIE1VHQ92u+lieoPi+kzyXVz+Pi4lLtSIyLR6hNYmNjfaesL0oYzdPJKxP0UkpfzPIY0Emuvhjj6+pkL+gFsJZd+znnB5UdcH980evqS6mgF8laD0HX5c9qvet1+Fx9cXMhL6X4BbWWT+uM203P1Tbm8uvkWe+Ny6/lC/KzOulMTd/g8uoDpl6X+6ReV++F8zVP5xt8P9rH9LNcL/oQyd+pvkfbkM/V+wxqf/1O/R7+rN6L+Yn0BfuIUL/iMaD2RccO9xvtQ/rCnPutjjPu4/qdQdfRc4PGYNC8Xa+rPwQEjY/knn14vOh11P9x+XXs8HcG2Vj9Hh3LajvZ7idne4JshtprzleboW3K3xN0HS2DXjfIPut1uI31XO1X/Fm1d3ovQT8KB42R5PwJt7nWkRJUR1wmvU5Qv9fy6L3xdybXhmzv9VzzEcmT7l5KJYX+osFop+S0vinVyRL/mqCDTw0qE2QA9NfyoBUu2mH1FwI+Vx2dGm5eCaKrlHSA8YOBPiSoUQqaUPLbZf1lRh+6+BdyvY7WPd9b0IowwHWaWtf6K4T+csOw8wLc+k7ugVbbggl6eaRv+bkO9T713rhva90HOZLkfvkKmqDraiO+n6AHJ8Dt2zoOuT71c1oG/lVK20zbhX99036tY0/vDQh+wPc8L+w+kiO15xvJU7BgQb/P8K+Z3PYA8MMPPzhp7g9qL3Vcsp378ccfnbyghwgdhzxOr7jiCidPV2Pu3LnTP2Y/kBRcPu1jaoevuuoq/1jtkZaX61NXCB08eNBJV6lSJcnyAO4v/urj1G5wHekv0Fo+/mxyK0A5X3/hVVvL/lx/Idf65PbX+1Ybw78Oly5d2snjOlK7o/6F+5m2A7cvAGzcuNE/1rlJmTJlnDTb0/Xr1zt5+lmekxUtWtTJ41+rAffXf71vfZHH/Vd9FY89rVv10dx3dFwGrTwL6mOar/OYpEitnzAfcfG56qqr/P7BK0W5XwLhKz14VZPOD2644QYnzStVdMUkz2F0zNWtWzdiuRs2bOikN23a5KS5j2/YsMHJ01VVbEN0JYr2cZ5H6bhXW/nxxx/7x2obGzdu7KQ5imTNmjVOHs9np06d6uTdfffdTnr16tX+8fTp0508nef179/fP3722WedPF05w88aalebNm3qpFesWOEfs+8Dwm0Ir7rZvXu3k6e2nW0cr0YFgOeff94/Vh+mK/eKFy/uH+v8QVdyXX/99f6x+k0dE8uWLfOPeSUhEO6v+dmM6wsIf0HEcy6e+wDuyigAGDNmjH+sK7B49aCOLbX7tWrV8o95JSEQHvG0bds2/1j7tc4XeWVfaB4a9COoPUu4/CVeShmGYaQnzJEYhmEYQdhLKcMwDCMS9izhYi+lDMMwUok5EsMwDCMIeyllGIZhRMKeJVzS7Usp1pTipdrJhTLwMm5dRhsUZ62w6F2QaDPgLv/WZZ/6nbwcVu8lKMxOl3lqp+RlwRp6qKEhvFRWl2vqMkO+V71vXr6uS3c17IrrSEPygmKe9b512Sej963LKrkMydU9X0u/U++Nl6nqsk9ejqshRRp+FiS+rO3CfTdI2wkIDkXUJbd8rSDtJwBhon2MjgOuQ13ezOMrSGsFcEMydXzrMvSgcA8dP0mFolr4XvonPj7etxG8xFqFP1XcmPuOjtkgfScNP+Nl9Nqv9TvZ9mvoj4bSsT1SuxEkIKu2icNVtIzJ6TMWKVLEP1Yboz6RbZcK9LKIqYrWqj1iO6L3ovWwa9cu/1jbLEi7SssepH+odaRhguxLVWZA66F8+fL+sYbHcRmChOwBt+9ouLOWgetQ+7mWgW0gz3+AcP/OfVnDLTQclttUz9XxxO0YZH81NF/rjH2X1p/KOrAouvqx5cuXO+mk5jz63Yy9lLr07N271x8nt956q//3iRMnOufpfILbQsOtJ02a5KRvuukm/7hixYpOHtsBDj0DwsPheExqH9dwLh7bQXqAgDvX5BCnpM5lG62+RoXFOUxM7bOGifE40bkah+G1atXKyXvmmWecNIdzqY3QkEwOBWzWrJmTt3Tp0ojXVduoYvDs01atWuXkaWhijx49/GMNu1ObHCSXwu39/fffO3l6b9x3de7L5QHcPqmblMyaNctJ16lTxz9WnzB//nwnzb5dx4/eG4eBakiePkvUr1/fP9Z+xJu3TJ48OfA7uS9rfd55551OmsP7NPy1bNmyTppDKUPzLx1jjD1LuKTbl1KGYRjpFXMkhmEYRhD2UsowDMOIhD1LuNhLKcMwjFRijsQwDMMIwl5KGYZhGJGwZwkXeyllGIaRSsyRGIZhGEHYSynDMAwjEvYs4ZJuX0qdOXPGj69lzQzVwFHdG44xVa0I3d6Z9QxUU4j1iFT3QmNp+bp6neS26GaCdJiC8rS8qisRpNuh2huqn8TaS0Fbx+rnVN+JY7+1DlSLJUhLS3WYuL61L2j7c1r7kZaJY721DbU+WW9FdQm4fLwlOBCuvcE6HlonWr9cR9rnNE5dtbUYrTNuN71v1RnhMgRpegCuNovWEZ+r8e/aj1jTRetP+z3r3OjWxvo9XL7QfQUZfnMk6YN169b5eiGFCxf2/666MRrTz1szB+nJAa6t1S2SWa9P9YYUPld1/lijQtFtpNX+sG1lnSUgXCNi7969/rHet9YRX0ttipb3hx9+8I+vuuoqJ0+3U2dUY6NGjRr+sWpAaP3ymFbbqnXEtkJ1UvR7WHNF7YT6H+4bqu+k5eX6VL0YtoGqM6PtxNu/q46LznHYr6luhtpLLh/rXwHh44ltK2tRAeFzCrateq6Wgcurvp/rQbW+tHysCVOzZs2I3wG4/oj7MRA+3pPSlArSdLSXUpeew4cP+/OTadOm+X9XnaCqVas6aZ5zb9++3ckbOHCgk3733Xf9Y+3/pUuX9o9Z+zWp67L2jmoT6VhRO8ao3WL7p/NitWmq6cOwbiMAJCQk+Meqw1S3bl0nzeNs7dq1Th7bXPUJtWvXdtLs79QvqV/lMmzcuNHJU9/I+nE67rUMrDGkZdA25nmz+kJNsx9TW7lkyRL/uGjRok6ezvHZP2s/0e+sVq2af/zmm286eay3B7j1q3N89ftXX321f6wan6oFxrZetXV5Xge4flXHGreFPiNXqVLFSfO8qkWLFk7eihUrnDT7VdatAsJ9Ofej//znPwDOtQ/bCMaeJVyikz/FMAzDMAzDMAzDMAzDMC4u6XallGEYRnrFft0wDMMwgrCVUoZhGEYk7FnCxV5KGYZhpBJzJIZhGEYQ9lLKMAzDiIQ9S7ik25dS0dHRfuwza10kp4PBceAaV60aBRxbrZpNnMe6SkC43g9rLalegca8cry0alBoPDTrCOl1NSaX48I1xlnP5XtVPQ29V45r1jri+tQ8/U5uN9VH0nho1otQXY4gTS5F65PLoBoUqgvB9aJ5qq3F+Rqvz+2vOida19xuqnmlcPm1TvS+uW207+r38L3p+NF24zhr1ZvScclx11p/HEOuda3l5fvWvqvl5fGl19E6Y02s0OcSExPD9K9CmCNJHxw/fty3NWwrtC+odgePNR2HqlfDfUDHC2t1qH9RfZ/8+fMn+f0AULBgQSfN36N9MOh7dPyoPgN/r96nak2wZpPqUmj9VqxY0T/esWOHk8fjUD+ndpjHod4363gArm6G2hQda6wTphoQatd27tzpH/N9AeFtyvZdNV+0PlmfI2/evE4et3dy9py1PDRPbSL3OfVNem7x4sX9Y/XJ+llG63P16tVOWv1e0HW5r+i9sZaHjgHVAGH9rCJFijh52u+5DMnpgnG/CvUbnevo+fZS6tLSokULv61YU4/7OwDUr1/fSX/11Vf+sdqIDz/80Ek3adLEP1ZNQtauUf0mtav//ve//WP1S6zRo+iY++677yJ+VvU/tQysWVenTh0nT23aokWL/OMKFSo4eUHPFiVLlkQkVq5c6aRVW4vrSLVCVX+I21vHtmo1sk3R+1y6dKmTZs09tWH8nQDw2Wef+ceq+Vi2bFknzfWybt06J4/1vFinDAifb48dO9Y/1jri9gXce+3YsaOTp32FbafeN+uCAa6/Xrx4sZPHOlaAO//ROtIxs2nTJv+4ZcuWTh5rAmr7qtYX+2D9Tq17rpetW7ciCL63kG6d2gTGniVc0u1LKcMwjPSKORLDMAwjCHspZRiGYUTCniVc7KWUYRhGKjFHYhiGYQRhL6UMwzCMSNizhEu6fSmVLVs2f6kgL/vUxtAloryUWpfc6hL/oO28eZmifqcuW+Rln7pNpsJhRLrsW5c/8n3rdXVZJS+31+WOuqSVlyrqMvigcDndqpXDEbSO9LpcPg1z0HbitIY/ajqovnW5Lrd/ctfhdtLr6PJ/vncNY+TwHl1iq2Ea3N4a8qB1xuEeujxX4RCOoHAewO3b2j81nILrU/uc1if3OS1vUPiDhvdwCKbWtY5vDinUNtTy8fLa0HdczLCM0GeMi8uxY8f8fsBLtzVMTMcs26OgEFHAHRM8lgB3TGt/1DHLvkq/88cff3TS3Ffi4uKcPO27fK7aeu3DbN81T7fBZhukWzrreOJ83tYaAIoVK+Yfq1/QMcuhJOoPtU25ftWmqH0PCjtQSQAOWdH71lA1Xuav4UBaXrb32v5cPu7HQHi/4v6ZnP/mEEgNGQwK52O/BQTbQu3n6ue4jLzlPRAeohQUys3zDx0D6sd4y3QNp9H5Rs2aNf1jDfPlMEC9VsOGDQGE2xbGXkpdembNmuXbmapVq/p/163dNeSNw8SmTJni5HHoMABs3rzZP9bQL+6r+p2a5u+pVKmSk7dw4UInzTaOw8mA8JBVDp9iSY6k4DGpdkrLxONl1apVTp76E54D1qhRw8njkKx69eo5eepr2N5oiLeORbbtGgbYs2dPJ831q/NknY+zbY+Pj3fyVMpi3759SX4OCA8p4+8NCi9VP/T222876Xbt2iV5Tb0O4PrZGTNmOHn8jAe49at1Mn/+fCddt25d/1jHi9pvbhsNGdVwOR7D+vzP41B9oYbes39p0aKFk6fjh+vok08+cfLYfwBu24T6nz4fMfYs4ZJuX0oZhmGkV8yRGIZhGEHYSynDMAwjEvYs4RJZvdIwDMMwDMMwDMMwDMMw/iRspZRhGEYqsV83DMMwjCBspZRhGIYRCXuWcEm3L6UyZszox69yHK5uVaxaAhyrqtt3a5q1GVQHgWNnNf5Vz+Xyaeyo6iJwZ9Lrqg5Gjhw5/GPVH1KNB46t1Vhf1Z9ijRLdDlY1KljPQvO4HrQdDh486KRZv0L1P1Qzg+9N44I1Hp7rTDUzgra+1XbhutYyqPaGXpfLoPHkQdo1qmXC9aD9SNuf+45ufapx67zFuerlaAw+13fQ1tmKjkvVwWEdDx0/3F/1O3XMch/U79R2YS2CIP0uwG2LUBmC9MrMkaQPcubM6euFcN/Q8ay2i/UvVD9Ht+/lfqD9iHXWVANJ7dyOHTsi5ul1efyorVINH7ZVqdHKU+0T7Z/8Wd2OXP0Gj1PVidKxxqgtYPuuGoZ6XbZzqjui9cBlUD0O9aW89bb6UrWfXCbVBdPtvrlNdVtu9p/a/1RbhO2e2kCtI/YpfF+Aq/UFBM95WPMFcLWhVLNJdUh47Kk/13kCl1f9GPdXbV+df/BY1PbVOcT69ev9Yx0DqinG2j2h62idM/ZS6tJz+PBhv7+wHZg6dapzXvXq1Z30pEmT/GMdk2rrFyxY4B83bdrUyWOdntAW8SFUY401plSbTzWvWMtGx8OKFSucNGv66HVKly7tpHlM6vxr2rRpTprH1vXXX+/kcZ0AwPLly/3jIBv81VdfOXnqn3m86VhWn1GqVClEQu+F57esSQeE+2duG7YfQLhOGGtKqZZWwYIFnfSyZcv8Y7WrS5cu9Y9Vx0r7LpdfbazOhbhd9BmKyw64mmKq0aV+iv1htWrVnDzV6uN5iWqR6bkTJkzwj++++24nj/ur1pE+f3Xs2NE/1rmQjqdFixb5x3qf2k78LBHSUdPrMfYs4ZJuX0oZhmGkZ/7OjsEwDMO4cMxPGIZhGJEwH/H/2EspwzCMVGK/bhiGYRhB2EopwzAMIxL2LOFiL6UMwzBSiTkSwzAMIwh7KWUYhmFEwp4lXNLtS6ljx475Mb6sFaOxvRqrGXRukDYUazIBbgy5av9oTC5rfmjcqpaBdRI0Tl11hPheVKNHtRlYx0G1f7Zv3+6kgzR8tLys8aDx25ynuiGq6cKojojqgfC1VCtC24LrTOtT64G/hzWOgPD65M+qJoXGuHMcu/ZHbhetW9Ug4fbWOtH+yX1Qz1VtJW5vvY7WZ9B9K3xdrfsgnbCgdtG+oeXje1PDrGOP81UbRPsVj6+QRlfQ/ZsjSR/kzp3b7z/cxr/99ptznvaNIkWK+Meq0aT9M3/+/P6xtiFrVujY0nGoeg2M2n62R1oe/R7WJQlpGITQccn9XvU2VOeItUZUq0Nh39q4cWMnjzUh1AerZgnbT/VNhw8fdtI8PtX/aHtzWjVAgjTuSpYs6eSpfh9rgmhda1twGVU3g8uneiZ6bzzfKFy4sJOnfmz//v0Rz1XbyvpofAyEzz+4H+n8Qr+H9TqqVq0aeF3uZ9qGfN/aF9QnB2l46HjifqbjsE6dOk6afXZIS0Q/w9hLqUvPFVdc4esQse3s1KmTc57q/LFfUPs3d+5cJ81aovPnz3fy2H+wDhQQrvczZ84c//iaa65x8nT+zTo9W7ZscfLUR7AOk2pI6djh665bt87JUy1B1pFSrSr1q//5z3/8Y7XtPK4efvhhJ081pvheVfNPNV353urXrx9YPp6fq+1Rux+kfadagtxuapeWLFnipNmWaDtxnan90/ks22TVuFKfy3pJqh2pmlKsYcj6epoHAG+//bZ/3L59eydP/SrXp+oXqv9r27atf6xzrAYNGvjH+nyl2lTvv/++f6zjReuB/dTGjRudvNatWzvpcePG+ceh52AtJ2PPEi7p9qWUYRhGesUciWEYhhGEvZQyDMMwImHPEi6Rt9MyDMMwLjkjRoxAsWLFkDlzZtSqVcvZoUXZsGEDOnTogGLFiiEqKgrDhw+/4GsahmEYhmEYhmH8WfwlVkrxW0FdVqlLETlsTJeI6tJJXg6u4Wa8jFK3/NWleMltHc9w+TXsKiikTEMDtLxcJr1PXabP5dWtWbU++Xt1uSuXX7ct161OuXwcUqDlAdzQNC27LuXk8gdtGa7fo3WkoXXcdzSMS8vEy7O1nbjO9Du1vLz8VfuuLlPm8Ar+fiA4lFKXqWof5CWvupxdP8tLubW8Wvdchxrap3XPaGgN15EuZ9d74TrSMBHtc7yMPjT2g36NSKtfN8aPH4/+/ftj5MiRqFWrFoYPH46EhARs3rw5rH2Ac2UvXrw4OnbsiPvuu++iXDM9kyFDBr/v8VJ9XW6v9pLthraL9g0ew+p/uI/p+NA091e1IRpCsXPnTkRCw494jGjYlY4RLv+OHTsCz2Vbpv1C65dDttQ/8thLbmk+2wId+xqGsHXrVv9Yw0q0vbmNOaQaCA9RYL+rNlpD0zi8JSh8GHDrUP0jhwRoaJ+GagRtG69+gutBwwK1nbi+tQzqA3meUKZMGSdP5xS8Zbb2OZVC4PLrGOBwFu2rWkfcbuqLdPzw+NZ+o3MyDpUN+SL1+YytlLr0/Pjjj34f4B9fNDxKbdHChQv94969ezt5b7zxhpPmsFQdV3Xr1vWPZ8yY4eStWLHCSXNIoZanUaNGTprD1bUP6lyIx5XOtzZv3uykeWwVL17cydPwW/YDhQoVcvJ0nsfPVGrbOe+TTz5x8jTsnUP0dGxraBXnq50vVqyYk+a+ofNFDaXkkGQtw5dffumk9VqM2j8uo/o09lsaIqih4jyP1/vUeuB203vRMDu2+zpnUdv+7LPP+scapqj9np+NNGS0du3aTpp9ntprbv+KFSs6edqXOeQxZ86cTp62Ifv9q666ysn7/PPPnTS3aaifa39nbKWUi62UMgzDSCUhR5Laf6nlxRdfRPfu3dG1a1eUL18eI0eORNasWfHuu+8meX6NGjXw/PPPo1OnTmETjPO9pmEYhpF60sJHGIZhGH9N0upZ4q9Cql5KhUJC9F+vXr0AnHvb2atXL+TOnRvZs2dHhw4dwlbFGIZh/NW5EEdy5MgR518kEcRTp05h5cqVaNasmf+36OhoNGvWLOyXp5TyZ1xTMT9hGIZhL6UiYT7CMAzDXkopqXoptXz5cuzdu9f/N2vWLABAx44dAQD33XcfJk+ejAkTJuCrr77Cnj17nN0ZDMMw/g5ciCMpXLgwcubM6f8bOnRokt/x888/4+zZs2Hhmfnz5w9bgp1S/oxrKuYnDMMw7KVUJMxHGIZh2EspJVWaUhrb+8wzz6BEiRJo2LAhDh8+jHfeeQfjxo1DkyZNAACjRo1CuXLlsHTp0rCY3OQ4fvy4H/vPcf2qeaRaNqw5o+Erul0yx6PqagW+rsaDahxw0Jb2QduOJrcVOZ+rMa/JbZfNaOwv36vGfat2RFAdcf2q9obWNceMqx6EwvHFGv+uW74yuiWpthPreOh2obqFPMdvq16WxjGXKFHCP9atg7k/6NbZql3D44tj1oFwzQDuK7oltWqbcF/Rsmu/Ya0b7Z+qV8Jtrvo52l/5V84gfSytkyBtBO2raqjZbmj9qV4D95VQe549exYbNmxAUlxIHPiPP/7o6AxECrP7q5KWfiJTpkxh+kxAuAZSUFvpdsraV1TbgeH+qPZHxyGPU7UT+p1sl9UvqD1nG6M6Clp2tq06vrUe2O5pH9V74+uqPeIxrbpGet9cL+ondLyzvo/aVtXv43bizwHh98af/eKLL5w81YKqUqWKf6zbsgfpSCxevNhJV69e3T9W364+j224arxoGbivzJ4928nT+mXtJ/1OPZftl97n6tWrnTTbcJ1DqG4Ut0WQr09Ov4v7kZ6r45T1QrgOgPB743EZ6tfq+7RcqfETf+eHDSYtfUTGjBn9eWToekD4/FC3jK9WrZp/PG/ePCdPddTYpqjtYd2lhIQEJ081AHnep3pmOg9lrSDN0/k335t+J98nAMyfP98/ZvsGhPsMnvvquNK5E79UfOqpp5y8+vXr+8c6N2PtLCD8XpnQS82kPtuyZUsnb9KkSU6abZzOH7RMbL+bNm3q5On8tkiRIv6x6nepJmCQ/itr9WkdbNu2zUmzfpJuYtO4cWMnzX1Fy646UWzL1YfpZ9nvq13VH0W5DDfddJOT9+qrrzrpWrVq+cf6nMF1rXWr/oR9e+fOnZ08vReeV02bNs3J02dfrvuQ7qXOXZgLeZZIKQsWLMDzzz+PlStXYu/evfjss8/Qvn17Pz+SLvZzzz2H+++/H8C5uWSfPn0wefJkREdHo0OHDnjppZccO7V27Vr06tULy5cvR968edGnTx888MADqSrreWtKnTp1Cu+//z66deuGqKgorFy5EqdPn3bCQsqWLYsiRYpctLAQwzCM9MCF/LoRFxfn/Iv0UipPnjzIkCFDWNjC/v37w8SaU8qfcc0gzE8YhvFPxX4BTx7zEYZh/FNJi5VSx44dQ+XKlTFixIgk83nV6t69e/Huu+8iKioKHTp08M/p3LkzNmzYgFmzZuHLL7/EggUL0KNHDz//yJEjaNGiBYoWLYqVK1fi+eefx6BBg/Dmm2+mqqznvfvepEmTcOjQIdx+++0Azin6x8TEhP3amlxYyMmTJ51fz/SNtGEYRnojLX7diImJQbVq1TBnzhz/V43ExETMmTMnbDegS3nNIMxPGIbxT8VWSiWP+QjDMP6ppMWzRKtWrdCqVauI+fqD9Oeff47GjRv7q742bdqE6dOnY/ny5f6q7ldeeQWtW7fGCy+8gEKFCuGDDz7AqVOn8O677yImJgYVKlTAmjVr8OKLLzovr5LjvFdKvfPOO2jVqlXYNqCpZejQoY6+im65bBiGkd5Iqzjw/v3746233sKYMWOwadMm9OzZE8eOHUPXrl0BALfddhsGDBjgn3/q1CmsWbMGa9aswalTp7B7926sWbPGWb6f3DUvJuYnDMP4p2IrpZLHfIRhGP9ULuRZIqWbJqWG/fv3Y8qUKbjjjjv8vy1ZsgSXXXaZIzPQrFkzREdH45tvvvHPadCggSOnkZCQgM2bN4fJ4wRxXiuldu7cidmzZ+PTTz/1/1agQAGcOnUKhw4dcn7hSC4sZMCAAejfv7+fPnLkCAoXLoyYmBhfS4N1pFRjSGMh+deR5LQZOBZUtao43lwbWn/B4VhqjX9WLQaOndWYYY1NZTQOXGNpuSNoeVWXh+ORVdMjqK1UX4HTGjOr7cTfqTH3el3Wq9A6Uf0YbrcgnRMAzq9sGjMcHx8f8XtUpyModlrLy21cvnx5J0/biWPwVe9AtWF4wqVtqP2cy6v6NNpfuQ417lu/hw2NtotqGvCYUR0zLpNqXincr7TfqKYBX1dtgdoN7p8hGxIUB55W3HTTTTh48CAGDhyIffv2oUqVKpg+fbrfNrt27XLuc8+ePbj66qv99AsvvIAXXngBDRs29LUikrvmxSIt/ESWLFl8+8b6NKr9o/aIx5P2R7XL3HeCdNa0j6kmCNtw7avqU9gG6i/+rC0BuLoPajdUw4l1FvQ6+lDIY/jbb7918ho0aOCkly5d6h+rfSxdurR/rHZCNStYL0bvW23KwYMH/WPWNgHCbWuQ5pVqY7BOk2oeXXnllU6a7ZW2oep8cBn0XL4XrT+1c+yP1N+oVg+H6Wr7qu3n7+H+B4RrgnDIsdpsHcfLly/3j1kHBwgfe9w/9Lrcbmq/dbxz+dR36ndyv7rqqqucvI0bNzpp1SPTchmpIy18xL/+9S/fLn7wwQd+PtslIHy+s3DhQv+4cuXKYeVmSpUq5R9rf+D5t+osNWzYMOK5qs2mekQ8Z1V7wuUBXNujfXzt2rVOmse+ah/qw2XZsmX9Y13FdsMNNzjpQYMG+ceq2cRzS7V/Ol7ZTuncXNuQx68+Q5UrV85Js+387LPPnDydN7NtUjuv53LdlyxZ0snTeQCvZvn444+dPPZT2t4VKlSIeG5oBWKI9957D5FQ+6xzBK4jtbmq0/vDDz/4x8npQ3J96hxLddh4nqVzBC4Da08B4TpcrKmk47JTp05Oes6cOf5x8+bNI+YBbv8N2R2df14s9MX7448/7oyz82HMmDHIkSOHowG3b9++sDGbMWNG5MqVyx/3+/btC3uODo3Hffv2BWpEOtc9n0KPGjUK+fLlQ5s2bfy/VatWDZkyZcKcOXP8OMTNmzdj165dqF27dsRrxcbG/u2Efg3D+HuTFktuQ/Tu3TtiaB2LkgLnJhUp+Z6ga14szE8YhvFPxsL3gjEfYRjGP5n0tmnSu+++i86dOwcukvkzSfVLqcTERIwaNQpdunQJ2x3ujjvuQP/+/ZErVy7ExcWhT58+qF27dqp3yzAMw0jPpOVLqb8i5icMw/inYy+lImM+wjCMfzoX8iwR2izpYrFw4UJs3rwZ48ePd/5eoECBsJXVZ86cwa+//uqvXi1QoECSGyiF8lJKql9KzZ49G7t27UK3bt3C8oYNG+ZvFXjy5EkkJCTgtddeS+1XADh3M6El2ry0T5dta8gWN5Auo9UldLycU8Ph+Lq6hFXDOzhMI7nKDwo3047J36NLDzU0jd+Q6hJ+PVeXjDJav7xkT5fR8nV06e7hw4edNC/11OvoUlRut6Cl0IBb93qfei+81FHPVfh7tXxBoYq6RJHDaXRZqoaj8HJyXQqr4Sm8RFjfjmsf5OXOWp/aV3hprNaRhsTw92ie3hu3k5aBx7eO0aB+rnnar3g7dM3T0BD+Xj03KeylVDBp5Se4HXhpvI41XVKvIQKM2hh2tBp+xjZG7Q0vXwfcrYLVBmvf4HM1VERDzXmJvYYq6blJbWkfQpew16lTxz/WUMTQVsdJfa/6R7ZlGv6kD5m7du3yj3W5uIav8LbsHP4GhNto3tp87ty5gWXgtqlRo4aTp8vlebm62mHtYzNmzPCP1b63aNHCP9Y2VN/P/kj9jYavcHhkUBg/4I4fzdPrcn1zm2n5ALd+1U/oZ3kepr/Usi/VkBntV+x/2A8A4f2er6t1pPNCnguG8jSsnbGXUpFJKx+xZMkSvy9xWI7OUdRHrFmzxj++7bbbnDwdvx9++KF/HCR7oGGw+kDJ/UttjfYznlOrvdOHQ2bdunVOWueWPBfSe9Hxy3Wk8zH1J40aNfKPNZR4+/bt/jGHTwPA7t27nTSHpulYVpvL4YX6UK31yfZbwxQ1DJ59Y3Kh7XyvOicoUqSIk/7oo49SVF79DrW5FStW9I+1r6qMCH9W20x9ET8zq63UOQ0/W6h91n7Pcwa9b40G4FBate08ZjS0U/0z93N9DlLbwONA52P6fqBmzZr+cWhMBGk9padniXfeeQfVqlULC1euXbs2Dh06hJUrV6JatWoAzs2jEhMT/TDJ2rVr45FHHsHp06f9dxuzZs1CmTJlUhy6B5zHS6kWLVpErJDMmTNjxIgREbcdNAzD+DuQnhxJesT8hGEY/3TspVRkzEcYhvFPJy2eJY4ePepsdrR9+3asWbMGuXLl8l+MHjlyBBMmTMD//ve/sM+XK1cOLVu2RPfu3TFy5EicPn0avXv3RqdOnfyXrrfccgsGDx6MO+64Aw8++CDWr1+Pl156CcOGDUtVWf8c9S3DMIy/MfZSyjAMwwjCXkoZhmEYkUiLZ4kVK1agcePGfjq0IUSXLl0wevRoAOdW6Xmeh5tvvjnJa3zwwQfo3bs3mjZt6q9iffnll/38nDlzYubMmejVqxeqVauGPHnyYODAgejRo0eqymovpQzDMM4De4AwDMMwgjA/YRiGYUTiz/YRjRo1SvY7evToEfgCKVeuXBg3blzgNa666ipn19LzId2+lLryyiv9GG+ON9XYaU2zZoZuZ6laMawloNobjOqIqF4Jx0uqnoZqM7COlObpvbCOh56r2jt8rt6LxkdzLLDqNmgdcX5q6khjnLleNH5X44s5NlljmjVWPkivQnVGGI0n13vj+tb60zh7jj/We+Hyq5aSxnYH6bJovD6ndSt63Wadr6U6Ztr+nK/tpH2DY791K3ptC25T1engulbDqduh83W0z+m9cLto+2p5eTzpdZPCVkqlD06cOOHrSnBfUU0AhfVD1O6qtghrMuhW22zn9HM6Lvl7VAtB7eWqVav8Y70X1osA3LGmY1RtF2tuqDaCaovwWFP7w1tt6/eqfgT3e61rte98r6wdApybE0S6rto11Z9iW6XjW31BUls6h1B7xN+jbaiaMLxF+oIFC5w8toGqpaXwvWp7a1/hLb5Vq0O1B1WfhVHbyloyWn+qNXL11Vf7x6pFpluZc19W/RUuv2qcqb9krS/VD1Ttk6DvVI2an3/+2T8O6WEF6VPaSqlLz5EjR/w2Hj58uP933hIecHWCAHcc6sOY6h4FaUHxnFXbV+0f+wX1H2q3eB6lmq46Hti2d+7c2ckL7XAYgvWK1N7pcwc/Y+mcT+0Nl3Hy5MlOHuvZqfaPjl+2eTqWp0+f7qTZxqld1fplfbuEhAQn75tvvnHSIR2dpMqnumHLli1L8jsA4MYbb3TS1atX94/Z1gCuz1WNHtUJY7ulcw2eWwCuXprqT+nzDNeZlk/bgucBrLmWVJlYQ1P9/B133OGkWfdK5yH8zKK+kdsBcPtG3bp1nTy9lyVLlvjHOl6OHDnipFlPOeSndH7C2LOES3TypxiGYRiGYRiGYRiGYRjGxSXdrpQyDMNIr9ivG4ZhGEYQtlLKMAzDiIQ9S7ik25dSp0+f9sMhOPRGl/3p0nFO69JrXSqbO3du/1iXcnJaw430ulw+XT6vnYfDATQ0QOEl/XquLk3kJfP6nRo2xktwdXmuhjxy+Id+J4eraOiHLsHlz+pSRg2HC6p7DTHgpdEaGqLhFBzOp/fN2+sCbptq6IIuo+al0kFhYtzfgPBlv9x3dctcDRHgvqyhnQqXSe9b4a1vdbwEbeup7RK0XJWXt+p1td/offNntR00fIrTuhxXwz84JDd0L9qfGHMk6YOoqCi/vXi7Yh0/utSc7aWGG2lf5mXoat95fKud1fHOtjQoRBBw+7b2c15KDrjL3dWW6hjm0IIyZco4eerX+Hs1lFe3DNawJ4ZDANSGaAghh4VpCMp3333npHl8avvq93BabaCGW7Av4z4FhIdfBNlsrU+uIw3BrF27tn88Z84cJ0/nPByGoH1VQ4e472r/1Dbk0Dq1jxr6EuR31QfydbVdNByIv0fLy9+j8yH1geyzdUt59ec8FvU79V44zCTUP/UzjL2UuvSULVvWH4s8tnX+9f777ztpHr8aBj1lyhQnXalSJf/422+/dfJYpkF9jdrn0qVL+8fqa9QGc5nU9qh0xddff+0fr1y50skLbfEegudcy5cvd/KqVKnipNm2awihhnGzzbjrrrucvKlTp/rHOuY4NBIAOnbs6B+/++67Tp7WZ40aNfxjDSfUEEIOM+bwXyD8vrdt2+Yfs00A3PAyALjpppv8Y9XbUb+1du1a/7hVq1ZOHrcLfz8Q7n+5P2oIns5LPv30U/9Y7bPaTg4v1O9Uf8LzFH2u1LnH3Llz/eN69eo5eSqZ0Lt3b//46aefdvLYH6uPaNKkiZPmfqThufr8wu0/bdo0J0/7Obdp6FlW53uMPUu4WPieYRhGKgk5ktT+MwzDMP4ZpIWPWLBgAdq1a4dChQohKioKkyZNSvYz8+fPR9WqVREbG4uSJUv6OzCFGDp0KGrUqIEcOXIgX758aN++fZhOm2EYhnFh2LOEi72UMgzDSCXmSAzDMIwg0sJHHDt2DJUrV8aIESNSdP727dvRpk0bNG7cGGvWrEG/fv1w5513YsaMGf45X331FXr16oWlS5di1qxZOH36NFq0aJHsBhKGYRhGyrFnCZd0G75nGIaRXrElt4ZhGEYQaRG+16pVq7BQnyBGjhyJ+Ph4/O9//wMAlCtXDosWLcKwYcP8Xcd0F7PRo0cjX758WLlypbM7m2EYhnH+2LOES7p9KZUzZ04/HpNjjFVfQ7ch5ThS1ToI0i7SOOYgHR6NC+fv0fLpudyZVMdKdRFYP0fjyVV7R2PeGY2PZR0H1b3QOFzW9VAtBi6/xi1ru3B5NZ54x44dTpp1pDT+mbV/tHxaB3ovXAatE42lZm0l/U5Nc7tpPXCdqT6S6nZwbPqWLVucPO0r/FndklR1uDjOWrVrtM5Ye0nLp4aQ+7bWp+qP8b1rP2ftL437D9LLUp0o1YjgOtP21c9yPwvViWlKpX+WLl3q29xy5cr5f1f7U6RIESfNY0Z1E1SfgzVjVN+H9QNUwy5IH037lvoJ1gb66aefnLwgXR49V7UMuJ8np3HHWyar9pPamHbt2vnHah+XLl3qHwf5F8D1DeqT1XYtXrzYP77mmmucvKC61y2nVZeJy6TX1fKy5ormlS1b1knz3EDt7urVq/1jtedcf4DrY1RDivVrAFffRG20hmNdddVV/rHqJKmuC7e/2mytT9bhSq7PsU6J+jwe0zq+VdNS8xkdEzzP0uuo5gu3Tcg3qa4pc74vpbQPxMbGhvXp82XJkiVo1qyZ87eEhAT069cv4mdCdaT276/Ajh07/HkY2/aJEyc657Vs2dJJ86qwkiVLOnn6TMBts27dOiePx6RqPdWsWdNJjx07NuJ3qs/gsa3tyfNXwLXf+hyk8zy2N6y7o98JuHN3HVeqm8faQPoMMGHCBP9Y9ev0uitWrPCP1RfqSj7WaFKbtmnTJifN9lp9eZD+r85R1Q9wP1N/or6SNbFUm4z9sfZdte1BemP6jMp6T1pHem88/rVd9Pnmlltu8Y9ffPFFJ0/rlzUV9b61Pr/55pskyw64GpCqQdm2bVsn/frrr/vHam+Vxo0b+8dadp0/cl8J+XW9B8aeJVwsfM8wDCOV2JJbwzAMI4jz9RGFCxdGzpw5/X9Dhw69aGXat29f2IuJ/Pnz48iRI0m+YEtMTES/fv1Qt27dsBcNhmEYxvljzxIu6XallGEYhmEYhmH8k/jxxx+dFXEXa5XU+dCrVy+sX78eixYtumRlMAzDMP7+2EspwzCMVGJLbg3DMIwgzjd8Ly4uLkwK4GJRoEAB7N+/3/nb/v37ERcXFxZG3Lt3b3z55ZdYsGBBWNirYRiGcWHYs4RLun0pdfjwYT9mlWNgVQtG46xZy0Y1kFRngD+rscl8Hf2c6uXwZzVGWM/VNKPxvPzrmOapNgPHUmscsNYRx8Nr51aNKZ6kaOw512+Q9g/g3rfGHgd9p5ZdY385nZzeAWuv6ORLy8TL2zlOOany8gSPdW0AN9ZbdVg0HprR+2bdC8CNu9+2bZuTF9QHNR5a73v79u3+scaiq1YI9wdtf9WDYd0RrT/W7dD7DNJ10v6oegI8fjRPtUy4DUNjIsjwmyNJH9SuXdvvT2ynVYNPtTy4f+q5qsvEtkK1JTjchfUggHCtPNYI0X6tKyHYz2neFVdc4aR5/LAuHRBuu/jcffv2OXnqA1mPQzWlgh6Y1UdXqlTJP161alXgdVjXQ8e3+uHvv//eP65QoYKTp1pAbJ9US0RhfQt9eNc2XrNmjX+sdm3jxo1Omv2T9iNuC9aX0jzA1ZFSOzZ//nwnzf5RNUA0zf1c7WWxYsWcNNen6mNpm7Lmhvoq1XorWLCgf6z9nv2Y+gn1YzwOtD+qHhBfS3W2uDyA61tD84mL6SfSwkfUrl0bU6dOdf42a9Ysp997noc+ffrgs88+w/z58xEfH/+nl+vPoly5cv7YHD58uP/35s2bO+exZg/gzndmz57t5LHGDODaep2Hss3QscH6SIBrr3X+pRp17AdYiy2p8vHY4Xmw5gHA1Vdf7R8vW7bMyWNdKMC1Pzqf/eKLL5w0j2cdkyzUn9x3sm2qXLmyk7d+/XonzeNXday0vDwP+Prrr5087f/si3TOr9qHH3zwgX+sek46T+bnDg2xZTuqGmILFy500tyvNCRX65Ovq+2icyEeE3fffbeTpyspOa3zrylTpjjpefPm+cd33nmnk6fjiW393r17nTz2SzoGlixZ4qTZR5QuXdrJ0/HEO5PeeOONTp5qSvFzccOGDQGcawPVAQthzxIu6fallGEYRnrFHIlhGIYRRFq8lDp69Kjzgnb79u1Ys2YNcuXKhSJFimDAgAHYvXs33nvvPQDnHiZfffVVPPDAA+jWrRvmzp2Ljz/+2HlQ7NWrF8aNG4fPP/8cOXLk8F+M5syZM+wHPcMwDOP8sGcJF3spZRiGkUrMkRiGYRhBpMVLqRUrVjirAvr37w8A6NKlC0aPHo29e/c6OyDGx8djypQpuO+++/DSSy/hyiuvxNtvv42EhAT/nNDOVI0aNXK+a9SoUbj99ttTXUbDMAwjHHuWcEm3L6Vy5MjhbwnMy9d1m2ANKeIQBA0p0mWrvHRStzXmZakaBqjX5eXfuiRel8YGhdnpkn4OBwja4hhwl/RriIEuGeX70e/U8vMSYS0vL8fVdtFlqRxGoCERujyT20nz9Lrc/rr8WreW5RAUDa3RpbLcTnpvCi/9VGPBISga3qHl462E+ZdPIDzch5fgalighidwfWobaigQL43V+9Z+xNuc6nU17IH7SlCon44X/VWW61fvM6h8Wh4NT+F+FqrrxMTEiFvFmiNJH0RHR/s2i0Mjtm7d6pynoWAchqN9TG1B0Fb0HNak/VptDC/rVzvL9htwwzj0XA2XYrun/VrD2ILOVTvC+brkX8MbeLm7hvpx2ESJEiWcPB3vfG+6zbWGW9xwww3+MS//B8LDwridtF00PI7tiNpADRPjkA/1Pxp+w/kaQsjl5xBqAKhSpYqTZrvG4YOAG3oIAG3atPGPx4wZ4+RpKDeHrqnf1T7HZVA/piEV7Lu0TqpXr+6k2d4GjUttB+1z3HeCwroBOFpJWnYNxWEbE/KrQSHmafFSqlGjRoGfGz16dJKf0TDRCy1HemXEiBF+W911113+36dNm+acp88HLJGh9aEhZmwHWrZs6eTx2OHt7AGgXr16Trp+/fr+sdoTDc3lMDud8wc9z6gP4zBtwA17uvbaa508DWN76aWX/GOVGAnyaRoWxnNjHYNqg7lMaivVprEt1zYrWrRoxHPVby5dutRJc/ic2mD1W4MGDfKP9d5Up61GjRr+8eTJk508DlEfO3ask6c2mK+r9ffdd9856Tp16vjH2i4amsg+QsPhNESP/RbfFxA+1vilufYjDXldsGCBf6zzEp7n6xyFxxbg1oPKHOh4atGihX88adIkJ099Loct3nfffQDOhUWGfixQ7FnCJd2+lDIMw0ivmCMxDMMwgkiPmlKGYRhG+sCeJVzspZRhGEYqMUdiGIZhBGEvpQzDMIxI2LOEi72UMgzDSCXmSAzDMIwg7KWUYRiGEQl7lnBJty+lfv31V19LQ2N0GdXp4XhU1avQOFZuWM1jnQDVrVKtA9a20TyN31ZtE0bLy9+reiWqr8H5HAuv5QPcuGvVV6hVq5aT3rJlS5KfA1wNEq0j1ffhmGfdgl11orhN9+zZEzEvuetqLDX3I40nV20lvlfV3VJdAP5ePZfR2GjW5QBczQxtb41x5jbV62p7cx9ULQ6tz6Btt1WXicuk40dj51krRLeo5X4UpMGm+cn1OS6TbnWraS5DaExoGxjpj6NHj/rtxO2tOoDaP1lzSDVh1Gazn9C+y/1cbWnQLlV6ro5v1kBi/SsgXJeJ9RhU30L7MOuU6PhRXR62y2qHVRuI9fB0bK1atco/Zm0TIFwLg+tBtQfVT/CYVZunbcq2jLU5gHBbxW2jui6qNcJl4DoAwrVl2B+pHgdrYWofU80r1rBQ/TPdNp51M1SbSm0/t5v2Xe2v7EdUo0u/h+cQJUuWdPK0frnN1Sez7odqXmm/4q3h1T+qj+a+oxpXPLYAd/tvLZ+RPrn22mv9/sy2Xe3Ahg0bnDSPX51LqhYQz5tV347nmsuXL3fy1O6zr9F+Wr58eSfNmmBqM9Susq7fBx984ORVrFjRSfN4UE011ssBgJo1a/rHap9ZfwhwbcjKlSudPPZxamuaN2/upFmPT22P6hF9++23/vGtt97q5Km/Y9vO9QWEP1N99dVX/nHdunUjXgcAxo8fH/Fc1ejiOazWJ/crvY4+D7Id1T6mNm7hwoX+sfrNypUrO2m2+3qf11xzTcQy6TOT+rS2bdv6x6r9xLuCAkCDBg38Y9XF4+/s0KGDkzdz5kwnzXWvdaTPZkFauzqP4vET0rXSfmlEJt2+lDIMw0iv2K8bhmEYRhC2UsowDMOIhD1LuNhLKcMwjPPg7+wYDMMwjAvH/IRhGIYRCfMR/4+9lDIMw0gl9uuGYRiGEYStlDIMwzAiYc8SLun2pVSmTJl8TSmO2VStENXMCH0GSF4rhHUcVLeDz1WtGo035e/U2G4tA3+nEhR3qtoL+j0c88z6PUC4PgSXV2NpNdabY+e1HjimWNtBNR9Y+0I1PFSfhGOpWT8DADZu3OikWftJ8zQGnzWRVFtJtS5++ukn/1gNQOHChZ30wYMHEQluF+0LrPcBuBoVeq7WJ9ehnst6NFo+jQPX+H3WXmG9FP1OwNU80DrSPsj6JaqrxuXXfq3jhetT70XblO2Gjn3VGOKxF/qc1itjjiR9kDdvXr9vsXaN6jGoBgj3c/Upqs/AY0SvyxpnqgGi2gPcl1U7TfU3+LNqv1X7hMeE+ibVDWJNEy2fjvcDBw5ELK/WJ+umqC4h60fofar+FNtdtcl6LuvNaZ2o7tZnn33mH6uNUc0htiuq2bR169aI52r9/fbbb06aNZGqVavm5G3bts0/Vn0k1Yli/Q21Kax1Arj1qfp8VatWddJ8LdXFUZ0o7suszwaE23fWJdH+yRpNALB9+3b/WPscl0/1DtU/cp/T+gzSD/3hhx+cPB3TPHcK+TjtT1pmeyl1admzZ49vw4sXL+7/vVGjRs55PAYB1z6qxhDr5AFun9L+zzp/mqdzQH4OUX1AHa+sibNixQonb/To0U6a71tto44dHpOsyQSc0+di2B7qfKxp06ZOmv0o2yUA+Pzzz/1jHZ86fj/66CP/+LHHHouYB7hzfrWNQX5q6dKlCILHvJZX9Q253bS9mzRp4qTZZ+gzKT/rsC8Bwv0Uz4VbtWrl5Gnf5evqM2ilSpWcNOt5qX1W/bG5c+f6x6qvqBpTPC5U71fnP9xX6tSp4+SxTpjO38uVK+ekefzoeNG5Bz/P6BjWucfVV1/tH4e0OC+mjwh95u9Kun0pZRiGkV4xR2IYhmEEYS+lDMMwjEjYs4RLdPKnuOzevRv//ve/kTt3bmTJkgWVKlVy3tJ7noeBAweiYMGCyJIlC5o1axb266JhGMZfmZAjSe2/fwrmJwzD+KdjPiIy5iMMw/inY88SLqlaKfXbb7+hbt26aNy4MaZNm4a8efNi69atzlLu5557Di+//DLGjBmD+Ph4PPbYY0hISMDGjRvDwiSCiI6O9pdK8/LI5MLjOLyClyWGrsnwUkUNE+KlqLrkXMMeeBmjfofeMy/t1BAnDT/iZYL6nbwcF3CXc2bKlMnJ0+X/vERUtzrVUDReyqv1yXWvy9x1m0wO/dNwPW1Trk9dGqtb3XJ5tQxa9xx+pu2kW5Ryfet1g0LgNMSRl9VqyIG2Ny/t5WXHQPjWt3nz5vWPdcmtLvvlfqb9SI0b15nWkY4Rbrfktt3mEA8NI+H71hAY7XO87FvLo+EyPNY0jEnbic8N9WsL3zs/0tJP7Nu3z1/ezfZSl3zrUnMOleUwNSB8G3juOzzuFP2c2lYelzoG1M5xuKnaR92Knm2/hpnw1tCAO/7VFugYZtulfkFDmNlHqn1v0aKFf/zee+85eVr3fK9advUpXH4NM9CxxttM67bs6t85reHsei7bCG1DtWUcWsChpoB739ouGvK4fv16/1hDjrRfsR9Rn6L1y/5Jtw3X8BAeoxoCrmOP61vD/DXNdlglCzg8RP2L9iPun+pnNZSb61tD3xUuX8iHBMku2EqppElLH5ElSxb/fLblOnY0ZIfH75w5c5w8DV/mfLX77BeaNWvm5Kl8BvdFDU3SsDAOgVKpCp1r8vjQOZXeC7eBzrd1TsRzar23UOhSCK4XtcG33nprxLK/9tprTprz2aYC4WOb7f78+fOdPA7pBoCaNWv6xxzaBYT7P24LvU8NE+S5ZteuXZ28L7/80klXrFjRP9awRfYD7FOB8DBoDnVXn6AvdvnZh+sAAGbNmuWkObxZpVW0TTnkW+3znXfe6aSnT5/uH99///1Onvp9HqfqI9inqY/4/vvvnTT7CL0X/U5ufw37XLx4sZPmMoU+p/MGxp4lXFL1UurZZ59F4cKFMWrUKP9vPHg9z8Pw4cPx6KOP4l//+heAc5PQ/PnzY9KkSejUqdNFKrZhGIaRHjE/YRiGYUTCfIRhGIahpCp874svvkD16tXRsWNH5MuXD1dffTXeeustP3/79u3Yt2+f88Y8Z86cqFWrFpYsWZLkNU+ePIkjR444/wzDMNIztuQ2MuYnDMMwLHwvEuYjDMMw7FlCSdVLqR9++AGvv/46SpUqhRkzZqBnz57o27cvxowZA+D/lwHqUu/8+fOHhUeFGDp0KHLmzOn/07AAwzCM9IY5ksiYnzAMw7CXUpEwH2EYhmHPEkqqwvcSExNRvXp1PP300wDObX24fv16jBw5El26dDmvAgwYMAD9+/f300eOHEHhwoVx5swZX9OG9RZUL0ljnFkHR3VkdEtp1gJQ3QG+jsaIa2w3dxC9jsZv82c1Ll7TrFGR3PabfK+qtaNaF3xv+p16rxy/rfHajN636pMExfrqlrR8bxr3r7G+rCOjsfE6cFmLZe3atU6e6njwZEjbWzVT+H60HlRnhtH7Zj001eW46qqrnDT3+4ULFzp5QVvRq06UxqJzHLvqHQRpzpQqVcrJ0zh2blONlWe0/yk8/jVOW9uf49hVu0TrgeszdN30oik1YsQIPP/889i3bx8qV66MV155JSz2n5kwYQIee+wx7NixA6VKlcKzzz6L1q1b+/lHjx7FQw89hEmTJuGXX35BfHw8+vbti7vvvvu8yqekpZ+Ii4sLs/NJwWMLcO2e9muFdTNUUypIo0l/qWd/o2NAbQHbOS27lpevG6TjAbj6Uzq+9Xu4XnXrarVzvHW4+io+t2HDhk6e3svUqVP9Y7WBrGcBuFuba55uK719+3b/uHnz5k5enjx5nDRv8axaGHou64SpForqUXG+6lCwT1EdF30wX7dunX+8ceNGJ091o1grQ7eyDtqeXvX5tJ3Yp2hf4PIBwIIFC/xj3ZZby8/aOOqbeDzpeGHNQgBYs2ZNxHNVj5NX3uj8UnW3uH5DeiEXc7vvv/PDBpOWPoLh/taqVSsnT/VpWBsoFEIYggXZAXduqTa3cuXK/rHafbWVrF3DK8cAV3cJcH2Rzn30e1jXSPVJ586d66TZ3qg/admypZPmZwuta9U2ZR/B8xHAnSfrdfr16+ekR4wY4R+rvqvaKdYq0vmczuO/+OIL/3jgwIEIgvWodMw2atQo4ueSE+pnP6a+kp+/9LlCy8B6aPp8oM+kbA/Vb7JPUPTZTP0J28Ubb7zRyZs5c6aT5vKzrhYQrqnIWlU6Z+EX1jq/0XHJ5dO+oGOC/Z1qZ7FuGeDO3ULfoT6SMU0pl1StlCpYsGCYoF65cuX8ARIy6urI9+/fH2bwQ8TGxiIuLs75ZxiGkZ5Jq183xo8fj/79++Pxxx/HqlWrULlyZSQkJIQ9LIdYvHgxbr75Ztxxxx1YvXo12rdvj/bt2zviyP3798f06dPx/vvvY9OmTejXrx969+7tTMouBPMThmEYtlIqEuYjDMMwbKWUkqqXUnXr1g17a7llyxb/7Wp8fDwKFCjg7ERx5MgRfPPNN2Hq9oZhGH9V0sqRvPjii+jevTu6du2K8uXLY+TIkciaNSvefffdJM9/6aWX0LJlS9x///0oV64cnnjiCVStWhWvvvqqf87ixYvRpUsXNGrUCMWKFUOPHj1QuXJlLFu27LzrgzE/YRiGYS+lImE+wjAMw15KKal6KXXfffdh6dKlePrpp/H9999j3LhxePPNN9GrVy8A55at9evXD08++SS++OILrFu3DrfddhsKFSqE9u3b/xnlNwzDSHPSwpGcOnUKK1eudMReo6Oj0axZs4hir0uWLAnbmjkhIcE5v06dOvjiiy+we/dueJ6HefPmYcuWLWHbDJ8v5icMwzDspVQkzEcYhmHYSyklVZpSNWrUwGeffYYBAwZgyJAhiI+Px/Dhw9G5c2f/nAceeADHjh1Djx49cOjQIdSrVw/Tp08P0y5KDo7/Za0O1s8AwvV+OJZW4001fpvjrGNjYyOWRb8zSFciSE9D09qxVK+Ez9UyaNy1auYwGvvL56ouT8mSJZ00x9qq3gKHEGmdqI4Vx0OrppRqcXBcs2pnaT2wNpBquKjWDLeT6nRoTD7ryKiWFvcxwI0h1nO5D+p36r1wf9S61n7EsenadzV2nttY+5x+D48n1mwBwjVzuM6CvhNw60x1ABgdP7p8n8ugda0aJKwlorZAP8vlD7Wn6pcx5+MYQudrP42NjU3S/vz88884e/ZskmKvkWL99+3bl6w47CuvvIIePXrgyiuvRMaMGREdHY233noLDRo0SNX9RCIt/cQvv/zijyO2OWqPVGODNRm0n2/ZssVJ8xjR8bJz507/WPu8ajew5oLqJen44XGo9kbtJ9uN5MY3h7Tofaod5vGjWhg6NoK0J7juVcdFtYtYB0J1HjTch8ew6jBpebgedM6gukase6S6HtoWIWFmIFxHT/1uvXr1/GPWoAGAVatW+ceqgaR1VKFCBf9YfYjCujjaH1U/JD4+PmKe+mHW6OL+BwDff/+9k+Z2076rIVbc/qp9w9odOr/QOmP9ENUSVaFsnifo+Amq31AdBZ2TWj/xd37YYNLSR9SqVcvvdzxGJ0yY4JynPoP1lLT/axm4r2rf5LTqLKk2UNWqVf1j1ar5+uuvnTTbR53PaPlYwyc5XSuuI9bD0usAwE033eQfz5gxI7AMfO9a1/yjmdrGTZs2OWm2z2qndG5Vp04d//ijjz5y8lQvi3XDVPNP/R2XsVKlSk6e2heuT/1Ote2lS5eOmMdzX34+Adx+o9/z7LPPOnn6o2XHjh39Y9VB1HZi/6crFlkPDTg37kJov9G5B/eN5DQVub/y/Eu/U5+ZtN/zc7D6av1OfjZnXw3AkcYAXH3d0HfqPIy5kGeJlLJgwQI8//zzWLlyJfbu3YvPPvss7OX+pk2b8OCDD+Krr77CmTNnUL58eUycONHXa2zUqJEznwCAu+66CyNHjvTTu3btQs+ePTFv3jxkz54dXbp0wdChQ8NsYhCpeikFAG3btkXbtm0j5kdFRWHIkCEYMmRIai9tGIbxt0dfKD/++OMYNGhQmn3/K6+8gqVLl+KLL75A0aJFsWDBAvTq1QuFChUKm7CcL+YnDMMwjEiYjzAMw/jzOXbsGCpXroxu3brh+uuvD8vftm0b6tWrhzvuuAODBw9GXFwcNmzYEPZiuXv37o495h90zp49izZt2qBAgQJYvHgx9u7di9tuuw2ZMmXyN7RICal+KWUYhvFP50J+3fjxxx+dFQKRVmnmyZMHGTJkSJXYa4ECBQLPP3HiBB5++GF89tlnaNOmDYBzOzuuWbMGL7zwwkV7KWUYhvFPx1ZKGYZhGJFIi5VSrVq1ClulzjzyyCNo3bo1nnvuOf9vGkEAnHsJFenZY+bMmdi4cSNmz56N/Pnzo0qVKnjiiSfw4IMPYtCgQWErqiORbl9KZciQwQ/N4tAGfXOnYRC8ZE+XjGn4ES+p0y3ieTmsLm/VJaL8WV2erqECnNZG0i0s+d50u01drsvX1U6jSwc5rcsWFV4Gr+GFfB19ENYwB24XrU8tL4cFFitWzMnT7+F60WWzvP054Na3lkFDV/gNsC7l1GW1QaGTvOxbty3Xbay5v+pyce3nvHxYw0aCtqbXkAgNG+LxldyScK4HHWtBdaKhklx+DYXQEExuJw390H7O52qdaCgLhxSGyq51w1yII0npzkAxMTGoVq0a5syZ4y+1TUxMxJw5c9C7d+8kP1O7dm3MmTPH2UZ51qxZ/lLr06dP4/Tp02H2LkOGDIH3m14pUqSI30fYNmg/52XxgLssXbd017HGS/fVvvMYUfuoLxs5jEjDAXTpO7ePluenn35y0jxmkgtrYmrWrOmka9So4aR5WbqGVCxYsMBJc5jbrFmznDwe3xp2oOEXbO81HPeqq65y0t98841/zMv2gfD2L1iwoH+stlXbib9XQwg/+eQTJ80hm1dffbWTpyEVHPKm5eW6Vt+kbcjfec0110S8DgCsWbPGP9YVmurzOHSf6xYAmjdv7qSrV6/uH6tgdVAot9p3nSewz9atwTnEQ8eP+kC+V50HaMgHzyGC5jiA6wND92Lhe+mbuXPn+mOc5x4crgqE24HQjzZAuC1SO9ukSRP/WG3auHHj/GPt0wkJCU6a+yqLvAPBzzrqE9Tf9e/f3z9W+6zl5bmQ2n2tIx53OlefPXu2k+Z8taN8L+vWrXPy1FZyaJq2y3333eekP/zwQ/9YV+Wp3+c5me5urOey71T7p+HCbIM11E9t8tKlS/3jV155xcljO7VhwwYnT+uBv+fjjz928vQZip+bdN6u9cD+Zt68eU6e9u0VK1b4xzrfZH8MuP1K5+Ya8sq+qXXr1k7ewoUL/WP1m3ov7K815FzbkJ/V1G/qMzSHCYaebS+mjwh9Bki5FEgQiYmJmDJlCh544AEkJCRg9erViI+Px4ABA8JC/D744AO8//77KFCgANq1a4fHHnvMn38vWbIElSpVcp6XExIS0LNnT2zYsCFsfhSJVAmdG4ZhGGknTti/f3+89dZbGDNmDDZt2oSePXvi2LFj6Nq1KwDgtttuw4ABA/zz7733XkyfPh3/+9//8N1332HQoEFYsWKF/xIrLi4ODRs2xP3334/58+dj+/btGD16NN577z1cd911F6dyDMMwDBOwNQzDMCJyIc8ShQsXRs6cOf1/Q4cOTfX3HzhwAEePHsUzzzyDli1bYubMmbjuuutw/fXXOxpSt9xyC95//33MmzcPAwYMwNixY/Hvf//bz4+kZxvKSynpdqWUYRhGeiUtltwC54REDx48iIEDB2Lfvn2oUqUKpk+f7hv7Xbt2Ob9C1alTB+PGjcOjjz6Khx9+GKVKlcKkSZOcX7E++ugjDBgwAJ07d8avv/6KokWL4qmnnsLdd9+d6vIZhmEYSWMrpQzDMIxIpIUUSBChlc3/+te//JWGVapUweLFizFy5Eh/FXyPHj38z1SqVAkFCxZE06ZNsW3btiRD/c4XeyllGIaRStLqpRQA9O7dO2K43vz588P+1rFjR2dHFaVAgQIYNWrUeZXFMAzDSBn2UsowDMOIRFpIgQSRJ08eZMyYMWx343LlymHRokURPxcKq/3+++9RokQJFChQAMuWLXPOCYWKRtKhSop0+1IqOjraXwHA27lrjLPG+nKMc3L6Tqwzo9oBHKupbx91S3sugy5f0xhdjqXV66jGlG5hyahuA6dVM0G1gDjW9vLLL3fyNDad9S1U64LRWGmNA+b4fdWR0G1xOeZZY/d5O1j9Ho3P1lhvbgvVGOI+Brh1pjHYqovC5xYvXtzJC+pH2r5r165NsqxAuOgcD3Id8BqTz3WoW7wG6aNpGVTficeajkONGeeYbO0bXCbV3dIxy31HddZUZ4S1B3S8aMw4j72QvkFyjsIeIC4927Zt87U2WLtGtxXWMRyUp/aSfYNu6Rw0DlW7iPX51NbreNm6dat/rP1MdYO4b6v/UXvO2xWrFoLqWvG5Ova1ftk38H0Crm3QbZonT57spNmmqK369ttvnTTbT9UN0jTXg9pd3Xqd7fDYsWOdPLUbrHei+k66ZJ3tk/YNruuNGzc6eepbuT9qu+g8hutQy65twZpXrC8FhPsC9pe6RXaVKlWcNM9ztM+pz+YxrNuTs46VjhfV4+QxoWNNJ/Hs87TutZ+zdktoXKrvU8xPXFoOHjzo9wHWWtL+r88WU6ZM8Y91Xqe6mKyvyVo6AFC5cmX/mG0LED6uuH+ppqfOH3msq37T3LlznTRrXumcSsfHjTfe6B+zNiUQrht15513+sevvfaak8f6PgDQuXNn/zhoTGjdql5SkC6v/kjHD8l16tRx8nSss/3W9tYx/t133/nH+gylabYhqouoc2Hug6rbyM9JqtHUrl07J83zaNUzUi1bLoM+F6kt52eL5HS3+DlJ61NfdrAIt44frSPWN+R2ANyxpvqAOm/iOQuvAgKAwYMHO2n2o6oVqc+O3K9Cem3p2UfExMSgRo0aYc/LW7ZsCdObY0JaaSFfXLt2bTz11FM4cOCA3+dnzZqFuLi4sBdeQaTbl1KGYRjplbRcKWUYhmH89bCVUoZhGEYk0uJZ4ujRo46Y+/bt27FmzRrkypULRYoUwf3334+bbroJDRo0QOPGjTF9+nRMnjzZf8m7bds2jBs3Dq1bt0bu3Lmxdu1a3HfffWjQoIH/orVFixYoX748br31Vjz33HPYt28fHn30UfTq1StVYYX2UsowDCOV2EspwzAMIwh7KWUYhmFEIi2eJVasWIHGjRv76dBunF26dMHo0aNx3XXXYeTIkRg6dCj69u2LMmXKYOLEiahXrx6Ac6upZs+ejeHDh+PYsWMoXLgwOnTogEcffdS/ZoYMGfDll1+iZ8+eqF27NrJly4YuXbpgyJAhqSqrvZQyDMMwDMMwDMMwDMP4m9CoUaNkX2R169YN3bp1SzKvcOHCzk58kShatCimTp16XmUMkW5fSv3xxx9+3CxrfKgugsZqckysakdw3Dfgau1oHus/qDaV6hxxbLeeq9flOHbVVlKNKY5j1fvUNJdB49T1XK7PIE0HwK1v1VLiWGnWAgFc3SrAvVeNcdb75lhgjX9XnQ6ON1Z9DY1j5mvpdVU74vDhw/6xxohr+/O9ajw8azapVo1qdHEbamy36r3wuaqJo9pK3B+0fKrhxGXSWG4dT2XKlPGPWY8EAC677DInzW2+fPnyiOVVjauQXlBS19H71CWiXIeapzH5TKg9ExMTw8ZvCFsplT44efKk3w9YT0Dj/HX8sK6MapPx2Adc7QTVo+H+qeNZbSKPH43f177Mdll9io5D1s9Rm6f6gvw9ao927NjhpFk/q0aNGk6elumbb77xj1kfSb9HfYi2C/sbHbNqd1nPSdtww4YNTpo1nFR3i5e1A6491/vUfsV2jzWPAODLL7900mx71ffPmTPHP1ZdKLVBnK91pFpafK/qZ9W+a7sxQZpsqiGmbcFl1Hth7TTA9bXaN7jOtC9oG7J913PVn/OcR+te51Lcl0N5eg5jK6UuPWXKlPHbjW20zhfV7rNOGfsW4JzeCsPaKyGtlRD8nWpzdf7NuoSFChVy8kaOHOmkedw988wzTp5q4bHdCtJlBeA8VLZu3drJ0/7J56puTM+ePZ3022+/7R+rP+E5P/sSIHzOynWvensffvihk+Yt63Wuqz533rx5/rH2jdAOZCFYN0jtlGoUsj6fzhHef/99J83+m8sOuPMQtaOqv8d+X7UZ1f5xP1OtQ64TAOjatat/rPeicxpuU63PBg0aOGnWfNRnfO2D3I7apjfddJN/zD4VCO9HrGep7aBaWlw+HfuqM8r6WKF2Uv06xp4lXNLtSynDMIz0ijkSwzAMIwh7KWUYhmFEwp4lXOyllGEYRioxR2IYhmEEYS+lDMMwjEjYs4RLun0pxQ3FYTgaiqZhWLpUkdElhLw8V5d4czooRBBwQ5V0a2L9LId/6BJMDXnjZbVaPg0N4fAz3cZRQ/J4GaOGrekyQ64zLR+ndQmrhm/xkn4NMdB24QGn4Vy6BSwvjdW+oFuWapkYXdLK6L3pdtRch3ovmzZt8o91y2ENveBl1RpepmEjHE6h9al9jpdK69JYXSLOfS65EEfeqldDGDScj8uvbcphJbrEVvsjLxnWLZP13KAwUB1PXKbQd3BYlGKOJH0QHx/vjzluY7V5Oka4v+pYU7vMIRYaTspjTW2/jm8Ot9CwMN3amO279nNdNs8hDNpn1R+yjdHr6lbMbGs1PErHadOmTf1jrQf2E7NmzXLydNtmXgqv4TQdOnRw0rw9ubaZtj9v960hhLp9Om8br2ioGod7vfnmm06e+huu79GjRzt5HEqiISm6jTz7H92iXW00b4Ot25FrX+FzeetvINx38feov5kwYYKTLlGihH+s/Uivy/ej9cBjVttbtznnUBINldR5gfZBRv0l+/eQLQiy6/ZS6tITExPj2zIOw1Hb+O233zrpRo0a+ce7du1y8nQ+wf1E/QmHxep3Llq0yEnfcsstEctz7733Omm2peo/tE+zndL+r2Fs1apV8481VFznwhzyrWNQr8v2R+uI7bWGE2roHIdFN2nSxMl74IEHnDTPQ3UOreHWHKqmc8kZM2Y46bJly/rHep8a6sx+VMPhevXq5aR5/KuGD8teaHiZ+iW2uRpOqmXgZwANwWO/Drj2W8PY1Bdx39E5v/pG9j3ad/k5A3Cf1bQNuXzcRkC4H2VbcN111zl5Gv7P4YY6P9BzeX4W6udBciH2LOGSbl9KGYZhpFfMkRiGYRhB2EspwzAMIxL2LOFiL6UMwzBSiTkSwzAMIwh7KWUYhmFEwp4lXOyllGEYRioxR2IYhmEEYS+lDMMwjEjYs4RLun0plSFDhiTjMPVvuv0wa8VoDLHGCfNn9bocg626Agrnq7aOapCwHlVyukG8faxu1am6RqyboNvOqhYUf4+eq1onrB2i97Zv3z7/WHVONA6YdSdUD0sJ2kJctZ84bllj5RW+lmpvaHw0a6+o7pJuGx3pOwBXM0PjqPXcIEOjceusTaDb1Gs/CorX1/h37ita16oVw+NHNZq4bwCuFoHqgnG9aOy51jVrehw/ftzJ0/HN+aoxo5/l8aN6EUb65dChQ75WD/dBbW/VF+B81ZvSsZY/f37/WPVz+LNq89SWsi/Q/qf2nPWo9DtVc4G3Edfxo5o9X3/9tX+sNk+vy2VUPYZVq1Y5aa5f9QVsU6pWrerk6b2x9pNqqqh95PGuuhmqP8XwNuYAcPXVVztptq3q89QPL1261D9W7SLWWwFcO612jfuualaqbQ3qc6oLxjofqr+ydetWJ80aXqp9wxolgNverAmYVPnZ37MGJODqtQGuLo2Wga+r7aBaKKw1ou2gWm88f9Oxr+OU+0PIF6k2m5G+KFCggN9W5cuX9/++c+dO57z4+HgnzX2sePHiTp76F9Z00rkl28PKlSs7eWqvP/vsM/9Y9Ul163m+ltpufWZh26njU8dO0DOK2n2uF9WLU/1AHs9qy9nHqp3SeR3rMKkml2pesY6Vzr+1fKwbtG3bNidPP8vz6Dp16jh5OvdlzUJt/+nTpztpnhuzXwfcOYL2m5UrVzpp9qtaPrW5TO/evZ30E0884aRZn7ZevXpOnrY/93vtN2pXuf217suUKeOk2V8vXrzYyeP+qv1a54CtWrXyj6dNm+bkqZ4hz0X0Oah169ZOmrWqQjbGfETKSbcvpQzDMNIr9uuGYRiGEYStlDIMwzAiYc8SLvZSyjAMI5WYIzEMwzCCsJdShmEYRiTsWcLFXkoZhmGkEnMkhmEYRhD2UsowDMOIhD1LuPwlXkpxnKjqdATpF2jcqsZds4aC6sjwdVW3SmOGWfNBNQlYkwkI1p/SGGLueBoPqzGqHCes5VPdKK5PPVf1NVizQu+NUT0NjQNn7SJtl9y5c0csL8cwJ3Vd1k/Sgap6Klx+1VJSTRfVDGC432h5VQeD21vLrnW2a9cu/1j1nDSmnetQr6taBNzeWteq78X9gfU9NE+vqzoyqvnBZdL6Y40P7WP6nUlpeoRQbS0eIzqGVeuN6zB03cTERETCHEn6IHPmzH5bcqy/6vBov58/f75/rHaXdSgAdwyrv2EtBO1/an/Y5ui4Uz/BOkyqEac6QpyvOlFapvXr1/vHqheivpU/q3p9Wn4e02oLWP+iYsWKTp7aXa5ftQXLli1z0qwbpPp8Wg+MjmttJ27/DRs2OHlapsaNG/vHWn9Llixx0qxDUrNmTSeP65p1bwBXr0avq/oW6uv5uu+9956TpzpRbJ9UH0S1v1j7pEqVKk6e+lauM9Uz0TT7Ne2f8+bN849VB07HN+t6qJ9V/8P9U/U3Vf+QfWKoH+l8hrGXUpeeokWL+v5/06ZN/t+1D6mt/O233/xj1WpTnRsedzp3Y9vE9hcIt5WMjiPVquJnFrUROl/kfqvaVGpvuF6++uorJ++mm25y0uw71SeobWI/xfpNgDuG9FlMbSWP140bNzp56qfYz6vd1/vm79W6Z/0hwPVb6nv02YHrSPVpVY+P61B1jh588EH/eMqUKU6e2nL2W9o31OZyGVRvj7V1AVe7Ub9T51zcxrVq1XLydF4/adIk/7hRo0ZOns7zuf1V14rHk7aDaiiybVdfo+3C96bl4+c2wK3D0HO7+k/GniVc/hIvpQzDMNIT5kgMwzCMIOyllGEYhhEJe5ZwsZdShmEYqcQciWEYhhGEvZQyDMMwImHPEi7p9qVUhgwZ/CV1HHqjW53qsjteIqrLtoOWyuqSW16Oqx1At4flpX661FC3fuYlgzly5HDydPkjl0HDHDRki8uoS3c1TJCvpUvPdeks14sux+VQNQ5NSOo7edmqthmHwOhntY60DTWchtEy8b0lF/bA52roly5/5nxd0sohZBo2oP2Tw1F0WbK2KfcVXYauS0U5rUvUtc9x3Wvf0DJs377dP9b+qPXJ9a3jicM7dPm1Xoe/R/M0zIn7jm6hq2EZXA+h+rLwvfTPb7/95vcJtg3adhqGxUvYkwsZ5XbTbYY5JFxDfzRcnPunjsOg62o/1zCOpUuX+sfazzUcm9Gxr3aNw7I0LLlYsWJOmpfKq91gm7h27Vonb/ny5U6al/mXLFnSyVu9erWT5jA29SnqdzmEQf23hu+xnVabF/Q92ufq1q3rpNmn6DyG8z744AMnT9uQwws5xAgIr08uk/oF7edsE7V9ta9wKIR+p86B2Eerz+OttgG3n2koCaNbdmvoUHx8vH+soTh6Lof+aRvydQC3vkO+Sn0fYy+lLj1LlizxbSj3P51baqha/fr1/WO1GepPeH6rc1+2oxoOpd/J5y5cuNDJU1vEPkNDukuXLu2kuc9r+dQW8feUKlXKyfvhhx+ctIZEMWq/+V5Xrlzp5PG4V9/41FNPOWn2f5UrV3by1B5ySPXcuXOdPLXP/LzAc3EA+Pzzz500yz+ojdBnFPbfGlK2bds2J83++4EHHnDy2E9p+OjXX3/tpP/1r3/5xx999JGTN3XqVCfdvn17/5jD6IDwOuKwbq1rtfscfqhzrJkzZzppDuvXebz2Qa4zteXsazScXmUa2Ocm14Z83RkzZjh56tMqVKjgH4fmcRczxDv0mb8r0cmfYhiGYRiGYRiGYRiGYRgXl1S9lBo0aBCioqKcfyzA/ccff6BXr17InTs3smfPjg4dOoT9SmUYhvFXJ/TrRmr//RMwP2EYhnF+fuKfgPkIwzAMe5ZQUr1SqkKFCti7d6//b9GiRX7efffdh8mTJ2PChAn46quvsGfPHlx//fUXtcCGYRiXGnMkwZifMAzjn475iMiYjzAM45+OPUu4pFpTKmPGjGGxmcA5HYZ33nkH48aNQ5MmTQAAo0aNQrly5bB06VJcc801qfqezJkz+/oNHLOvMf+qocE6BKqDoJ/lczUulOOLVdtAY2n5s6r9pLG0rOug5dMYWI55V10R1TniTqrXUR0M1gLSeF09l8ur8dAcm65bMmusL8fv6rlaXo5N1thevW/+rMYiq4YG6xXptu+qC8H6Kqqzpe3PMfCsIQW4scSqCaDncl/RPqf1yfWi56pWApdP60THJWuvqFaV1hHH1asmm+qVMFp/PA50u1rV4eIxrLpgqiPDWgl63zqe+NxQferYVf7OjuFCSSs/kSNHDr8fsq6G6lmobgZrUaguhvoUtiuqCcJjQvv14sWLnTTbAtWfUk0Q7q+qIaXbinM//Omnn5w81XlgW6G6QTrWWO9HNRFUh+nHH3/0j1UDietTbalup802UW2e6lqxvoWO727dujlp9hvq83bu3OmkWT9Cy6v2ietFtVqCNLFU75B1KbU8uu01+ybVnalRo4aT5r6jflb1Ydj2qx6azms4rX5N64z7Bh8DQOPGjZ009+UdO3Y4eTz/0PbWeQuPRfWHCtenzjfUR3MZQvWp362Yn0iatPIRMTExvt3juaeO1yD9JP1Otclsz3WezPMvfvEGuDYMcMe6aispbNs7d+7s5PEcH3DHy9ixY528J554wkmz3o/6TX0xyBpEal/Ux1155ZX+sfqwhx9+2D9etmyZk6d6XuynvvnmGyePtYk0X+tE65e/V31NkSJFnHRCQoJ/rHpT6iuDbLBqV7Hd1eu88sor/rHOdQcOHOikWb9L57Hql3gez9poALBixQonzfWrOlaspaSf1fq75ZZbnDTPlbTPtWrVyknznOvTTz918ooWLeofq25Z1apVnXTNmjX94y1btjh52o/4eVHbUPsVz8FC9xKkOwiYj2BSvVJq69atKFSoEIoXL47OnTtj165dAM51gNOnT6NZs2b+uWXLlkWRIkUcQVLDMIy/OvbrRjDmJwzD+KdjPiIy5iMMw/inY88SLqlaKVWrVi2MHj0aZcqUwd69ezF48GDUr18f69evx759+xATExP2q1n+/PkDV02cPHnS+TVKf6U2DMNIb5yPY/g7OxLG/IRhGIbtvhcJ8xGGYRj2LKGkaqVUq1at0LFjR1x11VVISEjA1KlTcejQIXz88cfnXYChQ4ciZ86c/j9dHm8YhpHesF83ImN+wjAMI21WSi1YsADt2rVDoUKFEBUVFbate1LMnz8fVatWRWxsLEqWLInRo0eHnTNixAgUK1YMmTNnRq1atcLCqi4E8xGGYRj2LKGkWlOKueyyy1C6dGl8//33aN68OU6dOoVDhw45v3Ds378/ybjxEAMGDED//v399JEjR1C4cGEcP37cj9Xn2F+NN82fP7+TDvp1RDWHOG5YdXlYV0LjizUWnWN2Y2JinDzVromPj/ePVYdHOxrr52hMqv6KxPem96Kx3dweWiesbQG4cetB+graxqq9wvH5XLdJlTfSdwDhmlJcRxqDredyGbU+tR9xPmtOAOFaLKxDoedyvLZqtmhf5ThxrWvVIOHrasy4XpfLpLHorLMFuBoveq7q03B/VW0YHSPcjqoVE6QDp2mG6wAIrzPOVx0z1T3hOPHQGNZ7YOzXjZTzZ/qJY8eO+TaAtSe0b+h4552cdPyolo3aMoZ1eNSeq8bCpk2b/ONVq1Y5eWrP161b5x+3b9/eydN7Ye230qVLO3mqu8bjQHWsVGePdUi0vOqHuW+rfV+6dGmS1wTg7LgFuPWZnA4D67xs3LjRyfviiy+cdJkyZfzjgwcPOnmqI8QakVpHqkPC7daxY0cnT7+HdTPU7laqVMk/Vn+tWmVc1zfccENg+dg/qp/t1auXk/7www/9Y/Wlel32ZeoXdByzHdVxqfMlnn/od/L4Ur02LQO3t/ZV9d88JnQM85gF3PlcqJ20rpi0WCl17NgxVK5cGd26dUuRGPj27dvRpk0b3H333fjggw8wZ84c3HnnnShYsKCvlTN+/Hj0798fI0eORK1atTB8+HAkJCRg8+bNYXpKF4M/00fkzJnT73dsD3VOpW3P41BXaKldYG037Yv8Mk/7ns5vWGNKr6Of/eijj/xjnd9s3rzZSbNt13HPWneAq/2kunNaD+zjdL6k2nis6aP6vqxfqD5Cdbfatm3rH2tf1JeabE9q1arl5OnLW/Yn+uwwY8YMJ81lVB+rfotths4J9PnwjTfe8I+bNm3q5LEWlOrk6fx77dq1/rHqN6nNZf+sWkqqHcm+qE6dOk6e6gWyP1RNqZEjRzppDtXl/ge4+liA28/atWvn5E2dOtU/vvnmm5081ZjiOYKWXXW32N8ltwso66WF2vBi+ojQZ/6upFpTijl69Ci2bduGggULolq1asiUKRPmzJnj52/evBm7du1C7dq1I14jNjYWcXFxzj/DMAzj74H5CcMwjD+HVq1a4cknn8R1112XovNHjhyJ+Ph4/O9//0O5cuXQu3dv3HDDDRg2bJh/zosvvoju3buja9euKF++PEaOHImsWbPi3Xff/VPuwXyEYRiGkaqVUv/973/Rrl07FC1aFHv27MHjjz+ODBky4Oabb0bOnDlxxx13oH///siVKxfi4uLQp08f1K5dO9W7ZRiGYaRn7NeNyJifMAzDOP+VUkntKh20ojw1LFmyxFmZAJzbTaxfv34Azq0iXrlyJQYMGODnR0dHo1mzZhdNaNx8hGEYhj1LKKl6KfXTTz/h5ptvxi+//IK8efOiXr16WLp0qb+N5bBhwxAdHY0OHTrg5MmTSEhIwGuvvXZeBePlnRxqo0sPNVyKHbcuq9SlsrwkT5fI8xJ+XWqqy3x5uaYu09PtI3n5sZ6rS9s5tC4oHApwO6nep3ZgrjNdPqqhA7x8U8NauF50+aMus+Z7Cwr9ANyQCF1iraE2HHrB24EmBS+71HYJ7fwSgpfH6lJeLS9PILVv8K91Gs6h4Wa8Nb2GwGjf4O1W9RdBbX/uO7o0WkPpuC/rWNP+ysuztT9qOCQv+9br8jL15EIwuV60PrXO+N70O/Vcvu9Qn9Jxz5gjiUxa+4lQO7EN1/7I9hxw+46ey9tyh74jhNo5thNVqlRx8jisDnDHcHJ+jO2Thg6oneOxVrduXSdPQx/YnmoIK4fOAa5vUP+jtou3U9dzeZtrHXda92wLtE6uvvrqiOXVUGO1a7xtt4agtGnTJuJ1OYwSCN82nu9t4sSJTp76GPZr6ktZ/yYkWxBCw77ZB+oW3ho6wuWbPHmyk6ef5VUo7IuAcL+2bdu2iOUNCvVTG6gvWvhcDUli36V1q23K962+U8M1GfUpGg7L3xMKkQoKMz3fl1Kqh/T4449j0KBBKb5OEPv27Qvzz/nz58eRI0dw4sQJ/Pbbbzh79myS56iNOF/S0kds2rTJ72fcF3TOr+FS8+bN8481/KhGjRpOmutK577cPzSsTudj3P80RGv+/PlOmvuIzpsKFizopHmMBo0VAJg1a5Z/rGHlulKuUaNG/rE+SygcClutWjUnj9uicuXKTt6WLVuc9OzZsyN+R+PGjZ30tGnT/GOtT50TsG/U0D6toylTpvjHGjqu7c+fVdujL5/5pavaXPZx6gu1L3Oo4jfffOPkBc0f1HePHz/eSXN/aNKkiZOnz1BsozVEVH0P9zmWuAHC/QuXX5/xue9oiPxPP/3kpLk/6ApMDhEF3HmUtmFQSH8o9FDnS4w9S7ik6qUUxzAnRebMmTFixAiMGDHiggplGIaRnjFHEhnzE4ZhGOf/UurHH390HnYu1iqp9IL5CMMwDHuWUC5I6NwwDOOfiDkSwzAMI4jzfSn1Z2oiFShQIEysd//+/YiLi0OWLFmQIUMGZMiQIclzgoTGDcMwjNRhzxIuFyR0bhiG8U/EtnE1DMMwgkiPPqJ27dqOiDhwLnwmFMISExODatWqOeckJiZizpw5gULjhmEYRuqwZwmXdLtSKlOmTH4sKceGajysxgVzvKdqwrBGE+DGzwbFQ2u8ruoZ8Hdq3LLqYnDcsupr6K9QHG+sMbj6PbyNpn6nwrodql2g+hUc867nsiaJxiKrngpv3ao6DarJwDHE+p06GLktdAtajbNnfSzN037EW8lqnWiMMy+t1xhxLpPqYWl/5P6gv5Kq/gvHqWsdsd4H4Nav6muoRgr/Oqqx8nny5HHSXPeqxaE6AFx+1SlgjTHVglGNM64j3TJZP8vtonlaD6zDFerLpimV/ilcuLDfD3isaXurjgbrM/D28UD4lr+8PbDqFLAd1u3jdWyxnghv0Q0Ea4uofdS+y2OEteaAcC2UBQsW+Me6nbZq/bGd1q2hVetv+/bt/rFqTfC24qpJo3XNtkz15PRBukSJEv6x6jxo/bJOhZZB7Sf7BtWOUT/M56puhOqGlS1b1j9WX8DtzVunA+G+gO2a3veGDRucNPcdbRfVLOFt2bVvqE/hute5k/pSbkcdh+pTeItvFdXmOqtUqZKTp+3N8xHVgVP/zajd0Prl9g5pyF3M7b7Px0ccPXrU0TzZvn071qxZg1y5cqFIkSIYMGAAdu/ejffeew8AcPfdd+PVV1/FAw88gG7dumHu3Ln4+OOPHZ2c/v37o0uXLqhevTpq1qyJ4cOH49ixY+jatWuqy3epKVasmG8zee7BczwgfM7CW97rXE218HgM6DyUNQp1zKk+EttK9R/abytWrOgfq1YVaz0BwIQJE/zj7t27O3lLly510sWLF/ePVZOwd+/eTpp9D/sAILzO2GbofbOukWr/qH1R/UBG55YtWrTwj1WTjW0N4M7zd+/e7eSxRhMALFy40D9WvSm1TaxzpPWp31OzZk3/ePr06U4e20rtR9xvALeOtE7UrrJvV9ut99a8eXP/WJ8PVAuKyz9w4EAnb9GiRU66fPny/rFq2a1cudJJd+7c2T/+8MMPnTyes7/11ltO3kMPPeSkFy9e7B9rOyxfvtxJsx/g+SAQPu9j/xwas0E+x54lXNLtSynDMIz0ijkSwzAMI4i0eCm1YsUKR+C5f//+AIAuXbpg9OjR2Lt3ryNCHB8fjylTpuC+++7DSy+9hCuvvBJvv/02EhIS/HNuuukmHDx4EAMHDsS+fftQpUoVTJ8+PeyB0TAMwzh/7FnCxcL3DMMwUklaLrkdMWIEihUrhsyZM6NWrVpYtmxZ4PkTJkxA2bJlkTlzZlSqVAlTp04NO2fTpk249tprkTNnTmTLlg01atQI2z3FMAzDOH/Swkc0atQoyeuMHj0aADB69OiwndsaNWqE1atX4+TJk9i2bRtuv/32sOv27t0bO3fuxMmTJ/HNN9+ErRYxDMMwLgwL33NJtyuleBkeL1vUXUh0S0heMq8hERoCxQ2rS+R5GaOGpmnIAS+P1PJoiBGHAupyR106zp/V62qYGIcGaIijlp/rQetTQwh5CS6HOAHufesSWw054XvRutZQBg4N0TyFl60mV/e8bFVDK3SLVV7+yqGR+p2AWy9B4T3JhXdwu2nIqC6j5e2yNZRTlwhzmJ2Gw2nf4H6vv4rqcmxekqptqt/Dn9Wl79wfdYzqd3KYi9afhtVyOJKG4mlbcEhUqD2DwvfSivHjx6N///4YOXIkatWqheHDhyMhIQGbN28OC7EBzi1JvvnmmzF06FC0bdsW48aNQ/v27bFq1Sp/uf+2bdtQr1493HHHHRg8eDDi4uKwYcOGZLd0To9kypTJ7we8pF1DtLQv8zjVcAA9d8eOHf6xLt3m/qkhrBrGwTZG+66Ob/YFHE4BhIcT8/hXexm0xF7vRcNXeHzrsn71rbx1tNYDf891113n5HHImJZfQ1LUX3IomIbZ6dhlLZxrr73WyVN73qlTJ/947ty5Tp6GqLAfVp9cv359J81hXnoul5f7GxDux7iddMxqv+e61xCCdevWOWn2nyoBsHfvXifN/l3nG2rfOfRB/YS+YOf5iIZVcp/TsmvIDNenfieHEQHuGFa/q7aA6zDkZ3UsGOmLEydO+HN97os6j9eQKO6bKrWh8we2TXfffbeTx2FCfAyE2+By5cpFPFdfCvIY0NBcvTfOHzNmjJOn9oWfiwoWLOjkrV+/3knzD1k6/9J5KdtODanlZzz9DpX74LBFDe1Sn8HPEt26dXPyNEyQwyz5O4BwW8Qhb9dcc42T9+qrrzrpWbNm+cdqV3keDwAzZ870j9W2c1/R66h/5pAyDo0DwufYHF6tdaIh9Bziq8+DWt5bb73VP9Z20vvmOYOGQ7dt29ZJr1692j9WyQH2PTq29FmnWLFi/rHOkzTMnOtbXwhpSD9L14T6kfmIlGMrpQzDMFJJWv268eKLL6J79+7o2rUrypcvj5EjRyJr1qx49913kzz/pZdeQsuWLXH//fejXLlyeOKJJ1C1alVnovTII4+gdevWeO6553D11VejRIkSuPbaa5N8yWUYhmGcH/YLuGEYhhGJtHiWWLBgAdq1a4dChQohKioKkyZNCjsnueiJP/74A7169ULu3LmRPXt2dOjQIezl965du9CmTRtkzZoV+fLlw/333x+my5gc9lLKMAwjlaSFIzl16hRWrlyJZs2a+X+Ljo5Gs2bNwsQqQyxZssQ5HwASEhL88xMTEzFlyhSULl0aCQkJyJcvH2rVqpWkkzIMwzDOH3spZRiGYUQiLZ4ljh07hsqVK2PEiBFJ5oeiJ8qWLYv58+dj7dq1eOyxx5zVb/fddx8mT56MCRMm4KuvvsKePXtw/fXX+/lnz55FmzZtcOrUKSxevBhjxozB6NGjw0TukyPdhu8ZhmGkV87HMYTO19Cd2NjYsDBa4NzuQGfPng0LIcmfP39YmE6Iffv2JXl+aDnygQMHcPToUTzzzDN48skn8eyzz2L69Om4/vrrMW/ePDRs2DBV92QYhmEkTWr9hL2UMgzD+OdwIc8SKaVVq1Zo1apVxHyOngjBYe6HDx/GO++8g3HjxvmhwKNGjUK5cuWwdOlSXHPNNZg5cyY2btyI2bNnI3/+/KhSpQqeeOIJPPjggxg0aFCYZEUk0u1LqRMnTvj6Ehzrrdu4aswrP/Bpw+lW2qwfoBoUvEWk6uOo/gd/VrfKVrhMGg+rMdkct67blqv+FH+vxsNqzHiQTpM+HPP3ah7HJqsWg24hzvHHWh6te36oXrt2rZOnW3eyDobqWKmWBPcdjicGwnWYWCdDrxsUS633zXmqtaLtxLodqnuhnw3qR3rfPH5UV0SXX3I7qm6C9hvWutEy6HVZX0B1oriOtK6DxoiOAY2V53P1vrXP8feGjGeQptSFOBLVJ3j88cfDtiv+swjd07/+9S/cd999AM5tbb548WKMHDnyL/dSKnPmzH7/YZ0otdG6hJjTqm+h9p7H09atW528IN0l1gcB3L6sY0v18IoUKZLkdwDhY4TtmtoJ1UlhvSLV0lKbyDt6zZkzx8nTF5884VHBfNZjueqqq5w8HUOsJ8H6FUB4fbL+odoC1bfg+lWNDdW3UN2ooDzWsFB7oZpiXN9B/lupXr26k2Zbqt+pKyjZ1laoUMHJU50orge1parVwv1Ky6BzAZ6TqZ9VLTCuIx0TXEeqsxY0t1NtG9VOY1uhcxPV9+I6Ct2XjkfGXkpdenLnzu33D+7z+pCmdovHet26dZ28adOmOemqVav6xzq/Yc061R9SPTYeH9oXdS7E84i3337bydM+zxpJ+syk87qFCxf6xz169HDydM760EMP+cdqr9XHsaaizpNZ5+qBBx5w8lijR79H7YlqInH9ql4u+1jA1Y5VnUHdKKB3797+sT5s63MSaxlVqVLFydN64H61fPlyJ491me69914nb8KECU6afa5qKQXNhdq1a+fkDRs2zEnzOND+qfMo1ovU71QdJvY3rFUMhPsetvU6Z2FNrvbt2zt5qg/JZVD9Ke1HXH6dw+izDveH0Heov2fS4gfuIELREw888AASEhKwevVqxMfHY8CAAX4drly5EqdPn3aiMMqWLYsiRYpgyZIluOaaa7BkyRJUqlTJmRsmJCSgZ8+e2LBhQ5hucyQsfM8wDCOVXMiS2x9//BGHDx/2/w0YMCDJ78iTJw8yZMgQ5vT2798fNsEKUaBAgcDz8+TJg4wZM4Y9mJcrV8523zMMw7iIWPieYRiGEYkLeZYoXLgwcubM6f8bOnRoqr+foydatmyJmTNn4rrrrsP111+Pr776CsC5l9ExMTFhLww5CiNSlEYoL6Wk25VShmEY6ZnzfYCIi4sLW82WFDExMahWrRrmzJnj/2KRmJiIOXPmOL/UMbVr18acOXPQr18//2+zZs3ydx+LiYlBjRo1wn592rJlS9ivtoZhGMaFYS+aDMMwjEicr4/48ccfnWeJ1K6SAtJf9IS9lDIMw0glaREHDgD9+/dHly5dUL16ddSsWRPDhw/HsWPH0LVrVwDAbbfdhiuuuML/heTee+9Fw4YN8b///Q9t2rTBRx99hBUrVuDNN9/0r3n//ffjpptuQoMGDdC4cWNMnz4dkydPDluibhiGYZw/Fr5nGIZhROJCniVS+gN3EEHRE4sWLQJwLgLj1KlTOHTokLNaiqMwChQogGXLljnXCEVtRIrsSIp0+1IqKirK19LgmGKO+wXC42WD9JK04fmtosa8sm6QahuophBrKmj5NN6Y9UpUK0TjQzlmVzUe9L4ZjifW8gFuPei5GuPM2g2snwG4cbKqZaKhQHny5PGPq1Wr5uSpTgcPMo0Z1uWB/FmtI/5OwI0h176guhDcN/TeVNeMY5xVy4TrSI2HnsvtpDHIOqi53VT3QpdKnjhxwj9W/RQ9l8uo/Vx1T1j/RbVrtF+tW7cuYhm47rV9NRSN20V/FdBzedyqfo6Wj9swlBekKZVW3HTTTTh48CAGDhyIffv2oUqVKpg+fbpfT7t27XLsSJ06dTBu3Dg8+uijePjhh1GqVClMmjTJ0ZS47rrrMHLkSAwdOhR9+/ZFmTJlMHHiRNSrVy/N7+9COXz4sG+XWNNJbavq57C+jmpIabvztXgsAa7f0DGq4501QbT/7dmzx0mz/dQl02rX+HtVa0dtA49T9ZWqEfHZZ5/5x+rXVq1a5aS5DlULg/2a2lnV/mIBf9VAUjtcunRp/1jtmGrycZuGJlohtM5YU0XrKD4+3klz/Wqbqr3k/CBdF9XO0jkE2yrVVNHy8urHfPnyOXmq6xFaTQkAH330UeC5lStX9o+1TlasWOGkue5VF0fbomDBgv6x6kRxnfF5ALBhw4aI5dOxpdpv3Cd1DGt96vg30j9nzpzx7TTbEJ1jqS3icTdv3jwnT+3hJ5984h+zlg5wzieH0Dn+jBkznDTPU7SPq84Rl1/HZ9++fZ0066hp/9cHydtuu80/Vn04LcOHH37oH+t8UZ8lNm3a5B8vXbrUyWNbr89iqgHHdiE5fR/2A/qcoXafy6Q2TB/Y+Vy+LyB87sH6XqVKlXLyFi9e7KTZP996661OHteD/oBYqVIlJ83+mPsmEF6/rPWzZs2aiHmAq+mrK2hUb5P9Xf369Z087jeA25f1Ojov4Wc3fY7r2LGjf6y+Ru+N+5HaAr0u6zrq/FCfUfm+Q2NCx0Z6IiXRE9WqVUOmTJkwZ84cdOjQAcA5ra9du3b584batWvjqaeewoEDB/y5xqxZsxAXFxc2foJIvzVlGIaRTkmrlVLAOVHNSOF6Sa1u6tixo+Ock6Jbt27o1q3beZXHMAzDSB5bKWUYhmFEIi2eJY4ePer8gLV9+3asWbMGuXLlQpEiRZKNnsiZMyfuuOMO9O/fH7ly5UJcXBz69OmD2rVr+5s3tGjRAuXLl8ett96K5557Dvv27cOjjz6KXr16pSqs0F5KGYZhpJK0fCllGIZh/PWwl1KGYRhGJNLiWWLFihXObsr9+/cHAHTp0gWjR49OUfTEsGHDEB0djQ4dOuDkyZNISEjAa6+95udnyJABX375JXr27InatWsjW7Zs6NKlC4YMGZKqsqbbl1I5cuQIWwYJhIdhaOgFh5jp5zXNy+x0G1de6s5hSkldh5eV69I+DeHg5bm6vF9D8vizeq4uaeS0hhFoHWma0c/ycli9F17eHLQkHnC3r9Ul8Bqewm2o98nXAdz61m1wtS24DjUUREPpeHtTNQAqCM3LYbWvcOipbm2r1+Ul2FqfGtbGy6i133BYC+AuTQ3arl3LpOGa2k5cnxqOovDyTd0qncMug8YW4C4n1zbT9ue+o+NH742vFToOCpG1l1LpAw7NYDRcU0OieLtq7Y/ar7gPapgEh1isXr3aydMl2zzWtm3bFngu21YdzxqOxLZK+6yGfvHWx3qujhG+Vw1ZVj/M21Xrdfk6ah81DIbbjUP5AKBMmTJO+ptvvvGPNVxP/QaHbmh4oYZscXix+htNc1/RMui28XyvGm7GYR3aVzUEk33y9OnTnTz9LNe3ho5wSDUAfPHFF/6x9hsOhwPcfq92TUPe2KZrSI+GXfK8IUiygNseCG9Drl8NLdewDkbHlvZzDmcJlS8opM9eSl16Nm/e7P9Sz7ZIw8J0Tti0aVP/WP3JnDlznHRI4xFww54B1y906tTJyVuyZImTLlu2rH+sYWEazsdoaJruwsU+onPnzk7etdde66RnzpzpH9esWdPJ0/kil1Ft+dSpU500h0fWqlXLyeP5LpcVAOrWreuk2a6q39RQYvazGmZ14MABJ83l17BADX3mMDadS2od8Wp2DX/UOmNpEw27Y1uu96mhp6Fd0wDX5wPhPozrm0NNgfBQOra7avc03J7rSPu5zqP42UjHpYYxjhkzxj/u2bOnk8eSHTof6969u5PmOY32I53DcDinhjTqvIl9WpUqVZK8HpMWzxKNGjVK9jPJRU9kzpwZI0aMwIgRIyKeU7Ro0bBxn1rS7UspwzCM9Iq9lDIMwzCCsJdShmEYRiTsWcLFXkoZhmGkEnMkhmEYRhD2UsowDMOIhD1LuNhLKcMwjFRijsQwDMMIwl5KGYZhGJGwZwmXdPtS6siRI742AetDBOnaAO522aprpGm+lmpdcGyqailpLC3rSmhsqn4nf49qHqkOBsfZcqwxEL6FJccxa+y5xjzzZ3mreCA89jXo3nhgqOaRaqbw9puqFRIUKx+k/wC4ehXaF7TduO5Vh+ann35y0ocPH/aPWWMECNcuYn0a/hzg6n/o51S3g7VtVP9D9Q54a2HdBld3OuA21O3kuZ8Dro6Hbges7c+x6XpuUD/S8vF40nsJMr5qC1SfJCmtoRDaFtw3Qu2p41PLZY7k0pM7d25ky5YNgNveqrGhNobHj+YF2WzV4+PvqVq1qpO3fv16J83aVGrrtd+zhoWOUR3DnJ4wYULE6wCujkJI7yCEaphwGVXrjQUwAWDUqFH+sfomLoNq/6xdu9ZJs6aP6smp9hdr+6n9Vr0k1uBQu6s6QlzfWr4uXbo46ebNm/vHqoGk/odtoLYLa+4lpwvGekmqD6L+kq/19ddfO3mqQ8Lfo/5b2437io4f1VThcal6baqBxWKsq1atcvJYG0p1HfVcHt/aH7Vf8dxEfYr6kG+//dY/DvkHtReMvZS69Pz6669+3+a+qRpwqpu2YMEC/1j7gWrisC1lzRvA1eVRLbnQzlUheM6vc5TixYtHPFfnqKqjxlpuEydOdPJUs4f7sz7r6G6/fN+qJadaRjzOJk2a5OSxXmCjRo2cPK17noeq5pWWl8c+tycQrl3VpEkT/1jnqGpf2E6oLVcNSJ4XqHax+jT+3vbt2zt53N6qeaUae/wspPq0bMMAVy9t4cKFgeVjzal58+Y5eVr37BfUF27evNlJt27d2j/WeTc/XwFAq1at/GNtU/ajWkc6J7jnnnv8Y60/bX9Oq76cPvvyHCv0HBf0HGvPEi7hSuKGYRiGYRiGYRiGYRiG8SeTbldKGYZhpFfs1w3DMAwjCFspZRiGYUTCniVc7KWUYRhGKjFHYhiGYQRhL6UMwzCMSNizhEu6fSmVmJgYFqMMhOsrhPREQnAMscb6a5w152vcMut0aIyraijwdVUPQsvAOg4aZxqkXRSkWwC4mj6q6aB6EJyvelOsxaDnqg4KxxtrzLCeu3v3bv9YY49Vi4N1MrRdNJ6X61Pjn1VLgttGtSxUU4x1JzRPdWW4jjRmnOtB21BjkTkGX7WpgmLPtTyqmbFjxw7/WLVh9N64zooUKeLkaV8J0mzSNt61a5d/rOOJ49+17KpjxXH1HLsNhNsCth/ab/Re2MiHrpOYmBim5cXnmyO59Hz33Xe+VgXbYdavAML7PetzbNmyxckL0ohQXR4e36rJxH0VcO2Ejm/Vw2Obrf1c742/9+qrr3by9F54XH7//fdOnvpb1txQv6b3xpqH6n/Yr40ePdrJu/baa520agUxNWrUcNJsK1RTSv0Y35v6XdXDY3taqVIlJ0/nHzNnzvSPtY7UdnEZGzZs6ORxO6nfUtvP/lF19Jo1a+akWStD7Y/6bNYaUW0l1SXZu3evf6z+W30r9+WlS5c6efpZ1vBS/6N9jtG5E9+b2nrV0uL213NVW4TbNKRTpj6UsZdSl57ExER/zsGaODwvAoAvv/zSSVeoUME/1r6nukHvvfeef6x2ittUNeCGDBnipG+55Rb/WOeHquHTrl07//jDDz908lTziu2N9v8DBw44adZASk4nim2a2jstA/setausoap6dqwTpOdq2fU72abpc5DqOPL8Qe2dXpfnDDr/njNnjpNmPS8tr85LWHNM/UmtWrX8Y9VH0mfS66+/3j9WG7Z69WonzXpOb7/9tpOn98b6i6qhqbacfZPaNe67gOuvdQ6gaZ6TB83V5s6d6+SpnuErr7yS5DWBcP+8YsUK/1h9rN4bX+uRRx4BEO5X9PP2LPH/pNuXUoZhGOkVcySGYRhGEPZSyjAMw4iEPUu4XJDQ+TPPPIOoqCj069fP/9sff/yBXr16IXfu3MiePTs6dOgQ9kbaMAzjr0zIkaT23z8N8xGGYfxTMR+RMsxPGIbxT8SeJVzOe6XU8uXL8cYbb4Rtu3jfffdhypQpmDBhAnLmzInevXvj+uuvDwt7SI6YmBh/aSMv09cQKG0cDm3QpacaUsTn6lJJDp3T6+jyf14WqMs+tXy83bQuq9Rl+7ycVMuuS3A5dCm5LZB56b1uh63n8tJ0rSNeRqtbZ/MWr4C7dFa3ONd74xA4/U4OAwPcJf663F/P5VAb/U4NN9SlyIyGe3Aoji5h5WWbunxUw99WrlzpH2s4j4aecntrOI8uFeUlw8mFWUYKVwPCQyQ4lIX7FBAensTLlLX+uI9peIzWJ9ehji1Ncx3qWNNl09zvQ0uJNcxQv8t+3Qjmz/YRwLm+H+r/3Fe0rr/77jsnzUvC1Z7r2Ofrar+58sor/WPd5lj7Lts9DSfl6wBu6IbaDbWffG8adqX9nv2n2hRNs99L7ly2/7p1OdsnvW+1gbxMXkNmNISCQxY0xFHrLKltmkOozeH+oOXVkEe2n1peDQvl79W+wWEb27dvd/L0XrhM2m90O3Wue+3Xug02+wataw2N5hDNUBhbCL1vDgPVEBr1G9wfNBSey6ehV3pv7Ks0tFNR/8no/IPHV8jPXszQjH+ajwD+fD+RJ08e3w5ySE9QmCkAVKlSxT/+4YcfnDwdOzwe9BmF5xHPPPOMk/foo486af4eDYPu0KGDk16wYIF/fM011zh5+vJu1qxZ/rE+O2iY4Pr16/1jDssGgJo1a0a8rt63jl/+HrXBJ0+e9I853A1wfQLg2lkNs9q4caOTZvuszwcagslhdxpm2aJFCyfN88UpU6Y4efzsCLi2VOcPQXME9d18b2o32cbqdTjsDwh/RuUQ9OrVqzt5+nzA38OhpgAwadIkJ83+mfsJEB4WyM8L2nc1fJ3rXp8zuQzqR1u2bOmkeRyw/wXc/ggAnTp18o+1XZYvX+6kOT8kT3D8+HFMnDgRSWHPEi7ntVLq6NGj6Ny5M9566y1ncnr48GG88847ePHFF9GkSRNUq1YNo0aNwuLFi8O0BAzDMP6q2K8bwZiPMAzjn475iGDMTxiG8U/GniVczuulVK9evdCmTZswwa+VK1fi9OnTzt/Lli2LIkWKYMmSJUle6+TJkzhy5IjzzzAMIz1jjiSYi+kjAPMThmH89TAfEYw9SxiG8U/GniVcUh2+99FHH2HVqlVhS9aAc2r7MTExYSEE+fPnD9slJsTQoUMxePDg1BbDMAzDSIdcbB8BmJ8wDMP4O2HPEoZhGAaTqpdSP/74I+69917MmjUrTOfgfBkwYAD69+/vp48cOYLChQsjOjraj7/luH6NwdW4a0bjdzXNmhkaB8xvIvU7NN6UNSlUk0fjT1lvQ52r6iKwNpReR+O1WaencuXKTp7+YsSx86ohpWVgvR/dfpNjfcuWLevk6WSCt3nVsufJk8dJcwy5apdo3D9fV9tQ24lRHRmNs+b+rWVQ7RDVGYl0HdWe0K2k+d6S007jMaH6U1q//Fn9Tu3bXIfaF1R/jGPGNQY/qM60DFz3qjelGlg8vrQNtS/zeA/SnlPUTiSFxYEnzZ/hI4DIfiImJsa3jTy+gvTvAKBYsWL+seoGqe1iP6E6BWzDVZtI7W69evX8Y9YDAcJtF2v46LhT/STeXlu18tRms//U71TfyuNAt+VWjRXeap3rFoATbqPaRIsXL3bS3GdUW0S1i1gHQm2V+tY1a9b4x6qLotdlDRh9YNYtvbm8et2gfqQ+L0gDSb+T20LrWrVG2I+orpr6Mdal0b6hY4THmmqpqQ1nvbGKFSs6eaojxGNItW9Yq0p1PtW3cnvrd6jtZ9+l26WrriB/b8h3qq9mTFMqadLyWeL06dO+LeNt7HX+oH6f5ynaF7UPsY3WeSfrMiUkJDh5QXo0qtOq+nHt2rXzjz/88EMnT/WyWBtI7Yn6Ex5nOpZVE2vhwoX+cUg/J8T48eOdNGsF6Rz17rvv9o9Z9wsI93+s0aS2sm3btk563LhxEc/V+uQ21jz9LNtD9Y1bt2510qzvpLqsqo3H/lE1r7iddK6htr1Nmzb+sT6TVqhQwUlzf9i0aZOTp8913G4fffSRk7dq1Sonfc899/jHOjdnHSvAna81bdrUyRs+fLiTHjJkiH+s+nJ8nc6dOzt53FcBd26kz9c8nwFc3bAaNWo4eWo3eN63bNkyAMHPovYs4ZKql1IrV67EgQMHHMN+9uxZLFiwAK+++ipmzJiBU6dO4dChQ86EbP/+/WEvE0LExsaGdQjDMIz0zt/ZMZwvf4aPAMxPGIbx18T8RDj2LGEYhnEO8xH/T6peSjVt2hTr1q1z/ta1a1eULVsWDz74IAoXLoxMmTJhzpw5/k4Rmzdvxq5du1C7du2LV2rDMIxLiP26kTTmIwzDMM5hK6WSxvyEYRiGPUsoqXoplSNHjrDl19myZUPu3Ln9v99xxx3o378/cuXKhbi4OPTp0we1a9cO27LUMAzjr4o5kqQxH2EYhnEOeymVNOYnDMMw7FlCSbXQeXIMGzYM0dHR6NChA06ePImEhAS89tprqb7OmTNn/FhN1snQOHBdrssaAKorovo0rAXwf+2de7yOVf7+L4dQU5lkIiJCDjkfQ1GSjU6k84H6+tVUVDIznb5FpxmZvonKpJrpzDAd6Ky0lUPOW5IUHZRSVFNoVMjevz+8njXXup793NtG2xPX+/Xyaq3W89zPutfh81n3vdfnWhojztoBqtmjcLlqeuh1Od5YdTBUg4JjillfCth6ZC6jMe+Mxm9zfCvHOwPpWkDcRvobHB+t2iWqdcEx2HovSXHWqjeg9eO4f/2s/g7H+mrbax04flt1UBTuN21rru+qVauiMr3uzz//XOj3gHSNFB5Xei+6vZ3rpMaM4/yB+F6qVKkSlenvcF7bWvVKOG5d5zCPDW4DrTsQa2DpfNb5w3NRNcMUnsOp31DdEsaOZPvZWT4C2DpeUnoyrFugfkE1Inh8qq6DPiyxvWRtDiCe0zqf69evH+V5zrIuBpA+zpN0zdQ2sGbEZ599FpXp73AddKeC6gjxPNW5oH6N9QXVTuTl5YU0ayoC6XpEbI9at24dlaluHWtBqS6K9hP7XfWdahtYR0V1rdQHsq6VrhNq1qyZsU6qQ8g2W/VgVBOE/Zzed/PmzaO8akwxqoHF7a1rJ9aZAeLxqXoZ2p6dOnUKadWHUZ946KGHhrT6FPYTqkuoedaQU1ug98J10nmoui6sYZlaa+qaj/FLqe1nZ/mJHj16BNvM9lHtidot1n1T7btTTz01yrNGoI4htrO6RlU7z+sk1UFUzT/WN1QdWdXP4Wvp2kxtcJ06dUJatXVGjx4d5VlXdOzYsVGZai1xnVhfEYg1e7R+up7l+ar2RO3zgAEDQvr555+PyvR32Oa99NJLUZn6LR4bqg+ofcx+VW2j+gjuC9Vt5DGnmpRqr9muqp6T2lxe5+tzkI6N3r17Z6zfrbfeGuUnTZoU0jp21bafcsopIc1jAUgfR1xfXe+wv1a/pOOI/bWu1e6+++4oz88ddevWjcp0bcl9k+oX1Txk/CwRs8Mvpd54440oX6FCBYwePTrNeBljzO6CHcm2Yx9hjNkT8Uupbcd+whizp+FniZidvlPKGGN2d+xIjDHGJOGXUsYYYzLhZ4mYrH0pVapUqRAywFvfdIutbtPnrflJRzUC8XZwPdaXr6vHIeu2QN4GqNsfdVslh17olj79Hd6aqkeA6pZ03tKoW9m1TtxGuqVR68shErxVF4i3YOok0S3tXH+tDx/frJ/VrbBJIWS6LVVDV3gbq4am6VZeDnvQY+B1izDXV8cGb8fWMt16mhTeo6EhHOamY0FD/bgNNVxTwyw5BEm32Gp4HLenHoOrIXo85vReuH4aQqTjkceZ3rdum+fPJoUBAnEoYMpO7M6Gf3ehXLlyYRzwWFF7uWTJkijPW+51e7jaYR4bGi7F80VDT9VOcBiWHjWvtoFDjDR0Tj+bdLSxhuhx/fVoaD2enMs1DGzp0qVRnttI5ze3r4ZbqE959tlnQ1pDG9QXcBtpCIWG6PF31f/ouoBDtDSUU/3lypUrQ1pDHTREhe9dQz25PTUsWW0p20gOmwTSQyi47Tn0Quuj9Vd/OGfOnCjftGnTjPVTeKxrn2ooHftlHfec17WTtifPET0uXX0r2wr9TQ2dZBuTSquPMtnF1KlTg13kNYyuk6dOnRrlORQoJycnKvv73/8e5VNi7ADwz3/+Myp77733QlrDa9nWAPFY1JAitddPPPFESP/P//xPVHb22WdH+c8//zyk1fd07NgxY50WL14clWn4Gc+XNm3aRGU6z1q0aBHSDzzwQFTGNk3XZhpez2u5/v37R2UcMgbENk3XxQr3t/qabt26RXkOVSyqPbn/9RmlZ8+eUZ79ia5Zub+1H/g5DUiXU2F052GXLl1CWtue7TwALFiwIKT1uWjhwoVRntfPOhbOP//8KM/2Wseu+lX2W7pW45Dc8ePHR2XqVznsTsMLNWyb+0KfbXXdd8EFF4R0qj9//PFHvPzyyzBFk7UvpYwxJlvxXzeMMcYk4Z1SxhhjMuFniRi/lDLGmGJiR2KMMSYJv5QyxhiTCT9LxPillDHGFBM7EmOMMUn4pZQxxphM+FkiJmtfSpUuXTpoQrHWjmqFsGYPEGsSaLypakcwet0knYCk4x1Vk0A1cjhmV4/V1jhm1v9Q7R/9Lg9SPaJStbVY10F1grSN+ChUjbvl7+ox5RoPzfG8WqZtxPeiukuqX6H9xmgcM8fV63V0kvO96pGq2vasoaLaJtyeqnuh7cmodgnXHQAqVaoU0nyEOZCulcD6aFoHHXNcnqSdBsQx+nodjXlnfSquOxDr1ei803htzqvmlcb2c7/pnNX68r2l7jvJ8NuRZAfLly8PeiGsf6B+QbWWWENQx6PqEbGtUFvA+gJqx3Qesm/Qeah6Bzzf1bYeeuihUZ61HdTWq64Rt4vOCdWTYNurmhWqz8G2Ys2aNVEZ/87MmTOjMj3KnDUCX3vttahM/Tm3t2oEqg9kHQrV+lI7zLp7qkekui5NmjQJadULUb1Ltl26TmBfpToejzzySJRnn6e6UHp0+dtvv53xN9Wec/smaZIAcbvoONIxyONV9Ti0/xs2bBjSqvPJfk61tNTX8xzW+aO6lKyhw78PpGsw8jxN+Tid54xfSu16PvnkkzC2TzvttPD/33rrrehzunZjLZtZs2ZFZWoHJkyYENKqk8c6UqrNd84550R5tlvPPfdc2n0wPXr0COmHHnooKtP1Dc9JXX/37ds3yvOaVTWv1J8krS1fffXVKM92Qu0L2wW1S+qXeD4/+uijUZmuH9kvnXjiiVGZ2k5eI6iv0XZo3bp1SJ9wwglRGesuAbFvUp+gbcZ5XZew3pSuF3TNwj5CtZ9OPvnkjNdN8h9A3PYdOnSIyu6+++4oX6dOnZDW+9T1A9vkuXPnRmXqM9hfq21nTcpjjjkmKtN+Yc02nZfqe7i9te1VT5fnXmpNoM9SjJ8lYrL2pZQxxmQrdiTGGGOS8EspY4wxmfCzRIxfShljTDGxIzHGGJOEX0oZY4zJhJ8lYvxSyhhjiokdiTHGmCT8UsoYY0wm/CwRk7UvpcqWLRvioDmmWPUfVHuH45Y1LlRj/zmGWLVrOMZUNRJUk4Djd1WTKUlHSPWQVBuINSA0rlpjp3mQahtpbDLrMWh8udaJ70evw7HfqtGkukusk1GUlsm///3vkFbNK43n5c/qfWssMmuJsF4AkN6erA/CmgBAenw595v2N7eZ1kfjjHmc6b2oRgqPV9VCULi9GzVqFJV9/fXXUZ7vRftQ6899o0ZS5xPPGdWCStLk0OvwvWiZ6v3wdXmcAMCBBx4Y5VlDIDUH8vPz02L1TXax7777hhh+tueq3cD6fEBsW9V21ahRI8qznVN7xHOYNQoKg8egjlXVN+HPqk1Zvnx5lGebrbpBem9s97QOhx9+eJRnzY2RI0dGZR07dozy48ePD2nVn2Ifff7550dl6gu4/mofWccDiG2kalSobhTrKC5ZsiQqUxuY5Eu1Tmwj9b7VznGd1J6zfpfqeKjOR5IGX5JOis6JDz/8MMrzeNDrap7totZP75t9iq4v1A/zeNB1V926dQutK5Duq1jXR/tMNX9Y30Z9iq5NuO1Tflf1Z0x20aVLlzA+WN/n6KOPjj6nzwuLFi0KadWuWbp0aZTv3r17SE+fPj0qY9ujvkXtFn9XnwfUv7DP0Lnds2fPKD9lypSQVv0cfQZIum+dH+xX+T6B9LUm63Lpuuqyyy4L6X/9619RmeqTtmnTJqQfe+yxqEz9CddJ7arq9Go7MKpz9eabb4a0ahXpuOI6qRaeamux/pP6nmOPPTakZ8yYEZWpneJ7Vb+k98nl+oyn2mnsK6tXrx6VaX+zXdT5ousoXuPo83WvXr2iPLfLCy+8EJWxjpWO3UGDBkV5HsuqQaqwn1K/qe8gWCcx1UYbNmzAXXfdlfgbZitZ+1LKGGOyFf91wxhjTBLeKWWMMSYTfpaI8UspY4wpJnYkxhhjkvBLKWOMMZnws0RM1r6UKlOmTNi+zVtcdSunblvkLbh6RLx+l7d86/ZM3h6u23p1iygPED0WnLflA/H2Qt32raFpHHqh22b1d/gYbt2ey9vegfjISt3Kqds3eXuuhkTwFkw+VhRID/X65z//GdLaZ7r1mEM49L41NIDbXrdRasiBtm/SdTl0QEMitP7cjzpWeLurbkvVtubf4SPhC6s737eORz0OmPtCjzjXI695rvFx50DykeJ635rncaRjjuepGlvdwpwU0qohetwvGuKoY4Ovm6pfkuG3I8kOqlevHuwHb8FWe66+gMOaNLxHQyp4G7iGVLCd0DAwHY+HHnpoSHN4MJC+xZ7rxLZd6651ULuhcy0p7EptONscPfa6ZcuWUV7DIxm2BY0bN47KNJyhXbt2Ia3zW0Md2G/o8eMaHse2QO9TQ0kaNGgQ0nzsOpAefsP9pqFoHOIBxKFD8+fPj8p4fKo/1Pvm8aDhcLre4Pv++OOPozINeeO+UX+o8PpIQzzU93OIjYZ46Pjk+aVtzWNMwz90TnD91O/qfOJ71fWGthHbgtRvqi9h/FJq1/PVV1+FNRGH9KsdOO6446I8h4mdeOKJUdnf/va3KD9t2rSQ1jC2d955J6R1zZ/0DKBh22qvX3/99ZBWO6VznUOf+b6AdBvSsGHDkFb7rNft3LlzSOfm5kZl6kd5Tqp/fvzxx0Naw9/0N9lWcqgUkB5SxqF1bNcLY+bMmSHdtWvXqEztFP8Oh24C6f3P4Yh9+/aNym677bYof9ZZZ4W0PsctXrw4pDt06BCV6Tqe17PvvvtuVKZyGRzOzDIrQLrvYXuodlT99VFHHRXS2ofqK/mz+jyj85R9RJcuXTKW6fx54IEHovzJJ58c0vq8ou3J/c1+vLDfefDBB0O6Xr16ANJ9IuNniZjSRX/EGGMMk3Ikxf23PYwePRq1atVChQoV0K5dO8ybNy/x808++SQaNGiAChUqoEmTJnjppZcyfvaSSy5BqVKl0jSDjDHG7Bgl5SOMMcb8+iiJZ4np06fjpJNOQrVq1VCqVClMmjQpKr/gggtQqlSp6J++bK1Vq1baZ26//fboM4sXL8bRRx+NChUqoEaNGvjrX/9a7PbwSyljjCkmJfVSasKECRg8eDCGDh2KhQsXolmzZsjJyckozDhr1iycffbZ6N+/P9566y306tULvXr1ShN3BoCJEydizpw5qFatWrHrZYwxJhm/lDLGGJOJkniW2LBhA5o1a4bRo0dn/Ez37t3x5Zdfhn8c3ZTilltuiT5z+eWXh7L169ejW7duOPTQQ5GXl4c77rgDN910U9oOtaLI2vA9Y4zJZkriAWLEiBG46KKLcOGFFwIAxowZgxdffBEPPfQQrr322rTPjxo1Ct27d8ef/vQnAMCtt96KKVOm4N5778WYMWPC51atWoXLL78cr7zySlpoljHGmJ2DXzQZY4zJxC/tI3r06JEW4quUL18+TQJH2W+//TJ+ZuzYsdi0aRMeeughlCtXDkcccQQWLVqEESNG4OKLL97mumbtSymOG+b4WNUGUZ0bjvnX2FSNG+U4T43t5u/q91Tfh/UMNK5W47VZf0Bje7UOHEOuZaxPUtjvMno0JutOqGaPXocnCx8VC8Sx57oTQ7UYuA21z1RbieOYNV6Xj0wF4jhxva7qQ/C96HVV/4V1PbSf9Khq1ivSeGONq2fWr18f5bnNtEzrwDoZatBUI4dj0TXmXmP7WTdMj3FVHTNuIx1HSTpXGl/N7an3rfOH20HLVEuL20jL9L75s6n5nZ+fn/a5FDsSB673WL58+TRNFGBrm+bl5eG6664L/6906dLo2rUrZs+eXehvzJ49G4MHD47+X05OTrRdNz8/H+effz7+9Kc/penB/Nr44YcfQruqzWHUZvN8V00h1fR57733QlptFetk6NxSG8P6HOpTUtoDKQ4//PCQVk0IHXdcrtppai95fi9cuDAqY90RIG7P5s2bR2Wq6cNtpna3ffv2If3RRx9FZaobxMc/T5gwISrjI9CB2E9omWoXsUaE1l3XFNyG6mdV34LHg9pHHQ/qG5j7778/pHv37h2VqbYf97/aXdVYmTx5ckirL9LFJWs2qU6S6rywNqEeka26VtwOaod5bGiddM3Dc0avo/3Cn9W5r/owvD5SP6tjg3U+U+xMvRC/wPpl4XGiNkLtAvtY1okB0o+Q53GjtjJJv1BtJWviaH1YmwrY6tdTJPkaIF4r6TpedRK5XPWn2rZtG+XZDui99evXL8qz7f/73/8elXXq1Cmk1U4df/zxGa+jdoA1pLS+rG0HAOeee26UZ71fbU9dI7A91Pb85JNPojzrbqmtVO0qXpe0aNEiKmPNrrlz50Zl6qf69OkT0qw9BqSPObaVet+q/cRrDdXBvPTSS6M8+1zV2dJnUl4Tqz1VPSq+V11LL1++PKR1raZt9PLLL4e0rn1q1aqVsX6qrfzCCy9E+dQfhIH/2g2dG0xJPEtsC2+88QYOOuggHHDAAejSpQtuu+02HHjggdFnbr/9dtx6662oWbMmzjnnHFx11VXh2Wn27Nno1KlTZGtycnIwfPhwfPfdd2l9ngmH7xljTDHZkS23NWrUQMWKFcO/YcOGFfob33zzDbZs2ZL2MFulSpWMLztXr15d5OeHDx+OsmXL4oorrtiRJjDGGJNANuoObt68Gbfccgvq1KmDChUqoFmzZtHLS2DrS8kbb7wRtWvXxt577406derg1ltv9UszY4zZiZTEs0RRdO/eHY899hhyc3MxfPhwTJs2DT169Ij+OHXFFVdg/PjxeP311/H73/8ef/nLX3D11VeH8kzPHqmybSVrd0oZY8zuyGeffRb99X57/7KxPeTl5WHUqFFYuHBh2s5CY4wxvy5SuoNjxoxBu3btMHLkSOTk5GDZsmVpp2cBwA033IAnnngCDz74IBo0aIBXXnkFvXv3xqxZs8LujOHDh+O+++7Do48+iiOOOAILFizAhRdeiIoVK/qPGcYYkwXsrGcJPvWxSZMmaNq0KerUqYM33ngjnErKERhNmzZFuXLl8Pvf/x7Dhg3bqc8w3illjDHFZEf+urH//vtH/zIZ9MqVK6NMmTJpR++uWbMmY1x31apVEz8/Y8YMfPXVV6hZsybKli2LsmXL4tNPP8Uf/vCHtC3Lxhhjtp+S2CnFuoONGjXCmDFjsM8+++Chhx4q9POPP/44rr/+evTs2ROHHXYYLr30UvTs2RN33nln+MysWbNwyimn4IQTTkCtWrVw2mmnoVu3bkWe/GqMMWbbKYlnieJy2GGHoXLlymnhpky7du3w888/h3DVTM8eqbJtJWt3SqWOHAQQnQ6lsb0aU6wxlozqBXDMqWoosMaMxuBqx7P2gWqFaP6LL74IadU60Fha1i9QrRDVW+A4V9XB0a1zX3/9dUirPpIuilh/QbfmcVtr2+qJXhxnyr8PpOtBsJbWhg0bojKN5ef6V6xYMSrTfuJ+LEr7i6+bpD8EIIq7Vb0Kbk+9DsfjA3Efavyt3guPDe0z7VMeZ9p+GkPO41N/U+vP41X7VMcy96OOFdaj0bbV3Tzcb6ohpPNU+yLTdYDCda2SHhB2JA58WylXrhxatWqF3NzcoLWTn5+P3NxcDBw4sNDvtG/fHrm5uRg0aFD4f1OmTAnaLeeff36ajkFOTg7OP//8IKb+a+LAAw8M44dtoo6bVatWRXnW4lGtAZ0/PF51zPH80d9gfQMg1p/S+nEZsPWhMIXq2qh2EeshqNaYajnwfavmgr6U5AdQtUdq3w855JCQVn1BtlWs2wHEuoRA3A5qf1TfgLVQ9D7VFnTp0iWk1aeoPgf/7rhx4zLWD4jtlY4bhbVQVC/mlFNOCWn19QcffHCU535T3672nX1248aNozLtC25PXQ9pm7EG2ueffx6VqU4YrxP0vuvWrRvleT2nOhw8v9RvqU/h8ai+SOcwz0XVk1PNLu7j1HzRtmF+aU2p7dEd3LhxY9q6ee+998bMmTNDvkOHDnjggQewfPlyHH744Xj77bcxc+ZMjBgxolj1ywZOP/30YDOnTZsW/j9r3gDptpPXNEVpt/EYeuKJJzLWRddJard4rqvm1WuvvRblVQeVYa1VIPYnRWnkDh06NKSff/75qEz1k3iNpbvy1DZNnz49pFkPC4htXocOHaIy1RTidtA2Ur0f1rc7+eSTozK1GWzTeB0MINLkBIC+ffuGtNo/9Y1sk3XeJWkN/e1vf4vy7JfatWsXlakf5ZcDqiupWoennnpqSBelVcW6ZuqP33777SjPdlefy9VX8vzRe9Nxzn6MdRCBeE5rfbSNeI7k5eVFZdov9evXD2k9lU7H1V133RXS3bp1A5A+1piSeJYoLp9//jn+/e9/p609mEWLFqF06dJh3rdv3x7/+7//i82bN4e1+JQpU1C/fv1t1pMCsvillDHGZCsl5UgGDx6Mfv36oXXr1mjbti1GjhyJDRs2hBdIffv2RfXq1UMs+ZVXXonOnTvjzjvvxAknnIDx48djwYIF4VjWAw88MG0xsddee6Fq1aqR4zXGGLNjbO9LqW0VsE3SHdQ/eqXIycnBiBEj0KlTJ9SpUwe5ubl45plnoheR1157LdavX48GDRqgTJky2LJlC/785z+niUMbY4zZfkriWeI///lPtOtpxYoVWLRoESpVqoRKlSrh5ptvRp8+fVC1alV89NFHuPrqq1G3bt3w8nj27NmYO3cujj32WOy3336YPXs2rrrqKpx33nnhhdM555yDm2++Gf3798c111yDJUuWYNSoUdFLum3BL6WMMaaYlNRLqTPPPBNff/01hgwZgtWrV6N58+aYPHlyeAhZuXJl9FenDh06YNy4cbjhhhtw/fXXo169epg0aVLaLgljjDG/LNv7Ukp3mw0dOhQ33XTTTqnTqFGjcNFFF6FBgwYoVaoU6tSpgwsvvDAK9/vXv/6FsWPHYty4ceFo70GDBqFatWppp6oZY4zZPkriWWLBggU49thjQz6lD9WvXz/cd999WLx4MR599FGsXbsW1apVQ7du3XDrrbeGP4SUL18e48ePx0033YSNGzeidu3auOqqqyKdqYoVK+LVV1/FgAED0KpVK1SuXBlDhgzBxRdfXKy6lirIsuM01q9fj4oVK+Kggw4KD1v8Fxz9a5GG6HC5bvHWbasc7qHhPLzdTo8B1uvylm/d3q/bfjkUQ+MsNfyMtxDqttSkUDUNgdLP8n3rdn99eOVtd3rf/Jt8bDqQ3tbaLoxuJ+XwFP1rn4b6cb/xMepAeqgAb/U84ogjojLdXsmhDHq8ro453pKr7clbeTW8R3+Twwp0WmoID4853RKuddi8eXOhdQXSwxZ5y7Vuv9YxyNfVv+zqlmYOOdF74fGqoRAaasHf1RBWbQf+rs4JHZ88zlNbgPPz8/HNN99g3bp1YYt7yj5dcMEFaW1XFJs2bcIjjzwSXc9sH6l+uO2228KY5u3Z2t96tDHPHx1jOr95Duu28w8++CCkdbeCzh/+TQ0T0pA8nls6VjXkg+2whlvoNnSeMxziBKTbAva7aqM11O+2224rtD5AbHP0exri2LNnz5CeOnVqVKY2hX3rGWecEZWp7WffxX0GxMdcA/FW/RkzZkRlGhbDfVNUGOjChQtDmsNKgPhocG0jtZcaWsfovfAaQ8MCtX7sozUsQrffs0/U62roJK8/1E9oaB2HcWiIKx+1fuSRR0Zl6vMY9aUatspzTcN2dByxz069OPrhhx9wzjnn7BQ/kfIRhQnYFrZTatOmTdhnn33w1FNPhRBvYOvDxtq1a/Hss89m/K2ffvoJ//73v1GtWjVce+21eOGFF8L4qVGjBq699loMGDAgfP62227DE088kXEHVraR6oMbb7wx2CBe1+lc1rU695s+A2hfcNiTji9G7b7KkXCI79lnnx2V6Xpn6dKlIa024vTTT4/yc+bMCWl94fmvf/0rynM7qE9LQm0Ey3AA8dzS0GHexaFtrffNdapdu3ZUpqdIsv/j8Dxgq6Azw/ZQr6Ohiey3eEwBsWg0ENsmtY1qy/m6+nzYuXPnkFZtN/WjrVq1CmkNU9X1DbevPismSd6o7dYwO24zff5T38NjTp/FNc9jXX1P0jMJ+w8gDpXV9Y36Z87rMxS/7NH6ptYHGzZsQO/evf0ssQ1Y6NwYY4rJjogTGmOM2f35pQVsWXcwRUp3MKUjmIkKFSqgevXq+Pnnn/H0009HumY//PBD2oNjmTJl0l5+GGOM2X78LBFTrJdS9913H5o2bRocZfv27fHyyy+H8p9++gkDBgzAgQceiH333Rd9+vRJU2M3xphfO3YkmbGfMMaYkjl9b/DgwXjwwQfx6KOP4r333sOll16apjvIQuhz587FM888g48//hgzZsxA9+7dkZ+fj6uvvjp85qSTTsKf//xnvPjii/jkk08wceJEjBgxAr17997xRoF9hDHGAH6WUIr1UuqQQw7B7bffjry8PCxYsABdunTBKaecErb8XnXVVXj++efx5JNPYtq0afjiiy8iZX9jjDG7N/YTxhhTMpx55pn4v//7PwwZMgTNmzfHokWL0nQHv/zyy/D5n376CTfccAMaNWqE3r17o3r16pg5c2YUqnjPPffgtNNOw2WXXYaGDRvij3/8I37/+9/j1ltv3Sl1to8wxhijFEvo/KSTToryf/7zn3Hfffdhzpw5OOSQQ/CPf/wD48aNC8cvP/zww2jYsCHmzJmTpgFQFJs3bw6aAhyHq/HFGvPPca6qT6Mxphz7qXHgrGegcbYa/5mk/6FvNFk7RE/BUg0Frp/ep9YpSRdBt2FzXLvet8bVszbL2rVrozJua43tTaqDtpFqhfC96meTYm9VR0ZjslkHQ6+r2+NZU0P7SePAWaMkSe9DY661vvw7Wh89Fpf1k7R/k7TTtH4cyw/EfaxtrX+p5Pml9dU4Z57Det8cEqAaOBq/zb+pGlcKzz39TdUR0nFfFNl4jGu2UJJ+omzZsqFvWU+nqDh71jtQW6osXrw4pFUThK+jv6lHQ7PuiB5lrRoG7Ce0fmpHeOyqVkfdunWjPOtc6W+qv2Sdpn/84x9RmWotcbvodVm7Qf236hTyseFNmzaNyvSo8DfffDOk1RaoRgTbMvV5atf4KGm1l2oTWddFtaCaN2+e8bs6znmsqH6J6gJxH7/66qtRmeqksM6LHg2ubc/3qvowTz/9dJTnUC9ta9Xs4vbVtm/Tpk2U5yPnVVuG9WO07mq/kzRK9LPsL1VvRzVgWE8pNV/U3zLbK3ReXAYOHIiBAwcWWvbGG29E+c6dO0fjtjD2228/jBw5EiNHjtyu+hRFSfqIJUuWBLvDenyql6N6qs8991xIq31hO6Xlavd5HaraZ0cddVSU1znK6Jqa56jWj20YEI95XW+rdtWsWbMK/Q0gXZcndToXELcXkO4jrrzyypBW7UOeo6eddlpUxv4XAB588MGQvvbaa6MyXfMvWbKk0N8A0tts7NixIc3PU0C6/iK3g55arP6P9XZbtGgRlWn7sv1T+N7Ur+szAPex3qfq/7KOo/pC9UU8tlUnitsPiNcEuv7W+rJPU60qtdc897QOzZo1C+kkzV6tn/p1DVPmOujzoK7HWLMr9Zy2M31E6ju7K9utKbVlyxaMHz8eGzZsQPv27ZGXl4fNmzdHgp0NGjRAzZo1MXv27IzX2bhxI9avXx/9M8aYbMZbbrcN+wljzJ6KfUTR2EcYY/ZU/CwRU+yXUu+88w723XdflC9fHpdccgkmTpyIRo0aYfXq1ShXrlzajp4qVaqknULEDBs2DBUrVgz/9C9VxhiTbdiRJGM/YYzZ07GPyIx9hDFmT8fPEjHFfilVv359LFq0CHPnzsWll16Kfv36FbkVOInrrrsO69atC/9066MxxmQbdiTJ2E8YY/Z07CMyYx9hjNnT8bNETLE0pYCtsZepeNZWrVph/vz5GDVqFM4880xs2rQJa9eujf7CsWbNmrQ4X6Z8+fKFHne73377hbjTTMfhAum6Axxjum7duqhM9UA4r9oBrO+k39N4Y/4sa/0A6fGnrJGjTlM1pTj+XeOhVf+D20j/wqT159hajTdetWpVlGddDP0rFcf+apnGInNMrbaJxlFzG+nY0VhfbrPvvvsuKvvoo4+iPI8NvU+NN+Zrqe7SYYcdFuW5DTUmm+OaNX5cdbh4DOqY1zzrf3AMM5Aer81tVNQJNmzsdE7oXGOtG+0XjaFmvR29LudVl0c1Xfg3NU6ddXiAeM5o/+rWfp4zqbml85FxHHgyJeUnypcvH+wFz0P9rMb9s71SW6pjmTWR1L7z3NN5p5oGbDeWL1+e8TpA7GNYpBhI9xuqI8WwjhUQ25zWrVtHZaoFxBohqkOh2m88n7i9gFg/q1+/flHZpEmTojzrgun8VtvFfk01XljzCABef/31kFZtRLUxbLvUl2p7sr3S+a36MDzOVPuJ/Y3+xrhx46I8+2S1YzqW2Yap9pO2A485Hbuqj8X2VMenwtfSsbFw4cIoz1qOag+S5ix/D4i1T3QdqOsE/ixrRgHp/c9rspQ+lrYVU1w/YR+x833EH//4x7D2mzp1avj/3377bfQ5tS9nnXVWSE+ZMiUqO++88zJ+V9chPGb0pZtqNLVr1y6kdV2sayq25fPmzYvK1LYzast1XXfMMceEtGo/sWYPAMyfPz+kdX2r2lAzZ84MaZ2DJ598ckir5pWuCTmsU7WAtI34GSUl/J9iwoQJUZ6fWdT36HUXLFgQ0vo8wL4GiDWndO2r7XDZZZeFtPb//fffH9L67KB+gH2azhkdn/wspGNBr8s+V7UudWcil+t961zjMaf9PXHixCjPfurEE09EJlSPVtcarEuofab6cmzjVRdMtd54/Zham+1MH5H6zu7KdmtKpcjPz8fGjRvRqlUr7LXXXsjNzQ1ly5Ytw8qVK9G+ffsd/RljjMka/NeN4mE/YYzZ07CP2HbsI4wxexp+logp1k6p6667Dj169EDNmjXx/fffY9y4cXjjjTfwyiuvoGLFiujfvz8GDx6MSpUqYf/998fll1+O9u3bF/u0DGOMyWb8143M2E8YY4x3SmXCPsIYY/wsoRTrpdRXX32Fvn374ssvv0TFihXRtGlTvPLKKzj++OMBAHfddRdKly6NPn36YOPGjcjJycHf/va37apY+fLlQ/gNb53UbZUKbz/UsDUNx+EtjvpZ3vapWyz1yErekq7X0frydkjdpqghRhyWoeFmGnbHg1S3qeo2S24j3Vaon+Vtlnl5eVEZb6NMChEE4u31Goqo4TJcf91qqqEW/DsaAqPhE9xP2vbaDnx0dcuWLaMyDb3hrak6Nri+GnKg22p5fGrI4McffxzlOQRS20+39nK7FDV/eMurhuhon/K81PvWbcAc2qRtzVuwtUzDJ7jfNJRKtwjz+NRjZXUccX1TYy4/Pz/tN0zRlKSf2G+//YLtYRupIdYausT9quHEOp/YXul2dp4TGiakx0jzHNGjjDUMgbf8r1y5Mirjre5AHNak41VDmGvXrh3Suu1cw7Dq1asX0tp+Gp7NoWB9+vSJynhOc7gHgLTTtDp06BDSSccoA3HYhPqme++9N8pz2IyGtqiN4fbV48i1n9hfqo3h8AAgtqdJ4fgaaqrHxnPYUZcuXaIyvRe2/TqukyQANBxOjyDna6lfUD/M5ToeObQJiH2g2nP2IXovuh7isaNzTdc4bCu0TP0a1yFVVtQ4NemUpI+YM2dOGIO85tIxpKGkbEt1LfTaa69FeV6n6rqT/ZLaUbUnHLqrn23QoEGUf+aZZ0K6Vq1aURmHXgOxvT788MOjMrVxHD6n61C1TQ0bNgxptT3qi9i/9OzZMypjW64hjRrOx2FiL730UlR29tlnR3leLy5btiwqY/8GxP12xBFHRGVvv/12lOe+OPbYY6OypHAzfU5q2rRpxjroWoP7UJ8dtK05VFKfezmcHojbWyUIZsyYEeU5fFNtd9u2baM8+0Oda+zngViS4J133onKTjjhhCjP40jbiH3uW2+9FZXxMx0Qr/l1zaJzmMeKlqn/43VVqq72EdtOsV5K/eMf/0gsr1ChAkaPHo3Ro0fvUKWMMSab8V83MmM/YYwx3imVCfsIY4zxs4RSbKFzY4wxu7djMMYYs+PYTxhjjMmEfcR/8UspY4wpJv7rhjHGmCS8U8oYY0wm/CwRk7Uvpb755psQC6v6C4zqNnCMtmo8JGnO6HX22muvjL+ZpBulmj2aZy0bjbPVIyw5Tli1DZIGperl6PGbrH2iceD6O3xEt7YfxzirjoTGMXOMtmqZ6L2wnoXqXmj9OE5Y21Pjrjn+WGOROTZef0fvTduB+03HKveF6mPp0e58rxojrn3Kcfba1vpd1nvR8ahtz5/V66iODGt1qIaPxm9zubZD0jzUOcFjQ9tE86wPorpwOq7YVqSukzTH7Eiyg59//jmMH9aG0jmhmiA8T1U3QzWmeFyp5gzr8OgcUC0b5quvvoryOie4vqoBolpVPE9ZNw9IP6ab53+S7hsQzzWtQ9JR3DqH+bhy1fFQ/RDWx+rdu3dUpv3C+liKXpevpbZfj7LmflQ9Lz1GnseRanKpzeE+Vn0THle6vtC2Zxut65TOnTtH+U8//TSk9Sh4HrtAvBbQOmifsn1X7Q7WwgTi+aO6a6q1Ua1atYxlSb5e5w+3kc59nXusD6O+Xf0Ej8FUe+lnGL+U2vU0bNgwjN/JkyeH/6/aOmrbWadMbc2iRYuifPPmzUNa5+Rxxx0X0qqXo88orGf39NNPR2WqP8QaPjpfde7wfGCbAKTrJzVp0iSkWZsISF/X8fOB2gjVwHr//fczXkfnM/PBBx9E+UMPPTSkdd7rmpXn+llnnRWVsSYXEPtOtRFKq1atQlr9iep78fOO+k1tX+4L1RFmbcFXXnklKmvcuHGUZ79flG4w94WuYbp37x7l+VlS10367MP9pusQ1cjltYfOyxdeeCHKDxo0KKTHjx8flfF4UF+tz1vsn7W/eT4DsX7WrFmzojJdc3EbpuyGPrcyfpaIydqXUsYYk63YkRhjjEnCL6WMMcZkws8SMX4pZYwxxcSOxBhjTBJ+KWWMMSYTfpaIydqXUhUqVAhb+nj7oW6v1pCdypUrR9dgdJslb6nTrfZ8lKj+hm7F423wOliSwgB1y6pu7eTth1rGW1iBeKuntpEeWcmhLUnHtgLxtlDdpsjtUlR4B2/71C38GlbAWzC1PTWEQ4+LZXSrNLc3hwkUdl3ecquhIBoOwNv3ta15W6iOPw0L5HHF262B5PBSHbsalsFhRLp1W+Hvah9q6B+Htmhon25L57mo84l/R/tb24y3COuWdW0Hnj86D3Us83b31PbbpC3ldiTZQcWKFcM44LFSpUqV6HO6lZtD1/g4aiDdPiWF4XEondoi3d7OY0x/Q20M23C1BTqWn3/++ZDmI5uB9PDs5cuXZ6yvhh3wdzXcQkMz+N4mTZoUlXFIns59DkkAYv+jn9U5y8d0s78urH5cfw3r1v7lsEVFt/lz36i9PPXUU6M8b/vXEDe2jzpW1ddzGI/aZPUpPD451AZIHxscrqThFXpvS5cuDWntF10f8THyWj8Ni+J5qW3NazuVJNC1HttaDeXV9mQ7r2U6T/leUt/T8Bith19K7VoeeOCBYDMvu+yy8P9nz54dfU7tDa8ndHzpepHRedahQ4eQVjul67rXXnstpDUET0MGOdRPr6uh2XPnzg3pli1bRmVPPfVUlOd1nc5ttY28Vq9evXpUpiFmHJo2Z86cqIxDq44//vioTEO+ef2t9oR9DRD70XvuuScqO++886L8ihUrkAkO3wLi56Jx48ZFZTqH2b/k5eVFZccee2yU5zGp4XscZs4hlkD6up5DO5944omorFevXlGen1/YBwDA/PnzozyHQGp4qfYbj0G23UD6/GEbrH2o4YbXXnttSGu/8JpLfayu8/iZReehjnPuC33G07Umr8FSazN9ZmT8LBFTuuiPGGOMMcYYY4wxxhizc8nanVLGGJOt+K8bxhhjkvBOKWOMMZnws0SMX0oZY0wxsSMxxhiThF9KGWOMyYSfJWKy9qXU5s2bg6YUx3PrsZOqF8B6BvpZPfKaY0OT9GlUc0L1iFjXQbUXNP6UNQr0OFOtA5drvLvqMLGugWpFaDx80rHg+lluT60f63RoTLvGOLOWiWoe6RHnfJSo9qHG+nKf6nU0z/XX+HHVn2JdDz3yWeODWZslSVtLj7ZVbZgk/THVCeNxrwZK5wTH3Wt7ap+yFpT2k943979qb+i4Z30EvU8eu/obet8cm651V30V1h3RNtFxz/eduq41pbIf7ge2ezp2VeOMx6Dq6qnmEF+X9TaA2I6w3gyQrrPG81+1G1RzgY8V1vmiY5ePHFd9G9Ul4XvRo5fVhnMd1Ae+9dZbUZ5tAWs9AbEew8svvxyVaVuzRoTOP7UFrBmhvl01K1hTZfHixVGZapZ89NFHIa3HsGubcZ+rPz/yyCOjPM9//g0gvletj/ox7mMdG2qHWd9G1xC6juE6qJaM2i6eXzrXVBOL66jjSK/Lvlb1YnisqM5IEjqOVKeQ9dx0POpR5uw/U/olOscYv5Ta9Zx88snBp/O6Wdt65syZad9LoZoz6jPY/qnNYH02XZvrOonXP6qLp/OM57ZeR+cHH3Gvmj1nnHFGlJ86dWpId+3aNSrTuc06eaecckpUpr/D2jv6fMDaco8//nhUpjaXtatOPPHEqExtO9vnbt26RWUNGzaM8s8++2xIF6UbNHr06JBmzTAg/dmCx87pp58elaneE98b62wBcf9r/fQZhbUOL7nkkqhMNcS4/gsWLIjK1Fbm5uaGNOtLAelrD9bl1XWTrnd4rOs41+cmHsvt27ePygYPHhzS3bt3j8p0fcbjs3Xr1lGZPg+yX1DtNNV64/Gamoc700ekvrO7krUvpYwxJluxIzHGGJOEX0oZY4zJhJ8lYvxSyhhjiokdiTHGmCT8UsoYY0wm/CwR45dSxhhTTOxIjDHGJOGXUsYYYzLhZ4mYrH0ptWXLlqAFwPGYqh2hmgqsiaQxrqpBw9fV2FnWVChKm4rzql2isegce67X+fLLL6N8UgyxxrSzng7HEwPp7XDYYYdlrJ/CGhA1atSIyliLQeOoVZuK25N1qgqjZcuWIa1tr5pDjOoGqRYHxxRre/K9APF4UJ0ObV8eV6ydBcT1V40MjZ1O0uhSXQweuxqfrbHT3P865lRnhNE20vnDceyqYaBGk/VgVBuG20h/Q/ufr6vjiDVwgHjsapnqv/C8TbWRNaWyn6+//jqMU9ZwYj0xIH2usZ6O6h2oHeY5ouORbUFROg+sYaF296uvvorybCtUR0/hOqkGjmoX1a9fP6RVRyFJj0Ovo23EOg9qh9lWnXnmmVGZ6sCxHVm2bFlUpraBbXatWrWiMtaxAmKtKtWAUA0sbiPVBVP/zn3KGldArEup5Vo/1qxQ3Ra172zDWSursO+ydpnaevWlbJ+0fqqFw5/V31Qfw3ZYx1GS9uBRRx0VlfGYUz0s9QWst6NrEdUoYT0bXSupP2/SpElaHZLWM34ptetZuXJlGAPsB7Rvzz777CjPY3P+/PlRWefOnaM860apXiD7Iv1NXRPyOkXnoK5DedyyriAAzJkzJ8rz/F24cGFUpnaKbVFKNy2Frn3Z3zz66KMZy4DY/vAaHwAeeuihkNa5rfOVdaTUz6uP4Lyu8ceOHRvlWSvo/fffj8r0+eboo48OaZ3/+mzWs2fPkH7xxRejst69e0f5adOmhbQ+b3Gfqmai2i32d//3f/8XlakOE49X1c6aPn16xu/q+NT1Dq+f2W4C6ZpirGOm/kPbgefBfffdF5U98sgjIa39q7pOST5M5yXXQeehPku8+eabIZ1aCyU9t/pZIiZrX0oZY0y2YkdijDEmCb+UMsYYkwk/S8SULvojxhhjjDHGGGOMMcbsXLJ2p1SFChXCNj7efq3herr1lLeIamiAhl7wNlXd2vf555+HtIaB6VY8DlfQ7a1aX94SXK5cuahM68vb1TVsQLc4cp30swqHJ2j9NMyFw0r03jhkQkNM9Drcvhouo3XgUD/dlqpviLnNdNuvhmxx2+u96JZMHkdaBw315KNbDz300Iz10/AJHXO8vVjDJbQ9uf91Duh2Yg6X09/UccRbcDUMR8PZeJxpiIRuYea5p+OTt79q22r9eDu29q/Ob75vPepbQxx5DKbaOim00X/dyA7Wr18fxhP3t84fHSu8PVtD6b7//vsozzZQQ6nYhuu41tAktjk6P3Su8ZzQOaDX5dARtT9t27aN8hx2kOQ7Nc8h30D6fGcbqEdt83c5rABIt7v8XQ2D0SPbeb6rzdO+YF+l4cP6WQ790q35Gt7A9lTDGTR8hUME2rVrF5VxW+tY0Dbisau+U4+r5mvpveiR7byGaNGiRVSmv/Pee++FtM4tnU/cLtom2sdJ8gvc32pLNXSEw0DV5+mc4HvTNZiOFV4XptZR2l+Md0rten766afQrroOYDQkuXHjxiGtYbw6LjjcS58XOnbsGNIcngUAHTp0iPIcUn333XdHZZ06dYryHM6l4Xoaote9e/eQ5vU/kB5a9cUXX4T08ccfH5VxeBQQz1f1PdpGPCc1HPKII44IaQ3J4/A3IPZbRx55ZFT297//Pcrzveo8nTVrVpTn9tWQMV1bchjblClTorI+ffpEef7dY445JirTEHW2wWor+blJ/XGXLl2i/MSJE0Nax436KQ6d41BrADj88MOjPPepPr+qhAf7LW17lSfhNY7et65/2NerBMFf//rXkL7sssuisnnz5kV5trXqE3gNoHn1J/rcyfeWClNMeib3s0RM1r6UMsaYbMWOxBhjTBJ+KWWMMSYTfpaI8UspY4wpJnYkxhhjkvBLKWOMMZnws0SMX0oZY0wxsSMxxhiThF9KGWOMyYSfJWKy9qVU6dKlQywpx6NqPKfG+laqVCmkNaZZtUJY14O/B8Sx56pzo7oSrPGgZarTwXXSuFq9F9bw0XvRPOs6aPyuaj6wVoPGAat2BNdRj0nl62q/JOX1N/QYV+4XjSfW+2ZUV0Rj07l9VU9FdcL4vjXOWg1Ckl4Sjx2NwdbPsl6Nal7pfXMsvx5nqp/l+mq8tmqBcXvrfWp9uY30N7XNuM9VI4VR7SzVQeE21N9UDRKeP6wLBKTPCc6n2lY1tBg7kuygWrVqwX6wfddxrTpMrEFTr169qEz1qHguql1jP6E6Hqq7xPNbx58e8cz3ojo3rN8ExDpR6uPUpzA6X1SfgY+DfuONN6Iy1bTgeao+hefp22+/HZWxlggQ6+qpDVQ9Km7fJUuWRGXqz1kLSMvUx5xyyikhrf5btWX4upwG0v0a+2X1P2yfVKNC68C/o/Zcx4qOQUbbgftQNfc++uijKM9aI2qj9bM8F7X9dJ6y31DdNbbHelS5asAwOp+T9AS1z1RDjv15Si9EbQLjl1K7nsMOOyzYf9bfU20l1TdjbSU9Il7HOK9b2rRpE5W98847Ia32WPOs2VS7du2oTDWvmjVrFtKqa3TBBRdE+aR1vNrVBg0ahLTaNC4D4jU33ycQtzUQ6/qpz+XnJv2eagH9z//8T0jff//9UZlqA82YMSOkVTtLn9XY9ixatCgqY58AALNnzw5p9R9qg9k+qD3WdfKpp54a0pMmTYrKeFypn9f+ZzurulDquxcvXhzSqjPYtGnTKN+oUaOQVlul6wmeM9r2qjvI6yH1jUlzRtuTf+fhhx+OyrQdeH4vX748KlM/wH7qtNNOi8pGjx4d5Vu2bBnSKc24DRs24J///CcKw88SMT59zxhjtoOUM9nWf8YYY/Ys7COMMcZk4pd+lpg+fTpOOukkVKtWDaVKlUp74XnBBRegVKlS0T8+HAHYuvnh3HPPxf7774/f/va36N+/f9rL+sWLF+Poo49GhQoVUKNGjUh4flvxSyljjCkmxXUifugwxpg9C/sIY4wxmSiJZ4kNGzagWbNmabu6mO7du+PLL78M/3Rn17nnnot3330XU6ZMwQsvvIDp06fj4osvDuXr169Ht27dcOihhyIvLw933HEHbrrpJjzwwAPFqqtfShljTBYzevRo1KpVCxUqVEC7du3StrMrTz75JBo0aIAKFSqgSZMmeOmll0LZ5s2bcc0116BJkyb4zW9+g2rVqqFv377RVmZjjDHGGGPMr5sePXrgtttuQ+/evTN+pnz58qhatWr4x9IA7733HiZPnoy///3vaNeuHY466ijcc889GD9+fHh2GDt2LDZt2oSHHnoIRxxxBM466yxcccUVGDFiRLHqmrWaUvvuu2+IHWVNBY31VL0F1iFQDR/9LKNvHpM0PZL0c5K0f4A4vnjlypVRmeoi8NY41eFRDRK9V0Y1PlhTQfWyVGeCtVi07JBDDsl4HdVz4ode1Q3S+GjuJ/4NIF1vg3VHVDdG45Y5Jr9u3bpRmW5D5DZSDSzVg+Gxo/oSrCOSpJ8BxG2o+lg6PrW+SdflGGy9rrYZk6RHAsRjXdtINbp4/Ooc5rzWXeP1+b61fhrTzuNI57COQdYdSd1LUfaiJOLAJ0yYgMGDB2PMmDFo164dRo4ciZycHCxbtixNnwUAZs2ahbPPPhvDhg3DiSeeiHHjxqFXr15YuHAhGjdujB9++AELFy7EjTfeiGbNmuG7777DlVdeiZNPPhkLFiwodv12NfPnzw99yVoJOs7VPrJN1HGjNpy1J3RM8LivXr16VKaagdz/eh0d96yFoRo4zZs3j/KsR3TwwQdHZe+9916U53vVeag6H9xmatdU74m1HNRXseaQ2pvXXnstyvfr1y+k69SpE5Wp7Z87d25Iq59QHQrW3VNbpW3Pules2wLEmldArM+imhWqscL6War7yAvAVatWRWV6361atQpp1QDRe2MfqH5WtQjZrxWlj8XX0nWMtueRRx6Z8bM6D1i38NBDD81YX9XSUv0pvq7aes3rtRhd1/AaLaUplaTbZk2pXc9HH30U+px1jVQ3SOfr6aefHtK6XtS1Jc+HmTNnRmVsF3ReqX3m8aY2Qn9z8uTJIa2hNnpdtik9evSIyljHCoh1g3SuqB9lfUPW0gGADz74IMofc8wxIa36WGzH1KZxnwGx5pTq2ekam32j1l3tFPebPjOppiLPef0sazQBsVajapGpjhn3mz5vsZ3SNlF/zPYwLy8v428AsS6m2lz9YyXft9putYM8HlQn6t13343yxx9/fEhr/7do0SLKz5kzJ6T1OZjXN40bN47KdE3I65233norKlOfwPpu6mtUd4vnWmotuTN1B1PfAdL9U/ny5dPm7Lbyxhtv4KCDDsIBBxyALl264LbbbgvtO3v2bPz2t7+NxnLXrl1RunRpzJ07F71798bs2bPRqVOnaJ7l5ORg+PDh+O677xL9LFOsnVLDhg1DmzZtsN9+++Gggw5Cr169sGzZsugzP/30EwYMGIADDzwQ++67L/r06ZP2gsIYY37NlFT43ogRI3DRRRfhwgsvRKNGjTBmzBjss88+eOihhwr9/KhRo9C9e3f86U9/QsOGDXHrrbeiZcuWuPfeewFsXUBNmTIFZ5xxBurXr48jjzwS9957L/Ly8tIeGLcX+wljjHH4XibsI4wxZseeJWrUqIGKFSuGf8OGDduuOnTv3h2PPfYYcnNzMXz4cEybNg09evQILx5Xr16d9kfwsmXLolKlSuEl4urVq9MOD0jl9UVjEsV6KTVt2jQMGDAAc+bMwZQpU7B582Z069YtOsnoqquuwvPPP48nn3wS06ZNwxdffBGdKGCMMb92dsSRrF+/Pvqnu8pSbNq0CXl5eejatWv4f6VLl0bXrl2j01+Y2bNnR58Htv61ItPnga07W0qVKpV2StX2Yj9hjDF+KZUJ+whjjNmxZ4nPPvsM69atC/+uu+667arDWWedhZNPPhlNmjRBr1698MILL2D+/PlpOwRLgmKF7/F2UWDrts+DDjoIeXl56NSpE9atW4d//OMfGDduHLp06QJg67GMDRs2xJw5c6Jt3EVWrGzZsB2Qt2RqaIWGSHB4gG7XU/iI4aRjPDWESMO3eLua/mbSEc26nV5DsnjLo2411ZAO3hqn4RNafz7mWLfla3gFb9HU7ZpLly4NaQ091DemjN6nbjfkLZi6LVXbgbeb6hZR3drLaD9pnn9Xx5xuA04KneSQA21bDT/j8aBtomOOx67CIRBA3P/6PR2f3L7antpvHCrE26SB9PrzvSaFtGrddZHO23V166puaeb6avvx4lfLU9vmk9p4R7bc6lwZOnQobrrpprTPf/PNN9iyZUuhf33QY51TZPprRaa/VPz000+45pprcPbZZ6fZ0u2lJP1EmzZtgg1Lsvc6R3l+a39oaAHPH/0Ntolqq9S+c+iGHsOtOwB4W79u8Vd7xDZcxyxvzQdie1RU6AP/ZUxDR9SWcaiJ+hQOWdD24+3gAJCbmxvSujU/yU/oVn31VRyyoCGO2vYcdqL3onaDyz/88MOoTNuej51W28E+WW2gHpHO/a9tpPOcx26SvwFie6p14PoBcXi2bsvXsCMNi2J0zLEN0nBiDtHUOavhEdxGGqbIoRhAvJbSPtM243wqpEvDfxmH7xVOSfqIffbZJ/Qb29ITTjgh+pw+gPF8VfunYU3t2rULaa3bs88+W+g1AeDSSy+N8o899lhIcxgdALzwwgtRnv/4pLZc7RSHgul81JAttgtFSTiwnVX7p7adfd78+fOjMg6B0zmoef5N9SdqKzn0S9tI10lsU9T2aCgdo+tktWnsk9UvcfgjEN+rhoPzvalNS1rz6/qhQ4cOUX7ixIkZ685hdQAwZcqUkFY/+vrrr0d5HmcdO3aMynRscOikrtV1XPG6vmHDhlEZh/TrPNTnYg6z03vR+c3+RdcLul7kNnv11VcBpPcPsyPPEvvvv/9OYmjIXQAAUIVJREFUW7czhx12GCpXrowPP/wQxx13HKpWrZr2jPfzzz/j22+/DWO2atWqaW2TyuscTmKHhM5TnZyasHl5edi8eXNkLBs0aICaNWsm/qXeGGN+TWTDXzd2lM2bN+OMM85AQUEB7rvvvl/sd+wnjDF7It4ptW3YRxhj9kRKSgqkOHz++ef497//HV7WtW/fHmvXro30yaZOnYr8/PzwQr59+/aYPn169FJxypQpqF+//jbrSQE7IHSen5+PQYMGoWPHjuGvlKtXr0a5cuXSwkCS/lK/cePG6K9uuvvBGGOyjZL460blypVRpkyZQv/6kOkvD5n+WqGfT72Q+vTTTzF16tRf5K8tgP2EMWbPxTulisY+whizp7IjzxLbyn/+859ox9mKFSuwaNEiVKpUCZUqVcLNN9+MPn36oGrVqvjoo49w9dVXo27dusjJyQGwdUda9+7dcdFFF2HMmDHYvHkzBg4ciLPOOiscxHLOOefg5ptvRv/+/XHNNddgyZIlGDVqFO66665i1XW7d0oNGDAAS5Yswfjx47f3EgC2Ch6yUJduyzbGmGyjJP66Ua5cObRq1SoKacrPz0dubi7at29f6Hfat28ffR7Y+tcK/nzqhdQHH3yA1157LS0EaGdiP2GM2VPJtr+AZyP2EcaYPZWSeJZYsGABWrRoEcJZBw8ejBYtWmDIkCEoU6YMFi9ejJNPPhmHH344+vfvj1atWmHGjBmRZMLYsWPRoEEDHHfccejZsyeOOuooPPDAA6G8YsWKePXVV7FixQq0atUKf/jDHzBkyBBcfPHFxarrdu2UGjhwIF544QVMnz49ivOvWrUqNm3ahLVr10Z/4Uj6y/51112HwYMHh/z69etRo0YNrFu3LsTFsm6DdobGn3Lst8ZxqiYOHzmtxzwmxVJrHDPHb6vmkWodcF41cPS4adV8YFQXgdtXY3D1OryVLknzCohjl/UvT9zWGous983l2kaa537SGGy9F85rm+hY4XGkOlt6b3wEsOp3qbYJ11f1Xjivf/XTHS16tCyjYth8LR2POu5Z40PbU7V2OD5aP6ttlqR5plosHFevMfiZPgekj0fuJz3+XDV9+Fqqc6L3wuUpjZAkTamSYvDgwejXrx9at26Ntm3bYuTIkdiwYQMuvPBCAEDfvn1RvXr1cOrGlVdeic6dO+POO+/ECSecgPHjx2PBggXBeWzevBmnnXYaFi5ciBdeeAFbtmwJf3muVKlSoj5acSkJP7H33nsHfSO2Bam/3qTQOczHF+vR1c2aNYvyb7/9dkirDgXrPNSvX7/QuqdgW6t+S+0l6zmpno/aES7X4591HrLOg9ZB7Q8fdXz00UdHZbpbgee0ajdwW6ud1XnIddAt37qbj+tQ1LHX/FnVHVF9LH5JO3369MQ68Ge1jZYsWRLl2Z7qEfM8dovSvOI6aJ/pC2bWsCmqv9kG6lhWDZPq1atnLNM+5c+qTVW7zKhP4fmzYsWKqExfQPAY1PmjR5dzHXTtpPN92rRpIZ3SyClKu9RkpiR8RO3atYMdZPuj4zSlXZWCdYV4DAPp9o/XZxMmTIjKeA3Tq1evqEzHW5MmTUJa50q/fv2iPNsBfpYB0te+rGvVoEGDqEztCz/PqI5Mt27dkIlFixZF+c6dO0d5lim48847ozKeV0OGDInKLr/88ijPvl3X26oxxLph2i/ah2xTWP8RSF/zs71RrSr1uay927Nnz6iM9RaBuM+feeaZqEzbk1FtKl1PMHPmzInyrPWltlxDZXmNrfd92WWXRXluX31u02eUTz/9NKS1/VTP7fTTTw/pRx55JCpjv7p48eKoTHXWWEtN1xo6f3hdrGND9bF4LZTq7x9++CHSiytpjjnmmMQXWa+88kqR16hUqRLGjRuX+JmmTZtixowZxa4fU6yXUgUFBbj88ssxceJEvPHGG2kvAVq1aoW99toLubm56NOnDwBg2bJlWLlyZca/7JcvXz5twhtjTDZTEltuAeDMM8/E119/jSFDhmD16tVo3rw5Jk+eHB6WVq5cGS2uO3TogHHjxuGGG27A9ddfj3r16mHSpEkhLGLVqlV47rnnAADNmzePfuv111/HMcccU+w6KvYTxhjj8L1M2EcYY0zJPUv8WijWS6kBAwZg3LhxePbZZ7HffvuFN6EVK1bE3nvvjYoVK6J///4YPHgwKlWqhP333x+XX3452rdvX6zTMowxJpspSUcycOBADBw4sNCywo5sPf3006O/JjG1atX6xR2a/YQxxvilVCbsI4wxxi+llGK9lEqd0KR/TX/44YdxwQUXAADuuusulC5dGn369MHGjRuRk5ODv/3tbzulssYYkw3YkWTGfsIYY/xSKhP2EcYY42cJpdjhe0VRoUIFjB49GqNHj97uSgFbY79Tsf4cr60x/grH5Kr+g+oXsO6Axm9z/KlqL2g7cMy4xspq/DvHLbNWif4mkK4dwiSdLKL1Vd0ovm/V/1BdniTtCL5XjWFWDReug2oV6Xe5fVUj47DDDovyrOekdVcNDdYOUT0BjU1P6QoB6bpGK1eujPKs/6JtxPHROv5UM4P7X/tMtX6437R+uoWd20HbROvL8yB1PHMKbV9uM+1T/R0u1/HJ9dc4f9WN4n7R9tQ5we2gc0nnO7dvavwlaUrZkWSmJP1EmTJlgkYM62roHC3KFzCqRcDjQPUOWN9CdRPUpvA417mlujSse6N6EWoT2daqXg7rWSiqo6h1ev/990NafRPPQyBuF9VfYbuhoTfqJzJdE0j3E6yFofZH/S7bAtauAdJ1Pbi+qk2mfuPwww8PabXRqlPBeR0bSTqKarvYRmqbJGnCaYiU2mgeO3qfel2eXzq3WrZsGeVZa0bHp+o98ZjUOcpaHqqLo2OX21D9lvY3a4uozVcd0lQYNPDfOat9yfilVOGUpI+YPXt2mP8dOnQI/1916Lp27RrlWQeVNW8AoEePHlGeyxs1ahSVsV9QfRwdm/xZXQM++uijUf5///d/Q/qJJ56IytRmdOzYMaQnTZoUlelc4nWU+povvvgiyrN91LWb6vodccQRIc3ahkBsk//4xz9GZa+++mqU535SPSzVlEpJFQDxPAfS9bxYQ5HrCqQ/d/C9svYYAJx00klRnv2oasPWq1cvyrOf1bUva/WoHdV+Yr+ktGnTJsovXLgwpNXeqR/t1KlTSOt656233orybK/VzutY4ftR/6x51jVSCQpeRyVpbwKxP9a66zMVjw29LpcBwOTJk0N65syZANLfCzB+lojZLqFzY4zZk7EjMcYYk4RfShljjMmEnyVi/FLKGGOKiR2JMcaYJPxSyhhjTCb8LBGTtS+lypcvH7aQ89Z73SqtoUCffPJJSOt2PQ054G3nSUdAJoUFAXEolZZpGAlvU9TQOT1ik7ct6jZ93V7I39VtldoO/Lu6hV/DSLj+upWTQy805ECvw+EAGpahR2fz72jYAG+p1s9y3wPp/c1hMHovGoLA7Ttv3jxsK9qe3BfaRtoOSaFpmuf21PvUsczbgPX4cw0x4fpqH+p2cq6/zstVq1ZFed62rnXgsa1blvW6PB40pEiPUud70e2zmuff1dCVwrAjyQ6++OKLMJ54K7xuZ1+2bFmU5/mkIRU6JzjEQu0P29IWLVpEZRpawFvjdaxqKDfXX4/7VjvCdk1tv4ao8JHjRc1ZDvlQ26q/w1vaf/e730VlScdnczg7ELe9hrbofbNvUruh98K2Srfb6zHt3GYa3qx14hAV9fV6rxzqotfhz3I4OJAevslh32qTNSSTx7b6ED66GojbSMND9Bhsvhf1Y3rkOI8z/ayGcfCaQuchh+Zo3TW8itdOOtf42HAgvjf1TRrOx74h5evV5zN+KbXradiwYejX3Nzc8P85FAlIn0u8RtC1us4lnr+6Zv3qq69CWu2Srs15rmjYUv/+/aP8m2++GdIacn7JJZdEefY9OqbVvvC80+crDT/T8GZG12fsT3T9zeFTajePPvrojPmkEHkgtrMaKq7rW557Og+1fdmuajic2gP2h2r3dVzxGOzcuXNUxj5Y/Z32Kdtvtbm6FmLU32loJ/u7L7/8Mirr1q1blOcwO+1TleHhe9Prargch1JyaDgQh2hqiKhe57zzzgtp9W/K1KlTQ1rDfDUclt8XNGvWDMDWZ5wJEyYUem0/S8Rk7UspY4zJVuxIjDHGJOGXUsYYYzLhZ4mYorcEGGOMMcYYY4wxxhizk/FOKWOMKSb+64YxxpgkvFPKGGNMJvwsEZO1L6X23nvvEL/PMdka46xHWHJMscZ2a2wtxyNrTC7nNW5Z42M59lx/U7U3WLNA70V1HDg2VXVPFNaS0NhZPQKZ66D3rd9lfQvV/2CND9ViYN0LIG5DPb5Z25cnnGpHaCw1xx9rnL+2GbevXlf1BPi6qjGkBoG1RPRe+Loaa65jl8eOtpHqRvHv6LjRMci/q2WqEcDl+lmNwee8tj3ryGj9Vc+J54HOF21PvpeijldOOuJ8Zxj13dkx/FrYd999g12fNWtW+P+qWaC2n8t1XCdpxNSqVSvK83dVL0RtCs8n1WjS67IOjtoq9od6XbW7Xbp0ifJcrvpD1atXj/I8Z7RMfSD7Kj0KnL+rc1/9ButAqD6jznfOa9urDhcfy612go/EBuJ7S9IdAWIbo9pF2kacV41I1vLQ48e1Dvybavt1TcFjQzWvVNeFtR31SPTatWtnrK+uL/S6POZ0faF2meep9vdLL70U0tpG2g4891SLTOcea9+oFp1qrLAGUOo39beVkvATo0ePxh133IHVq1ejWbNmuOeee9C2bdtCP7t582YMGzYMjz76KFatWoX69etj+PDh6N69e/S5VatW4ZprrsHLL7+MH374AXXr1sXDDz+M1q1b/+L3szM58sgjwxjkvtc1lmoOsfYYa9UAwMSJE6M86wapFhBrDqk20QcffBDleZ5NmzYtKtM5yfNV15Jz586N8kcddVRIN27cOGPdgVifTX2E2m9+ftD2ZA04/axqAbHPqFmzZlSm45j9n35W9X9Zl0u17lTPi+e6+iVd37Kd1XGj6wf2C9p+auNY70n9FK+p1Y5qP3Fenx10LcT2SXXMdBx16NAhpLWNXn755SjPfkDXLKqbyGNONZu0j5cuXRrS2oc8v/U+1T9zW6uN1nZ49913Q1rXJfpZHq+p+9I+UPws8V8cvmeMMcUk9deN4v4zxhizZ1ASPmLChAkYPHgwhg4dioULF6JZs2bIyclJexBMccMNN+D+++/HPffcg6VLl+KSSy5B7969I7Hp7777Dh07dsRee+2Fl19+GUuXLsWdd96ZJj5vjDFm+/GzRIxfShljTDGxIzHGGJNESfiIESNG4KKLLsKFF16IRo0aYcyYMdhnn33w0EMPFfr5xx9/HNdffz169uyJww47DJdeeil69uyJO++8M3xm+PDhqFGjBh5++GG0bdsWtWvXRrdu3dJ2IBpjjNl+/CwR45dSxhhTTOxIjDHGJLG9PmL9+vXRPw2zT7Fp0ybk5eVFIS+lS5dG165dMXv27EK/s3HjxkjmAtgaijNz5syQf+6559C6dWucfvrpOOigg9CiRQs8+OCDO9ocxhhjCD9LxGStptR//vOfoDfA8aeq06GaMxyjq45c9TY4zlPjY1nHQfVJVq9eHeU5vli1IjT+lDVz9t9//6hM46FZg0J1ET777LMoz+UaX6zaQGvWrAlpvTeNY+bFi04Ebl+N39UYd46V15hrrQNrVOh9c5w/EMdor1u3LirT3+G+0LZWbS2Oh1bdi48//jjKsx6M6nZw+2mbKKyXpfVL0kTSeHfVuOA6qD6Nzh/tR0bnE49frZ/GzrPegI4jrQOjc5bj/rVM5wTH4H/zzTdRmdaBx0qqH7Rd9fvFdQy7syPZVRxwwAFhzPJ4ULur2gOsQaR6TqpPxL5BdfX4N1X7R30K23Odozp/2Jap7oRqNvHvqp6P6nywL1Dbqnp9PF7ffvvtqEzvjTUh1J7Xq1cvpNUG8veA2Cdr/XQ+slaL1l3tebVq1UJabaBel33V8uXLEz/7xRdfhLSuTdSXcv+rRhP3i64vVAsjScdMP8vjSNcBqpfE9pLvC0jvC7b9qvmhcH11baJtxmNO/Sxr1CT5OP0d7TNdo/G11McdfvjhUZ79ss6Bwiiun0h9VjUZhw4diptuuint89988w22bNmSNueqVKkS6agxOTk5GDFiBDp16oQ6deogNzcXzzzzTNTHH3/8Me677z4MHjwY119/PebPn48rrrgC5cqVQ79+/bb5frKBZ599NowP1sNSLTm1nbwuOf3006My1Ytju6bzntezqjelekRch6L0h3hs6nX13qZMmRLSquejfov1c3Ru63f5eYZ1WIH0dmBtwaeffjoqa968ecb66HV5/mobNWnSJMqzxpBq/+gam8NX2V8AQLNmzaL8U089FdInn3xyVDZv3rwoz2uPBQsWRGXap6wVpX6gV69eIT1ixIioTPW72B6oz1Vbwdpkuk5ivT0AmDFjRkg3aNAgKlPdNV6PN23aNCrTPmbNM9XL0v7nvNpJXu+oLde2Zl0zXT/oGou1BnUtpD6Y/WrK7qjPYfwsEZO1L6WMMSZbsSMxxhiTxPa+lPrss8+iF3/6kn1HGDVqFC666CI0aNAApUqVQp06dXDhhRdG4X75+flo3bo1/vKXvwAAWrRogSVLlmDMmDG/updSxhiTrfhZIsbhe8YYY4wxxmQB+++/f/Qv00upypUro0yZMtHud2Drbnjd+ZPid7/7HSZNmoQNGzbg008/xfvvv4999903OtHw4IMPRqNGjaLvNWzYMG0nuDHGGLOzyNqdUnvvvXcIaeDQmqSwGyAOg9Ay3erJ27p1aztv3dVj6jUkgq+jIUUaNsTbI3VrpG4H5y1/GuKkWxz5u5lOXSnsWrp9WLepcrvowohDBbSNdOsk34v+pm6V5Xv7/PPPozLtfw690XBI7SfeTqr9rVuNuY30OE/9LNdJr8v3rXXX7aTcLhpyoGF1vN1UQ1UU7ouk4+6BeP7odXVs83W1TfTe+Lq6bZrvTX9Dt9VyqIXOLT3amLcta3iHjlfu7235q7T/upEdfP/99yHshMeYzlkNG2LbpTZGxz2PHbXR/DCoD4G6VZ/Hsto8DQtkO6KhXhrCzHZPj7vXrfs8/3WcH3300Rmvq9vPP/zwwyjP7aChLRwCp/NZ+4VD69QGqs/jLfbaZxwOop/VB3i1rdOnTw9pDWPjUEQgtjnaRmpz2L6rf+TrcPgMEIe9AHEIja4LtB14vaH3rbaWx5zab/0sh91paJ+GR/KY0/pqGAf/DssiAPH8Vlv/zjvvRHlex+gY0/HJ19XQFm0znhPqQwpje3dKbSvlypVDq1atkJubG8J78vPzkZubi4EDByZ+t0KFCqhevTo2b96Mp59+GmeccUYo69ixY3RkOrB1HmuIzq+Bpk2bhjm+ePHi8P/VBietbx555JGoTNuBx7WGTXII3PPPPx+V6Vr4xBNPzFg/9VNsF7SvdBzzfFC7qr6nRYsWIV1UCCG/yFRfo/Ns/vz5IX3HHXdEZVwnbSMOswKADh06hPSbb74ZlWn4HtsQDW3W+ct1UFuu4fXcT7rW0BC4uXPnhjSHgRVG48aNQ1p9GN9rq1atojJdI/AYfOONN6Iy/S7/TibtuhQ8JvUkTvVbPCd43gHJ4dZq93VdwuF72i9JYbQaKs7hkccff3zGugNxKHlS6C4QhyqmDobQZ33GzxIxWftSyhhjshU7EmOMMUn80i+lAGDw4MHo168fWrdujbZt22LkyJHYsGEDLrzwQgBA3759Ub16dQwbNgzA1ofkVatWoXnz5li1ahVuuukm5Ofn4+qrrw7XvOqqq9ChQwf85S9/wRlnnIF58+bhgQcewAMPPFDs+hljjCkcP0vE+KWUMcYUEzsSY4wxSZTES6kzzzwTX3/9NYYMGYLVq1ejefPmmDx5ctgts3LlymhHwU8//YQbbrgBH3/8Mfbdd1/07NkTjz/+eLQTsU2bNpg4cSKuu+463HLLLahduzZGjhyJc889t9j1M8YYUzh+lojxSyljjCkmdiTGGGOSKImXUgAwcODAjOF6Gr7TuXNnLF26tMhrnnjiiVGYkjHGmJ2LnyVisval1Nq1a0OML8d6q2aCHg3MscCq/6Hxpxw/q9fluFaNRdU8xxQXFZPLn9W4WtX44DhUjYfW73Ics2r2aJ14QGvsrMbHcpy1ajaxPoj+hrY934t+Vo9+5t/R+9ZjUlnHhY+/LqwOHA+vcfUaO89tqJoUqhvF96btyXobqomiceusk6F6Sap7wuNI6666LVwH1QTQccTX0s8mHWtaVKw8t4u2A6Nx4Dq/+d60H5K0tfS6OpbVNhSFHUl2sHbt2mBP2Fbo3Fcbw3oSrIsBpGts8NjW439Z10NtkwoFsz4Ra+EB6ZoLbNfUpqifYJutQsRq57hddM6++OKLUf7MM88MaZ2HOvf4aOZPP/00KuOjy1XvUNuBr5OXlxeVqZ4Ttz3rlQDpWlDcN6qBNGvWrCjP7asaJWonuJw1C4F07QnWglIfzbojeuy6/iaPT7XfqovDdk71kdReFme9weOMjxQH0nUp+Sh29WtaB87r2GX/o3owOo74XtX2q94O+zz1cbqm4Hb46KOPCv0OU1IvpUxmqlevHuYF22+1ueoHeC6x1g+QriPE80XHNOvvqR6gas2wdtWpp54alek4Zjum2lSq1cbf1TmotohfRKrNUO22+vXrh/RTTz2VWF+ed08//XRUdsQRR4S06iOldHlSzJs3L6TZXwD/nZMp+FlN2yRJh0v9/GuvvRbl/9//+38hreNI9cb4WVJ1J9VvsW5U//79o7KpU6dmvA6XAbGdatmyZVSmzwe8NlI9LNZv0uuyTiOQ7nu437Rf2rVrF+VZd5LTQPrzV8OGDUNanyV4vOo413ZgbS1dN2n78vjUcZPkp/75z38CSH9GY/wsEZO1L6WMMSZbsSMxxhiThF9KGWOMyYSfJWL8UsoYY4qJHYkxxpgk/FLKGGNMJvwsEVO8mBVjjDHGGGOMMcYYY3YCWbtTaq+99gqxmqxfofGwqk9UHL0a/qxeh/Oq6aHx5Bxnq1obqsuzefPmkFZtKtXbqFatWkirppDeC7eLxrjqW1Wur95bkiaW1oH1ForqB46zVU0P1atg/RJuLyCO7QXiuGHVWlFtE+4b1aRQ3Q6OTdaYe42d5nh+1RNgTQ+9F20zHg+qiaPx8NyG2n7ap/y7qqWk1+Wxo/XTscz3quNR61CpUqWQ1rbnzxal7cRtr3ofOs65PXVeap7vNZVO0qjyXzeyg7333jvMFR7nqifA4w+Ix3JRehw8XnWusc1Wu6B2uHnz5iGtc4v1kYBYy0H1fbS+bGu1THWjWBOEtX4AoEGDBlH+2WefDWm9N9VI4PtRO5ykWaGwHpH+ptpP/h29F9UE0X5jtH1ZT0LHjdoE9g2qSaP+kn9HNUp69eoV0qpZofaS/aOuIVSjhj/L2iZAuh/+9ttvQ1p1kpJ0zFgLEUjXIeH662e17Vl/R/ub21NtqdaX10OqO6N+g+fp559/HpWpz+P2TdUnyWd5p9SuZ8WKFaGvuD91jaLrRS5v06ZNVKbrHdaw02cUXsvpeFe7z/ZZdfFU75Xto+oY6b1xnXieA+nPHXfffXdIq0/Q+fDwww+HtIrn9+zZM8qzrXz33Xejsvbt24e0avxpG/Xo0SOkb7rppqjs2muvjfKzZ88OafXrHTt2jPKsYahjoW/fvlGe9QO1TVgfEIj1yLSttX2POuqokH7ppZeiMn72Ua3DMWPGRHn2J/r88t1330V5fp7VcaMHHUyfPj2k1ebqvXzyySchzfqUAPDMM89Eefa52t/3339/lOe+0HXIAQccENLqa9Tf8ZxQX60apJxXbSpts5kzZ4Z0Sh9tZ+oOpr6zu5K1L6WMMSZbsSMxxhiThF9KGWOMyYSfJWL8UsoYY4qJHYkxxpgk/FLKGGNMJvwsEZO1L6V+97vfha3nvEVPt2BqnjtLt7brFj3eiq/b8pOOnld4K69usdWjtPlIWN1GqyEGHBKh4Vy6JZO3k+qRx0nf5e2OhdWft+1rGfeLbmHX47G5TjqhdCs095v2oX6WQxK0/bSN+Fq6xVqPtU7qcz36na+VdNyuhk9om3E/aRvp+OSxoduHFd7mraGn2g5cJw1d0e9y/2t4h/Ybf1aPSud+0z7T7bkcPqHtqSFRPPe0rbU9+V5T89Lhe9nPN998E8YPb2/Xrdo6nzk8V8eYhg1xOJKGSfB2fB0vWgfeTn7QQQdFZTo2OMxNQ/A0NI3nTK1ataIytVV8XT32WLeY8zxVu6Z2mK+rYXc893RbvG595/mtIXl6PPmECRNCWo8R1zDBevXqhXRSmC8Qt+GKFSuiskaNGkV5Dk3T62g/cUiIjiPuJx2PGg7H7anh7NrffK9FHZ/O9lTtsI5B9hs6bnSs8LpBw/o5ZEZ/V8MkeK5pSKP6QO4Lnc86T3m86pzVEFwO70u1p65BGb+U2vUsXbo0+AgOoeZQYSB93HKYp44DtS/8XQ1j6969e0i///77UZnaAQ7vU9ujv8k2T8PhdK3GtlRDfNXO8njmEG4gDp0D4nZRW6RzieeOrt24rTmEDYhDegFg+fLlIa0hgnpvHOam/oPDrACgU6dOIa22/OWXX874WfW5l1xySZQfPXp0SBcln8Ghf2qn+NlRQ7xbtGgR5efMmRPSOs5feeWVKH/RRReFtNp9vW/uf72O2i4eV+r3O3ToEOWffPLJkOb5AqSHVPO6ZNGiRVEZ+zQOmwSASZMmRfn+/fuH9LRp06IynZfsXzRMUeF5mWoTnY+MnyVisvallDHGZCt2JMYYY5LwSyljjDGZ8LNETLFP35s+fTpOOukkVKtWDaVKlUp7+1hQUIAhQ4bg4IMPxt57742uXbvigw8+2Fn1NcaYrCDlTLb1356CfYQxxmzFPqJw7CeMMcbPEkyxX0pt2LABzZo1i7YkMn/9619x9913Y8yYMZg7dy5+85vfICcnJ1F93hhjfk0U14nsCc4khX2EMcZsn5/YU7CfMMbs6fhZIqbY4Xs9evRIiytOUVBQgJEjR+KGG27AKaecAgB47LHHUKVKFUyaNAlnnXXWNv9O6dKlg3aCahkxqmWTpIugugP83SRdEdUjUW0q/q5qL6gD5bzWR3Vv+L71eFgdlKyxoNfRPMdLa4yzHlnKuhiqP8VaQHqf+pvcRvpZ1QKqVq1aSCcdX6rf1X5SfQ2OTdbYc40h12NJGf0dHmca9//1118X+rnCfpN1WjQeX3UxuO21THWiNM/o8disa6UaCxrjzn2sY1k1Z5J0zHiMadvqdbm+OmdVv43HmfaL6rQUpltnTanto6R8BLC1X1NjlrUndGyobgbPbz42GkjXiOC5pvOFdaJUy081hZh///vfUV7nMNs11Z3Qccm2X3WDdK6xpo9q8E2dOjXKcxuprdJ75Tqof2R7rtdRvQi+b7XRel3WfXjvvfeissWLF0f5888/P6TVrqnd0D5m1C5zG6qui/of1vbQOrDWjd5306ZNo3ySxqb2C9scbSPVdWENm3bt2kVlui5gDT7VrNSxzbZX21rXH3Xq1AlpnRN83/Xr14/KdBzxd7U/dU7wmNQ20fHKujmp9ZmudRiH72WmpPzEt99+G9b68+bNC/9ftS2POeaYKP/GG2+EtO7QUr0z1itS/8F+QMetakwtWLAgpM8444yoTOfV9OnTM5Ydd9xxUf7pp58OadXz0fHLOn9aX17PArFN1rGr7cD+Wa/L81U1uXQOsv6r6g9xnwGxf9a1pD5XTpkyJaQ7d+4clWl933777ZBWu6/6SWz/itJp5T7V8chrVl2z6Hjksa32OScnJ8qzzdP2U60q3s2o2pFqV1977bWQVv1KnXtHH310SOta/fTTT4/y8+fPD2kdy/zd1q1bR2XqG3nOqI5ZmzZtojzrj+k6RJ9Z+Fk99eyV9CLdzxIxxd4plcSKFSuwevVqdO3aNfy/ihUrol27dmmTKMXGjRuxfv366J8xxpjdj+3xEYD9hDHG7Cn4WcIYY3YORYVKM5dccglKlSqFkSNHRv+/Vq1aKFWqVPTv9ttvjz6zePFiHH300ahQoQJq1KiBv/71r8Wu6059KZX6y6C+kaxSpUraXw1TDBs2DBUrVgz/9C+9xhiTbXjL7faxPT4CsJ8wxvz6sI/YPvwsYYzZEyiJZ4miQqVTTJw4EXPmzIl2tzO33HILvvzyy/Dv8ssvD2Xr169Ht27dcOihhyIvLw933HEHbrrpJjzwwAPFqutOfSm1PVx33XVYt25d+PfZZ5/t6ioZY0wifilVsthPGGN+bdhHlBz2EcaYXxsl8SzRo0cP3Hbbbejdu3fGz6xatQqXX345xo4dm1HyZb/99kPVqlXDPw6JHTt2LDZt2oSHHnoIRxxxBM466yxcccUVGDFiRLHqWmxNqSRScaNr1qzBwQcfHP7/mjVrIs0Epnz58mm6UMDWuOZUw3McrmrVaDwnx3ZrDLFqF3FMKWtiALE2B8dYA+mxtFw/jRHWz3LstOpBqL4CX1c/q3UqVapUSKueimpUcCy16jbwdfR39LOsD8Gx20B6nDq3b5LGkaJjQ+vAv6OLEI1x/vbbb0Na9VS0zVh/Q2PPNeaZ20H7ieuvbav1Y+0LbT/9ro57RuvLbabtqcaN55f2qf4mjw1tP20HjltXXQK+jraJwu2gsfIK11djunVecn1TczjJ8JdkHPjo0aNxxx13YPXq1WjWrBnuuecetG3bNuPnn3zySdx444345JNPUK9ePQwfPhw9e/aM6jF06FA8+OCDWLt2LTp27Ij77rsP9erV2676FYft8RFAsp9IjR/ub9UsSNIxU70I9RNsr3ScH3TQQSGtmj36F322P1o/tUc8VnTssi4UEM9Z9QsNGjSI8qxlpHNUtSZYu+Hwww+PylTrj+el2gn+TQ2pURvDY3DRokVRmfpWbjP19Ty2tA563+r7WatK22/lypUZ66B+TbUn2E+odlHSukVtFWuz6JzQ3+Tr6nV0fLIepo4jrROPSb2Orje4XXTcJ63JtI14XKmv1zHH2l+1a9eOylRPkMek6rVpnu87NV+SNE+tKbV97MxniVSoCRDPZ53348ePj/L8O6y3BqRr1jFqG9neqI9Q+3fqqaeGNPsLIN0G83xVu6QaPn369Alp1bN76623onyrVq1CWrVV9buM+g/1uawbpDpMbBv1GU/twDXXXBPSuvMjpT+WgtfUqstbs2bNKM+/+8knn0RlunOE+0LbT/X42Mapvp36l44dO4a0jhW2yaqzpFpQrKGpfabPUGy/ue+B9L7gPtV1CesBAsCrr74a0suWLYvK2rdvH+VZR0zHfZMmTTJ+Vn+T57+uH1SbjNtI5/OTTz4Z5QcMGBDSr7/+elSmmo/8fJjS1dJ1I7MjzxK6nspkA4siPz8f559/Pv70pz9FuonK7bffjltvvRU1a9bEOeecg6uuuiq06+zZs9GpU6fIF+fk5GD48OH47rvviny2S7FTd0rVrl0bVatWRW5ubvh/69evx9y5c9MGoTHG/FopqZ1SEyZMwODBgzF06FAsXLgQzZo1Q05OTprwcIpZs2bh7LPPRv/+/fHWW2+hV69e6NWrVyQOuitPNbKPMMbsKXin1PZhP2GM2RPYkWeJGjVqRCHLw4YN2646DB8+HGXLlsUVV1yR8TNXXHEFxo8fj9dffx2///3v8Ze//AVXX311KF+9enWh4dapsm2l2Dul/vOf/0R/qVuxYgUWLVqESpUqoWbNmhg0aBBuu+021KtXD7Vr18aNN96IatWqoVevXsX9KWOMyUpKaqfUiBEjcNFFF+HCCy8EAIwZMwYvvvgiHnroIVx77bVpnx81ahS6d++OP/3pTwCAW2+9FVOmTMG9996LMWPGoKBg555+Vxj2EcYY451SSdhPGGP2dHbkWeKzzz6LIqy2Z5dUXl4eRo0ahYULF6ZF5DCDBw8O6aZNm6JcuXL4/e9/j2HDhm3X72ai2C+lFixYgGOPPTbkUxXt168fHnnkEVx99dXYsGEDLr74YqxduxZHHXUUJk+enBaeUBQbN24MIUy8hVq3lWvIG/+1X7cp6vY93s6u4VK8hTApbEmvo2jIAW+D1y19el2uvx5FrSEIvEVe66P3xlv89ShZ/S63p2535vAp3Rqr1+G21/vUUAve6qlhAzppuF10q71uPeXP6k4THSt83zputN94C6VuNeZ20bAb7UPO69HZus2b21A/q3ON+1+Nn7Yvh4zqdbR9+e23viFPCvfQOctzRH9Dwzv4vjVcRuca97eOOQ394/qm5kd+fn4U5sWUxEupTZs2IS8vD9ddd134f6VLl0bXrl0znkA0e/bsyHkAW7fQpk7bKOpUo53xUqqkfASwNVQiNW/YMSb5BSAeZ7q9Xecah+fodXVOM2o32O7qNn6dLzy2dYs2h5cBcTiSzgm1c3y0uW47V3vEIey81R1In08cIqVzlsMQdGs47+ADYjusv6nw8e5qv/WIZ7bZ6kMaNWoU5dkGaqix9im3mdZB+5T7Qn0phwqpndAjxvm66ts1rI3tnI45bV8eZzpfdG5yH2vIYN26daM8j0/1ydpm/F2dWzwP1X5rv/DLDg5zApKlBoryKXzfqX7RzzB+KZWZkvIT9erVC+OF+0rH4gknnBDlOQxPQ1J1vPF1dXwtX748pDW8TMNZH3vssZDWeaRhTTyu1Z7ozgT2cXovumbl0D8OwQLS12etW7cO6U8//TQq+/zzz6M8rxF1Dc0h9A0bNozKxo4dG+XHjRsX0iqlob5yxowZIa0hmPrcwSF5al+SZGM0xHHatGlRnseDrh/Uv7At0utwaJ8+t+nYZZ+R9HwKxL78/fffj8o0dG7q1KkhffPNN0dlU6ZMifIsbaDtqf6F/fXjjz8elWl4dOoPtADw6KOPRmV9+/YNaQ7zA7a+fGHYlteqVSsq02eq5557LqTVr2v/87ok9Vm1F8yOPEvsv//+aWOquMyYMQNfffVVFM66ZcsW/OEPf8DIkSPTQllTtGvXDj///DM++eQT1K9fH1WrVo3kAoD/ygdoOHESxX4pdcwxxyQ2YKlSpXDLLbfglltuKe6ljTHmV0FJxIF/88032LJlS6FbYnXxkCLTFtrUInV7T78rDvYRxhjjl1JJ2E8YY/Z0SlKftjDOP//86I/UwNY/ZJ9//vnRC0Bl0aJFKF26dHj52L59e/zv//4vNm/eHP64M2XKFNSvX3+b9aSAnSx0bowxJhk9qnro0KG46aabdk1ljDHGGGOMMbsdRYVK627ivfbaC1WrVg07GGfPno25c+fi2GOPxX777YfZs2fjqquuwnnnnRdeOJ1zzjm4+eab0b9/f1xzzTVYsmQJRo0ahbvuuqtYdfVLKWOMKSYlEQdeuXJllClTptAtsZm2w2baQpv6/PaefmeMMaZ4eKeUMcaYTJTETqmiQqWLonz58hg/fjxuuukmbNy4EbVr18ZVV10VSYVUrFgRr776KgYMGIBWrVqhcuXKGDJkCC6++OJi1TVrX0rxMa4c85wUyw3EMbmqHaGaGRw3qg+GHAOrA0BjOPk6GtutMdhcX62f1kHry2g8NP+u6jRo/DaX670kaZ1oLD9fV3UltM34NzXGWX+T70X1vDRe/6OPPsr4WdXh4vYu6thZHkfap9q+rEOhcf+s26FjVfuFP6sx46rhwvVnDRQgve35XvQ6+lkec9rfWn9+MaL11b5I0lLj2HMt0zpwW6s2gsZ687hK0uQC4j5Oxbvr3GVKIg68XLlyaNWqFXJzc4O4a35+PnJzczFw4MBCv9O+fXvk5uZi0KBB4f9NmTIlnFjEpxqlXkKlTjW69NJLi3U/2UB+fn7oJ/YNOjZUu4E1kHT+qJYNa+Ik6TmpboLqFPB40fGn84V1ClRTiG2eonZYdT54jujLS9V7Yjut2lQ6T1l/RecFH9usGiq6a5A58sgjo7xqlLBen/qUww47LGP99L7Vl86ZMyekddu5amxwHdq0aROV8dgAYt+gv8njQe9TNbrYr+nYVZ/Hdlk/q7Cf0P7Wsc12WP2C+kfuCx03OtdYf0eP++Y5rP5a24zXUjq3VHeL20g1uvQvyHzdlH9JWqf5pdSuZ8uWLWFe8BjSucxaRUDs/1V/T8ctzw9dW55xxhkhreN/7ty5Uf7MM88MaZ33bMMA4O233w5p1RRSu89rSx3jqgfLGjI5OTlRma5vuY6qrTNhwoQoz+2ic5LvhbWdgHSNKV7L6fxU3Sgu1/UhrzuBWE9Jw5lU2oDtY7Vq1aKyxYsXR3n2J/qArm3GPkN9OY+5WbNmRWWq0cTll112WVQ2fvz4KM9rUdYVAoBnn302yvN4eOWVV6IyHXO8dtexrH3K7XvSSSdFZQsWLIjyr7/+ekhr+7H9VH1AHcvsM3Tto5qtvG5p2bJlVKZrTb7X1NhIOtm6JF5KFRUqraiOVMuWLaNxnImmTZtGOm7bQ9a+lDLGmGylpOLABw8ejH79+qF169Zo27YtRo4ciQ0bNoRY7759+6J69erhKNgrr7wSnTt3xp133okTTjgB48ePx4IFC/DAAw8A2Ppi1qcaGWPML49fShljjMnErtaUyjb8UsoYY4pJSTmSM888E19//TWGDBmC1atXo3nz5pg8eXIQKl+5cmX0l88OHTpg3LhxuOGGG3D99dejXr16mDRpEho3bhw+szNPvzPGGFM4filljDEmE34pFVOqIMvubv369ahYsSLq1q0bwuKStkfrSVa8xVG/p6F/vM1cQ+l465+WaegFb8nVbYq6ZZ6Pt0w6Mly/q9tHdfs//65eV48853J9ENXQBt6WrCFHvJVXNW50CzNvX9QQDg0N4PbVttatxxw6oFs5FW573X6dFC6n40i3YnKIh27l5G3Kei9639yHeiSpjituQw1/1PAJ3qasdU+aExoKoluu+XeLeqGRFBbI80u3WOuc0LGcBPe3jrkkUvdVUFCA77//HuvWrQtjP2WfKleunDYWiyI/Px/ffPNNdD2zfaT64Z577gnziENrNPRS3RzPRZ0v2jc89zSUl8PG9Dc1pIzthP6mhiFwiNGqVauiMvVH/F0NN1O7sXLlykLTQPoc4W3qeh2172yvNJyBfVdRYXY8v9UWJB2fraEYarvY5mi4itp+DpPREAAN32QbrqEtamu5z7W+7DfUnmuoGod46JHoei/chmq/te3feeedkC5qDcHzSf2Y+mHucx0b7733XpTntZSGOrEPUZ/M4bhaPw1F1BAVHtvafjqneTyk2vbHH3/EJZdcslP8hH3EziPVB4MGDQq+gW1e27Zto89rP7E/0XWI+gEOd9HxlgqdB9JDXfX5hcP3eD4CwJNPPhnlOby1Y8eOUVlubm6Ub9WqVUir7dY1K495na+6Lp06dWpIa/iytkODBg1CWn0a11/DnhcuXBjl+VlD7YD2Ifeb3rf6u9atW4e06uyozWCfpmNDfUSLFi0yXldlJVjXU8M1OYxN1wtaB14/nHLKKVGZ+n32Nxoyr1IBjRo1CmkNmdbP8vjU/tb1GI85fQbVccX111Ok+brqs3Q9weM+yScA8RhU7VVtB/5s7969AWyd9xdccIGfJbYB75Qyxphi4r9uGGOMScI7pYwxxmTCzxIxfilljDHFxI7EGGNMEn4pZYwxJhN+logp3p4xY4wxxhhjjDHGGGN2Alm7U2rDhg0hzpJjuzXWU7WBOLZW47c1/pivq7HSrKmgZaozwfokqpejuggcx6y6DZrnuGWN+9b4XdZx0NhpfavK2hJFaQyxrpXG0XMcbFFaDBw7rTHD2p6soaFxwdr/3N6q2aRtxvfGekNA+ljhco1FVg2NJB0mvo5+T7VD+F5Un0S1WLjftM90zHH9dCzzHNDvapm2Gd+rjjEdV9zHGgPN/ab9q+MoKfZaNQJ47Kp2Q9Ix66nxl5+fnzbmU/ivG9nBhg0bQt/xuNJj33XssoaAzgkdK7Vq1QrppHGu+huqXcRjuahj6tk26FjV44vZr6nukvoCthWs8QGk6xZyHXQeqi1j2/XBBx9EZTy3tB9U14iPBlf7re3L93rsscdmLANiG6g2RPuUNbtUH0nvjW2X2mHW1ADiPlb7zu2rOiNJGmI6dnU9xG2mv6lHeLOP0f7Ve+O5pvNFfSCvDfQ6Wn/uC9XdWrp0aUirzpbe91tvvRXSqjui84l/U32ejk+eE6k5rGsHvbZ3Su1aPv/887D+0P5l9Plg3rx5Ia0aPjo/eA6w7g4QayCpPpKu1VhzqEOHDlHZgAEDovwTTzwR0itWrIjKjj/++Cj/7rvvhvTRRx8dlY0fPz7Ksy7hK6+8EpXpd9nu6/pLbT37LdWbYhun9lnXkjzXVQvozTffjPKsG6ZrOdWumjlzZkirbpD6F9bs0rGh9uX9998PafUJH374YZRnv8VaVADw6quvhrS2idpKHjtPP/10VHb44YdHefYRbDeB9PUDr6lVS0l1hdl/63V4TQXEvlHn4axZs6I8+wxdIyQ9v+gcSXo203tjra3p06dnrA8Qa3Gm1lRJuth+lojJ2pdSxhiTzezOjsEYY8yOYz9hjDEmE/YR/8UvpYwxpphsjxOx4zHGmD0H/wXcGGNMJvwsEeOXUsYYU0zsSIwxxiThl1LGGGMy4WeJmKx9KbVp06YQW8x6C0maMkAcY6pxohrjzLHAGtvNMc4aI6yf5bjqpOtoudZPv8vaDBq3qpokHM+t2kqqR5Skr6GwhoLqOOi9MaodwdepXLlyVHbwwQdH+e+++67QugLpehXc9vy9wurA19L4cs1zm2nMuMbD8+9oW3Ofar9o+3GcuPYva6UBcey56mnoffOcUH0sbTPVe2K037jN9F60/hxTrvXjsqQ20fKiNMQ4plzbT+caz4OUvVEdHcaOJDsoXbp0sI3c/6pvoPA4UpuiGjQ831U/huePjhedLzxedTyqLg/rPrC2CQAsW7Ysyh9yyCEhrfeten2rVq0KadU/VM0m9jlJ2nNA7COTbAxrUgCx3kZhdWCWLFkS5Vn3Y/ny5VGZtn2SrpXqRLHehfof9cOsNaH6h+oneHzoGGONSG1r7SfWfEmyY0Bsc3Ts6hhke65zQvNsl1VLRm0263Po2FDfz75A113t2rULaZ0DavubNm2asUz7lOuraxzW4gHi+aU+rjD8UmrX06dPn7SxDqSvNbSvWcNJy+bPnx/l2RapzeXxpfVQLUHWEdK5/dRTT0V5Xmvq/FQtqCZNmoS02lz9Hb7vF198MSp7/fXXozzbynfeeScqY78EAIsWLQppnTuPPfZYSKt+U5s2baI86w7qXFY9PtaEmzt3blSm/c+aijVq1IjKXnjhhSjP9vvjjz+Oytg+67V0fmsbffLJJyGt2kWtW7cO6WbNmkVl+vzC6wf1Q9r206ZNC+nOnTtHZfp8wGNZ/fFll10W5dknaz/xWADidcExxxwTlenaiOeM+gi+LrcXAFx11VVR/sEHHwxpfW5r3759lM/Lywtp9aPnnntulGett7PPPhtAug9i/CwRk7UvpYwxJluxIzHGGJOEX0oZY4zJhJ8lYvxSyhhjiokdiTHGmCT8UsoYY0wm/CwRk7UvpcqWLRu2onO4j25T1aMWeXu4bqfXbeb8XQ0F4tA6DTfSOvB2ew3h0G2LvHVStyXq1l6ur15HtwjztveirsvbdfW6GlLI2yO1jTisQEMitB04r+EJfGQ0EB/VqqFzutWYr6X9onVKCsnUscHbLXWbrxoE/l1tI66fjlUdV9xGGrKh98L9pH2mbc8hJzo2dCsvhwVqmyhJoX7aDjxek+ashn7oVlne7qx9qHm+lpZpmAvXL5V2+F7207BhwzB+kmygbkNnu6LzW7/LW+PVp/C2cx27euQ0b/nXsA0djzz/NeRpzZo1UZ7nodrAxo0bR3m2BTzXtX5APGf0N/WzPE/1umxH1OZpmB3bOQ6nAJKPYde21zBBDi3hkA4gPjYciMMv1AboNv8kG6NjpW7duiG9cuXKqIz9RFGyA1y/6tWrR2UciqHfVb+rtp/DwPU39chxDgPVPlS/wOsPPWpbxwrbYZ0THIaiv6E+he9Vr6N14PbUNlLbwPM2NZd07DF+KbXr+eyzz8Kc4rA7DpUD0uf6xIkTQ1rH0PXXXx/ln3322UK/BwA333xzSHPoGQAcddRRUZ5tmoaB6dqX506rVq2isjfffDPK8zH13377bVSmY55tiK6/tR04zyGChcHhXurTDj300JDWOachtezn+/XrF5W9/PLLUZ79oYa2q/3jUGINz9SxwrZd16haX7ZpS5cujco0XJ1tk7Ynf5bDyYD08PW+ffuGtK7j33vvvSjfsWPHkNYxps9m7PdPO+20xOtyX6jESO3ataM823P1abqOYls/e/bsqOyUU04Jae0zbTMe2zondJ6yD9PQPg1jPOGEE0I6FSqr/onxs0RMsqCQMcYYY4wxxhhjjDG/AFm7U8oYY7IV/3XDGGNMEt4pZYwxJhN+lojxSyljjCkmdiTGGGOS8EspY4wxmfCzREzWvpQqVapUiC1mrQbVEtB4Xo4/Vf0H1dvg2GU96pnROGqNwWZUCyhJW0m1nvS7rPekMamq2cNaHRoXrPpTSfet8dysb6HaCVpfRvU1uM00RljrwBNOj9JM0irSuGBtM45r1rqvW7cuynN7qu6WjiPuU9UD4e/qMbMcG6/XUbQ9k/pF25Nj53W+6Fjmsa6fVe0QLletGK0v95OWcV/oWNV74bGTpBEHxBouql2ifci2IjXG9DOMHUl28OGHH4a5wO1blMYQ6z6oLdDjlVk/QnU+2B+pv2HdHSC2T6xfASRr+2nd1X6yDolqZ6lN/PTTT0Na549qafF817GrWiOMthHPYZ3PK1asiPJsq9Sm1KlTJ8qz7VJfqu3AfaN1Vw0QPpZb7ZGuBdiG62+q7haPK9VU+eKLL0JadTy0vqxHpX5LfQzbWrXn2qesI6V6JjxugFjbb9myZRl/E4jHmc5LbV/27+r7WVNF52j9+vWjPPeF+lXWrwHi9k7SQ9Nrpfp+Z2oP2kfsfH7++edgX7l/VW9P9Wl4but8HTt2bMbf0yPt2U6phtTHH38c5dkPqE7QSSedFOXZdqrtbt26dZTn+at2VfWo5syZE9Kqv6dac+wj1DbqWpg1nXQtybpGTz31VFSmdpXtwFtvvRWV6Zr6ww8/DGltI9UcYjt23HHHZbyOXku1D9u0aZPxu9ovqnPFtkQ1Cfk62oe81gWAJ554IqR1zOkzNNucefPmRWWdOnWK8rwmnjt3blTWq1evKM/179KlS1Smmk3cbw8++GBUprphPHaaN28elXEf6ppAdTzZtqtv1H7i7xalZcvaWqm23Zm6g9v7nV8LWftSyhhjshU7EmOMMUn4pZQxxphM+Fkixi+ljDGmmNiRGGOMScIvpYwxxmTCzxIxfilljDHFxI7EGGNMEn4pZYwxJhN+lojJ2pdSpUuXTotRBtL1kjQ+ljUKNI5TtSO4XLUuWItDdQ80/pQ1NLTOqkvD9VV9JNU24fpprLTqdnBsssYia/15QGs8rOo4sE5Pkp6Xlqk2Ff+OaqRonDrH72t/q8YD35vGsOvvcHurlpLmuR20jZK0lVTXiOukhkT1QPjedCwk9aFqWui44ryOT70ua7ronNCYfI7Z5u8VVn/uY60D97FeR8dVks6T9vdXX30V0jqukzTFUvXZmVoh2/sdk8yBBx4YxiFrAam2js5L1hZRW6pjhfVzZs2aFZWdcMIJIa3zQ6/DNlq1lFR/iLUR1N/UrFkzyvM8VK0dtQWso6HjUW3t559/HtJqz1UDi+epzhuuE89JIL3t2QZq/VRbhNtByzTP9l3trupusd1TX6prCtbyKMoH8nU/+OCDqIx1UlQLQzXEknS31GZzn2r/6r3x+kg1VPR3+LuqeaXtsGbNmkJ/A0j32dxPSXpOOm4WL16c8bNKcfTRWL8LAKpXrx7SBx10EIB025L0W0VhH7HzmTdvXljncF9Pnjw5+pzqRp1//vkhvXTp0qhM14tTp04NafYXWqb6TWqvuf9VW06fAXgNo/NTdY1Y33DGjBmJn2WtrUWLFkVlZ599dpRnG6d2inXyAOB3v/tdSKfmTor3338/pNleAOnrOta5WrhwYVSmWo3cvqeeempU9uabb0Z5tu2qraTrCf4d1ajTNQLXX9cISbpbqlXF/k51/NQesq6ZPq+oreQ6dO/ePSrTMXfttdeG9I033hiVPf7441Ge+/vvf/97VHbsscdGeR6/uhZSX8njl9dfQNxG2g+1atWK8qwxrOsx1oUC4ucObU9tI/bfKc0z9b2MnyVi0t/67CRGjx6NWrVqoUKFCmjXrl3aJDfGGLPnYh9hjDEmCfsJY4zZM/hFXkpNmDABgwcPxtChQ7Fw4UI0a9YMOTk5aX8lNcaYXyMFBQXb9c9sxT7CGLO7Yx+xY9hPGGN2Z/wsEVOq4Be4u3bt2qFNmza49957AWzdSl2jRg1cfvnl0fa/wli/fj0qVqyIWrVqhTAf3tqn2x91y23S9lfdVs5hRLoFj39Tt5zrdm3eyqvhR7qtltFQJL0uf1e3eepnecughsMldbGGMWn7ch011IvrpGEN+pv8WW1r3QqtISeMbunn39GQEg3T4C23et8a7pHU9rog4j7X0DS+blKYgP6mhpTocfMcnqBlStJxpNrfvK1a70XHFaPjPmnMJY177UO9Ny7XcZMUyqLzJSkMMEV+fj6+/PJLrFu3LtiOlH0qXbp0WnhOURQUFCA/Pz+63p7KjvgI4L/98OCDDxYavqdzTcN5eKxomfoUthX6WfYNeuSwHvfMISAakqA2kW2tzlEd97x1X+uu3+Xr6pzQcEPefq9zX+0nz3+dW+ybOGxSy7R+iv4m96HaZK0vh6hoP2noA4ed6JHtGgLA39Xf1C37Scen82c51AZI71Mu12PNNWSmdu3aIc0hMkB623OIivpDDWHnsBi9Fw2/4O+qb+ej6rUOWj+uU1Fh3ezHNDQjyZ/r+kL9GodZptaXP/74Iy677LKd4ifsI2J2xrPEsGHDgu3gkK0ePXpEn08Kl9I1VJKMhNp2nnfvvvtuVNa0adMoz7ZJr8OhwkAcLjd37tyobNCgQVGefaN+VsP37rrrrpDW0DR9FmIfweFQQHqYE899tXHs09S/tWzZMsq/8sorIX344YdHZRqaxqHv6o+T1s0aMtaoUaMoz/2oNlh9GI8rndNqrzk8WNuPbaXuFjzppJOiPI/zjh07RmX6XMzjQX1h27Ztozy3p64t9JmF7fU555wTlb399ttRntceHJ4JpMsV8NjR8cjhnBoiqvDzoM7nf/zjH1FefRGj475OnTohnWqvH3/8EX/4wx/8LLEN7PSdUps2bUJeXh66du363x8pXRpdu3bF7Nmz0z6/ceNGrF+/PvpnjDHZjP+6sf0U10cA9hPGmF8f9hHbj58ljDG7O36WiNnpL6W++eYbbNmyJe2vZVWqVEn7SwQADBs2DBUrVgz/VFDOGGOyDTuS7ae4PgKwnzDG/Pqwj9h+/CxhjNnd8bNEzC4/fe+6667D4MGDQ37dunWoWbNmFCbBW7M1fELz3FnaccU5kYU/W9RvZqorkH4CD6OfLc519bNJbVScUKqkOhSnbGe1p5L0O0XVge91Z7b9to6rpM9peVFjN6l+SddNqp9eqzjzpzhjLuleinPfOzJuiuoL/kyme9mdHUO2kclPcAgah1hoaFpxwvc0HClpzPF19Df11Lyk+ml4CM/DokKj+Vrqb/R3OISiqDpkatvC6pBkW5N+Q+ubFLpdnLbX3+HwgKTwey3XsLWk72qZhu/xeEgK39O662e5TjrGksZgUW2fdIqstgNfS+undeJ70/5Nat+ksawhMjrmku5F4XvREPBtuW7qv/YTu5ZMPoL7l8P/d8T+6SmXPB6T7InahKR5pr+ZNNf1uhpKzN/VOafX5Tmq102yA0X5v231aWrnNaycv6u/ofVLak/tw6TrJNn2JB+rv6t2K+l3ktYPKnORZHOL8hFJJ7TqZ5PmhN4LXyvpVHe9ltZXv8vlaq+TxqPC103yx4X9DpPUh6l0ql72EUWz019KVa5cGWXKlEk71nPNmjVpMb3AVgPORjy15VaP4t0ZbO92XtVXMLsO1SQpDrt6O3eSJlNRlNQYzLaxng31+f7770Pserly5VC1atWMO3qKomrVqmk6JXsaxfURQGY/ccUVV/xyFTXGmG1kZ/kJ+4it7KxniZtvvrnQ6z/77LM7sbbZxeOPP76rq2BKkKeeeipj2YMPPliCNSmchx56aFdXISvws0TR7PSXUuXKlUOrVq2Qm5uLXr16Adj65js3NxcDBw4s8vvVqlXDZ599hoKCAtSsWROfffbZbifktbNYv349atSo4TZKwG2UjNsnMwUFBfj+++8jcdsKFSpgxYoVaX9J2VbKlSuXJti8p7GjPgKwn9hWPL+Lxm1UNG6jzOxsP2EfsRU/S5Qcnt9F4zYqGrdR4fhZYtv5RcL3Bg8ejH79+qF169Zo27YtRo4ciQ0bNuDCCy8s8rulS5fGIYccEv7Ksf/++3twF4HbqGjcRsm4fQpHT/cAtjqT3dEZlCQ74iMA+4ni4vYpGrdR0biNCsd+4pfBzxIli9uoaNxGReM2Ssc+Ytv4RV5KnXnmmfj6668xZMgQrF69Gs2bN8fkyZPTBAuNMcbsedhHGGOMScJ+whhj9hx+MaHzgQMHbnMohjHGmD0L+whjjDFJ2E8YY8yeQemiP7JrKF++PIYOHZp2koX5L26jonEbJeP2Mb9mPH6TcfsUjduoaNxG5teKx27RuI2Kxm1UNG4js6OUKvBZhMYYY4wxxhhjjDGmhMnanVLGGGOMMcYYY4wxZvfFL6WMMcYYY4wxxhhjTInjl1LGGGOMMcYYY4wxpsTxSyljjDHGGGOMMcYYU+Jk7Uup0aNHo1atWqhQoQLatWuHefPm7eoq7RKGDRuGNm3aYL/99sNBBx2EXr16YdmyZdFnfvrpJwwYMAAHHngg9t13X/Tp0wdr1qzZRTXe9dx+++0oVaoUBg0aFP6f2whYtWoVzjvvPBx44IHYe++90aRJEyxYsCCUFxQUYMiQITj44IOx9957o2vXrvjggw92YY2NyYx9xH+xnyge9hGFYx9hdjfsJ7ZiH1F87CcKx37C/FJk5UupCRMmYPDgwRg6dCgWLlyIZs2aIScnB1999dWurlqJM23aNAwYMABz5szBlClTsHnzZnTr1g0bNmwIn7nqqqvw/PPP48knn8S0adPwxRdf4NRTT92Ftd51zJ8/H/fffz+aNm0a/f89vY2+++47dOzYEXvttRdefvllLF26FHfeeScOOOCA8Jm//vWvuPvuuzFmzBjMnTsXv/nNb5CTk4OffvppF9bcmHTsI2LsJ7Yd+4jCsY8wuxv2E//FPqJ42E8Ujv2E+UUpyELatm1bMGDAgJDfsmVLQbVq1QqGDRu2C2uVHXz11VcFAAqmTZtWUFBQULB27dqCvfbaq+DJJ58Mn3nvvfcKABTMnj17V1Vzl/D9998X1KtXr2DKlCkFnTt3LrjyyisLCgrcRgUFBQXXXHNNwVFHHZWxPD8/v6Bq1aoFd9xxR/h/a9euLShfvnzBP//5z5KoojHbjH1EMvYThWMfkRn7CLO7YT+RGfuIzNhPZMZ+wvySZN1OqU2bNiEvLw9du3YN/6906dLo2rUrZs+evQtrlh2sW7cOAFCpUiUAQF5eHjZv3hy1V4MGDVCzZs09rr0GDBiAE044IWoLwG0EAM899xxat26N008/HQcddBBatGiBBx98MJSvWLECq1evjtqoYsWKaNeu3R7TRubXgX1E0dhPFI59RGbsI8zuhP1EMvYRmbGfyIz9hPklybqXUt988w22bNmCKlWqRP+/SpUqWL169S6qVXaQn5+PQYMGoWPHjmjcuDEAYPXq1ShXrhx++9vfRp/d09pr/PjxWLhwIYYNG5ZW5jYCPv74Y9x3332oV68eXnnlFVx66aW44oor8OijjwJAaAfPO5Pt2EckYz9ROPYRydhHmN0J+4nM2Edkxn4iGfsJ80tSdldXwGw7AwYMwJIlSzBz5sxdXZWs4rPPPsOVV16JKVOmoEKFCru6OllJfn4+Wrdujb/85S8AgBYtWmDJkiUYM2YM+vXrt4trZ4zZWdhPpGMfUTT2EcbsGdhHFI79RNHYT5hfkqzbKVW5cmWUKVMm7TSDNWvWoGrVqruoVruegQMH4oUXXsDrr7+OQw45JPz/qlWrYtOmTVi7dm30+T2pvfLy8vDVV1+hZcuWKFu2LMqWLYtp06bh7rvvRtmyZVGlSpU9vo0OPvhgNGrUKPp/DRs2xMqVKwEgtIPnncl27CMyYz9ROPYRRWMfYXYn7CcKxz4iM/YTRWM/YX5Jsu6lVLly5dCqVSvk5uaG/5efn4/c3Fy0b99+F9Zs11BQUICBAwdi4sSJmDp1KmrXrh2Vt2rVCnvttVfUXsuWLcPKlSv3mPY67rjj8M4772DRokXhX+vWrXHuueeG9J7eRh07dkw7/nf58uU49NBDAQC1a9dG1apVozZav3495s6du8e0kfl1YB+Rjv1EMvYRRWMfYXYn7Cdi7COKxn6iaOwnzC/KLhZaL5Tx48cXlC9fvuCRRx4pWLp0acHFF19c8Nvf/rZg9erVu7pqJc6ll15aULFixYI33nij4Msvvwz/fvjhh/CZSy65pKBmzZoFU6dOLViwYEFB+/btC9q3b78La73r4RMzCgrcRvPmzSsoW7ZswZ///OeCDz74oGDs2LEF++yzT8ETTzwRPnP77bcX/Pa3vy149tlnCxYvXlxwyimnFNSuXbvgxx9/3IU1NyYd+4gY+4niYx8RYx9hdjfsJ/6LfcT2YT8RYz9hfkmy8qVUQUFBwT333FNQs2bNgnLlyhW0bdu2YM6cObu6SrsEAIX+e/jhh8Nnfvzxx4LLLrus4IADDijYZ599Cnr37l3w5Zdf7rpKZwHqSNxGBQXPP/98QePGjQvKly9f0KBBg4IHHnggKs/Pzy+48cYbC6pUqVJQvnz5guOOO65g2bJlu6i2xiRjH/Ff7CeKj31EOvYRZnfDfmIr9hHbh/1EOvYT5peiVEFBQUFJ784yxhhjjDHGGGOMMXs2WacpZYwxxhhjjDHGGGN2f/xSyhhjjDHGGGOMMcaUOH4pZYwxxhhjjDHGGGNKHL+UMsYYY4wxxhhjjDEljl9KGWOMMcYYY4wxxpgSxy+ljDHGGGOMMcYYY0yJ45dSxhhjjDHGGGOMMabE8UspY4wxxhhjjDHGGFPi+KWUMcYYY4wxxhhjjClx/FLKGGOMMcYYY4wxxpQ4filljDHGGGOMMcYYY0ocv5QyxhhjjDHGGGOMMSXO/wcBW4iaLHvcaQAAAABJRU5ErkJggg==", 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", 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" ] }, "metadata": {}, @@ -260,22 +216,19 @@ "source": [ "# Simulate each image, drawing from the stochastic parts of the model\n", "key = jax.random.PRNGKey(0)\n", - "fig, axes = plt.subplots(ncols=3, figsize=(12, 6))\n", - "ax1, ax2, ax3 = axes\n", + "fig, axes = plt.subplots(ncols=2, figsize=(7, 4))\n", + "ax1, ax2 = axes\n", "im1 = plot_image(\n", - " compute_noisy_image(scattering_pipeline, key),\n", + " compute_image(imaging_pipeline),\n", " fig,\n", " ax1,\n", - " label=\"Phase shifts at exit plane\",\n", + " label=\"Image contrast\",\n", ")\n", "im2 = plot_image(\n", - " compute_noisy_image(optics_pipeline, key),\n", + " compute_noisy_image(imaging_pipeline, key),\n", " fig,\n", " ax2,\n", - " label=\"Squared wavefunction at detector plane\",\n", - ")\n", - "im3 = plot_image(\n", - " compute_noisy_image(detector_pipeline, key), fig, ax3, label=\"Detector readout\"\n", + " label=\"Image contrast with solvent noise\",\n", ")\n", "plt.tight_layout()" ] @@ -284,25 +237,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "What if we did not want to include noise in the simulation? In this case, the three outputs are\n", - "\n", - "**1. If there the `Instrument` just has an accelerating voltage and a dose:** The returned \"image\" is the phase shifts in the exit plane including stochasticity, which here we use in the `solvent` model.\n", - "\n", - "**2. If the `Instrument` also has an optics model:** Again, the returned \"image\" is the squared wavefunction in the detector plane.\n", - "\n", - "**3. If the `Instrument` also has a detector model:** Now, the returned \"image\" is the expected number of electron counts for each pixel. This is nothing but the poisson rate." + "Note that the `compute_noisy_image` function draws an image from the noise models contained in the image formation `imaging_pipeline`. These noise models are meant to be physical noise models, so in theory, these do not need to cleanly correspond to sampling from a particular statistical distribution. Alternatively, the user can simulate an image from a specific distribution from the `cryojax.inference.distributions` module. In this example, we use the `IndependentGaussianFourierModes` distribution, which simulates images from an arbitrary noise power spectrum." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -310,24 +257,96 @@ } ], "source": [ - "# Simulate each image without stochasticity\n", - "key = jax.random.PRNGKey(0)\n", - "fig, axes = plt.subplots(ncols=3, figsize=(12, 6))\n", - "ax1, ax2, ax3 = axes\n", + "import jax.numpy as jnp\n", + "\n", + "from cryojax.image import operators as op\n", + "from cryojax.inference import distributions as dist\n", + "\n", + "\n", + "@eqx.filter_jit\n", + "def compute_image_with_distribution(distribution: dist.AbstractDistribution):\n", + " \"\"\"Simulate an image with noise from a `imaging_pipeline`.\"\"\"\n", + " return distribution.compute_signal()\n", + "\n", + "\n", + "@eqx.filter_jit\n", + "def compute_noisy_image_with_distribution(\n", + " distribution: dist.AbstractDistribution, key: PRNGKeyArray\n", + "):\n", + " \"\"\"Simulate an image with noise from a `imaging_pipeline`.\"\"\"\n", + " return distribution.sample(key)\n", + "\n", + "\n", + "# Passing the ImagePipeline and a variance function, instantiate the distribution\n", + "distribution = dist.IndependentGaussianFourierModes(\n", + " imaging_pipeline,\n", + " signal_scale_factor=jnp.sqrt(instrument_config.n_pixels),\n", + " variance_function=op.Constant(1.0),\n", + ")\n", + "# ... then, either simulate an image from this distribution\n", + "key = jax.random.PRNGKey(seed=0)\n", + "\n", + "fig, axes = plt.subplots(ncols=2, figsize=(7, 4))\n", + "ax1, ax2 = axes\n", "im1 = plot_image(\n", - " compute_image(scattering_pipeline),\n", + " compute_image_with_distribution(distribution),\n", " fig,\n", " ax1,\n", - " label=\"Phase shifts at exit plane\",\n", + " label=\"Underlying image\",\n", ")\n", "im2 = plot_image(\n", - " compute_image(optics_pipeline),\n", + " compute_noisy_image_with_distribution(distribution, key),\n", " fig,\n", " ax2,\n", - " label=\"Squared wavefunction at detector plane\",\n", + " label=\"Image with additive gaussian white noise\",\n", + ")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we can directly control image SNR through the parameters `distribution.signal_scale_factor` (a phenomenological scale factor for the underlying signal) and `distribution.variance_function` (a function that computes the variance, or power spectrum, of the gaussian noise).\n", + "\n", + "Notice that in order to simulate the image with additive white noise, we chose the power spectrum to be a constant. In particular, we set `variance_function = op.Constant(1.0)`. We can instead use the `cryojax.image.operators` module to build a more complex power spectrum. In this example, we choose the variance to be a lorenzian envelope modulated by the CTF, plus additive white noise." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "variance_function = op.Constant(0.75) + ctf * ctf * op.Lorenzian(\n", + " amplitude=5.0, length_scale=instrument_config.pixel_size * 3.0\n", ")\n", - "im3 = plot_image(\n", - " compute_image(detector_pipeline), fig, ax3, label=\"Expected electron counts\"\n", + "# Passing the ImagePipeline and a variance function, instantiate the distribution\n", + "non_white_noise_distribution = dist.IndependentGaussianFourierModes(\n", + " imaging_pipeline,\n", + " signal_scale_factor=jnp.sqrt(instrument_config.n_pixels),\n", + " variance_function=variance_function,\n", + ")\n", + "# ... then, either simulate an image from this distribution\n", + "key = jax.random.PRNGKey(seed=0)\n", + "\n", + "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n", + "im = plot_image(\n", + " compute_noisy_image_with_distribution(non_white_noise_distribution, key),\n", + " fig,\n", + " ax,\n", + " label=\"Image with additive gaussian noise \\n from an arbitrary power spectrum\",\n", ")\n", "plt.tight_layout()" ] diff --git a/docs/examples/simulate-micrograph.ipynb b/docs/examples/simulate-micrograph.ipynb index 0d207461..96274cbc 100644 --- a/docs/examples/simulate-micrograph.ipynb +++ b/docs/examples/simulate-micrograph.ipynb @@ -6,7 +6,7 @@ "source": [ "In this tutorial, we will simulate a naive model of a micrograph. In particular, we will simulate a batch of images of the same particle at random poses, then sum over them.\n", "\n", - "The goal of this tutorial is to learn how to vmap in `cryojax`'s recommended pattern. Namely, we will demonstrate this pattern using utilities in `cryojax.core`. These utilities are lightweight wrappers around `equinox`." + "The goal of this tutorial is to learn how to vmap in `cryojax`'s recommended pattern. This uses the lightweight wrappers around `equinox` in `cryojax`." ] }, { @@ -49,9 +49,13 @@ "outputs": [], "source": [ "# CryoJAX imports\n", - "import cryojax.simulator as cs\n", - "from cryojax.io import read_array_with_spacing_from_mrc\n", - "from cryojax.rotations import SO3" + "from jaxtyping import install_import_hook\n", + "\n", + "\n", + "with install_import_hook(\"cryojax\", \"typeguard.typechecked\"):\n", + " import cryojax.simulator as cxs\n", + " from cryojax.data import read_array_with_spacing_from_mrc\n", + " from cryojax.rotations import SO3" ] }, { @@ -70,33 +74,33 @@ "# First, load the scattering potential and projection method\n", "filename = \"./data/ribosome_4ug0_scattering_potential_from_cistem.mrc\"\n", "real_voxel_grid, voxel_size = read_array_with_spacing_from_mrc(filename)\n", - "potential = cs.FourierVoxelGridPotential.from_real_voxel_grid(\n", + "potential = cxs.FourierVoxelGridPotential.from_real_voxel_grid(\n", " real_voxel_grid, voxel_size, pad_scale=2\n", ")\n", - "integrator = cs.FourierSliceExtract(interpolation_order=1)\n", - "\n", - "# ... and build the instrument\n", - "voltage_in_kilovolts = 300.0\n", - "optics = cs.WeakPhaseOptics(\n", - " ctf=cs.CTF(\n", - " defocus_u_in_angstroms=10000.0,\n", - " defocus_v_in_angstroms=10000.0,\n", + "# ... now the projection method\n", + "potential_integrator = cxs.FourierSliceExtraction(interpolation_order=1)\n", + "# ... and the contrast transfer theory\n", + "transfer_theory = cxs.ContrastTransferTheory(\n", + " ctf=cxs.ContrastTransferFunction(\n", + " defocus_in_angstroms=10000.0,\n", + " astigmatism_in_angstroms=0.0,\n", " )\n", ")\n", - "instrument = cs.Instrument(voltage_in_kilovolts, optics=optics)\n", - "\n", - "# ... and finally the config\n", + "# ... finally, the instrument_config\n", "shape = (400, 600)\n", "pixel_size = potential.voxel_size # Angstroms\n", - "image_size = np.asarray(shape) * pixel_size\n", - "config = cs.ImageConfig(shape, pixel_size, pad_scale=1.1)" + "voltage_in_kilovolts = 300.0\n", + "instrument_config = cxs.InstrumentConfig(\n", + " shape, pixel_size, voltage_in_kilovolts, pad_scale=1.1\n", + ")\n", + "image_size = np.asarray(shape) * pixel_size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we will construct an `ImagePipeline` by batching over a set of random number generator keys." + "Now we will construct a `ContrastImagingPipeline` by batching over a set of random number generator keys." ] }, { @@ -113,8 +117,10 @@ "\n", "\n", "@partial(eqx.filter_vmap, in_axes=(0, None), out_axes=eqxi.if_mapped(axis=0))\n", - "def make_pipeline(key: PRNGKeyArray, no_vmap: tuple[PyTree, ...]) -> cs.ImagePipeline:\n", - " config, potential, integrator, instrument = no_vmap\n", + "def make_imaging_pipeline(\n", + " key: PRNGKeyArray, no_vmap: tuple[PyTree, ...]\n", + ") -> cxs.ContrastImagingPipeline:\n", + " config, potential, potential_integrator = no_vmap\n", " # ... instantiate rotations\n", " rotation = SO3.sample_uniform(key)\n", " # ... now in-plane translation\n", @@ -128,12 +134,14 @@ " # zero\n", " offset_in_angstroms = jnp.pad(in_plane_offset_in_angstroms, ((0, 1),))\n", " # ... build the pose\n", - " pose = cs.QuaternionPose.from_rotation_and_translation(\n", - " rotation, offset_in_angstroms\n", + " pose = cxs.QuaternionPose.from_rotation_and_translation(rotation, offset_in_angstroms)\n", + " # ... build the ensemble\n", + " structural_ensemble = cxs.SingleStructureEnsemble(potential, pose)\n", + " # ... and finally the scattering theory and return\n", + " theory = cxs.LinearScatteringTheory(\n", + " structural_ensemble, potential_integrator, transfer_theory\n", " )\n", - " # ... build the Specimen and ImagePipeline as usual and return\n", - " specimen = cs.Specimen(potential, integrator, pose)\n", - " return cs.ImagePipeline(config, specimen, instrument)" + " return cxs.ContrastImagingPipeline(config, theory)" ] }, { @@ -145,7 +153,7 @@ " When we create a pytree with `eqx.filter_vmap` (or `jax.vmap`), `out_axes` should have the same structure as the output pytree. If `out_axes` is set to `None` at a particular leaf, this\n", " says that we do not want to broadcast that leaf (of course, this only works for unmapped leaves). By default `jax.vmap` sets `out_axes=0`, so all unmapped leaves get broadcasted. `equinox` allows us to pass `out_axes=eqxi.if_mapped(axes=0)`, which specifies *not* to broadcast pytree leaves unless the leaves are directly mapped.\n", "\n", - " When building an `ImagePipeline`, it is very important that we do not broadcast arbitrary leaves! For example, an `ImageConfig` stores the coordinate systems for our image. Without the `out_axes=eqxi.if_mapped(axes=0)` specification, the `make_pipeline` would output an `ImagePipeline.config` whose coordinate systems have a batch dimension. This takes up unecessary memory." + " When building a `ContrastImagingPipeline`, it is very important that we do not broadcast arbitrary leaves! For example, an `InstrumentConfig` stores the coordinate systems for our image. Without the `out_axes=eqxi.if_mapped(axes=0)` specification, the `make_imaging_pipeline` would output an `ContrastImagingPipeline.instrument_config` whose coordinate systems have a batch dimension. This takes up unecessary memory." ] }, { @@ -158,17 +166,19 @@ "number_of_poses = 20\n", "keys = jax.random.split(jax.random.PRNGKey(12345), number_of_poses)\n", "\n", - "# ... instantiate the pipeline\n", - "pipeline = make_pipeline(keys, (config, potential, integrator, instrument))" + "# ... instantiate the instrument_pipeline\n", + "imaging_pipeline = make_imaging_pipeline(\n", + " keys, (instrument_config, potential, potential_integrator)\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This may be a little odd at first. We have contructed a pipeline, where if we were to directly call its `render` method, it would not work. Think of it this way: because we created our `pipeline`s with a `vmap`, functions can now only be called after crossing `vmap` boundaries. There is very good reason for this! To learn more, read the section of the equinox documentation on [model ensembling](https://docs.kidger.site/equinox/tricks/#ensembling).\n", + "This may be a little odd at first. We have contructed an `imaging_pipeline`, where if we were to directly call its `render` method, it would not work. Think of it this way: because we created our `imaging_pipeline`s with a `vmap`, functions can now only be called after crossing `vmap` boundaries. There is very good reason for this! To learn more, read the section of the equinox documentation on [model ensembling](https://docs.kidger.site/equinox/tricks/#ensembling).\n", "\n", - "Now that we have an `ImagePipeline` with a batched set of poses, we need some way of telling our `vmap` exactly what pytree leaves have batch dimensions. One way `equinox` does this is by using pointers to particular pytree leaves to create what is called a `filter_spec`." + "Now that we have a `ContrastImagingPipeline` with a batched set of poses, we need some way of telling our `vmap` exactly what pytree leaves have batch dimensions. One way `equinox` does this is by using pointers to particular pytree leaves to create what is called a `filter_spec`." ] }, { @@ -177,22 +187,22 @@ "metadata": {}, "outputs": [], "source": [ - "import cryojax.core as cjc\n", + "import cryojax as cx\n", "\n", "\n", "# ... specify which leaves we would like to vmap over\n", - "where = lambda pipeline: pipeline.specimen.pose\n", + "where = lambda p: p.scattering_theory.structural_ensemble.pose\n", "# ... use a cryojax wrapper to return a filter_spec\n", - "filter_spec = cjc.get_filter_spec(pipeline, where)" + "filter_spec = cx.get_filter_spec(imaging_pipeline, where)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here, `filter_spec` is a pytree of booleans of the same structure as `pipeline`. The values are `True` at leaves that we do want to `vmap` over and `False` where we don't. Filtered transformations are a cornerstone to `equinox` and it is highly recommended to learn about them. See [here](https://docs.kidger.site/equinox/examples/frozen_layer/) in the equinox documentation for reading.\n", + "Here, `filter_spec` is a pytree of booleans of the same structure as `imaging_pipeline`. The values are `True` at leaves that we do want to `vmap` over and `False` where we don't. Filtered transformations are a cornerstone to `equinox` and it is highly recommended to learn about them. See [here](https://docs.kidger.site/equinox/examples/frozen_layer/) in the equinox documentation for reading.\n", "\n", - "Above we have used a `cryojax` utility routine for creating a `filter_spec`, called `cryojax.core.get_filter_spec`. Next, we will finally define functions to batch and sum over images! To do this, we will again use a `cryojax` wrapper to `equinox` called `filter_vmap_with_spec`. This batches over a pytree, only at leaves specified by `filter_spec`. " + "Above we have used a `cryojax` utility routine for creating a `filter_spec`, called `cryojax.get_filter_spec`. Next, we will finally define functions to batch and sum over images! To do this, we will again use a `cryojax` wrapper to `equinox` called `filter_vmap_with_spec`. This batches over a pytree, only at leaves specified by `filter_spec`. " ] }, { @@ -204,19 +214,18 @@ "import equinox as eqx\n", "\n", "\n", - "@partial(cjc.filter_vmap_with_spec, filter_spec=filter_spec)\n", - "def compute_image_stack(pipeline):\n", + "@partial(cx.filter_vmap_with_spec, filter_spec=filter_spec)\n", + "def compute_image_stack(imaging_pipeline):\n", " \"\"\"Compute a batch of images at different poses,\n", " specified by the `filter_spec`.\n", " \"\"\"\n", - " image = pipeline.render()\n", - " return image - image.mean()\n", + " return imaging_pipeline.render()\n", "\n", "\n", "@eqx.filter_jit\n", - "def compute_micrograph(pipeline):\n", + "def compute_micrograph(imaging_pipeline):\n", " \"\"\"Sum together the image stack.\"\"\"\n", - " return jnp.sum(compute_image_stack(pipeline), axis=0)" + " return jnp.sum(compute_image_stack(imaging_pipeline), axis=0)" ] }, { @@ -237,7 +246,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -249,7 +258,7 @@ "source": [ "# Compute the image and plot\n", "fig, ax = plt.subplots(figsize=(5.5, 5.5))\n", - "micrograph = compute_micrograph(pipeline)\n", + "micrograph = compute_micrograph(imaging_pipeline)\n", "plot_image(\n", " micrograph,\n", " fig,\n", diff --git a/docs/index.md b/docs/index.md index b649a122..d5433020 100644 --- a/docs/index.md +++ b/docs/index.md @@ -42,66 +42,62 @@ The [`jax-finufft`](https://github.com/dfm/jax-finufft) package is an optional d The following is a basic workflow to simulate an image. -First, instantiate the scattering potential representation and its respective method for computing image projections. +First, instantiate the spatial potential energy distribution representation and its respective method for computing image projections. ```python import jax import jax.numpy as jnp -import cryojax.simulator as cs -from cryojax.io import read_array_with_spacing_from_mrc +import cryojax.simulator as cxs +from cryojax.data import read_array_with_spacing_from_mrc -# Instantiate the scattering potential. +# Instantiate the scattering potential filename = "example_scattering_potential.mrc" real_voxel_grid, voxel_size = read_array_with_spacing_from_mrc(filename) -potential = cs.FourierVoxelGridPotential.from_real_voxel_grid(real_voxel_grid, voxel_size) -# ... now instantiate fourier slice extraction -integrator = cs.FourierSliceExtract(interpolation_order=1) -``` - -Here, the 3D scattering potential array is read from `filename`. Then, the abstraction of the scattering potential is then loaded in fourier-space into a `FourierVoxelGridPotential`, and the fourier-slice projection theorem is initialized with `FourierSliceExtract`. The scattering potential can be generated with an external program, such as the [cisTEM](https://github.com/timothygrant80/cisTEM) simulate tool. - -We can now instantiate the representation of a biological specimen, which also includes a pose. - -```python -# First instantiate the pose. Angles are given in degrees -pose = cs.EulerAnglePose( +potential = cxs.FourierVoxelGridPotential.from_real_voxel_grid(real_voxel_grid, voxel_size) +# ... now, instantiate the pose. Angles are given in degrees +pose = cxs.EulerAnglePose( offset_x_in_angstroms=5.0, offset_y_in_angstroms=-3.0, view_phi=20.0, view_theta=80.0, view_psi=-10.0, ) -# ... now, build the biological specimen -specimen = cs.Specimen(potential, integrator, pose) +# ... now, build the ensemble. In this case, the ensemble is just a single structure +structural_ensemble = cxs.SingleStructureEnsemble(potential, pose) ``` -Next, build the model for the electron microscope. Here, we simply include a model for the CTF in the weak-phase approximation (linear image formation theory). +Here, the 3D scattering potential array is read from `filename`. Then, the abstraction of the scattering potential is then loaded in fourier-space into a `FourierVoxelGridPotential`. The scattering potential can be generated with an external program, such as the [cisTEM](https://github.com/timothygrant80/cisTEM) simulate tool. Then, the representation of a biological specimen is instantiated, which also includes a pose and conformational heterogeneity. Here, the `SingleStructureEnsemble` class takes a pose but has no heterogeneity. + +Next, build the *scattering theory*. The simplest `scattering_theory` is the `LinearScatteringTheory`. This represents the usual image formation pipeline in cryo-EM, which forms images by computing projections of the potential and convolving the result with a contrast transfer function. ```python from cryojax.image import operators as op -# First, initialize the CTF and its optics model -ctf = cs.CTF( - defocus_u_in_angstroms=10000.0, - defocus_v_in_angstroms=9800.0, +# Initialize the scattering theory. First, instantiate fourier slice extraction +potential_integrator = cxs.FourierSliceExtraction(interpolation_order=1) +# ... next, the contrast transfer theory +ctf = cxs.ContrastTransferFunction( + defocus_in_angstroms=9800.0, + astigmatism_in_angstroms=200.0, astigmatism_angle=10.0, - amplitude_contrast_ratio=0.1) -optics = cs.WeakPhaseOptics(ctf, envelope=op.FourierGaussian(b_factor=5.0)) # b_factor is given in Angstroms^2 -# ... these are stored in the Instrument -voltage_in_kilovolts = 300.0 -instrument = cs.Instrument(voltage_in_kilovolts, optics) + amplitude_contrast_ratio=0.1 +) +transfer_theory = cxs.ContrastTransferTheory(ctf, envelope=op.FourierGaussian(b_factor=5.0)) +# ... now for the scattering theory +scattering_theory = cxs.LinearScatteringTheory(structural_ensemble, potential_integrator, transfer_theory) ``` -The `CTF` has all parameters used in CTFFIND4, which take their default values if not -explicitly configured here. Finally, we can instantiate the `ImagePipeline` and simulate an image. +The `ContrastTransferFunction` has parameters used in CTFFIND4, which take their default values if not +explicitly configured here. Finally, we can instantiate the `imaging_pipeline`--the highest level of imaging abstraction in `cryojax`--and simulate an image. Here, we choose a `ContrastImagingPipeline`, which simulates image contrast from a linear scattering theory. ```python -# Instantiate the image configuration -config = cs.ImageConfig(shape=(320, 320), pixel_size=voxel_size) -# Build the image formation model -pipeline = cs.ImagePipeline(config, specimen, instrument) -# ... simulate an image and return a normalized image in real-space -image = pipeline.render(get_real=True, normalize=True) +# Finally, build the image formation model +# ... first instantiate the instrument configuration +instrument_config = cxs.InstrumentConfig(shape=(320, 320), pixel_size=voxel_size, voltage_in_kilovolts=300.0) +# ... now the imaging pipeline +imaging_pipeline = cxs.ContrastImagingPipeline(instrument_config, scattering_theory) +# ... finally, simulate an image and return in real-space! +image_without_noise = imaging_pipeline.render(get_real=True) ``` ## Next steps diff --git a/mkdocs.yml b/mkdocs.yml index 686f8581..4bdfca82 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -64,5 +64,6 @@ nav: - Read a particle stack: 'examples/read-dataset.ipynb' - Intermediate: - Simulate a batch of images: 'examples/simulate-micrograph.ipynb' + - Run a cross-correlation search: 'examples/cross-correlation-search.ipynb' - Simulator API: - 'api/simulator/scattering_potential.md' diff --git a/pyproject.toml b/pyproject.toml index 3af778b7..ded864d6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ dependencies = [ "starfile", "pandas", "typing_extensions>=4.5.0", + "tqdm", ] [project.optional-dependencies] @@ -45,6 +46,7 @@ version-file = "src/cryojax/cryojax_version.py" [tool.ruff] extend-include = ["*.ipynb"] lint.fixable = ["I001", "F401"] +line-length = 90 lint.ignore = ["E402", "E721", "E731", "E741", "F722"] lint.ignore-init-module-imports = true lint.select = ["E", "F", "I001"] @@ -56,6 +58,9 @@ extra-standard-library = ["typing_extensions"] lines-after-imports = 2 order-by-type = false +[tool.black] +line-length = 90 + [tool.pyright] reportIncompatibleMethodOverride = true reportIncompatibleVariableOverride = false # Incompatible with eqx.AbstractVar diff --git a/src/cryojax/__init__.py b/src/cryojax/__init__.py index c49d4717..13427f4b 100644 --- a/src/cryojax/__init__.py +++ b/src/cryojax/__init__.py @@ -1,11 +1,15 @@ from . import ( coordinates as coordinates, - core as core, data as data, image as image, inference as inference, - io as io, rotations as rotations, simulator as simulator, ) +from ._filter_specs import get_filter_spec as get_filter_spec +from ._filtered_transformations import ( + filter_grad_with_spec as filter_grad_with_spec, + filter_value_and_grad_with_spec as filter_value_and_grad_with_spec, + filter_vmap_with_spec as filter_vmap_with_spec, +) from .cryojax_version import __version__ as __version__ diff --git a/src/cryojax/core/_errors.py b/src/cryojax/_errors.py similarity index 93% rename from src/cryojax/core/_errors.py rename to src/cryojax/_errors.py index a66862c3..8ee97098 100644 --- a/src/cryojax/core/_errors.py +++ b/src/cryojax/_errors.py @@ -1,5 +1,5 @@ """ -Utilities for runtime errors, wrapping `equinox.error_if`. +Utilities for runtime errors, wrapping `equinox.error_if`. """ import equinox as eqx diff --git a/src/cryojax/_filter_specs.py b/src/cryojax/_filter_specs.py new file mode 100644 index 00000000..6d66645f --- /dev/null +++ b/src/cryojax/_filter_specs.py @@ -0,0 +1,46 @@ +""" +Utilities for creating equinox filter_specs. +""" + +from typing import Any, Callable, Optional, Sequence, Union + +import equinox as eqx +import jax.tree_util as jtu +from jaxtyping import PyTree + + +def get_filter_spec( + pytree: PyTree, + where: Callable[[PyTree], Union[Any, Sequence[Any]]], + *, + inverse: bool = False, + is_leaf: Optional[Callable[[Any], bool]] = None, +) -> PyTree[bool]: + """A lightweight wrapper around `equinox` for creating a "filter specification". + + A filter specification, or `filter_spec`, is a pytree whose + leaves are either `True` or `False`. These are commonly used with + `equinox` [filtered transformations](https://docs.kidger.site/equinox/all-of-equinox/#2-filtering). + + In `cryojax`, it is a very common pattern to need to finely specify which leaves + we would like to take JAX transformations with respect to. This is done with a + pointer to individual leaves, which is referred to as a `where` function. See + [`here`](https://docs.kidger.site/equinox/examples/frozen_layer/#freezing-parameters) + in the `equinox` documentation for an example. + + **Returns:** + + The filter specification. This is a pytree of the same structure as `pytree` with + `True` where the `where` function points to, and `False` where it does not + (or the opposite, if `inverse = True`). + """ + if not inverse: + false_pytree = jtu.tree_map(lambda _: False, pytree) + return eqx.tree_at( + where, false_pytree, replace_fn=lambda _: True, is_leaf=is_leaf + ) + else: + true_pytree = jtu.tree_map(lambda _: True, pytree) + return eqx.tree_at( + where, true_pytree, replace_fn=lambda _: False, is_leaf=is_leaf + ) diff --git a/src/cryojax/core/_filtered_transformations.py b/src/cryojax/_filtered_transformations.py similarity index 91% rename from src/cryojax/core/_filtered_transformations.py rename to src/cryojax/_filtered_transformations.py index 1cdbc091..dd244a73 100644 --- a/src/cryojax/core/_filtered_transformations.py +++ b/src/cryojax/_filtered_transformations.py @@ -18,6 +18,10 @@ def filter_grad_with_spec( *, has_aux: bool = False, ) -> Callable: + """A lightweight wrapper around `equinox.filter_grad` that accepts a + `filter_spec`. + """ + @wraps(func) def partition_and_recombine_fn(pytree: PyTree, *args: Any, **kwargs: Any): @partial( @@ -45,6 +49,10 @@ def filter_value_and_grad_with_spec( *, has_aux: bool = False, ) -> Callable: + """A lightweight wrapper around `equinox.filter_value_and_grad` that + accepts a `filter_spec`. + """ + @wraps(func) def partition_and_recombine_fn(pytree: PyTree, *args: Any, **kwargs: Any): @partial( @@ -79,6 +87,10 @@ def filter_vmap_with_spec( axis_name: Hashable = None, axis_size: Optional[int] = None, ) -> Callable: + """A lightweight wrapper around `equinox.filter_vmap` that accepts a + `filter_spec`. + """ + @wraps(func) def partition_and_recombine_fn(pytree: PyTree, *args: Any): @partial( diff --git a/src/cryojax/coordinates/__init__.py b/src/cryojax/coordinates/__init__.py index 5da3d886..36813a1d 100644 --- a/src/cryojax/coordinates/__init__.py +++ b/src/cryojax/coordinates/__init__.py @@ -1,10 +1,12 @@ -from ._coordinates import ( - AbstractCoordinates as AbstractCoordinates, +from ._coordinate_functions import ( cartesian_to_polar as cartesian_to_polar, + make_coordinates as make_coordinates, + make_frequencies as make_frequencies, +) +from ._coordinate_wrappers import ( + AbstractCoordinates as AbstractCoordinates, CoordinateGrid as CoordinateGrid, CoordinateList as CoordinateList, FrequencyGrid as FrequencyGrid, FrequencySlice as FrequencySlice, - make_coordinates as make_coordinates, - make_frequencies as make_frequencies, ) diff --git a/src/cryojax/coordinates/_coordinate_functions.py b/src/cryojax/coordinates/_coordinate_functions.py new file mode 100644 index 00000000..6aab214b --- /dev/null +++ b/src/cryojax/coordinates/_coordinate_functions.py @@ -0,0 +1,154 @@ +""" +Functions for creating and operating on coordinate systems. +""" + +from typing import Optional + +import jax.numpy as jnp +import numpy as np +from jaxtyping import Array, Float, Inexact + + +def make_coordinates( + shape: tuple[int, ...], grid_spacing: float | Float[np.ndarray, ""] = 1.0 +) -> Float[Array, "*shape ndim"]: + """ + Create a real-space cartesian coordinate system on a grid. + + Arguments + --------- + shape : + Shape of the voxel grid, with + ``ndim = len(shape)``. + grid_spacing : + The grid spacing, in units of length. + + Returns + ------- + coordinate_grid : + Cartesian coordinate system in real space. + """ + coordinate_grid = _make_coordinates_or_frequencies( + shape, grid_spacing=grid_spacing, real_space=True + ) + return coordinate_grid + + +def make_frequencies( + shape: tuple[int, ...], + grid_spacing: float | Float[np.ndarray, ""] = 1.0, + half_space: bool = True, +) -> Float[Array, "*shape ndim"]: + """ + Create a fourier-space cartesian coordinate system on a grid. + The zero-frequency component is in the beginning. + + Arguments + --------- + shape : + Shape of the voxel grid, with + ``ndim = len(shape)``. + grid_spacing : + The grid spacing, in units of length. + half_space : + Return a frequency grid on the half space. + ``shape[-1]`` is the axis on which the negative + frequencies are omitted. + + Returns + ------- + frequency_grid : + Cartesian coordinate system in frequency space. + """ + frequency_grid = _make_coordinates_or_frequencies( + shape, + grid_spacing=grid_spacing, + real_space=False, + half_space=half_space, + ) + return frequency_grid + + +def cartesian_to_polar( + freqs: Float[Array, "y_dim x_dim 2"], square: bool = False +) -> tuple[Inexact[Array, "y_dim x_dim"], Inexact[Array, "y_dim x_dim"]]: + """ + Convert from cartesian to polar coordinates. + + Arguments + --------- + freqs : + The cartesian coordinate system. + square : + If ``True``, return the square of the + radial coordinate :math:`|r|^2`. Otherwise, + return :math:`|r|`. + """ + theta = jnp.arctan2(freqs[..., 0], freqs[..., 1]) + k_sqr = jnp.sum(jnp.square(freqs), axis=-1) + if square: + return k_sqr, theta + else: + kr = jnp.sqrt(k_sqr) + return kr, theta + + +def _make_coordinates_or_frequencies( + shape: tuple[int, ...], + grid_spacing: float | Float[np.ndarray, ""] = 1.0, + real_space: bool = False, + half_space: bool = True, +) -> Float[Array, "*shape ndim"]: + ndim = len(shape) + coords1D = [] + for idx in range(ndim): + if real_space: + c1D = _make_coordinates_or_frequencies_1d( + shape[idx], grid_spacing, real_space + ) + else: + if not half_space: + rfftfreq = False + else: + rfftfreq = False if idx < ndim - 1 else True + c1D = _make_coordinates_or_frequencies_1d( + shape[idx], grid_spacing, real_space, rfftfreq + ) + coords1D.append(c1D) + if ndim == 2: + y, x = coords1D + xv, yv = jnp.meshgrid(x, y, indexing="xy") + coords = jnp.stack([xv, yv], axis=-1) + elif ndim == 3: + z, y, x = coords1D + xv, yv, zv = jnp.meshgrid(x, y, z, indexing="xy") + xv, yv, zv = [ + jnp.transpose(rv, axes=[2, 0, 1]) for rv in [xv, yv, zv] + ] # Change axis ordering to [z, y, x] + coords = jnp.stack([xv, yv, zv], axis=-1) + else: + raise ValueError( + "Only 2D and 3D coordinate grids are supported. " + f"Tried to create a grid of shape {shape}." + ) + + return coords + + +def _make_coordinates_or_frequencies_1d( + size: int, + grid_spacing: float | Float[np.ndarray, ""], + real_space: bool = False, + rfftfreq: Optional[bool] = None, +) -> Float[Array, " size"]: + """One-dimensional coordinates in real or fourier space""" + if real_space: + make_1d = lambda size, dx: jnp.fft.fftshift(jnp.fft.fftfreq(size, 1 / dx)) * size + else: + if rfftfreq is None: + raise ValueError("Argument rfftfreq cannot be None if real_space=False.") + else: + fn = jnp.fft.rfftfreq if rfftfreq else jnp.fft.fftfreq + make_1d = lambda size, dx: fn(size, grid_spacing) + + return make_1d(size, grid_spacing) diff --git a/src/cryojax/coordinates/_coordinate_wrappers.py b/src/cryojax/coordinates/_coordinate_wrappers.py new file mode 100644 index 00000000..6a197001 --- /dev/null +++ b/src/cryojax/coordinates/_coordinate_wrappers.py @@ -0,0 +1,151 @@ +""" +Coordinate abstractions. +""" + +from abc import abstractmethod +from typing import Any +from typing_extensions import Self + +import equinox as eqx +import jax.numpy as jnp +import numpy as np +from equinox import AbstractVar +from jaxtyping import Array, Float + +from ._coordinate_functions import make_coordinates, make_frequencies + + +class AbstractCoordinates(eqx.Module, strict=True): + """ + A base class that wraps a coordinate array. + """ + + array: AbstractVar[Any] + + @abstractmethod + def get(self) -> Any: + """Get the coordinates.""" + raise NotImplementedError + + def __mul__( + self, real_number: float | Float[np.ndarray, ""] | Float[Array, ""] + ) -> Self: + # The following line seems to be required for differentiability with + # respect to arr + rescaled_array = jnp.where( + self.array != 0.0, self.array * jnp.asarray(real_number), 0.0 + ) + return eqx.tree_at(lambda x: x.array, self, rescaled_array) + + def __rmul__( + self, real_number: float | Float[np.ndarray, ""] | Float[Array, ""] + ) -> Self: + rescaled_array = jnp.where( + self.array != 0.0, jnp.asarray(real_number) * self.array, 0.0 + ) + return eqx.tree_at(lambda x: x.array, self, rescaled_array) + + def __truediv__( + self, real_number: float | Float[np.ndarray, ""] | Float[Array, ""] + ) -> Self: + rescaled_array = jnp.where( + self.array != 0.0, self.array / jnp.asarray(real_number), 0.0 + ) + return eqx.tree_at(lambda x: x.array, self, rescaled_array) + + +class CoordinateList(AbstractCoordinates, strict=True): + """ + A Pytree that wraps a coordinate list. + """ + + array: Float[Array, "size 3"] | Float[Array, "size 2"] = eqx.field( + converter=jnp.asarray + ) + + def __init__(self, coordinate_list: Float[Array, "size 2"] | Float[Array, "size 3"]): + self.array = coordinate_list + + def get(self) -> Float[Array, "size 3"] | Float[Array, "size 2"]: + return self.array + + +class CoordinateGrid(AbstractCoordinates, strict=True): + """ + A Pytree that wraps a coordinate grid. + """ + + array: Float[Array, "y_dim x_dim 2"] | Float[Array, "z_dim y_dim x_dim 3"] = ( + eqx.field(converter=jnp.asarray) + ) + + def __init__( + self, + shape: tuple[int, ...], + grid_spacing: float | Float[np.ndarray, ""] = 1.0, + ): + self.array = make_coordinates(shape, grid_spacing) + + def get( + self, + ) -> Float[Array, "y_dim x_dim 2"] | Float[Array, "z_dim y_dim x_dim 3"]: + return self.array + + +class FrequencyGrid(AbstractCoordinates, strict=True): + """ + A Pytree that wraps a frequency grid. + """ + + array: Float[Array, "y_dim x_dim 2"] | Float[Array, "z_dim y_dim x_dim 3"] = ( + eqx.field(converter=jnp.asarray) + ) + + def __init__( + self, + shape: tuple[int, ...], + grid_spacing: float | Float[np.ndarray, ""] = 1.0, + half_space: bool = True, + ): + self.array = make_frequencies(shape, grid_spacing, half_space=half_space) + + def get( + self, + ) -> Float[Array, "y_dim x_dim 2"] | Float[Array, "z_dim y_dim x_dim 3"]: + return self.array + + +class FrequencySlice(AbstractCoordinates, strict=True): + """ + A Pytree that wraps a frequency slice. + + Unlike a `FrequencyGrid`, a `FrequencySlice` has the zero frequency + component in the center. + """ + + array: Float[Array, "1 y_dim x_dim 3"] = eqx.field(converter=jnp.asarray) + + def __init__( + self, + shape: tuple[int, int], + grid_spacing: float | Float[np.ndarray, ""] = 1.0, + half_space: bool = True, + ): + frequency_slice = make_frequencies(shape, grid_spacing, half_space=half_space) + if half_space: + frequency_slice = jnp.fft.fftshift(frequency_slice, axes=(0,)) + else: + frequency_slice = jnp.fft.fftshift(frequency_slice, axes=(0, 1)) + frequency_slice = jnp.expand_dims( + jnp.pad( + frequency_slice, + ((0, 0), (0, 0), (0, 1)), + mode="constant", + constant_values=0.0, + ), + axis=0, + ) + self.array = frequency_slice + + def get(self) -> Float[Array, "1 y_dim x_dim 3"]: + return self.array diff --git a/src/cryojax/coordinates/_coordinates.py b/src/cryojax/coordinates/_coordinates.py deleted file mode 100644 index 0dc17bb8..00000000 --- a/src/cryojax/coordinates/_coordinates.py +++ /dev/null @@ -1,298 +0,0 @@ -""" -Coordinate functionality in cryojax. -""" - -from abc import abstractmethod -from typing import Any, Optional -from typing_extensions import Self - -import equinox as eqx -import jax.numpy as jnp -import numpy as np -from equinox import AbstractVar -from jaxtyping import Array, Float, Inexact - - -class AbstractCoordinates(eqx.Module, strict=True): - """ - A base class that wraps a coordinate array. - """ - - array: AbstractVar[Any] - - @abstractmethod - def get(self) -> Any: - """Get the coordinates.""" - raise NotImplementedError - - def __mul__( - self, real_number: float | Float[np.ndarray, ""] | Float[Array, ""] - ) -> Self: - # The following line seems to be required for differentiability with - # respect to arr - rescaled_array = jnp.where( - self.array != 0.0, self.array * jnp.asarray(real_number), 0.0 - ) - return eqx.tree_at(lambda x: x.array, self, rescaled_array) - - def __rmul__( - self, real_number: float | Float[np.ndarray, ""] | Float[Array, ""] - ) -> Self: - rescaled_array = jnp.where( - self.array != 0.0, jnp.asarray(real_number) * self.array, 0.0 - ) - return eqx.tree_at(lambda x: x.array, self, rescaled_array) - - def __truediv__( - self, real_number: float | Float[np.ndarray, ""] | Float[Array, ""] - ) -> Self: - rescaled_array = jnp.where( - self.array != 0.0, self.array / jnp.asarray(real_number), 0.0 - ) - return eqx.tree_at(lambda x: x.array, self, rescaled_array) - - -class CoordinateList(AbstractCoordinates, strict=True): - """ - A Pytree that wraps a coordinate list. - """ - - array: Float[Array, "size 3"] | Float[Array, "size 2"] = eqx.field( - converter=jnp.asarray - ) - - def __init__( - self, coordinate_list: Float[Array, "size 2"] | Float[Array, "size 3"] - ): - self.array = coordinate_list - - def get(self) -> Float[Array, "size 3"] | Float[Array, "size 2"]: - return self.array - - -class CoordinateGrid(AbstractCoordinates, strict=True): - """ - A Pytree that wraps a coordinate grid. - """ - - array: Float[Array, "y_dim x_dim 2"] | Float[Array, "z_dim y_dim x_dim 3"] = ( - eqx.field(converter=jnp.asarray) - ) - - def __init__( - self, - shape: tuple[int, ...], - grid_spacing: float | Float[np.ndarray, ""] = 1.0, - ): - self.array = make_coordinates(shape, grid_spacing) - - def get( - self, - ) -> Float[Array, "y_dim x_dim 2"] | Float[Array, "z_dim y_dim x_dim 3"]: - return self.array - - -class FrequencyGrid(AbstractCoordinates, strict=True): - """ - A Pytree that wraps a frequency grid. - """ - - array: Float[Array, "y_dim x_dim 2"] | Float[Array, "z_dim y_dim x_dim 3"] = ( - eqx.field(converter=jnp.asarray) - ) - - def __init__( - self, - shape: tuple[int, ...], - grid_spacing: float | Float[np.ndarray, ""] = 1.0, - half_space: bool = True, - ): - self.array = make_frequencies(shape, grid_spacing, half_space=half_space) - - def get( - self, - ) -> Float[Array, "y_dim x_dim 2"] | Float[Array, "z_dim y_dim x_dim 3"]: - return self.array - - -class FrequencySlice(AbstractCoordinates, strict=True): - """ - A Pytree that wraps a frequency slice. - - Unlike a `FrequencyGrid`, a `FrequencySlice` has the zero frequency - component in the center. - """ - - array: Float[Array, "1 y_dim x_dim 3"] = eqx.field(converter=jnp.asarray) - - def __init__( - self, - shape: tuple[int, int], - grid_spacing: float | Float[np.ndarray, ""] = 1.0, - half_space: bool = True, - ): - frequency_slice = make_frequencies(shape, grid_spacing, half_space=half_space) - if half_space: - frequency_slice = jnp.fft.fftshift(frequency_slice, axes=(0,)) - else: - frequency_slice = jnp.fft.fftshift(frequency_slice, axes=(0, 1)) - frequency_slice = jnp.expand_dims( - jnp.pad( - frequency_slice, - ((0, 0), (0, 0), (0, 1)), - mode="constant", - constant_values=0.0, - ), - axis=0, - ) - self.array = frequency_slice - - def get(self) -> Float[Array, "1 y_dim x_dim 3"]: - return self.array - - -def make_coordinates( - shape: tuple[int, ...], grid_spacing: float | Float[np.ndarray, ""] = 1.0 -) -> Float[Array, "*shape ndim"]: - """ - Create a real-space cartesian coordinate system on a grid. - - Arguments - --------- - shape : - Shape of the voxel grid, with - ``ndim = len(shape)``. - grid_spacing : - The grid spacing, in units of length. - - Returns - ------- - coordinate_grid : - Cartesian coordinate system in real space. - """ - coordinate_grid = _make_coordinates_or_frequencies( - shape, grid_spacing=grid_spacing, real_space=True - ) - return coordinate_grid - - -def make_frequencies( - shape: tuple[int, ...], - grid_spacing: float | Float[np.ndarray, ""] = 1.0, - half_space: bool = True, -) -> Float[Array, "*shape ndim"]: - """ - Create a fourier-space cartesian coordinate system on a grid. - The zero-frequency component is in the beginning. - - Arguments - --------- - shape : - Shape of the voxel grid, with - ``ndim = len(shape)``. - grid_spacing : - The grid spacing, in units of length. - half_space : - Return a frequency grid on the half space. - ``shape[-1]`` is the axis on which the negative - frequencies are omitted. - - Returns - ------- - frequency_grid : - Cartesian coordinate system in frequency space. - """ - frequency_grid = _make_coordinates_or_frequencies( - shape, - grid_spacing=grid_spacing, - real_space=False, - half_space=half_space, - ) - return frequency_grid - - -def cartesian_to_polar( - freqs: Float[Array, "y_dim x_dim 2"], square: bool = False -) -> tuple[Inexact[Array, "y_dim x_dim"], Inexact[Array, "y_dim x_dim"]]: - """ - Convert from cartesian to polar coordinates. - - Arguments - --------- - freqs : - The cartesian coordinate system. - square : - If ``True``, return the square of the - radial coordinate :math:`|r|^2`. Otherwise, - return :math:`|r|`. - """ - theta = jnp.arctan2(freqs[..., 0], freqs[..., 1]) - k_sqr = jnp.sum(jnp.square(freqs), axis=-1) - if square: - return k_sqr, theta - else: - kr = jnp.sqrt(k_sqr) - return kr, theta - - -def _make_coordinates_or_frequencies( - shape: tuple[int, ...], - grid_spacing: float | Float[np.ndarray, ""] = 1.0, - real_space: bool = False, - half_space: bool = True, -) -> Float[Array, "*shape ndim"]: - ndim = len(shape) - coords1D = [] - for idx in range(ndim): - if real_space: - c1D = _make_coordinates_or_frequencies_1d( - shape[idx], grid_spacing, real_space - ) - else: - if not half_space: - rfftfreq = False - else: - rfftfreq = False if idx < ndim - 1 else True - c1D = _make_coordinates_or_frequencies_1d( - shape[idx], grid_spacing, real_space, rfftfreq - ) - coords1D.append(c1D) - if ndim == 2: - y, x = coords1D - xv, yv = jnp.meshgrid(x, y, indexing="xy") - coords = jnp.stack([xv, yv], axis=-1) - elif ndim == 3: - z, y, x = coords1D - xv, yv, zv = jnp.meshgrid(x, y, z, indexing="xy") - xv, yv, zv = [ - jnp.transpose(rv, axes=[2, 0, 1]) for rv in [xv, yv, zv] - ] # Change axis ordering to [z, y, x] - coords = jnp.stack([xv, yv, zv], axis=-1) - else: - raise ValueError( - "Only 2D and 3D coordinate grids are supported. " - f"Tried to create a grid of shape {shape}." - ) - - return coords - - -def _make_coordinates_or_frequencies_1d( - size: int, - grid_spacing: float | Float[np.ndarray, ""], - real_space: bool = False, - rfftfreq: Optional[bool] = None, -) -> Float[Array, " size"]: - """One-dimensional coordinates in real or fourier space""" - if real_space: - make_1d = ( - lambda size, dx: jnp.fft.fftshift(jnp.fft.fftfreq(size, 1 / dx)) * size - ) - else: - if rfftfreq is None: - raise ValueError("Argument rfftfreq cannot be None if real_space=False.") - else: - fn = jnp.fft.rfftfreq if rfftfreq else jnp.fft.fftfreq - make_1d = lambda size, dx: fn(size, grid_spacing) - - return make_1d(size, grid_spacing) diff --git a/src/cryojax/core/__init__.py b/src/cryojax/core/__init__.py deleted file mode 100644 index 4479a861..00000000 --- a/src/cryojax/core/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from ._errors import ( - error_if_negative as error_if_negative, - error_if_not_fractional as error_if_not_fractional, - error_if_not_positive as error_if_not_positive, - error_if_zero as error_if_zero, -) -from ._filter_specs import get_filter_spec as get_filter_spec -from ._filtered_transformations import ( - filter_grad_with_spec as filter_grad_with_spec, - filter_value_and_grad_with_spec as filter_value_and_grad_with_spec, - filter_vmap_with_spec as filter_vmap_with_spec, -) diff --git a/src/cryojax/core/_filter_specs.py b/src/cryojax/core/_filter_specs.py deleted file mode 100644 index 95da591c..00000000 --- a/src/cryojax/core/_filter_specs.py +++ /dev/null @@ -1,28 +0,0 @@ -""" -Utilities for creating equinox filter_specs. -""" - -from typing import Any, Callable, Optional, Sequence, Union - -import equinox as eqx -import jax.tree_util as jtu -from jaxtyping import PyTree - - -def get_filter_spec( - pytree: PyTree, - where: Callable[[PyTree], Union[Any, Sequence[Any]]], - *, - inverse: bool = False, - is_leaf: Optional[Callable[[Any], bool]] = None, -) -> PyTree[bool]: - if not inverse: - false_pytree = jtu.tree_map(lambda _: False, pytree) - return eqx.tree_at( - where, false_pytree, replace_fn=lambda _: True, is_leaf=is_leaf - ) - else: - true_pytree = jtu.tree_map(lambda _: True, pytree) - return eqx.tree_at( - where, true_pytree, replace_fn=lambda _: False, is_leaf=is_leaf - ) diff --git a/src/cryojax/core/_serialization.py b/src/cryojax/core/_serialization.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/cryojax/data/__init__.py b/src/cryojax/data/__init__.py index 066a192e..0ef3ded2 100644 --- a/src/cryojax/data/__init__.py +++ b/src/cryojax/data/__init__.py @@ -1,9 +1,24 @@ from ._dataset import AbstractDataset as AbstractDataset +from ._io import ( + clean_gemmi_structure as clean_gemmi_structure, + extract_atom_positions_and_names as extract_atom_positions_and_names, + extract_gemmi_atoms as extract_gemmi_atoms, + get_atom_info_from_gemmi_model as get_atom_info_from_gemmi_model, + get_atom_info_from_mdtraj as get_atom_info_from_mdtraj, + mdtraj_load_from_file as mdtraj_load_from_file, + read_and_validate_starfile as read_and_validate_starfile, + read_array_from_mrc as read_array_from_mrc, + read_array_with_spacing_from_mrc as read_array_with_spacing_from_mrc, + read_atoms_from_cif as read_atoms_from_cif, + read_atoms_from_pdb as read_atoms_from_pdb, + write_image_stack_to_mrc as write_image_stack_to_mrc, + write_image_to_mrc as write_image_to_mrc, + write_volume_to_mrc as write_volume_to_mrc, +) from ._particle_stack import ( AbstractParticleStack as AbstractParticleStack, ) from ._relion import ( - default_relion_make_config as default_relion_make_config, HelicalRelionDataset as HelicalRelionDataset, RelionDataset as RelionDataset, RelionParticleStack as RelionParticleStack, diff --git a/src/cryojax/io/__init__.py b/src/cryojax/data/_io/__init__.py similarity index 70% rename from src/cryojax/io/__init__.py rename to src/cryojax/data/_io/__init__.py index e2e7b3c6..f408abf5 100644 --- a/src/cryojax/io/__init__.py +++ b/src/cryojax/data/_io/__init__.py @@ -1,20 +1,20 @@ -from ._cif import read_atoms_from_cif as read_atoms_from_cif -from ._gemmi import ( +from .cif import read_atoms_from_cif as read_atoms_from_cif +from .gemmi import ( clean_gemmi_structure as clean_gemmi_structure, extract_atom_positions_and_names as extract_atom_positions_and_names, extract_gemmi_atoms as extract_gemmi_atoms, get_atom_info_from_gemmi_model as get_atom_info_from_gemmi_model, ) -from ._mdtraj import ( +from .mdtraj import ( get_atom_info_from_mdtraj as get_atom_info_from_mdtraj, mdtraj_load_from_file as mdtraj_load_from_file, ) -from ._mrc import ( +from .mrc import ( read_array_from_mrc as read_array_from_mrc, read_array_with_spacing_from_mrc as read_array_with_spacing_from_mrc, write_image_stack_to_mrc as write_image_stack_to_mrc, write_image_to_mrc as write_image_to_mrc, write_volume_to_mrc as write_volume_to_mrc, ) -from ._pdb import read_atoms_from_pdb as read_atoms_from_pdb -from ._starfile import read_and_validate_starfile as read_and_validate_starfile +from .pdb import read_atoms_from_pdb as read_atoms_from_pdb +from .starfile import read_and_validate_starfile as read_and_validate_starfile diff --git a/src/cryojax/io/_cif.py b/src/cryojax/data/_io/cif.py similarity index 98% rename from src/cryojax/io/_cif.py rename to src/cryojax/data/_io/cif.py index e303bf2e..b81ea606 100644 --- a/src/cryojax/io/_cif.py +++ b/src/cryojax/data/_io/cif.py @@ -1,7 +1,7 @@ import numpy as np from jaxtyping import Float, Int -from ._gemmi import ( +from .gemmi import ( clean_gemmi_structure, extract_atom_positions_and_names, extract_gemmi_atoms, diff --git a/src/cryojax/io/_gemmi.py b/src/cryojax/data/_io/gemmi.py similarity index 100% rename from src/cryojax/io/_gemmi.py rename to src/cryojax/data/_io/gemmi.py diff --git a/src/cryojax/io/_mdtraj.py b/src/cryojax/data/_io/mdtraj.py similarity index 100% rename from src/cryojax/io/_mdtraj.py rename to src/cryojax/data/_io/mdtraj.py diff --git a/src/cryojax/io/_mrc.py b/src/cryojax/data/_io/mrc.py similarity index 100% rename from src/cryojax/io/_mrc.py rename to src/cryojax/data/_io/mrc.py diff --git a/src/cryojax/io/_pdb.py b/src/cryojax/data/_io/pdb.py similarity index 98% rename from src/cryojax/io/_pdb.py rename to src/cryojax/data/_io/pdb.py index 50ef3094..d3b703c4 100644 --- a/src/cryojax/io/_pdb.py +++ b/src/cryojax/data/_io/pdb.py @@ -6,7 +6,7 @@ import numpy as np from jaxtyping import Float, Int -from ._gemmi import ( +from .gemmi import ( clean_gemmi_structure, extract_atom_positions_and_names, extract_gemmi_atoms, diff --git a/src/cryojax/io/_starfile.py b/src/cryojax/data/_io/starfile.py similarity index 100% rename from src/cryojax/io/_starfile.py rename to src/cryojax/data/_io/starfile.py diff --git a/src/cryojax/data/_relion.py b/src/cryojax/data/_relion.py index 4f60f0ab..22c8426d 100644 --- a/src/cryojax/data/_relion.py +++ b/src/cryojax/data/_relion.py @@ -11,9 +11,9 @@ import pandas as pd from jaxtyping import Array, Float, Int -from ..io import read_and_validate_starfile -from ..simulator import CTF, EulerAnglePose, ImageConfig +from ..simulator import ContrastTransferFunction, EulerAnglePose, InstrumentConfig from ._dataset import AbstractDataset +from ._io import read_and_validate_starfile from ._particle_stack import AbstractParticleStack @@ -39,28 +39,25 @@ class RelionParticleStack(AbstractParticleStack): """ image_stack: Float[Array, "... y_dim x_dim"] - config: ImageConfig + instrument_config: InstrumentConfig pose: EulerAnglePose - ctf: CTF + ctf: ContrastTransferFunction def __init__( self, image_stack: Float[Array, "... y_dim x_dim"], - config: ImageConfig, + instrument_config: InstrumentConfig, pose: EulerAnglePose, - ctf: CTF, + ctf: ContrastTransferFunction, ): # Set image stack and config as is self.image_stack = jnp.asarray(image_stack) - self.config = config + self.instrument_config = instrument_config # Set CTF using the defocus offset in the EulerAnglePose self.ctf = eqx.tree_at( - lambda ctf: (ctf.defocus_u_in_angstroms, ctf.defocus_v_in_angstroms), + lambda tf: tf.defocus_in_angstroms, ctf, - ( - ctf.defocus_u_in_angstroms + pose.offset_z_in_angstroms, - ctf.defocus_v_in_angstroms + pose.offset_z_in_angstroms, - ), + ctf.defocus_in_angstroms + pose.offset_z_in_angstroms, ) # Set defocus offset to zero self.pose = eqx.tree_at( @@ -73,22 +70,25 @@ def __init__( - `image_stack`: The stack of images. The shape of this array is a leading batch dimension followed by the shape of an image in the stack. -- `config`: The image configuration. Any subset of pytree leaves may +- `instrument_config`: The instrument configuration. Any subset of pytree leaves may have a batch dimension. - `pose`: The pose, represented by euler angles. Any subset of pytree leaves may have a batch dimension. Upon instantiation, `pose.offset_z_in_angstroms` is set to zero. - `ctf`: The contrast transfer function. Any subset of pytree leaves may - have a batch dimension. Upon instantiation, `ctf.defocus_u_in_angstroms` - is set to `ctf.defocus_u_in_angstroms + pose.offset_z_in_angstroms` (and - also for `ctf.defocus_v_in_angstroms`). -""" + have a batch dimension. Upon instantiation, + `ctf.defocus_in_angstroms` is set to + `ctf.defocus_in_angstroms + pose.offset_z_in_angstroms`. +""" # noqa: E501 -def default_relion_make_config( - shape: tuple[int, int], pixel_size: float | Float[np.ndarray, "..."], **kwargs: Any +def _default_make_instrument_config_fn( + shape: tuple[int, int], + pixel_size: Float[Array, ""], + voltage_in_kilovolts: Float[Array, ""], + **kwargs: Any, ): - return ImageConfig(shape, jnp.asarray(pixel_size), **kwargs) + return InstrumentConfig(shape, pixel_size, voltage_in_kilovolts, **kwargs) @dataclasses.dataclass(frozen=True) @@ -100,8 +100,8 @@ class RelionDataset(AbstractDataset): path_to_relion_project: pathlib.Path data_blocks: dict[str, pd.DataFrame] - make_config: Callable[ - [tuple[int, int], float | Float[np.ndarray, "..."]], ImageConfig + make_instrument_config_fn: Callable[ + [tuple[int, int], Float[Array, "..."], Float[Array, "..."]], InstrumentConfig ] @final @@ -109,9 +109,10 @@ def __init__( self, path_to_starfile: str | pathlib.Path, path_to_relion_project: str | pathlib.Path, - make_config: Callable[ - [tuple[int, int], float | Float[np.ndarray, "..."]], ImageConfig - ] = default_relion_make_config, + make_instrument_config_fn: Callable[ + [tuple[int, int], Float[Array, "..."], Float[Array, "..."]], + InstrumentConfig, + ] = _default_make_instrument_config_fn, ): """**Arguments:** @@ -124,7 +125,7 @@ def __init__( object.__setattr__( self, "path_to_relion_project", pathlib.Path(path_to_relion_project) ) - object.__setattr__(self, "make_config", make_config) + object.__setattr__(self, "make_instrument_config_fn", make_instrument_config_fn) @final def __getitem__( @@ -142,7 +143,7 @@ def __getitem__( if index > n_rows - 1: raise IndexError(index_error_msg(index)) elif isinstance(index, slice): - if index.start > n_rows - 1: + if index.start is not None and index.start > n_rows - 1: raise IndexError(index_error_msg(index.start)) elif isinstance(index, np.ndarray): pass # catch exceptions later @@ -211,9 +212,7 @@ def __getitem__( np.asarray(image_stack_filename, dtype=object)[0], ) # ... relion convention starts indexing at 1, not 0 - particle_index = ( - np.asarray(relion_particle_index.astype(int), dtype=int) - 1 - ) + particle_index = np.asarray(relion_particle_index.astype(int), dtype=int) - 1 else: raise IOError( "Could not read `rlnImageName` in STAR file for `RelionDataset` " @@ -223,26 +222,32 @@ def __getitem__( image_stack = np.asarray(mrc.data[particle_index]) # type: ignore # Read metadata into a RelionParticleStack # ... particle data - defocus_u_in_angstroms = np.asarray(particle_blocks["rlnDefocusU"]) - defocus_v_in_angstroms = np.asarray(particle_blocks["rlnDefocusV"]) - astigmatism_angle = np.asarray(particle_blocks["rlnDefocusAngle"]) - phase_shift = np.asarray(particle_blocks["rlnPhaseShift"]) + defocus_in_angstroms = jnp.asarray(particle_blocks["rlnDefocusU"]) + astigmatism_in_angstroms = jnp.asarray( + particle_blocks["rlnDefocusV"] + ) - jnp.asarray(particle_blocks["rlnDefocusU"]) + astigmatism_angle = jnp.asarray(particle_blocks["rlnDefocusAngle"]) + phase_shift = jnp.asarray(particle_blocks["rlnPhaseShift"]) # ... optics group data - image_size = np.asarray(optics_group["rlnImageSize"]) - pixel_size = np.asarray(optics_group["rlnImagePixelSize"]) - voltage_in_kilovolts = np.asarray(optics_group["rlnVoltage"]) - spherical_aberration_in_mm = np.asarray(optics_group["rlnSphericalAberration"]) - amplitude_contrast_ratio = np.asarray(optics_group["rlnAmplitudeContrast"]) + image_size = jnp.asarray(optics_group["rlnImageSize"]) + pixel_size = jnp.asarray(optics_group["rlnImagePixelSize"]) + voltage_in_kilovolts = float(optics_group["rlnVoltage"]) + spherical_aberration_in_mm = jnp.asarray(optics_group["rlnSphericalAberration"]) + amplitude_contrast_ratio = jnp.asarray(optics_group["rlnAmplitudeContrast"]) # ... create cryojax objects - config = self.make_config((int(image_size), int(image_size)), pixel_size) - ctf = CTF( - defocus_u_in_angstroms, - defocus_v_in_angstroms, - astigmatism_angle, - voltage_in_kilovolts, - spherical_aberration_in_mm, - amplitude_contrast_ratio, - phase_shift, + instrument_config = self.make_instrument_config_fn( + (int(image_size), int(image_size)), + pixel_size, + jnp.asarray(voltage_in_kilovolts), + ) + ctf = ContrastTransferFunction( + defocus_in_angstroms=defocus_in_angstroms, + astigmatism_in_angstroms=astigmatism_in_angstroms, + astigmatism_angle=astigmatism_angle, + voltage_in_kilovolts=voltage_in_kilovolts, + spherical_aberration_in_mm=spherical_aberration_in_mm, + amplitude_contrast_ratio=amplitude_contrast_ratio, + phase_shift=phase_shift, ) pose = EulerAnglePose() # ... values for the pose are optional, so look to see if @@ -293,9 +298,7 @@ def __getitem__( if particle_blocks["rlnAnglePsi"] == -999.0 else particle_blocks["rlnAnglePsi"] ) - pose_parameter_names_and_values.append( - ("view_psi", particle_blocks_for_psi) - ) + pose_parameter_names_and_values.append(("view_psi", particle_blocks_for_psi)) elif "rlnAnglePsiPrior" in particle_keys: # support for helices pose_parameter_names_and_values.append( ("view_psi", particle_blocks["rlnAnglePsiPrior"]) @@ -312,7 +315,7 @@ def __getitem__( tuple([jnp.asarray(value) for value in pose_parameter_values]), ) - return RelionParticleStack(jnp.asarray(image_stack), config, pose, ctf) + return RelionParticleStack(jnp.asarray(image_stack), instrument_config, pose, ctf) @final def __len__(self) -> int: diff --git a/src/cryojax/image/__init__.py b/src/cryojax/image/__init__.py index 1ef42a0e..c18baaeb 100644 --- a/src/cryojax/image/__init__.py +++ b/src/cryojax/image/__init__.py @@ -22,5 +22,8 @@ normalize_image as normalize_image, rescale_image as rescale_image, ) -from ._rescale_pixel_size import rescale_pixel_size as rescale_pixel_size +from ._rescale_pixel_size import ( + maybe_rescale_pixel_size as maybe_rescale_pixel_size, + rescale_pixel_size as rescale_pixel_size, +) from ._spectrum import powerspectrum as powerspectrum diff --git a/src/cryojax/image/_average.py b/src/cryojax/image/_average.py index b109d8f4..26f51bff 100644 --- a/src/cryojax/image/_average.py +++ b/src/cryojax/image/_average.py @@ -113,9 +113,7 @@ def radial_average( average_as_profile, ).reshape(radial_grid.shape) else: - raise ValueError( - f"interpolation_mode = {interpolation_mode} not supported." - ) + raise ValueError(f"interpolation_mode = {interpolation_mode} not supported.") return average_as_profile, average_as_grid else: return average_as_profile diff --git a/src/cryojax/image/_edges.py b/src/cryojax/image/_edges.py index ee92810d..399db384 100644 --- a/src/cryojax/image/_edges.py +++ b/src/cryojax/image/_edges.py @@ -24,9 +24,7 @@ def crop_to_shape( def crop_to_shape( - image_or_volume: ( - Inexact[Array, "y_dim x_dim"] | Inexact[Array, "z_dim y_dim x_dim"] - ), + image_or_volume: Inexact[Array, "y_dim x_dim"] | Inexact[Array, "z_dim y_dim x_dim"], shape: tuple[int, int] | tuple[int, int, int], ) -> ( Inexact[Array, " {shape[0]} {shape[1]}"] @@ -124,9 +122,7 @@ def pad_to_shape( def pad_to_shape( - image_or_volume: ( - Inexact[Array, "y_dim x_dim"] | Inexact[Array, "z_dim y_dim x_dim"] - ), + image_or_volume: Inexact[Array, "y_dim x_dim"] | Inexact[Array, "z_dim y_dim x_dim"], shape: tuple[int, int] | tuple[int, int, int], **kwargs: Any, ) -> ( diff --git a/src/cryojax/image/_fft.py b/src/cryojax/image/_fft.py index 15654a9c..217357ec 100644 --- a/src/cryojax/image/_fft.py +++ b/src/cryojax/image/_fft.py @@ -121,9 +121,7 @@ def irfftn( axes: Optional[tuple[int, ...]] = None, **kwargs: Any, ) -> ( - Float[Array, "y_dim x_dim"] - | Float[Array, "z_dim y_dim x_dim"] - | Float[Array, " *s"] + Float[Array, "y_dim x_dim"] | Float[Array, "z_dim y_dim x_dim"] | Float[Array, " *s"] ): """ Helper routine to compute the inverse fourier transform diff --git a/src/cryojax/image/_normalize.py b/src/cryojax/image/_normalize.py index bae32b57..b2698164 100644 --- a/src/cryojax/image/_normalize.py +++ b/src/cryojax/image/_normalize.py @@ -104,7 +104,7 @@ def compute_mean_and_std_from_fourier_image( else: N_modes = N1 * N2 # The mean is just the zero mode divided by the number of modes - mean = fourier_image[0, 0] / N_modes + mean = fourier_image[0, 0].real / N_modes # The standard deviation is square root norm squared of the zero mean image std = ( jnp.sqrt( diff --git a/src/cryojax/image/_rescale_pixel_size.py b/src/cryojax/image/_rescale_pixel_size.py index 4c5f49e4..84234639 100644 --- a/src/cryojax/image/_rescale_pixel_size.py +++ b/src/cryojax/image/_rescale_pixel_size.py @@ -2,10 +2,14 @@ Routines for rescaling image pixel size. """ +from typing import Optional + import jax import jax.numpy as jnp from jax.image import scale_and_translate -from jaxtyping import Array, Float +from jaxtyping import Array, Complex, Float + +from ._fft import irfftn, rfftn def rescale_pixel_size( @@ -63,3 +67,51 @@ def rescale_pixel_size( ) return rescaled_image + + +def maybe_rescale_pixel_size( + real_or_fourier_image: ( + Float[Array, "padded_y_dim padded_x_dim"] + | Complex[Array, "padded_y_dim padded_x_dim//2+1"] + ), + current_pixel_size: Float[Array, ""], + new_pixel_size: Float[Array, ""], + is_real: bool = True, + shape_in_real_space: Optional[tuple[int, int]] = None, + method: str = "bicubic", +) -> ( + Float[Array, "padded_y_dim padded_x_dim"] + | Complex[Array, "padded_y_dim padded_x_dim//2+1"] +): + """Rescale the image pixel size using real-space interpolation. Only + interpolate if the `pixel_size` is not the `current_pixel_size`.""" + if is_real: + rescale_fn = lambda im: rescale_pixel_size( + im, current_pixel_size, new_pixel_size, method=method + ) + else: + if shape_in_real_space is None: + rescale_fn = lambda im: rfftn( + rescale_pixel_size( + irfftn(im), + current_pixel_size, + new_pixel_size, + method=method, + ) + ) + else: + rescale_fn = lambda im: rfftn( + rescale_pixel_size( + irfftn(im, s=shape_in_real_space), + current_pixel_size, + new_pixel_size, + method=method, + ) + ) + null_fn = lambda im: im + return jax.lax.cond( + jnp.isclose(current_pixel_size, new_pixel_size), + null_fn, + rescale_fn, + real_or_fourier_image, + ) diff --git a/src/cryojax/image/_spectrum.py b/src/cryojax/image/_spectrum.py index b8471969..1e9cfe2c 100644 --- a/src/cryojax/image/_spectrum.py +++ b/src/cryojax/image/_spectrum.py @@ -44,9 +44,7 @@ def powerspectrum( k_max: Optional[Float[Array, ""] | float] = None, ) -> ( tuple[Float[Array, " n_bins"], Float[Array, " n_bins"]] - | tuple[ - Float[Array, " n_bins"], Float[Array, "y_dim x_dim"], Float[Array, " n_bins"] - ] + | tuple[Float[Array, " n_bins"], Float[Array, "y_dim x_dim"], Float[Array, " n_bins"]] ): ... diff --git a/src/cryojax/image/operators/_fourier_operator.py b/src/cryojax/image/operators/_fourier_operator.py index cae6e77d..28a3d5b0 100644 --- a/src/cryojax/image/operators/_fourier_operator.py +++ b/src/cryojax/image/operators/_fourier_operator.py @@ -16,7 +16,7 @@ from equinox import field from jaxtyping import Array, Float, Inexact -from ...core import error_if_not_positive +from ..._errors import error_if_not_positive from ._operator import AbstractImageOperator diff --git a/src/cryojax/image/operators/_real_operator.py b/src/cryojax/image/operators/_real_operator.py index d5e43c96..edaa9035 100644 --- a/src/cryojax/image/operators/_real_operator.py +++ b/src/cryojax/image/operators/_real_operator.py @@ -10,7 +10,7 @@ from equinox import field from jaxtyping import Array, Float -from ...core import error_if_not_positive +from ..._errors import error_if_not_positive from ._operator import AbstractImageOperator diff --git a/src/cryojax/inference/__init__.py b/src/cryojax/inference/__init__.py index e0fcfba8..7b09ac52 100644 --- a/src/cryojax/inference/__init__.py +++ b/src/cryojax/inference/__init__.py @@ -1 +1,20 @@ -from . import distributions as distributions, transforms as transforms +from . import distributions as distributions +from ._grid_search import ( + AbstractGridSearchMethod as AbstractGridSearchMethod, + MinimumSearchMethod as MinimumSearchMethod, + run_grid_search as run_grid_search, + tree_grid_shape as tree_grid_shape, + tree_grid_take as tree_grid_take, + tree_grid_unravel_index as tree_grid_unravel_index, +) +from ._transforms import ( + AbstractLieGroupTransform as AbstractLieGroupTransform, + AbstractParameterTransform as AbstractParameterTransform, + apply_updates_with_lie_transform as apply_updates_with_lie_transform, + ComposedTransform as ComposedTransform, + ExpTransform as ExpTransform, + RescalingTransform as RescalingTransform, + resolve_transforms as resolve_transforms, + SE3Transform as SE3Transform, + SO3Transform as SO3Transform, +) diff --git a/src/cryojax/inference/_grid_search/__init__.py b/src/cryojax/inference/_grid_search/__init__.py new file mode 100644 index 00000000..577a2108 --- /dev/null +++ b/src/cryojax/inference/_grid_search/__init__.py @@ -0,0 +1,10 @@ +from .pytree_manipulation import ( + tree_grid_shape as tree_grid_shape, + tree_grid_take as tree_grid_take, + tree_grid_unravel_index as tree_grid_unravel_index, +) +from .search_loop import run_grid_search as run_grid_search +from .search_method import ( + AbstractGridSearchMethod as AbstractGridSearchMethod, + MinimumSearchMethod as MinimumSearchMethod, +) diff --git a/src/cryojax/inference/_grid_search/custom_types.py b/src/cryojax/inference/_grid_search/custom_types.py new file mode 100644 index 00000000..a0fe41c3 --- /dev/null +++ b/src/cryojax/inference/_grid_search/custom_types.py @@ -0,0 +1,10 @@ +from typing import TypeAlias, TypeVar + +from jaxtyping import Array, Int, PyTree, Shaped + + +SearchSolution = TypeVar("SearchSolution") +SearchState = TypeVar("SearchState") +PyTreeGrid: TypeAlias = PyTree[Shaped[Array, "_ ..."] | None, " Y"] +PyTreeGridPoint: TypeAlias = PyTree[Shaped[Array, "..."] | None, " Y"] +PyTreeGridIndex: TypeAlias = PyTree[Int[Array, "..."] | None, "... Y"] diff --git a/src/cryojax/inference/_grid_search/pytree_manipulation.py b/src/cryojax/inference/_grid_search/pytree_manipulation.py new file mode 100644 index 00000000..c0e6b1a9 --- /dev/null +++ b/src/cryojax/inference/_grid_search/pytree_manipulation.py @@ -0,0 +1,205 @@ +from collections.abc import Callable +from typing import Any, Optional + +import equinox as eqx +import jax.numpy as jnp +import jax.tree_util as jtu +from jaxtyping import Array, Int, PyTree + +from .custom_types import PyTreeGrid, PyTreeGridIndex, PyTreeGridPoint + + +def tree_grid_shape( + tree_grid: PyTreeGrid, + *, + is_leaf: Optional[Callable[[Any], bool]] = None, +) -> tuple[int, ...]: + """Get the shape of a pytree grid. + + **Arguments:** + + - `tree_grid`: A sparse grid cartesian grid, represented as a pytree. + See [`run_grid_search`][] for more information. + - `is_leaf`: As [`jax.tree_util.tree_flatten`](https://jax.readthedocs.io/en/latest/_autosummary/jax.tree_util.tree_flatten.html). + + **Returns:** + + The shape of `tree_grid`. + + !!! Example + + ```python + # A simple "pytree grid" + simple_tree_grid = (jnp.zeros(10), jnp.zeros(10), jnp.zeros(10)) + # Its shape is just the shape of the cartesian product of its leaves + assert tree_grid_shape(simple_tree_grid) == (10, 10, 10) + ``` + + !!! Example + + ```python + # Library code + import equinox as eqx + import jax + + class SomeModule(eqx.Module): + + a: jax.Array + + # End-user script + # ... create a more complicated grid + complicated_tree_grid = (SomeModule(jnp.zeros(10)), jnp.zeros(10), (jnp.zeros(10), None)) + # Its shape is still just the shape of the cartesian product of its leaves + assert tree_grid_shape(complicated_tree_grid) == (10, 10, 10) + ``` + """ # noqa: E501 + n_leaves = len(jtu.tree_leaves(tree_grid, is_leaf=is_leaf)) + if n_leaves == 0: + raise ValueError( + "The pytree passed to `tree_grid_shape` should have at least " + f"one leaf. The pytree was equal to {tree_grid}, which has " + "no leaves." + ) + else: + _leading_dim_resolver = jtu.tree_map( + _LeafLeadingDimension, tree_grid, is_leaf=is_leaf + ) + _reduce_fn = lambda x, y: ( + x.get() + y.get() if isinstance(x, _LeafLeadingDimension) else x + y.get() + ) + shape = jtu.tree_reduce( + _reduce_fn, + _leading_dim_resolver, + is_leaf=lambda x: isinstance(x, _LeafLeadingDimension), + ) + return shape if n_leaves > 1 else shape.get() + + +def tree_grid_unravel_index( + raveled_index: int | Int[Array, ""] | Int[Array, " _"], + tree_grid: PyTreeGrid, + *, + is_leaf: Optional[Callable[[Any], bool]] = None, +) -> PyTreeGridIndex: + """Get a "grid index" for a pytree grid. + + Roughly, this can be thought of as `jax.numpy.unravel_index`, but with a + pytree grid. See [`tree_grid_take`][] for an example of how to use this + function to sample a grid point. + + **Arguments:** + + - `raveled_index`: A flattened index for `tree_grid`. Simply pass an integer + valued index, as one would with a flattened array. Passing + a 1D array of indices is also supported. + - `tree_grid`: A sparse grid cartesian grid, represented as a pytree. + See [`run_grid_search`][] for more information. + - `is_leaf`: As [`jax.tree_util.tree_flatten`](https://jax.readthedocs.io/en/latest/_autosummary/jax.tree_util.tree_flatten.html). + + **Returns:** + + The grid index. This is a pytree of the same structure as `tree_grid`, with the + result of `jax.numpy.unravel_index(raveled_index, shape)` inserted into the + appropriate leaf. Here, `shape` is given by the output of [`tree_grid_shape`][]. + """ + raveled_index = jnp.asarray(raveled_index) + shape = tree_grid_shape(tree_grid, is_leaf=is_leaf) + # raveled_index = eqx.error_if( + # raveled_index, + # jnp.logical_or(raveled_index < 0, raveled_index >= math.prod(shape)), + # "The flattened grid index must be greater than 0 and less than the " + # f"grid size. Got index {raveled_index}, but the grid has shape {shape}, " + # f"so its maximum index is {math.prod(shape) - 1}.", + # ) + unraveled_index = jnp.unravel_index(raveled_index, shape) + tree_grid_def = jtu.tree_structure(tree_grid, is_leaf=is_leaf) + tree_grid_index = jtu.tree_unflatten(tree_grid_def, unraveled_index) + + return tree_grid_index + + +def tree_grid_take( + tree_grid: PyTreeGrid, + tree_grid_index: PyTreeGridIndex, +) -> PyTreeGridPoint: + """Get a grid point of the pytree grid, given a + pytree grid index. See [`tree_grid_unravel_index`][] to see + how to return a pytree grid index. + + Roughly, this can be thought of as `jax.numpy.take`, but with a + pytree grid. + + **Arguments:** + + - `tree_grid`: A sparse cartesian grid, represented as a pytree. + See [`run_grid_search`][] for more information. + - `tree_grid_index`: An index for `tree_grid`, also represented as a pytree. + See [`tree_grid_unravel_index`][] for more information. + + **Returns:** + + A grid point of a pytree grid. This is a pytree of the same + structure as `tree_grid` (or a prefix of it), where each leaf + is indexed by the leaf at `tree_grid_index`. + + !!! Example + + ```python + # A simple "pytree grid" + simple_tree_grid = (jnp.zeros(10), jnp.zeros(10), jnp.zeros(10)) + # Its shape is just the shape of the cartesian product of its leaves + raveled_index = 7 + tree_grid_index = tree_grid_unravel_index(raveled_index, simple_tree_grid) + tree_grid_point = tree_grid_take(simple_tree_grid, tree_grid_index) + assert tree_grid_point == (jnp.asarray(0.), jnp.asarray(0.), jnp.asarray(0.)) + ``` + """ + tree_grid_point = _tree_take(tree_grid, tree_grid_index, axis=0) + return tree_grid_point + + +def _tree_take( + pytree_of_arrays: PyTree[Array], + pytree_of_indices: PyTree[Int[Array, "..."]], + axis: Optional[int] = None, + mode: Optional[str] = None, + fill_value: Optional[Array] = None, +) -> PyTree[Array]: + return jtu.tree_map( + lambda i, l: _leaf_take(i, l, axis=axis, mode=mode, fill_value=fill_value), + pytree_of_indices, + pytree_of_arrays, + ) + + +def _leaf_take(index, leaf, **kwargs): + _take_fn = lambda array: jnp.take(jnp.atleast_1d(array), index, **kwargs) + if eqx.is_array(leaf): + return _take_fn(leaf) + else: + return jtu.tree_map(_take_fn, leaf) + + +def _get_leading_dim(array): + return (array.shape[0],) + + +class _LeafLeadingDimension(eqx.Module): + _leaf: Any + + def get(self): + if eqx.is_array(self._leaf): + return _get_leading_dim(self._leaf) + else: + leaves = jtu.tree_leaves(self._leaf) + if len(leaves) > 0: + _leading_dim = _get_leading_dim(leaves[0]) + if not all([_get_leading_dim(leaf) == _leading_dim for leaf in leaves]): + raise ValueError( + "Arrays stored in PyTree leaves should share the same " + "leading dimension. Found that this is not true for " + f"leaf {self._leaf}." + ) + return _leading_dim + else: + raise ValueError(f"No arrays found at leaf {self._leaf}") diff --git a/src/cryojax/inference/_grid_search/search_loop.py b/src/cryojax/inference/_grid_search/search_loop.py new file mode 100644 index 00000000..0a427c61 --- /dev/null +++ b/src/cryojax/inference/_grid_search/search_loop.py @@ -0,0 +1,253 @@ +"""The main search loop for the grid search.""" + +import math +from collections.abc import Callable +from typing import Any, Optional + +import equinox as eqx +import equinox.internal as eqxi +import jax +import jax.numpy as jnp +import jax.tree_util as jtu +from jax.experimental import host_callback +from jaxtyping import Array, PyTree +from tqdm.auto import tqdm + +from .custom_types import PyTreeGrid, PyTreeGridPoint +from .pytree_manipulation import ( + tree_grid_shape, + tree_grid_take, + tree_grid_unravel_index, +) +from .search_method import AbstractGridSearchMethod + + +@eqx.filter_jit +def run_grid_search( + fn: Callable[[PyTreeGridPoint, Any], Array], + method: AbstractGridSearchMethod, + tree_grid: PyTreeGrid, + args: Any, + *, + is_leaf: Optional[Callable[[Any], bool]] = None, + progress_bar: bool = False, + print_every: Optional[int] = None, +) -> PyTree[Any]: + """Run a grid search to minimize the function `fn`. + + !!! question "What is a `tree_grid`?" + + For the grid search, we represent the grid as an arbitrary + pytree whose leaves are JAX arrays with a leading dimension. + For a particular leaf, its leading dimension indexes a set + grid points. The entire grid is then the cartesian product + of the grid points of all of its leaves. + + !!! warning + + A `tree_grid` can only have leaves that are JAX arrays of + grid points and `None`. It is difficult to precisely check this + condition even with a run-time type checker, so breaking it may + result in unhelpful errors. + + To learn more, see the `tree_grid` manipulation routines [`tree_grid_shape`][] and + [`tree_grid_take`][]. + + **Arguments:** + + - `fn`: The function we would like to minimize with grid search. This + should be evaluated at arguments `fn(y, args)`, where `y` is a + particular grid point of `tree_grid`. The value returned by `fn` + must be compatible with the respective `method`. + - `method`: An interface that specifies what we would like to do with + each evaluation of `fn`. + - `tree_grid`: The grid as a pytree. Importantly, its leaves can only be JAX + arrays with leading dimensions and `None`. + - `args`: Arguments passed to `fn`, as `fn(y, args)`. + - `is_leaf`: As [`jax.tree_util.tree_flatten`](https://jax.readthedocs.io/en/latest/_autosummary/jax.tree_util.tree_flatten.html). + This specifies what is to be treated as a leaf in `tree_grid`. + - `progress_bar`: Add a [`tqdm`](https://github.com/tqdm/tqdm) progress bar to the + search loop. + - `print_every`: An interval for the number of iterations at which to update the + tqdm progress bar. By default, this is 1/20 of the total number + of iterations. Ignored if `progress_bar = False`. + + **Returns:** + + Any pytree, as specified by the method `AbstractGridSearchMethod.postprocess`. + """ + # Evaluate the shape and dtype of the output of `fn` using + # eqx.filter_closure_convert + test_tree_grid_point = tree_grid_take( + tree_grid, + tree_grid_unravel_index(0, tree_grid, is_leaf=is_leaf), + ) + fn = eqx.filter_closure_convert(fn, test_tree_grid_point, args) + f_struct = jtu.tree_map( + lambda x: x.value, + jtu.tree_map(eqxi.Static, fn.out_struct), + is_leaf=lambda x: isinstance(x, eqxi.Static), + ) + # Get the initial state of the search method + init_state = method.init(tree_grid, f_struct, is_leaf=is_leaf) + dynamic_init_state, static_state = eqx.partition(init_state, eqx.is_array) + # Finally, build the loop + init_carry = (dynamic_init_state, tree_grid) + + def brute_force_body_fun(iteration_index, carry): + dynamic_state, tree_grid = carry + state = eqx.combine(static_state, dynamic_state) + tree_grid_point = tree_grid_take( + tree_grid, + tree_grid_unravel_index(iteration_index, tree_grid, is_leaf=is_leaf), + ) + new_state = method.update(fn, tree_grid_point, args, state, iteration_index) + new_dynamic_state, new_static_state = eqx.partition(new_state, eqx.is_array) + assert eqx.tree_equal(static_state, new_static_state) is True + return new_dynamic_state, tree_grid + + def batched_body_fun(iteration_index, carry): + dynamic_state, tree_grid = carry + state = eqx.combine(static_state, dynamic_state) + raveled_grid_index_batch = jnp.linspace( + iteration_index * method.batch_size, + (iteration_index + 1) * method.batch_size - 1, + method.batch_size, # type: ignore + dtype=int, + ) + tree_grid_points = tree_grid_take( + tree_grid, + tree_grid_unravel_index(raveled_grid_index_batch, tree_grid, is_leaf=is_leaf), + ) + new_state = method.batch_update( + fn, tree_grid_points, args, state, raveled_grid_index_batch + ) + new_dynamic_state, new_static_state = eqx.partition(new_state, eqx.is_array) + assert eqx.tree_equal(static_state, new_static_state) is True + return new_dynamic_state, tree_grid + + # Get the number of iterations of the loop (the size of the grid) + grid_size = math.prod(tree_grid_shape(tree_grid, is_leaf=is_leaf)) + if method.batch_size is None: + n_iterations = grid_size + body_fun = brute_force_body_fun + else: + if grid_size % method.batch_size != 0: + raise ValueError( + "The size of the grid must be an integer multiple " + "of the `method.batch_size`. Found that the grid size " + f"is equal to {grid_size}, and the batch size is equal " + f"to {method.batch_size}." + ) + n_iterations = grid_size // method.batch_size + body_fun = batched_body_fun + # Run and unpack results + if progress_bar: + body_fun = _loop_tqdm(n_iterations, print_every)(body_fun) + final_carry = jax.lax.fori_loop(0, n_iterations, body_fun, init_carry) + dynamic_final_state, _ = final_carry + final_state = eqx.combine(static_state, dynamic_final_state) + # Return the solution + solution = method.postprocess(tree_grid, final_state, f_struct, is_leaf=is_leaf) + return solution + + +def _loop_tqdm( + n_iterations: int, + print_every: Optional[int] = None, + **kwargs, +) -> Callable: + """Add a tqdm progress bar to `body_fun` used in `jax.lax.fori_loop`. + This function is based on the implementation in [`jax_tqdm`](https://github.com/jeremiecoullon/jax-tqdm) + """ + + _update_progress_bar, close_tqdm = _build_tqdm(n_iterations, print_every, **kwargs) + + def _fori_loop_tqdm_decorator(func): + def wrapper_progress_bar(i, val): + _update_progress_bar(i) + result = func(i, val) + return close_tqdm(result, i) + + return wrapper_progress_bar + + return _fori_loop_tqdm_decorator + + +def _build_tqdm( + n_iterations: int, + print_every: Optional[int] = None, + **kwargs, +) -> tuple[Callable, Callable]: + """Build the tqdm progress bar on the host.""" + + desc = kwargs.pop("desc", f"Running for {n_iterations:,} iterations") + message = kwargs.pop("message", desc) + for kwarg in ("total", "mininterval", "maxinterval", "miniters"): + kwargs.pop(kwarg, None) + + tqdm_bars = {} + + if print_every is None: + if n_iterations > 20: + print_every = int(n_iterations / 20) + else: + print_every = 1 + else: + if print_every < 1: + raise ValueError( + "The number of iterations per progress bar update should " + f"be greater than 0. Got {print_every}." + ) + elif print_every > n_iterations: + raise ValueError( + "The number of iterations per progress bar update should be less " + f"than the number of iterations, equal to {n_iterations}. " + f"Got {print_every}." + ) + + remainder = n_iterations % print_every + + def _define_tqdm(arg, transform): + tqdm_bars[0] = tqdm(range(n_iterations), **kwargs) + tqdm_bars[0].set_description(message, refresh=False) + + def _update_tqdm(arg, transform): + tqdm_bars[0].update(arg) + + def _update_progress_bar(iter_num): + _ = jax.lax.cond( + iter_num == 0, + lambda _: host_callback.id_tap(_define_tqdm, None, result=iter_num), + lambda _: iter_num, + operand=None, + ) + + _ = jax.lax.cond( + # update tqdm every multiple of `print_rate` except at the end + (iter_num % print_every == 0) & (iter_num != n_iterations - remainder), + lambda _: host_callback.id_tap(_update_tqdm, print_every, result=iter_num), + lambda _: iter_num, + operand=None, + ) + + _ = jax.lax.cond( + # update tqdm by `remainder` + iter_num == n_iterations - remainder, + lambda _: host_callback.id_tap(_update_tqdm, remainder, result=iter_num), + lambda _: iter_num, + operand=None, + ) + + def _close_tqdm(arg, transform): + tqdm_bars[0].close() + + def close_tqdm(result, iter_num): + return jax.lax.cond( + iter_num == n_iterations - 1, + lambda _: host_callback.id_tap(_close_tqdm, None, result=result), + lambda _: result, + operand=None, + ) + + return _update_progress_bar, close_tqdm diff --git a/src/cryojax/inference/_grid_search/search_method.py b/src/cryojax/inference/_grid_search/search_method.py new file mode 100644 index 00000000..51accc41 --- /dev/null +++ b/src/cryojax/inference/_grid_search/search_method.py @@ -0,0 +1,283 @@ +"""An interface for a grid search method.""" + +import math +from abc import abstractmethod +from typing import Any, Callable, Generic, Optional + +import equinox as eqx +import jax +import jax.numpy as jnp +import jax.tree_util as jtu +from jaxtyping import Array, Int, PyTree + +from .custom_types import PyTreeGrid, PyTreeGridPoint, SearchSolution, SearchState +from .pytree_manipulation import ( + tree_grid_shape, + tree_grid_take, + tree_grid_unravel_index, +) + + +class AbstractGridSearchMethod( + eqx.Module, Generic[SearchState, SearchSolution], strict=True +): + """An abstract interface that determines the behavior of the grid + search. + """ + + batch_size: eqx.AbstractVar[Optional[int]] + + @abstractmethod + def init( + self, + tree_grid: PyTreeGrid, + f_struct: PyTree[jax.ShapeDtypeStruct], + *, + is_leaf: Optional[Callable[[Any], bool]] = None, + ) -> SearchState: + """Initialize the state of the search method. + + **Arguments:** + + - `tree_grid`: As [`run_grid_search`][]. + - `f_struct`: A container that stores the `shape` and `dtype` + returned by `fn`. + - `is_leaf`: As [`run_grid_search`][]. + + **Returns:** + + Any pytree that represents the state of the grid search. + """ + raise NotImplementedError + + @abstractmethod + def update( + self, + fn: Callable[[PyTreeGridPoint, Any], Array], + tree_grid_point: PyTreeGridPoint, + args: Any, + state: SearchState, + raveled_grid_index: Int[Array, ""], + ) -> SearchState: + """Update the state of the grid search. + + **Arguments:** + + - `fn`: As [`run_grid_search`][]. + - `tree_grid_point`: The grid point at which to evaluate `fn`. Specifically, + `fn` is evaluated as `fn(tree_grid_point, args)`. + - `args`: As [`run_grid_search`][]. + - `state`: The current state of the search. + - `raveled_grid_index`: The current index of the grid. This is + used to index `tree_grid` to extract the + `tree_grid_point`. + + **Returns:** + + The updated state of the grid search. + """ + raise NotImplementedError + + @abstractmethod + def batch_update( + self, + fn: Callable[[PyTreeGridPoint, Any], Array], + tree_grid_point_batch: PyTreeGridPoint, + args: Any, + state: SearchState, + raveled_grid_index_batch: Int[Array, " _"], + ) -> SearchState: + """Update the state of the grid search with a batch of grid points as + input. + + **Arguments:** + + - `fn`: As [`run_grid_search`][]. + - `tree_grid_point_batch`: The grid points at which to evaluate `fn` in + parallel. + - `args`: As [`run_grid_search`][]. + - `state`: The current state of the search. + - `raveled_grid_index_batch`: The current batch of indices on which to evaluate + the grid. + + **Returns:** + + The updated state of the grid search. + """ + raise NotImplementedError + + @abstractmethod + def postprocess( + self, + tree_grid: PyTreeGrid, + final_state: SearchState, + f_struct: PyTree[jax.ShapeDtypeStruct], + *, + is_leaf: Optional[Callable[[Any], bool]] = None, + ) -> SearchSolution: + """Post-process the final state of the grid search into a + solution. + + **Arguments:** + + - `tree_grid`: As [`run_grid_search`][]. + - `final_state`: The final state of the grid search. + - `f_struct`: A container that stores the `shape` and `dtype` + returned by `fn`. + - `is_leaf`: As [`run_grid_search`][]. + + **Returns:** + + Any pytree that represents the solution of the grid search. + """ + raise NotImplementedError + + +class MinimumState(eqx.Module, strict=True): + current_minimum_eval: Array + current_best_raveled_index: Array + + +class MinimumSolution(eqx.Module, strict=True): + value: Optional[PyTreeGridPoint] + stats: dict[str, Any] + state: MinimumState + + +class MinimumSearchMethod( + AbstractGridSearchMethod[MinimumState, MinimumSolution], strict=True +): + """Simply find the minimum value returned by `fn` over all grid points. + + The minimization is done *elementwise* for the output returned by `fn(y, args)`. + This allows for more clever grid searches than a brute-force approach--for example, + `fn` can explore its own region of parameter space in parallel. + """ + + get_solution_value: bool + batch_size: Optional[int] + + def __init__( + self, *, get_solution_value: bool = True, batch_size: Optional[int] = None + ): + """**Arguments:** + + - `get_solution_value`: If `True`, the grid search solution will contain the + best grid point found. If `False`, only the flattened + index corresponding to these grid points are returned + and [`tree_grid_take`][] must be used to extract the + actual grid points. Setting this to `False` may be + necessary if the grid contains large arrays. + - `batch_size`: The stride of grid points over which to evaluate in parallel. + """ + self.get_solution_value = get_solution_value + self.batch_size = batch_size + + def init( + self, + tree_grid: PyTreeGrid, + f_struct: PyTree[jax.ShapeDtypeStruct], + *, + is_leaf: Optional[Callable[[Any], bool]] = None, + ) -> MinimumState: + # Initialize the state, just keeping track of the best function values + # and their respective grid index + state = MinimumState( + current_minimum_eval=jnp.full(f_struct.shape, jnp.inf), + current_best_raveled_index=jnp.full(f_struct.shape, 0, dtype=int), + ) + return state + + def update( + self, + fn: Callable[[PyTreeGridPoint, Any], Array], + tree_grid_point: PyTreeGridPoint, + args: Any, + state: MinimumState, + raveled_grid_index: Int[Array, ""], + ) -> MinimumState: + # Evaluate the function + value = fn(tree_grid_point, args) + # Unpack the current state + last_minimum_value = state.current_minimum_eval + last_best_raveled_index = state.current_best_raveled_index + # Update the minimum and best grid index, elementwise + is_less_than_last_minimum = value < last_minimum_value + current_minimum_eval = jnp.where( + is_less_than_last_minimum, value, last_minimum_value + ) + current_best_raveled_index = jnp.where( + is_less_than_last_minimum, raveled_grid_index, last_best_raveled_index + ) + return MinimumState(current_minimum_eval, current_best_raveled_index) + + def batch_update( + self, + fn: Callable[[PyTreeGridPoint, Any], Array], + tree_grid_point_batch: PyTreeGridPoint, + args: Any, + state: MinimumState, + raveled_grid_index_batch: Int[Array, " _"], + ) -> MinimumState: + # Evaluate the batch of grid points and extract the best one + value_batch = jax.vmap(fn, in_axes=[0, None])(tree_grid_point_batch, args) + best_batch_index = jnp.argmin(value_batch, axis=0) + raveled_grid_index = jnp.take(raveled_grid_index_batch, best_batch_index) + value = jnp.amin(value_batch, axis=0) + # Unpack the current state + last_minimum_value = state.current_minimum_eval + last_best_raveled_index = state.current_best_raveled_index + # Update the minimum and best grid index, elementwise + is_less_than_last_minimum = value < last_minimum_value + current_minimum_eval = jnp.where( + is_less_than_last_minimum, value, last_minimum_value + ) + current_best_raveled_index = jnp.where( + is_less_than_last_minimum, raveled_grid_index, last_best_raveled_index + ) + return MinimumState(current_minimum_eval, current_best_raveled_index) + + def postprocess( + self, + tree_grid: PyTreeGrid, + final_state: MinimumState, + f_struct: PyTree[jax.ShapeDtypeStruct], + *, + is_leaf: Optional[Callable[[Any], bool]] = None, + ) -> MinimumSolution: + # Make sure that shapes did not get modified during loop + if final_state.current_best_raveled_index.shape != f_struct.shape: + raise ValueError( + "The shape of the search state solution does " + "not match the shape of the output of `fn`. Got " + f"output shape {f_struct.shape} for `fn`, but got " + f"shape {final_state.current_best_raveled_index.shape} for the " + "solution." + ) + if self.get_solution_value: + # Extract the solution of the search, i.e. the grid point(s) corresponding + # to the raveled grid index + if f_struct.shape == (): + raveled_index = final_state.current_best_raveled_index + else: + raveled_index = final_state.current_best_raveled_index.ravel() + # ... get the pytree representation of the index + tree_grid_index = tree_grid_unravel_index( + raveled_index, tree_grid, is_leaf=is_leaf + ) + # ... index the full grid, reshaping the solution's leaves to be the same + # shape as returned by `fn` + _reshape_fn = lambda x: ( + x.reshape((*f_struct.shape, *x.shape[1:])) + if x.ndim > 1 + else x.reshape(f_struct.shape) + ) + value = jtu.tree_map(_reshape_fn, tree_grid_take(tree_grid, tree_grid_index)) + else: + value = None + # ... build and return the solution + return MinimumSolution( + value, + {"n_iterations": math.prod(tree_grid_shape(tree_grid, is_leaf=is_leaf))}, + final_state, + ) diff --git a/src/cryojax/inference/transforms/__init__.py b/src/cryojax/inference/_transforms/__init__.py similarity index 80% rename from src/cryojax/inference/transforms/__init__.py rename to src/cryojax/inference/_transforms/__init__.py index 21be6fa7..833b9740 100644 --- a/src/cryojax/inference/transforms/__init__.py +++ b/src/cryojax/inference/_transforms/__init__.py @@ -1,14 +1,13 @@ -from ._lie_group_transforms import ( +from .lie_group_transforms import ( AbstractLieGroupTransform as AbstractLieGroupTransform, apply_updates_with_lie_transform as apply_updates_with_lie_transform, SE3Transform as SE3Transform, SO3Transform as SO3Transform, ) -from ._transforms import ( +from .transforms import ( AbstractParameterTransform as AbstractParameterTransform, ComposedTransform as ComposedTransform, ExpTransform as ExpTransform, - insert_transforms as insert_transforms, RescalingTransform as RescalingTransform, resolve_transforms as resolve_transforms, ) diff --git a/src/cryojax/inference/transforms/_lie_group_transforms.py b/src/cryojax/inference/_transforms/lie_group_transforms.py similarity index 97% rename from src/cryojax/inference/transforms/_lie_group_transforms.py rename to src/cryojax/inference/_transforms/lie_group_transforms.py index 67acb5ff..16ee4d31 100644 --- a/src/cryojax/inference/transforms/_lie_group_transforms.py +++ b/src/cryojax/inference/_transforms/lie_group_transforms.py @@ -17,7 +17,7 @@ from ...rotations import AbstractMatrixLieGroup, SE3, SO3 from ...simulator import QuaternionPose -from ._transforms import AbstractParameterTransform +from .transforms import AbstractParameterTransform def _apply_update_with_lie_transform(u, p): @@ -68,7 +68,6 @@ class SO3Transform(AbstractLieGroupTransform, strict=True): **Attributes:** - `transformed_parameter`: The local tangent vector. - - `group_element`: The element of SO3. """ @@ -100,7 +99,6 @@ class SE3Transform(AbstractLieGroupTransform, strict=True): **Attributes:** - `transformed_parameter`: The local tangent vector. - - `group_element`: The element of SE3. """ @@ -123,9 +121,7 @@ def __init__(self, quaternion_pose: QuaternionPose): def get(self) -> Float[Array, "6"]: """An implementation of the `jaxlie.manifold.rplus`.""" local_tangent = self.transformed_parameter - group_element = jax.lax.stop_gradient(self.group_element) @ SE3.exp( - local_tangent - ) + group_element = jax.lax.stop_gradient(self.group_element) @ SE3.exp(local_tangent) return QuaternionPose.from_rotation_and_translation( group_element.rotation, group_element.xyz ) diff --git a/src/cryojax/inference/transforms/_transforms.py b/src/cryojax/inference/_transforms/transforms.py similarity index 65% rename from src/cryojax/inference/transforms/_transforms.py rename to src/cryojax/inference/_transforms/transforms.py index 5a267211..f6a52692 100644 --- a/src/cryojax/inference/transforms/_transforms.py +++ b/src/cryojax/inference/_transforms/transforms.py @@ -3,8 +3,7 @@ """ from abc import abstractmethod -from typing import Any, Callable, Optional, Sequence, Union -from typing_extensions import overload +from typing import Any, Callable, Sequence import equinox as eqx import jax @@ -13,7 +12,7 @@ from equinox import AbstractVar, field, Module from jaxtyping import Array, Float, PyTree -from ...core import error_if_not_positive, error_if_zero +from ..._errors import error_if_not_positive, error_if_zero def _is_transformed(x: Any) -> bool: @@ -27,76 +26,6 @@ def _resolve_transform(x: Any) -> Any: return x -def _apply_transform( - pytree: PyTree, - where: Callable[[PyTree], Union[Any, Sequence[Any]]], - replace_fn: Callable[[Any], "AbstractParameterTransform"], - is_leaf: Optional[Callable[[Any], bool]] = None, -) -> PyTree: - return eqx.tree_at(where, pytree, replace_fn=replace_fn, is_leaf=is_leaf) - - -@overload -def insert_transforms( - pytree: PyTree, - wheres: Sequence[Callable[[PyTree], Union[Any, Sequence[Any]]]], - replace_fns: Sequence[Callable[[Any], "AbstractParameterTransform"]], - *, - is_leaf: Optional[Callable[[Any], bool]] = None, -) -> PyTree: ... - - -@overload -def insert_transforms( - pytree: PyTree, - wheres: Callable[[PyTree], Union[Any, Sequence[Any]]], - replace_fns: Callable[[Any], "AbstractParameterTransform"], - *, - is_leaf: Optional[Callable[[Any], bool]] = None, -) -> PyTree: ... - - -def insert_transforms( - pytree: PyTree, - wheres: ( - Callable[[PyTree], Union[Any, Sequence[Any]]] - | Sequence[Callable[[PyTree], Union[Any, Sequence[Any]]]] - ), - replace_fns: ( - Callable[[Any], "AbstractParameterTransform"] - | Sequence[Callable[[Any], "AbstractParameterTransform"]] - ), - *, - is_leaf: Optional[Callable[[Any], bool]] = None, -) -> PyTree: - """Applies an `AbstractParameterTransform` to pytree node(s). - - This function performs a sequence of `equinox.tree_at` calls to apply each - `replace_fn` in `replace_fns` to each `where` in `wheres`. - """ - if isinstance(replace_fns, Callable) and isinstance(wheres, Callable): - where, replace_fn = wheres, replace_fns - return _apply_transform(pytree, where, replace_fn, is_leaf=is_leaf) - elif isinstance(replace_fns, Sequence) and isinstance(wheres, Sequence): - if len(replace_fns) != len(wheres): - raise TypeError( - "If arguments `wheres` and `replace_fns` are sequences, they " - "must be sequences of the same length. Got " - f"`wheres, replace_fns = {wheres}, {replace_fns}`." - ) - transformed_pytree = pytree - for where, replace_fn in zip(wheres, replace_fns): - transformed_pytree = _apply_transform( - pytree, where, replace_fn, is_leaf=is_leaf - ) - return transformed_pytree - else: - raise TypeError( - "Input arguments `wheres` and `replace_fns` must both either be functions " - f"or sequences. Got `wheres, replace_fns = {wheres}, {replace_fns}`." - ) - - def resolve_transforms(pytree: PyTree) -> PyTree: """Transforms a pytree whose parameters have entries that are `AbstractParameterTransform`s back to its @@ -173,9 +102,7 @@ def __init__( """**Arguments:** - `parameter`: The parameter to be rescaled. - - `scaling`: The scale factor. - - `shift`: The shift. """ self.scaling = jnp.asarray(scaling) @@ -195,7 +122,6 @@ class ComposedTransform(AbstractParameterTransform, strict=True): **Attributes:** - `transformed_parameter`: The transformed parameter. - - `transforms`: The sequence of `AbstractParameterTransform`s. """ @@ -210,7 +136,6 @@ def __init__( """**Arguments:** - `parameter`: The parameter to be transformed. - - `transform_fns`: A sequence of functions that take in a parameter and return an `AbstractParameterTransform`. """ diff --git a/src/cryojax/inference/distributions/__init__.py b/src/cryojax/inference/distributions/__init__.py index 0a287f6e..be3036b7 100644 --- a/src/cryojax/inference/distributions/__init__.py +++ b/src/cryojax/inference/distributions/__init__.py @@ -1,7 +1,7 @@ -from ._distribution import ( +from ._base_distribution import ( AbstractDistribution as AbstractDistribution, AbstractMarginalDistribution as AbstractMarginalDistribution, ) from ._gaussian_distributions import ( - IndependentFourierGaussian as IndependentFourierGaussian, + IndependentGaussianFourierModes as IndependentGaussianFourierModes, ) diff --git a/src/cryojax/inference/distributions/_distribution.py b/src/cryojax/inference/distributions/_base_distribution.py similarity index 71% rename from src/cryojax/inference/distributions/_distribution.py rename to src/cryojax/inference/distributions/_base_distribution.py index ed13c310..61ef15df 100644 --- a/src/cryojax/inference/distributions/_distribution.py +++ b/src/cryojax/inference/distributions/_base_distribution.py @@ -4,7 +4,7 @@ from abc import abstractmethod -from equinox import AbstractVar, Module +from equinox import Module from jaxtyping import Array, Float, Inexact, PRNGKeyArray @@ -12,9 +12,7 @@ class AbstractDistribution(Module, strict=True): """An image formation model equipped with a probabilistic model.""" @abstractmethod - def log_likelihood( - self, observed: Inexact[Array, "y_dim x_dim"] - ) -> Float[Array, ""]: + def log_likelihood(self, observed: Inexact[Array, "y_dim x_dim"]) -> Float[Array, ""]: """Evaluate the log likelihood. **Arguments:** @@ -25,27 +23,25 @@ def log_likelihood( @abstractmethod def sample( - self, key: PRNGKeyArray, *, get_real: bool = True + self, rng_key: PRNGKeyArray, *, get_real: bool = True ) -> Inexact[Array, "y_dim x_dim"]: """Sample from the distribution. **Arguments:** - - `key` : The RNG key or key(s). See `AbstractPipeline.sample` for + - `rng_key` : The RNG key or key(s). See `AbstractPipeline.sample` for more documentation. """ raise NotImplementedError @abstractmethod - def render(self, *, get_real: bool = True) -> Inexact[Array, "y_dim x_dim"]: + def compute_signal(self, *, get_real: bool = True) -> Inexact[Array, "y_dim x_dim"]: """Render the image formation model.""" raise NotImplementedError class AbstractMarginalDistribution(AbstractDistribution, strict=True): - """An image formation model equipped with a probabilistic model.""" - - distribution: AbstractVar[AbstractDistribution] + """An `AbstractDistribution` equipped with a marginalized likelihood.""" @abstractmethod def marginal_log_likelihood( diff --git a/src/cryojax/inference/distributions/_gaussian_distributions.py b/src/cryojax/inference/distributions/_gaussian_distributions.py index 6a5c203a..99aacffe 100644 --- a/src/cryojax/inference/distributions/_gaussian_distributions.py +++ b/src/cryojax/inference/distributions/_gaussian_distributions.py @@ -7,76 +7,116 @@ import jax.numpy as jnp import jax.random as jr -import numpy as np -from equinox import field from jaxtyping import Array, Complex, Float, PRNGKeyArray -from ...core import error_if_not_positive +from ..._errors import error_if_not_positive +from ...image import rescale_image from ...image.operators import Constant, FourierOperatorLike -from ...simulator import AbstractPipeline -from ._distribution import AbstractDistribution +from ...simulator import AbstractImagingPipeline +from ._base_distribution import AbstractDistribution -class IndependentFourierGaussian(AbstractDistribution, strict=True): +class IndependentGaussianFourierModes(AbstractDistribution, strict=True): r"""A gaussian noise model, where each fourier mode is independent. This computes the likelihood in Fourier space, so that the variance to be an arbitrary noise power spectrum. """ - pipeline: AbstractPipeline - variance: FourierOperatorLike - contrast_scale: Float[Array, ""] = field(converter=error_if_not_positive) + imaging_pipeline: AbstractImagingPipeline + variance_function: FourierOperatorLike + signal_scale_factor: Float[Array, ""] def __init__( self, - pipeline: AbstractPipeline, - variance: Optional[FourierOperatorLike] = None, - contrast_scale: float | Float[Array, ""] = 1.0, + imaging_pipeline: AbstractImagingPipeline, + variance_function: Optional[FourierOperatorLike] = None, + signal_scale_factor: Optional[float | Float[Array, ""]] = None, ): """**Arguments:** - - `pipeline`: The image formation model. - - `variance`: The variance of each fourier mode. By default, - `cryojax.image.operators.Constant(1.0)`. - - `contrast_scale`: The standard deviation of an image simulated - from `pipeline`, excluding the noise. By default, - `1.0`. - """ - self.pipeline = pipeline - self.variance = variance or Constant(1.0) - self.contrast_scale = jnp.asarray(contrast_scale) + - `imaging_pipeline`: The image formation model. + - `variance_function`: The variance of each fourier mode. By default, + `cryojax.image.operators.Constant(1.0)`. + - `signal_scale_factor`: A scale factor for the standard deviation of the + underlying signal simulated from `imaging_pipeline`. + The standard deviation of the signal is rescaled to be + equal to `signal_scale_factor / jnp.sqrt(n_pixels)`, + where the inverse square root of `n_pixels` is included + so that the scale of the signal does not depend on the + number of pixels. As a result, a good starting value for + `signal_scale_factor` should be on the order of the + extent of the object in pixels. By default, + `signal_scale_factor = sqrt(imaging_pipeline.instrument_config.n_pixels)`. + """ # noqa: E501 + self.imaging_pipeline = imaging_pipeline + self.variance_function = variance_function or Constant(1.0) + if signal_scale_factor is None: + signal_scale_factor = jnp.sqrt( + jnp.asarray(imaging_pipeline.instrument_config.n_pixels, dtype=float) + ) + self.signal_scale_factor = error_if_not_positive(jnp.asarray(signal_scale_factor)) @override - def render( + def compute_signal( self, *, get_real: bool = True ) -> ( - Float[Array, "{self.pipeline.config.y_dim} {self.pipeline.config.x_dim}"] - | Complex[Array, "{self.pipeline.config.y_dim} {self.config.x_dim//2+1}"] + Float[ + Array, + "{self.imaging_pipeline.instrument_config.y_dim} " + "{self.imaging_pipeline.instrument_config.x_dim}", + ] + | Complex[ + Array, + "{self.imaging_pipeline.instrument_config.y_dim}" + " {self.imaging_pipeline.instrument_config.x_dim//2+1}", + ] ): """Render the image formation model.""" - return self.contrast_scale * self.pipeline.render( - normalize=True, get_real=get_real + n_pixels = self.imaging_pipeline.instrument_config.n_pixels + shape = self.imaging_pipeline.instrument_config.shape + simulated_image = self.imaging_pipeline.render(get_real=get_real) + return rescale_image( + simulated_image, + std=self.signal_scale_factor / jnp.sqrt(n_pixels), + mean=0.0, + is_real=get_real, + shape_in_real_space=shape, ) @override def sample( - self, key: PRNGKeyArray, *, get_real: bool = True + self, rng_key: PRNGKeyArray, *, get_real: bool = True ) -> ( - Float[Array, "{self.pipeline.config.y_dim} {self.pipeline.config.x_dim}"] - | Complex[Array, "{self.pipeline.config.y_dim} {self.config.x_dim//2+1}"] + Float[ + Array, + "{self.imaging_pipeline.instrument_config.y_dim} " + "{self.imaging_pipeline.instrument_config.x_dim}", + ] + | Complex[ + Array, + "{self.imaging_pipeline.instrument_config.y_dim} " + "{self.imaging_pipeline.instrument_config.x_dim//2+1}", + ] ): """Sample from the gaussian noise model.""" - N_pix = np.prod(self.pipeline.config.padded_shape) - freqs = self.pipeline.config.wrapped_padded_frequency_grid_in_angstroms.get() + pipeline = self.imaging_pipeline + freqs = ( + pipeline.instrument_config.wrapped_padded_frequency_grid_in_angstroms.get() + ) # Compute the zero mean variance and scale up to be independent of the number of # pixels - std = jnp.sqrt(N_pix * self.variance(freqs)) - noise = self.pipeline.crop_and_apply_operators( - std * jr.normal(key, shape=freqs.shape[0:-1]).at[0, 0].set(0.0), + padded_n_pixels = pipeline.instrument_config.padded_n_pixels + std = jnp.sqrt(padded_n_pixels * self.variance_function(freqs)) + noise = pipeline.postprocess( + std + * jr.normal(rng_key, shape=freqs.shape[0:-1]) + .at[0, 0] + .set(0.0) + .astype(complex), get_real=get_real, ) - image = self.render(get_real=get_real) + image = self.compute_signal(get_real=get_real) return image + noise @override @@ -84,7 +124,8 @@ def log_likelihood( self, observed: Complex[ Array, - "{self.pipeline.config.y_dim} {self.pipeline.config.x_dim//2+1}", + "{self.imaging_pipeline.instrument_config.y_dim} " + "{self.imaging_pipeline.instrument_config.x_dim//2+1}", ], ) -> Float[Array, ""]: """Evaluate the log-likelihood of the gaussian noise model. @@ -93,12 +134,13 @@ def log_likelihood( - `observed` : The observed data in fourier space. """ - N_pix = np.prod(self.pipeline.config.shape) - freqs = self.pipeline.config.wrapped_frequency_grid_in_angstroms.get() + pipeline = self.imaging_pipeline + n_pixels = pipeline.instrument_config.n_pixels + freqs = pipeline.instrument_config.wrapped_frequency_grid_in_angstroms.get() # Compute the variance and scale up to be independent of the number of pixels - variance = N_pix * self.variance(freqs) + variance = n_pixels * self.variance_function(freqs) # Create simulated data - simulated = self.render(get_real=False) + simulated = self.compute_signal(get_real=False) # Compute residuals residuals = simulated - observed # Compute standard normal random variables @@ -108,7 +150,7 @@ def log_likelihood( # real space (parseval's theorem) log_likelihood_per_mode = ( squared_standard_normal_per_mode - jnp.log(2 * jnp.pi * variance) / 2 - ) / N_pix + ) / n_pixels # Compute log-likelihood, throwing away the zero mode. Need to take care # to compute the loss function in fourier space for a real-valued function. log_likelihood = -1.0 * ( diff --git a/src/cryojax/rotations/__init__.py b/src/cryojax/rotations/__init__.py index fca3f0ac..a2ad2b06 100644 --- a/src/cryojax/rotations/__init__.py +++ b/src/cryojax/rotations/__init__.py @@ -4,3 +4,6 @@ SO3 as SO3, ) from ._rotation import AbstractRotation as AbstractRotation +from ._utils import ( + convert_quaternion_to_euler_angles as convert_quaternion_to_euler_angles, +) diff --git a/src/cryojax/rotations/_lie_group.py b/src/cryojax/rotations/_lie_group.py index cced3935..c41a160d 100644 --- a/src/cryojax/rotations/_lie_group.py +++ b/src/cryojax/rotations/_lie_group.py @@ -106,9 +106,7 @@ def compose(self, other: Self) -> Self: def inverse(self) -> Self: # Negate complex terms. - return eqx.tree_at( - lambda R: R.wxyz, self, self.wxyz * jnp.array([1, -1, -1, -1]) - ) + return eqx.tree_at(lambda R: R.wxyz, self, self.wxyz * jnp.array([1, -1, -1, -1])) @classmethod def from_x_radians(cls, angle: Float[Array, ""]) -> Self: @@ -306,9 +304,7 @@ def adjoint(self) -> Float[Array, "3 3"]: @override def normalize(self) -> Self: - return eqx.tree_at( - lambda R: R.wxyz, self, self.wxyz / jnp.linalg.norm(self.wxyz) - ) + return eqx.tree_at(lambda R: R.wxyz, self, self.wxyz / jnp.linalg.norm(self.wxyz)) @classmethod def sample_uniform(cls, key: PRNGKeyArray) -> Self: diff --git a/src/cryojax/rotations/_utils.py b/src/cryojax/rotations/_utils.py new file mode 100644 index 00000000..7054718e --- /dev/null +++ b/src/cryojax/rotations/_utils.py @@ -0,0 +1,59 @@ +import jax.numpy as jnp +from jaxtyping import Array, Float + + +def convert_quaternion_to_euler_angles( + wxyz: Float[Array, "4"], convention: str = "zyz" +) -> Float[Array, "3"]: + """Convert a quaternion to a sequence of euler angles about an extrinsic + coordinate system. + + Adapted from https://github.com/chrisflesher/jax-scipy-spatial/. + """ + if len(convention) != 3 or not all([axis in ["x", "y", "z"] for axis in convention]): + raise ValueError( + f"`convention` should be a string of three characters, each " + f"of which is 'x', 'y', or 'z'. Instead, got '{convention}'" + ) + if convention[0] == convention[1] or convention[1] == convention[2]: + raise ValueError( + f"`convention` cannot have axes repeating in a row. For example, " + f"'xxy' or 'zzz' are not allowed. Got '{convention}'." + ) + xyz_axis_to_array_axis = {"x": 0, "y": 1, "z": 2} + axes = [xyz_axis_to_array_axis[axis] for axis in convention] + xyzw = jnp.roll(wxyz, shift=-1) + angle_first = 0 + angle_third = 2 + i = axes[0] + j = axes[1] + k = axes[2] + symmetric = i == k + k = jnp.where(symmetric, 3 - i - j, k) + sign = jnp.array((i - j) * (j - k) * (k - i) // 2, dtype=xyzw.dtype) + eps = 1e-7 + a = jnp.where(symmetric, xyzw[3], xyzw[3] - xyzw[j]) + b = jnp.where(symmetric, xyzw[i], xyzw[i] + xyzw[k] * sign) + c = jnp.where(symmetric, xyzw[j], xyzw[j] + xyzw[3]) + d = jnp.where(symmetric, xyzw[k] * sign, xyzw[k] * sign - xyzw[i]) + angles = jnp.empty(3, dtype=xyzw.dtype) + angles = angles.at[1].set(2 * jnp.arctan2(jnp.hypot(c, d), jnp.hypot(a, b))) + case = jnp.where(jnp.abs(angles[1] - jnp.pi) <= eps, 2, 0) + case = jnp.where(jnp.abs(angles[1]) <= eps, 1, case) + half_sum = jnp.arctan2(b, a) + half_diff = jnp.arctan2(d, c) + angles = angles.at[0].set( + jnp.where(case == 1, 2 * half_sum, 2 * half_diff * -1) + ) # any degenerate case + angles = angles.at[angle_first].set( + jnp.where(case == 0, half_sum - half_diff, angles[angle_first]) + ) + angles = angles.at[angle_third].set( + jnp.where(case == 0, half_sum + half_diff, angles[angle_third]) + ) + angles = angles.at[angle_third].set( + jnp.where(symmetric, angles[angle_third], angles[angle_third] * sign) + ) + angles = angles.at[1].set(jnp.where(symmetric, angles[1], angles[1] - jnp.pi / 2)) + angles = (angles + jnp.pi) % (2 * jnp.pi) - jnp.pi + return -jnp.rad2deg(angles) diff --git a/src/cryojax/simulator/__init__.py b/src/cryojax/simulator/__init__.py index ffc2d670..f8813b42 100644 --- a/src/cryojax/simulator/__init__.py +++ b/src/cryojax/simulator/__init__.py @@ -2,53 +2,39 @@ AbstractAssembly as AbstractAssembly, compute_helical_lattice_positions as compute_helical_lattice_positions, compute_helical_lattice_rotations as compute_helical_lattice_rotations, - Helix as Helix, -) -from ._config import ImageConfig as ImageConfig -from ._conformation import ( - AbstractConformation as AbstractConformation, - DiscreteConformation as DiscreteConformation, + HelicalAssembly as HelicalAssembly, ) from ._detector import ( AbstractDetector as AbstractDetector, AbstractDQE as AbstractDQE, GaussianDetector as GaussianDetector, + IdealCountingDQE as IdealCountingDQE, IdealDQE as IdealDQE, PoissonDetector as PoissonDetector, ) -from ._dose import ElectronDose as ElectronDose -from ._ice import ( - AbstractIce as AbstractIce, - GaussianIce as GaussianIce, -) -from ._instrument import Instrument as Instrument -from ._integrators import ( - AbstractPotentialIntegrator as AbstractPotentialIntegrator, - extract_slice as extract_slice, - extract_slice_with_cubic_spline as extract_slice_with_cubic_spline, - FourierSliceExtract as FourierSliceExtract, - NufftProject as NufftProject, - project_with_nufft as project_with_nufft, -) -from ._optics import ( - AbstractOptics as AbstractOptics, - CTF as CTF, - WeakPhaseOptics as WeakPhaseOptics, -) -from ._pipeline import ( - AbstractPipeline as AbstractPipeline, - AssemblyPipeline as AssemblyPipeline, - ImagePipeline as ImagePipeline, +from ._imaging_pipeline import ( + AbstractImagingPipeline as AbstractImagingPipeline, + ContrastImagingPipeline as ContrastImagingPipeline, + ElectronCountingImagingPipeline as ElectronCountingImagingPipeline, + IntensityImagingPipeline as IntensityImagingPipeline, ) +from ._instrument_config import InstrumentConfig as InstrumentConfig from ._pose import ( AbstractPose as AbstractPose, AxisAnglePose as AxisAnglePose, EulerAnglePose as EulerAnglePose, QuaternionPose as QuaternionPose, ) -from ._potential import ( +from ._potential_integrator import ( + AbstractFourierVoxelExtraction as AbstractFourierVoxelExtraction, + AbstractPotentialIntegrator as AbstractPotentialIntegrator, + AbstractVoxelPotentialIntegrator as AbstractVoxelPotentialIntegrator, + FourierSliceExtraction as FourierSliceExtraction, + NufftProjection as NufftProjection, +) +from ._potential_representation import ( AbstractFourierVoxelGridPotential as AbstractFourierVoxelGridPotential, - AbstractScatteringPotential as AbstractScatteringPotential, + AbstractPotentialRepresentation as AbstractPotentialRepresentation, AbstractVoxelPotential as AbstractVoxelPotential, build_real_space_voxels_from_atoms as build_real_space_voxels_from_atoms, evaluate_3d_atom_potential as evaluate_3d_atom_potential, @@ -58,9 +44,29 @@ RealVoxelCloudPotential as RealVoxelCloudPotential, RealVoxelGridPotential as RealVoxelGridPotential, ) -from ._specimen import ( - AbstractEnsemble as AbstractEnsemble, - AbstractSpecimen as AbstractSpecimen, - DiscreteEnsemble as DiscreteEnsemble, - Specimen as Specimen, +from ._scattering_theory import ( + AbstractLinearScatteringTheory as AbstractLinearScatteringTheory, + AbstractScatteringTheory as AbstractScatteringTheory, + LinearScatteringTheory as LinearScatteringTheory, + LinearSuperpositionScatteringTheory as LinearSuperpositionScatteringTheory, +) +from ._solvent import ( + AbstractIce as AbstractIce, + GaussianIce as GaussianIce, +) +from ._structural_ensemble import ( + AbstractConformationalVariable as AbstractConformationalVariable, + AbstractStructuralEnsemble as AbstractStructuralEnsemble, + AbstractStructuralEnsembleBatcher as AbstractStructuralEnsembleBatcher, + DiscreteConformationalVariable as DiscreteConformationalVariable, + DiscreteStructuralEnsemble as DiscreteStructuralEnsemble, + SingleStructureEnsemble as SingleStructureEnsemble, +) +from ._transfer_theory import ( + AbstractContrastTransferFunction as AbstractContrastTransferFunction, + AbstractTransferFunction as AbstractTransferFunction, + AbstractTransferTheory as AbstractTransferTheory, + ContrastTransferFunction as ContrastTransferFunction, + ContrastTransferTheory as ContrastTransferTheory, + IdealContrastTransferFunction as IdealContrastTransferFunction, ) diff --git a/src/cryojax/simulator/_assembly/__init__.py b/src/cryojax/simulator/_assembly/__init__.py index 169b6a14..d2144ff6 100644 --- a/src/cryojax/simulator/_assembly/__init__.py +++ b/src/cryojax/simulator/_assembly/__init__.py @@ -1,6 +1,6 @@ -from ._assembly import AbstractAssembly as AbstractAssembly -from ._helix import ( +from .assembly import AbstractAssembly as AbstractAssembly +from .helix import ( compute_helical_lattice_positions as compute_helical_lattice_positions, compute_helical_lattice_rotations as compute_helical_lattice_rotations, - Helix as Helix, + HelicalAssembly as HelicalAssembly, ) diff --git a/src/cryojax/simulator/_assembly/_assembly.py b/src/cryojax/simulator/_assembly/assembly.py similarity index 68% rename from src/cryojax/simulator/_assembly/_assembly.py rename to src/cryojax/simulator/_assembly/assembly.py index 94fe1279..f57b98b0 100644 --- a/src/cryojax/simulator/_assembly/_assembly.py +++ b/src/cryojax/simulator/_assembly/assembly.py @@ -1,12 +1,12 @@ """ Abstraction of a biological assembly. This assembles a structure -by computing an Ensemble of subunits, parameterized by -some geometry. +by computing a batch of subunits, parameterized by some geometry. """ from abc import abstractmethod from functools import cached_property from typing import Optional +from typing_extensions import override import equinox as eqx import jax @@ -14,17 +14,17 @@ from jaxtyping import Array, Float from ...rotations import SO3 -from .._conformation import AbstractConformation from .._pose import AbstractPose -from .._specimen import AbstractEnsemble, AbstractSpecimen +from .._structural_ensemble import ( + AbstractConformationalVariable, + AbstractStructuralEnsemble, + AbstractStructuralEnsembleBatcher, +) -class AbstractAssembly(eqx.Module, strict=True): +class AbstractAssembly(AbstractStructuralEnsembleBatcher, strict=True): """Abstraction of a biological assembly. - This class acts just like an ``AbstractSpecimen``, however - it creates an assembly from a subunit. - To subclass an `AbstractAssembly`, 1) Overwrite the `AbstractAssembly.n_subunits` property @@ -32,22 +32,20 @@ class AbstractAssembly(eqx.Module, strict=True): and `AbstractAssembly.rotations` properties. """ - subunit: AbstractVar[AbstractSpecimen] + subunit: AbstractVar[AbstractStructuralEnsemble] pose: AbstractVar[AbstractPose] - conformation: AbstractVar[Optional[AbstractConformation]] + conformation: AbstractVar[Optional[AbstractConformationalVariable]] n_subunits: AbstractVar[int] def __check_init__(self): - if self.conformation is not None and not isinstance( - self.subunit, AbstractEnsemble - ): + if self.conformation is not None and self.subunit.conformation is None: # Make sure that if conformation is set, subunit is an AbstractEnsemble raise AttributeError( - f"If {type(self)}.conformation is set, {type(self)}.subunit must be an " - "AbstractEnsemble." + f"If {type(self)}.conformation is set, " + "{type(self)}.subunit.conformation cannot be `None`." ) - if self.conformation is not None and isinstance(self.subunit, AbstractEnsemble): + if self.conformation is not None and self.subunit.conformation is not None: # ... if it is an AbstractEnsemble, the AbstractConformation must be the # right type if not isinstance(self.conformation, type(self.subunit.conformation)): @@ -75,19 +73,19 @@ def poses(self) -> AbstractPose: Draw the poses of the subunits in the lab frame, measured from the rotation relative to the first subunit. """ - # Transform the subunit positions by pose of the helix + # Transform the subunit positions by the center of mass pose of the assembly. transformed_positions = ( self.pose.rotate_coordinates(self.offsets_in_angstroms, inverse=False) + self.pose.offset_in_angstroms ) - # Transform the subunit rotations by the pose of the helix. This operation - # left multiplies by the pose of the helix, taking care that first subunits - # are rotated to the center of mass frame, then the lab frame. + # Transform the subunit rotations by the center of mass pose of the assembly. + # This operation left multiplies by the pose rotation matrix, taking care that + # first subunits are rotated to the center of mass frame, then the lab frame. transformed_rotations = jax.vmap( lambda com_rotation, subunit_rotation: com_rotation @ subunit_rotation, in_axes=[None, 0], )(self.pose.rotation, self.rotations) - # Function to construct AbstractPoses + # Construct the batch of `AbstractPose`s cls = type(self.pose) make_assembly_poses = jax.vmap( lambda rot, pos: cls.from_rotation_and_translation(rot, pos) @@ -96,12 +94,16 @@ def poses(self) -> AbstractPose: return make_assembly_poses(transformed_rotations, transformed_positions) @cached_property - def subunits(self) -> AbstractSpecimen: + def subunits(self) -> AbstractStructuralEnsemble: """Draw a realization of all of the subunits in the lab frame.""" # Compute a list of subunits, configured at the correct conformations - if isinstance(self.subunit, AbstractEnsemble): + if self.subunit.conformation is not None: where = lambda s: (s.conformation, s.pose) return eqx.tree_at(where, self.subunit, (self.conformation, self.poses)) else: where = lambda s: s.pose return eqx.tree_at(where, self.subunit, self.poses) + + @override + def get_batched_structural_ensemble(self) -> AbstractStructuralEnsemble: + return self.subunits diff --git a/src/cryojax/simulator/_assembly/_helix.py b/src/cryojax/simulator/_assembly/helix.py similarity index 95% rename from src/cryojax/simulator/_assembly/_helix.py rename to src/cryojax/simulator/_assembly/helix.py index d9e2fcf4..546d52ce 100644 --- a/src/cryojax/simulator/_assembly/_helix.py +++ b/src/cryojax/simulator/_assembly/helix.py @@ -7,17 +7,18 @@ import jax import jax.numpy as jnp -from equinox import field from jaxtyping import Array, Float from ...rotations import SO3 -from .._conformation import AbstractConformation from .._pose import AbstractPose, EulerAnglePose -from .._specimen import AbstractSpecimen -from ._assembly import AbstractAssembly +from .._structural_ensemble import ( + AbstractConformationalVariable, + AbstractStructuralEnsemble, +) +from .assembly import AbstractAssembly -class Helix(AbstractAssembly, strict=True): +class HelicalAssembly(AbstractAssembly, strict=True): """ Abstraction of a helical polymer. @@ -28,23 +29,23 @@ class Helix(AbstractAssembly, strict=True): image, pointing out-of-plane (i.e. along the z direction). """ - subunit: AbstractSpecimen + subunit: AbstractStructuralEnsemble rise: Float[Array, ""] twist: Float[Array, ""] pose: AbstractPose - conformation: Optional[AbstractConformation] + conformation: Optional[AbstractConformationalVariable] - n_subunits: int = field(static=True) - n_start: int = field(static=True) + n_subunits: int + n_start: int def __init__( self, - subunit: AbstractSpecimen, + subunit: AbstractStructuralEnsemble, rise: Float[Array, ""] | float, twist: Float[Array, ""] | float, pose: Optional[AbstractPose] = None, - conformation: Optional[AbstractConformation] = None, + conformation: Optional[AbstractConformationalVariable] = None, n_start: int = 1, n_subunits: int = 1, ): diff --git a/src/cryojax/simulator/_config.py b/src/cryojax/simulator/_config.py deleted file mode 100644 index 994275c8..00000000 --- a/src/cryojax/simulator/_config.py +++ /dev/null @@ -1,207 +0,0 @@ -""" -The image configuration and utility manager. -""" - -import math -from functools import cached_property -from typing import Any, Callable, Optional, Union - -import jax -import jax.numpy as jnp -from equinox import field, Module -from jaxtyping import Array, Complex, Float - -from ..coordinates import CoordinateGrid, FrequencyGrid -from ..core import error_if_not_positive -from ..image import ( - crop_to_shape, - irfftn, - pad_to_shape, - rescale_pixel_size, - resize_with_crop_or_pad, - rfftn, -) - - -class ImageConfig(Module, strict=True): - """Configuration and utilities for an electron microscopy image. - - **Attributes:** - - - `shape`: - Shape of the imaging plane in pixels. - ``width, height = shape[0], shape[1]`` - is the size of the desired imaging plane. - - `pixel_size`: - The pixel size of the image in Angstroms. - - `padded_shape`: - The shape of the image affter padding. This is - set with the `pad_scale` variable during initialization. - - `pad_mode`: - The method of image padding. By default, ``"constant"``. - For all options, see ``jax.numpy.pad``. - - `rescale_method`: - The interpolation method for pixel size rescaling. See - ``jax.image.scale_and_translate`` for options. - - `wrapped_frequency_grid_in_pixels`: - The fourier wavevectors in the imaging plane, wrapped in - a `FrequencyGrid` object. - - `wrapped_padded_frequency_grid_in_pixels`: - The fourier wavevectors in the imaging plane - in the padded coordinate system, wrapped in - a `FrequencyGrid` object. - - `wrapped_coordinate_grid_in_pixels`: - The coordinates in the imaging plane, wrapped - in a `CoordinateGrid` object. - - `wrapped_padded_coordinate_grid_in_pixels`: - The coordinates in the imaging plane - in the padded coordinate system, wrapped in a - `CoordinateGrid` object. - """ - - shape: tuple[int, int] = field(static=True) - pixel_size: Float[Array, ""] = field(converter=error_if_not_positive) - - padded_shape: tuple[int, int] = field(static=True) - pad_mode: Union[str, Callable] = field(static=True) - rescale_method: str = field(static=True) - - wrapped_frequency_grid_in_pixels: FrequencyGrid - wrapped_padded_frequency_grid_in_pixels: FrequencyGrid - wrapped_coordinate_grid_in_pixels: CoordinateGrid - wrapped_padded_coordinate_grid_in_pixels: CoordinateGrid - - def __init__( - self, - shape: tuple[int, int], - pixel_size: float | Float[Array, ""], - padded_shape: Optional[tuple[int, int]] = None, - *, - pad_scale: float = 1.0, - pad_mode: Union[str, Callable] = "constant", - rescale_method: str = "bicubic", - ): - """**Arguments:** - - - `pad_scale`: A scale factor at which to pad the image. This is - optionally used to set `padded_shape` and must be - greater than `1`. If `padded_shape` is set, this - argument is ignored. - """ - self.shape = shape - self.pixel_size = jnp.asarray(pixel_size) - self.pad_mode = pad_mode - self.rescale_method = rescale_method - # Set shape after padding - if padded_shape is None: - self.padded_shape = (int(pad_scale * shape[0]), int(pad_scale * shape[1])) - else: - self.padded_shape = padded_shape - # Set coordinates - self.wrapped_frequency_grid_in_pixels = FrequencyGrid(shape=self.shape) - self.wrapped_padded_frequency_grid_in_pixels = FrequencyGrid( - shape=self.padded_shape - ) - self.wrapped_coordinate_grid_in_pixels = CoordinateGrid(shape=self.shape) - self.wrapped_padded_coordinate_grid_in_pixels = CoordinateGrid( - shape=self.padded_shape - ) - - def __check_init__(self): - if self.padded_shape[0] < self.shape[0] or self.padded_shape[1] < self.shape[1]: - raise AttributeError( - "ImageConfig.padded_shape is less than ImageConfig.shape in one or " - "more dimensions." - ) - - @cached_property - def wrapped_coordinate_grid_in_angstroms(self) -> CoordinateGrid: - return self.pixel_size * self.wrapped_coordinate_grid_in_pixels # type: ignore - - @cached_property - def wrapped_frequency_grid_in_angstroms(self) -> FrequencyGrid: - return self.wrapped_frequency_grid_in_pixels / self.pixel_size - - @cached_property - def wrapped_padded_coordinate_grid_in_angstroms(self) -> CoordinateGrid: - return self.pixel_size * self.wrapped_padded_coordinate_grid_in_pixels # type: ignore - - @cached_property - def wrapped_padded_frequency_grid_in_angstroms(self) -> FrequencyGrid: - return self.wrapped_padded_frequency_grid_in_pixels / self.pixel_size - - def rescale_to_pixel_size( - self, - real_or_fourier_image: ( - Float[Array, "{self.padded_y_dim} {self.padded_x_dim}"] - | Complex[Array, "{self.padded_y_dim} {self.padded_x_dim//2+1}"] - ), - current_pixel_size: Float[Array, ""], - is_real: bool = True, - ) -> Complex[Array, "{self.padded_y_dim} {self.padded_x_dim//2+1}"]: - """Rescale the image pixel size using real-space interpolation. Only - interpolate if the `pixel_size` is not the `current_pixel_size`.""" - if is_real: - rescale_fn = lambda im: rescale_pixel_size( - im, current_pixel_size, self.pixel_size, method=self.rescale_method - ) - else: - rescale_fn = lambda im: rfftn( - rescale_pixel_size( - irfftn(im, s=self.padded_shape), - current_pixel_size, - self.pixel_size, - method=self.rescale_method, - ) - ) - null_fn = lambda im: im - return jax.lax.cond( - jnp.isclose(current_pixel_size, self.pixel_size), - null_fn, - rescale_fn, - real_or_fourier_image, - ) - - def crop_to_shape( - self, image: Float[Array, "y_dim x_dim"] - ) -> Float[Array, "{self.y_dim} {self.x_dim}"]: - """Crop an image.""" - return crop_to_shape(image, self.shape) - - def pad_to_padded_shape( - self, image: Float[Array, "y_dim x_dim"], **kwargs: Any - ) -> Float[Array, "{self.padded_y_dim} {self.padded_x_dim}"]: - """Pad an image.""" - return pad_to_shape(image, self.padded_shape, mode=self.pad_mode, **kwargs) - - def crop_or_pad_to_padded_shape( - self, image: Float[Array, "y_dim x_dim"], **kwargs: Any - ) -> Float[Array, "{self.padded_y_dim} {self.padded_x_dim}"]: - """Reshape an image using cropping or padding.""" - return resize_with_crop_or_pad( - image, self.padded_shape, mode=self.pad_mode, **kwargs - ) - - @property - def n_pix(self) -> int: - return math.prod(self.shape) - - @property - def y_dim(self) -> int: - return self.shape[0] - - @property - def x_dim(self) -> int: - return self.shape[1] - - @property - def padded_y_dim(self) -> int: - return self.padded_shape[0] - - @property - def padded_x_dim(self) -> int: - return self.padded_shape[1] - - @property - def padded_n_pix(self) -> int: - return math.prod(self.padded_shape) diff --git a/src/cryojax/simulator/_conformation.py b/src/cryojax/simulator/_conformation.py deleted file mode 100644 index e40b48ef..00000000 --- a/src/cryojax/simulator/_conformation.py +++ /dev/null @@ -1,26 +0,0 @@ -""" -Representations of conformational variables. -""" - -from typing import Any - -from equinox import AbstractVar, field, Module -from jaxtyping import Array, Int - -from ..core import error_if_negative - - -class AbstractConformation(Module, strict=True): - """ - A conformational variable wrapped in a Module. - """ - - value: AbstractVar[Any] - - -class DiscreteConformation(AbstractConformation, strict=True): - """ - A conformational variable wrapped in a Module. - """ - - value: Int[Array, ""] = field(converter=error_if_negative) diff --git a/src/cryojax/simulator/_detector.py b/src/cryojax/simulator/_detector.py index 3ff3fdcd..22bff7e2 100644 --- a/src/cryojax/simulator/_detector.py +++ b/src/cryojax/simulator/_detector.py @@ -12,10 +12,10 @@ from equinox import AbstractVar, field, Module from jaxtyping import Array, Complex, Float, PRNGKeyArray -from ..core import error_if_not_fractional +from .._errors import error_if_not_fractional from ..image import irfftn, rfftn from ..image.operators import AbstractFourierOperator -from ._config import ImageConfig +from ._instrument_config import InstrumentConfig class AbstractDQE(AbstractFourierOperator, strict=True): @@ -42,8 +42,8 @@ def __call__( raise NotImplementedError -class IdealDQE(AbstractDQE, strict=True): - r"""The model for an ideal DQE. +class IdealCountingDQE(AbstractDQE, strict=True): + r"""A perfect DQE for a detector at a discrete pixel size. See Ruskin et. al. "Quantitative characterization of electron detectors for transmission electron microscopy." (2013) for details. @@ -61,9 +61,7 @@ def __call__( pixel_size: Optional[Float[Array, ""]] = None, ) -> Float[Array, "y_dim x_dim"]: if pixel_size is None: - frequency_grid_in_nyquist_units = ( - frequency_grid_in_angstroms_or_pixels / 0.5 - ) + frequency_grid_in_nyquist_units = frequency_grid_in_angstroms_or_pixels / 0.5 else: frequency_grid_in_nyquist_units = ( frequency_grid_in_angstroms_or_pixels * pixel_size @@ -75,6 +73,26 @@ def __call__( ) +class IdealDQE(AbstractDQE, strict=True): + r"""A DQE that is perfect across all spatial frequencies.""" + + fraction_detected_electrons: Float[Array, ""] = field( + default=1.0, converter=error_if_not_fractional + ) + + @override + def __call__( + self, + frequency_grid_in_angstroms_or_pixels: Float[Array, "y_dim x_dim 2"], + *, + pixel_size: Optional[Float[Array, ""]] = None, + ) -> Float[Array, "y_dim x_dim"]: + return jnp.full( + frequency_grid_in_angstroms_or_pixels.shape[0:2], + self.fraction_detected_electrons, + ) + + class AbstractDetector(Module, strict=True): """Base class for an electron detector.""" @@ -84,26 +102,72 @@ def __init__(self, dqe: AbstractDQE): self.dqe = dqe @abstractmethod - def sample( + def sample_readout_from_expected_events( self, key: PRNGKeyArray, expected_electron_events: Float[Array, "y_dim x_dim"] ) -> Float[Array, "y_dim x_dim"]: """Sample a realization from the detector noise model.""" raise NotImplementedError - def __call__( + def compute_expected_electron_events( + self, + fourier_squared_wavefunction_at_detector_plane: Complex[ + Array, + "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}", + ], + instrument_config: InstrumentConfig, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + """Compute the expected electron events from the detector.""" + fourier_expected_electron_events = ( + self._compute_expected_events_or_detector_readout( + fourier_squared_wavefunction_at_detector_plane, + instrument_config, + key=None, + ) + ) + + return fourier_expected_electron_events + + def compute_detector_readout( self, + key: PRNGKeyArray, fourier_squared_wavefunction_at_detector_plane: Complex[ - Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}" + Array, + "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}", ], - config: ImageConfig, - electrons_per_angstrom_squared: Float[Array, ""], + instrument_config: InstrumentConfig, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + """Measure the readout from the detector.""" + fourier_detector_readout = self._compute_expected_events_or_detector_readout( + fourier_squared_wavefunction_at_detector_plane, + instrument_config, + key, + ) + + return fourier_detector_readout + + def _compute_expected_events_or_detector_readout( + self, + fourier_squared_wavefunction_at_detector_plane: Complex[ + Array, + "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}", + ], + instrument_config: InstrumentConfig, key: Optional[PRNGKeyArray] = None, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: """Pass the image through the detector model.""" - N_pix = np.prod(config.padded_shape) - frequency_grid = config.wrapped_padded_frequency_grid_in_pixels.get() + N_pix = np.prod(instrument_config.padded_shape) + frequency_grid = instrument_config.wrapped_padded_frequency_grid_in_pixels.get() # Compute the time-integrated electron flux in pixels - electrons_per_pixel = electrons_per_angstrom_squared * config.pixel_size**2 + electrons_per_pixel = ( + instrument_config.electrons_per_angstrom_squared + * instrument_config.pixel_size**2 + ) # ... now the total number of electrons over the entire image electrons_per_image = N_pix * electrons_per_pixel # Normalize the squared wavefunction to a set of probabilities @@ -123,9 +187,11 @@ def __call__( # ... otherwise, go to real space, sample, go back to fourier, # and return. expected_electron_events = irfftn( - fourier_expected_electron_events, s=config.padded_shape + fourier_expected_electron_events, s=instrument_config.padded_shape + ) + return rfftn( + self.sample_readout_from_expected_events(key, expected_electron_events) ) - return rfftn(self.sample(key, expected_electron_events)) class GaussianDetector(AbstractDetector, strict=True): @@ -134,19 +200,19 @@ class GaussianDetector(AbstractDetector, strict=True): """ @override - def sample( + def sample_readout_from_expected_events( self, key: PRNGKeyArray, expected_electron_events: Float[Array, "y_dim x_dim"] ) -> Float[Array, "y_dim x_dim"]: - return expected_electron_events + jnp.sqrt( - expected_electron_events - ) * jr.normal(key, expected_electron_events.shape) + return expected_electron_events + jnp.sqrt(expected_electron_events) * jr.normal( + key, expected_electron_events.shape + ) class PoissonDetector(AbstractDetector, strict=True): """A detector with a poisson noise model.""" @override - def sample( + def sample_readout_from_expected_events( self, key: PRNGKeyArray, expected_electron_events: Float[Array, "y_dim x_dim"] ) -> Float[Array, "y_dim x_dim"]: return jr.poisson(key, expected_electron_events).astype(float) diff --git a/src/cryojax/simulator/_dose.py b/src/cryojax/simulator/_dose.py deleted file mode 100644 index a01f65ce..00000000 --- a/src/cryojax/simulator/_dose.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Models the electron dose. -""" - -from equinox import field, Module -from jaxtyping import Array, Float - -from ..core import error_if_not_positive - - -class ElectronDose(Module, strict=True): - """Models the exposure to electrons during image formation. - - **Attributes:** - - `electrons_per_angstrom_squared`: The integrated electron flux. - """ - - electrons_per_angstrom_squared: Float[Array, ""] = field( - converter=error_if_not_positive - ) diff --git a/src/cryojax/simulator/_imaging_pipeline.py b/src/cryojax/simulator/_imaging_pipeline.py new file mode 100644 index 00000000..bbb3b1ac --- /dev/null +++ b/src/cryojax/simulator/_imaging_pipeline.py @@ -0,0 +1,394 @@ +""" +Image formation models. +""" + +from abc import abstractmethod +from typing import Optional +from typing_extensions import override + +import jax +from equinox import AbstractVar, Module +from jaxtyping import Array, Complex, Float, PRNGKeyArray + +from ..image import irfftn, rfftn +from ..image.operators import AbstractFilter, AbstractMask +from ._detector import AbstractDetector +from ._instrument_config import InstrumentConfig +from ._scattering_theory import AbstractScatteringTheory + + +class AbstractImagingPipeline(Module, strict=True): + """Base class for an image formation model. + + Call an `AbstractImagingPipeline`'s `render` routine. + """ + + instrument_config: AbstractVar[InstrumentConfig] + filter: AbstractVar[Optional[AbstractFilter]] + mask: AbstractVar[Optional[AbstractMask]] + + @abstractmethod + def render( + self, + rng_key: Optional[PRNGKeyArray] = None, + *, + postprocess: bool = True, + get_real: bool = True, + ) -> ( + Float[Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim}"] + | Float[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim}", + ] + | Complex[ + Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim//2+1}" + ] + | Complex[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim//2+1}", + ] + ): + """Render an image without any stochasticity. + + **Arguments:** + + - `rng_key`: The random number generator key. If not passed, render an image + with no stochasticity. + - `postprocess`: If `True`, view the cropped, filtered, and masked image. + If `postprocess = False`, `ImagePipeline.filter`, + `ImagePipeline.mask`, and cropping to `InstrumentConfig.shape` + are not applied. Instead, an image at the shape + `Instrument.padded_shape` is returned. + - `get_real`: If `True`, return the image in real space. + """ + raise NotImplementedError + + def postprocess( + self, + image: Complex[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim//2+1}", + ], + *, + get_real: bool = True, + ) -> ( + Float[Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim}"] + | Complex[ + Array, + "{self.instrument_config.y_dim} {self.instrument_config.x_dim//2+1}", + ] + ): + """Return an image postprocessed with filters, cropping, and masking + in either real or fourier space. + """ + instrument_config = self.instrument_config + if ( + self.mask is None + and instrument_config.padded_shape == instrument_config.shape + ): + # ... if there are no masks and we don't need to crop, + # minimize moving back and forth between real and fourier space + if self.filter is not None: + image = self.filter(image) + return irfftn(image, s=instrument_config.shape) if get_real else image + else: + # ... otherwise, apply filter, crop, and mask, again trying to + # minimize moving back and forth between real and fourier space + is_filter_applied = True if self.filter is None else False + if ( + self.filter is not None + and self.filter.buffer.shape + == instrument_config.wrapped_padded_frequency_grid_in_pixels.get().shape[ + 0:2 + ] + ): + # ... apply the filter here if it is the same size as the padded + # coordinates + is_filter_applied = True + image = self.filter(image) + image = irfftn(image, s=instrument_config.padded_shape) + image = instrument_config.crop_to_shape(image) + if self.mask is not None: + image = self.mask(image) + if is_filter_applied or self.filter is None: + return image if get_real else rfftn(image) + else: + # ... otherwise, apply the filter here and return. assume + # the filter is the same size as the non-padded coordinates + image = self.filter(rfftn(image)) + return irfftn(image, s=instrument_config.shape) if get_real else image + + def _maybe_postprocess( + self, + image: Complex[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim//2+1}", + ], + *, + postprocess: bool = True, + get_real: bool = True, + ) -> ( + Float[Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim}"] + | Float[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim}", + ] + | Complex[ + Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim//2+1}" + ] + | Complex[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim//2+1}", + ] + ): + instrument_config = self.instrument_config + if postprocess: + return self.postprocess(image, get_real=get_real) + else: + return irfftn(image, s=instrument_config.padded_shape) if get_real else image + + +class ContrastImagingPipeline(AbstractImagingPipeline, strict=True): + """An image formation pipeline that returns the image contrast from a linear + scattering theory. + + **Attributes:** + + - `instrument_config`: The configuration of the instrument, such as for the pixel size + and the wavelength. + - `scattering_theory`: The scattering theory. This must be a linear scattering + theory. + - `filter: `A filter to apply to the image. + - `mask`: A mask to apply to the image. + """ + + instrument_config: InstrumentConfig + scattering_theory: AbstractScatteringTheory + + filter: Optional[AbstractFilter] + mask: Optional[AbstractMask] + + def __init__( + self, + instrument_config: InstrumentConfig, + scattering_theory: AbstractScatteringTheory, + *, + filter: Optional[AbstractFilter] = None, + mask: Optional[AbstractMask] = None, + ): + self.instrument_config = instrument_config + self.scattering_theory = scattering_theory + self.filter = filter + self.mask = mask + + @override + def render( + self, + rng_key: Optional[PRNGKeyArray] = None, + *, + postprocess: bool = True, + get_real: bool = True, + ) -> ( + Float[Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim}"] + | Float[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim}", + ] + | Complex[ + Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim//2+1}" + ] + | Complex[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim//2+1}", + ] + ): + # Compute the squared wavefunction + fourier_contrast_at_detector_plane = ( + self.scattering_theory.compute_fourier_contrast_at_detector_plane( + self.instrument_config, rng_key + ) + ) + + return self._maybe_postprocess( + fourier_contrast_at_detector_plane, postprocess=postprocess, get_real=get_real + ) + + +class IntensityImagingPipeline(AbstractImagingPipeline, strict=True): + """An image formation pipeline that returns an intensity distribution---or in other + words a squared wavefunction. + + **Attributes:** + + - `instrument_config`: The configuration of the instrument, such as for the pixel size + and the wavelength. + - `scattering_theory`: The scattering theory. + - `filter: `A filter to apply to the image. + - `mask`: A mask to apply to the image. + """ + + instrument_config: InstrumentConfig + scattering_theory: AbstractScatteringTheory + + filter: Optional[AbstractFilter] + mask: Optional[AbstractMask] + + def __init__( + self, + instrument_config: InstrumentConfig, + scattering_theory: AbstractScatteringTheory, + *, + filter: Optional[AbstractFilter] = None, + mask: Optional[AbstractMask] = None, + ): + self.instrument_config = instrument_config + self.scattering_theory = scattering_theory + self.filter = filter + self.mask = mask + + @override + def render( + self, + rng_key: Optional[PRNGKeyArray] = None, + *, + postprocess: bool = True, + get_real: bool = True, + ) -> ( + Float[Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim}"] + | Float[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim}", + ] + | Complex[ + Array, + "{self.instrument_config.y_dim} {self.instrument_config.x_dim//2+1}", + ] + | Complex[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim//2+1}", + ] + ): + theory = self.scattering_theory + fourier_squared_wavefunction_at_detector_plane = ( + theory.compute_fourier_squared_wavefunction_at_detector_plane( + self.instrument_config, rng_key + ) + ) + + return self._maybe_postprocess( + fourier_squared_wavefunction_at_detector_plane, + postprocess=postprocess, + get_real=get_real, + ) + + +class ElectronCountingImagingPipeline(AbstractImagingPipeline, strict=True): + """An image formation pipeline that returns electron counts, given a + model for the detector. + + **Attributes:** + + - `instrument_config`: The configuration of the instrument, such as for the pixel size + and the wavelength. + - `scattering_theory`: The scattering theory. + - `detector`: The electron detector. + - `filter: `A filter to apply to the image. + - `mask`: A mask to apply to the image. + """ + + instrument_config: InstrumentConfig + scattering_theory: AbstractScatteringTheory + detector: AbstractDetector + + filter: Optional[AbstractFilter] + mask: Optional[AbstractMask] + + def __init__( + self, + instrument_config: InstrumentConfig, + scattering_theory: AbstractScatteringTheory, + detector: AbstractDetector, + *, + filter: Optional[AbstractFilter] = None, + mask: Optional[AbstractMask] = None, + ): + self.instrument_config = instrument_config + self.scattering_theory = scattering_theory + self.detector = detector + self.filter = filter + self.mask = mask + + @override + def render( + self, + rng_key: Optional[PRNGKeyArray] = None, + *, + postprocess: bool = True, + get_real: bool = True, + ) -> ( + Float[Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim}"] + | Float[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim}", + ] + | Complex[ + Array, "{self.instrument_config.y_dim} {self.instrument_config.x_dim//2+1}" + ] + | Complex[ + Array, + "{self.instrument_config.padded_y_dim} " + "{self.instrument_config.padded_x_dim//2+1}", + ] + ): + if rng_key is None: + # Compute the squared wavefunction + theory = self.scattering_theory + fourier_squared_wavefunction_at_detector_plane = ( + theory.compute_fourier_squared_wavefunction_at_detector_plane( + self.instrument_config + ) + ) + # ... now measure the expected electron events at the detector + fourier_expected_electron_events = ( + self.detector.compute_expected_electron_events( + fourier_squared_wavefunction_at_detector_plane, self.instrument_config + ) + ) + + return self._maybe_postprocess( + fourier_expected_electron_events, + postprocess=postprocess, + get_real=get_real, + ) + else: + keys = jax.random.split(rng_key) + # Compute the squared wavefunction + theory = self.scattering_theory + fourier_squared_wavefunction_at_detector_plane = ( + theory.compute_fourier_squared_wavefunction_at_detector_plane( + self.instrument_config, keys[0] + ) + ) + # ... now measure the detector readout + fourier_detector_readout = self.detector.compute_detector_readout( + keys[1], + fourier_squared_wavefunction_at_detector_plane, + self.instrument_config, + ) + + return self._maybe_postprocess( + fourier_detector_readout, + postprocess=postprocess, + get_real=get_real, + ) diff --git a/src/cryojax/simulator/_instrument.py b/src/cryojax/simulator/_instrument.py deleted file mode 100644 index 767676ec..00000000 --- a/src/cryojax/simulator/_instrument.py +++ /dev/null @@ -1,163 +0,0 @@ -""" -Abstraction of the electron microscope. This includes models -for the optics, electron dose, and detector. -""" - -from typing import Optional - -import jax.numpy as jnp -from equinox import field, Module -from jaxtyping import Array, Complex, Float, PRNGKeyArray - -from ..constants import convert_keV_to_angstroms -from ..core import error_if_not_positive -from ._config import ImageConfig -from ._detector import AbstractDetector -from ._dose import ElectronDose -from ._optics import AbstractOptics - - -class Instrument(Module, strict=True): - """An abstraction of an electron microscope. - - **Attributes:** - - - `voltage_in_kilovolts`: The accelerating voltage of the - instrument in kilovolts (kV). - - `optics`: The model for the instrument optics. - - `dose`: The model for the exposure to electrons - during image formation. - - `detector` : The model of the detector. - """ - - voltage_in_kilovolts: Float[Array, ""] = field(converter=error_if_not_positive) - dose: Optional[ElectronDose] - optics: Optional[AbstractOptics] - detector: Optional[AbstractDetector] - - def __init__( - self, - voltage_in_kilovolts: float | Float[Array, ""], - *, - dose: Optional[ElectronDose] = None, - optics: Optional[AbstractOptics] = None, - detector: Optional[AbstractDetector] = None, - ): - if (optics is None or dose is None) and isinstance(detector, AbstractDetector): - raise AttributeError( - "Cannot set Instrument.detector without passing an AbstractOptics and " - "an ElectronDose." - ) - self.voltage_in_kilovolts = jnp.asarray(voltage_in_kilovolts) - self.optics = optics - self.dose = dose - self.detector = detector - - @property - def wavelength_in_angstroms(self) -> Float[Array, ""]: - return convert_keV_to_angstroms(self.voltage_in_kilovolts) - - def propagate_to_detector_plane( - self, - fourier_phase_at_exit_plane: Complex[ - Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}" - ], - config: ImageConfig, - defocus_offset: Float[Array, ""] | float = 0.0, - ) -> ( - Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"] - | Complex[Array, "{config.padded_y_dim} {config.padded_x_dim}"] - ): - if self.optics is None: - raise AttributeError( - "Tried to call `Instrument.propagate_to_detector_plane`, " - "but the `Instrument`'s optics model is `None`. This " - "is not allowed!" - ) - """Propagate the scattering potential with the optics model.""" - fourier_contrast_at_detector_plane = self.optics( - fourier_phase_at_exit_plane, - config, - self.wavelength_in_angstroms, - defocus_offset=defocus_offset, - ) - - return fourier_contrast_at_detector_plane - - def compute_fourier_squared_wavefunction( - self, - fourier_contrast_at_detector_plane: ( - Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"] - | Complex[Array, "{config.padded_y_dim} {config.padded_x_dim}"] - ), - config: ImageConfig, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: - """Compute the squared wavefunction at the detector plane, given the - contrast. - """ - N1, N2 = config.padded_shape - if self.optics is None: - raise AttributeError( - "Tried to call `compute_fourier_squared_wavefunction`, " - "but the `Instrument`'s optics model is `None`. This " - "is not allowed!" - ) - elif self.optics.is_linear: - # ... compute the squared wavefunction directly from the image contrast - # as |psi|^2 = 1 + 2C. - fourier_contrast_at_detector_plane = fourier_contrast_at_detector_plane - fourier_squared_wavefunction_at_detector_plane = ( - (2 * fourier_contrast_at_detector_plane).at[0, 0].add(1.0 * N1 * N2) - ) - return fourier_squared_wavefunction_at_detector_plane - else: - raise NotImplementedError( - "Functionality for AbstractOptics.is_linear = False not supported." - ) - - def compute_expected_electron_events( - self, - fourier_squared_wavefunction_at_detector_plane: Complex[ - Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}" - ], - config: ImageConfig, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: - """Compute the expected electron events from the detector.""" - if self.detector is None: - raise AttributeError( - "Tried to call `Instrument.compute_expected_electron_events`, " - "but the `Instrument`'s detector model is `None`. This " - "is not allowed!" - ) - fourier_expected_electron_events = self.detector( - fourier_squared_wavefunction_at_detector_plane, - config, - self.dose.electrons_per_angstrom_squared, - key=None, - ) - - return fourier_expected_electron_events - - def measure_detector_readout( - self, - key: PRNGKeyArray, - fourier_squared_wavefunction_at_detector_plane: Complex[ - Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}" - ], - config: ImageConfig, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: - """Measure the readout from the detector.""" - if self.detector is None: - raise AttributeError( - "Tried to call `Instrument.measure_detector_readout`, " - "but the `Instrument`'s detector model is `None`. This " - "is not allowed!" - ) - fourier_detector_readout = self.detector( - fourier_squared_wavefunction_at_detector_plane, - config, - self.dose.electrons_per_angstrom_squared, - key, - ) - - return fourier_detector_readout diff --git a/src/cryojax/simulator/_instrument_config.py b/src/cryojax/simulator/_instrument_config.py new file mode 100644 index 00000000..3de7b767 --- /dev/null +++ b/src/cryojax/simulator/_instrument_config.py @@ -0,0 +1,180 @@ +""" +The image configuration and utility manager. +""" + +import math +from functools import cached_property +from typing import Any, Callable, Optional, Union + +import jax.numpy as jnp +from equinox import Module +from jaxtyping import Array, Float + +from .._errors import error_if_not_positive +from ..constants import convert_keV_to_angstroms +from ..coordinates import CoordinateGrid, FrequencyGrid +from ..image import ( + crop_to_shape, + pad_to_shape, + resize_with_crop_or_pad, +) + + +class InstrumentConfig(Module, strict=True): + """Configuration and utilities for an electron microscopy image.""" + + shape: tuple[int, int] + pixel_size: Float[Array, ""] + voltage_in_kilovolts: Float[Array, ""] + electrons_per_angstrom_squared: Float[Array, ""] + + padded_shape: tuple[int, int] + pad_mode: Union[str, Callable] + + def __init__( + self, + shape: tuple[int, int], + pixel_size: float | Float[Array, ""], + voltage_in_kilovolts: float | Float[Array, ""], + electrons_per_angstrom_squared: float | Float[Array, ""] = 100.0, + padded_shape: Optional[tuple[int, int]] = None, + *, + pad_scale: float = 1.0, + pad_mode: Union[str, Callable] = "constant", + ): + """**Arguments:** + + - `shape`: + Shape of the imaging plane in pixels. + ``width, height = shape[0], shape[1]`` + is the size of the desired imaging plane. + - `pixel_size`: + The pixel size of the image in Angstroms. + - `padded_shape`: + The shape of the image affter padding. This is + set with the `pad_scale` variable during initialization. + - `pad_scale`: A scale factor at which to pad the image. This is + optionally used to set `padded_shape` and must be + greater than `1`. If `padded_shape` is set, this + argument is ignored. + - `pad_mode`: + The method of image padding. By default, ``"constant"``. + For all options, see ``jax.numpy.pad``. + """ + self.shape = shape + self.pixel_size = error_if_not_positive(jnp.asarray(pixel_size)) + self.voltage_in_kilovolts = error_if_not_positive( + jnp.asarray(voltage_in_kilovolts) + ) + self.electrons_per_angstrom_squared = error_if_not_positive( + jnp.asarray(electrons_per_angstrom_squared) + ) + self.pad_mode = pad_mode + # Set shape after padding + if padded_shape is None: + self.padded_shape = (int(pad_scale * shape[0]), int(pad_scale * shape[1])) + else: + self.padded_shape = padded_shape + + def __check_init__(self): + if self.padded_shape[0] < self.shape[0] or self.padded_shape[1] < self.shape[1]: + raise AttributeError( + "ImageConfig.padded_shape is less than ImageConfig.shape in one or " + "more dimensions." + ) + + @property + def wavelength_in_angstroms(self) -> Float[Array, ""]: + return convert_keV_to_angstroms(self.voltage_in_kilovolts) + + @cached_property + def wrapped_coordinate_grid_in_pixels(self) -> CoordinateGrid: + return CoordinateGrid(shape=self.shape) + + @cached_property + def wrapped_coordinate_grid_in_angstroms(self) -> CoordinateGrid: + return self.pixel_size * self.wrapped_coordinate_grid_in_pixels # type: ignore + + @cached_property + def wrapped_frequency_grid_in_pixels(self) -> FrequencyGrid: + return FrequencyGrid(shape=self.shape) + + @cached_property + def wrapped_frequency_grid_in_angstroms(self) -> FrequencyGrid: + return self.wrapped_frequency_grid_in_pixels / self.pixel_size + + @cached_property + def wrapped_full_frequency_grid_in_pixels(self) -> FrequencyGrid: + return FrequencyGrid(shape=self.shape, half_space=False) + + @cached_property + def wrapped_full_frequency_grid_in_angstroms(self) -> FrequencyGrid: + return self.wrapped_full_frequency_grid_in_pixels / self.pixel_size + + @cached_property + def wrapped_padded_coordinate_grid_in_pixels(self) -> CoordinateGrid: + return CoordinateGrid(shape=self.padded_shape) + + @cached_property + def wrapped_padded_coordinate_grid_in_angstroms(self) -> CoordinateGrid: + return self.pixel_size * self.wrapped_padded_coordinate_grid_in_pixels # type: ignore + + @cached_property + def wrapped_padded_frequency_grid_in_pixels(self) -> FrequencyGrid: + return FrequencyGrid(shape=self.padded_shape) + + @cached_property + def wrapped_padded_frequency_grid_in_angstroms(self) -> FrequencyGrid: + return self.wrapped_padded_frequency_grid_in_pixels / self.pixel_size + + @cached_property + def wrapped_padded_full_frequency_grid_in_pixels(self) -> FrequencyGrid: + return FrequencyGrid(shape=self.padded_shape, half_space=False) + + @cached_property + def wrapped_padded_full_frequency_grid_in_angstroms(self) -> FrequencyGrid: + return self.wrapped_padded_full_frequency_grid_in_pixels / self.pixel_size + + def crop_to_shape( + self, image: Float[Array, "y_dim x_dim"] + ) -> Float[Array, "{self.y_dim} {self.x_dim}"]: + """Crop an image.""" + return crop_to_shape(image, self.shape) + + def pad_to_padded_shape( + self, image: Float[Array, "y_dim x_dim"], **kwargs: Any + ) -> Float[Array, "{self.padded_y_dim} {self.padded_x_dim}"]: + """Pad an image.""" + return pad_to_shape(image, self.padded_shape, mode=self.pad_mode, **kwargs) + + def crop_or_pad_to_padded_shape( + self, image: Float[Array, "y_dim x_dim"], **kwargs: Any + ) -> Float[Array, "{self.padded_y_dim} {self.padded_x_dim}"]: + """Reshape an image using cropping or padding.""" + return resize_with_crop_or_pad( + image, self.padded_shape, mode=self.pad_mode, **kwargs + ) + + @property + def n_pixels(self) -> int: + return math.prod(self.shape) + + @property + def y_dim(self) -> int: + return self.shape[0] + + @property + def x_dim(self) -> int: + return self.shape[1] + + @property + def padded_y_dim(self) -> int: + return self.padded_shape[0] + + @property + def padded_x_dim(self) -> int: + return self.padded_shape[1] + + @property + def padded_n_pixels(self) -> int: + return math.prod(self.padded_shape) diff --git a/src/cryojax/simulator/_integrators/__init__.py b/src/cryojax/simulator/_integrators/__init__.py deleted file mode 100644 index bf9f6bcb..00000000 --- a/src/cryojax/simulator/_integrators/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from ._fourier_slice_extract import ( - extract_slice as extract_slice, - extract_slice_with_cubic_spline as extract_slice_with_cubic_spline, - FourierSliceExtract as FourierSliceExtract, -) -from ._nufft_project import ( - NufftProject as NufftProject, - project_with_nufft as project_with_nufft, -) -from ._potential_integrator import ( - AbstractPotentialIntegrator as AbstractPotentialIntegrator, -) diff --git a/src/cryojax/simulator/_integrators/_fourier_slice_extract.py b/src/cryojax/simulator/_integrators/_fourier_slice_extract.py deleted file mode 100644 index 79296808..00000000 --- a/src/cryojax/simulator/_integrators/_fourier_slice_extract.py +++ /dev/null @@ -1,179 +0,0 @@ -""" -Using the fourier slice theorem for computing volume projections. -""" - -from typing import Any - -import jax.numpy as jnp -from equinox import field -from jaxtyping import Array, Complex, Float - -from ...image import ( - irfftn, - map_coordinates, - map_coordinates_with_cubic_spline, - rfftn, -) -from .._config import ImageConfig -from .._potential import ( - FourierVoxelGridPotential, - FourierVoxelGridPotentialInterpolator, -) -from ._potential_integrator import AbstractPotentialIntegrator - - -class FourierSliceExtract(AbstractPotentialIntegrator, strict=True): - """Integrate points to the exit plane using the - Fourier-projection slice theorem. - - This extracts slices using resampling techniques housed in - ``cryojax.image._map_coordinates``. See here for more documentation. - - Attributes - ---------- - interpolation_order : - The interpolation order. This can be ``0`` (nearest-neighbor), ``1`` - (linear), or ``3`` (cubic). - Note that this argument is ignored if a ``FourierVoxelGridInterpolator`` - is passed. - interpolation_mode : - Specify how to handle out of bounds indexing. - interpolation_cval : - Value for filling out-of-bounds indices. Used only when - ``interpolation_mode = "fill"``. - """ - - interpolation_order: int = field(static=True, default=1) - interpolation_mode: str = field(static=True, default="fill") - interpolation_cval: complex = field(static=True, default=0.0 + 0.0j) - - def __call__( - self, - potential: FourierVoxelGridPotential | FourierVoxelGridPotentialInterpolator, - wavelength_in_angstroms: Float[Array, ""], - config: ImageConfig, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: - """Compute a projection of the real-space potential by extracting - a central slice in fourier space. - """ - frequency_slice = potential.wrapped_frequency_slice_in_pixels.get() - N = frequency_slice.shape[1] - if potential.shape != (N, N, N): - raise AttributeError( - "Only cubic boxes are supported for fourier slice extraction." - ) - # Compute the fourier projection - if isinstance(potential, FourierVoxelGridPotentialInterpolator): - fourier_projection = extract_slice_with_cubic_spline( - potential.coefficients, - frequency_slice, - mode=self.interpolation_mode, - cval=self.interpolation_cval, - ) - elif isinstance(potential, FourierVoxelGridPotential): - fourier_projection = extract_slice( - potential.fourier_voxel_grid, - frequency_slice, - interpolation_order=self.interpolation_order, - mode=self.interpolation_mode, - cval=self.interpolation_cval, - ) - else: - raise ValueError( - "Supported density representations are FourierVoxelGrid and " - "FourierVoxelGridInterpolator." - ) - - # Resize the image to match the ImageConfig.padded_shape - if config.padded_shape != (N, N): - fourier_projection = rfftn( - config.crop_or_pad_to_padded_shape(irfftn(fourier_projection, s=(N, N))) - ) - # Rescale the voxel size to the ImageConfig.pixel_size - return config.rescale_to_pixel_size( - fourier_projection, potential.voxel_size, is_real=False - ) - - -def extract_slice( - fourier_voxel_grid: Complex[Array, "dim dim dim"], - frequency_slice: Float[Array, "1 dim dim 3"], - interpolation_order: int = 1, - **kwargs: Any, -) -> Complex[Array, "dim dim//2+1"]: - """ - Project and interpolate 3D volume point cloud - onto imaging plane using the fourier slice theorem. - - Arguments - --------- - fourier_voxel_grid : shape `(N, N, N)` - Density grid in fourier space. The zero frequency component - should be in the center. - frequency_slice : shape `(1, N, N, 3)` - Frequency central slice coordinate system, with the zero - frequency component in the corner. - interpolation_order : int - Order of interpolation, either 0, 1, or 3. - kwargs - Keyword arguments passed to ``cryojax.image.map_coordinates`` - or ``cryojax.image.map_coordinates_with_cubic_spline``. - - Returns - ------- - projection : shape `(N, N//2+1)` - The output image in fourier space. - """ - # Convert to logical coordinates - N = frequency_slice.shape[1] - logical_frequency_slice = (frequency_slice * N) + N // 2 - # Convert arguments to map_coordinates convention and compute - k_z, k_y, k_x = jnp.transpose(logical_frequency_slice, axes=[3, 0, 1, 2]) - projection = map_coordinates( - fourier_voxel_grid, (k_x, k_y, k_z), interpolation_order, **kwargs - )[0, :, :] - # Shift zero frequency component to corner and take upper half plane - projection = jnp.fft.ifftshift(projection)[:, : N // 2 + 1] - # Set last line of frequencies to zero if image dimension is even - if N % 2 == 0: - projection = projection.at[:, -1].set(0.0 + 0.0j).at[N // 2, :].set(0.0 + 0.0j) - return projection - - -def extract_slice_with_cubic_spline( - spline_coefficients: Complex[Array, "dim+2 dim+2 dim+2"], - frequency_slice: Float[Array, "1 dim dim 3"], - **kwargs: Any, -) -> Complex[Array, "dim dim//2+1"]: - """ - Project and interpolate 3D volume point cloud - onto imaging plane using the fourier slice theorem, using cubic - spline coefficients as input. - - Arguments - --------- - spline_coefficients : shape `(N+2, N+2, N+2)` - Coefficients for cubic spline. - frequency_slice : shape `(1, N, N, 3)` - Frequency central slice coordinate system, with the zero - frequency component in the corner. - kwargs - Keyword arguments passed to ``cryojax.image.map_coordinates_with_cubic_spline``. - - Returns - ------- - projection : shape `(N, N//2+1)` - The output image in fourier space. - """ - # Convert to logical coordinates - N = frequency_slice.shape[1] - logical_frequency_slice = (frequency_slice * N) + N // 2 - # Convert arguments to map_coordinates convention and compute - k_z, k_y, k_x = jnp.transpose(logical_frequency_slice, axes=[3, 0, 1, 2]) - projection = map_coordinates_with_cubic_spline( - spline_coefficients, (k_x, k_y, k_z), **kwargs - )[0, :, :] - # Shift zero frequency component to corner and take upper half plane - projection = jnp.fft.ifftshift(projection)[:, : N // 2 + 1] - # Set last line of frequencies to zero if image dimension is even - return projection if N % 2 == 1 else projection.at[:, -1].set(0.0 + 0.0j) diff --git a/src/cryojax/simulator/_integrators/_nufft_project.py b/src/cryojax/simulator/_integrators/_nufft_project.py deleted file mode 100644 index f68dc540..00000000 --- a/src/cryojax/simulator/_integrators/_nufft_project.py +++ /dev/null @@ -1,110 +0,0 @@ -""" -Using non-uniform FFTs for computing volume projections. -""" - -import math - -import jax.numpy as jnp -from equinox import field -from jaxtyping import Array, Complex, Float - -from .._config import ImageConfig -from .._potential import RealVoxelCloudPotential, RealVoxelGridPotential -from ._potential_integrator import AbstractPotentialIntegrator - - -class NufftProject(AbstractPotentialIntegrator, strict=True): - """Integrate points onto the exit plane using - non-uniform FFTs. - - Attributes - ---------- - eps : `float` - See ``jax-finufft`` for documentation. - """ - - eps: float = field(static=True, default=1e-6) - - def __call__( - self, - potential: RealVoxelGridPotential | RealVoxelCloudPotential, - wavelength_in_angstroms: Float[Array, ""], - config: ImageConfig, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: - """Rasterize image with non-uniform FFTs.""" - if isinstance(potential, RealVoxelGridPotential): - shape = potential.shape - fourier_projection = project_with_nufft( - potential.real_voxel_grid.ravel(), - potential.wrapped_coordinate_grid_in_pixels.get().reshape( - (math.prod(shape), 3) - ), - config.padded_shape, - eps=self.eps, - ) - elif isinstance(potential, RealVoxelCloudPotential): - fourier_projection = project_with_nufft( - potential.voxel_weights, - potential.wrapped_coordinate_list_in_pixels.get(), - config.padded_shape, - eps=self.eps, - ) - else: - raise ValueError( - "Supported density representations are RealVoxelGrid and VoxelCloud." - ) - # Rescale the voxel size to the ImageConfig.pixel_size - return config.rescale_to_pixel_size( - fourier_projection, potential.voxel_size, is_real=False - ) - - -def project_with_nufft( - weights: Float[Array, " size"], - coordinate_list: Float[Array, "size 2"] | Float[Array, "size 3"], - shape: tuple[int, int], - eps: float = 1e-6, -) -> Complex[Array, "{shape[0]} {shape[1]}"]: - """ - Project and interpolate 3D volume point cloud - onto imaging plane using a non-uniform FFT. - - Arguments - --------- - weights : shape `(N,)` - Density point cloud. - coordinates : shape `(N, 2)` or shape `(N, 3)` - Coordinate system of point cloud. - shape : - Shape of the imaging plane in pixels. - ``width, height = shape[0], shape[1]`` - is the size of the desired imaging plane. - - Returns - ------- - projection : - The output image in fourier space. - """ - from jax_finufft import nufft1 - - weights, coordinate_list = ( - jnp.asarray(weights).astype(complex), - jnp.asarray(coordinate_list), - ) - # Get x and y coordinates - coordinates_xy = coordinate_list[:, :2] - # Normalize coordinates betweeen -pi and pi - M1, M2 = shape - image_size = jnp.asarray((M1, M2), dtype=float) - coordinates_periodic = 2 * jnp.pi * coordinates_xy / image_size - # Unpack and compute - x, y = coordinates_periodic[:, 0], coordinates_periodic[:, 1] - projection = nufft1(shape, weights, y, x, eps=eps, iflag=-1) - # Shift zero frequency component to corner and take upper half plane - projection = jnp.fft.ifftshift(projection)[:, : M2 // 2 + 1] - # Set last line of frequencies to zero if image dimension is even - if M2 % 2 == 0: - projection = projection.at[:, -1].set(0.0 + 0.0j) - if M1 % 2 == 0: - projection = projection.at[M1 // 2, :].set(0.0 + 0.0j) - return projection diff --git a/src/cryojax/simulator/_integrators/_potential_integrator.py b/src/cryojax/simulator/_integrators/_potential_integrator.py deleted file mode 100644 index da7d1547..00000000 --- a/src/cryojax/simulator/_integrators/_potential_integrator.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -Methods for integrating the scattering potential onto the exit plane. -""" - -from abc import abstractmethod -from typing import Generic, TypeVar - -from equinox import Module -from jaxtyping import Array, Complex, Float - -from .._config import ImageConfig -from .._potential import AbstractScatteringPotential - - -ScatteringPotentialT = TypeVar( - "ScatteringPotentialT", bound="AbstractScatteringPotential" -) - - -class AbstractPotentialIntegrator(Module, Generic[ScatteringPotentialT], strict=True): - """Base class for a method of integrating the scattering - potential to a set of phase shifts the exit plane.""" - - @abstractmethod - def __call__( - self, - potential: ScatteringPotentialT, - wavelength_in_angstroms: Float[Array, ""], - config: ImageConfig, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: - """Compute the scattering potential in the exit plane at - the `ImageConfig` settings. - - **Arguments:** - - - `potential`: The scattering potential representation. - - `wavelength_in_angstroms`: The wavelength of the electron beam. - - `config`: The configuration of the resulting image. - """ - raise NotImplementedError diff --git a/src/cryojax/simulator/_optics.py b/src/cryojax/simulator/_optics.py deleted file mode 100644 index 367fdcc5..00000000 --- a/src/cryojax/simulator/_optics.py +++ /dev/null @@ -1,207 +0,0 @@ -""" -Models of instrument optics. -""" - -from abc import abstractmethod -from typing import ClassVar, Optional -from typing_extensions import override - -import jax.numpy as jnp -from equinox import AbstractClassVar, AbstractVar, field, Module -from jaxtyping import Array, Complex, Float - -from ..constants import convert_keV_to_angstroms -from ..coordinates import cartesian_to_polar -from ..core import error_if_negative, error_if_not_fractional, error_if_not_positive -from ..image.operators import ( - AbstractFourierOperator, - Constant, - FourierOperatorLike, -) -from ._config import ImageConfig - - -class CTF(AbstractFourierOperator, strict=True): - """Compute the Contrast Transfer Function (CTF) in for a weakly - scattering specimen. - """ - - defocus_u_in_angstroms: Float[Array, ""] = field( - default=10000.0, converter=error_if_not_positive - ) - defocus_v_in_angstroms: Float[Array, ""] = field( - default=10000.0, converter=error_if_not_positive - ) - astigmatism_angle: Float[Array, ""] = field(default=0.0, converter=jnp.asarray) - voltage_in_kilovolts: Float[Array, ""] | float = field( - default=300.0, static=True - ) # Mark `static=True` so that the voltage is not part of the model pytree - # It is treated as part of the pytree upstream, in the Instrument! - spherical_aberration_in_mm: Float[Array, ""] = field( - default=2.7, converter=error_if_negative - ) - amplitude_contrast_ratio: Float[Array, ""] = field( - default=0.1, converter=error_if_not_fractional - ) - phase_shift: Float[Array, ""] = field(default=0.0, converter=jnp.asarray) - - def __call__( - self, - frequency_grid_in_angstroms: Float[Array, "y_dim x_dim 2"], - *, - wavelength_in_angstroms: Optional[Float[Array, ""] | float] = None, - defocus_offset: Float[Array, ""] | float = 0.0, - ) -> Float[Array, "y_dim x_dim"]: - # Convert degrees to radians - phase_shift = jnp.deg2rad(self.phase_shift) - astigmatism_angle = jnp.deg2rad(self.astigmatism_angle) - # Convert spherical abberation coefficient to angstroms - spherical_aberration_in_angstroms = self.spherical_aberration_in_mm * 1e7 - # Get the wavelength. It can either be passed from upstream or stored in the - # CTF - if wavelength_in_angstroms is None: - wavelength_in_angstroms = convert_keV_to_angstroms( - jnp.asarray(self.voltage_in_kilovolts) - ) - else: - wavelength_in_angstroms = jnp.asarray(wavelength_in_angstroms) - # Compute phase shifts for CTF - phase_shifts = _compute_phase_shifts( - frequency_grid_in_angstroms, - self.defocus_u_in_angstroms + jnp.asarray(defocus_offset), - self.defocus_v_in_angstroms + jnp.asarray(defocus_offset), - astigmatism_angle, - wavelength_in_angstroms, - spherical_aberration_in_angstroms, - self.amplitude_contrast_ratio, - phase_shift, - ) - # Compute the CTF - return jnp.sin(phase_shifts).at[0, 0].set(0.0) - - -CTF.__init__.__doc__ = """**Arguments:** - -- `defocus_u_in_angstroms`: The major axis defocus in Angstroms. -- `defocus_v_in_angstroms`: The minor axis defocus in Angstroms. -- `astigmatism_angle`: The defocus angle. -- `voltage_in_kilovolts`: The accelerating voltage in kV. -- `spherical_aberration_in_mm`: The spherical aberration coefficient in mm. -- `amplitude_contrast_ratio`: The amplitude contrast ratio. -- `phase_shift`: The additional phase shift. -""" - - -class AbstractOptics(Module, strict=True): - """Base class for an optics model.""" - - ctf: AbstractVar[CTF] - envelope: AbstractVar[FourierOperatorLike] - - is_linear: AbstractClassVar[bool] - - @property - def wavelength_in_angstroms(self) -> Float[Array, ""]: - return self.ctf.wavelength_in_angstroms - - @abstractmethod - def __call__( - self, - fourier_phase_in_exit_plane: Complex[ - Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}" - ], - config: ImageConfig, - wavelength_in_angstroms: Float[Array, ""] | float, - defocus_offset: Float[Array, ""] | float = 0.0, - ) -> ( - Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"] - | Complex[Array, "{config.padded_y_dim} {config.padded_x_dim}"] - ): - """Pass an image through the optics model.""" - raise NotImplementedError - - -class WeakPhaseOptics(AbstractOptics, strict=True): - """An optics model in the weak-phase approximation. Here, compute the image - contrast by applying the CTF directly to the exit plane phase shifts. - """ - - ctf: CTF - envelope: FourierOperatorLike - - is_linear: ClassVar[bool] = True - - def __init__( - self, - ctf: CTF, - envelope: Optional[FourierOperatorLike] = None, - ): - self.ctf = ctf - self.envelope = envelope or Constant(1.0) - - @override - def __call__( - self, - fourier_phase_in_exit_plane: Complex[ - Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}" - ], - config: ImageConfig, - wavelength_in_angstroms: Float[Array, ""] | float, - defocus_offset: Float[Array, ""] | float = 0.0, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: - """Apply the CTF directly to the phase shifts in the exit plane.""" - frequency_grid = config.wrapped_padded_frequency_grid_in_angstroms.get() - # Compute the CTF - ctf = self.envelope(frequency_grid) * self.ctf( - frequency_grid, - wavelength_in_angstroms=wavelength_in_angstroms, - defocus_offset=defocus_offset, - ) - # ... compute the contrast as the CTF multiplied by the exit plane - # phase shifts - fourier_contrast_in_detector_plane = ctf * fourier_phase_in_exit_plane - - return fourier_contrast_in_detector_plane - - -WeakPhaseOptics.__init__.__doc__ = """**Arguments:** - -- `ctf`: The contrast transfer function model. -- `envelope`: The envelope function of the optics model. -""" - - -def _compute_phase_shifts( - frequency_grid_in_angstroms: Float[Array, "y_dim x_dim 2"], - defocus_u_in_angstroms: Float[Array, ""], - defocus_v_in_angstroms: Float[Array, ""], - astigmatism_angle: Float[Array, ""], - wavelength_in_angstroms: Float[Array, ""], - spherical_aberration_in_angstroms: Float[Array, ""], - amplitude_contrast_ratio: Float[Array, ""], - phase_shift: Float[Array, ""], -) -> Float[Array, "y_dim x_dim"]: - k_sqr, azimuth = cartesian_to_polar(frequency_grid_in_angstroms, square=True) - defocus = 0.5 * ( - defocus_u_in_angstroms - + defocus_v_in_angstroms - + (defocus_u_in_angstroms - defocus_v_in_angstroms) - * jnp.cos(2.0 * (azimuth - astigmatism_angle)) - ) - amplitude_contrast_phase_shifts = jnp.arctan( - amplitude_contrast_ratio / jnp.sqrt(1.0 - amplitude_contrast_ratio**2) - ) - defocus_phase_shifts = -0.5 * defocus * wavelength_in_angstroms * k_sqr - aberration_phase_shifts = ( - 0.25 - * spherical_aberration_in_angstroms - * (wavelength_in_angstroms**3) - * (k_sqr**2) - ) - phase_shifts = ( - (2 * jnp.pi) * (defocus_phase_shifts + aberration_phase_shifts) - - phase_shift - - amplitude_contrast_phase_shifts - ) - - return phase_shifts diff --git a/src/cryojax/simulator/_pipeline.py b/src/cryojax/simulator/_pipeline.py deleted file mode 100644 index 8e937c60..00000000 --- a/src/cryojax/simulator/_pipeline.py +++ /dev/null @@ -1,586 +0,0 @@ -""" -Image formation models. -""" - -from abc import abstractmethod -from typing import Callable, Optional -from typing_extensions import override - -import equinox as eqx -import jax -import jax.numpy as jnp -from equinox import AbstractVar, Module -from jaxtyping import Array, Complex, Float, PRNGKeyArray - -from ..image import irfftn, normalize_image, rfftn -from ..image.operators import AbstractFilter, AbstractMask -from ._assembly import AbstractAssembly -from ._config import ImageConfig -from ._ice import AbstractIce -from ._instrument import Instrument -from ._pose import AbstractPose -from ._specimen import AbstractConformation, AbstractSpecimen - - -class AbstractPipeline(Module, strict=True): - """Base class for an image formation model. - - Call an `AbstractPipeline`'s `render` and `sample`, - routines. - """ - - config: AbstractVar[ImageConfig] - filter: AbstractVar[Optional[AbstractFilter]] - mask: AbstractVar[Optional[AbstractMask]] - - @abstractmethod - def render( - self, - *, - view_cropped: bool = True, - get_real: bool = True, - normalize: bool = False, - ) -> ( - Float[Array, "{self.config.y_dim} {self.config.x_dim}"] - | Float[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim}"] - | Complex[Array, "{self.config.y_dim} {self.config.x_dim//2+1}"] - | Complex[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim//2+1}"] - ): - """Render an image without any stochasticity. - - **Arguments:** - - - `view_cropped`: If `True`, view the cropped image. - If `view_cropped = False`, `ImagePipeline.filter`, - `ImagePipeline.mask`, and normalization with - `normalize = True` are not applied. - - `get_real`: If `True`, return the image in real space. - - `normalize`: If `True`, normalize the image to mean zero - and standard deviation 1. - """ - raise NotImplementedError - - @abstractmethod - def sample( - self, - key: PRNGKeyArray, - *, - view_cropped: bool = True, - get_real: bool = True, - normalize: bool = False, - ) -> ( - Float[Array, "{self.config.y_dim} {self.config.x_dim}"] - | Float[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim}"] - | Complex[Array, "{self.config.y_dim} {self.config.x_dim//2+1}"] - | Complex[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim//2+1}"] - ): - """ - Sample an image from a realization of the `AbstractIce` and - `AbstractDetector` models. - - **Arguments:** - - - `key`: The random number generator key. - - See `ImagePipeline.render` for documentation of keyword arguments. - """ - raise NotImplementedError - - def crop_and_apply_operators( - self, - image: Complex[ - Array, "{self.config.padded_y_dim} {self.config.padded_x_dim//2+1}" - ], - *, - get_real: bool = True, - normalize: bool = False, - ) -> ( - Float[Array, "{self.config.y_dim} {self.config.x_dim}"] - | Complex[Array, "{self.config.y_dim} {self.config.x_dim//2+1}"] - ): - """Return an image postprocessed with filters, cropping, and masking - in either real or fourier space. - """ - config = self.config - if self.mask is None and config.padded_shape == config.shape: - # ... if there are no masks and we don't need to crop, - # minimize moving back and forth between real and fourier space - if self.filter is not None: - image = self.filter(image) - if normalize: - image = normalize_image( - image, is_real=False, shape_in_real_space=config.shape - ) - return irfftn(image, s=config.shape) if get_real else image - else: - # ... otherwise, apply filter, crop, and mask, again trying to - # minimize moving back and forth between real and fourier space - is_filter_applied = True if self.filter is None else False - if ( - self.filter is not None - and self.filter.buffer.shape - == config.wrapped_padded_frequency_grid_in_pixels.get().shape[0:2] - ): - # ... apply the filter here if it is the same size as the padded - # coordinates - is_filter_applied = True - image = self.filter(image) - image = irfftn(image, s=config.padded_shape) - if self.mask is not None: - image = self.mask(image) - image = config.crop_to_shape(image) - if is_filter_applied or self.filter is None: - # ... normalize and return if the filter has already been applied - if normalize: - image = normalize_image(image, is_real=True) - return image if get_real else rfftn(image) - else: - # ... otherwise, apply the filter here, normalize, and return. assume - # the filter is the same size as the non-padded coordinates - image = self.filter(rfftn(image)) - if normalize: - image = normalize_image( - image, is_real=False, shape_in_real_space=config.shape - ) - return irfftn(image, s=config.shape) if get_real else image - - def _get_final_image( - self, - image: Complex[ - Array, "{self.config.padded_y_dim} {self.config.padded_x_dim//2+1}" - ], - *, - view_cropped: bool = True, - get_real: bool = True, - normalize: bool = False, - ) -> ( - Float[Array, "{self.config.y_dim} {self.config.x_dim}"] - | Float[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim}"] - | Complex[Array, "{self.config.y_dim} {self.config.x_dim//2+1}"] - | Complex[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim//2+1}"] - ): - config = self.config - if view_cropped: - return self.crop_and_apply_operators( - image, - get_real=get_real, - normalize=normalize, - ) - else: - return irfftn(image, s=config.padded_shape) if get_real else image - - -class ImagePipeline(AbstractPipeline, strict=True): - """Standard image formation pipeline. - - **Attributes:** - - - `config`: The image configuration. - - `specimen`: The abstraction of the biological specimen. - - `instrument`: The abstraction of the electron microscope. - - `solvent: `The solvent around the specimen. - - `filter: `A filter to apply to the image. - - `mask`: A mask to apply to the image. - """ - - config: ImageConfig - specimen: AbstractSpecimen - instrument: Instrument - solvent: Optional[AbstractIce] - - filter: Optional[AbstractFilter] - mask: Optional[AbstractMask] - - def __init__( - self, - config: ImageConfig, - specimen: AbstractSpecimen, - instrument: Instrument, - solvent: Optional[AbstractIce] = None, - *, - filter: Optional[AbstractFilter] = None, - mask: Optional[AbstractMask] = None, - ): - self.config = config - self.specimen = specimen - self.instrument = instrument - self.solvent = solvent - self.filter = filter - self.mask = mask - - def render( - self, - *, - view_cropped: bool = True, - get_real: bool = True, - normalize: bool = False, - ) -> ( - Float[Array, "{self.config.y_dim} {self.config.x_dim}"] - | Float[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim}"] - | Complex[Array, "{self.config.y_dim} {self.config.x_dim//2+1}"] - | Complex[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim//2+1}"] - ): - """Render an image without any stochasticity.""" - # Compute the phase shifts in the exit plane - fourier_phase_at_exit_plane = self.specimen.scatter_to_exit_plane( - self.instrument, self.config - ) - if self.instrument.optics is None: - return self._get_final_image( - fourier_phase_at_exit_plane, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - else: - # ... propagate the potential to the detector plane - fourier_contrast_at_detector_plane = ( - self.instrument.propagate_to_detector_plane( - fourier_phase_at_exit_plane, - self.config, - defocus_offset=self.specimen.pose.offset_z_in_angstroms, - ) - ) - # ... compute the squared wavefunction - fourier_squared_wavefunction_at_detector_plane = ( - self.instrument.compute_fourier_squared_wavefunction( - fourier_contrast_at_detector_plane, - self.config, - ) - ) - if self.instrument.detector is None: - return self._get_final_image( - fourier_squared_wavefunction_at_detector_plane, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - else: - # ... now measure the expected electron events at the detector - fourier_expected_electron_events = ( - self.instrument.compute_expected_electron_events( - fourier_squared_wavefunction_at_detector_plane, self.config - ) - ) - - return self._get_final_image( - fourier_expected_electron_events, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - - def sample( - self, - key: PRNGKeyArray, - *, - view_cropped: bool = True, - get_real: bool = True, - normalize: bool = False, - ) -> ( - Float[Array, "{self.config.y_dim} {self.config.x_dim}"] - | Float[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim}"] - | Complex[Array, "{self.config.y_dim} {self.config.x_dim//2+1}"] - | Complex[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim//2+1}"] - ): - """Sample the assembly from the stochastic parts of the model.""" - idx = 0 # Keep track of number of stochastic models - if self.solvent is not None and self.instrument.detector is not None: - keys = jax.random.split(key) - else: - keys = jnp.expand_dims(key, axis=0) - if self.solvent is not None: - # Compute the phase shifts in the exit plane, including - # potential of the solvent - fourier_phase_at_exit_plane = ( - self.specimen.scatter_to_exit_plane_with_solvent( - keys[idx], self.instrument, self.solvent, self.config - ) - ) - idx += 1 - else: - # ... otherwise, just compute the potential of the specimen - fourier_phase_at_exit_plane = self.specimen.scatter_to_exit_plane( - self.instrument, self.config - ) - if self.instrument.optics is None: - return self._get_final_image( - fourier_phase_at_exit_plane, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - else: - # ... propagate the potential to the contrast at the detector plane - fourier_contrast_at_detector_plane = ( - self.instrument.propagate_to_detector_plane( - fourier_phase_at_exit_plane, - self.config, - defocus_offset=self.specimen.pose.offset_z_in_angstroms, - ) - ) - # ... compute the squared wavefunction - fourier_squared_wavefunction_at_detector_plane = ( - self.instrument.compute_fourier_squared_wavefunction( - fourier_contrast_at_detector_plane, - self.config, - ) - ) - if self.instrument.detector is None: - return self._get_final_image( - fourier_squared_wavefunction_at_detector_plane, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - else: - # ... now measure the detector readout - fourier_detector_readout = self.instrument.measure_detector_readout( - keys[idx], - fourier_squared_wavefunction_at_detector_plane, - self.config, - ) - - return self._get_final_image( - fourier_detector_readout, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - - -class AssemblyPipeline(AbstractPipeline, strict=True): - """Compute an image from a superposition of subunits in - the `AbstractAssembly`. - - **Attributes:** - - - `config`: The image configuration. - - `assembly`: The assembly from which to render images. - - `instrument`: The abstraction of the electron microscope. - - `solvent: `The solvent around the specimen. - - `filter: `A filter to apply to the image. - - `mask`: A mask to apply to the image. - """ - - config: ImageConfig - assembly: AbstractAssembly - instrument: Instrument - solvent: Optional[AbstractIce] - - filter: Optional[AbstractFilter] - mask: Optional[AbstractMask] - - def __init__( - self, - config: ImageConfig, - assembly: AbstractAssembly, - instrument: Instrument, - solvent: Optional[AbstractIce] = None, - *, - filter: Optional[AbstractFilter] = None, - mask: Optional[AbstractMask] = None, - ): - self.config = config - self.assembly = assembly - self.instrument = instrument - self.solvent = solvent - self.filter = filter - self.mask = mask - - @override - def sample( - self, - key: PRNGKeyArray, - *, - view_cropped: bool = True, - get_real: bool = True, - normalize: bool = False, - ) -> ( - Float[Array, "{self.config.y_dim} {self.config.x_dim}"] - | Float[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim}"] - | Complex[Array, "{self.config.y_dim} {self.config.x_dim//2+1}"] - | Complex[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim//2+1}"] - ): - """Sample the superposition of `AbstractAssembly.subunits` from - stochastic models. - """ - idx = 0 # Keep track of number of stochastic models - if self.solvent is not None and self.instrument.detector is not None: - keys = jax.random.split(key) - else: - keys = jnp.expand_dims(key, axis=0) - if self.instrument.optics is None: - compute_fourier_phase_fn = ( - lambda spec, conf, ins: spec.scatter_to_exit_plane(ins, conf) - ) - fourier_phase_in_exit_plane = self._compute_subunit_superposition( - compute_fourier_phase_fn - ) - if self.solvent is not None: - # Compute the solvent potential in the detector plane - # and add to that of the specimen - fourier_solvent_potential_at_exit_plane = self.solvent.sample( - keys[idx], self.config - ) - fourier_phase_in_exit_plane += fourier_solvent_potential_at_exit_plane - idx += 1 - return self._get_final_image( - fourier_phase_in_exit_plane, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - else: - compute_fourier_contrast_fn = ( - lambda spec, conf, ins: ins.propagate_to_detector_plane( - spec.scatter_to_exit_plane(ins, conf), - conf, - defocus_offset=spec.pose.offset_z_in_angstroms, - ) - ) - # Compute the contrast in the detector plane - fourier_contrast_at_detector_plane = self._compute_subunit_superposition( - compute_fourier_contrast_fn - ) - if self.solvent is not None: - # Compute the solvent contrast in the detector plane - # and add to that of the specimen - fourier_solvent_potential_at_exit_plane = self.solvent.sample( - keys[idx], self.config - ) - fourier_contrast_at_detector_plane += ( - self.instrument.propagate_to_detector_plane( - fourier_solvent_potential_at_exit_plane, self.config - ) - ) - idx += 1 - # ... compute the squared wavefunction - fourier_squared_wavefunction_at_detector_plane = ( - self.instrument.compute_fourier_squared_wavefunction( - fourier_contrast_at_detector_plane, - self.config, - ) - ) - if self.instrument.detector is None: - return self._get_final_image( - fourier_squared_wavefunction_at_detector_plane, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - else: - # ... now measure the detector readout - fourier_detector_readout = self.instrument.measure_detector_readout( - keys[idx], - fourier_squared_wavefunction_at_detector_plane, - self.config, - ) - - return self._get_final_image( - fourier_detector_readout, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - - @override - def render( - self, - *, - view_cropped: bool = True, - get_real: bool = True, - normalize: bool = False, - ) -> ( - Float[Array, "{self.config.y_dim} {self.config.x_dim}"] - | Float[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim}"] - | Complex[Array, "{self.config.y_dim} {self.config.x_dim//2+1}"] - | Complex[Array, "{self.config.padded_y_dim} {self.config.padded_x_dim//2+1}"] - ): - """Render the superposition of images from the - `AbstractAssembly.subunits`. - """ - if self.instrument.optics is None: - compute_fourier_phase_fn = ( - lambda spec, conf, ins: spec.scatter_to_exit_plane(ins, conf) - ) - fourier_phase_in_exit_plane = self._compute_subunit_superposition( - compute_fourier_phase_fn - ) - return self._get_final_image( - fourier_phase_in_exit_plane, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - else: - compute_fourier_contrast_fn = ( - lambda spec, conf, ins: ins.propagate_to_detector_plane( - spec.scatter_to_exit_plane(ins, conf), - conf, - defocus_offset=spec.pose.offset_z_in_angstroms, - ) - ) - # Compute the contrast in the detector plane - fourier_contrast_at_detector_plane = self._compute_subunit_superposition( - compute_fourier_contrast_fn - ) - # ... compute the squared wavefunction - fourier_squared_wavefunction_at_detector_plane = ( - self.instrument.compute_fourier_squared_wavefunction( - fourier_contrast_at_detector_plane, - self.config, - ) - ) - if self.instrument.detector is None: - return self._get_final_image( - fourier_squared_wavefunction_at_detector_plane, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - else: - # ... now measure the expected electron events at the detector - fourier_expected_electron_events = ( - self.instrument.compute_expected_electron_events( - fourier_squared_wavefunction_at_detector_plane, self.config - ) - ) - - return self._get_final_image( - fourier_expected_electron_events, - view_cropped=view_cropped, - get_real=get_real, - normalize=normalize, - ) - - def _compute_subunit_superposition(self, compute_image_fn: Callable): - # Get the assembly subunits - subunits = self.assembly.subunits - # Setup vmap over the pose and conformation - is_vmap = lambda x: isinstance(x, (AbstractPose, AbstractConformation)) - to_vmap = jax.tree_util.tree_map(is_vmap, subunits, is_leaf=is_vmap) - vmap, novmap = eqx.partition(subunits, to_vmap) - # ... vmap to compute a stack of images to superimpose - compute_stack = jax.vmap( - lambda vmap, novmap, conf, ins: compute_image_fn( - eqx.combine(vmap, novmap), conf, ins - ), - in_axes=(0, None, None, None), - ) - # ... sum over the stack of images and jit - compute_stack_and_sum = jax.jit( - lambda vmap, novmap, conf, ins: jnp.sum( - compute_stack(vmap, novmap, conf, ins), - axis=0, - ) - ) - # ... compute the superposition. depending on the Instrument, - # this will either be a - superposition_image = ( - (compute_stack_and_sum(vmap, novmap, self.config, self.instrument)) - .at[0, 0] - .divide(self.assembly.n_subunits) - ) - - return superposition_image diff --git a/src/cryojax/simulator/_pose.py b/src/cryojax/simulator/_pose.py index fc1c44fa..b2bcd591 100644 --- a/src/cryojax/simulator/_pose.py +++ b/src/cryojax/simulator/_pose.py @@ -13,7 +13,7 @@ from equinox import AbstractVar, field, Module from jaxtyping import Array, Complex, Float -from ..rotations import SO3 +from ..rotations import convert_quaternion_to_euler_angles, SO3 class AbstractPose(Module, strict=True): @@ -47,9 +47,7 @@ def rotate_coordinates( def rotate_coordinates( self, - volume_coordinates: ( - Float[Array, "z_dim y_dim x_dim 3"] | Float[Array, "size 3"] - ), + volume_coordinates: Float[Array, "z_dim y_dim x_dim 3"] | Float[Array, "size 3"], inverse: bool = False, ) -> Float[Array, "z_dim y_dim x_dim 3"] | Float[Array, "size 3"]: """Rotate coordinates from a particular convention.""" @@ -75,9 +73,7 @@ def compute_shifts( given a frequency grid coordinate system. """ xy = self.offset_in_angstroms[0:2] - return jnp.exp( - -1.0j * (2 * jnp.pi * jnp.matmul(frequency_grid_in_angstroms, xy)) - ) + return jnp.exp(-1.0j * (2 * jnp.pi * jnp.matmul(frequency_grid_in_angstroms, xy))) @cached_property def offset_in_angstroms(self) -> Float[Array, "3"]: @@ -163,7 +159,7 @@ def rotation(self) -> SO3: @override @classmethod def from_rotation(cls, rotation: SO3): - view_phi, view_theta, view_psi = _convert_quaternion_to_euler_angles( + view_phi, view_theta, view_psi = convert_quaternion_to_euler_angles( rotation.wxyz, "zyz", ) @@ -265,62 +261,3 @@ def from_rotation(cls, rotation: SO3): - `euler_vector`: The axis-angle parameterization, represented as a vector $\boldsymbol{\omega} = (\omega_x, \omega_y, \omega_z)$. """ - - -def _convert_quaternion_to_euler_angles( - wxyz: jax.Array, convention: str = "zyz" -) -> jax.Array: - """Convert a quaternion to a sequence of euler angles about an extrinsic - coordinate system. - - Adapted from https://github.com/chrisflesher/jax-scipy-spatial/. - """ - if len(convention) != 3 or not all( - [axis in ["x", "y", "z"] for axis in convention] - ): - raise ValueError( - f"`convention` should be a string of three characters, each " - f"of which is 'x', 'y', or 'z'. Instead, got '{convention}'" - ) - if convention[0] == convention[1] or convention[1] == convention[2]: - raise ValueError( - f"`convention` cannot have axes repeating in a row. For example, " - f"'xxy' or 'zzz' are not allowed. Got '{convention}'." - ) - xyz_axis_to_array_axis = {"x": 0, "y": 1, "z": 2} - axes = [xyz_axis_to_array_axis[axis] for axis in convention] - xyzw = jnp.roll(wxyz, shift=-1) - angle_first = 0 - angle_third = 2 - i = axes[0] - j = axes[1] - k = axes[2] - symmetric = i == k - k = jnp.where(symmetric, 3 - i - j, k) - sign = jnp.array((i - j) * (j - k) * (k - i) // 2, dtype=xyzw.dtype) - eps = 1e-7 - a = jnp.where(symmetric, xyzw[3], xyzw[3] - xyzw[j]) - b = jnp.where(symmetric, xyzw[i], xyzw[i] + xyzw[k] * sign) - c = jnp.where(symmetric, xyzw[j], xyzw[j] + xyzw[3]) - d = jnp.where(symmetric, xyzw[k] * sign, xyzw[k] * sign - xyzw[i]) - angles = jnp.empty(3, dtype=xyzw.dtype) - angles = angles.at[1].set(2 * jnp.arctan2(jnp.hypot(c, d), jnp.hypot(a, b))) - case = jnp.where(jnp.abs(angles[1] - jnp.pi) <= eps, 2, 0) - case = jnp.where(jnp.abs(angles[1]) <= eps, 1, case) - half_sum = jnp.arctan2(b, a) - half_diff = jnp.arctan2(d, c) - angles = angles.at[0].set( - jnp.where(case == 1, 2 * half_sum, 2 * half_diff * -1) - ) # any degenerate case - angles = angles.at[angle_first].set( - jnp.where(case == 0, half_sum - half_diff, angles[angle_first]) - ) - angles = angles.at[angle_third].set( - jnp.where(case == 0, half_sum + half_diff, angles[angle_third]) - ) - angles = angles.at[angle_third].set( - jnp.where(symmetric, angles[angle_third], angles[angle_third] * sign) - ) - angles = angles.at[1].set(jnp.where(symmetric, angles[1], angles[1] - jnp.pi / 2)) - angles = (angles + jnp.pi) % (2 * jnp.pi) - jnp.pi - return -jnp.rad2deg(angles) diff --git a/src/cryojax/simulator/_potential_integrator/__init__.py b/src/cryojax/simulator/_potential_integrator/__init__.py new file mode 100644 index 00000000..c1067e68 --- /dev/null +++ b/src/cryojax/simulator/_potential_integrator/__init__.py @@ -0,0 +1,11 @@ +from .base_potential_integrator import ( + AbstractPotentialIntegrator as AbstractPotentialIntegrator, + AbstractVoxelPotentialIntegrator as AbstractVoxelPotentialIntegrator, +) +from .fourier_voxel_extract import ( + AbstractFourierVoxelExtraction as AbstractFourierVoxelExtraction, + FourierSliceExtraction as FourierSliceExtraction, +) +from .nufft_project import ( + NufftProjection as NufftProjection, +) diff --git a/src/cryojax/simulator/_potential_integrator/base_potential_integrator.py b/src/cryojax/simulator/_potential_integrator/base_potential_integrator.py new file mode 100644 index 00000000..64d2d907 --- /dev/null +++ b/src/cryojax/simulator/_potential_integrator/base_potential_integrator.py @@ -0,0 +1,82 @@ +""" +Methods for integrating the scattering potential directly onto the exit plane. +""" + +from abc import abstractmethod +from typing import Generic, TypeVar +from typing_extensions import override + +from equinox import AbstractVar, Module +from jaxtyping import Array, Complex + +from ...image import maybe_rescale_pixel_size +from .._instrument_config import InstrumentConfig +from .._potential_representation import AbstractVoxelPotential + + +PotentialT = TypeVar("PotentialT") +VoxelPotentialT = TypeVar("VoxelPotentialT", bound="AbstractVoxelPotential") + + +class AbstractPotentialIntegrator(Module, Generic[PotentialT], strict=True): + """Base class for a method of integrating a potential directly onto + an imaging plane.""" + + @abstractmethod + def compute_fourier_integrated_potential( + self, + potential: PotentialT, + instrument_config: InstrumentConfig, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + """Compute the scattering potential in the exit plane at + the `InstrumentConfig` settings. + + **Arguments:** + + - `potential`: The scattering potential representation. + - `wavelength_in_angstroms`: The wavelength of the electron beam. + - `instrument_config`: The configuration of the resulting image. + """ + raise NotImplementedError + + +class AbstractVoxelPotentialIntegrator( + AbstractPotentialIntegrator[AbstractVoxelPotential], + Generic[VoxelPotentialT], + strict=True, +): + """Base class for a method of integrating a voxel-based potential.""" + + pixel_rescaling_method: AbstractVar[str] + + @abstractmethod + def compute_raw_fourier_image( + self, + potential: VoxelPotentialT, + instrument_config: InstrumentConfig, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + raise NotImplementedError + + @override + def compute_fourier_integrated_potential( + self, + potential: AbstractVoxelPotential, + instrument_config: InstrumentConfig, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + fourier_projected_potential_without_postprocess = self.compute_raw_fourier_image( + potential, # type: ignore + instrument_config, + ) + return maybe_rescale_pixel_size( + potential.voxel_size * fourier_projected_potential_without_postprocess, + potential.voxel_size, + instrument_config.pixel_size, + is_real=False, + shape_in_real_space=instrument_config.padded_shape, + ) diff --git a/src/cryojax/simulator/_potential_integrator/fourier_voxel_extract.py b/src/cryojax/simulator/_potential_integrator/fourier_voxel_extract.py new file mode 100644 index 00000000..59d3f224 --- /dev/null +++ b/src/cryojax/simulator/_potential_integrator/fourier_voxel_extract.py @@ -0,0 +1,242 @@ +""" +Using the fourier slice theorem for computing volume projections. +""" + +from abc import abstractmethod +from typing_extensions import override + +import jax.numpy as jnp +from jaxtyping import Array, Complex, Float + +from ...image import ( + irfftn, + map_coordinates, + map_coordinates_with_cubic_spline, + rfftn, +) +from .._instrument_config import InstrumentConfig +from .._potential_representation import ( + FourierVoxelGridPotential, + FourierVoxelGridPotentialInterpolator, +) +from .base_potential_integrator import AbstractVoxelPotentialIntegrator + + +class AbstractFourierVoxelExtraction( + AbstractVoxelPotentialIntegrator[ + FourierVoxelGridPotential | FourierVoxelGridPotentialInterpolator + ], + strict=True, +): + """Integrate points to the exit plane by extracting a voxel surface + from a 3D voxel grid. + + This extracts values using resampling techniques housed in + `cryojax.image._map_coordinates`. See here for more documentation. + """ + + pixel_rescaling_method: str = "bicubic" + interpolation_order: int = 1 + interpolation_mode: str = "fill" + interpolation_cval: complex = 0.0 + 0.0j + + @abstractmethod + def extract_voxels_from_spline_coefficients( + self, + spline_coefficients: Complex[Array, "dim+2 dim+2 dim+2"], + frequency_slice: Float[Array, "1 dim dim 3"], + instrument_config: InstrumentConfig, + ) -> Complex[Array, "dim dim//2+1"]: + """Extract voxels values from the spline coefficients of the + fourier-space voxel grid. + + **Arguments:** + + - `fourier_voxel_grid`: + Density grid in fourier space. The zero frequency component + should be in the center. + - `frequency_slice`: + Frequency central slice coordinate system, with the zero + frequency component in the corner. + - `instrument_config`: + The `InstrumentConfig`. + + **Returns:** + + The output image in fourier space. + """ + raise NotImplementedError + + @abstractmethod + def extract_voxels_from_grid_points( + self, + fourier_voxel_grid: Complex[Array, "dim dim dim"], + frequency_slice: Float[Array, "1 dim dim 3"], + instrument_config: InstrumentConfig, + ) -> Complex[Array, "dim dim//2+1"]: + """Extract voxels values from the potential as a fourier-space + voxel grid. + + **Arguments:** + + - `fourier_voxel_grid`: + Density grid in fourier space. The zero frequency component + should be in the center. + - `frequency_slice`: + Frequency central slice coordinate system, with the zero + frequency component in the corner. + - `instrument_config`: + The `InstrumentConfig`. + + **Returns:** + + The output image in fourier space. + """ + raise NotImplementedError + + @override + def compute_raw_fourier_image( + self, + potential: FourierVoxelGridPotential | FourierVoxelGridPotentialInterpolator, + instrument_config: InstrumentConfig, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + """Compute a projection of the real-space potential by extracting + a central slice in fourier space. + """ + frequency_slice = potential.wrapped_frequency_slice_in_pixels.get() + N = frequency_slice.shape[1] + if potential.shape != (N, N, N): + raise AttributeError( + "Only cubic boxes are supported for fourier slice extraction." + ) + # Compute the fourier projection + if isinstance(potential, FourierVoxelGridPotentialInterpolator): + fourier_projection = self.extract_voxels_from_spline_coefficients( + potential.coefficients, frequency_slice, instrument_config + ) + elif isinstance(potential, FourierVoxelGridPotential): + fourier_projection = self.extract_voxels_from_grid_points( + potential.fourier_voxel_grid, frequency_slice, instrument_config + ) + else: + raise ValueError( + "Supported density representations are FourierVoxelGrid and " + "FourierVoxelGridInterpolator." + ) + + # Resize the image to match the InstrumentConfig.padded_shape + if instrument_config.padded_shape != (N, N): + fourier_projection = rfftn( + instrument_config.crop_or_pad_to_padded_shape( + irfftn(fourier_projection, s=(N, N)) + ) + ) + return fourier_projection + + +AbstractFourierVoxelExtraction.__init__.__doc__ = """**Arguments:** + +- `pixel_rescaling_method`: + Method for rescaling the final image to the `InstrumentConfig` + pixel size. See `cryojax.image._rescale_pixel_size` for documentation. +- `interpolation_order`: + The interpolation order. This can be ``0`` (nearest-neighbor), ``1`` + (linear), or ``3`` (cubic). + Note that this argument is ignored if a ``FourierVoxelGridInterpolator`` + is passed. +- `interpolation_mode`: + Specify how to handle out of bounds indexing. +- `interpolation_cval`: + Value for filling out-of-bounds indices. Used only when + ``interpolation_mode = "fill"``. +""" + + +class FourierSliceExtraction(AbstractFourierVoxelExtraction, strict=True): + """Integrate points to the exit plane using the + Fourier-projection slice theorem. + """ + + @override + def extract_voxels_from_spline_coefficients( + self, + spline_coefficients: Complex[Array, "dim+2 dim+2 dim+2"], + frequency_slice: Float[Array, "1 dim dim 3"], + instrument_config: InstrumentConfig, + ) -> Complex[Array, "dim dim//2+1"]: + return _extract_slice_with_cubic_spline( + spline_coefficients, + frequency_slice, + mode=self.interpolation_mode, + cval=self.interpolation_cval, + ) + + @override + def extract_voxels_from_grid_points( + self, + fourier_voxel_grid: Complex[Array, "dim dim dim"], + frequency_slice: Float[Array, "1 dim dim 3"], + instrument_config: InstrumentConfig, + ) -> Complex[Array, "dim dim//2+1"]: + return _extract_slice( + fourier_voxel_grid, + frequency_slice, + interpolation_order=self.interpolation_order, + mode=self.interpolation_mode, + cval=self.interpolation_cval, + ) + + +def _extract_slice( + fourier_voxel_grid, + frequency_slice, + interpolation_order, + **kwargs, +) -> Complex[Array, "dim dim//2+1"]: + return _extract_surface_from_voxel_grid( + fourier_voxel_grid, + frequency_slice, + is_spline_coefficients=False, + interpolation_order=interpolation_order, + **kwargs, + ) + + +def _extract_slice_with_cubic_spline( + spline_coefficients, frequency_slice, **kwargs +) -> Complex[Array, "dim dim//2+1"]: + return _extract_surface_from_voxel_grid( + spline_coefficients, frequency_slice, is_spline_coefficients=True, **kwargs + ) + + +def _extract_surface_from_voxel_grid( + voxel_grid, + frequency_coordinates, + is_spline_coefficients=False, + interpolation_order=1, + **kwargs, +): + # Convert to logical coordinates + N = frequency_coordinates.shape[1] + logical_frequency_slice = (frequency_coordinates * N) + N // 2 + # Convert arguments to map_coordinates convention and compute + k_z, k_y, k_x = jnp.transpose(logical_frequency_slice, axes=[3, 0, 1, 2]) + if is_spline_coefficients: + spline_coefficients = voxel_grid + projection = map_coordinates_with_cubic_spline( + spline_coefficients, (k_x, k_y, k_z), **kwargs + )[0, :, :] + else: + fourier_voxel_grid = voxel_grid + projection = map_coordinates( + fourier_voxel_grid, (k_x, k_y, k_z), interpolation_order, **kwargs + )[0, :, :] + # Shift zero frequency component to corner and take upper half plane + projection = jnp.fft.ifftshift(projection)[:, : N // 2 + 1] + # Set last line of frequencies to zero if image dimension is even + if N % 2 == 0: + projection = projection.at[:, -1].set(0.0 + 0.0j).at[N // 2, :].set(0.0 + 0.0j) + return projection diff --git a/src/cryojax/simulator/_integrators/_gaussian_mixture.py b/src/cryojax/simulator/_potential_integrator/gaussian_mixture.py similarity index 100% rename from src/cryojax/simulator/_integrators/_gaussian_mixture.py rename to src/cryojax/simulator/_potential_integrator/gaussian_mixture.py diff --git a/src/cryojax/simulator/_potential_integrator/nufft_project.py b/src/cryojax/simulator/_potential_integrator/nufft_project.py new file mode 100644 index 00000000..81d494b6 --- /dev/null +++ b/src/cryojax/simulator/_potential_integrator/nufft_project.py @@ -0,0 +1,117 @@ +""" +Using non-uniform FFTs for computing volume projections. +""" + +import math +from typing_extensions import override + +import jax.numpy as jnp +from jaxtyping import Array, Complex, Float + +from .._instrument_config import InstrumentConfig +from .._potential_representation import RealVoxelCloudPotential, RealVoxelGridPotential +from .base_potential_integrator import AbstractVoxelPotentialIntegrator + + +class NufftProjection( + AbstractVoxelPotentialIntegrator[RealVoxelGridPotential | RealVoxelCloudPotential], + strict=True, +): + """Integrate points onto the exit plane using + non-uniform FFTs. + """ + + pixel_rescaling_method: str = "bicubic" + eps: float = 1e-6 + + def project_voxel_cloud_with_nufft( + self, + weights: Float[Array, " size"], + coordinate_list: Float[Array, "size 2"] | Float[Array, "size 3"], + shape: tuple[int, int], + ) -> Complex[Array, "{shape[0]} {shape[1]}"]: + """Project and interpolate 3D volume point cloud + onto imaging plane using a non-uniform FFT. + + **Arguments:** + + - `weights`: + Density point cloud. + - `coordinates`: + Coordinate system of point cloud. + - `shape`: + Shape of the imaging plane in pixels. + ``width, height = shape[0], shape[1]`` + is the size of the desired imaging plane. + + **Returns:** + + The output image in fourier space. + """ + return _project_with_nufft(weights, coordinate_list, shape, self.eps) + + @override + def compute_raw_fourier_image( + self, + potential: RealVoxelGridPotential | RealVoxelCloudPotential, + instrument_config: InstrumentConfig, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + """Rasterize image with non-uniform FFTs.""" + if isinstance(potential, RealVoxelGridPotential): + shape = potential.shape + fourier_projection = self.project_voxel_cloud_with_nufft( + potential.real_voxel_grid.ravel(), + potential.wrapped_coordinate_grid_in_pixels.get().reshape( + (math.prod(shape), 3) + ), + instrument_config.padded_shape, + ) + elif isinstance(potential, RealVoxelCloudPotential): + fourier_projection = self.project_voxel_cloud_with_nufft( + potential.voxel_weights, + potential.wrapped_coordinate_list_in_pixels.get(), + instrument_config.padded_shape, + ) + else: + raise ValueError( + "Supported density representations are RealVoxelGrid and VoxelCloud." + ) + return fourier_projection + + +NufftProjection.__init__.__doc__ = """**Arguments:** + +- `pixel_rescaling_method`: + Method for interpolating the final image to the `InstrumentConfig` + pixel size. See `cryojax.image._rescale_pixel_size` for documentation. +- `eps` : `float` + See ``jax-finufft`` for documentation. +""" + + +def _project_with_nufft(weights, coordinate_list, shape, eps=1e-6): + from jax_finufft import nufft1 + + weights, coordinate_list = ( + jnp.asarray(weights).astype(complex), + jnp.asarray(coordinate_list), + ) + # Get x and y coordinates + coordinates_xy = coordinate_list[:, :2] + # Normalize coordinates betweeen -pi and pi + M1, M2 = shape + image_size = jnp.asarray((M1, M2), dtype=float) + coordinates_periodic = 2 * jnp.pi * coordinates_xy / image_size + # Unpack and compute + x, y = coordinates_periodic[:, 0], coordinates_periodic[:, 1] + projection = nufft1(shape, weights, y, x, eps=eps, iflag=-1) + # Shift zero frequency component to corner and take upper half plane + projection = jnp.fft.ifftshift(projection)[:, : M2 // 2 + 1] + # Set last line of frequencies to zero if image dimension is even + if M2 % 2 == 0: + projection = projection.at[:, -1].set(0.0 + 0.0j) + if M1 % 2 == 0: + projection = projection.at[M1 // 2, :].set(0.0 + 0.0j) + return projection diff --git a/src/cryojax/simulator/_potential/__init__.py b/src/cryojax/simulator/_potential_representation/__init__.py similarity index 81% rename from src/cryojax/simulator/_potential/__init__.py rename to src/cryojax/simulator/_potential_representation/__init__.py index abdeb211..e86476a4 100644 --- a/src/cryojax/simulator/_potential/__init__.py +++ b/src/cryojax/simulator/_potential_representation/__init__.py @@ -1,7 +1,7 @@ -from ._scattering_potential import ( - AbstractScatteringPotential as AbstractScatteringPotential, +from .base_potential import ( + AbstractPotentialRepresentation as AbstractPotentialRepresentation, ) -from ._voxel_potential import ( +from .voxel_potential import ( AbstractFourierVoxelGridPotential as AbstractFourierVoxelGridPotential, AbstractVoxelPotential as AbstractVoxelPotential, build_real_space_voxels_from_atoms as build_real_space_voxels_from_atoms, diff --git a/src/cryojax/simulator/_potential/_atom_potential.py b/src/cryojax/simulator/_potential_representation/atom_potential.py similarity index 87% rename from src/cryojax/simulator/_potential/_atom_potential.py rename to src/cryojax/simulator/_potential_representation/atom_potential.py index f3f3152e..a9ba706e 100644 --- a/src/cryojax/simulator/_potential/_atom_potential.py +++ b/src/cryojax/simulator/_potential_representation/atom_potential.py @@ -1,7 +1,4 @@ """ -Atomic-based electron density representations. -""" - from typing import Any, ClassVar, Type import equinox as eqx @@ -10,13 +7,13 @@ from jaxtyping import Array from .._pose import AbstractPose -from ._scattering_potential import AbstractScatteringPotential +from .scattering_potential import AbstractScatteringPotential class AtomCloud(AbstractScatteringPotential): - """ + ''' Abstraction of a point cloud of atoms. - """ + ''' weights: Array = field(converter=jnp.asarray) coordinate_list: Array = field(converter=jnp.asarray) @@ -38,11 +35,12 @@ def from_file( filename: str, **kwargs: Any, ) -> "AtomCloud": - """ + ''' Load an Atom Cloud TODO: What is the file format appropriate here? Q. for Michael... - """ + ''' raise NotImplementedError # return cls.from_mrc(filename, config=config, **kwargs) +""" diff --git a/src/cryojax/simulator/_potential/_scattering_potential.py b/src/cryojax/simulator/_potential_representation/base_potential.py similarity index 50% rename from src/cryojax/simulator/_potential/_scattering_potential.py rename to src/cryojax/simulator/_potential_representation/base_potential.py index 86a03df2..a8ad3b14 100644 --- a/src/cryojax/simulator/_potential/_scattering_potential.py +++ b/src/cryojax/simulator/_potential_representation/base_potential.py @@ -10,15 +10,17 @@ from .._pose import AbstractPose -class AbstractScatteringPotential(Module, strict=True): - """Abstract interface for an electron scattering potential.""" +class AbstractPotentialRepresentation(Module, strict=True): + """Abstract interface for the spatial potential energy distribution of a + scatterer. + """ @abstractmethod def rotate_to_pose(self, pose: AbstractPose) -> Self: - """Return a new `AbstractScatteringPotential` at the given pose. + """Return a new `AbstractPotentialRepresentation` at the given pose. **Arguments:** - - `pose`: The pose at which to view the `AbstractScatteringPotential`. + - `pose`: The pose at which to view the `AbstractPotentialRepresentation`. """ raise NotImplementedError diff --git a/src/cryojax/simulator/_potential/_voxel_potential.py b/src/cryojax/simulator/_potential_representation/voxel_potential.py similarity index 92% rename from src/cryojax/simulator/_potential/_voxel_potential.py rename to src/cryojax/simulator/_potential_representation/voxel_potential.py index 4cfef6fd..0994bcf7 100644 --- a/src/cryojax/simulator/_potential/_voxel_potential.py +++ b/src/cryojax/simulator/_potential_representation/voxel_potential.py @@ -9,7 +9,6 @@ cast, ClassVar, Optional, - overload, ) from typing_extensions import override, Self @@ -20,9 +19,9 @@ from equinox import AbstractClassVar, AbstractVar, field from jaxtyping import Array, Complex, Float, Int +from ..._errors import error_if_not_positive from ...constants import get_form_factor_params from ...coordinates import CoordinateGrid, CoordinateList, FrequencySlice -from ...core import error_if_not_positive from ...image import ( compute_spline_coefficients, crop_to_shape, @@ -31,10 +30,10 @@ ) from ...image.operators import AbstractFilter from .._pose import AbstractPose -from ._scattering_potential import AbstractScatteringPotential +from .base_potential import AbstractPotentialRepresentation -class AbstractVoxelPotential(AbstractScatteringPotential, strict=True): +class AbstractVoxelPotential(AbstractPotentialRepresentation, strict=True): """Abstract interface for a voxel-based scattering potential representation.""" voxel_size: AbstractVar[Float[Array, ""]] @@ -53,7 +52,7 @@ def from_real_voxel_grid( real_voxel_grid: Float[Array, "dim dim dim"] | Float[np.ndarray, "dim dim dim"], voxel_size: Float[Array, ""] | Float[np.ndarray, ""] | float, ) -> Self: - """Load an `AbstractVoxels` from real-valued 3D electron + """Load an `AbstractVoxelPotential` from real-valued 3D electron scattering potential. """ raise NotImplementedError @@ -72,7 +71,7 @@ def from_atoms( ] = None, **kwargs: Any, ) -> Self: - """Load an `AbstractVoxels` from atom positions and identities.""" + """Load an `AbstractVoxelPotential` from atom positions and identities.""" raise NotImplementedError @@ -184,9 +183,7 @@ def from_atoms( - `**kwargs`: Passed to `AbstractFourierVoxelGridPotential.from_real_voxel_grid` """ - form_factors = ( - form_factors if form_factors is None else jnp.asarray(form_factors) - ) + form_factors = form_factors if form_factors is None else jnp.asarray(form_factors) a_vals, b_vals = get_form_factor_params( jnp.asarray(atom_identities), form_factors ) @@ -281,9 +278,7 @@ def __init__( @property def shape(self) -> tuple[int, int, int]: - return cast( - tuple[int, int, int], tuple([s - 2 for s in self.coefficients.shape]) - ) + return cast(tuple[int, int, int], tuple([s - 2 for s in self.coefficients.shape])) class RealVoxelGridPotential(AbstractVoxelPotential, strict=True): @@ -330,26 +325,6 @@ def rotate_to_pose(self, pose: AbstractPose) -> Self: ), ) - @overload - @classmethod - def from_real_voxel_grid( - cls, - real_voxel_grid: Float[Array, "dim dim dim"] | Float[np.ndarray, "dim dim dim"], - voxel_size: Float[Array, ""] | Float[np.ndarray, ""] | float, - *, - coordinate_grid: Optional[CoordinateGrid] = None, - ) -> Self: ... - - @overload - @classmethod - def from_real_voxel_grid( - cls, - real_voxel_grid: Float[Array, "dim dim dim"] | Float[np.ndarray, "dim dim dim"], - voxel_size: Float[Array, ""] | Float[np.ndarray, ""] | float, - *, - crop_scale: Optional[float] = None, - ) -> Self: ... - @classmethod def from_real_voxel_grid( cls, @@ -408,9 +383,7 @@ def from_atoms( - `**kwargs`: Passed to `RealVoxelGridPotential.from_real_voxel_grid` """ - form_factors = ( - form_factors if form_factors is None else jnp.asarray(form_factors) - ) + form_factors = form_factors if form_factors is None else jnp.asarray(form_factors) a_vals, b_vals = get_form_factor_params( jnp.asarray(atom_identities), form_factors ) @@ -438,7 +411,8 @@ class RealVoxelCloudPotential(AbstractVoxelPotential, strict=True): of storing the whole voxel grid, a `RealVoxelCloudPotential` need only store points of non-zero scattering potential. Therefore, a `RealVoxelCloudPotential` stores a point cloud of scattering potential - voxel values. + voxel values. Instantiating with the `from_real_voxel_grid` constructor + will automatically mask points of zero scattering potential. """ voxel_weights: Float[Array, " size"] @@ -465,8 +439,8 @@ def __init__( self.voxel_size = jnp.asarray(voxel_size) @property - def shape(self) -> tuple[int, int]: - return cast(tuple[int, int], self.voxel_weights.shape) + def shape(self) -> tuple[int]: + return cast(tuple[int], self.voxel_weights.shape) @cached_property def wrapped_coordinate_list_in_angstroms(self) -> CoordinateList: @@ -491,6 +465,8 @@ def from_real_voxel_grid( coordinate_grid_in_pixels: Optional[CoordinateGrid] = None, rtol: float = 1e-05, atol: float = 1e-08, + size: Optional[int] = None, + fill_value: Optional[float] = None, ) -> Self: """Load an `RealVoxelCloudPotential` from a real-valued 3D electron scattering potential voxel grid. @@ -499,10 +475,15 @@ def from_real_voxel_grid( - `real_voxel_grid`: An electron scattering potential voxel grid in real space. - `voxel_size`: The voxel size of `real_voxel_grid`. - - `rtol`: Argument passed to `jnp.isclose`, used for removing - points of zero scattering potential. - - `atol`: Argument passed to `jnp.isclose`, used for removing - points of zero scattering potential. + - `rtol`: Argument passed to `jnp.isclose`, used for masking + voxels of zero scattering potential. + - `atol`: Argument passed to `jnp.isclose`, used for masking + voxels of zero scattering potential. + - `size`: Argument passed to `jnp.where`, used for fixing the size + of the masked scattering potential. This argument is required + for using this function with a JAX transformation. + - `fill_value`: Argument passed to `jnp.where`, used if `size` is specified and + the mask has fewer than the indicated number of elements. """ # Cast to jax array real_voxel_grid, voxel_size = ( @@ -514,7 +495,11 @@ def from_real_voxel_grid( coordinate_grid_in_pixels = CoordinateGrid(real_voxel_grid.shape) # ... mask zeros to store smaller arrays. This # option is not jittable. - nonzero = jnp.where(~jnp.isclose(real_voxel_grid, 0.0, rtol=rtol, atol=atol)) + nonzero = jnp.where( + ~jnp.isclose(real_voxel_grid, 0.0, rtol=rtol, atol=atol), + size=size, + fill_value=fill_value, + ) flat_potential = real_voxel_grid[nonzero] coordinate_list = CoordinateList(coordinate_grid_in_pixels.get()[nonzero]) @@ -539,9 +524,7 @@ def from_atoms( - `**kwargs`: Passed to `RealVoxelCloudPotential.from_real_voxel_grid` """ - form_factors = ( - form_factors if form_factors is None else jnp.asarray(form_factors) - ) + form_factors = form_factors if form_factors is None else jnp.asarray(form_factors) a_vals, b_vals = get_form_factor_params( jnp.asarray(atom_identities), form_factors ) diff --git a/src/cryojax/simulator/_scattering_theory/__init__.py b/src/cryojax/simulator/_scattering_theory/__init__.py new file mode 100644 index 00000000..28eab427 --- /dev/null +++ b/src/cryojax/simulator/_scattering_theory/__init__.py @@ -0,0 +1,6 @@ +from .base_scattering_theory import AbstractScatteringTheory as AbstractScatteringTheory +from .linear_scattering_theory import ( + AbstractLinearScatteringTheory as AbstractLinearScatteringTheory, + LinearScatteringTheory as LinearScatteringTheory, + LinearSuperpositionScatteringTheory as LinearSuperpositionScatteringTheory, +) diff --git a/src/cryojax/simulator/_scattering_theory/base_scattering_theory.py b/src/cryojax/simulator/_scattering_theory/base_scattering_theory.py new file mode 100644 index 00000000..287bd9a3 --- /dev/null +++ b/src/cryojax/simulator/_scattering_theory/base_scattering_theory.py @@ -0,0 +1,31 @@ +from abc import abstractmethod +from typing import Optional + +import equinox as eqx +from jaxtyping import Array, Complex, PRNGKeyArray + +from .._instrument_config import InstrumentConfig + + +class AbstractScatteringTheory(eqx.Module, strict=True): + """Base class for a scattering theory.""" + + @abstractmethod + def compute_fourier_contrast_at_detector_plane( + self, + instrument_config: InstrumentConfig, + rng_key: Optional[PRNGKeyArray] = None, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + raise NotImplementedError + + @abstractmethod + def compute_fourier_squared_wavefunction_at_detector_plane( + self, + instrument_config: InstrumentConfig, + rng_key: Optional[PRNGKeyArray] = None, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + raise NotImplementedError diff --git a/src/cryojax/simulator/_scattering_theory/linear_scattering_theory.py b/src/cryojax/simulator/_scattering_theory/linear_scattering_theory.py new file mode 100644 index 00000000..03faf598 --- /dev/null +++ b/src/cryojax/simulator/_scattering_theory/linear_scattering_theory.py @@ -0,0 +1,278 @@ +from abc import abstractmethod +from functools import partial +from typing import Optional +from typing_extensions import override + +import equinox as eqx +import jax +import jax.numpy as jnp +from jaxtyping import Array, Complex, PRNGKeyArray + +from .._instrument_config import InstrumentConfig +from .._pose import AbstractPose +from .._potential_integrator import AbstractPotentialIntegrator +from .._solvent import AbstractIce +from .._structural_ensemble import ( + AbstractConformationalVariable, + AbstractStructuralEnsemble, + AbstractStructuralEnsembleBatcher, +) +from .._transfer_theory import ContrastTransferTheory +from .base_scattering_theory import AbstractScatteringTheory + + +class AbstractLinearScatteringTheory(AbstractScatteringTheory, strict=True): + """Base class for a scattering theory in linear image formation theory + (the weak-phase approximation). + """ + + @abstractmethod + def compute_fourier_phase_shifts_at_exit_plane( + self, + instrument_config: InstrumentConfig, + rng_key: Optional[PRNGKeyArray] = None, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + raise NotImplementedError + + @override + def compute_fourier_squared_wavefunction_at_detector_plane( + self, + instrument_config: InstrumentConfig, + rng_key: Optional[PRNGKeyArray] = None, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + """Compute the squared wavefunction at the detector plane, given the + contrast. + """ + N1, N2 = instrument_config.padded_shape + # ... compute the squared wavefunction directly from the image contrast + # as |psi|^2 = 1 + 2C. + fourier_contrast_at_detector_plane = ( + self.compute_fourier_contrast_at_detector_plane(instrument_config, rng_key) + ) + fourier_squared_wavefunction_at_detector_plane = ( + (2 * fourier_contrast_at_detector_plane).at[0, 0].add(1.0 * N1 * N2) + ) + return fourier_squared_wavefunction_at_detector_plane + + +class LinearScatteringTheory(AbstractLinearScatteringTheory, strict=True): + """Base linear image formation theory.""" + + structural_ensemble: AbstractStructuralEnsemble + potential_integrator: AbstractPotentialIntegrator + transfer_theory: ContrastTransferTheory + solvent: Optional[AbstractIce] = None + + @override + def compute_fourier_phase_shifts_at_exit_plane( + self, + instrument_config: InstrumentConfig, + rng_key: Optional[PRNGKeyArray] = None, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + # Compute the phase shifts in the exit plane + fourier_phase_shifts_at_exit_plane = ( + _compute_phase_shifts_from_projected_potential( + self.structural_ensemble, self.potential_integrator, instrument_config + ) + ) + + if rng_key is not None: + # Get the potential of the specimen plus the ice + if self.solvent is not None: + fourier_phase_shifts_at_exit_plane = ( + self.solvent.compute_fourier_phase_shifts_with_ice( + rng_key, fourier_phase_shifts_at_exit_plane, instrument_config + ) + ) + + return fourier_phase_shifts_at_exit_plane + + @override + def compute_fourier_contrast_at_detector_plane( + self, + instrument_config: InstrumentConfig, + rng_key: Optional[PRNGKeyArray] = None, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + fourier_phase_shifts_at_exit_plane = ( + self.compute_fourier_phase_shifts_at_exit_plane(instrument_config, rng_key) + ) + fourier_contrast_at_detector_plane = self.transfer_theory( + fourier_phase_shifts_at_exit_plane, + instrument_config, + defocus_offset=self.structural_ensemble.pose.offset_z_in_angstroms, + ) + + return fourier_contrast_at_detector_plane + + +LinearScatteringTheory.__init__.__doc__ = """**Arguments:** + +- `structural_ensemble`: The structural ensemble of scattering potentials. +- `potential_integrator`: The method for integrating the scattering potential. +- `transfer_theory`: The contrast transfer theory. +- `solvent`: The model for the solvent. +""" + + +class LinearSuperpositionScatteringTheory(AbstractLinearScatteringTheory, strict=True): + """Compute the superposition of images of the structural ensemble batch returned by + the `AbstractStructuralEnsembleBatcher`. + """ + + structural_ensemble_batcher: AbstractStructuralEnsembleBatcher + potential_integrator: AbstractPotentialIntegrator + transfer_theory: ContrastTransferTheory + solvent: Optional[AbstractIce] = None + + @override + def compute_fourier_phase_shifts_at_exit_plane( + self, + instrument_config: InstrumentConfig, + rng_key: Optional[PRNGKeyArray] = None, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + @partial(eqx.filter_vmap, in_axes=(0, None, None)) + def compute_image_stack(ensemble_vmap, ensemble_no_vmap, instrument_config): + ensemble = eqx.combine(ensemble_vmap, ensemble_no_vmap) + fourier_phase_shifts_at_exit_plane = ( + _compute_phase_shifts_from_projected_potential( + ensemble, self.potential_integrator, instrument_config + ) + ) + return fourier_phase_shifts_at_exit_plane + + @eqx.filter_jit + def compute_image_superposition( + ensemble_vmap, ensemble_no_vmap, instrument_config + ): + return jnp.sum( + compute_image_stack(ensemble_vmap, ensemble_no_vmap, instrument_config), + axis=0, + ) + + # Get the batch + ensemble_batch = ( + self.structural_ensemble_batcher.get_batched_structural_ensemble() + ) + # Setup vmap over the pose and conformation + is_vmap = lambda x: isinstance(x, (AbstractPose, AbstractConformationalVariable)) + to_vmap = jax.tree_util.tree_map(is_vmap, ensemble_batch, is_leaf=is_vmap) + vmap, novmap = eqx.partition(ensemble_batch, to_vmap) + + fourier_phase_shifts_at_exit_plane = compute_image_superposition( + vmap, novmap, instrument_config + ) + + if rng_key is not None: + # Get the potential of the specimen plus the ice + if self.solvent is not None: + fourier_phase_shifts_at_exit_plane = ( + self.solvent.compute_fourier_phase_shifts_with_ice( + rng_key, fourier_phase_shifts_at_exit_plane, instrument_config + ) + ) + + return fourier_phase_shifts_at_exit_plane + + @override + def compute_fourier_contrast_at_detector_plane( + self, + instrument_config: InstrumentConfig, + rng_key: Optional[PRNGKeyArray] = None, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + @partial(eqx.filter_vmap, in_axes=(0, None, None)) + def compute_image_stack(ensemble_vmap, ensemble_no_vmap, instrument_config): + ensemble = eqx.combine(ensemble_vmap, ensemble_no_vmap) + fourier_phase_shifts_at_exit_plane = ( + _compute_phase_shifts_from_projected_potential( + ensemble, self.potential_integrator, instrument_config + ) + ) + fourier_contrast_at_detector_plane = self.transfer_theory( + fourier_phase_shifts_at_exit_plane, instrument_config + ) + + return fourier_contrast_at_detector_plane + + @eqx.filter_jit + def compute_image_superposition( + ensemble_vmap, ensemble_no_vmap, instrument_config + ): + return jnp.sum( + compute_image_stack(ensemble_vmap, ensemble_no_vmap, instrument_config), + axis=0, + ) + + # Get the batch + ensemble_batch = ( + self.structural_ensemble_batcher.get_batched_structural_ensemble() + ) + # Setup vmap over the pose and conformation + is_vmap = lambda x: isinstance(x, (AbstractPose, AbstractConformationalVariable)) + to_vmap = jax.tree_util.tree_map(is_vmap, ensemble_batch, is_leaf=is_vmap) + vmap, novmap = eqx.partition(ensemble_batch, to_vmap) + + fourier_contrast_at_detector_plane = compute_image_superposition( + vmap, novmap, instrument_config + ) + + if rng_key is not None: + # Get the contrast from the ice and add to that of the image batch + if self.solvent is not None: + fourier_ice_contrast_at_detector_plane = self.transfer_theory( + self.solvent.sample_fourier_phase_shifts_from_ice( + rng_key, instrument_config + ), + instrument_config, + ) + fourier_contrast_at_detector_plane += ( + fourier_ice_contrast_at_detector_plane + ) + + return fourier_contrast_at_detector_plane + + +LinearSuperpositionScatteringTheory.__init__.__doc__ = """**Arguments:** + +- `structural_ensemble_batcher`: The batcher that computes the states that over which to + compute a superposition of images. Most commonly, this + would be an `AbstractAssembly` concrete class. +- `potential_integrator`: The method for integrating the specimen potential. +- `transfer_theory`: The contrast transfer theory. +- `solvent`: The model for the solvent. +""" + + +def _compute_phase_shifts_from_projected_potential( + structural_ensemble, potential_integrator, instrument_config +): + # Get potential in the lab frame + potential = structural_ensemble.get_potential_in_lab_frame() + # Compute the phase shifts in the exit plane + fourier_projected_potential = ( + potential_integrator.compute_fourier_integrated_potential( + potential, instrument_config + ) + ) + # Compute in-plane translation through fourier phase shifts + translational_phase_shifts = structural_ensemble.pose.compute_shifts( + instrument_config.wrapped_padded_frequency_grid_in_angstroms.get() + ) + # The phase shifts in the exit plane multiplies the wavelength x + # projected potential (here with units of inverse angstroms) x the translation + return ( + instrument_config.wavelength_in_angstroms + * fourier_projected_potential + * translational_phase_shifts + ) diff --git a/src/cryojax/simulator/_ice.py b/src/cryojax/simulator/_solvent.py similarity index 64% rename from src/cryojax/simulator/_ice.py rename to src/cryojax/simulator/_solvent.py index dfe2b117..87aa933e 100644 --- a/src/cryojax/simulator/_ice.py +++ b/src/cryojax/simulator/_solvent.py @@ -12,31 +12,38 @@ from jaxtyping import Array, Complex, PRNGKeyArray from ..image.operators import FourierOperatorLike -from ._config import ImageConfig +from ._instrument_config import InstrumentConfig class AbstractIce(Module, strict=True): """Base class for an ice model.""" @abstractmethod - def sample( - self, key: PRNGKeyArray, config: ImageConfig - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: + def sample_fourier_phase_shifts_from_ice( + self, key: PRNGKeyArray, instrument_config: InstrumentConfig + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: """Sample a stochastic realization of the phase shifts due to the ice at the exit plane.""" raise NotImplementedError - def __call__( + def compute_fourier_phase_shifts_with_ice( self, key: PRNGKeyArray, fourier_phase_at_exit_plane: Complex[ - Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}" + Array, + "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}", ], - config: ImageConfig, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: + instrument_config: InstrumentConfig, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: """Compute the combined phase of the ice and the specimen.""" # Sample the realization of the phase due to the ice. - fourier_ice_phase_at_exit_plane = self.sample(key, config) + fourier_ice_phase_at_exit_plane = self.sample_fourier_phase_shifts_from_ice( + key, instrument_config + ) return fourier_phase_at_exit_plane + fourier_ice_phase_at_exit_plane @@ -58,13 +65,15 @@ def __init__(self, variance: FourierOperatorLike): self.variance = variance @override - def sample( - self, key: PRNGKeyArray, config: ImageConfig - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: + def sample_fourier_phase_shifts_from_ice( + self, key: PRNGKeyArray, instrument_config: InstrumentConfig + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: """Sample a realization of the ice phase shifts as colored gaussian noise.""" - N_pix = np.prod(config.padded_shape) + N_pix = np.prod(instrument_config.padded_shape) frequency_grid_in_angstroms = ( - config.wrapped_padded_frequency_grid_in_angstroms.get() + instrument_config.wrapped_padded_frequency_grid_in_angstroms.get() ) # Compute standard deviation, scaling up by the variance by the number # of pixels to make the realization independent pixel-independent in real-space. diff --git a/src/cryojax/simulator/_specimen.py b/src/cryojax/simulator/_specimen.py deleted file mode 100644 index 400c3e4c..00000000 --- a/src/cryojax/simulator/_specimen.py +++ /dev/null @@ -1,171 +0,0 @@ -""" -Abstractions of biological specimen. -""" - -from abc import abstractmethod -from functools import cached_property -from typing import Any, Optional -from typing_extensions import override - -import jax -from equinox import AbstractVar, Module -from jaxtyping import Array, Complex, PRNGKeyArray - -from ._config import ImageConfig -from ._conformation import AbstractConformation, DiscreteConformation -from ._ice import AbstractIce -from ._instrument import Instrument -from ._integrators import AbstractPotentialIntegrator -from ._pose import AbstractPose, EulerAnglePose -from ._potential import AbstractScatteringPotential - - -class AbstractSpecimen(Module, strict=True): - """ - Abstraction of a of biological specimen. - - **Attributes:** - - - `potential`: The scattering potential of the specimen. - - `integrator`: A method of integrating the `potential` onto the exit - plane of the specimen. - - `pose`: The pose of the specimen. - """ - - potential: AbstractVar[Any] - integrator: AbstractVar[Any] - pose: AbstractVar[AbstractPose] - - @cached_property - @abstractmethod - def potential_in_com_frame(self) -> AbstractScatteringPotential: - """Get the scattering potential in the center of mass - frame.""" - raise NotImplementedError - - @cached_property - def potential_in_lab_frame(self) -> AbstractScatteringPotential: - """Get the scattering potential in the lab frame.""" - return self.potential_in_com_frame.rotate_to_pose(self.pose) - - def scatter_to_exit_plane( - self, - instrument: Instrument, - config: ImageConfig, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: - """Scatter the specimen potential to the exit plane.""" - # Get potential in the lab frame - potential = self.potential_in_lab_frame - # Compute the scattering potential in fourier space - fourier_phase_at_exit_plane = self.integrator( - potential, instrument.wavelength_in_angstroms, config - ) - # Apply translation through phase shifts - fourier_phase_at_exit_plane *= self.pose.compute_shifts( - config.wrapped_padded_frequency_grid_in_angstroms.get() - ) - - return fourier_phase_at_exit_plane - - def scatter_to_exit_plane_with_solvent( - self, - key: PRNGKeyArray, - instrument: Instrument, - solvent: AbstractIce, - config: ImageConfig, - ) -> Complex[Array, "{config.padded_y_dim} {config.padded_x_dim//2+1}"]: - """Scatter the specimen potential to the exit plane, including - the phase shifts due to the solvent.""" - # Compute the phase in fourier space - fourier_phase_at_exit_plane = self.scatter_to_exit_plane(instrument, config) - # Get the potential of the specimen plus the ice - fourier_phase_at_exit_plane_with_solvent = solvent( - key, fourier_phase_at_exit_plane, config - ) - - return fourier_phase_at_exit_plane_with_solvent - - -class Specimen(AbstractSpecimen, strict=True): - """ - Abstraction of a of biological specimen. - - **Attributes:** - - - `potential`: The scattering potential representation of the - specimen as a single scattering potential object. - """ - - potential: AbstractScatteringPotential - integrator: AbstractPotentialIntegrator - pose: AbstractPose - - def __init__( - self, - potential: AbstractScatteringPotential, - integrator: AbstractPotentialIntegrator, - pose: Optional[AbstractPose] = None, - ): - self.potential = potential - self.integrator = integrator - self.pose = pose or EulerAnglePose() - - @cached_property - @override - def potential_in_com_frame(self) -> AbstractScatteringPotential: - """Get the scattering potential in the center of mass - frame.""" - return self.potential - - -class AbstractEnsemble(AbstractSpecimen, strict=True): - """ - Abstraction of an ensemble of a biological specimen which can - occupy different conformations. - - **Attributes:** - - - `conformation`: The conformation at which to evaluate the scattering potential. - """ - - conformation: AbstractVar[AbstractConformation] - - -class DiscreteEnsemble(AbstractEnsemble, strict=True): - """ - Abstraction of an ensemble with discrete conformational - heterogeneity. - - **Attributes:** - - - `potential`: A tuple of scattering potential representations. - - `pose`: The pose of the specimen. - - `conformation`: A conformation with a discrete index at which to evaluate - the scattering potential tuple. - """ - - potential: tuple[AbstractScatteringPotential, ...] - integrator: AbstractPotentialIntegrator - pose: AbstractPose - conformation: DiscreteConformation - - def __init__( - self, - potential: tuple[AbstractScatteringPotential, ...], - integrator: AbstractPotentialIntegrator, - pose: Optional[AbstractPose] = None, - conformation: Optional[DiscreteConformation] = None, - ): - self.potential = potential - self.integrator = integrator - self.pose = pose or EulerAnglePose() - self.conformation = conformation or DiscreteConformation(0) - - @cached_property - @override - def potential_in_com_frame(self) -> AbstractScatteringPotential: - """Get the scattering potential at configured conformation.""" - funcs = [lambda i=i: self.potential[i] for i in range(len(self.potential))] - potential = jax.lax.switch(self.conformation.value, funcs) - - return potential diff --git a/src/cryojax/simulator/_structural_ensemble/__init__.py b/src/cryojax/simulator/_structural_ensemble/__init__.py new file mode 100644 index 00000000..f6af3f15 --- /dev/null +++ b/src/cryojax/simulator/_structural_ensemble/__init__.py @@ -0,0 +1,14 @@ +from .base_conformation import ( + AbstractConformationalVariable as AbstractConformationalVariable, +) +from .base_ensemble import ( + AbstractStructuralEnsemble as AbstractStructuralEnsemble, + SingleStructureEnsemble as SingleStructureEnsemble, +) +from .discrete_ensemble import ( + DiscreteConformationalVariable as DiscreteConformationalVariable, + DiscreteStructuralEnsemble as DiscreteStructuralEnsemble, +) +from .ensemble_batcher import ( + AbstractStructuralEnsembleBatcher as AbstractStructuralEnsembleBatcher, +) diff --git a/src/cryojax/simulator/_structural_ensemble/base_conformation.py b/src/cryojax/simulator/_structural_ensemble/base_conformation.py new file mode 100644 index 00000000..4a35e7c0 --- /dev/null +++ b/src/cryojax/simulator/_structural_ensemble/base_conformation.py @@ -0,0 +1,19 @@ +""" +Representations of conformational variables. +""" + +from typing import Any + +from equinox import AbstractVar, Module + + +class AbstractConformationalVariable(Module, strict=True): + """A conformational variable wrapped in an `equinox.Module`.""" + + value: AbstractVar[Any] + + +AbstractConformationalVariable.__init__.__doc__ = """**Arguments:** + +- `value`: The value of the integer conformation. +""" diff --git a/src/cryojax/simulator/_structural_ensemble/base_ensemble.py b/src/cryojax/simulator/_structural_ensemble/base_ensemble.py new file mode 100644 index 00000000..abc931bc --- /dev/null +++ b/src/cryojax/simulator/_structural_ensemble/base_ensemble.py @@ -0,0 +1,62 @@ +""" +Abstractions of ensembles of biological specimen. +""" + +from abc import abstractmethod +from typing import Any, Optional +from typing_extensions import override + +from equinox import AbstractVar, Module + +from .._pose import AbstractPose, EulerAnglePose +from .._potential_representation import AbstractPotentialRepresentation +from .base_conformation import AbstractConformationalVariable + + +class AbstractStructuralEnsemble(Module, strict=True): + """A map from a pose and conformational variable to an `AbstractPotential`.""" + + conformational_space: AbstractVar[Any] + pose: AbstractVar[AbstractPose] + conformation: AbstractVar[Optional[AbstractConformationalVariable]] + + @abstractmethod + def get_potential_at_conformation(self) -> AbstractPotentialRepresentation: + """Get the scattering potential in the center of mass + frame.""" + raise NotImplementedError + + def get_potential_in_lab_frame(self) -> AbstractPotentialRepresentation: + """Get the scattering potential in the lab frame.""" + potential = self.get_potential_at_conformation() + return potential.rotate_to_pose(self.pose) + + +class SingleStructureEnsemble(AbstractStructuralEnsemble, strict=True): + """An "ensemble" with one conformation.""" + + conformational_space: AbstractPotentialRepresentation + pose: AbstractPose + conformation: None + + def __init__( + self, + conformational_space: AbstractPotentialRepresentation, + pose: Optional[AbstractPose] = None, + ): + """**Arguments:** + + - `conformational_space`: The scattering potential representation of the + specimen as a single scattering potential object. + - `pose`: The pose of the specimen. + """ + self.conformational_space = conformational_space + self.pose = pose or EulerAnglePose() + self.conformation = None + + @override + def get_potential_at_conformation(self) -> AbstractPotentialRepresentation: + """Get the scattering potential in the center of mass + frame. + """ + return self.conformational_space diff --git a/src/cryojax/simulator/_structural_ensemble/discrete_ensemble.py b/src/cryojax/simulator/_structural_ensemble/discrete_ensemble.py new file mode 100644 index 00000000..ff59ad24 --- /dev/null +++ b/src/cryojax/simulator/_structural_ensemble/discrete_ensemble.py @@ -0,0 +1,60 @@ +""" +Abstractions of ensembles on discrete conformational variables. +""" + +from typing import Optional +from typing_extensions import override + +import jax +from equinox import field +from jaxtyping import Array, Int + +from ..._errors import error_if_negative +from .._pose import AbstractPose, EulerAnglePose +from .._potential_representation import AbstractPotentialRepresentation +from .base_conformation import AbstractConformationalVariable +from .base_ensemble import AbstractStructuralEnsemble + + +class DiscreteConformationalVariable(AbstractConformationalVariable, strict=True): + """A conformational variable as a discrete index.""" + + value: Int[Array, ""] = field(converter=error_if_negative) + + +class DiscreteStructuralEnsemble(AbstractStructuralEnsemble, strict=True): + """Abstraction of an ensemble with discrete conformational + heterogeneity. + """ + + conformational_space: tuple[AbstractPotentialRepresentation, ...] + pose: AbstractPose + conformation: DiscreteConformationalVariable + + def __init__( + self, + conformational_space: tuple[AbstractPotentialRepresentation, ...], + pose: Optional[AbstractPose] = None, + conformation: Optional[DiscreteConformationalVariable] = None, + ): + """**Arguments:** + + - `conformational_space`: A tuple of `AbstractPotential` representations. + - `pose`: The pose of the specimen. + - `conformation`: A conformation with a discrete index at which to evaluate + the scattering potential tuple. + """ + self.conformational_space = conformational_space + self.pose = pose or EulerAnglePose() + self.conformation = conformation or DiscreteConformationalVariable(0) + + @override + def get_potential_at_conformation(self) -> AbstractPotentialRepresentation: + """Get the scattering potential at configured conformation.""" + funcs = [ + lambda i=i: self.conformational_space[i] + for i in range(len(self.conformational_space)) + ] + potential = jax.lax.switch(self.conformation.value, funcs) + + return potential diff --git a/src/cryojax/simulator/_structural_ensemble/ensemble_batcher.py b/src/cryojax/simulator/_structural_ensemble/ensemble_batcher.py new file mode 100644 index 00000000..5a59cadf --- /dev/null +++ b/src/cryojax/simulator/_structural_ensemble/ensemble_batcher.py @@ -0,0 +1,13 @@ +from abc import abstractmethod + +import equinox as eqx + +from .base_ensemble import AbstractStructuralEnsemble + + +class AbstractStructuralEnsembleBatcher(eqx.Module, strict=True): + """A batching utility for structural ensembles.""" + + @abstractmethod + def get_batched_structural_ensemble(self) -> AbstractStructuralEnsemble: + raise NotImplementedError diff --git a/src/cryojax/simulator/_transfer_theory/__init__.py b/src/cryojax/simulator/_transfer_theory/__init__.py new file mode 100644 index 00000000..25d0b2f6 --- /dev/null +++ b/src/cryojax/simulator/_transfer_theory/__init__.py @@ -0,0 +1,10 @@ +from .base_transfer_theory import ( + AbstractTransferFunction as AbstractTransferFunction, + AbstractTransferTheory as AbstractTransferTheory, +) +from .contrast_transfer_theory import ( + AbstractContrastTransferFunction as AbstractContrastTransferFunction, + ContrastTransferFunction as ContrastTransferFunction, + ContrastTransferTheory as ContrastTransferTheory, + IdealContrastTransferFunction as IdealContrastTransferFunction, +) diff --git a/src/cryojax/simulator/_transfer_theory/base_transfer_theory.py b/src/cryojax/simulator/_transfer_theory/base_transfer_theory.py new file mode 100644 index 00000000..d55384a7 --- /dev/null +++ b/src/cryojax/simulator/_transfer_theory/base_transfer_theory.py @@ -0,0 +1,56 @@ +from abc import abstractmethod +from typing import Optional + +from equinox import Module +from jaxtyping import Array, Complex, Float + +from ...image.operators import ( + AbstractFourierOperator, +) +from .._instrument_config import InstrumentConfig + + +class AbstractTransferFunction(AbstractFourierOperator, strict=True): + """An abstract base class for a transfer function.""" + + @abstractmethod + def __call__( + self, + frequency_grid_in_angstroms: Float[Array, "y_dim x_dim 2"], + *, + wavelength_in_angstroms: Optional[Float[Array, ""] | float] = None, + defocus_offset: Float[Array, ""] | float = 0.0, + ) -> Float[Array, "y_dim x_dim"] | Complex[Array, "y_dim x_dim"]: + raise NotImplementedError + + +class AbstractTransferTheory(Module, strict=True): + """Base class for a transfer theory.""" + + @abstractmethod + def __call__( + self, + fourier_phase_or_wavefunction_at_exit_plane: ( + Complex[ + Array, + "{instrument_config.padded_y_dim} " + "{instrument_config.padded_x_dim//2+1}", + ] + | Complex[ + Array, + "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim}", + ] + ), + instrument_config: InstrumentConfig, + defocus_offset: Float[Array, ""] | float = 0.0, + ) -> ( + Complex[ + Array, + "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}", + ] + | Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim}" + ] + ): + """Pass an image through the transfer theory.""" + raise NotImplementedError diff --git a/src/cryojax/simulator/_transfer_theory/common_functions.py b/src/cryojax/simulator/_transfer_theory/common_functions.py new file mode 100644 index 00000000..b3319d6e --- /dev/null +++ b/src/cryojax/simulator/_transfer_theory/common_functions.py @@ -0,0 +1,63 @@ +import jax.numpy as jnp +from jaxtyping import Array, Float + +from ...coordinates import cartesian_to_polar + + +# Not currently public API +def compute_phase_shifts( + frequency_grid_in_angstroms: Float[Array, "y_dim x_dim 2"], + defocus_axis_1_in_angstroms: Float[Array, ""], + defocus_axis_2_in_angstroms: Float[Array, ""], + astigmatism_angle: Float[Array, ""], + wavelength_in_angstroms: Float[Array, ""], + spherical_aberration_in_angstroms: Float[Array, ""], + phase_shift: Float[Array, ""], +) -> Float[Array, "y_dim x_dim"]: + k_sqr, azimuth = cartesian_to_polar(frequency_grid_in_angstroms, square=True) + defocus = 0.5 * ( + defocus_axis_1_in_angstroms + + defocus_axis_2_in_angstroms + + (defocus_axis_1_in_angstroms - defocus_axis_2_in_angstroms) + * jnp.cos(2.0 * (azimuth - astigmatism_angle)) + ) + defocus_phase_shifts = -0.5 * defocus * wavelength_in_angstroms * k_sqr + aberration_phase_shifts = ( + 0.25 + * spherical_aberration_in_angstroms + * (wavelength_in_angstroms**3) + * (k_sqr**2) + ) + phase_shifts = (2 * jnp.pi) * ( + defocus_phase_shifts + aberration_phase_shifts + ) - phase_shift + + return phase_shifts + + +# Not currently public API +def compute_phase_shifts_with_amplitude_contrast_ratio( + frequency_grid_in_angstroms: Float[Array, "y_dim x_dim 2"], + defocus_axis_1_in_angstroms: Float[Array, ""], + defocus_axis_2_in_angstroms: Float[Array, ""], + astigmatism_angle: Float[Array, ""], + wavelength_in_angstroms: Float[Array, ""], + spherical_aberration_in_angstroms: Float[Array, ""], + phase_shift: Float[Array, ""], + amplitude_contrast_ratio: Float[Array, ""], +) -> Float[Array, "y_dim x_dim"]: + phase_shifts = compute_phase_shifts( + frequency_grid_in_angstroms, + defocus_axis_1_in_angstroms, + defocus_axis_2_in_angstroms, + astigmatism_angle, + wavelength_in_angstroms, + spherical_aberration_in_angstroms, + phase_shift, + ) + amplitude_contrast_phase_shifts = jnp.arctan( + amplitude_contrast_ratio / jnp.sqrt(1.0 - amplitude_contrast_ratio**2) + ) + phase_shifts -= amplitude_contrast_phase_shifts + + return phase_shifts diff --git a/src/cryojax/simulator/_transfer_theory/contrast_transfer_theory.py b/src/cryojax/simulator/_transfer_theory/contrast_transfer_theory.py new file mode 100644 index 00000000..ae4fe703 --- /dev/null +++ b/src/cryojax/simulator/_transfer_theory/contrast_transfer_theory.py @@ -0,0 +1,175 @@ +from abc import abstractmethod +from typing import Optional +from typing_extensions import override + +import jax.numpy as jnp +from equinox import field +from jaxtyping import Array, Complex, Float + +from ..._errors import error_if_negative, error_if_not_fractional, error_if_not_positive +from ...constants import convert_keV_to_angstroms +from ...image.operators import ( + Constant, + FourierOperatorLike, +) +from .._instrument_config import InstrumentConfig +from .base_transfer_theory import AbstractTransferFunction, AbstractTransferTheory +from .common_functions import compute_phase_shifts_with_amplitude_contrast_ratio + + +class AbstractContrastTransferFunction(AbstractTransferFunction, strict=True): + """An abstract base class for a transfer function.""" + + @abstractmethod + def __call__( + self, + frequency_grid_in_angstroms: Float[Array, "y_dim x_dim 2"], + *, + wavelength_in_angstroms: Optional[Float[Array, ""] | float] = None, + defocus_offset: Float[Array, ""] | float = 0.0, + ) -> Float[Array, "y_dim x_dim"]: + raise NotImplementedError + + +class ContrastTransferFunction(AbstractContrastTransferFunction, strict=True): + """Compute an astigmatic Contrast Transfer Function (CTF) with a + spherical aberration correction and amplitude contrast ratio. + """ + + defocus_in_angstroms: Float[Array, ""] = field( + default=10000.0, converter=error_if_not_positive + ) + astigmatism_in_angstroms: Float[Array, ""] = field(default=0.0, converter=jnp.asarray) + astigmatism_angle: Float[Array, ""] = field(default=0.0, converter=jnp.asarray) + voltage_in_kilovolts: Float[Array, ""] | float = field( + default=300.0, static=True + ) # Mark `static=True` so that the voltage is not part of the model pytree + # It is treated as part of the pytree upstream, in the Instrument! + spherical_aberration_in_mm: Float[Array, ""] = field( + default=2.7, converter=error_if_negative + ) + amplitude_contrast_ratio: Float[Array, ""] = field( + default=0.1, converter=error_if_not_fractional + ) + phase_shift: Float[Array, ""] = field(default=0.0, converter=jnp.asarray) + + def __call__( + self, + frequency_grid_in_angstroms: Float[Array, "y_dim x_dim 2"], + *, + wavelength_in_angstroms: Optional[Float[Array, ""] | float] = None, + defocus_offset: Float[Array, ""] | float = 0.0, + ) -> Float[Array, "y_dim x_dim"]: + # Convert degrees to radians + phase_shift = jnp.deg2rad(self.phase_shift) + astigmatism_angle = jnp.deg2rad(self.astigmatism_angle) + # Convert spherical abberation coefficient to angstroms + spherical_aberration_in_angstroms = self.spherical_aberration_in_mm * 1e7 + # Get the wavelength. It can either be passed from upstream or stored in the + # CTF + if wavelength_in_angstroms is None: + wavelength_in_angstroms = convert_keV_to_angstroms( + jnp.asarray(self.voltage_in_kilovolts) + ) + else: + wavelength_in_angstroms = jnp.asarray(wavelength_in_angstroms) + defocus_axis_1_in_angstroms = self.defocus_in_angstroms + jnp.asarray( + defocus_offset + ) + defocus_axis_2_in_angstroms = ( + self.defocus_in_angstroms + + self.astigmatism_in_angstroms + + jnp.asarray(defocus_offset) + ) + # Compute phase shifts for CTF + phase_shifts = compute_phase_shifts_with_amplitude_contrast_ratio( + frequency_grid_in_angstroms, + defocus_axis_1_in_angstroms, + defocus_axis_2_in_angstroms, + astigmatism_angle, + wavelength_in_angstroms, + spherical_aberration_in_angstroms, + phase_shift, + self.amplitude_contrast_ratio, + ) + # Compute the CTF + return jnp.sin(phase_shifts).at[0, 0].set(0.0) + + +ContrastTransferFunction.__init__.__doc__ = """**Arguments:** + +- `defocus_u_in_angstroms`: The major axis defocus in Angstroms. +- `defocus_v_in_angstroms`: The minor axis defocus in Angstroms. +- `astigmatism_angle`: The defocus angle. +- `voltage_in_kilovolts`: The accelerating voltage in kV. +- `spherical_aberration_in_mm`: The spherical aberration coefficient in mm. +- `amplitude_contrast_ratio`: The amplitude contrast ratio. +- `phase_shift`: The additional phase shift. +""" + + +class IdealContrastTransferFunction(AbstractContrastTransferFunction, strict=True): + """Compute a perfect CTF, where frequency content is delivered equally + over all frequencies. + """ + + def __call__( + self, + frequency_grid_in_angstroms: Float[Array, "y_dim x_dim 2"], + *, + wavelength_in_angstroms: Optional[Float[Array, ""] | float] = None, + defocus_offset: Float[Array, ""] | float = 0.0, + ) -> Float[Array, "y_dim x_dim"]: + return jnp.ones(frequency_grid_in_angstroms.shape[0:2]) + + +class ContrastTransferTheory(AbstractTransferTheory, strict=True): + """An optics model in the weak-phase approximation. Here, compute the image + contrast by applying the CTF directly to the exit plane phase shifts. + """ + + ctf: AbstractContrastTransferFunction + envelope: FourierOperatorLike + + def __init__( + self, + ctf: AbstractContrastTransferFunction, + envelope: Optional[FourierOperatorLike] = None, + ): + self.ctf = ctf + self.envelope = envelope or Constant(1.0) + + @override + def __call__( + self, + fourier_phase_at_exit_plane: Complex[ + Array, + "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}", + ], + instrument_config: InstrumentConfig, + defocus_offset: Float[Array, ""] | float = 0.0, + ) -> Complex[ + Array, "{instrument_config.padded_y_dim} {instrument_config.padded_x_dim//2+1}" + ]: + """Apply the CTF directly to the phase shifts in the exit plane.""" + frequency_grid = ( + instrument_config.wrapped_padded_frequency_grid_in_angstroms.get() + ) + # Compute the CTF + ctf_array = self.envelope(frequency_grid) * self.ctf( + frequency_grid, + wavelength_in_angstroms=instrument_config.wavelength_in_angstroms, + defocus_offset=defocus_offset, + ) + # ... compute the contrast as the CTF multiplied by the exit plane + # phase shifts + fourier_contrast_at_detector_plane = ctf_array * fourier_phase_at_exit_plane + + return fourier_contrast_at_detector_plane + + +ContrastTransferTheory.__init__.__doc__ = """**Arguments:** + +- `ctf`: The contrast transfer function model. +- `envelope`: The envelope function of the optics model. +""" diff --git a/tests/conftest.py b/tests/conftest.py index d3b5a24f..450ea5f1 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,5 @@ import os -import equinox as eqx import jax import jax.numpy as jnp import jax.random as jr @@ -11,8 +10,8 @@ with install_import_hook("cryojax", "typeguard.typechecked"): import cryojax as cryojax import cryojax.simulator as cs + from cryojax.data import read_array_with_spacing_from_mrc from cryojax.image import operators as op, rfftn - from cryojax.io import read_array_with_spacing_from_mrc # jax.config.update("jax_numpy_dtype_promotion", "strict") @@ -73,13 +72,18 @@ def pixel_size(): @pytest.fixture -def config(pixel_size): - return cs.ImageConfig((65, 66), pixel_size, pad_scale=1.1) +def voltage_in_kilovolts(): + return 300.0 @pytest.fixture -def integrator(): - return cs.FourierSliceExtract(interpolation_order=1) +def config(pixel_size, voltage_in_kilovolts): + return cs.InstrumentConfig((65, 66), pixel_size, voltage_in_kilovolts, pad_scale=1.1) + + +@pytest.fixture +def projection_method(): + return cs.FourierSliceExtraction(interpolation_order=1) @pytest.fixture @@ -104,14 +108,13 @@ def masks(config): @pytest.fixture -def instrument(): - voltage_in_kilovolts = 300.0 - return cs.Instrument( - voltage_in_kilovolts, - optics=cs.WeakPhaseOptics(cs.CTF()), - dose=cs.ElectronDose(electrons_per_angstrom_squared=1000.0), - detector=cs.GaussianDetector(cs.IdealDQE(fraction_detected_electrons=1.0)), - ) +def transfer_theory(): + return cs.ContrastTransferTheory(ctf=cs.ContrastTransferFunction()) + + +@pytest.fixture +def detector(): + return cs.PoissonDetector(cs.IdealDQE()) @pytest.fixture @@ -126,8 +129,8 @@ def pose(): @pytest.fixture -def specimen(potential, integrator, pose): - return cs.Specimen(potential, integrator, pose) +def specimen(potential, pose): + return cs.SingleStructureEnsemble(potential, pose) @pytest.fixture @@ -136,45 +139,34 @@ def solvent(): @pytest.fixture -def noiseless_model(config, specimen, instrument): - instrument = eqx.tree_at(lambda ins: ins.detector, instrument, None) - return cs.ImagePipeline(config=config, specimen=specimen, instrument=instrument) +def theory(specimen, projection_method, transfer_theory, solvent): + return cs.LinearScatteringTheory( + specimen, projection_method, transfer_theory, solvent + ) @pytest.fixture -def noisy_model(config, specimen, instrument, solvent): - return cs.ImagePipeline( - config=config, - specimen=specimen, - instrument=instrument, - solvent=solvent, +def theory_with_solvent(specimen, projection_method, transfer_theory, solvent): + return cs.LinearScatteringTheory( + specimen, projection_method, transfer_theory, solvent ) @pytest.fixture -def filtered_model(config, specimen, instrument, solvent, filters): - return cs.ImagePipeline( - config=config, - specimen=specimen, - instrument=instrument, - solvent=solvent, - filter=filters, - ) +def noiseless_model(config, theory): + return cs.IntensityImagingPipeline(instrument_config=config, scattering_theory=theory) @pytest.fixture -def filtered_and_masked_model(config, specimen, instrument, solvent, filters, masks): - return cs.ImagePipeline( - config=config, - specimen=specimen, - instrument=instrument, - solvent=solvent, - filter=filters, - mask=masks, +def noisy_model(config, theory_with_solvent, detector): + return cs.ElectronCountingImagingPipeline( + instrument_config=config, + scattering_theory=theory_with_solvent, + detector=detector, ) @pytest.fixture def test_image(noisy_model): - image = noisy_model.sample(jr.PRNGKey(1234)) + image = noisy_model.render(jr.PRNGKey(1234)) return rfftn(image) diff --git a/tests/test_agree_with_cistem.py b/tests/test_agree_with_cistem.py index 178bf3d4..507badac 100644 --- a/tests/test_agree_with_cistem.py +++ b/tests/test_agree_with_cistem.py @@ -2,13 +2,13 @@ import jax.numpy as jnp import numpy as np import pytest -from pycistem.core import AnglesAndShifts, CTF as cisCTF, Image +from pycistem.core import AnglesAndShifts, CTF as cisCTF, Image # pyright: ignore import cryojax.simulator as cs from cryojax.coordinates import cartesian_to_polar, make_frequencies +from cryojax.data import read_array_with_spacing_from_mrc from cryojax.image import irfftn, powerspectrum -from cryojax.io import read_array_with_spacing_from_mrc -from cryojax.simulator import CTF, EulerAnglePose +from cryojax.simulator import ContrastTransferFunction, EulerAnglePose jax.config.update("jax_enable_x64", True) @@ -36,15 +36,15 @@ def test_ctf_with_cistem(defocus1, defocus2, asti_angle, kV, cs, ac, pixel_size) freqs = make_frequencies(shape, pixel_size) k_sqr, theta = cartesian_to_polar(freqs, square=True) # Compute cryojax CTF - optics = CTF( - defocus_u_in_angstroms=defocus1, - defocus_v_in_angstroms=defocus2, + optics = ContrastTransferFunction( + defocus_in_angstroms=defocus1, + astigmatism_in_angstroms=defocus2 - defocus1, astigmatism_angle=asti_angle, voltage_in_kilovolts=kV, spherical_aberration_in_mm=cs, amplitude_contrast_ratio=ac, ) - ctf = np.array(optics(freqs)) + ctf = jnp.array(optics(freqs)) # Compute cisTEM CTF cisTEM_optics = cisCTF( kV=kV, @@ -55,9 +55,9 @@ def test_ctf_with_cistem(defocus1, defocus2, asti_angle, kV, cs, ac, pixel_size) astig_angle=asti_angle, pixel_size=pixel_size, ) - cisTEM_ctf = np.vectorize( - lambda k_sqr, theta: cisTEM_optics.Evaluate(k_sqr, theta) - )(k_sqr.ravel() * pixel_size**2, theta.ravel()).reshape(freqs.shape[0:2]) + cisTEM_ctf = np.vectorize(lambda k_sqr, theta: cisTEM_optics.Evaluate(k_sqr, theta))( + k_sqr.ravel() * pixel_size**2, theta.ravel() + ).reshape(freqs.shape[0:2]) cisTEM_ctf[0, 0] = 0.0 # Compute cryojax and cisTEM power spectrum @@ -122,14 +122,15 @@ def test_compute_projection_with_cistem( real_voxel_grid, voxel_size ) pose = cs.EulerAnglePose(view_phi=phi, view_theta=theta, view_psi=psi) - integrator = cs.FourierSliceExtract() - specimen = cs.Specimen(potential, integrator, pose) + projection_method = cs.FourierSliceExtraction() box_size = potential.shape[0] - config = cs.ImageConfig((box_size, box_size), pixel_size) - instrument = cs.Instrument(voltage_in_kilovolts=300.0) - pipeline = cs.ImagePipeline(config, specimen, instrument) + config = cs.InstrumentConfig((box_size, box_size), pixel_size, 300.0) cryojax_projection = irfftn( - pipeline.render(get_real=False).at[0, 0].set(0.0 + 0.0j) + projection_method.compute_raw_fourier_image( + potential.rotate_to_pose(pose), config + ) + .at[0, 0] + .set(0.0 + 0.0j) / np.sqrt(np.prod(config.shape)), s=config.padded_shape, ) diff --git a/tests/test_detector.py b/tests/test_detector.py index 5a28206b..7ce22132 100644 --- a/tests/test_detector.py +++ b/tests/test_detector.py @@ -8,33 +8,33 @@ def test_gaussian_limit(): # Pick a large integrated electron flux to test - electrons_per_angstrom_squared = 10000.0 - # Create ImageConfig - config = cs.ImageConfig((25, 25), 1.0) + # Create InstrumentConfig, picking a large electron flux to test + config = cs.InstrumentConfig( + (25, 25), + 1.0, + voltage_in_kilovolts=300.0, + electrons_per_angstrom_squared=10000.0, + ) N_pix = np.prod(config.padded_shape) - electrons_per_pixel = electrons_per_angstrom_squared * config.pixel_size**2 + electrons_per_pixel = config.electrons_per_angstrom_squared * config.pixel_size**2 # Create squared wavefunction of just vacuum, i.e. 1 everywhere vacuum_squared_wavefunction = jnp.ones(config.shape, dtype=float) fourier_vacuum_squared_wavefunction = rfftn(vacuum_squared_wavefunction) - # Instantiate the electron dose - dose = cs.ElectronDose(electrons_per_angstrom_squared) # Create detector models key = jax.random.PRNGKey(1234) dqe = cs.IdealDQE() gaussian_detector = cs.GaussianDetector(dqe) poisson_detector = cs.PoissonDetector(dqe) # Compute detector readout - fourier_gaussian_detector_readout = gaussian_detector( + fourier_gaussian_detector_readout = gaussian_detector.compute_detector_readout( + key, fourier_vacuum_squared_wavefunction, config, - dose.electrons_per_angstrom_squared, - key, ) - fourier_poisson_detector_readout = poisson_detector( + fourier_poisson_detector_readout = poisson_detector.compute_detector_readout( + key, fourier_vacuum_squared_wavefunction, config, - dose.electrons_per_angstrom_squared, - key, ) # Compare to see if the autocorrelation has converged np.testing.assert_allclose( diff --git a/tests/test_ensemble.py b/tests/test_ensemble.py index 965ea916..b3ca2b2a 100644 --- a/tests/test_ensemble.py +++ b/tests/test_ensemble.py @@ -5,39 +5,39 @@ import jax.numpy as jnp import jax.tree_util as jtu -from cryojax.simulator import DiscreteConformation, DiscreteEnsemble, Instrument +import cryojax.simulator as cxs +from cryojax.simulator import DiscreteConformationalVariable, DiscreteStructuralEnsemble -def test_conformation(potential, pose, integrator, config): +def test_conformation(potential, pose, projection_method, transfer_theory, config): potential = tuple([potential for _ in range(3)]) - ensemble = DiscreteEnsemble( - potential, integrator, pose, conformation=DiscreteConformation(0) + ensemble = DiscreteStructuralEnsemble( + potential, pose, conformation=DiscreteConformationalVariable(0) ) - instrument = Instrument(300.0) - _ = ensemble.scatter_to_exit_plane(instrument, config) + theory = cxs.LinearScatteringTheory(ensemble, projection_method, transfer_theory) + _ = theory.compute_fourier_phase_shifts_at_exit_plane(config) -def test_conformation_vmap(potential, pose, integrator, config): +def test_conformation_vmap(potential, pose, projection_method, transfer_theory, config): # Build Ensemble - stacked_potential = tuple([potential for _ in range(3)]) - ensemble = DiscreteEnsemble( - stacked_potential, - integrator, + state_space = tuple([potential for _ in range(3)]) + ensemble = DiscreteStructuralEnsemble( + state_space, pose, - conformation=jax.vmap(lambda value: DiscreteConformation(value))( + conformation=jax.vmap(lambda value: DiscreteConformationalVariable(value))( jnp.asarray((0, 1, 2, 1, 0)) ), ) + theory = cxs.LinearScatteringTheory(ensemble, projection_method, transfer_theory) # Setup vmap - is_vmap = lambda x: isinstance(x, DiscreteConformation) - to_vmap = jtu.tree_map(is_vmap, ensemble, is_leaf=is_vmap) - vmap, novmap = eqx.partition(ensemble, to_vmap) + is_vmap = lambda x: isinstance(x, DiscreteConformationalVariable) + to_vmap = jtu.tree_map(is_vmap, theory, is_leaf=is_vmap) + vmap, novmap = eqx.partition(theory, to_vmap) @partial(jax.vmap, in_axes=[0, None, None]) def compute_conformation_stack(vmap, novmap, config): - ensemble = eqx.combine(vmap, novmap) - instrument = Instrument(300.0) - return ensemble.scatter_to_exit_plane(instrument, config) + theory = eqx.combine(vmap, novmap) + return theory.compute_fourier_phase_shifts_at_exit_plane(config) # Vmap over conformations image_stack = compute_conformation_stack(vmap, novmap, config) diff --git a/tests/test_fft.py b/tests/test_fft.py index df36914f..22cb699a 100644 --- a/tests/test_fft.py +++ b/tests/test_fft.py @@ -29,9 +29,7 @@ def test_fft(model, request): # Run tests with an image np.testing.assert_allclose(image, ifftn(fftn(image)).real) # ... test zero mode separately - np.testing.assert_allclose( - fftn(image)[1:, 1:], fftn(ifftn(fftn(image)).real)[1:, 1:] - ) + np.testing.assert_allclose(fftn(image)[1:, 1:], fftn(ifftn(fftn(image)).real)[1:, 1:]) np.testing.assert_allclose( fftn(image)[0, 0], fftn(ifftn(fftn(image)).real)[0, 0], atol=1e-12 ) diff --git a/tests/test_filters_and_masks.py b/tests/test_filters_and_masks.py deleted file mode 100644 index 8d21302d..00000000 --- a/tests/test_filters_and_masks.py +++ /dev/null @@ -1,40 +0,0 @@ -import equinox as eqx -import jax -import jax.numpy as jnp -import numpy as np -import pytest - - -@pytest.mark.parametrize("model", ["noisy_model"]) -def test_compute_with_filters_and_masks( - model, filtered_and_masked_model, request, filters, masks -): - """Make sure that adding null filters and masks does not change output""" - model = request.getfixturevalue(model) - # Add null filters and masks - null_mask = eqx.tree_at(lambda m: m.buffer, masks, jnp.asarray(1.0)) - null_filter = eqx.tree_at(lambda f: f.buffer, filters, jnp.asarray(1.0)) - where = lambda m: (m.filter, m.mask) - model_with_null_mask = eqx.tree_at( - where, filtered_and_masked_model, (None, null_mask) - ) - model_with_null_filter = eqx.tree_at( - where, filtered_and_masked_model, (null_filter, None) - ) - model_with_null_filter_and_mask = eqx.tree_at( - where, - filtered_and_masked_model, - (null_filter, null_mask), - ) - # Compute images - key = jax.random.PRNGKey(0) - image = model.render() - noisy_image = model.sample(key) - # Check render - np.testing.assert_allclose(model_with_null_mask.render(), image) - np.testing.assert_allclose(model_with_null_filter.render(), image) - np.testing.assert_allclose(model_with_null_filter_and_mask.render(), image) - # Check sample - np.testing.assert_allclose(model_with_null_mask.sample(key), noisy_image) - np.testing.assert_allclose(model_with_null_filter.sample(key), noisy_image) - np.testing.assert_allclose(model_with_null_filter_and_mask.sample(key), noisy_image) diff --git a/tests/test_grid_search.py b/tests/test_grid_search.py new file mode 100644 index 00000000..5ce042dc --- /dev/null +++ b/tests/test_grid_search.py @@ -0,0 +1,96 @@ +import equinox as eqx +import jax +import jax.numpy as jnp +import numpy as np +import pytest +from jaxtyping import Array, install_import_hook + + +with install_import_hook("cryojax", "typeguard.typechecked"): + import cryojax.inference as cxi + from cryojax.coordinates import make_coordinates + + +class ExampleModule(eqx.Module): + a_1: Array + a_2: Array + a_3: Array + placeholder: None + + def __init__(self, a_1, a_2, a_3): + self.a_1 = a_1 + self.a_2 = a_2 + self.a_3 = a_3 + self.placeholder = None + + +def test_pytree_grid_manipulation(): + # ... make three arrays with the same leading dimension + a_1, a_2, a_3 = tuple([jnp.arange(5) for _ in range(3)]) + # ... now two other arrays with different leading dimensions + b, c = jnp.arange(7), jnp.arange(20) + # Build a random tree grid + is_leaf = lambda x: isinstance(x, ExampleModule) + tree_grid = [ExampleModule(a_1, a_2, a_3), b, None, (c, (None,))] + # Get grid point + shape = cxi.tree_grid_shape(tree_grid, is_leaf=is_leaf) + tree_grid_point = cxi.tree_grid_take( + tree_grid, cxi.tree_grid_unravel_index(0, tree_grid, is_leaf=is_leaf) + ) + tree_grid_points = cxi.tree_grid_take( + tree_grid, + cxi.tree_grid_unravel_index(jnp.asarray([0, 10]), tree_grid, is_leaf=is_leaf), + ) + # Define ground truth + true_shape = (a_1.size, b.size, c.size) + true_tree_grid_point = [ + ExampleModule(a_1[0], a_2[0], a_3[0]), + b[0], + None, + (c[0], (None,)), + ] + true_tree_grid_points = [ + ExampleModule(a_1[([0, 0],)], a_2[([0, 0],)], a_3[([0, 0],)]), + b[([0, 0],)], + None, + (c[([0, 10],)], (None,)), + ] + assert shape == true_shape + assert eqx.tree_equal(tree_grid_point, true_tree_grid_point) + assert eqx.tree_equal(tree_grid_points, true_tree_grid_points) + + +@eqx.filter_jit +def cost_fn(grid_point, variance_plus_offset): + variance, offset = variance_plus_offset + mu_x, mu_y = offset + x, y = grid_point + return -jnp.exp(-((x - mu_x) ** 2 + (y - mu_y) ** 2) / (2 * variance)) / jnp.sqrt( + 2 * jnp.pi * variance + ) + + +@pytest.mark.parametrize("batch_size", [None, 1, 10]) +def test_run_grid_search(batch_size): + # Compute full landscape of simple analytic "cost function" + dim = 200 + coords = make_coordinates((dim, dim)) + variance, offset = jnp.asarray(10.0), jnp.asarray((2.0, -1.0)) + landscape = jax.vmap(jax.vmap(cost_fn, in_axes=[0, None]), in_axes=[0, None])( + coords, (variance, offset) + ) + # Find the true minimum value and its location + true_min_eval = landscape.min() + true_min_idx = jnp.squeeze(jnp.argwhere(landscape == true_min_eval)) + true_min_pos = tuple(coords[true_min_idx[0], true_min_idx[1]]) + # Generate a sparse representation of coordinate grid + x, y = ( + jnp.fft.fftshift(jnp.fft.fftfreq(dim)) * dim, + jnp.fft.fftshift(jnp.fft.fftfreq(dim)) * dim, + ) + grid = (x, y) + # Run the grid search + method = cxi.MinimumSearchMethod(batch_size=batch_size) + solution = cxi.run_grid_search(cost_fn, method, grid, (variance, offset)) + np.testing.assert_allclose(solution.state.current_minimum_eval, true_min_eval) + np.testing.assert_allclose(solution.value, true_min_pos) diff --git a/tests/test_helix.py b/tests/test_helix.py index d33c7d57..d59f957a 100644 --- a/tests/test_helix.py +++ b/tests/test_helix.py @@ -5,21 +5,21 @@ import pytest import cryojax.simulator as cs -from cryojax.io import read_array_with_spacing_from_mrc +from cryojax.data import read_array_with_spacing_from_mrc +from cryojax.image import irfftn, normalize_image -def build_helix(sample_subunit_mrc_path, n_subunits_per_start) -> cs.Helix: +def build_helix(sample_subunit_mrc_path, n_subunits_per_start) -> cs.HelicalAssembly: real_voxel_grid, voxel_size = read_array_with_spacing_from_mrc( sample_subunit_mrc_path ) subunit_density = cs.FourierVoxelGridPotential.from_real_voxel_grid( real_voxel_grid, voxel_size, pad_scale=2 ) - integrator = cs.FourierSliceExtract() r_0 = jnp.asarray([-88.70895129, 9.75357114, 0.0], dtype=float) subunit_pose = cs.EulerAnglePose(*r_0) - subunit = cs.Specimen(subunit_density, integrator, subunit_pose) - return cs.Helix( + subunit = cs.SingleStructureEnsemble(subunit_density, subunit_pose) + return cs.HelicalAssembly( subunit, rise=21.8, twist=29.4, @@ -30,7 +30,7 @@ def build_helix(sample_subunit_mrc_path, n_subunits_per_start) -> cs.Helix: def build_helix_with_conformation( sample_subunit_mrc_path, n_subunits_per_start -) -> cs.Helix: +) -> cs.HelicalAssembly: subunit_density = tuple( [ cs.FourierVoxelGridPotential.from_real_voxel_grid( @@ -42,17 +42,15 @@ def build_helix_with_conformation( n_start = 6 r_0 = jnp.asarray([-88.70895129, 9.75357114, 0.0], dtype=float) subunit_pose = cs.EulerAnglePose(*r_0) - integrator = cs.FourierSliceExtract() - subunit = cs.DiscreteEnsemble( + subunit = cs.DiscreteStructuralEnsemble( subunit_density, - integrator, subunit_pose, - conformation=cs.DiscreteConformation(0), + conformation=cs.DiscreteConformationalVariable(0), ) - conformation = jax.vmap(lambda value: cs.DiscreteConformation(value))( + conformation = jax.vmap(lambda value: cs.DiscreteConformationalVariable(value))( np.random.choice(2, n_start * n_subunits_per_start) ) - return cs.Helix( + return cs.HelicalAssembly( subunit, conformation=conformation, rise=21.8, @@ -64,20 +62,30 @@ def build_helix_with_conformation( def test_superposition_pipeline_without_conformation(sample_subunit_mrc_path, config): helix = build_helix(sample_subunit_mrc_path, 1) - pipeline = cs.AssemblyPipeline( - config=config, assembly=helix, instrument=cs.Instrument(300.0) + projection_method = cs.FourierSliceExtraction() + transfer_theory = cs.ContrastTransferTheory(cs.IdealContrastTransferFunction()) + theory = cs.LinearSuperpositionScatteringTheory( + helix, projection_method, transfer_theory + ) + pipeline = cs.ContrastImagingPipeline( + instrument_config=config, scattering_theory=theory ) _ = pipeline.render() - _ = pipeline.sample(jax.random.PRNGKey(0)) + _ = pipeline.render(jax.random.PRNGKey(0)) def test_superposition_pipeline_with_conformation(sample_subunit_mrc_path, config): helix = build_helix_with_conformation(sample_subunit_mrc_path, 2) - pipeline = cs.AssemblyPipeline( - config=config, instrument=cs.Instrument(300.0), assembly=helix + projection_method = cs.FourierSliceExtraction() + transfer_theory = cs.ContrastTransferTheory(cs.IdealContrastTransferFunction()) + theory = cs.LinearSuperpositionScatteringTheory( + helix, projection_method, transfer_theory + ) + pipeline = cs.ContrastImagingPipeline( + instrument_config=config, scattering_theory=theory ) _ = pipeline.render() - _ = pipeline.sample(jax.random.PRNGKey(0)) + _ = pipeline.render(jax.random.PRNGKey(0)) @pytest.mark.parametrize( @@ -88,20 +96,27 @@ def test_c6_rotation( sample_subunit_mrc_path, config, rotation_angle, n_subunits_per_start ): helix = build_helix(sample_subunit_mrc_path, n_subunits_per_start) + projection_method = cs.FourierSliceExtraction() + transfer_theory = cs.ContrastTransferTheory(cs.IdealContrastTransferFunction()) + theory = cs.LinearSuperpositionScatteringTheory( + helix, projection_method, transfer_theory + ) + pipeline = cs.ContrastImagingPipeline( + instrument_config=config, scattering_theory=theory + ) - @jax.jit - def compute_rotated_image(config, helix, pose): - helix = eqx.tree_at(lambda m: m.pose, helix, pose) - pipeline = cs.AssemblyPipeline( - config=config, instrument=cs.Instrument(300.0), assembly=helix + @eqx.filter_jit + def compute_rotated_image(pipeline, pose): + pipeline = eqx.tree_at( + lambda m: m.scattering_theory.structural_ensemble_batcher.pose, + pipeline, + pose, ) - return pipeline.render(normalize=True) + return normalize_image(pipeline.render()) np.testing.assert_allclose( - compute_rotated_image(config, helix, cs.EulerAnglePose()), - compute_rotated_image( - config, helix, cs.EulerAnglePose(view_phi=rotation_angle) - ), + compute_rotated_image(pipeline, cs.EulerAnglePose()), + compute_rotated_image(pipeline, cs.EulerAnglePose(view_phi=rotation_angle)), ) @@ -115,29 +130,44 @@ def compute_rotated_image(config, helix, pose): def test_agree_with_3j9g_assembly( sample_subunit_mrc_path, potential, config, translation, euler_angles ): - instrument = cs.Instrument(voltage_in_kilovolts=300.0) helix = build_helix(sample_subunit_mrc_path, 2) - specimen_39jg = cs.Specimen(potential, helix.subunit.integrator) - pipeline_for_assembly = cs.AssemblyPipeline( - config=config, instrument=instrument, assembly=helix + specimen_39jg = cs.SingleStructureEnsemble(potential, cs.EulerAnglePose()) + superposition_theory = cs.LinearSuperpositionScatteringTheory( + helix, + cs.FourierSliceExtraction(), + cs.ContrastTransferTheory(cs.IdealContrastTransferFunction()), + ) + theory = cs.LinearScatteringTheory( + specimen_39jg, + cs.FourierSliceExtraction(), + cs.ContrastTransferTheory(cs.IdealContrastTransferFunction()), ) - pipeline_for_3j9g = cs.ImagePipeline( - config=config, instrument=instrument, specimen=specimen_39jg + pipeline_for_assembly = cs.ContrastImagingPipeline( + instrument_config=config, scattering_theory=superposition_theory + ) + pipeline_for_3j9g = cs.ContrastImagingPipeline( + instrument_config=config, scattering_theory=theory ) @eqx.filter_jit def compute_rotated_image_with_helix( - pipeline: cs.AssemblyPipeline, pose: cs.AbstractPose + pipeline: cs.ContrastImagingPipeline, pose: cs.AbstractPose ): - pipeline = eqx.tree_at(lambda m: m.assembly.pose, pipeline, pose) - return pipeline.render(normalize=True) + pipeline = eqx.tree_at( + lambda m: m.scattering_theory.structural_ensemble_batcher.pose, + pipeline, + pose, + ) + return normalize_image(pipeline.render()) @eqx.filter_jit def compute_rotated_image_with_3j9g( - pipeline: cs.ImagePipeline, pose: cs.AbstractPose + pipeline: cs.ContrastImagingPipeline, pose: cs.AbstractPose ): - pipeline = eqx.tree_at(lambda m: m.specimen.pose, pipeline, pose) - return pipeline.render(normalize=True) + pipeline = eqx.tree_at( + lambda m: m.scattering_theory.structural_ensemble.pose, pipeline, pose + ) + return normalize_image(pipeline.render()) pose = cs.EulerAnglePose(*translation, 0.0, *euler_angles) reference_image = compute_rotated_image_with_3j9g( @@ -152,15 +182,22 @@ def compute_rotated_image_with_3j9g( def test_transform_by_rise_and_twist(sample_subunit_mrc_path, pixel_size): helix = build_helix(sample_subunit_mrc_path, 12) - config = cs.ImageConfig((50, 20), pixel_size, pad_scale=6) + config = cs.InstrumentConfig((50, 20), pixel_size, 300.0, pad_scale=6) - @jax.jit + @eqx.filter_jit def compute_rotated_image(config, helix, pose): helix = eqx.tree_at(lambda m: m.pose, helix, pose) - pipeline = cs.AssemblyPipeline( - config=config, instrument=cs.Instrument(300.0), assembly=helix + theory = cs.LinearSuperpositionScatteringTheory( + helix, + cs.FourierSliceExtraction(), + cs.ContrastTransferTheory(cs.IdealContrastTransferFunction()), ) - return pipeline.render(normalize=True) + return config.crop_to_shape( + irfftn( + theory.compute_fourier_phase_shifts_at_exit_plane(config), + s=config.padded_shape, + ) + ) # noqa: E501 np.testing.assert_allclose( compute_rotated_image( diff --git a/tests/test_jit.py b/tests/test_jit.py deleted file mode 100644 index 7a180ef6..00000000 --- a/tests/test_jit.py +++ /dev/null @@ -1,19 +0,0 @@ -import jax -import jax.random as jr -import numpy as np -import pytest - - -jax.config.update("jax_enable_x64", True) - - -@pytest.mark.parametrize("model", ["noisy_model"]) -def test_jit(model, test_image, request): - model = request.getfixturevalue(model) - key = jr.PRNGKey(0) - - @jax.jit - def compute_image(model, key): - return model.sample(key) - - np.testing.assert_allclose(compute_image(model, key), model.sample(key)) diff --git a/tests/test_normalize.py b/tests/test_normalize.py index 65d17e73..22e6044d 100644 --- a/tests/test_normalize.py +++ b/tests/test_normalize.py @@ -1,36 +1,25 @@ import jax import jax.numpy as jnp import numpy as np -import pytest -from cryojax.image import irfftn +from cryojax.image import irfftn, normalize_image jax.config.update("jax_enable_x64", True) -@pytest.mark.parametrize( - "model", - [ - "noisy_model", - "noiseless_model", - "filtered_model", - "filtered_and_masked_model", - ], -) -def test_compute_with_filters_and_masks(model, request): - model = request.getfixturevalue(model) +def test_fourier_vs_real_normalized_image(noisy_model): key = jax.random.PRNGKey(1234) - im1 = model.render(get_real=True, normalize=True) - im2 = model.sample(key, get_real=True, normalize=True) - im3 = irfftn( - model.render(get_real=False, normalize=True), - s=model.config.shape, - ) - im4 = irfftn( - model.render(get_real=False, normalize=True), - s=model.config.shape, - ) - for im in [im1, im2, im3, im4]: + im1 = normalize_image(noisy_model.render(key, get_real=True), is_real=True) + im2 = irfftn( + normalize_image( + noisy_model.render(get_real=False), + is_real=False, + half_space=True, + shape_in_real_space=im1.shape, # type: ignore + ), + s=noisy_model.instrument_config.shape, + ) # type: ignore + for im in [im1, im2]: np.testing.assert_allclose(jnp.std(im), jnp.asarray(1.0), rtol=1e-3) - np.testing.assert_allclose(jnp.mean(im), jnp.asarray(0.0), atol=1e-12) + np.testing.assert_allclose(jnp.mean(im), jnp.asarray(0.0), atol=1e-8) diff --git a/tests/test_pose_agreement.py b/tests/test_pose_agreement.py index edb61b16..ad30962e 100644 --- a/tests/test_pose_agreement.py +++ b/tests/test_pose_agreement.py @@ -21,9 +21,7 @@ def test_translation_agreement(): offset = jnp.asarray((0.0, -1.4, 4.5)) quat = cs.QuaternionPose.from_rotation_and_translation(rotation, offset) axis_angle = cs.AxisAnglePose.from_rotation_and_translation(rotation, offset) - np.testing.assert_allclose( - quat.rotation.as_matrix(), axis_angle.rotation.as_matrix() - ) + np.testing.assert_allclose(quat.rotation.as_matrix(), axis_angle.rotation.as_matrix()) np.testing.assert_allclose(quat.offset_in_angstroms, axis_angle.offset_in_angstroms) @@ -34,17 +32,19 @@ def test_pose_conversion(): euler = cs.EulerAnglePose.from_rotation(rotation) axis_angle = cs.AxisAnglePose.from_rotation(rotation) np.testing.assert_allclose(quat.rotation.as_matrix(), euler.rotation.as_matrix()) - np.testing.assert_allclose( - quat.rotation.as_matrix(), axis_angle.rotation.as_matrix() - ) + np.testing.assert_allclose(quat.rotation.as_matrix(), axis_angle.rotation.as_matrix()) def test_default_pose_images(noiseless_model): euler = cs.EulerAnglePose() quat = cs.QuaternionPose() - model_euler = eqx.tree_at(lambda m: m.specimen.pose, noiseless_model, euler) - model_quat = eqx.tree_at(lambda m: m.specimen.pose, noiseless_model, quat) + model_euler = eqx.tree_at( + lambda m: m.scattering_theory.structural_ensemble.pose, noiseless_model, euler + ) + model_quat = eqx.tree_at( + lambda m: m.scattering_theory.structural_ensemble.pose, noiseless_model, quat + ) np.testing.assert_allclose(model_euler.render(), model_quat.render()) diff --git a/tests/test_potential.py b/tests/test_potential.py index b8daaedb..068dc8a9 100644 --- a/tests/test_potential.py +++ b/tests/test_potential.py @@ -1,15 +1,8 @@ -from functools import partial - -import equinox as eqx -import jax import jax.numpy as jnp -import jax.tree_util as jtu from jaxtyping import Array, Float import cryojax.simulator as cs -from cryojax.constants import convert_keV_to_angstroms from cryojax.coordinates import ( - AbstractCoordinates, CoordinateGrid, CoordinateList, FrequencySlice, @@ -31,9 +24,7 @@ def test_voxel_electron_potential_loaders(): for potential in [real_potential, fourier_potential, cloud_potential]: assert potential.voxel_size == jnp.asarray(voxel_size) - assert isinstance( - fourier_potential.wrapped_frequency_slice_in_pixels, FrequencySlice - ) + assert isinstance(fourier_potential.wrapped_frequency_slice_in_pixels, FrequencySlice) assert isinstance( fourier_potential.wrapped_frequency_slice_in_pixels.get(), Float[Array, "1 _ _ 3"], @@ -46,69 +37,3 @@ def test_voxel_electron_potential_loaders(): assert isinstance( cloud_potential.wrapped_coordinate_list_in_pixels.get(), Float[Array, "_ 3"] ) - - -def test_electron_potential_vmap(potential, integrator, config): - filter_spec = jtu.tree_map( - lambda x: not isinstance(x, AbstractCoordinates), - potential, - is_leaf=lambda x: isinstance(x, AbstractCoordinates), - ) - # Add a leading dimension to scattering potential leaves - potential = jtu.tree_map( - lambda spec, x: jnp.expand_dims(x, axis=0) if spec else x, - filter_spec, - potential, - is_leaf=lambda x: isinstance(x, AbstractCoordinates), - ) - vmap, novmap = eqx.partition(potential, filter_spec) - - @partial(jax.vmap, in_axes=[0, None, None, None]) - def compute_image_stack(vmap, novmap, integrator, config): - wavelength_in_angstroms = convert_keV_to_angstroms(300.0) - potential = eqx.combine(vmap, novmap) - return integrator(potential, wavelength_in_angstroms, config) - - # vmap over first axis - image_stack = compute_image_stack(vmap, novmap, integrator, config) - assert image_stack.shape[:1] == (1,) - - -def test_electron_potential_vmap_with_pipeline(potential, pose, integrator, config): - instrument = cs.Instrument(voltage_in_kilovolts=300.0) - pipeline = cs.ImagePipeline( - config, cs.Specimen(potential, integrator, pose), instrument - ) - - def is_potential_leaves_without_coordinates(element): - if isinstance(element, cs.AbstractScatteringPotential): - return jtu.tree_map( - lambda x: not isinstance(x, AbstractCoordinates), - potential, - is_leaf=lambda x: isinstance(x, AbstractCoordinates), - ) - else: - return False - - # Get filter spec for scattering potential - filter_spec = jtu.tree_map( - is_potential_leaves_without_coordinates, - pipeline, - is_leaf=lambda x: isinstance(x, cs.AbstractScatteringPotential), - ) - # Add a leading dimension to scattering potential leaves - pipeline = jtu.tree_map( - lambda spec, x: jnp.expand_dims(x, axis=0) if spec else x, - filter_spec, - pipeline, - ) - vmap, novmap = eqx.partition(pipeline, filter_spec) - - @partial(jax.vmap, in_axes=[0, None]) - def compute_image_stack(vmap, novmap): - pipeline = eqx.combine(vmap, novmap) - return pipeline.render() - - # vmap over first axis - image_stack = compute_image_stack(vmap, novmap) - assert image_stack.shape[:1] == (1,) diff --git a/tests/test_projection_agreement.py b/tests/test_projection_agreement.py deleted file mode 100644 index a16c9107..00000000 --- a/tests/test_projection_agreement.py +++ /dev/null @@ -1,33 +0,0 @@ -import jax -import numpy as np -import pytest - -import cryojax.simulator as cs -from cryojax.image import crop_to_shape -from cryojax.io import read_array_with_spacing_from_mrc - - -jax.config.update("jax_enable_x64", True) - - -@pytest.mark.parametrize("shape", [(65, 65), (65, 64), (64, 65)]) -def test_even_vs_odd_image_shape(shape, sample_mrc_path, pixel_size): - control_shape = (64, 64) - real_voxel_grid, voxel_size = read_array_with_spacing_from_mrc(sample_mrc_path) - potential = cs.FourierVoxelGridPotential.from_real_voxel_grid( - real_voxel_grid, voxel_size - ) - assert control_shape == potential.fourier_voxel_grid.shape[0:2] - pose = cs.EulerAnglePose() - integrator = cs.FourierSliceExtract() - specimen = cs.Specimen(potential, integrator, pose) - config_control = cs.ImageConfig(control_shape, pixel_size) - config_test = cs.ImageConfig(shape, pixel_size) - instrument = cs.Instrument(voltage_in_kilovolts=300.0) - pipeline_control = cs.ImagePipeline(config_control, specimen, instrument) - pipeline_test = cs.ImagePipeline(config_test, specimen, instrument) - - np.testing.assert_allclose( - crop_to_shape(pipeline_test.render(), control_shape), - pipeline_control.render(), - ) diff --git a/tests/test_shape.py b/tests/test_shape.py index c79581ce..85f08e3e 100644 --- a/tests/test_shape.py +++ b/tests/test_shape.py @@ -1,24 +1,65 @@ +import jax +import numpy as np import pytest +import cryojax.simulator as cs +from cryojax.data import read_array_with_spacing_from_mrc +from cryojax.image import crop_to_shape -@pytest.mark.parametrize("model", ["noisy_model", "filtered_and_masked_model"]) + +jax.config.update("jax_enable_x64", True) + + +@pytest.mark.parametrize("model", ["noisy_model"]) def test_real_shape(model, request): """Make sure shapes are as expected in real space.""" model = request.getfixturevalue(model) image = model.render() - padded_image = model.render(view_cropped=False) - assert image.shape == model.config.shape - assert padded_image.shape == model.config.padded_shape + padded_image = model.render(postprocess=False) + assert image.shape == model.instrument_config.shape + assert padded_image.shape == model.instrument_config.padded_shape -@pytest.mark.parametrize("model", ["noisy_model", "filtered_and_masked_model"]) +@pytest.mark.parametrize("model", ["noisy_model"]) def test_fourier_shape(model, request): """Make sure shapes are as expected in fourier space.""" model = request.getfixturevalue(model) image = model.render(get_real=False) - padded_image = model.render(view_cropped=False, get_real=False) - assert image.shape == model.config.wrapped_frequency_grid_in_pixels.get().shape[0:2] + padded_image = model.render(postprocess=False, get_real=False) + assert ( + image.shape + == model.instrument_config.wrapped_frequency_grid_in_pixels.get().shape[0:2] + ) assert ( padded_image.shape - == model.config.wrapped_padded_frequency_grid_in_pixels.get().shape[0:2] + == model.instrument_config.wrapped_padded_frequency_grid_in_pixels.get().shape[ + 0:2 + ] + ) + + +@pytest.mark.parametrize("shape", [(65, 65), (65, 64), (64, 65)]) +def test_even_vs_odd_image_shape(shape, sample_mrc_path, pixel_size): + control_shape = (64, 64) + real_voxel_grid, voxel_size = read_array_with_spacing_from_mrc(sample_mrc_path) + potential = cs.FourierVoxelGridPotential.from_real_voxel_grid( + real_voxel_grid, voxel_size + ) + assert control_shape == potential.fourier_voxel_grid.shape[0:2] + pose = cs.EulerAnglePose() + method = cs.FourierSliceExtraction() + specimen = cs.SingleStructureEnsemble(potential, pose) + transfer_theory = cs.ContrastTransferTheory(cs.ContrastTransferFunction()) + theory = cs.LinearScatteringTheory(specimen, method, transfer_theory) + config_control = cs.InstrumentConfig( + control_shape, pixel_size, voltage_in_kilovolts=300.0 + ) + config_test = cs.InstrumentConfig(shape, pixel_size, voltage_in_kilovolts=300.0) + pipeline_control = cs.ContrastImagingPipeline(config_control, theory) + pipeline_test = cs.ContrastImagingPipeline(config_test, theory) + + np.testing.assert_allclose( + crop_to_shape(pipeline_test.render(), control_shape), + pipeline_control.render(), + atol=1e-4, ) diff --git a/tests/test_voxels_from_atoms.py b/tests/test_voxels_from_atoms.py index 05b59c4b..9a321b6c 100644 --- a/tests/test_voxels_from_atoms.py +++ b/tests/test_voxels_from_atoms.py @@ -5,8 +5,8 @@ from jax import config from cryojax.coordinates import CoordinateGrid +from cryojax.data import read_atoms_from_pdb from cryojax.image import ifftn -from cryojax.io import read_atoms_from_pdb from cryojax.simulator import ( build_real_space_voxels_from_atoms, FourierVoxelGridPotential,