diff --git a/notebooks/diffusion.ipynb b/notebooks/diffusion.ipynb new file mode 100644 index 0000000..f7d09f1 --- /dev/null +++ b/notebooks/diffusion.ipynb @@ -0,0 +1,1146 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "from jax import lax\n", + "\n", + "interior_slice = slice(1, -1), slice(1, -1)\n", + "\n", + "\n", + "def compute_laplacian_2d(array, dx=1.0, dy=1.0):\n", + " laplacian = jnp.zeros_like(array)\n", + " laplacian = laplacian.at[interior_slice].set(\n", + " (array[:-2, 1:-1] - 2 * array[1:-1, 1:-1] + array[2:, 1:-1]) / (dx * dx)\n", + " + (array[1:-1, :-2] - 2 * array[1:-1, 1:-1] + array[1:-1, 2:]) / (dy * dy)\n", + " )\n", + " return laplacian\n", + "\n", + "\n", + "def enforce_boundary_conditions(\n", + " field: jnp.ndarray, region_mask: jnp.ndarray, rescaling_factor: float\n", + "):\n", + " masked_field = field * region_mask\n", + " smoothed_values = jnp.zeros_like(masked_field)\n", + " smoothed_values = smoothed_values.at[1:, :].add(masked_field[:-1, :])\n", + " smoothed_values = smoothed_values.at[:-1, :].add(masked_field[1:, :])\n", + " smoothed_values = smoothed_values.at[:, 1:].add(masked_field[:, :-1])\n", + " smoothed_values = smoothed_values.at[:, :-1].add(masked_field[:, 1:])\n", + " smoothed_values *= rescaling_factor\n", + " return smoothed_values\n", + "\n", + "\n", + "def simulate_diffusion(\n", + " initial_field: jnp.ndarray,\n", + " total_time: float,\n", + " dt: float,\n", + " diffusion_coefficient: float,\n", + " region_mask: jnp.ndarray,\n", + " rescaling_factor: float,\n", + " dx: float = 1.0,\n", + " dy: float = 1.0,\n", + "):\n", + "\n", + " def diffusion_step(\n", + " field,\n", + " _,\n", + " diffusion_coefficient=diffusion_coefficient,\n", + " dt=dt,\n", + " region_mask=region_mask,\n", + " rescaling_factor=rescaling_factor,\n", + " ):\n", + " # Enforce boundary conditions\n", + " smoothed_values = enforce_boundary_conditions(\n", + " field, region_mask, rescaling_factor\n", + " )\n", + "\n", + " # Compute the Laplacian\n", + " laplacian = compute_laplacian_2d(field * region_mask + smoothed_values, dx, dy)\n", + "\n", + " # Update field with diffusion term\n", + " field += diffusion_coefficient * dt * laplacian\n", + "\n", + " return field, field\n", + "\n", + " n_total_steps = int(total_time / dt)\n", + "\n", + " _, field_ts = lax.scan(\n", + " f=diffusion_step,\n", + " init=initial_field,\n", + " xs=None,\n", + " length=n_total_steps,\n", + " )\n", + "\n", + " return field_ts\n", + "\n", + "\n", + "def calculate_diffusion_coefficient(sigma, dt):\n", + " return (sigma**2) / (2 * dt)\n", + "\n", + "\n", + "def calculate_minimum_grid_spacing(sigma):\n", + " return sigma * jnp.sqrt(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Define region_mask and rescaling_factor as jax arrays\n", + "region_mask = jnp.ones((100, 100))\n", + "rescaling_factor = (\n", + " jnp.ones_like(region_mask) / 4.0\n", + ") # Normalize by the number of neighbors (4 in this case)\n", + "\n", + "# Initialize some initial condition for the field\n", + "initial_field = jnp.zeros_like(region_mask)\n", + "initial_field = initial_field.at[\n", + " region_mask.shape[0] // 2, region_mask.shape[1] // 2\n", + "].set(\n", + " 1.0\n", + ") # Set a single point with high value\n", + "\n", + "# Define simulation parameters\n", + "total_time = 1.00\n", + "dt = 0.001\n", + "\n", + "desired_standard_deviation = jnp.sqrt(6.0) # Desired standard deviation of the Gaussian\n", + "\n", + "# Calculate diffusion coefficient and grid spacing\n", + "diffusion_coefficient = calculate_diffusion_coefficient(desired_standard_deviation, dt)\n", + "dy = dx = calculate_minimum_grid_spacing(desired_standard_deviation)\n", + "dx = dy = desired_standard_deviation\n", + "\n", + "\n", + "# Run the diffusion simulation\n", + "field_ts = simulate_diffusion(\n", + " initial_field,\n", + " total_time,\n", + " dt,\n", + " diffusion_coefficient,\n", + " region_mask,\n", + " rescaling_factor,\n", + " dx=dx,\n", + " dy=dy,\n", + ")\n", + "\n", + "\n", + "# Visualization\n", + "def plot_field(field, title):\n", + " x, y = jnp.meshgrid(\n", + " jnp.arange(field.shape[1]) * dx, jnp.arange(field.shape[0]) * dy\n", + " )\n", + " plt.pcolormesh(field, cmap=\"viridis\", vmin=0)\n", + " plt.colorbar()\n", + " plt.title(title)\n", + " plt.xlabel(\"X\")\n", + " plt.ylabel(\"Y\")\n", + " plt.show()\n", + "\n", + "\n", + "# Plot initial field\n", + "plot_field(initial_field, \"Initial Field\")\n", + "\n", + "# Plot final field\n", + "plot_field(field_ts[0], \"Final Field after Diffusion\")" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 219, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "dx = 0.5\n", + "state_x = np.arange(0, 50, dx)\n", + "n_states = state_x.shape[0]\n", + "\n", + "\n", + "initial_state = np.zeros((n_states,))\n", + "initial_state[initial_state.shape[0] // 2] = 1.0\n", + "\n", + "\n", + "plt.plot(state_x, initial_state, label=\"Initial State\")\n", + "\n", + "diffusion_coefficient = 0.5\n", + "dt = 0.001\n", + "total_time = 10.0\n", + "\n", + "n_total_steps = int(total_time / dt)\n", + "\n", + "u = np.zeros((n_total_steps, n_states))\n", + "u[0] = initial_state\n", + "\n", + "diffusion_constant = diffusion_coefficient * dt / (dx**2)\n", + "\n", + "d = np.zeros((n_states,))\n", + "\n", + "for n in range(n_total_steps - 1):\n", + " # for i in range(1, n_states-2):\n", + " # u[n + 1, i] = u[n, i] + diffusion_constant * (\n", + " # (u[n, i + 1] - u[n, i]) + (u[n, i - 1] - u[n, i])\n", + " # )\n", + " u[n + 1, slice(1, -1)] = u[n, slice(1, -1)] + diffusion_constant * (\n", + " u[n, 2:] - 2 * u[n, 1:-1] + u[n, :-2]\n", + " )\n", + "\n", + "plt.plot(state_x, u[::2000].T)" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dx = 0.5\n", + "state_x = np.arange(0, 50, dx)\n", + "n_states = state_x.shape[0]\n", + "\n", + "initial_state = np.zeros((n_states,))\n", + "initial_state[initial_state.shape[0] // 2] = 1.0\n", + "\n", + "diffusion_coefficient = 0.5\n", + "dt = 0.001\n", + "total_time = 10.0\n", + "\n", + "n_total_steps = int(total_time / dt)\n", + "\n", + "\n", + "def _step(u_t, _):\n", + " u_t = u_t.at[slice(1, -1)].set(\n", + " u_t[slice(1, -1)] + diffusion_constant * (u_t[2:] - 2 * u_t[1:-1] + u_t[:-2])\n", + " )\n", + " return u_t, u_t\n", + "\n", + "\n", + "_, u_ts = lax.scan(\n", + " f=_step,\n", + " init=initial_state,\n", + " xs=None,\n", + " length=n_total_steps,\n", + ")\n", + "\n", + "plt.plot(state_x, u_ts[::2000].T)" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 242, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def impulse(x, x0, sigma):\n", + " return jnp.exp(-((x - x0) ** 2) / (2 * sigma**2))\n", + "\n", + "\n", + "dd_impluse = jax.vmap(\n", + " jax.grad(jax.grad(impulse, argnums=0), argnums=0), in_axes=(0, None, None)\n", + ")\n", + "\n", + "plt.plot(state_x, dd_impluse(state_x, 25, 0.1))" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 283, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "diffusion_coefficient = 0.5\n", + "dt = 0.001\n", + "total_time = 10.0\n", + "\n", + "n_total_steps = int(total_time / dt)\n", + "\n", + "dd_impluse = jax.vmap(\n", + " jax.grad(jax.grad(impulse, argnums=0), argnums=0), in_axes=(0, None, None)\n", + ")\n", + "\n", + "impulse_mean = 25\n", + "impulse_sigma = 0.5\n", + "\n", + "\n", + "def _step(u_t, _):\n", + " u_t += diffusion_constant * dd_impluse(state_x, impulse_mean, impulse_sigma)\n", + " return u_t, u_t\n", + "\n", + "\n", + "_, u_ts = lax.scan(\n", + " f=_step,\n", + " init=impulse(state_x, impulse_mean, impulse_sigma),\n", + " xs=None,\n", + " length=n_total_steps,\n", + ")\n", + "\n", + "plt.plot(state_x, u_ts[::2000].T)" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "from jax import lax\n", + "\n", + "\n", + "# Define a function to compute the Laplacian in 1D using the second derivative\n", + "def compute_laplacian_1d(u):\n", + " def wrapped_u(i):\n", + " return u[jnp.astype(i, jnp.int32)]\n", + "\n", + " second_derivative = jax.vmap(jax.grad(jax.grad(wrapped_u)))\n", + " index = jnp.arange(len(u)).astype(jnp.float32)\n", + "\n", + " return second_derivative(index)\n", + "\n", + "\n", + "dx = 0.5\n", + "state_x = np.arange(0, 50, dx)\n", + "n_states = state_x.shape[0]\n", + "\n", + "initial_state = np.zeros((n_states,))\n", + "initial_state[initial_state.shape[0] // 2] = 1.0\n", + "\n", + "diffusion_coefficient = 0.5\n", + "dt = 0.001\n", + "total_time = 10.0\n", + "\n", + "n_total_steps = int(total_time / dt)\n", + "\n", + "\n", + "def _step(u_t, _):\n", + " u_t += diffusion_coefficient * compute_laplacian_1d(u_t)\n", + " return u_t, u_t\n", + "\n", + "\n", + "# Perform the simulation\n", + "_, u_ts = lax.scan(\n", + " f=_step,\n", + " init=initial_state,\n", + " xs=None,\n", + " length=n_total_steps,\n", + ")\n", + "\n", + "# Plot results at intervals\n", + "plt.plot(state_x, u_ts[::2000].T)\n", + "plt.xlabel(\"Position\")\n", + "plt.ylabel(\"Field Value\")\n", + "plt.title(\"Diffusion Simulation\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Array([1., 1.], dtype=float32),\n", + " Array([[2., 0.],\n", + " [0., 2.]], dtype=float32))" + ] + }, + "execution_count": 305, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from typing import Callable\n", + "from jax.scipy.optimize import minimize\n", + "\n", + "\n", + "def laplace(f: Callable, x0: jnp.ndarray) -> jnp.ndarray:\n", + " mode, *details = minimize(lambda x: -f(x), x0, method=\"BFGS\")\n", + " H = jax.hessian(f)(mode)\n", + " return mode, H\n", + "\n", + "\n", + "laplace(lambda x: x[0] ** 2 + x[1] ** 2, jnp.array([1.0, 1.0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def receptive_field_model(\n", + " position: np.ndarray, log_max_rate: float, place_field_center: float, scale: float\n", + "):\n", + " return jnp.exp(log_max_rate - (position - place_field_center) ** 2 / (2 * scale**2))\n", + "\n", + "\n", + "position = np.arange(0, 300, 1)\n", + "plt.plot(position, receptive_field_model(position, np.log(10), 250.0, np.sqrt(12)))\n", + "plt.plot(position, receptive_field_model(position, np.log(30), 150.0, np.sqrt(20)))\n", + "\n", + "F = np.identity(3)\n", + "Q = np.diag([1e-5, 1e-3, 1e-4])\n", + "dt = 0.020 # seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 328, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grad_receptive_field_model = jax.grad(receptive_field_model, argnums=(1, 2, 3))\n", + "hess_receptive_field_model = jax.hessian(receptive_field_model, argnums=(1, 2, 3))\n", + "\n", + "plt.plot(\n", + " position,\n", + " [grad_receptive_field_model(x, np.log(10), 250.0, np.sqrt(12)) for x in position],\n", + ")\n", + "# hess_receptive_field_model(250.0, np.log(10), 250.0, np.sqrt(12))" + ] + }, + { + "cell_type": "code", + "execution_count": 349, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 354, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.0, -0.41666666666666674, 0.6014065304058602]" + ] + }, + "execution_count": 354, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 371, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array(True, dtype=bool)" + ] + }, + "execution_count": 371, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def log_receptive_field_model(\n", + " position: np.ndarray, params: tuple[float, float, float]\n", + ") -> np.ndarray:\n", + " log_max_rate, place_field_center, scale = params\n", + " return log_max_rate - (position - place_field_center) ** 2 / (2 * scale**2)\n", + "\n", + "\n", + "log_max_rate = np.log(10)\n", + "place_field_center = 250.0\n", + "scale = np.sqrt(12)\n", + "x = 245.0\n", + "\n", + "grad_log_receptive_field_model = jax.grad(log_receptive_field_model, argnums=1)\n", + "np.allclose(\n", + " grad_log_receptive_field_model(x, (log_max_rate, place_field_center, scale)),\n", + " [\n", + " 1.0,\n", + " (scale**-2) * (x - place_field_center),\n", + " (scale**-3) * (x - place_field_center) ** 2,\n", + " ],\n", + ")\n", + "\n", + "hess_log_receptive_field_model = jax.hessian(log_receptive_field_model, argnums=1)\n", + "jnp.allclose(\n", + " jnp.array(\n", + " hess_log_receptive_field_model(x, (log_max_rate, place_field_center, scale))\n", + " ),\n", + " jnp.array(\n", + " [\n", + " [0, 0, 0],\n", + " [0, -(scale**-2), -2 * scale**-3 * (x - place_field_center)],\n", + " [\n", + " 0,\n", + " -2 * scale**-3 * (x - place_field_center),\n", + " -3 * scale**-4 * (x - place_field_center) ** 2,\n", + " ],\n", + " ]\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 472, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 472, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def log_receptive_field_model(\n", + " position: np.ndarray, params: tuple[float, float, float]\n", + ") -> np.ndarray:\n", + " log_max_rate, place_field_center, scale = params\n", + " return log_max_rate - (position - place_field_center) ** 2 / (2 * scale**2)\n", + "\n", + "\n", + "def receptive_field_model(\n", + " position: np.ndarray, params: tuple[float, float, float]\n", + ") -> np.ndarray:\n", + " return jnp.exp(log_receptive_field_model(position, params))\n", + "\n", + "\n", + "grad_log_receptive_field_model = jax.grad(log_receptive_field_model, argnums=1)\n", + "hess_log_receptive_field_model = jax.hessian(log_receptive_field_model, argnums=1)\n", + "\n", + "\n", + "dt = 0.020 # seconds\n", + "total_time = 8000.0 # seconds\n", + "n_total_steps = int(total_time / dt)\n", + "\n", + "time = np.arange(0, total_time, dt)\n", + "\n", + "speed = 125.0 # cm/s\n", + "track_length = 300.0 # cm\n", + "\n", + "run1 = np.arange(0, track_length, speed * dt)\n", + "run2 = np.arange(track_length, 0, -speed * dt)\n", + "run = np.concatenate((run1, run2))\n", + "\n", + "position = np.concatenate([run] * int(np.ceil(n_total_steps / run.shape[0])))\n", + "position = position[:n_total_steps]\n", + "\n", + "true_params1 = (np.log(10), 250.0, np.sqrt(12))\n", + "true_params2 = (np.log(30), 150.0, np.sqrt(20))\n", + "true_rate1 = receptive_field_model(position[: position.shape[0] // 2], true_params1)\n", + "true_rate2 = receptive_field_model(position[position.shape[0] // 2 :], true_params2)\n", + "true_rate = np.concatenate((true_rate1, true_rate2))\n", + "spike_indicator = np.random.poisson(true_rate * dt)\n", + "\n", + "plt.figure(figsize=(12, 3))\n", + "plt.plot(time[:5_000], position[:5_000])\n", + "plt.eventplot(time[spike_indicator[:5_000].nonzero()[0]], color=\"red\", linelengths=10.0)\n", + "plt.figure(figsize=(12, 3))\n", + "plt.plot(time[-5_000:], position[-5_000:])\n", + "\n", + "plt.eventplot(\n", + " time[(spike_indicator.shape[0] - 5000) + spike_indicator[-5_000:].nonzero()[0]],\n", + " color=\"red\",\n", + " linelengths=10.0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 548, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(position, params)>" + ] + }, + "execution_count": 548, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 482, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 300.0)" + ] + }, + "execution_count": 482, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(\n", + " time[spike_indicator.nonzero()[0]], position[spike_indicator.nonzero()[0]], s=1\n", + ")\n", + "plt.ylim((0, 300))" + ] + }, + { + "cell_type": "code", + "execution_count": 463, + "metadata": {}, + "outputs": [], + "source": [ + "# random walk\n", + "transition_matrix = np.identity(3)\n", + "covariance_matrix = np.diag([1e-5, 1e-3, 1e-4])\n", + "\n", + "params_prev = np.array((np.log(10), 250.0, np.sqrt(12)))\n", + "one_step_mean = transition_matrix @ params_prev\n", + "\n", + "t = 0\n", + "conditional_intensity = receptive_field_model(position[t], one_step_mean)\n", + "innovation = spike_indicator[t] - conditional_intensity * dt\n", + "one_step_grad = grad_log_receptive_field_model(position[t], one_step_mean)[None]\n", + "one_step_hess = hess_log_receptive_field_model(position[t], one_step_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 465, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([[ 1.0000000e+00, -2.0833334e+01, 1.5035162e+03],\n", + " [-2.0833334e+01, 4.3402780e+02, -3.1323256e+04],\n", + " [ 1.5035162e+03, -3.1323256e+04, 2.2605610e+06]], dtype=float32)" + ] + }, + "execution_count": 465, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "one_step_grad.T @ one_step_grad" + ] + }, + { + "cell_type": "code", + "execution_count": 458, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array(0., dtype=float32)" + ] + }, + "execution_count": 458, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "innovation" + ] + }, + { + "cell_type": "code", + "execution_count": 544, + "metadata": {}, + "outputs": [], + "source": [ + "def point_process_adaptive_filter(\n", + " init_params,\n", + " x,\n", + " spike_indicator,\n", + " dt,\n", + " transition_matrix,\n", + " covariance_matrix,\n", + " receptive_field_model,\n", + " grad_log_receptive_field_model,\n", + " hess_log_receptive_field_model,\n", + "):\n", + "\n", + " def _update(params_prev, args):\n", + " \"\"\"Point Process Adaptive Filter update step\n", + "\n", + " F : transition matrix\n", + " Q : covariance matrix\n", + " \\theta_{k | k-1} :\n", + " W_{k | k-1}: one_step_variance_params\n", + " \\theta_{k | k} : posterior_mean\n", + " W_{k | k} : posterior_variance\n", + " \"\"\"\n", + " mean_prev, variance_prev = params_prev\n", + " x_t, spike_indicator_t = args\n", + " one_step_mean = transition_matrix @ mean_prev\n", + " one_step_variance = (\n", + " transition_matrix @ variance_prev @ transition_matrix.T + covariance_matrix\n", + " )\n", + " conditional_intensity = receptive_field_model(x_t, one_step_mean) * dt\n", + " innovation = spike_indicator_t - conditional_intensity\n", + " one_step_grad = grad_log_receptive_field_model(x_t, one_step_mean)[None]\n", + " one_step_hess = hess_log_receptive_field_model(x_t, one_step_mean)\n", + "\n", + " inverse_posterior_variance = (\n", + " jnp.linalg.pinv(one_step_variance)\n", + " + (one_step_grad.T * conditional_intensity @ one_step_grad)\n", + " - innovation * one_step_hess\n", + " )\n", + " posterior_variance = jnp.linalg.pinv(inverse_posterior_variance)\n", + " posterior_mean = one_step_mean + posterior_variance @ (\n", + " one_step_grad.squeeze() * innovation\n", + " )\n", + "\n", + " return (posterior_mean, posterior_variance), (\n", + " posterior_mean,\n", + " posterior_variance,\n", + " )\n", + "\n", + " return jax.lax.scan(_update, init_params, (x, spike_indicator))[1]\n", + "\n", + "\n", + "(\n", + " posterior_mean,\n", + " posterior_variance,\n", + ") = point_process_adaptive_filter(\n", + " (np.array(true_params1), np.identity(3)),\n", + " position,\n", + " spike_indicator,\n", + " dt,\n", + " transition_matrix,\n", + " covariance_matrix,\n", + " receptive_field_model,\n", + " grad_log_receptive_field_model,\n", + " hess_log_receptive_field_model,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 545, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, 'Stochastic State Point Process Filter (SSPPF)')" + ] + }, + "execution_count": 545, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_results(time, true_params1, true_params2, posterior_mean):\n", + " fig, axes = plt.subplots(3, 1, figsize=(12, 6), sharex=True)\n", + " axes[0].plot(time, posterior_mean[:, 0])\n", + " axes[0].plot(\n", + " time[:time.shape[0] // 2],\n", + " true_params1[0] * np.ones_like(time[:time.shape[0] // 2]),\n", + " linestyle=\"--\", color=\"red\"\n", + " )\n", + " axes[0].plot(\n", + " time[time.shape[0] // 2 :],\n", + " true_params2[0] * np.ones_like(time[time.shape[0] // 2 :]),\n", + " linestyle=\"--\", color=\"red\"\n", + " )\n", + " axes[0].set_title(\"Log Max Rate\")\n", + " axes[1].plot(time, posterior_mean[:, 1])\n", + " axes[1].plot(\n", + " time[:time.shape[0] // 2],\n", + " true_params1[1] * np.ones_like(time[:time.shape[0] // 2]),\n", + " linestyle=\"--\", color=\"red\"\n", + " )\n", + " axes[1].plot(\n", + " time[time.shape[0] // 2 :],\n", + " true_params2[1] * np.ones_like(time[time.shape[0] // 2 :]),\n", + " linestyle=\"--\", color=\"red\"\n", + " )\n", + " axes[1].set_title(\"Place Field Center\")\n", + " axes[2].plot(time, posterior_mean[:, 2])\n", + " axes[2].plot(\n", + " time[:time.shape[0] // 2],\n", + " true_params1[2] * np.ones_like(time[:time.shape[0] // 2]),\n", + " linestyle=\"--\", color=\"red\"\n", + " )\n", + " axes[2].plot(\n", + " time[time.shape[0] // 2 :],\n", + " true_params2[2] * np.ones_like(time[time.shape[0] // 2 :]),\n", + " linestyle=\"--\", color=\"red\"\n", + " )\n", + " axes[2].set_title(\"Scale\")\n", + " axes[-1].set_xlabel(\"Time (s)\")\n", + "\n", + "plot_results(time, true_params1, true_params2, posterior_mean)\n", + "plt.suptitle(\"Stochastic State Point Process Filter (SSPPF)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 542, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.98, 'Steepest Descent Point Process Filter (SDPPF)')" + ] + }, + "execution_count": 542, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def steepest_descent_point_process_filter(\n", + " init_params,\n", + " x,\n", + " spike_indicator,\n", + " dt,\n", + " receptive_field_model,\n", + " grad_log_receptive_field_model,\n", + " epsilon,\n", + "):\n", + "\n", + " def _update(mean_prev, args):\n", + " \"\"\"Steepest Descent Point Process Filter update step\"\"\"\n", + "\n", + " x_t, spike_indicator_t = args\n", + " conditional_intensity = receptive_field_model(x_t, mean_prev) * dt\n", + " innovation = spike_indicator_t - conditional_intensity\n", + " one_step_grad = grad_log_receptive_field_model(x_t, mean_prev)\n", + " posterior_mean = mean_prev + epsilon @ one_step_grad * innovation\n", + "\n", + " return posterior_mean, posterior_mean\n", + "\n", + " return jax.lax.scan(_update, init_params, (x, spike_indicator))[1]\n", + "\n", + "epsilon = np.diag([0.02, 10.0, 1.0])\n", + "\n", + "posterior_mean2 = steepest_descent_point_process_filter(\n", + " np.array(true_params1),\n", + " position,\n", + " spike_indicator,\n", + " dt,\n", + " receptive_field_model,\n", + " grad_log_receptive_field_model,\n", + " epsilon,\n", + ")\n", + "\n", + "plot_results(time, true_params1, true_params2, posterior_mean2)\n", + "plt.suptitle(\"Steepest Descent Point Process Filter (SDPPF)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 541, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.302585092994046, 250.0, 3.4641016151377544)" + ] + }, + "execution_count": 541, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "true_params1" + ] + }, + { + "cell_type": "code", + "execution_count": 478, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.02, 0. , 0. ],\n", + " [ 0. , 10. , 0. ],\n", + " [ 0. , 0. , 1. ]])" + ] + }, + "execution_count": 478, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "epsilon" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "non_local_detector", + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/diffusion2.ipynb b/notebooks/diffusion2.ipynb new file mode 100644 index 0000000..e9213c5 --- /dev/null +++ b/notebooks/diffusion2.ipynb @@ -0,0 +1,1503 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n", + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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44Yf97dLS0vDEE0/g97//PdLS0tCvXz9s374ds2fPxtixY9GuXTuzfxpCIg+3M09J5OPL0Pc9EhISpCZNmkg333yz9OSTT4acZihJ1neXSJIkFRQUSHfddZeUlpYmpaamSr/+9a+lHTt2BO04OHHihDRq1CipTZs2Ur169aT69etLHTt2lJ599lmpsrIyaIzA3SWSdHHnxPjx46X09HSpdu3aUmZmppSTkyOdO3cuqB0AaeLEiSHzy8zMFNoN8qc//Ulq1aqVlJiYKLVt21Z6+eWXFa/Pnj17pB49ekh169aVAITMVw0zu0uM/B0yMzOlQYMGBZX997//lSZNmiRlZWVJderUkdLS0qSuXbtKM2bMkMrLy1Xn+tNPP0nz5s2TevfuLTVv3lxKSEiQ6tWrJ3Xq1EmaN2+edObMmaD2q1atktq0aSPVqVNHAiDNmjXLX/fVV19JQ4cOlZo0aSLVqVNHatasmdS7d2/ppZde8rfx/dv98MMPpfvuu0/yeDxScnKyNHDgQOnbb79VnWcgH3/8sdSvXz+pWbNmUnJystS6dWvp4YcfloqLi0Pa7tixQ7r11lulunXrSikpKdKQIUNCDvLy8dxzz0mtW7eWEhISpMsvv1yaNWuWdP78eaE5ERItxEmSS9sCCCHEYV555RWMHj0a27dvR3Z2ttvTIaTGwZwMQgghhDgCRQYhhBBCHIHhEkIIIYQ4gquejE2bNmHw4MHIyMhAXFyc0I2BNm7ciK5duyIpKQlXXHGF6jkIhBBCCHEXV0XG6dOncd111+HFF18Uan/kyBEMHDgQPXv2xO7du/H4449j0qRJeOuttxyeKSGEEEKMEjHhkri4OKxdu1bzIJrHHnsM77zzTtA9H8aPH4+vvvrKfzgOIYQQQiKDqDqMa9u2bejXr19QWf/+/bFs2TJcuHABderUCelTUVERdIRzdXU1Tp06hYYNG/JGRIQQEsVIkoSysjJkZGSgVi1nHPPnzp3D+fPnbbGVkJAQcs+gWCeqREZhYSGaNm0aVNa0aVNUVlaiuLgY6enpIX1yc3MxZ86ccE2REEJImDl27JjqKbNWOHfuHLKyshTvfWOGZs2a4ciRIzVKaESVyABCb4Psi/aoeSVycnIwbdo0//uSkhJcfvnlSAFAPwYhhEQvEoBSIOReNXZx/vx5FBYW4tixb5GSkmLJVmlpKVq2vBrnz5+nyIhUmjVrFqIoi4qKULt2bcVbcQMXb7Lku9FSIHGgyCCEkFjA6dB3SkqKZZFRU4mqw7i6deuGvLy8oLIPP/wQ2dnZivkYhBBCCHEPV0VGeXk59uzZgz179gC4uEV1z549yM/PB3Ax1DFixAh/+/Hjx+Po0aOYNm0aDh48iOXLl2PZsmV45JFH3Jg+IYQQQjRwNVyyY8cO9OrVy//elzsxcuRIvPLKKygoKPALDgDIysrC+vXrMXXqVCxcuBAZGRl4/vnnceedd4Z97oQQQgjRJmLOyQgXpaWlSE1NRSqYk0EIIdGMBKAEFxP6nciZ8K0XJSUnbEn8TE1t6thcI5WoSvwkhBBCws9JAFbPyiizYyJRR1QlfhJCCCEkeqDIIIQQQogjUGQQQgghxBEoMgghhJAIpKysDFOmTEFmZiaSk5PRvXt3bN++XbNPRUUFZsyYgczMTCQmJuLKK6/E8uXLwzTjUJj4SQghhEQgY8eOxb59+/C3v/0NGRkZeO2119CnTx8cOHAAzZs3V+wzdOhQnDhxAsuWLcNVV12FoqIiVFZWhnnm/4NbWAkhhEQl4dvCegApKdbuj1JaWobU1HbCcz179iwaNGiAt99+G4MGDfKXd+rUCbfddhvmzZsX0uf999/HPffcg8OHDyMtLc3SfO2C4RJCCCEkTJSWlgY9KioqFNtVVlaiqqoq5GZqycnJ2LJli2Kfd955B9nZ2Zg/fz6aN2+O1q1b45FHHsHZs2dt/xyiMFxCCCGEaHIK1s/JKAcAtGzZMqh01qxZmD17dkjrBg0aoFu3bvjDH/6Atm3bomnTpli1ahW++OILXH311YojHD58GFu2bEFSUhLWrl2L4uJiTJgwAadOnXItL4MigxBCCAkTx44dCwqXKN0l3Mff/vY33H///WjevDni4+PRpUsXDB8+HLt27VJsX11djbi4OKxcuRKpqakAgGeeeQZ33XUXFi5ciOTkZHs/jAAMlxBCCCFhwnfbeN9DS2RceeWV2LhxI8rLy3Hs2DF8+eWXuHDhArKyshTbp6eno3nz5n6BAQBt27aFJEn44YcfbP8sIlBkEEIIIRFMvXr1kJ6ejp9++gkffPAB7rjjDsV2PXr0wI8//ojy8nJ/2TfffINatWqhRYsW4ZpuEBQZhBBCSATywQcf4P3338eRI0eQl5eHXr164ZprrsHo0aMBADk5ORgxYoS//fDhw9GwYUOMHj0aBw4cwKZNm/Doo4/i/vvvdyVUAlBkEEIIIRFJSUkJJk6ciDZt2mDEiBG46aab8OGHH6JOnToAgIKCAuTn5/vb169fH3l5efB6vcjOzsa9996LwYMH4/nnn3frI/CcDEIIIdFJ+M7J2IKUlPoWbZUjNfWmGnerd3oyCCGEEOII3MJKCCGEaHISwDmLNk7bMZGog54MQgghhDgCRQYhhBBCHIEigxBCCCGOQJFBCCGEEEegyCCEEEKII1BkEEIIIcQRKDIIIYQQ4gg8J4MQQgjR5ASAuhZtnLFjIlEHPRmEEEIIcQSKDEIIIYQ4AkUGIYQQQhyBIoMQQgghjkCRQQghhBBHoMgghBBCiCNwCyshhBCiyU8Azlq0YfVW8dEJPRmEEEIIcQSKDEIIIYQ4AkUGIYQQQhyBIoMQQgghjkCRQQghhBBHoMgghBBCiCNQZBBCCCHEEXhOBiGEEKLJSQCJFm1U2DGRqIOeDEIIIYQ4AkUGIYQQQhyBIoMQQgghjkCRQQghhBBHoMgghBBCiCNwdwkhUUgDh+yWOWSXEFIzocggJAJxSkRYHZcihBBiBIoMQlzELTFhFrX5UnyQ2OYkgASLNs7bMZGogyKDkDARbYLCCEqfjcKDEEKRQYhDxLKoEEH++Sk6CKl5cHcJITbSIOBBguG1IUScyspKPPHEE8jKykJycjKuuOIKzJ07F9XV1UL9t27ditq1a6NTp07OTlQHejIIsQgXTePQy0GINk899RReeukl/PWvf0X79u2xY8cOjB49GqmpqZg8ebJm35KSEowYMQK33norTpw4EaYZK0ORQYgJKCzsJfB6UnAQAmzbtg133HEHBg0aBABo1aoVVq1ahR07duj2HTduHIYPH474+HisW7fO4Zlqw3AJIYLQ3R8eeI1JLFNaWhr0qKhQvjvrTTfdhA0bNuCbb74BAHz11VfYsmULBg4cqGl/xYoV+O677zBr1izb524GejII0YELnjvQu0Eih1MA6li0cQEA0LJly6DSWbNmYfbs2SGtH3vsMZSUlKBNmzaIj49HVVUV/vjHP+L//b//pzrCt99+i+nTp2Pz5s2oXTsylvfImAUhEQaFRWTh+3tQbJBo59ixY0hJSfG/T0xMVGy3evVqvPbaa3j99dfRvn177NmzB1OmTEFGRgZGjhwZ0r6qqgrDhw/HnDlz0Lp1a8fmb5Q4SZIktycRTkpLS5GamopUAHFuT4ZEHBQX0QMFB5EAlOBiomPgwm0XvvWipOSXSEmx5skoLb2A1NS1wnNt2bIlpk+fjokTJ/rL5s2bh9deew3//ve/Q9p7vV5cdtlliI+P95dVV1dDkiTEx8fjww8/RO/evS19BjPQk0EIKC6iEXo3SCxz5swZ1KoVnDYZHx+vuoU1JSUFe/fuDSpbtGgRPv74Y7z55pvIyspybK5aUGSQGk20igsn5x1tizbFBolFBg8ejD/+8Y+4/PLL0b59e+zevRvPPPMM7r//fn+bnJwcHD9+HK+++ipq1aqFDh06BNlo0qQJkpKSQsrDCUUGqZFEurhwc37RepM0ig0SS7zwwgv4/e9/jwkTJqCoqAgZGRkYN24cZs6c6W9TUFCA/Px8F2epD3MySI0i0sRFpM3HLJG4sEfinIi9xHJORqxATwapEUTKYh4p87CbSDzBswEiYx6E1GQoMkhMEwmLeiTMIdxEiuhgCIXYw0lYXy4r7ZhI1EGRQWIWtxb3migq9HD7YC2KDULcgSKDxBxuLPIUFuK4KTgoNggJLxQZJGaguIg+3BIczNcgJDy4foO0RYsWISsrC0lJSejatSs2b96s2X7lypW47rrrULduXaSnp2P06NE4efJkmGZLIpVwLvaRdKO0BjY+3Cbcc4mUz01ILOOqyFi9ejWmTJmCGTNmYPfu3ejZsycGDBiguu93y5YtGDFiBMaMGYP9+/fjjTfewPbt2zF27Ngwz5xECrG8KIVbGESSCAn335UQ4gyuioxnnnkGY8aMwdixY9G2bVssWLAALVu2xOLFixXbf/7552jVqhUmTZqErKws3HTTTRg3bhx27NgR5pmTSCAci0O4Flm3F3UR3BAf4boWkXrNCYl2XBMZ58+fx86dO9GvX7+g8n79+uGzzz5T7NO9e3f88MMPWL9+PSRJwokTJ/Dmm29i0KBBquNUVFSgtLQ06EGim1hY3CJdUBghHJ8lnGKDEGIfriV+FhcXo6qqCk2bNg0qb9q0KQoLCxX7dO/eHStXrsSwYcNw7tw5VFZW4vbbb8cLL7ygOk5ubi7mzJlj69yJe4RDXEST3UjEyWTOcOwO4Q4UEspJAPG6rbSpsmMiUYfriZ9xccGHe0uSFFLm48CBA5g0aRJmzpyJnTt34v3338eRI0cwfvx4Vfs5OTkoKSnxP44dO2br/En4CIdnwQmbNUlgyHHKyxEubxYhxBqueTIaNWqE+Pj4EK9FUVFRiHfDR25uLnr06IFHH30UANCxY0fUq1cPPXv2xLx585Cenh7SJzExEYmJifZ/ABI2nBYXkWxPBI8NNrw22BDBbi+H014HbnUlxBqueTISEhLQtWtX5OXlBZXn5eWhe/fuin3OnDmDWrWCpxwff9GFVcPu81ZjiAaB4bTHwqPziJYx5Nh5zZzOByGEmMPVw7imTZuG++67D9nZ2ejWrRuWLl2K/Px8f/gjJycHx48fx6uvvgoAGDx4MB544AEsXrwY/fv3R0FBAaZMmYIbbrgBGRkZbn4U4gCRvmjYPT+PzfbsxKNQ5rXJtp3eDac8G8zTIMQcroqMYcOG4eTJk5g7dy4KCgrQoUMHrF+/HpmZmQCAgoKCoDMzRo0ahbKyMrz44ot4+OGH4fF40Lt3bzz11FNufQTiEE4IjEgTFx6b7LiFR6HMa9GmXYu5U2EOhk8IMUacVMPiDKWlpUhNTUUqAOX0UuImkeq9sGNeHhtsRBNeG2zYsaA7IQooNCIDCUAJgJKSEqSkpNhu37delJS0R0qKtd0lpaVVSE3d79hcIxXeu4REDJHovbDa32Oxvyhm5+nkYukJeO01acMOz4YT3gd6NGoap2A9hbHajolEHRQZJCKINIFhpa/HQl8l3E5+tWMx9QS89prob1VsOJFTQaFBiD4UGcR1ImkbqdviIhJ3MqjNyewC6wl47TXY1w6xQaFBSPigyCCuEu0Cw2NhPLNjRgryuZtZbD2Xnr0mxzYzpt1eDe48IUQdigziGnafABnOvp4wjhUtWNmK6rn07DU5plmxQa8GIc5CkUFcIRIERrjERaRtnRXFarKlGTuegNdeg+NRaBASeVBkkLATjQLD47B9u/vbgV25GFYFh9fgOGbnZ2f4hEKDkItQZJCw4rbAiERxEQmCwghWcjHMCA7PpWevgTHc9mpQaBByEYoMEjbcDhsY6edxyK6VPnbbtDvx0ahdox4ED8Lj1aDQIKGchPXjG2vUuZd+KDJIWHBTYESKuHBra61Vm06HR4wIAs+lZ6+BuVBoEOIeFBnEcWJNYBixGSleDisozceot0Kkj5G2nkvPXgNzMBPWset29BQapKZCkUEcJRoEhsdme+H0cLiFmRCJkYVbdGH2IHq8GoTURCgyiGO4JTDs9l44IS4iLXQix8kQiWhbUVHiufTs1Wnns+mG0KBYITUVigziCJEuMDw22hJpF21hEyvbV80IDrvEhlenjRF78j4UGoQYhyKD2E5NERh2i4toCJsYDZPY6bUQaeO59OzVnNX/7FFoEOIsFBkkInFicfbYZEuvjVO5G4GkWOirR6mBtm54LUQWag8oNAiJBCgyiK3Y8Ws8UgWGHeLC6PVxUkwYHVNPfITTa2GnV4NCg+hy+jwQb9WGLTOJOigyiG1Eq8BwWlyIztMNQWEE+fy0RIeI4DDSxqrY8GrU++xQaBBiPxQZxBYiUWB4LNoJh7gwIywiZXeJqOiwS0xYCaF4EJlCg5BYhyKDWCYaBYYVgWBVXIgKi0jbXaK3oAZ+Lj3BYUZMWBUiHogJDa0xjI7pdH9CIp1abk+AkEgSGA006kXq1OpTAh56Y2vZ0ULeX+9h1b4Wop/VaB0E6rTqPbB3+7LRtk70J7FJq1atEBcXF/KYOHGiYvs1a9agb9++aNy4MVJSUtCtWzd88MEHYZ51KBQZxBLh/IINh8AwWqe3qBkRFno4LRqM2hRpqyeurIo6rblp4dGpF7Fhtq0T/UnssX37dhQUFPgfeXl5AIC7775bsf2mTZvQt29frF+/Hjt37kSvXr0wePBg7N69O5zTDiFOkqQadWu40tJSpKamIhXW76lX04kmgWG3uNCq0wuHiAqKSEXEva/VRithVKufWp2ZPj68OvUiNoy2c9pGTUICUAKgpKQEKSn2p0771ouSHwGr5ktLgdQM83OdMmUK/vWvf+Hbb79FXJzY6tW+fXsMGzYMM2fONDyeXTAng5gi1gWG3eLCjiTRQDwG24viFWhjNZHTd52UxIZWP7X8Bb0+anWAvQmhzK8gIpSWBv/LT0xMRGJiomaf8+fP47XXXsO0adOEBUZ1dTXKysqQlpZmeq52QJFBwk60Cgy7xYXodfAItrMDtbG8KuV6gkOrXk9smBEUZpJCPYgcoUGhEqGUw7rru/ziU8uWLYOKZ82ahdmzZ2t2XbduHbxeL0aNGiU83NNPP43Tp09j6NChBidqLxQZxDDhcuU7JTDcFBd25AoYtamH6N1OA/HqzMOIQFATG2a9Gk4JDVEoNIgWx44dCwqX6HkxAGDZsmUYMGAAMjIyhMZYtWoVZs+ejbfffhtNmjQxPVc7oMgghghXmMTqYuyWwDAjLjwadaI2rKBlV2tR9uHVsSm3YZfYsNvb4YG20DCy+FMoEDVSUlIM5WQcPXoUH330EdasWSPUfvXq1RgzZgzeeOMN9OnTx+w0bYMig4SNaBIYTosLj0q5Xj9RzAgHETtKfT0Br70aNuwQG6I21NprlQORIzQoUoiPFStWoEmTJhg0aJBu21WrVuH+++/HqlWrhNqHA4oMIoyVhc8ugWG2rxEhoVQeDnERrl0nIjZEjgJXaucJeO1V6WtEbNjh1XBTaFiBQoNUV1djxYoVGDlyJGrXDl6uc3JycPz4cbz66qsALgqMESNG4LnnnsPPfvYzFBYWAgCSk5ORmpoa9rn74DkZRIhwCAwRPCbGcEJgNNCwKy/3QP0QqAZQtyWvD1cujNK4IvOT44H2Z1Yql6N2xobVv6lWOWCfpymcfzMSe3z00UfIz8/H/fffH1JXUFCA/Px8//slS5agsrISEydORHp6uv8xefLkcE45BJ6TQYSIBC+Gx2A/q+ERI94LtUVWtL9InZE2VrDjXAi1Oq9gW6UypV0oRs7OMHPOhlejTq+vmXZ29411wnZOxjdAisX/eKVlQGpr5+YaqTBcQnShwDDW12PDnIzU243SeEa3p6qFNTwQC6MolSnlahgJh7gZOmF+RpRTHiE2ohCKDKJJJAsMo/bsFBii4sKMsHDTm2E2F0NNcCiJBc+lZ6+CDRFhIM/VMJKn4YTQIISoQ5FBXMWKwLAal5eXmfVeeCzOQ6tcr85u1MYSFRWBdWbEhlpf0aRQp4SGFvRmEKIORQZRxe1kT49B+04LDBHvhdMJiUbamEHEk2HFi2FEbIiET8IlNDxwP2xCSDRCkUEUcTtM4jHYT3TBFgmP2OW9sENw6NXZjUg+hh1eDDWx4RXoZyZPI5KEhlkoUEg0QpFBXMHswmmknx0Cwy7vhRHBoVcHAPUc2nx+ujq0TCsfw6oXQ17mufTslbUxGj5xS2iIwLAJqUlQZJAQnPZimM3DsBIOsUNgeGwa16jgcEpQiI4lFx56wiKw3IjY0PNqGA2fWBUaasjnZsYWxQKpKVBkENtwMg8j0gWG3eJCV1g4uc1eluggn0ug6BD1ZIiIDT2vhmj4xC6h4XQiqFkoUEg0QZFBgnA6/m82D0PUVrgFhpk5GBYWRgWFyB9Ra5WSj6chOpQEhxmxYdWrEW6hIZ+TGSgWoohSAAqhREPwnAxCzGNHmMRIv3AIDI9B+2bEhSlhYYcS1LOhlF3pI2AF983fqthQeu+G0FDCjNCgN4OQi1BkED/hSMZUw2PAthMCw0p4xDZxYfQubHLM3AOpRHBMNdFxaSVX8m6Y8VoEvlcKn3gN2DIjNNQWb6eEBsUCiXUoMkhYMBMmsSNp0oddAsOIoBASF6IngPmw+2aKavbk4kMt+UJhD6ncu2HVixH43nPp2SvQ1jc9kWPIYbCNnURq7gchdkCRQQA468VwMs9DRHSYFRh2ei9MiwsRUWFHEqj8VCv5uIGiQ0lwaHg3jIgNM+ETq0LDyfwMCgFS0+Gt3omreFTKzXos3BYYDWTv69WSCQz5vcsbKHRKDXjISVF42IGeXbU5yeceaOsS8mtg5doG4hHs55uSVr1Vr5lHpVyrj9E2dvYjJFzQk0Fc82J4DPQxk4dhh8AwuyDqei7kndU8Fk4ngAaidMJVID53QOBcfR4OLe9GgGdDy6sh6uGwy6Mhh14HQuyHIoNEJZEkMDRDI2bEhdUEUL22aiupUh+dhE///JXCKYFiQyOEYlRcyNt5IC40IFin1capsAlzM0gsQpFRw4lGL0a4BYao2ND0XuiJC6MJoFa8GHp99Y7xBEITPtW8GzZ4NUQTQr0C7ZzOzyAxSjkAyaKN03ZMJPqgyCARg9k8DKM2fXhU2pkRG6reC6PiQm3CehfC7IVS26+p1EYrJAIoezeUvBqX2up5NewUGoGYERqiBM5BDr0ZpCZCkVGDiTQvhpmxtbwYWm09KuVGXwt7L8yIC7OCwwhqtvS8GQI5GEhFaM6GSgjFitAIxANloaGXn6EnNOwKmxBS06DIIGHFo1LuRJhEra1HpdySwBD1Xti1zUGkzghKK6hSvZbgUBIb8pwNldiFFaEhX+w9MCc09LAjbEJvBqlpUGQQQ1j1Yoi2N2pDNA/DI9DGNoFhRVzY4cVQams26VNEXGh4K1RDKCrhk8AmVr0bZtrptdXDA56dQQhAkVFjsdPjLorHQl/RNVheF3ECw6i4sNuLoddHJOlTT1yoiAgAoSEUQa+GUaHhgdgi72TYxAoUIiRWoMggwrjlxbCSh2FkfEMCw05xYTVkIlKvhp1hEqX3al6NMAsNrUXbaNhElMDxwwXFCYk0KDJqIJHsxbAaJlGz5VEpVypzVGAYERd2hEz0ULNlVVz43qt5NeThkzALDSthEzu8GSLtKRgiiHJYv9X7GTsmEn1QZBAhwuXF0GvjVJhEr15YYIiICyN1WmWAtRhUIF6N8dQEh9p7vZVfyashFyNhFhpGb6QmKgACxw4XFCckkqDIII7jEWxnNCIgsp57BNo00KnXFBhWvBdmPBkehTI5IupNvgop2fUq2BMJjah5MlSEhGL4xAahYRStsIkTngd6M0hNgCKjhuFUqCQcXoxAjN4XTHSdl5dZEhhmxIURYWHlj6kXJpGP65X10xMUvjqt1/LwiaDQgKyZ/LVSvQfhCZsoETg2ITUNigyii5W1zGNyDLvDJHptlXSBYYFh1HuhJS48CEX0IhhBNPHTNx+vrJ0R74VerEJQaBgRF1bDJkZxwpsRjnkQ4hS81TuxjBNeDC2shEm0+jkqMBoIvPZN3CPrL2/TQOFhFj1bavPzqNQbvQ4+fNcx8No2kNXhf38Xvb+fnnNIFCviNxCPjXMgJJqgJ6MGYebLKtK9GEZs64kNH7YIDKPeC4/OxNQugl0rkNr5GL66BgptPZeevQH1Rj0ZJjwakFXplQXiQc3xZhASCdCTQcJKuL0YWm2VykLuRRJYaUVgqHkvPFD2XOi91/JgKHkn9PoZ8WYoeTcC67Re65UJeDQC/0aiZvX+fcixcn6aGiLj2gk9ICQSoCeDWMLqF5mRL3CRZE+Phi29MsWTPM0KDKPeCy2XixNeDLW+Zs/H8Fx67ZWVq3kv5PV6Hg0E1+klgmqVqdXb6VGw2ztBb4fLlAGosmgjSs7JqK6uxqFDh1BUVITq6uD/ZD//+c8N26PIqCFEQqjEzl9WRsIgWvWKc1ISEVp1IgLDDnGhdQGNbrfxIY8LaIkLX5mS2NDKrhQNmWgJDYU6rURQPcGhNE05gWETeRu990ahiCCRwOeff47hw4fj6NGjkCQpqC4uLg5VVcaVFsMlxDROu2NFvBh6bnC99d6HYh6GD/lBW04LDL3whG8ugQ+zyO3I4wRac1F77UFwYihkr0Wvn49U9TqlRFCR4dRwSgj78Nhsz4k+pOYyfvx4ZGdnY9++fTh16hR++ukn/+PUqVOmbNKTQWzHI9jObGzbzEIhHCaRV9olMDwqkzHiyRBRWmYJ/BkdOE7gT/nAdnL3ga9OyV2gFx5R82ionQyqkgjqQzRs4pue0sdRSgJ12ttAbwZxm2+//RZvvvkmrrrqKttsuu7JWLRoEbKyspCUlISuXbti8+bNmu0rKiowY8YMZGZmIjExEVdeeSWWL18eptlGJ5HwC8ioPbu9GPIyoTCJ0lHhaoaMCgw1L4D8vZ6HQe1D6j30+vhQG1tt3vJyj0p94LNWnZaXRpYIalRcWsWsSPbYND6JfY4fP45f//rXaNiwIerWrYtOnTph586dmn02btyIrl27IikpCVdccQVeeukl4fFuvPFGHDp0yOq0g3DVk7F69WpMmTIFixYtQo8ePbBkyRIMGDAABw4cwOWXX67YZ+jQoThx4gSWLVuGq666CkVFRaisrAzzzInVL2q3vRg+QsIkWo3VFkIzAkNpclrKSm1eZv8QSv2UtrEG/qQHlD0bap4M32sPlPM09JIn5F4LpfyMS8jzM6x6MwKx4s2w2ztBb0fN4aeffkKPHj3Qq1cvvPfee2jSpAm+++47eDwe1T5HjhzBwIED8cADD+C1117D1q1bMWHCBDRu3Bh33nmn7pgPPfQQHn74YRQWFuLaa69FnTp1guo7duxo+HPESfLsjjBy4403okuXLli8eLG/rG3bthgyZAhyc3ND2r///vu45557cPjwYaSlpZkas7S0FKmpqUgFEGd24lGGE54MtXqPYFstkaEVJVBapz069WqaQGg3ibzcCYFhRFxoKqZEjUoFTleo1ymtZIFlpSrlSq99z16NOr3nwDFLZHWXyn27TbSmoFSnNC21oeXlZt7Lx5SjJyLMiIxYFSYSLv5zKCkpQUqKleQkZXzrRcnLQEpdi7bOAKkPiM91+vTp2Lp1q653P5DHHnsM77zzDg4ePOgvGz9+PL766its27ZNt3+tWqHBjbi4OEiSFH2Jn+fPn8fOnTvRr1+/oPJ+/frhs88+U+zzzjvvIDs7G/Pnz0fz5s3RunVrPPLIIzh79qzqOBUVFSgtLQ16EG3sDpXo2dcaT+u8Aj27gWWafY0KDK3B5QJDHl5QKwscT95G6T1wUVQEPowi7x9oQyl0Evg+MIyi9nmMiC4REaeGQNhEPmwgStPSwuj/D70xSc1Cvh5VVCiLfd96d/fdd6NJkybo3LkzXn75ZU3b27ZtC1lT+/fvjx07duDChQu6czty5EjI4/Dhw/5nM7gWLikuLkZVVRWaNm0aVN60aVMUFhYq9jl8+DC2bNmCpKQkrF27FsXFxZgwYQJOnTqlmpeRm5uLOXPm2D7/aMEJweC0CDGCR6FMZJExFCZRM6L27JG9V2oT+NrIyU+qYqKRSrkIxcr2fZ4Oeegk0GevFEtQCpkYCZ2oPQuETXwYicqoofYxtdqRGKUc1s/JuPRbuGXLlkHFs2bNwuzZs0OaHz58GIsXL8a0adPw+OOP48svv8SkSZOQmJiIESNGKA5RWFiouKZWVlaiuLgY6enpmlPMzMwU/zyCuL67JC4uOGjhc8soUV1djbi4OKxcuRKpqRd/bj7zzDO46667sHDhQiQnJ4f0ycnJwbRp0/zvS0tLQ/7IxB48CmVWfvWJnO4pUmZoDkbDJGYEhtEPqSgsrIgKLVsKgkNJbAS+DszXaKBQryU05JgVGhqHdKkNEYjWlIxiVXQYET922STh49ixY0HhksRE5R8O1dXVyM7OxpNPPgkA6Ny5M/bv34/FixerigxAeU1VKlfju+++w4IFC3Dw4EHExcWhbdu2mDx5Mq688kqh/nJcC5c0atQI8fHxIV6LoqKiECXmIz09Hc2bN/cLDOBiDockSfjhhx8U+yQmJiIlJSXoQdzDSKhED4+AfaU6VS+GXQJDaVAtgSEPN2gKjEYIFRgNLT6U7AeMoRRKUXptZJuvR6XcRkQiL3plInrQyFwC8Ri0QWID+XqkJjLS09PRrl27oLK2bdsiPz9f1XazZs0U19TatWujYUP5//VQPvjgA7Rr1w5ffvklOnbsiA4dOuCLL75A+/btkZeXJ/DpQnFNZCQkJKBr164hE8/Ly0P37t0V+/To0QM//vgjysvL/WXffPMNatWqhRYtWjg632jEzPe2Xh8nQyVm4+G2eTHsQiS/QO/urUELu8LCryoSAmkE5b5y1ESHhtiQCyMfdggNvWf5/U1k5Yrnnsiwe9F35d8ZiWl69OiB//znP0Fl33zzjWZIo1u3biFr6ocffojs7OyQnSJKTJ8+HVOnTsUXX3yBZ555Bs8++yy++OILTJkyBY899pipz+HqORnTpk3DX/7yFyxfvhwHDx7E1KlTkZ+fj/HjxwO4GOoIdAsNHz4cDRs2xOjRo3HgwAFs2rQJjz76KO6//37FUAkJHx6FMitfvGZ+NUaEF8OqwAhJ4hQRFnIxoSYq1NqpeUbk/QLmKJ93oOhwUmgYxA5vhp5to3V2QFET+0ydOhWff/45nnzySRw6dAivv/46li5diokTJ/rbyNfI8ePH4+jRo5g2bRoOHjyI5cuXY9myZXjkkUeExjx48CDGjBkTUn7//ffjwIEDpj6HqzkZw4YNw8mTJzF37lwUFBSgQ4cOWL9+vV+pFRQUBLmG6tevj7y8PDz00EPIzs5Gw4YNMXToUMybN8+tj0AM4HSoRG7X8hdxOASGamhELi7k6OVkaNUXK5Qp5WX4xj0pa1McnK8RGPCX505oJQNYSS5wKDdDr53V28CL4kQOBfMyoovrr78ea9euRU5ODubOnYusrCwsWLAA9957r7+NfI3MysrC+vXrMXXqVCxcuBAZGRl4/vnnhc7IAIDGjRtjz549uPrqq4PK9+zZgyZNmpj6HK6ek+EGNemcjHCGSzwCbbXey+v0ogkejXq1Z1u9GK4JDDUPhVWUhIe87KRynS8xVOmQiVLZe7Vnr2C7wGcbz80IbCOfivy16JkZSgu6kTMzeF6GPmE7J+NZIMWis7z0LJA61bm52sHcuXPx7LPPYvr06ejevTvi4uKwZcsWPPXUU3j44YfxxBNPGLbp+u4SEjm4mY8RiN5/P49Cme05GDa77cUEhhFxoScstHI1TiqUKXkyGsneN0SwV+NSeb1EcY+G2rMH/7vPidqeUzmC3gwRU0ptrP7yp+cghigBoHF2nRDn7JiIs/z+979HgwYN8PTTTyMnJwcAkJGRgdmzZ2PSpEmmbFJkxCjhjNl6TIxvdH5G4+aWvRgiRj0q5fJnSwJDRFzoZ43rtw8UHnJxEfg+MIQiC59YFRpKmOkjgFEzaiETN4UERQyxm7i4OEydOhVTp05FWdnFf10NGlhbTVy/QRqpGZhJkhP9p227oDLivQibwLBjd4nIDhOl/krzURg3cOeJGTwI7i9y/QV3mhhxRHl0W5hHaXy18cL5Q4EQOQ0aNLAsMAB6MkiEYSZU4kP4v4OdXgyttlp2NQWG2sIe2AYabbRQahuYeyFP9vT1KVZ47QufaIROzIZNlLDJi6GGHSGTSPcuRPr8SPjp0qULNmzYgMsuuwydO3fWPLRr165dhu1TZBAAkZOPYQe6oRIjnbV+ehrN1/BPyIzAsCoutBDdWeKw0AjESBt5boYg4QiZcFEnkc4dd9zhPxDsjjvuED4ZVBSKDGIJj0JZOPMxLIsfM14MrTq9MIktAsPOHSbyHSRKyZ5ASA5GSJ6GTGj4UBMaStjlzZCNJXIbeDlaUyAklpg1a5b/tdI9VKzCnAziOE7mYwi3V0v41DIm4sUQGStEYARiRGAo5VXo5VrooZavoRWm0Uo+vVSnd1dYES+QkZwYm3YEak2DkFjniiuuwMmTJ0PKvV4vrrjiClM2KTJikGj9UrSSj+FDNVRipJPZOuEL71ukjQoMuQ21xV7koTYvvWRT+Xw0PDLyRFCRXBWtnBeR6yxPABXEzv8zZjx5HhttEWKW77//HlVVVSHlFRUVqvcH04PhEhJ12PbFqqVqzLhfAm3qhkm0BIZWeMSOLaxKfdQSPeXvlc7KUAiT+PDlZ2ghkneh1U8LnZCJlgkPQkMmau2ZlxHjlAO4YNGG1XM2HOSdd97xv/7ggw+CbkJaVVWFDRs2ICsry5RtigwSVuwMgxgWG0ZCJUplHo06rX6qAiMQkTwLkZ0mcppdei7UbKV5hLj/vYjQUMnP0EsCDUQkEVSpLlxnfl8izMMR4hhDhgwBcPGcjJEjRwbV1alTB61atcLTTz9tyjZFBjHtkvWYsCWCWSGiGioR8ViIBuO12oskkQYhEHIIeR3YRk4zlXK1OiXhESggfGPrCQ15W5nQUDqoS45SnQfBp4DqtfdhcJeJyFRiiVj/fMQ41dUXz+DPysrC9u3b0aiRXTvXmJNBHEbUE2FHPoYhjIZKlCYgkiMgFCaBrE4LpTbNoC0w1GgG5b7yvA29nBGDX0hayZpGXVc2JoXa6jUjJAo5cuSIrQIDoCeDRBmWv+zNhkpEJ2E5CTSwTP5aLjBEhIU87KGGUlhFKSwifw1ZmQlvhlaZB2KJEQI/z83kZZhFzya9CSRSOX36NDZu3Ij8/HycP38+qM7M/UsoMkjEYuuvRzt+IWuVKf1a1vVi6OVhqAkMNXGh9QtE76TPQNtGhIZS2EQAs3kXemU2J0p4IJ786cRYTo5HiJzdu3dj4MCBOHPmDE6fPo20tDQUFxejbt26aNKkiSmRwXBJjFFT3Lp6DgND+Rh6ZR6DfQ3/EYxsZVUSGGbPylDrpxQ+UZqXvF4ungTOznDqjtdaHisbCZy+W+GWmvJ/njjP1KlTMXjwYJw6dQrJycn4/PPPcfToUXTt2hX/93//Z8omRQYJG+H4ohUaw8rCppXwqYgRL4a8T2A/QF1gWEVJbBgJxRjEDs+RXplInf3dCIlq9uzZg4cffhjx8fGIj49HRUUFWrZsifnz5+Pxxx83ZZMig0QUoo6EsAxsdtsNoH/ipR/RRFD5oq/lvTBzEJfSHALHtOrNUMApT5ISsruyGpmSSB2JccpsekQ4derU8d+7pGnTpsjPzwcApKam+l8bhSKD2EbEfwkrudDt+LUs3M6oF8NoGz0RIW8ncl6HmtDQm4tKO9GQiZW/gQBq254Jqcl07twZO3bsAAD06tULM2fOxMqVKzFlyhRce+21pmxSZBBNjJyRIdpXXudUWF5oInp4BO0pjqH3a96sF0OprZlTP+Xj+LAidASxS0QYDl8RQtR48sknkZ6eDgD4wx/+gIYNG+K3v/0tioqKsHTpUlM2ubuERA2G1yA7kj7NjCEcKpFjZnE3Ky7kNtQO1gKCd5wYOIRLZAeKk3s9LR7KZZVI3hUSyXMj7iBJEho3boz27dsDABo3boz169dbtktPRg0nFt3EQjdF08PuHA0/ArkKql4MJwSGqC07kk4F25u5zg7+Q/aEdzhCXEGSJFx99dWmb4SmBkUGiUjMnHdl+ovfLje9InqnYorkaZihkcZDDbnQ0Mr9sICZraweC+NRERCiS61atXD11Vcr3urdkl1brRHiAobWEDPxels8GGYw48UQOTPDbkETiMmjxpVw7bobw2wKSIRMnxA/8+fPx6OPPop9+/bZZpM5GYQ4gtlF1oqXwMiYardn17pBmvw00MB6NXsat4EP93neBk8DNTM95jqQaObXv/41zpw5g+uuuw4JCQlITk4Oqj916pRhmxQZpGYgegJkVPx6trKVVN5H754mdiF4/HgNv+mHB8pHixOXKQdwXreVNlb7h4Fnn33Wf06GXVBkEGInQjtLREIKWqESO1ESGiKCwOQ9S2owMa6PSAwwatQo220yJ4PUXKx6JQwH450SC06KEEJITSE+Ph5FRUUh5SdPnkR8fLwpm8Iiw+5tLYREPJ5wDVSTRILCZw1zCMqWLc6ExCCSJCmWV1RUICEhwZRN4XBJhw4d8MILL+C+++4zNRAhxA7sPBvDh15uRjhzNxSo4XkahDjN888/DwCIi4vDX/7yF9SvX99fV1VVhU2bNqFNmzambAuLjCeffBITJ07EunXrsHTpUjRs6MSXHSGxSE3yVBAnoI4iTvLss88CuOjJeOmll4JCIwkJCWjVqhVeeuklU7aFRcaECRMwYMAAjBkzBu3bt8fSpUtx++23mxqUkJqFb4tntOKiF0OEGrD61oCPSFzkyJEjAC7eFG3NmjW47LLLbLNtaHdJVlYWPv74Y7z44ou488470bZtW9SuHWxi165dtk2OkJqBERFyEvaHTMIpIhTG0ltBucIStykHUMeijQt2TMRZPvnkE9ttGt7CevToUbz11ltIS0vDHXfcESIyCIkZvAhj8qcVot1TEl5OV7s9A0Iik6qqKrzyyivYsGEDioqKUF0d/J/l448/NmzTkEJ4+eWX8fDDD6NPnz7Yt28fGjdubHhAQiKGMljb2VAKg9tY5WJASxwUQv8W74QQYh+TJ0/GK6+8gkGDBqFDhw62HMwlLDJ+8Ytf4Msvv8SLL76IESNGWB6YkLBSAvFTP61wukLgQC6jIQ8RT4VZb4ZSqETkkC2jB3HZdHBXFIdOonjqpIbw97//Hf/4xz8wcOBA22wK7xivqqrC119/TYFBYhvX8wOsLPBGcytE2ge2kd+3xMyYAShdSyevr4H7lgDuiAKvC2OSyCc3NxdxcXGYMmWKZruVK1fiuuuuQ926dZGeno7Ro0cbuqtqQkICrrrqKouzDUZYZOTl5aFFixa2Dk5ITKC4Grm1I0N0XLV2Zj0OcnvFKuUGMCP4XFAGgdqF3gpiN9u3b8fSpUvRsWNHzXZbtmzBiBEjMGbMGOzfvx9vvPEGtm/fjrFjxwqP9fDDD+O5555TPZTLDMzaJBGJSLqEr438WROlPAq9jpZyN0RDI0rhjsC8DHm9ll3fwq4UPnFa/AiIlNMVDs8hOqAgIXqUl5fj3nvvxcsvv4x58+Zptv3888/RqlUrTJo0CcDF3aDjxo3D/PnzhcfbsmULPvnkE7z33nto37496tQJ3lKzZs0aw5+BB+ySqEPvy9mW3QO2/4qW/7I/KXsvgryt3oJerPDQQm5PLVTiwI3R9EIZXgu2uZqTCKK0tDToUVGhLronTpyIQYMGoU+fPrp2u3fvjh9++AHr16+HJEk4ceIE3nzzTQwaNEh4bh6PB7/85S9x8803o1GjRkhNTQ16mIGejBqO1Q0WEY3W7g+lD26XR0Mo+VOLQC+F3i4Tu87N0BIYaoiGRDQElZmQh2ifEh07gngFhzPThkQJ5bC+WlZefGrZsmVQ8axZszB79uyQ5n//+9+xa9cubN++Xch89+7dsXLlSgwbNgznzp1DZWUlbr/9drzwwgvCU1yxYoVwW1HoySCaWPmitPtL1pUvba9OveYvcKP5EUYTMX1Y8SycFOgv6sWQiwmNzyMqFLhSOwIvq3scO3YMJSUl/kdOTo5im8mTJ+O1115DUlKSkN0DBw5g0qRJmDlzJnbu3In3338fR44cwfjx4w3Nr7KyEh999BGWLFmCsrKL/1J+/PFHlJeXG7Ljg54MYgovrJ1TFegUMHzchJXBjNQZsadpx+dt8OVV+J61vBBa3gyl/A3fAm/Eq2Fll4rJ3A4r+RiiIsTALhIutiTcpKSkICVF+xtv586dKCoqQteuXf1lvhuVvfjii6ioqAi59Xpubi569OiBRx99FADQsWNH1KtXDz179sS8efOQnp6uO7ejR4/iF7/4BfLz81FRUYG+ffuiQYMGmD9/Ps6dO2fq/iX0ZBDbcOoL22uXIVH3uZVf2ZYvQuDiHSgC5NtHtXaHBD7UypUEhlLehp4XQ55jolauIUqURIFDO0t8+TrypmVQLjdonhBbuPXWW7F3717s2bPH/8jOzsa9996LPXv2hAgMADhz5gxq1Qpe0n3tRHeLTJ48GdnZ2fjpp5+QnJzsL//lL3+JDRs2mPos9GSQsGFkx4ijiO4wES1TsufPy5B7LdS8GYHeCbWDtUQ8GnJEQylKIkBNYGh5MQQ8HForulKZV9+kLgbPyCDETRo0aIAOHToEldWrVw8NGzb0l+fk5OD48eN49dVXAQCDBw/GAw88gMWLF6N///4oKCjAlClTcMMNNyAjI0No3C1btmDr1q1ISEgIKs/MzMTx48dNfRZ6MmKMmvZrS75e+Z6FdphYWeQc92L4kIsEJY+GlW2pav1FBIaaF0NpDGiHSpzyHpn8m9S0/0ck+igoKEB+fr7//ahRo/DMM8/gxRdfRIcOHXD33XfjmmuuMbTttLq6GlVVVSHlP/zwAxo0MPfzj54MErHYkkphNuHDimfDV6a7y0QrNyPQSyGvV9pxonU2htrYaogkeir1V9umq4FoqMSouHBwZ0kgogdxiYgWvbGs2CbRz6effhr0/pVXXglp89BDD+Ghhx4yPUbfvn2xYMECLF26FAAQFxeH8vJyzJo1y/RR4xQZJKowHU7xdVS6h4lRNSMaMvGjFRrxoRc2URIagLrYMIPcS6KWZ6GUN6Izrs+LYUeoRGtVDUNYxI5F3YgNiggSLp599ln06tUL7dq1w7lz5zB8+HB8++23aNSoEVatWmXKJkUGiQgc32EiMqCW2PAidDuNlgAJ8Wbo5WYEoiU0ADGxYQSle5KInJshkggqkPDpdFjk0jjypE+RZE+zUBjEGGWw7ZyMSCYjIwN79uzB3//+d+zcuRPV1dUYM2YM7r333qBEUCNQZBDT3gEv9Lexmgl5KJUbGcvQ5xERCqLtTSslpbCJViKoUnglUCiICA4lYRFoXz4/+WulPA0VgSHixYBGnVaZ1iFcBld67iwhBEhOTsbo0aMxevRoW+wx8ZPoYqdr184va701y5/8qfWLWW5MtE6kvT8L1ZfwKPIrXy/JMrCdWs5DocBDCblNeVKoVh6GSbT+iF6N9krYFCqJdUER65+PmCc3NxfLly8PKV++fDmeeuopUzYpMkhE48gmDt8vXa2kQ6OLnu/Z8EKnlecgL1faCSJyYqcWaudmqN1VNXB+Wh4OHS+G0Wtvk5vByL8dr049775KYo0lS5agTZs2IeXt27c3dRAXQJFBiHkvhi3ejMAyEaHhe68mNrQO3DJzIFfg+8A+AgJDDblnSUvQWb3+snwMIya0zNrdx2vCLiF2U1hYqHgyaOPGjVFQUGDKJkVGDBJNv6oC56r3y9ArYCdkbTcTMtFa2JQmofYr3XahISI25GgJCj1bot4Vef0lzORimBUVvmtucuuqWSeKli2z9U7ZIkSPli1bYuvWrSHlW7duFT7QSw4TP4njmN526qRt31ZWkTu1iiSHGp6IVrKn1o4Std0oPuw4J0PJS6JUp5GjoRcmMevFgKyOqywhtjF27FhMmTIFFy5cQO/evQEAGzZswO9+9zs8/PDDpmxSZBAA+mukWr0XYjs7jdSLtLdduIiIBV+dF+pbXeQ7TeR2gw7o0hMagPI5GVqiwurpn4GI7DIJbKciMOSouZ1EvBhaokK+q8TC1lWvRl2AaV07JEYoAxBv0UboQZoRx+9+9zucOnUKEyZMwPnz5wEASUlJeOyxxxTvFisCwyUkqvBq1BkOmWi51q0sgMJhE0A7dCJ/rZagafZo8WKo9xfdZaIiMAIxkhTrlfVRwnKyrbI5u9pZ7eMkkTYfElnExcXhqaeewn//+198/vnn+Oqrr3Dq1CnMnDnTtE16MojrBDoPAqMXot4Ky14NNa+D1mBeXPRmiIZLND0aPrTOyQC0D+UKbGcWrcO15K+1ElMhLrjs8mLI+6h4MUS66pUZtaFX7zU4BiFOU79+fVx//fW22KLIIGFBvgaHO2RyuhqoVwvGDsxSy7sQESHysIlSm6DTQAHl0ImvXOm9XBTIRYcIagmhBpNAAz0YZgWGV/ZeCTXbggmfdoRKwgWTPkm4OX36NP70pz9hw4YNKCoqQnV18Lasw4cPG7ZJkRGjOJFsaWdehhWUxpOPq6sHfBXyBFAjiZy+ieiJEbntwPFVczQA9WRPtZuhWTkvI9CuVplJgSGKEU+HDZgJlTAfg8QqY8eOxcaNG3HfffchPT0dcXFxlm1SZBA/TgoDI2O7EjLRM6z27IVDQgMIFhtKyZ5yASC6syQQtRCL4A4TLYERiKgXQwm9nBeDCZ9aQ+iVaSFvTwFCoo333nsP7777Lnr06GGbTYoMEjasCgGjIZOQNAh5yETUm2HEu6E1AT2hASiET4DQe5WoeTKs5mQo2dDYYaInMMyGScLkvQh3foTb+RgUPUSPyy67DGlpabba5O4S4hhOxpS9GvYc/zIVXSSV+igdChb4WnHnCaC/s8SswFCzIR9PVh+4TdVJgREBXgw3QiXMxyBu8Ic//AEzZ87EmTNnbLNJT0YM43ZehhXbRkImrngzjIZNAicTOAYC2vle63o1fAR6NwDrngy93SVQ9l4EvlZahcPkoRBBa2ivjXYpAmKMUlj/SV6t38Rtnn76aXz33Xdo2rQpWrVqhTp16gTV79q1y7BNigwShNN5GXaGTLzQTwA1ZNQNoSGfbODrkFwNH3btLtFKFNUQF4B5gRGIV6etg14MUb2j5sUwituhEkJEGDJkiO02KTKIq2iJATPeDKV2ut4MOzArNADlPA1AwavhQ+3ETwd2l4iIC0BcYDgVJhFAtKlIOzdDJZFik8Qes2bNst0mRQaxDS+sb2UVaS9qU6idXWGTQLxQFxpQ6KcUPoHsfeBiryo4AHvuXQJtcSF/r5VnYkVgGMGEFyMQr0KZKOEOlVAwEKfZuXMnDh48iLi4OLRr1w6dO3c2bcv1xM9FixYhKysLSUlJ6Nq1KzZv3izUb+vWrahduzY6derk7ASjHDNfSEZcyGaw80vZK2A/sMz3LL/1dxC+X8miyYtKK5pXoy6wLPAXeqlCudL70xX/e4QgT+RUe8gItCnPu1Cbi3zOSp9PXubVaCPHZJhEy5SRMj1HjSgMlZBooaioCL1798b111+PSZMm4cEHH0TXrl1x66234r///a8pm66KjNWrV2PKlCmYMWMGdu/ejZ49e2LAgAHIz8/X7FdSUoIRI0bg1ltvDdNMiRWsfCmrfbmbWTTkGLoNvNygWaGhtQAHzkXePvC9muBQekCwrfwziooLI5/Pq1CnZEPp2cDJnuH0YtgNvRTETR566CGUlpZi//79OHXqFH766Sfs27cPpaWlmDRpkimbcZIkSTbPU5gbb7wRXbp0weLFi/1lbdu2xZAhQ5Cbm6va75577sHVV1+N+Ph4rFu3Dnv27BEes7S0FKmpqUgFYP0ss+jATKKlXh+teo9AeyPvU1TKA197NOq1yuoFyuwUWWWqSrmIYdHJab2W54uoXXSrmbpqK5u8XJ75qCaURASXUjstwWbAi6FlxujUAodWqhPxyhkRNk54EWNZuEi4+E+ipKQEKSl2JVj9D996UdIcSLH4k7y0Gkg97txc7SA1NRUfffRRyH1LvvzyS/Tr1w9er9ewTdc8GefPn8fOnTvRr1+/oPJ+/frhs88+U+23YsUKfPfdd8IJKhUVFSgtLQ16EOuEM2Ri1psh8ovWlrCJXp1Xo73WayWPgdzDIC8381CzpTcPvdc+vBqfU+u6BY4PqAoM+fTVzNjhxWCohMQy1dXVIdtWAaBOnToh9zERxTWRUVxcjKqqKjRt2jSovGnTpigsLFTs8+2332L69OlYuXIlatcWy1nNzc296Lm49GjZsqXluUcb4f7147XBntH2gWOKeOMDEQqbmBUaSqECkQVaKTyhJjiUxIIIeja0xAUEX3tlZUpCROtaCuwm0brTqpZppXby11o/SYwIZRFi2eMQ9ZTDupgvD/usDdO7d29MnjwZP/74o7/s+PHjmDp1qun0BNcTP+U3YJEkSfGmLFVVVRg+fDjmzJmD1q1bC9vPyclBSUmJ/3Hs2DHLcyYXsftLVcueiDfDKzBGYJnieHIxoZQDYERoBL72KpQpvdZ6Xwpl0RHY1owHQ8u21pzUXnshnvwqR01gyOcKZU+UqDfDq1CvhdNCOdz2CJHz4osvoqysDK1atcKVV16Jq666CllZWSgrK8MLL7xgyqZrW1gbNWqE+Pj4EK9FUVFRiHcDAMrKyrBjxw7s3r0bDz74IICLrh1JklC7dm18+OGH6N27d0i/xMREJCYmhpST8FMG+7ezGumrVeY/O0OpUr6tNbBO/qxUF1jmvfTeg/+tGvLtrdB4H1gGqP/U1gr5ikQMjfzcl7/3KpSLCjG5wFAaw+YwSTi9GF7dFvZCYUKM0LJlS+zatQt5eXn497//DUmS0K5dO/Tp08e0Tdc8GQkJCejatSvy8vKCyvPy8tC9e/eQ9ikpKdi7dy/27Nnjf4wfPx7XXHMN9uzZgxtvvDFcU49KzHzZWPmC8pocw2lvhtZiFBI2CWwoz89QMib/Va9WJp+omjcg8L1amdoFk3sl1LwfejblZVpeDS/Uw0KQvRYVGBp5GCJhEr0yUZxerCkGiJt8/PHHaNeunT9nsW/fvnjooYcwadIkXH/99Wjfvr3w8RJyXD2Ma9q0abjvvvuQnZ2Nbt26YenSpcjPz8f48eMBXAx1HD9+HK+++ipq1aqFDh06BPVv0qQJkpKSQspJ+LDibXDavpIjQak+sE7zNFARj4ZIGRDq1YCsTslzoebNCIdfXk8NelXqjAoNLYERgJLA0DJrZKryacgREcZ64xqFIoQ4yYIFC/DAAw8o7npJTU3FuHHj8Mwzz6Bnz56GbbuakzFs2DAsWLAAc+fORadOnbBp0yasX78emZmZAICCggLdMzOIs9jtzTCzfgVi1Jth6VeuUiKoiEdDXqZWr+QBkNcpvQ8s0/No6KFlR6vMR+Dc1TwedgkMgTwMtbKa6MWgMCGifPXVV/jFL36hWt+vXz/s3LnTlG1Xz8lwg5p4TkYgkXhmhlKZ6LkZ8jozx1MolWmenwGEnqGhZ1D0NRB60Zw6H0OO2qpkxB0gf6/nXrAgMERNiwhQLS+GnlgRFclelXIRMWBGMNQEkRG2czJSgRSLC0apBKSWROY5GUlJSdi3bx+uuuoqxfpDhw7h2muvxdmzZw3b5r1LiGWMhjS0QhciNo3e10w0bBL4OigRVH4fE0A/dAKTrwH1MEpgG3m5EkoXUHTlEfH3i4gLkdcRJDDk6F0u0cvp1W1hfQyrfYgGpbD+qzSCf843b94ce/fuVRUZX3/9NdLT003Zdn0LK4l87A6Z2DEHK2ET0QUqyC2vFzrRuoeH3mulEAPwv1CEV6G9SJjESttA1OYg8hnUwic2CAz59LXqRdG6R4mIx8Lo2BQDJBIYOHAgZs6ciXPnzoXUnT17FrNmzcJtt91myjbDJTUQJ0ImWm08Btrbcdy4/L1HodxICEU4dKI1KdFQid57Hx6VcjvwqpQbWXVFXgsKDEA70TPwtVnBKZ+OvE7kvVqZV6FMq72Rerv7RRthC5fE2RQukSIzXHLixAl06dIF8fHxePDBB3HNNdcgLi4OBw8exMKFC1FVVYVdu3YpHi+hB8MlRAijIZFAvHBmTVSKVIiiFkIJjHiE7DgJHFQpdKI1Ka3XEHgfWAYor1wehTItlGwEorZSiYoLrTqlg7ZcFBhyotmLUVMEBrGPpk2b4rPPPsNvf/tb5OTkwOd7iIuLQ//+/bFo0SJTAgNguKRGEilfbiJf1CJf7kp1RhcZvUVLNXTia1SiUK9kWCusoPRerUztJ7ORhxwt+/JytfdKn0ceHnFAYMin6sOr01ZhSFvx6rZQh2KhZrN48WJ07NgRKSkpSElJQbdu3fDee+9p9qmoqMCMGTOQmZmJxMREXHnllVi+fLnQeJmZmVi/fj2Ki4vxxRdf4PPPP0dxcTHWr1+PVq1amf4c9GQQYZzwZojYlLcJfC9PAlVzGASOL+pgEPJoBDb2LZq+hFDgf14PaAwkr4NAmbzcbsx4MfTeK4VHVNqo5WAYERtahCNMIjo+IUq0aNECf/rTn/zJmH/9619xxx13YPfu3Wjfvr1in6FDh+LEiRNYtmwZrrrqKhQVFaGystLQuJdddlnIXVitwJyMGopZsRCJuRmAtfwMkde6ORryRmp5GlqDGS0TqRPFSOxAqcwmcQFYExhmwiRGkj1F28jH17Nhto0dfaKZmpaTkZaWhj//+c8YM2ZMSN3777+Pe+65B4cPH0ZaWpq1ydoIwyU1FDe+jLwq5XaETbQWCrU52Bo68TUKDJ8o7T6Rt9MKi0ChTO1iWX1o2dOag977KBIYItjx/8YpgUGig9LS0qBHRUWFbp+qqir8/e9/x+nTp9GtWzfFNu+88w6ys7Mxf/58NG/eHK1bt8Yjjzxi6mwLO2G4hBjCTHgjEC/MJ4FqhU202ppppxY6Af53jgYQcAQ5oJzwKU8KlbcDggdSeh9YJi+XYzT71WwbI6pPfhdVlwSGHCfDJF6VciehMHGO0xIQb9Hnf/rSc8uWLYPKZ82ahdmzZyv22bt3L7p164Zz586hfv36WLt2Ldq1a6fY9vDhw9iyZQuSkpKwdu1aFBcXY8KECTh16pRwXoYTMFxSwwn3dlYgfGETeb1a2ESrnchrofAJEBxCkbdVaq9WJlJnFqthE/nKLeC9AMIjMDSGNy0olMq8CmVa7c20sbNfNBOucMmPMHYAoKItABkAjh07FjRXrTuFnz9/Hvn5+fB6vXjrrbfwl7/8BRs3blQUGv369cPmzZtRWFiI1NSLXzZr1qzBXXfdhdOnTyM5OdniJzAHPRnEMJHszdA6DTSwrdYcjHg3AIWEUEA54TMwMVTeFgheJfS8GfI6JxD1YgDi4kLWVuuQLSsCQ45R7STSJpIW9UiaC9HGt1tEhISEBH/iZ3Z2NrZv347nnnsOS5YsCWmbnp6O5s2b+wUGALRt2xaSJOGHH37A1Vdfbc8HMAhzMmo4kfTlZPbXo9aPaa22XsF2Iq9PVwvmagDB+Rq+tko/rbVyIpRyKaxgJE/Dh9Lt4+WfTan9JbS8F1YFhpU8DKfCJMzFIFaRJEk1h6NHjx748ccfUV5e7i/75ptvUKtWLbRo0SJcUwyBIoOYwuoXptdAHzNjmRUaZlz1crERNAkzYkNEcCjVWXno2Q1ETVjIxYVKfEIuyESvrRMCQ0RQEOIGjz/+ODZv3ozvv/8ee/fuxYwZM/Dpp5/i3nvvBQDk5ORgxIgR/vbDhw9Hw4YNMXr0aBw4cACbNm3Co48+ivvvv9+1UAnAcAlxGLvCJnphEiv95fMQDZdApS4oKRTQT/gMXJzloZTAfoF9lTCToyGyqqr99JcndCrZE8y9kL8XrfMasOFkHobSXET6GG1jZz8S2Zw4cQL33XcfCgoKkJqaio4dO+L9999H3759AQAFBQXIz8/3t69fvz7y8vLw0EMPITs7Gw0bNsTQoUMxb948tz4CACZ+kku4cW4GYC0JVKnMSCKo/L18LqK3GNGbQz25v1Ak4VOeJKrV32604gkiwkLBRpCHB+YEhfy9V2caKo4UxbZ6/bXKlOYi0sdsO7v6xQLRmPgZifcucRJ6MoglRDwKZrwZSn1EyuTvtU4Elb+Xz0XNi6FUB422QYmhvkkB2gmf8sU8UHToJRXofX8ZPRxCSVgAlsWF3ns3BUa4ocAgsQpFBgFgPPxgJ15EltAA1I8gh857tbrAxVY1jBLYMbAzoLzQq3k7rNyIQ01Q+BAQFoA1cSF/L6/zGugrcimiNUxCwkc5rHu+y/WbxCQUGcSPWaFh1ZsRjvGMCA1A/V4neu/1hAigkbMBhUn6UPrAeoLADrRWQBPiQqlM1HsBWN9FEml5GFagOCHRAEUGCRt2hU1EbdstNABjXg2t94CC2PBN0odIwqed7ie9VUvFLSAXFmqmrHgzAGMCQwm7BYYdUCiQWIcigwThpDdDDy/sDZsolVkRGiLtld4DYmID0BAcgHKuhdOrlEa8IRLEhdJ7Oz0YWng16ujFIOQiFBnENpwMm7gtNADzXg2tNvJ2qoLDN3El7EhUF0heUBIVPsyIC5E2XoPt7RYYToZJzAoFCgwSTVBkkBDszJ8wat8LseO+rZYpCQ1AWyjI52bGa6FVJi+XL+ohosOHlURPDbREBSC+MJsVIF6DfcwKDDXMCAyrtgmJNSgyiK1YyaHw4YWxe5vYJTSU2oiET6DTx4iw0Mr1VFv0VcWHIHpiwofWwmglv0HPe6HUxozAUMPOPAyGSQgJhiKDKOK0N0MPL4wlgloRGoC18IlaHwiUiZRDpd6HqEgwit6CZiTEIFrmNdFPVGDYkejp1ahzOkxCSDRCkUFsxw5vBhA+oQGI5WkA2mLDanhET1RoLU5WBKEdIQS1OrPiQqmdUj8rIRI7BYYoVgQGxYl72HHGBc/JIESGFW+GXULDaD+7hQag7dUAxEMoMFAur1Oq12prByI2rS7gXgt9rZzkafR6eXXqKQAIUYYig2gSDqGhhRfGEkHVys0KDaV2Il4NtXYi5Up18no5kerJ0Cr3CrQ1670wMx+ziZ7hCJNQxJBohSKDuI7ZsIlWXyNCA7JyrdO+rYgNtf7ycnmdUr0cpxYhu70ZgL3eCyN9zZR7Vcr1+pltZ3dfQtyGIoPoEglhEy/sExpQKTfj1VCz6b307DEwPlTq5PVy7EjQddqbAVgTF0D0CgxCajIUGcRxIk1oqJWrCQ1A36uh1t976dmj0NZHpHoxjNg3ulAbWfCNbE+1q9xO6MUgNRmKDCKE1fyKaBIaUChX82rI26r19wa89qjYUeonrw/E7i3GdnkzAOfEhVEbdnpdRPuaaWd3X0IiBYoMIowdiZx2jOOFs0JDrVzJq+FrCyiLDXk5oO7dkPdT6qvV1klExvKa6G8kNKLW3oyXItIFBoksygBIFm1wCyshDmNEpDglNKBQZ7Rcyathxo434LVHwV5g30CcFnpGFkevSVt2iAs7y314derDJTAoUEisQJFBDBGusIkIXhgXGlp1dng1fO2h0EfEu+HDo2BXyU648Qq0MeNBMCoutOrcFhhWocAgsQRFBjFMpORnAM4IDSjUaXk1AGNiI7BOrd6rUOZRKHMar2A7vYXRqLjQ6mN3eASwV2BQJBDyPygyiCmiSWhAxYadIsSs2Ais12oDaC+EHo06LbRs6mFWWADmxIVWnVP5F3r9rbR1oj8hkQZFBol4rAoNLRsi3gYjAkVEbKjZlLfRaifHK9jOLHblItgtLqzUeTXqRPpbaetEf0IiEYoMYppw5mc4KTTM1mmJkMDF1KzgkLcLxMnkT6OLnUj7SBIXAAUGIeGCIoNYItaEBlTqzdYB2t6NwP4+RMNIbmJVWOjZsBKKiTaBQUgsQ5FBLBOJQgMwl6ehN4aVZE4974aSHTVb4cTIIqonLETsOSU+gMgUGBQpkU85rJ+TcdqOiUQhFBkkIjAqNCDQ3gtnvBp21IsKjkBbStglQKwsdCLCQmQMJ8UHQIFBiBtQZBBbsOrNMGPDrvAJNOzYVa/VRr5I64kONfvhQlRUAGLzc1J8APZtwzXb1kkbhEQ6FBnENiJZaADmvRq+emi0EfGuiHpglBZxI8LDTowICh92CAuRNnZ5L0Rt2QkFBqkpUGQQW4lUoQFY92qIjGVEbPgQmbveYm9FhJgREnLs2uJqZxuvQBtRW1baE1KTocggthPNQkPEllEhYfRW7WaunR1CwQhOLMw1RVxQpJCaBEUGcQS3hAYE+ngvPXss2jJz1oWZ7anRtLvETD+7xAVAgUFIpEGRQRzDDaFhpI8XYl4NCNgz2k6krVIfJewQIW4sonaKEK8D45pt77QdQqIJigziKNEgNIDwi43AtqLtReyEGycXbLe9F2b7OGmHuEM5gGqLNs7YMZEohCKDRAVGFu/APkbu/eGxcR5WQySi/cKN06ETI229Do1vpY+TdgiJRigyiOPY4c0wa8uIOPFeevbYbNesx0JtcXJafERy6AQwfjM4CgxC3KOW2xMgNQM7v2ydXjS8MOaCN7qYGu2jZcOJh13zMtJHFK+J+RiFAoNEArm5ubj++uvRoEEDNGnSBEOGDMF//vMf4f5bt25F7dq10alTJ+cmKQBFBgkbkSA0nBYbZgVHNC5IVubv5N/CZ98odv4dovHvSSKLjRs3YuLEifj888+Rl5eHyspK9OvXD6dP698FpaSkBCNGjMCtt94ahplqw3AJCStuhk7M9vNeevYYsA+DYwT2CySS8jKsLpxm+nvDMIaVfk7bIjWX999/P+j9ihUr0KRJE+zcuRM///nPNfuOGzcOw4cPR3x8PNatW+fgLPWhyCBhx26hARP2zPTzXnr2GBzD6DhqNuTYLUCcWBzN2vSGcSwKDBJOSkuDj85LTExEYmKibr+SkhIAQFpamma7FStW4LvvvsNrr72GefPmmZ+oTVBkEFewU2hYsRcOsRE4jo9IOtvCbsIpLKyMZ7Wvk7ZIZFEC4LxFG2cvPbds2TKofNasWZg9e7ZmX0mSMG3aNNx0003o0KGDartvv/0W06dPx+bNm1G7dmQs75ExC1IjiRShYbav99Kzx+R4PiIpJGIGq4ur14Vx7RYEFBhElGPHjiEl5X93GxLxYjz44IP4+uuvsWXLFtU2VVVVGD58OObMmYPWrVvbMlc7iJMkSXJ7EuGktLQUqampSAUQ5/ZkiB+7F1or9qz09Vjoa9ccnMauBdXr0vgUGLGDhItehpKSkqCF2y5868VSAMkWbZ0F8BsYn+tDDz2EdevWYdOmTcjKylJt5/V6cdlllyE+Pt5fVl1dDUmSEB8fjw8//BC9e/e28AnMQU8GiQgizasBk/29Aa89JscPnIOccIkPpxZOr4W+kSYunLJJCHAxRPLQQw9h7dq1+PTTTzUFBgCkpKRg7969QWWLFi3Cxx9/jDfffFO3v1NQZJCIwQmhAQs2rfb3Brz2mLQhJxoXNa/F/nZ8ZgoMEm1MnDgRr7/+Ot5++200aNAAhYWFAIDU1FQkJ1/0q+Tk5OD48eN49dVXUatWrZB8jSZNmiApKUkzj8NpeE4GiSgicTGw4/wEL4yf9RDNeGH989px3Z06g4QCgzjN4sWLUVJSgltuuQXp6en+x+rVq/1tCgoKkJ+f7+Is9WFOBolInAoL2GXXzvl5bLTlFl4bbUXygVgUF5FFTcjJiHYYLiERid2hk0C7sMG2XXYA5QXaY4Ndp/A6ZDeSxYWTdgmJZSgySMRi50KuZNvusyrsnKdXpdxj4xhm52An0XJOBQVGzaYMQKVFG2f1m8QkFBkk4ol0r4ZT9pTwOmg7XETbFlIKDELMQ5FBogKnhIbPNmy0H0sHbdlFNOZHUFwQYh2KDBI1OO0pcMK+E8eJRwvRnBtBgUGIPbi+hXXRokXIyspCUlISunbtis2bN6u2XbNmDfr27YvGjRsjJSUF3bp1wwcffBDG2ZJIIBy/YJ1cIKP59u5aOP3ZwnXNYu3vQoibuCoyVq9ejSlTpmDGjBnYvXs3evbsiQEDBqju+920aRP69u2L9evXY+fOnejVqxcGDx6M3bt3h3nmxG3CtdiES9BEm+iQz9vppMtY+XsTUtNw9ZyMG2+8EV26dMHixYv9ZW3btsWQIUOQm5srZKN9+/YYNmwYZs6cKdSe52TEHuEMQbgV7nBrXDcX3XCOTXERnYTrnIynYc85GQ+D52SEjfPnz2Pnzp2YPn16UHm/fv3w2WefCdmorq5GWVkZ0tLSVNtUVFSgoqLC/760tNTchEnE4mRSqNJYCON48nFjnXB/zppyXQlxC9dERnFxMaqqqtC0adOg8qZNm/rPaNfj6aefxunTpzF06FDVNrm5uZgzZ46luZLIJ9yLP3eQ2Isbiz0FBhGlHNbPyThnx0SiENcTP+PigoMWkiSFlCmxatUqzJ49G6tXr0aTJk1U2+Xk5KCkpMT/OHbsmOU5k8jFjbg6Y/nmcCsXhX8vQsKHa56MRo0aIT4+PsRrUVRUFOLdkLN69WqMGTMGb7zxBvr06aPZNjExEYmJiZbnS6KLcIZQAscMhB6OUGpKjgch5CKueTISEhLQtWtX5OXlBZXn5eWhe/fuqv1WrVqFUaNG4fXXX8egQYOcniaJYtz+xRqNu0bsJhJ2z9T0vwEhbuLqYVzTpk3Dfffdh+zsbHTr1g1Lly5Ffn4+xo8fD+BiqOP48eN49dVXAVwUGCNGjMBzzz2Hn/3sZ34vSHJyMlJTU137HCSycStZU2kOPmLZyxFJC3okzYWQmoirImPYsGE4efIk5s6di4KCAnTo0AHr169HZmYmAKCgoCDozIwlS5agsrISEydOxMSJE/3lI0eOxCuvvBLu6ZMoIxLEho9YER2RuohH6rwIqWm4ek6GG/CcDOIjWhb2SJhntCza0TJPYg/hOidjDoAki7bOAZgFnpNBSI0hkjwbWhhZOEU/S6wtxrH2eUhkUQbgvEUbFfpNYhKKDFLjiRaxIUJNW2xr2uclJNqgyCDkErEkNmIdigtCogOKDEJkUGxEJhQWhEQfFBmEqMCjwyMDigtCoheKDEIEoHcjvFBYEBIbUGQQYgB6N5yDwoKQ2IMigxCTUHBYh8KCkNiGIoMQG4iVEzzDAYUFiTZ4ToZ5KDIIcQB6Of4HRQUhNReKDEIcpqZ5OSgqCCE+KDIICTNqi3A0ig8KCkKIFhQZhEQIkSo+KCQIIWahyCAkwrG6yDewwQYhhJihltsTIIQ4CwUGIcQtKDIIIYQQ4ggMlxBCCCEalMP6ORlW+0cr9GQQQgghxBEoMgghhBDiCBQZhBBCCHEEigxCCCEkwti0aRMGDx6MjIwMxMXFYd26dZrt16xZg759+6Jx48ZISUlBt27d8MEHH4RnshpQZBBCCCERxunTp3HdddfhxRdfFGq/adMm9O3bF+vXr8fOnTvRq1cvDB48GLt373Z4ptpwdwkhhBASYQwYMAADBgwQbr9gwYKg908++STefvtt/POf/0Tnzp1tnp04FBmEEEJImCgtLQ16n5iYiMTERNvHqa6uRllZGdLS0my3bQSGSwghhBANynHx5Fwrj/JLtlq2bInU1FT/Izc315E5P/300zh9+jSGDh3qiH1R6MkghBBCwsSxY8eQkpLif++EF2PVqlWYPXs23n77bTRp0sR2+0agyCCEEELCREpKSpDIsJvVq1djzJgxeOONN9CnTx/HxhGF4RJCCCEkBli1ahVGjRqF119/HYMGDXJ7OgDoySCEEEIijvLychw6dMj//siRI9izZw/S0tJw+eWXIycnB8ePH8err74K4KLAGDFiBJ577jn87Gc/Q2FhIQAgOTkZqamprnwGgJ4MQgghJOLYsWMHOnfu7N9+Om3aNHTu3BkzZ84EABQUFCA/P9/ffsmSJaisrMTEiRORnp7uf0yePNmV+fuIkyRJcnUGYaa0tPRiVi+AOLcnQwghxDQSgBIAJSUljuQ5+NaLOwDUsWjrAoC34dxcIxWGSwghhBANymF9say0YyJRCMMlhBBCCHEEigxCCCGEOAJFBiGEEEIcgSKDEEIIIY5AkUEIIYQQR6DIIIQQQogjUGQQQgghxBF4TgYhhBCigRc8J8Ms9GQQQgghxBEoMgghhBDiCBQZhBBCCHEEigxCCCGEOAJFBiGEEEIcgSKDEEIIIY5AkUEIIYQQR+A5GYQQQogG5QDiLdqosmMiUQg9GYQQQghxBIoMQgghhDgCRQYhhBBCHIEigxBCCCGOQJFBCCGEEEegyCCEEEKII3ALKyGEEKJBOaz/Iq+2YyJRCD0ZhBBCCHEEigxCCCGEOAJFBiGEEEIcgSKDEEIIIY5AkUEIIYQQR6DIIIQQQogjUGQQQgghxBF4TgYhhBCiQTmAOIs2JDsmEoXQk0EIIYQQR6DIIIQQQogjUGQQQgghxBFcFxmLFi1CVlYWkpKS0LVrV2zevFmz/caNG9G1a1ckJSXhiiuuwEsvvRSmmRJCCCHhJdrXSFdFxurVqzFlyhTMmDEDu3fvRs+ePTFgwADk5+crtj9y5AgGDhyInj17Yvfu3Xj88ccxadIkvPXWW2GeOSGEEOIssbBGxkmS5FrS64033oguXbpg8eLF/rK2bdtiyJAhyM3NDWn/2GOP4Z133sHBgwf9ZePHj8dXX32Fbdu2CY1ZWlqK1NRUpMJ6tjAhhBD3kACUACgpKUFKSort9u1cL8zM1Y010m5c82ScP38eO3fuRL9+/YLK+/Xrh88++0yxz7Zt20La9+/fHzt27MCFCxcU+1RUVKC0tNT/KCkpAXDxD84HH3zwwUd0PwDA6d/Kds41cD0qLS1FRUWF4pjhWiOdxrVzMoqLi1FVVYWmTZsGlTdt2hSFhYWKfQoLCxXbV1ZWori4GOnp6SF9cnNzMWfOnJDyUgtzJ4QQEjmcPHkSqamptttNSEhAs2bNVNcko9SvXx8tW7YMKps1axZmz54d0jZca6TTuH4YV1xcsBNKkqSQMr32SuU+cnJyMG3aNP97r9eLzMxM5OfnO/KPMpYoLS1Fy5YtcezYMUdckbECr5M4vFZi8DqJUVJSgssvvxxpaWmO2E9KSsKRI0dw/vx5W+wprW+JiYmafZxeI53GNZHRqFEjxMfHhyiyoqKiECXmQ0lRFhUVoXbt2mjYsKFin8TERMU/YmpqKv/zCpKSksJrJQCvkzi8VmLwOolRq5Zzkf+kpCQkJSU5Zl+NcK2RTuNaTkZCQgK6du2KvLy8oPK8vDx0795dsU+3bt1C2n/44YfIzs5GnTp1HJsrIYQQEk5iZo2UXOTvf/+7VKdOHWnZsmXSgQMHpClTpkj16tWTvv/+e0mSJGn69OnSfffd529/+PBhqW7dutLUqVOlAwcOSMuWLZPq1Kkjvfnmm8JjlpSUSACkkpIS2z9PrMFrJQavkzi8VmLwOokR69fJjTXSblzNyRg2bBhOnjyJuXPnoqCgAB06dMD69euRmZkJACgoKAjaD5yVlYX169dj6tSpWLhwITIyMvD888/jzjvvFB4zMTERs2bN0o2DEV4rUXidxOG1EoPXSYxYv05urJF24+o5GYQQQgiJXVw/VpwQQgghsQlFBiGEEEIcgSKDEEIIIY5AkUEIIYQQR4hJkRHtt8YNJ0au1Zo1a9C3b180btwYKSkp6NatGz744IMwztY9jP6b8rF161bUrl0bnTp1cnaCEYLR61RRUYEZM2YgMzMTiYmJuPLKK7F8+fIwzdZdjF6rlStX4rrrrkPdunWRnp6O0aNH4+TJk2GarTts2rQJgwcPRkZGBuLi4rBu3TrdPjX5+zwicW3zrEP49hW//PLL0oEDB6TJkydL9erVk44eParY3revePLkydKBAwekl19+2fV9xeHC6LWaPHmy9NRTT0lffvml9M0330g5OTlSnTp1pF27doV55uHF6HXy4fV6pSuuuELq16+fdN1114Vnsi5i5jrdfvvt0o033ijl5eVJR44ckb744gtp69atYZy1Oxi9Vps3b5Zq1aolPffcc9Lhw4elzZs3S+3bt5eGDBkS5pmHl/Xr10szZsyQ3nrrLQmAtHbtWs32Nfn7PFKJOZFxww03SOPHjw8qa9OmjTR9+nTF9r/73e+kNm3aBJWNGzdO+tnPfubYHCMFo9dKiXbt2klz5syxe2oRhdnrNGzYMOmJJ56QZs2aVSNEhtHr9N5770mpqanSyZMnwzG9iMLotfrzn/8sXXHFFUFlzz//vNSiRQvH5hhpiIiMmvx9HqnEVLgkVm6NGw7MXCs51dXVKCsrc+zmRJGA2eu0YsUKfPfdd5g1a5bTU4wIzFynd955B9nZ2Zg/fz6aN2+O1q1b45FHHsHZs2fDMWXXMHOtunfvjh9++AHr16+HJEk4ceIE3nzzTQwaNCgcU44aaur3eSTj+l1Y7SRWbo0bDsxcKzlPP/00Tp8+jaFDhzoxxYjAzHX69ttvMX36dGzevBm1a8fUfzFVzFynw4cPY8uWLUhKSsLatWtRXFyMCRMm4NSpUzGdl2HmWnXv3h0rV67EsGHDcO7cOVRWVuL222/HCy+8EI4pRw019fs8kokpT4aPaL81bjgxeq18rFq1CrNnz8bq1avRpEkTp6YXMYhep6qqKgwfPhxz5sxB69atwzW9iMHIv6fq6mrExcVh5cqVuOGGGzBw4EA888wzeOWVV2LemwEYu1YHDhzApEmTMHPmTOzcuRPvv/8+jhw5gvHjx4djqlFFTf4+j0Ri6mdWrNwaNxyYuVY+Vq9ejTFjxuCNN95Anz59nJym6xi9TmVlZdixYwd2796NBx98EMDFxVSSJNSuXRsffvghevfuHZa5hxMz/57S09PRvHlzpKam+svatm0LSZLwww8/4Oqrr3Z0zm5h5lrl5uaiR48eePTRRwEAHTt2RL169dCzZ0/MmzePv9AvUVO/zyOZmPJkxMytccOAmWsFXPRgjBo1Cq+//nqNiAcbvU4pKSnYu3cv9uzZ43+MHz8e11xzDfbs2YMbb7wxXFMPK2b+PfXo0QM//vgjysvL/WXffPMNatWqhRYtWjg6Xzcxc63OnDmDWrWCv67j4+MB/O+XOqm53+cRjUsJp44RC7fGDRdGr9Xrr78u1a5dW1q4cKFUUFDgf3i9Xrc+Qlgwep3k1JTdJUavU1lZmdSiRQvprrvukvbv3y9t3LhRuvrqq6WxY8e69RHChtFrtWLFCql27drSokWLpO+++07asmWLlJ2dLd1www1ufYSwUFZWJu3evVvavXu3BEB65plnpN27d/u3+vL7PPKJOZEhSZK0cOFCKTMzU0pISJC6dOkibdy40V83cuRI6eabbw5q/+mnn0qdO3eWEhISpFatWkmLFy8O84zdw8i1uvnmmyUAIY+RI0eGf+Jhxui/qUBqisiQJOPX6eDBg1KfPn2k5ORkqUWLFtK0adOkM2fOhHnW7mD0Wj3//PNSu3btpOTkZCk9PV269957pR9++CHMsw4vn3zyieZ3Dr/PIx/e6p0QQgghjhBTORmEEEIIiRwoMgghhBDiCBQZhBBCCHEEigxCCCGEOAJFBiGEEEIcgSKDEEIIIY5AkUEIIYQQR6DIIIQQQogjUGQQQgghxBEoMgiJEaqqqtC9e3fceeedQeUlJSVo2bIlnnjiCZdmRgipqfBYcUJiiG+//RadOnXC0qVLce+99wIARowYga+++grbt29HQkKCyzMkhNQkKDIIiTGef/55zJ49G/v27cP27dtx991348svv0SnTp3cnhohpIZBkUFIjCFJEnr37o34+Hjs3bsXDz30EEMlhBBXoMggJAb597//jbZt2+Laa6/Frl27ULt2bbenRAipgTDxk5AYZPny5ahbty6OHDmCH374we3pEEJqKPRkEBJjbNu2DT//+c/x3nvvYf78+aiqqsJHH32EuLg4t6dGCKlh0JNBSAxx9uxZjBw5EuPGjUOfPn3wl7/8Bdu3b8eSJUvcnhohpAZCkUFIDDF9+nRUV1fjqaeeAgBcfvnlePrpp/Hoo4/i+++/d3dyhJAaB8MlhMQIGzduxK233opPP/0UN910U1Bd//79UVlZybAJISSsUGQQQgghxBEYLiGEEEKII1BkEEIIIcQRKDIIIYQQ4ggUGYQQQghxBIoMQgghhDgCRQYhhBBCHIEigxBCCCGOQJFBCCGEEEegyCCEEEKII1BkEEIIIcQRKDIIIYQQ4gj/H02UqGhbM1Y4AAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from functools import partial\n", + "\n", + "# Enable 64-bit precision if needed\n", + "jax.config.update(\"jax_enable_x64\", True)\n", + "\n", + "# 1. Set Up the Grid and Parameters\n", + "Lx, Ly = 1.0, 1.0 # Domain size\n", + "Nx, Ny = 100, 100 # Number of grid points\n", + "dx, dy = Lx / Nx, Ly / Ny\n", + "dt = 0.0001 # Time step\n", + "D = 0.1 # Diffusion coefficient\n", + "num_steps = 1000 # Number of time steps\n", + "\n", + "x = jnp.linspace(0, Lx, Nx)\n", + "y = jnp.linspace(0, Ly, Ny)\n", + "X, Y = jnp.meshgrid(x, y, indexing=\"ij\")\n", + "\n", + "# 2. Initialize the Concentration Field\n", + "C = jnp.zeros((Nx, Ny))\n", + "# Place a point source at the center\n", + "center_x, center_y = Nx // 2, Ny // 2\n", + "C = C.at[center_x, center_y].set(1.0 / (dx * dy))\n", + "\n", + "\n", + "# 3. Define the Laplacian Operator\n", + "def laplacian(C):\n", + " C_up = jnp.roll(C, -1, axis=0)\n", + " C_down = jnp.roll(C, 1, axis=0)\n", + " C_left = jnp.roll(C, -1, axis=1)\n", + " C_right = jnp.roll(C, 1, axis=1)\n", + " laplacian_C = (C_up + C_down + C_left + C_right - 4 * C) / (dx * dy)\n", + " return laplacian_C\n", + "\n", + "\n", + "# 4. Time-Stepping Function\n", + "@jax.jit\n", + "def update(C):\n", + " C_new = C + D * dt * laplacian(C)\n", + " return C_new\n", + "\n", + "\n", + "# 5. Run the Simulation\n", + "C_list = []\n", + "for step in range(num_steps):\n", + " C = update(C)\n", + " if step % 100 == 0:\n", + " C_list.append(C)\n", + "\n", + "# 6. Visualization\n", + "for i, C_snapshot in enumerate(C_list):\n", + " plt.figure(figsize=(6, 5))\n", + " plt.contourf(X, Y, C_snapshot, levels=50, cmap=\"hot\")\n", + " plt.colorbar(label=\"Concentration\")\n", + " plt.title(f\"Diffusion at Time Step {i * 100}\")\n", + " plt.xlabel(\"X\")\n", + " plt.ylabel(\"Y\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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hBOzbtw+bNm3CK6+8gtmzZ9et6tDzxRdfYNCgQRg6dCj69u2Ltm3bYvfu3Xj55Zcxd+5cnHXWWejTpw8A4JhjjkGjRo3w1FNPoXPnzmjatCmKi4tRXFyMOXPmYPDgwRg0aBBGjhyJI488Ert27cJnn32GtWvX4rnnnksZd/Xq1bjmmmtw2WWXYevWrbj99ttx5JFH1qVRzLjuuutQUFCA0047Da1bt8aOHTvw3HPPYf78+bjlllvq3vOVlZUYMGAAhg0bhuOOOw6NGjXCl19+iYcffhjV1dWYPHlync05c+Zg+fLlGDhwIEpKSrB3714sX74cjzzyCPr06YOLLrqoru1VV12Fxx57DJdddhnuu+8+FBUVYebMmfjiiy9SNtsiJDRCLiglMSZRcY+kavmioiKtb9++2rRp07SKioq0Pl5Xi2iapm3btk279NJLtebNm2uFhYXar3/9a2316tUpKwi2b9+ujRw5UuvUqZPWpEkTrWnTptqJJ56o/fGPf9QOHjyYMkbyahFNO7QSYsyYMVrbtm213NxcrX379tqkSZO0ffv2pbQDoN1www1p/rVv315odcd9992ndejQQcvPz9c6d+6s/fnPfza8PuvWrdPOOOMMrXHjxhqANH/NcLNaxMnfoX379tp5552Xcuy7777Txo0bp3Xs2FFr0KCB1rx5c61nz57a7bffrn3//femvu7evVu79957tbPPPls78sgjtby8PK1JkybaySefrN17773aDz/8kNL+mWee0Tp16qQ1aNBAA6BNnjy57twHH3ygXX755VpRUZHWoEEDrU2bNtrZZ5+tzZ49u65N4r372muvaWVlZZqiKFqjRo20IUOGaOvXrzf1M8Ff//pX7cwzz9Ratmyp5ebmaoqiaH379tX+93//N6Xdvn37tGuuuUbr3Lmz1rRpUy03N1c76qijtF//+tdpq2fefvtt7fzzz9eKi4u1vLw8rXHjxtpJJ52k3XPPPdrevXvTfCgvL9eGDx+uNW/eXGvYsKF2+umna4sWLbL1nZAgyNG0kMr5CSEkJJ544gmMGjUKq1atQq9evcJ2h5CMgzUXhBBCCJEKxQUhhBBCpMK0CCGEEEKkwsgFIYQQEmMOHjyIO+64Ax07dkSjRo1w9NFH4+677xbaldgvuBSVEEIIiTH3338/Zs+ejb/97W/o2rUrVq9ejVGjRqGwsBC/+c1vQvGJ4oIQQgiJMStXrsRFF11Ud5fgDh064JlnnsHq1atD84niwoba2lr897//RbNmzXgzIEIIiTGapmHPnj0oLi5GvXryqwL27duH/fv3S7On6W5DABzaoTaxS22Cn/3sZ5g9eza+/PJLHH/88fjggw/w1ltvYcaMGdJ8cUyYm2zEga1bt6ZsFMUHH3zwwUe8H1u3bpU+V/z4449amzatpfrZtGnTtGPJG8YlqK2t1SZOnKjl5ORoubm5Wk5OjjZt2jTpr9EJjFzYkLgXwdatW1FQUBCyN4QQQtxSVVWFkpKStHvMyGD//v0oL9+OrVs/R0GBd/tVVXtQUtIpbe7RRy0AYP78+XjyySfx9NNPo2vXrli3bh3Gjx+P4uJijBgxwrMvbqC4sCERkiooKKC4IISQDMDPFHdBQTOpc4XI3HPLLbdg4sSJGDp0KIBD9yPavHkzpk+fHpq44FJUQgghJMb88MMPaTUk9evX51JUQgghhLjjggsuwO9//3u0a9cOXbt2xfvvv4+HHnoIV111VWg+UVwQQgghMeaRRx7BnXfeieuvvx4VFRUoLi7G6NGjcdddd4XmE8UFIYQQEmOaNWuGGTNmhLv0VAdrLgghhBAiFUYuCIkwLTJ047advF8iIRkNxQUhIZCpokEU0ddPEULiRwWAHyXY2SPBRngwLUJIwGS7sHACrxUh8YSRC0J8gJOiPKyuJSMbhEQTRi4IkQyFRXDwWhMSTRi5IMQlnNiigdnfgVENQsKDkQtCXEBhEX34NyIkPBi5IMQGTlLxxehvx4gGIf7DyAUhFlBYZB78mxLiP4xcEHIYTjrZAyMahPgLIxeEgMKC8D1AiEwYuSBZCScSYoT+fcFoBnFOBYC9EuzIsBEejFyQrIPCgojC9woh7mDkgmQFnCSIW5LfO4xkECIGIxck46GwILLge4kQMRi5IBkJJwHiF4xkEGIPxQXJGCgoSNBQaBBiDNMiJCOgsCBhw/cgIT/ByAWJLfwwJ1GDkQxCDsHIBYklFBYk6vA9SrIZRi5IbOCHNYkbjGSQbIXigsQCCgsSd1rk5FBgZAXfAmgswc4PEmyEB8UFiSwUFCTTYCSDZAusuSCRhMKCZDp8j5NMhpELEin4gUuyCUYySKbCyAWJDBQWJJvh+59kEoxckFDhByohP5H4f2AUg8QdRi5IaFBYEGIM/zdI3KG4IKHAD09CrOH/CIkzFBckcPihSYgY/F8hcYU1FyQQ+CFJiDu4oiRufAegoQQ7+yTYCA9GLojvUFgQIgf+L5G4EDtxMXPmTHTs2BENGzZEz549sXz5cqF+b7/9NnJzc3HyySf76yBJgR+GhMiF/1MkDsRKXMyfPx/jx4/H7bffjvfffx9nnnkmBg8ejC1btlj2q6ysxPDhw3HOOecE5CkB+CFIiF/wf4tEnViJi4ceeghXX301rrnmGnTu3BkzZsxASUkJZs2aZdlv9OjRGDZsGEpLS23HqK6uRlVVVcqDOIcffoT4C//HSJSJjbjYv38/1qxZg4EDB6YcHzhwIFasWGHab968efj6668xefJkoXGmT5+OwsLCukdJSYknv7OJFjk5dQ9CiP/wf45EldiIix07dqCmpgatW7dOOd66dWuUl5cb9lm/fj0mTpyIp556Crm5YgtjJk2ahMrKyrrH1q1bPfueDfDDjZBw4f9g9tKhQwfk5OSkPW644YbQfIrdUtQc3T+QpmlpxwCgpqYGw4YNw9SpU3H88ccL28/Pz0d+fr5nP7MJfqgREg1a5ORwuWoWsmrVKtTU1NQ9//jjjzFgwABcdtllofkUG3HRsmVL1K9fPy1KUVFRkRbNAIA9e/Zg9erVeP/99zF27FgAQG1tLTRNQ25uLl577TWcffbZgfieyVBYEBItKDCyj1atWqU8v++++3DMMcegb9++IXkUI3GRl5eHnj17YtGiRbjkkkvqji9atAgXXXRRWvuCggJ89NFHKcdmzpyJxYsX45///Cc6duzou8+ZDoVFfFAk21Ml2yNyocDIHPSLCuyi6/v378eTTz6JCRMmGEb1gyI24gIAJkyYgLKyMvTq1QulpaWYO3cutmzZgjFjxgA4VC/x7bff4u9//zvq1auHbt26pfQvKipCw4YN044T51BYRAMl4uOqPvpArKHACItvAchIrVcDQNqigsmTJ2PKlCmmvV588UWoqoqRI0dK8ME9sRIXV1xxBXbu3Im7774b27ZtQ7du3fDKK6+gffv2AIBt27bZ7nlB3ENBER5K2A64RDE5rgboQzbDrcPjz9atW1FQUFD33K4m8PHHH8fgwYNRXFzst2uW5Gga33FWVFVVobCwEJWVlSl/4GyDwiI4lLAdCAk1bAeygGwXGH5+nv9kexwKCrxHLqqqqlFY+CdHvm7evBlHH300FixYYFguECSxilyQcKCw8BclbAcigqJ7robgQ6bDVElmM2/ePBQVFeG8884L2xWKC2INhYV8lLAdiAmK7rkagg+ZCAVGZlJbW4t58+ZhxIgRwvs6+Un4HpDIQmEhByVsBzIERfdcDcGHTIECI/N4/fXXsWXLFlx11VVhuwKA4oKYQGHhDSVsB7IAJel3NSQf4gwFRmYxcOBARKmEkuKCpEFh4Q4lbAeyGCXpdzUkH+IIBQbxi9jcW4QEA4WFcxRQWEQJBfx7OIH/88QPGLkgdfBDRhwlbAeILUrS72pIPsQFRjBkUgEgT4Kd/RJshAcjFwQAhYUoCigs4ogC/t3s4GcAkQkjF4QfKjYoYTtApKEk/a6G5EOUYQSDyIKRiyyHwsIcBRQWmYwC/n2N4GcCkQEjF1kMP0SMUcJ2IGBamBzfGagX4aEc/qmG6EPUYASDeIXiIkuhsEhHCdsBj5iJhKDtxVWUKId/qiH6ECUoMIgXKC6yEAqLVJSwHXCIbBEhm7hHQpTDP9UQfYgKFBjELRQXWQaFxU8oYTsgQNSFhBOMXkuUBYdy+Kcaog9RgAKDuIHiIougsDiEErYDFmSSmBBB/3qjKDaUwz/VEH0IGwoM4hSKiyyBwuIQStgO6Mg2MWFHlMWGAgoMCgwiCsVFFkBhER1RQTHhjOTrFQWhoRz+qYboQ5hQYIhQATlT60EJNsKD4iLDyXZhoYTtACgoZBEloaEc/qmG6ENYUGAQESguMhgKi3ChqPCPqAgNBRQYhBhBcZGhZLOwUEIcm4IieMIWGsrhn2oIY4cJBQaxguIiA6GwCJ4oiYrCkMatDGncZBJ/h7BEhhrCuGFCgUHMoLjIMLJVWCghjBmmoAhLQFhh5VPQwiOsaIZy+Kca4JhhQ4FBjKC4ILFHCXi8oEVFFIWEU4xeQ1CCI4xohgIKDJLdUFyQ2KIEPF5QoiITxIQI+tfpt9gIWmQoh3+qAY1HSJSguMggsiklogQ4lt+iIlvEhB1BiY0wRIYa0FiERAWKiwyBwkI+fooKCgp7kq+RH0IjSJGhIPMFBlMjJBmKiwwgW4SFEtA4fomKKAoKxea8GoAPIvgpNIISGcrhn6rP44QJBQYAfAugvgQ7NRJshAfFRcyhsJBHJogKJWR7quTxjUhczziLDNXnMcKEAoMAFBexhsJCHrKFhd+CQvHZvlsUk+OqD2P5Fc1oAQoMr1BgEIqLmEJhIYc4iArFB5tBo+ieq5Lty45mBBHFUECBQTIXigsSSRSf7csUFbIFhSLZXhRRdM9VSXZlRzP8FhnK4Z+qT/YJCQuKCxI5FJ/tyxIWMkWFItFWHFF0z1UJNmVGM/xOlSigwCCZBcVFDMnklIjio+2oiQpFkh0Rmku2t0uyPT1K0u+qR1uyREYQUQzVJ9thwdRI9kJxETMoLNwhQ1jIEBWKBBtGyBYPXseTKT6UpN9VD3ZkigwKDHEoMLITiosYQWHhnEwUFUELCTcY+ShDcCiHf6oebBQi2lEMBRQYJP5QXMQECgvnhC0sFAnjA/EQEyLoX4cXsaEk/a666B/1KIYCCoz4UgGgngQ7tRJshAfFRQygsHCOV2ERpqjIFDFhhyyxoRz+qbroK0NkUGCIkz0Cg1BcRBwKC2fEVVT4KSgUn+yqku0lXwM3QkM5/FN10deryPArTaKAAoPEE4oLEgqKDzbDEhaKy34yBYVbH/waU/Vo24vQUDz44LUew48ohoLMExgk86G4IIGj+GDTi7AIUlTIEBRuxg0axeCY6tKWW6GR8MHpuDKiGBQYJNuhuIgwmZgSUXywGbSwUFz08SIq3IwXRRTdc9WFjcR1DEpkUGD4A1MjmY+MklbiAxQW9rSAe2FRCOfCQoGz19A86eFmHKfjidDC40MmCty/Ti/X1Qlu3icJ/LjLruKDzbDIxM+4MPn222/x61//Gi1atEDjxo1x8sknY82aNaH5w8hFBMnEfzpFsr0goxWKw/ZuohROxzDDr9vGi9r38m1dSfpdFezjJmWSGEd0DMB9FMOPQk8FjGCQVHbv3o0zzjgD/fr1w3/+8x8UFRXh66+/hqIooflEcUF8R5FsL6rCws03abf4LSLcYuaX08lVSfpdFezjNGWiOLANRCtNoiBzBAbxzv3334+SkhLMmzev7liHDh3CcwhMixCfUSTbCyoNokDcd6cheie2E/iZnggCL/4rcJeO8sN2lNIkimR7YZGJkVqZVFVVpTyqq6vT2rz00kvo1asXLrvsMhQVFaFHjx7485//HIK3P8HIRcTIpH80RbI9L8LCCYpgO6eCwimyJyNZN1yTeStzQPwbvZL0uyrQvjmiF8VgBMOYjEqP7N8N7Jdh59CPkpKSlMOTJ0/GlClTUo5t2LABs2bNwoQJE3Dbbbfhvffew7hx45Cfn4/hw4dLcMY5FBcRgsLCnCCEheKgrZNvxk4IY0mt7HGcTrpehIZq085JqkTUZgIKDBIEW7duRUFBQd3z/Pz8tDa1tbXo1asXpk2bBgDo0aMHPvnkE8yaNYvighAz3Ey4YUcrRO0Bwby+IDHyTXQSdio0lMM/VZt2TkWGnb0EUREYJHMpKChIERdGtG3bFl26dEk51rlzZzz//PN+umYJxUVEYNTCGL8nXkWwnWxR4fR1RVlMiKD3X2RCdiI0lMM/VZt2oiJD1B7gftMtmQJDQfyjFxmVGgmYM844A1988UXKsS+//BLt27cPySOKi0hAYWFMnISFqC0nr8mroFA89hdFddEn+bU5ERoyRUYUohgUGKlQYLjjpptuQp8+fTBt2jRcfvnleO+99zB37lzMnTs3NJ8oLog0FIm2oiAswhAVQe0YKhOz8VXB/k6EhhORYTe+kyiGna0EFBgkDE499VS88MILmDRpEu6++2507NgRM2bMwJVXXhmaTxQXRAqKRFt+CgtFsJ0sYSHyWvze1CssFN1zVaCPqNAQERmJ8e3GFYliKAJ2ElBgkDA4//zzcf7554ftRh0UFyGTCSkRRaKtsIVFFEWFyHhW+HU7d7d3K02g2rQXqWWQJTJEBYadnQRhC4w4w9RIZkBxESKZICxkkgnCwu41+Cko/BIRTsdzug03YD1hyxQZVuPITpOEKTAUxDt6QYERfyguiCcUSXaiLizsbMgQFXZjJBO0kHCCkW+iUQHAfFIUSZnYiYzEOGZjAHLTJBQYJFuhuCCuUSTZCVNYeI1WBCkqonTbdtVheyc3GFMExrCLZoiIDCv7YQsMWSigwAicSgC1EuzskWAjRCguQoIpEfcEJSzs+lsJCzsf7WwD4d5d1e04qkBfUaGRPIaZXRGR4TaKIZImUSz6J+NUYLD+gqmRuENxEQKZICwUSXb82kxKsTnvRVh4iVZY2U3g19biQaAYHFMt2jsVGma2rCZvv6MYdv0ThCUwFMQ3ekGBEV8oLohjFEl2oigs7Pq6jVbY2ZW5p4YRsm+C5mTSU3TPVZN2IkIjYcvIhtcohplfAAUGIU6huCCOUCTZiZuwCEtU2PXXE9Tt2M3GcXI/EMB9SiJhw6i/lciwimJY2Uz4RIFBiBgUFwGTCSkRr2SDsPDLl2SCEhJOMPLJyx4UIiLDrK9dqsRNFCPuAiOOMDUSTyguAiTuwkIJYcyoCgu30QqvoiJqNzyTdQMyJel31eC8lchI9DXqZxfFiJPAkIGCeEYvKDDiB8UFEUKRZMePG3cpNufdTOiyoxV+RE30hHHnVKe3V3ciNFSDc15Ehps0iZGthB9BC4xsjl6Q+FEvbAecMnPmTHTs2BENGzZEz549sXz5ctO2CxYswIABA9CqVSsUFBSgtLQUr776aoDekmT8COMrFueaQ66wKIRzYWHmg5L0MPNBZFVK8sMpiuDDKaJ+tYD167Qa3+pva9bHyh8rH8yQXS8jgoz/IUWCDULsiFXkYv78+Rg/fjxmzpyJM844A3PmzMHgwYPx6aefol27dmnt33zzTQwYMADTpk2DoiiYN28eLrjgArz77rvo0aNHoL7HOSWiSLDhRzhfsTgXVBrEzJbTic9qbDsfnI7jFBFbqsU5vd9WEQQg/dt5YnyjMcwiGVZ9rKIYYUQwwqi/UBC/9EhsUiNVkLOJ1vcSbIRIjqbF4a91iN69e+OUU07BrFmz6o517twZF198MaZPny5ko2vXrrjiiitw1113CbWvqqpCYWEhKisrUVBQ4MrvOAsLIHhxIWNXS5mTu9/CwstmXHa2w0IVbGc1qZpNoGa2zSZ5s/ZmY1tN3E7HtuuXjBOBISM9okqwEQZeBIaMz3Nb22uBgqYS7H0PFJ4CX3wNgthELvbv3481a9Zg4sSJKccHDhyIFStWCNmora3Fnj170Ly5+ffa6upqVFdX1z2vqqpy53CGoEiwkWnCwsxOkKLCzKYZ9Z12sKFGtT6vH86suZtlownbeptOoxhOIxgJW3o7ibGDjGBka/SCxIfYiIsdO3agpqYGrVu3TjneunVrlJeXC9l48MEHsXfvXlx++eWmbaZPn46pU6d68jVTUCTYiLqw8DNaIWtMO3vJyBYRTscxEx3JzY2aWN2UzI3IMCv41Lc1EzdhCQwnUGCQKBO7gs4cXYpB07S0Y0Y888wzmDJlCubPn4+ioiLTdpMmTUJlZWXdY+vWrZ78jXtKJEooNufDEBZWBZtG4xmNaVcomngYUV9JfThGX33psUpUxB8F1q/JbGiz62dkx8nfJTGm6HhWdrwWeYax4idu8DM1HsQmctGyZUvUr18/LUpRUVGRFs3QM3/+fFx99dV47rnn0L9/f8u2+fn5yM/P9+wvEO9/AkWCjSCXUfopLMxseI1WuN2AS1hEyJipRGxYxPKTfTWKaiROG5xyFFUws2MUTVAsxpNR6ClyR1UrmB6xJzbFnVlMbCIXeXl56NmzJxYtWpRyfNGiRejTp49pv2eeeQYjR47E008/jfPOO89vN8lhgkyHBC0svEYrzL6ZKyY2AIHohISog2sEx7Z6DQrMX7+RSadRDLPxjMYywulqJ6+bpTn580Vxp1ZCYhO5AIAJEyagrKwMvXr1QmlpKebOnYstW7ZgzJgxAA6lNL799lv8/e9/B3BIWAwfPhwPP/wwTj/99LqoR6NGjVBYyACkGUrYDuhQLM45vS25DGEh0k5GZMQ2QhHmDGT3ddmqiALWEY3EKd1hw2/0RvUYRv2tij1FxkmMZRQx0fdPHtPNtuVhoCBa/pD4EytxccUVV2Dnzp24++67sW3bNnTr1g2vvPIK2rdvDwDYtm0btmzZUtd+zpw5OHjwIG644QbccMMNdcdHjBiBJ554wldf45wS8YrMqIVicc7pt8MwhYU0UWF3waJ45zJBoSEiMpymSnQmhdMkTlITRv2txhPp59SHbNy9k6mRaBOrfS7CwO266LiKC8Vj/7ilQ2QKC99Ehd02l05xG7RzeiMMu9nOxJ5RbYbBIUMTRkMa9TWa8I3aGbkoa/8Nqz52PhiRjXtfOBUX3OciOGIVuSDZhWJxLkxhYdTGi33HokJEUPiR9bOyabftJpA++5mEIYwiGYcP2UYYnKRJ3EYwZBZ4mvVxQzZGLyLJLgDVtq3s2SvBRojEpqCT+I/isX/Y6RCzPkELC6MCRMWgr2Fxo1VRpNXaSBnFnEZOiiIyvsO1uEbXR0G6i2aXSo++n2iaS9S+WX+zsez6WI1vhtesmOKxPyEJGLnwgbimRIIi04WFnW3DSIWbKIWMXce89lFNjovsigWkftV2GMlIHtqoq0gdhh8RDDO8LFF1ev+RbIF1F9GFkQvJxFVYKB77R3E5nFthYbTUVN/GbImpvo++n7CwsPuWL7LrltFYshEZz2k0xiKSYTS0vqudeX0f2REMo752uOljRrZFL+L6mZvpMHJBAiWoqIXX4kqrNr5GK5zstmU0kBlBLUVVdM9V3XOziIZRsYRBOEI0imEXZdD3kR3BMOprNo5dH6uxCYkqFBcksKiFlxpDNzcjsxvbqK9IxMKpXcO6Cj0yt/AMIoxkNIZZlWMCVXfOLJeht2UiMrymSfR9nOzmKYJZX687eIrgtbhTQfxWjpBoQXFBIoMiqb3I3GrU16uw0LcXilbIEBVB7rNu9dXZbnWIcvinqjvuRGTYCAy9eaPVJF4Fhoz6CzP0Y+lh9ILEBYqLLEfx2F9W1MLKDye7cIrM1UZj+S4sRKIVTvI2dhferw1ozezaLUU1WhsKOA836Nq4SZMEKTD0/azGsOtjNbYRjF6QMKG4kAgLi+TjR52F3Rh629KFhVsFZNbXqL8dTlMnTrb7BrzdN11k8wqBKIadSacCQ4+fAoM4g6tGogfFhSTiKCwUj/2DiFp4aS+6MsTqvO/CQkRUuBUUMmsvrGw52fLbLpqh6vrbRTECFBj680Y2zTDq66U9oxfpREZgqAAOSLAT8020KC5IZPGaDtGj2NjXn/ckLJyKChEHrPpatbfDSarDblwroSF61zHVoq++j0GaJGiBocfJhM7oBclUKC6IK/yOWshOh+j7yRQW0qMVTvI7fm4FbtfP6ZbfViLDbr9uO1WQdF5fh6E4NKVvbycwvKZHzLBrH1T0ghA3cBOtLEUJ2wH454PXWsZICQsnm08lt7fbbEsGImNZbZJVaNNOMeint21xPvlv49SUU/xYZh0VlLAdILGEkQviGL/3tfAatbDrZxW18E1YuBEVerwUc+rH84pqctxprYVd8aaiG88oTSJYh6HAPuKQQN9WVnrErJ9ZesRuHEYvSFRh5EICcSvmVMJ2AHKKOP1Ih4jashQW+m/hXoWFwxt+pW3JrR9PBiL2RaMZRu2cRDFsrrdoBMPpNuFWtsxsGvWLA0rYDggSt8/iTIbiwiPZ9mYOK2rhZkzFxrb+vKgesBUWZucUnSG7SVY0pZBsW+9bMs0lPYxQYO6Dmc92KSD969fbdhApshIYVi7p29q9P/1Ij+h9cDtmEBu3RoFs+0wGgClTpiAnJyfl0aZNm1B9YlqEBI4iob3smzNZ2Utu60lYWA3oZnWJkd0EfibyjWwb7ToF2O/GKbCHhWXFpVVbi5yBqAk7ku2YIaO4kxA7unbtitdff73uef369UP0huIi61ACGCMOUYtknGgC04ZuhYWMJasJRC6gWV8RVItzyWPrixOM+jtcYipDYFgtU7UykdwOcLd6RBS/ay+8YOcDCZfc3NzQoxXJMC1ChJERVlUktBf9Up/AbZ2Fvp3pPhaiwsKuFkOkmlTvlFW6QjF4eEHUnplPRn2cXAOjNImInaRzhvd7MTGRjL6bUyEcldqLbEmNZBJVVVUpj+rqasN269evR3FxMTp27IihQ4diw4YNAXuaCiMXJBKIfli7WR1idc5qrk9GirAwa+dm106vt4mVtf23fjxV9zzhp1E0I9FWJIohElZwEcFI7q434SU9Ekb0gkSE3QCM539n/HDoR0lJScrhyZMnY8qUKSnHevfujb///e84/vjjsX37dtx7773o06cPPvnkE7RoEY6kpLjIIhQPff0u5DRCcTGevo+TdIiZHanCwus+GGYRCitkfLYY2XByi3UzkZHcxkn9hKjASEZQYCQjMz3id+1FEMtSFVDgBM3WrVtRUFBQ9zw/Pz+tzeDBg+t+7969O0pLS3HMMcfgb3/7GyZMmBCIn3ooLjyQjVXJXlBMjsuMWrgZHzDXBVZhdCEDosJCpqhweqHMFKHdTGW1GydgXNSpFxn6Nla1FlbnFBgLDIuZ1ExgONn/wi8YvfBGZO4zIoGCgoIUcSFCkyZN0L17d6xfv164T21tLb766itUVFSgtrY25dzPf/5zR+MDFBeuobBIJ65RC88FnEELC/15vT0j3PxxrPq4ucW6mnTMydd+q42ynAoMwa/3oumRuEYvsoFMEhhOqa6uxmeffYYzzzxTqP0777yDYcOGYfPmzdB01ywnJwc1NTWOfWBBZ5ageOjrZyGnX1ELJ7twmiGUDnEqLKyKOhWdDX1RpP68kb1ku2b7S8jAzr7Zdt5K0nO71yd63USKPAUKPJO76jEbIgxkjO/lf1rG+EQuv/3tb7Fs2TJs3LgR7777Li699FJUVVVhxIgRQv3HjBmDXr164eOPP8auXbuwe/fuuseuXe5urcfIBYkcikAbkZpHkXNC6RCZwkJkcCB90tUjsj2pHiM7blBtxrbazjvhR7INq6//onUYHiIYXtMjfkUveMdUIso333yDX/3qV9ixYwdatWqF008/He+88w7at28v1H/9+vX45z//iWOPPVaaTxQXRAp+fDl2i5t0iGO8CAsrZeT0dq1G9sz6ykJvV9U919dGAOkiI2Ej0deoFiNxzqvA8IDb9EiYMDWSfTz77LOe+vfu3RtfffUVxQVxhuKhb9ApEaO2blZqipwzaye8OkSPn8JCVFQoBsf05Ln8q+43mVqTx1STfjcTGaJRjORzTlaS6PEQvbDCqp3+nEj0wgg/Czu5aoQkuPHGG3HzzTejvLwc3bt3R4MGDVLOn3jiiY5tUlyQjMJrEaendIiTQfV9rdIgXnbtBNyLCTs7RmIj4YeadMxuHwt9H1GBYYTP6RE/oxfJYxISJL/85S8BAFdddVXdsZycHGia5rqgk+KCeMbJzoYJ3BZy+hG1cOSEm5UhRiT3FRUWTkSFI0HR2uT4dutuyWPohYZy+KeadMzu67sCZwLDS/2FJJLN251zG71wA1MjxAkbN26UbpPiwgXZsgw16H3dFI/9fYtaGJHc1mk6JLmvmbBwo6osBYWZgLDCgegwExrK4Z/q4Z8iUYxEWz8EBgzOS4xeeCV5zARRTY3EgVCWo1YC2C/Bzo8SbAgiWvjpBC5FdUjchIUStgMesBM3istztojWS1idD1JY5LUwERatkx4yaQ1L20b+KLo2Vvt+JLc1u0ZO/kZm40pSz0rS707uYxN3lLAdECRun9lh8fXXX+PGG29E//79MWDAAIwbNw5ff/21a3sUF8QTfqZEnIxlheOohd3gRm39EBb6PSQUXVtbUWHGUS4fZgiKDL3/+tcnS2DY2TN6IwnsfSFDl1hpKivc/M9EaQUXiTavvvoqunTpgvfeew8nnngiunXrhnfffRddu3bFokWLXNlkWoREAsVjf7ttIYSxm0EUm/MyhYXZuICBqLATEzLQ2/nGxAdd2iSvRXqqRE06L5LOcFLk6SSH4WL3TjuCLOx00pYQMyZOnIibbroJ9913X9rxW2+9FQMGDHBsk5ELYkjQ9RZ24zsp5DSzIyVqIXpelrBQIBCtMBIWIhGHBG1MHnaYjWEQyTCKYiTjJYJh1M+u+NXmb+o0emHkjsg5vZ0wCHt8Ej6fffYZrr766rTjV111FT799FNXNikuMhjFZ/tuwq5uUyKB2JQZtTDq40ZYJGMoKvTCwkpQOBURTto7EBkJFDifuY3+2G7TI0bnJeUSrN6TQaYr/B5L8dk+CYZWrVph3bp1acfXrVuHoqIiVzaZFiG+oPjU1q598jnfohZ2k5HRMV+ERTJWgkI2yTbLTfxITpm0RkqqxCpN4mTFh1k/GBwzSo/YpEGcrhwxctEtRrZ4p1TiF9deey2uu+46bNiwAX369EFOTg7eeust3H///bj55ptd2aS4IJFDZM8o6TiJWlj1sSv8TD7mSljIFBWiq0is9rtIjGMkMvQCI8mWG4GRwKz+IoFdHYWbPh7RDxnknheE2HHnnXeiWbNmePDBBzFp0iQAQHFxMaZMmYJx48a5sklxQdKIUw7WaUpESfrdc9TC6liybSMnfRMWZqLCy3JUfV8jsWEkMmyiGKICw+i80dd4O1HiMXrhlCjdb8QOipvsJicnBzfddBNuuukm7NmzBwDQrFkzTzYpLjIUxWf7spagmrV1alt/znUa3U3UwuqYk2Us+vOOhIUfosKMZJt6oWEmMgQFhhF2K0iSj9mlRxIk9zEaxwCzYaKQGrGCu3UGTBXkbKK1T4INF3gVFQkoLkikCColYrsbp6yohdExEeVjKixEoxV2osLp8lT9slP9OEYiw4XAUOC9/iKBl+hFwFuFR9l2lMcm7jnllFPwxhtv4IgjjkCPHj2QY7HZ2Nq1ax3bp7hwAHd6ixZeUiKG+Bm1MBrDLB0iLCycigov+10k9zUSGkYiQx/F0KdJPAiMBHbpEatjyeMkMBjDzR1Trdwzc4mpCf8IZRvwiHPRRRchPz+/7ncrceEGigtBOhQWIhukRZzqLZJRkn53lBIJOmphNEbyedfCwkxUyNpAy8ymyEZaVlEMhwIjQfJ5q2Oi0QsjbKIXQaRGiDxa5ORgYyUTRAkmT55c9/uUKVOk2+c+FyQQ/K63cIJtIacVsqIWCZJ9kSosRDbQ0t8jxOxhhdk4+n5Wq1eS2trdzdXotN3eF1bHRM7B43smCadmjNr7sVcMEN8vFsQ7Rx99NHbuTFfcqqri6KOPdmWT4oI4xk0xpwhO6i1CS4k4bW90zDacYjWhiwoLM7tubmYm0s+pwBCIqChJv9vtuGl1zGrWdNrepEsUUCzO8T4jxIpNmzahpqYm7Xh1dTW++cas3soapkUyECVsBwJGSfo9sJSIVXvPUYtkkidhO2FhJSpkYlbEKbCBVkqKxGF6JIGX2gsrGy4KO53eFsWqv5lbhPjFSy+9VPf7q6++isLCnz7Qampq8MYbb6Bjx46ubFNckKxCOLxt1M5pyEbUhlA6xI2w8GMZqpF9I5EhUWAk8FJ7YWWDeEYB60ziyMUXXwzg0D4XI0aMSDnXoEEDdOjQAQ8++KAr2xQXJBQUn9s7wmmy2aq9m6hFHUEJC7dFnlbLUZ0KDKu2OhS4j15YtXdo38uGWnbDERIGtbW1AICOHTti1apVaNmypTTbrLkgdfhV0OVXAZooitFBv1IiVoPaRi1EsBMWdnURXpej2tVz6Nvr2yQQ2PTL6NqI1l5YtU/g4Q1vNaRsjMYK+3+KZBYbN26UKiwARi6IQ6JWzJk8pg83txQzmHDC96iFk8nbqp1XjGorkn3YrmtrFsEQSI8kUOBsR02j9gmsUiMe6y7McFJ3ESas+ZDALgD5EuxUS7DhgL1792LZsmXYsmUL9u9P3WLUzf1FKC5I1mBYbyFjlYhIf1dRC6vlmyLCwo89LvT23QgMB4jWXiSQmRpxgYz9LrxM8FZjRknEkGjx/vvvY8iQIfjhhx+wd+9eNG/eHDt27EDjxo1RVFTkSlwwLUKIGW5TIlb9FaOGdtt727VxIyzaOHyYIbrPhdk5s+WpBv0VC5NW59ymRgzOydrvgpAocdNNN+GCCy7Arl270KhRI7zzzjvYvHkzevbsiT/84Q+ubFJckMijBNHX7QZLIgN4jlo4XfVhJSzsxILbfnZixs4nC6yuldM9LBJ42FBLxDwhcWLdunW4+eabUb9+fdSvXx/V1dUoKSnBAw88gNtuu82VTYqLDEMJ2wEBlLAdcFuQ4XSViGNE0hh2UQszG25FhRM7TnzzWB8ic4c1r+O5QJFrjhBPNGjQoO7eIq1bt8aWLVsAAIWFhXW/O4XigviK7M95EXue5gHF4wBW/Q3P2UUlRNMLgLWwkI2oTS97bUhMjSRwG+mwwe8dMBWf7ctACdsB4poePXpg9erVAIB+/frhrrvuwlNPPYXx48eje/furmxSXJDIIfpBrST9bpdkEC7mFDWod8BJf8Mwv9W+FkZtRPFDWFjZdpIeEam9OIzb1EiCAPe/NnMnDKFNiAjTpk1D27ZtAQD33HMPWrRogf/3//4fKioqMHfuXFc2uVqEhE5G3zDJt0lNJGrhNbpgtumVfoxy3TEnG2h5QGT5gwL7JRtWyzM8bqYlMjzAlRwkPDRNQ6tWrdC1a1cAQKtWrfDKK694tsvIBQGQ4RO8F0TEgchXSMXooNGkblbIaRURcCMsRG5gJnqTM6fREZHaC4vzisAQvufP/IX/jyQoNE3Dcccd5/oGZWZQXBApKGE7YIAi2tDtslLXA3rF6z1D3PT3umJFpL+EFI7i3QSASAsPQmRSr149HHfccYa3XPdkV6o1ktFkxOdtEC/CU72FDKwmackFlkJjusFh3YUTJIYFFHmmpKF47M+oiUeqcCjH5fVRFZzLDzzwAG655RZ8/PHH0myy5oKQBIpAm8AUlpeUiIhNtzipn7DamdPDrp1GBLFvdQYURmTASyA+8Otf/xo//PADTjrpJOTl5aFRo0Yp53ftMtv21hyKCxJplLAd8IKlEHE70butf5B5+3UzgWFU3CnSz6eCTz2yCkAJyTD++Mc/1u1zIQuKC5JRBBZYUPww6uey0bhgJFAsxEeMv4oroI4h0WDkyJHSbbLmgpCMR2bUwk+bPqGE7QAh0aZ+/fqoqKhIO75z507Ur1/flU1hcSF7mYpbZs6ciY4dO6Jhw4bo2bMnli9fbtl+2bJl6NmzJxo2bIijjz4as2fPDshTEltkV7Qpku3V4fddT2UQBx/lkBEFzyQjmD59OnJycjB+/Hih9pqmGR6vrq5GXl6eKx+E0yLdunXDI488grKyMlcDyWD+/PkYP348Zs6ciTPOOANz5szB4MGD8emnn6Jdu3Zp7Tdu3IghQ4bg2muvxZNPPom3334b119/PVq1aoVf/vKXIbwCknFEapvEKKRV7OouJKGAOQVCDFi1ahXmzp2LE0880bbtn/70JwBATk4O/vKXv6Bp06Z152pqavDmm2+iU6dOrvwQFhfTpk3DDTfcgBdffBFz585FixbBL1h66KGHcPXVV+Oaa64BAMyYMQOvvvoqZs2ahenTp6e1nz17Ntq1a4cZM2YAADp37ozVq1fjD3/4A8UFIYSQjOL777/HlVdeiT//+c+49957bdv/8Y9/BHAocjF79uyUFEheXh46dOjgOtovnBa5/vrr8cEHH2D37t3o2rUrXnrpJVcDumX//v1Ys2YNBg4cmHJ84MCBWLFihWGflStXprUfNGgQVq9ejQMHDhj2qa6uRlVVVcqDEEIICQP9fFRdXW3a9oYbbsB5552H/v37C9neuHEjNm7ciL59++KDDz6oe75x40Z88cUXePXVV9G7d29XfjtaLdKxY0csXrwYjz76KH75y1+ic+fOyM1NNbF27VpXjtixY8cO1NTUoHXr1EKy1q1bo7zcOAxbXl5u2P7gwYPYsWNH3Y1akpk+fTqmTp0qz3FCCCHZwy4ADSTYOfz9t6SkJOXw5MmTMWXKlLTmzz77LNauXYtVq1Y5HmrJkiVuPLTE8VLUzZs34/nnn0fz5s1x0UUXpYkLv9GvxdU0zXJ9rlF7o+MJJk2ahAkTJtQ9r6qqSvvjElLHLkSs7iJsAqi3ICSL2Lp1KwoKCuqe5+fnG7b5zW9+g9deew0NGzZ0PEZNTQ2eeOIJvPHGG6ioqEBtbW3K+cWLFzu26UgZ/PnPf8bNN9+M/v374+OPP0arVq0cD+iWli1bon79+mlRioqKirToRII2bdoYts/NzTWtGcnPzzf845EsYidiugdyOaJR1JmMT6vMVH/MeiGm222QiFNQUJAiLoxYs2YNKioq0LNnz7pjiYLMRx99FNXV1ZZLSn/zm9/giSeewHnnnYdu3bpJ2VBLWFyce+65eO+99/Doo49i+PDhngd2Sl5eHnr27IlFixbhkksuqTu+aNEiXHTRRYZ9SktL8a9//Svl2GuvvYZevXqhQQMZcStCBFDh03LUb5BNSz0JIcacc845+Oijj1KOjRo1Cp06dcKtt95qu1fFs88+i3/84x8YMmSINJ+ExUVNTQ0+/PBDHHVUeB9mEyZMQFlZGXr16oXS0lLMnTsXW7ZswZgxYwAcSml8++23+Pvf/w4AGDNmDB599FFMmDAB1157LVauXInHH38czzzzTGivgfhLJbjfQDrbIX/TK7+26/YhraLKNykDNWwHSMbQrFkzdOvWLeVYkyZN0KJFi7TjRuTl5eHYY4+V6pOwuFi0aJHUgd1wxRVXYOfOnbj77ruxbds2dOvWDa+88grat28PANi2bRu2bNlS175jx4545ZVXcNNNN+Gxxx5DcXEx/vSnP3EZKvGOCo/RCKMJP4ppDZm4FSQW/ZiLIMQzN998Mx5++GE8+uij0u4xErt7i1x//fW4/vrrDc898cQTacf69u3r2woW4j8qAty9WWQwJ6ERT2EUs2iDXRTCTKDIjF6YTfZOog7JtRgB3LQMEBMiqt9OhAu1WPawdOlS4bZvvfUWlixZgv/85z/o2rVrWtnAggULHI8fO3FBiCeCyJtILwi1qq1wUnchQ2A4EQKSizn3W9xT3e/brQOGM7MawLCE+I2iKCm1jDKguCDCxK2eQYXEqIcswbB/J5CnNyS7MNMqveJFYFgJC9m1Ei6FiSrQRkSIxPgrvuqxfxA6jUSLefPmSbfJu6ISAN4/UFQZToSFrIlEdXkuBZFJWj/JG03EVna2G9iwG8+psND7tN3inJUdA1SxZrbEWEAQIpuDBw/i9ddfx5w5c7Bnzx4AwH//+198//33ruwxckGIFSLhGtcbadlFEZLP+7HsVC8YWpsct8JrxMJuLJfFnLvc+BIdGD2IMZWQM7MelGBDkM2bN+Pcc8/Fli1bUF1djQEDBqBZs2Z44IEHsG/fPlf3F2HkgoROEB+kNarDgUWcMrKpR/jbsdM0gNfohZlNGcWVVlELN/1hXW9hhSrQxuH7wPC9ZNJFZHiAQRQSLr/5zW/Qq1cv7N69G40aNao7fskll+CNN95wZZPigviKmy+Qol9I1aTfA/2mJzITWDlkO1EmiwKRdILV+XL4syW3mV07YWG2SsSDj7JqKFT3LoggO5gS8+AMiRBvvfUW7rjjDuTl5aUcb9++Pb799ltXNikuMgw1bAcEUH227+lboGpxzms0w/Ccl0iBUV+P9QxCmNmSvdW3wetTLZpbnUvgVoXavKn8jjyoPtuXgRq2A8Q1tbW1qKmpSTv+zTffoFmzZq5sUlyQyKPKNmg0E1jNDiIzh9XXSKP+htGL5MlZNHrhVGB4ERlW/Y3GdBO1EEyJeK238Pr3hrf3pZe+hMhmwIABmDFjRt3znJwcfP/995g8ebLrLcFZ0EkyGhU+b8JlNYDw8lWny0P1xZ1G/a0KQJMnc7sdQUXEiFNhIYJg1MIqEmHU3kl/QrKEP/7xj+jXrx+6dOmCffv2YdiwYVi/fj1atmzp+nYZFBcka6hRgfqK7qCVALA6Z7WKRIW54LA6ByBVFCTvV+FkZYmRLTO8pkvcCAsHUQtRVItzbnMWLoo5CYkjxcXFWLduHZ599lmsWbMGtbW1uPrqq3HllVemFHg6geKCOMLtnGqH2z2qkseUtjFmslE9VoNYLUk1unApG2qJRC/slqaaCQwYtPWKmRDwsLTUqI3MlIjXKIcAMlaKeAmmWI3JFSnEikaNGmHUqFEYNWqUFHusuSB1RClC7GdK3bSB2zy8anHO6KJatQdgXnth1S6B2eT9jUl7p1jZcVL/ATiKWqgGx9yKBad/ZwnFnHFZ2RGlzwASHNOnT8df//rXtON//etfcf/997uySXFBfMfog1UNcHxPY1l92joVHLaFnWaFm2bFnfp2Zm307Z0KDZE+IsJCJB3iImqhHv5p9Eaz+ht5mElV2xby+hq1j4tYyUqqcOh95/VRFZzLc+bMQadOndKOd+3a1dUGWgDTIiQmqJBTmClcd2E1oNPUiJ19w/uN6LGqvzBLkQDWqRYZkQyrSIlZO4E6j2RhoRqcdxq1kNCe9RYkUykvL0fbtm3Tjrdq1Qrbtm1zZZORiwxEDduBgFGTfvcc1nUbMlcNzhkds42hi26aZRTBMIti+HFLczO7Rn5YCQubW6+rSb9bRS2MkJH6clCo4KbeIlNQw3aAeKKkpARvv/122vG3334bxcXFrmwyckEig/4LvlXxqMjtPGxvCyJ6m1ejyIOv0Quzwk393U6drBBJnrj9uCtqYmwnfRykQ5IxOu20kFNSSkT2Fih+FXMSYsU111yD8ePH48CBAzj77LMBAG+88QZ+97vf4eabb3Zlk+KCOMavFSN+ouInvzylRpy+eDsbyeddCwwgPU0CmK8QMZvwW1ucs0JEVAhGLMzSIaJRi8Qx0SiEkQ0fUyIyzMjeUp+Q3/3ud9i1axeuv/567N+/HwDQsGFD3HrrrZg0aZIrm0yLkBT8qhaXUdTptL0QopOQ08JOoxecbMPxp72+wFM/WYumKKxwc4Mxr8JCgORr5VfUwmNKxC/UAMfiSpHsJScnB/fffz++++47vPPOO/jggw+wa9cu3HXXXa5tUlyQjEBN+t1sDk9u48qwHtFlpsnH7PqYrh4B0idlI4FhJTJkLkUVre+wExYCUYsERkUNRneykxy1MOtipnmS23BVB4kTTZs2xamnnopu3bohPz/fky2mRQTZVFmJjoUiCXriBdl1F2Y43q0zGSOnjI4lO6hCQnoESK+p0KdJEn0A49oKI1Fglj4RFSNmEQ8JwsJpOiQZL1GLJPxaJSKz3oLYs1PTUFUV4PrOGLF3717cd999eOONN1BRUYHa2tqU8xs2bHBsk+LCATs1DS1ycsJ2QwgV4dU++Dm2L7ZFxYJdYWeij4qfnLQr7pQmMABnIiMZtxENUVFhNIZDYSGaDvEStbDrY4KoKLAb0kt7q7a8Y+uhz25izjXXXINly5ahrKwMbdu2RY6EeY7igrhCdKFFMkbzrAp5YiHZlsj8bYibwk7RPk4dTBMYQGqRJ2AfxUjum9zfLVZ1GQ5FBeBcWCS3sTrmZoWITSFn8jBmE3ZyGz9SIkyzED/4z3/+g5dffhlnnHGGNJsUFyQNaffokEQgqRE30Quv6RHHAgMQj2IA9kJDFqK3YZcoLGRFLUIq5IxaSiTs8TOKXZBTzVhr30QWRxxxBJo3d/tJagwLOknksPugU13YcTRf2BVdin4rTu5jVHhoRXKbtH0fRDfQMlpVIhMz+3YbaUGesEg+btTP6piD88nDOAmqiJwjJGzuuece3HXXXfjhhx+k2WTkgrjGzX4XfqRG7Oom9SS3cRS9MDLupI+T+gt9X8MIBiC2v4VeABhFNESwEyoCy1L1QklN+t3pjlR2fT1GLbwUcnpNXxgNbWbTqG2CCKymJTHgwQcfxNdff43WrVujQ4cOaNCgQcr5tWvXOrZJcZHBqIjmhlZucFPj4Qm73JBocSfgvP4CJn0TE7NlmgSw30RLdjRD8C6pToSFkzoLo1CCXTGo3XkTXHYzteHFThRQw3aASOHiiy+WbpPighgSdt2F3fgqzIVT8jmR+V5q9MKuJkOk/sJuTKEoBpA66ZsJDbc4vEOqDGFhV2fhNZ9mErWw62bXRqR/mMRZ3BA5TJ48WbpNigviGyrCSY1Iw04omJ23Wz3iVmAAAlGMBFZCIxk70eFxrwsrUQG4ExZ6G/p+PkUtRIlKSoQQp6xZswafffYZcnJy0KVLF/To0cO1LYoL4gk/0xV2G2qpCDB64dS4kdPJ/awEBixsJrcFTEQG4P8eF/pxdBjdfEzVPbea5JPbuq2zsEutmAgSkeWnbgs5g0yJsN6CiFJRUYGhQ4di6dKlUBQFmqahsrIS/fr1w7PPPotWrVo5tsnVIhmOGrYDAeLLHgBu9xIX6Wf1bVzfX29D337/TpO7iW7XPbygt2WS/jCKVqhJz/Wvx6uwsFsdYtbPBjfd4rYPBe/CSgDgxhtvRFVVFT755BPs2rULu3fvxscff4yqqiqMGzfOlU1GLhwSp106vSKj7kKF3NSIk0hJsj1P0QsZ6RHRCAZMnDYaW98eSJ3Y06IZgC/7XJjdIl01OOakcNOpsLBTAAL9RFaIyIpaiPQBmBLxCnfntGfhwoV4/fXX0blz57pjXbp0wWOPPYaBAwe6ssnIhQv4Zk3Fz/Cr0xo90W+OZsGElMnF7oUltxXJ+YtEMIzsWkUxEu1VpJOIIphN/l6wsm3kj1G0wi9hITr76/o53Y1Tj9OoBVMi/sPPajFqa2vTlp8CQIMGDdLuMyIKIxckUqgIJnrhGJEohKwIhv6c3kbCDmAcyQDSL6KVwDCKcDgVJKrJcaNZTjQNoj/nRVhIKuJ0oluScRu1iCpq2A5EmSoAMoLbAeqis88+G7/5zW/wzDPPoLi4GADw7bff4qabbsI555zjyiYjF1mA6qGvjG9XZuOLftOLTPRCJArhNYKh/8aut62/GEaRjOS+qsE5PclRCCeRDqsxjPwyilYk9w1aWHgo4tRj5boRov9XfqZEuASVJHj00UexZ88edOjQAccccwyOPfZYdOzYEXv27MEjjzziyiYjF0QKgW9y5QAVzqMXjusvzAZx2hcQi2IA9pGM5P56FINjVhjZMEIkUmFkz6q4NSRhYYVoO70LMuzJGI+QZEpKSrB27VosWrQIn3/+OTRNQ5cuXdC/f3/XNhm5IIGgmhw3+mZm1NboC7tVH6vohdsFIK4RjWAkO2AXxUjYMotkmEU09PZEH2ZYjWfkn96e0evU20+2Z3dcIpkctSAEABYvXowuXbqgqqoKADBgwADceOONGDduHE499VR07doVy5cvd2Wb4iJLUD30zYTwqWpxzvf0iGh/vQ3AeObStzGaxJPti4gNUUTsiYgKwD4N4kZY6G3Y9HdTxKk6OBd0FEF0PC5BJQAwY8YMXHvttSgoKEg7V1hYiNGjR+Ohhx5yZZvigkjD7Qdp2NELKwITGE6iGEZtEjathEbyWG4fZpiNbeSnXVTGarlqAMJCj+iiFhFEMkRu7JLsZtasWTjxxBNRUFCAgoIClJaW4j//+Y9tvw8++ADnnnuu6fmBAwdizZo1rnyiuHBJti1x8rOw0wt2AsPqnKv0iBuBITo5iqglEZGRPK6I4HCKiG0jv8z8T8ZquapPwkKPrHSIH1EL/RhuyIRIpAjZ9Bl91FFH4b777sPq1auxevVqnH322bjooovwySefWPbbvn274RLUBLm5ufjuu+9c+cSCTg/EbUMtFf7fJVVmYaeKdH+dLie1q41MxqzGMqW4U99QtEjTqsgTunOA+T1FgJ9mNf0LS5DcNhm/ZxXV5LhIaMoqWqE/71FYWLniNh0igt9RiyBSMGoAY8ggm4QFAFxwwQUpz3//+99j1qxZeOedd9C1a1fTfkceeSQ++ugjHHvssYbnP/zwQ7Rt29aVT4xckEBRTY57CQP7lR5JtmO5c6NoCMRqshOJYujt7YJ1NMOoj0ysxrHzLRm7NIhXYaHDTTpET1yjFiR+VFVVpTyqq6st29fU1ODZZ5/F3r17UVpaatl2yJAhuOuuu7Bv3760cz/++CMmT56M888/35XfOZqWZRLPIVVVVSgsLERlZaVh0UucIheA98iFaNTAKnph5oPZXU2N2uv90I+n76O3rT+fbM/KVkoEQ99Q1Iid83bn9faScXNrWDNbqgtbTpY4OBVYVsUPgukmK2EhqmEA5+JCRtTCqL3ZeGZ4DV5Z+RAlzCIXdp/nXkjY3gVAhuUqGP87T548GVOmTEk7/tFHH6G0tBT79u1D06ZN8fTTT2PIkCGWY2zfvh2nnHIK6tevj7Fjx+KEE05ATk4OPvvsMzz22GOoqanB2rVr0bq13Q0Q06G4sCHTxAXgTWDIEBdWPhj9Mxm1NfLDi8Cwms/1dkIRGEZtjOzqkX4feh1uZkORmTeDhIWITTPbdu2txjQiW1aJZJK42Lp1a4qv+fn5yM/PT2u/f/9+bNmyBaqq4vnnn8df/vIXLFu2DF26dLEcZ/Pmzfh//+//4dVXX0VCDuTk5GDQoEGYOXMmOnTo4Mp/igsbKC7SEREYMsWFWXs3X/AjJTDsBjU6b9TGzLYVTkWHaN5KtTgnMuvaCQ+9fRfCQm/GiY6RISyM+hnZtmtvNaYR2RK1ADJLXLj1tX///jjmmGMwZ84cofa7d+/GV199BU3TcNxxx+GII45wPGYyLOjMQlSEX9hp5oPR3VLN2uuLO70Wk1rZ049vuoOn3pBRgaZi0tbMFgzaJDC7twhg/UeWudZRP24yVnth2LUTra+wsedWWOhxc8lEhYUVTtv7gRq2A8QxmqbZ1mckc8QRR+DUU0+VNj7FBYkcZgJDBP3crML5ztrSBYaRISQZs1sxYtQGurb69snjJKMYHBPFyJ4Rdvth2LV3Eq0wai9JWOiH1WPnlhOivEKERJ/bbrsNgwcPRklJCfbs2YNnn30WS5cuxcKFC0PzieLCI3FbjioD0eWgbqMXTtqL+KLv51RgWNlyLTBEBrYSGTBw0kpoJI/pB04FhVEfka/5ohtsQa6w8DMdYoXT9mZky94WQPYtQwUOFWaWlZVh27ZtKCwsxIknnoiFCxdiwIABoflEcSGBOAoMFf6nRrwgM3oBeBMYdpogTWAgqYNRVMIolaGYtDeyCV1bffvkPsnI2oBE5Ouy3S6hdu1Vmz42aRUrYeF0aKf3DjGyaUXUoxZqQOPIIBuFBQA8/vjjYbuQBsUFcUXUoxci9RduN9gyamu50ZbeSSOxoDfopNYiub2+TzJ+z0ZOBYVZH9Wmr0dh4WVliBGil1VvW3Z7M7IpakGiA8VFFqMintELFe4EhlE/K5xmNRwJjMR5wLwWI9EHcJYCMZpNnGxrKoLIjOU0VaIK9A9QWBhhZc/MrpVtRi1IpkJxQVwTRPTCSXokiPoLKQIDAkZh4EgCN7UWQX19dVN7oQrYEVhd4kRYiBBmOkRkLBEYtSBhQXEhiTjWXQRJEOkR0XH1fWUIDCT1SUxypnUYZkb1bfSGk/sCxkpKP4PKqrMww27GFt1f3cqexGiFmUv6PiLCIqx0CKMWxkSl3qISQK0EO3sk2AgTiguJxFFgqPCWGnF6IzE3yE6PGPV1IzAAn6IYRoYT6J1KRkRsGGEmQNzOYk4FhdlYAstWoyQsgkiHOCGbohZRERbkJyguSGCEmR6RUeApmtFI7mMoMAB3IiMxAGB8oUTEhhFevwqLzGKqg7FdiAqjIeIoLMz6WI1NSBShuCCxiF6YocJ8nnVT4CmygsRNHQbgk8iAgYN6e3rc/rGcfBVWLc45WTkiUFthNJwMYWGEqLBwgyrJDpBdW32TaEJxQQLFj+iFVR+78Y36yhIYgE0Uw6hjwjgMBkhgtxOnfgw9fsXLVZvzTkSFQXsRUWE0jMiqECNhoW/nRFgYjWE2jiiMWpA4QXFBAAQbvQhKYIj6ZNTXSGAAcuowAIEoRmIAwPnqENXgGODtDyxi3wgZO3fCXbTCaAgDM0LCwglmfYNKhzBqQaIAxYVk4ljUKYsw0yOA3AJPwFjI6NuJpl8ACSIDJoMlY6fa/MbL6hGXosJsWJnCwq/tvd32MSObijgTsJgzmlBc+EBcBYaK4DbVCqq4EwhGYADWaRIz+5YiAwZG7Hbi9HPbb5GxjHCxc6dMUWHWV7awsCIu6RA1wLFkQGERXSguiFRkRi9UyKu/8FtgGI3hZOsKQ5FhZiR5wARWFz2MZL3d7OtAVADxEBZG/c3GsutjNb4Z2Ri1INGF4oKkoCI60Qsg/gIjMQYgSWToDSUPnEzQ+SmP24FHTVQ4GcfKRhC7cMpCDdsBklFQXBDpyCzutMNPgQGdjcREIVLoCQ/jABYiI2Eogdvtvp2KD7dfi22+ejsRFWbm4igsRGDUIp5wh85DUFyQNFR4j14EtXrECrN+TjbZMrLhJIoBONu2Qm8jefK1FRpGhs3wazYSmBHNBAXg32QftLCww65f0MJClWCDkGQoLnwirkWdCVRE646pKtwVeJr180tgwKCtaKok2YaRHVuhkWxYj+yiTod1HG4EhdUwXnfENGrrdBsOs/HMxhTpFwZq2A64hMWc0aZe2A6Isnv3bpSVlaGwsBCFhYUoKyuDqqqm7Q8cOIBbb70V3bt3R5MmTVBcXIzhw4fjv//9b3BOZzlOvlGJzFWqxTk3H+ZmG0Ia+WJkY5fJuEZtdzocL2HHyBZwaLJOfthSKflhg4h/KqyjB2YRBCdpkDgKC6ZDSCYQG3ExbNgwrFu3DgsXLsTChQuxbt06lJWVmbb/4YcfsHbtWtx5551Yu3YtFixYgC+//BIXXnhhgF7HG1WCjTgKDDNfVBM7ZhOYUVsvIsPIXgL9ZC4kOCThZGwV8kWFkS0nf5NsFhaqBBuEGJGjadGPLX322Wfo0qUL3nnnHfTu3RsA8M4776C0tBSff/45TjjhBCE7q1atwmmnnYbNmzejXbt2Qn2qqqpQWFiIyspKFBQUOPY9zqkRQE5qxGntoF0EX7E5b7UHhllfMx/NfDGzYza203GtxrazKYppakWHV6Fi193NJp5mNs0md7P2QQkLu75WvpiRzeLCbUrE6+e5iO11AJpJsLcHwMmAL74GQSxqLlauXInCwsI6YQEAp59+OgoLC7FixQphcVFZWYmcnBwoimLaprq6GtXV1XXPq6qqXPsNsPYCkL9zpwprn2TVYADO6jCsxrYaFxZjJxDZ5dvIvhV+RDdETXrZyNNsjDgLC6dQWJCoE4u0SHl5OYqKitKOFxUVoby8XMjGvn37MHHiRAwbNsxSBU6fPr2urqOwsBAlJSWu/c4UVAk2gkyPAPJTJE7TJE7C8omx7TaytCt3UE0efuF0LJHXYHUdzMZwer2tfAhLWDAdQjKNUMXFlClTkJOTY/lYvXo1ACDH4Nu/pmmGx/UcOHAAQ4cORW1tLWbOnGnZdtKkSaisrKx7bN261d2LI54IU2A4qcOwsuVFZHgVGkbjyX6I4ERQyBIViT5m/lj54MRWwgcrrPomCGMDVUL8JtS0yNixYzF06FDLNh06dMCHH36I7du3p5377rvv0Lp1a8v+Bw4cwOWXX46NGzdi8eLFtrmr/Px85Ofn2zvvgLinRoBw0iNed/AE3KVIAHdpEpjYs0qVJDDqJ7K7d5C3ErFD5rdv1eKc26JJp9EKL37Y9U3AOgtnxCElsgvAfgl2vpdgI0xCFRctW7ZEy5YtbduVlpaisrIS7733Hk477TQAwLvvvovKykr06dPHtF9CWKxfvx5LlixBixbh3bMzEwSGDDJFYMDEL/XwT71No909Rfol+5LA7vpZTVhehYeXb9kiE6NqcU62qACyT1jEmTgIC/ITsSjo7Ny5M84991xce+21mDNnDgDguuuuw/nnn59SzNmpUydMnz4dl1xyCQ4ePIhLL70Ua9euxb///W/U1NTU1Wc0b94ceXl5obyWOKNCzuqRKAoMmPS3K7g088vMn+RJyU00I9mnBE6vZVCIToaqzXkvE7nsaAUQb2GhSrJDiB2xEBcA8NRTT2HcuHEYOHAgAODCCy/Eo48+mtLmiy++QGXloX/bb775Bi+99BIA4OSTT05pt2TJEpx11lm++6wnE6IXKqK1c2cyKtwLDLv+MqMYyf7Awic16XczG4DxxBPF+5Ylowq08UNUAOELC6dQWJA4Ehtx0bx5czz55JOWbZK37OjQoQOiuIVHJggMGfgRvQDCERiAvyIj2YaVnWREJiTR6x/05OaXqACiISzCKOBUQxhTJkyJxI/YiAsSHVRENz0CiAkMwH0dBmAtMqxSJbCwbZcy0dtJYGbPDr9z+KqDtiJ3ELWz5zZaYWdbhm8JWGfhHAqLeEJxQVyhIt4CA3BfhwG4j2KI2AbEhUayvWSsbPuBkQ92iN6S3M62X6ICyAxhoUqyQ4gTKC5CIFNSIyoyW2DY2RCJYgD2IgMWYwDOhIaRbSusxnViRxRZggLwlgIRGYPCInwYtYgvFBchkSkCQxZRFxiwsONVZIiMkUA/4YmKDbtx/UJUTADyJmqvogLIDGERdygs4g3FBfGECnkheD8FBuCtDiNhx8qGTJEBm7ESGE2CXgWHW5wIiWRUgTYiE7TXfTQAuaICCFdYqBJtEeIUigviGRXhLU8VFRhAMFEMQFxkAOJCw27MZEQmSKcCxK1wMEJ10DYoUQFQWBA5VAI4KMHOXgk2woTiIkQyKTWiIpz6CyB4gSFqx05kAGLRjOQxE9iNbYdMsWCH6qJP1ESFqK0EYd4vRA1xbFkwJRJ/KC5CJpMEhiyiIjAA71EMwJnIAJwLjQR2fgSF6rKf6IQsa+dPQL6wcCsqWGfxExQWmQHFBZGGivDqLwB3UQDFpp1oFEPElojIAJwLDb0fRigO7HgZxyl+3GpcFWgTpWgF0yEkE6G4IFJREa7AAMKJYiRsQcCeqMgA0icstzceU132k42bCVimqAAoLAgJAoqLCJBpqREVmScwAP9EBiD+GmWJjaAIYtJVBdv5sSwWoLCQCVMimUO9sB0gh8i0fypVoi23H8JOPvRV+PPNV9TmzqSHEypNHkEjww+n10CFeArEj78ZQGEhk0z7DAyS6dOn49RTT0WzZs1QVFSEiy++GF988UWoPjFyQXxDRbwiGID8KEbCJgTtAu4iGnrCXK3gBDeTrOqgbSZHK4DMERbEG8uWLcMNN9yAU089FQcPHsTtt9+OgQMH4tNPP0WTJk1C8YnigsQGJ7UKyTgp9ASciQE3IkPUNpA+GQV9K3XZuJ1cVYft/RIVAIUFCZaqqqqU5/n5+cjPz085tnDhwpTn8+bNQ1FREdasWYOf//znvvtoBMVFhMi02gvAnw22ohbFAJyJjITtBKJjAPESG14nU9VFHz9FBRAdYZFpZFJKZDeAagl2fjj8s6SkJOX45MmTMWXKFMu+lZWH3qnNm4e1Xy/FReSgwBAjSIEB+Ccy3IyRjNWkFZTwiMI3cqebgjkdw0uayQ9hofpgMywySVj4wdatW1FQUFD3XB+10KNpGiZMmICf/exn6Natm9/umUJxQQJBRbQEBhBdkZFAdCwz4vJtWXXZz80uo27GorAgYVJQUJAiLuwYO3YsPvzwQ7z11ls+emUPxQUJDBXRERiA8ygG4F5kAM7v56Em/S46XlxQPfQNSlQAFBYkXtx444146aWX8Oabb+Koo44K1ReKiwiSiamRBCqiJzAA/0UG4C6aoR8vGSdjh4kqyU5cRAVAYSEKUyJy0DQNN954I1544QUsXboUHTt2DNsliouoQoHhDLcrSRK4iWIA3kQG4O326KrBMSd++IEq2Z6ft3E3I2rRCoDCglhzww034Omnn8b//d//oVmzZigvLwcAFBYWolGjRqH4RHERYSgwnBNGFANwX5QpS2jo/RBB8cGmDLzcsVX10DeK0QqAwoLYM2vWLADAWWedlXJ83rx5GDlyZPAOgeKChIiK6AkMIByRAaRPqn4vIlN9ti+KjNu/qx76ythwjMKChIkWQbFGcUFCRYV/AgPwLjK83ihM8TC+7KhGlJAhKIDMFRUAhQWJNxQXESeTUyMJVPhXKxBmFAOQt+LDaDKOi+CQJSQSqBJsUFiEB1Mi2QHFRQygwPCGV4EBeBcZgJxoRjJmk3ZYokO2iNCjSrBBUREu2SAsKgHsl2DnRwk2woTiIiZki8AAopsmAeSKDMCf1+r3JB8kqiQ7sm7kRmHhnmwQFuQnKC5iRDYIDCD6UQxAjsgAMnujLDeoku3FQVQAFBYk86C4iBkUGN6RFcUA5IkMQP7233FB9cGmzFvOU1h4g8IiO6G4iCEUGHLwQ2QAcoQGkLliQ/XJrkxBAQRzbxY1gDHChMIie6G4IJFGhf+TqqxUSQKZ0YxkVINjiuQxZKIGNI5sUQFQWBDiFYoLEnnUwz8VH8eQGcVI4Ec0Q49qc17xaVzR8f3CD0EBUFQQIguKi5iSLamRZFQEE8UA5IoMIBihYYQa4Fh+45egAIK7Pb0a0DhRgCmR7IbiIsZQYPiHXyIDCE9oxBE/BQUQnKgAKCxIdkFxEXOyVWAA8RcZAIWGEX4LCoCiwk8oLAhAcZERZKPAAIKLYgD+iwwgfVLNFrERhJhIEKSoACgsspEqyNmhc58EG2FCcZEhUGAEQxAiI0Gmio0gxUSCoEUFQGFBshuKiwwimwUGkLkiI4HZpBxV0RGGiNBDUUFIOFBckIxBRfD7PiRPXkEKjWTsJnG/xEcUxIMRYQiKBGqIY4cJoxZED8VFhpGt0YsE6uGfSghjhxHNECGqIkA2FBXhQGFBjKC4yECyXWAA4UQxEkQhmpEthCkoEqhhOxAiFBbEDIqLDIUCI9woRgIKDflEQVAA2S0qAAoLYg3FRQZDgXEI9fBPJUQfAAoNL0RFUAAUFQCFBbGH4iLDocD4CRXhC4wEFBr2RElQJFDDdiACUFgQESgusgAKjJ9QD/9UQvRBj34SzVaxEUUxkUAN24GIQGFhzy4A+RLsVEuwESYUF1kCBUYq6uGfSog+mGE0yWaa4IiykEhGDduBCEFhQZxAcZFFUGCkox7+qYTogwhxFhxxERLJqGE7EDEoLIhTKC6yDAoMY9TDP5UQfXCK3aQdlPiIo3gwQw3bgQhCYUHcQHGRhVBgmKMe/qmE6IMsMmnS9xs1bAciCoUFcUu9sB0g4cAPDWtUcMLJBlTw72wGPyOIFygushh+eNijgpNPJqKCf1cr+NlAvMK0SJbDFIkYatLvSkg+EG+oYTsQEygsiAwYuSD8MHGICk5UcUIF/16i8LOAyIKRCwKAEQw3qEm/KyH5QIxRw3YghlBYEJlQXJA6KDDcoyb9roTkQ7ajhu1AjKGwkEclgDwJdvZLsBEmFBckBQoM76hJvysh+ZAtqGE7kAFQWBA/oLggaVBgyENN+l0JyYdMQw3bgQyCwoL4BcUFMYQCQz5q0u9KSD7EETVsBzIUCgviJxQXxBQKDP9Qdc+VEHyIMmrYDmQ4FBbEbyguiCUUGMGgGhxTAvYhLNSwHcgyKCxIEHCfC2ILP4zCQTV4xBnV5EGCg//Lmcubb76JCy64AMXFxcjJycGLL74Yqj8UF0QIfihFA9XmETaqxYOEC/+HM5u9e/fipJNOwqOPPhq2KwCYFiEOYIok+qhhO0AiCYVF5jN48GAMHjw4bDfqoLggjkh8SFFkEBJ9KCriT1VVVcrz/Px85Ofnh+SNOLFJi+zevRtlZWUoLCxEYWEhysrKoKqqcP/Ro0cjJycHM2bM8M3HbIIfWoREG/6PhsNuALskPHYftldSUlI37xUWFmL69OkBvhr3xCZyMWzYMHzzzTdYuHAhAOC6665DWVkZ/vWvf9n2ffHFF/Huu++iuLjYbzezCqZJCIkmFBaZw9atW1FQUFD3PA5RCyAm4uKzzz7DwoUL8c4776B3794AgD//+c8oLS3FF198gRNOOMG077fffouxY8fi1VdfxXnnnReUy1kDBQYh0YLCIrMoKChIERdxIRZpkZUrV6KwsLBOWADA6aefjsLCQqxYscK0X21tLcrKynDLLbega9euQmNVV1ejqqoq5UGs4YcZIdGA/4skKsQiclFeXo6ioqK040VFRSgvLzftd//99yM3Nxfjxo0THmv69OmYOnWqKz+zmeQPNUYyCAkOCgoCAN9//z2++uqruucbN27EunXr0Lx5c7Rr1y5wf0KNXEyZMgU5OTmWj9WrVwMAcgwmLE3TDI8DwJo1a/Dwww/jiSeeMG1jxKRJk1BZWVn32Lp1q7sXl8Xww46QYOD/GkmwevVq9OjRAz169AAATJgwAT169MBdd90Vij+hRi7Gjh2LoUOHWrbp0KEDPvzwQ2zfvj3t3HfffYfWrVsb9lu+fDkqKipSFFtNTQ1uvvlmzJgxA5s2bTLsF5dlPlGHtRiE+AuFBUnmrLPOghah90So4qJly5Zo2bKlbbvS0lJUVlbivffew2mnnQYAePfdd1FZWYk+ffoY9ikrK0P//v1Tjg0aNAhlZWUYNWqUd+eJLRQYhPgDhQWJOrGouejcuTPOPfdcXHvttZgzZw6AQ0tRzz///JSVIp06dcL06dNxySWXoEWLFmjRokWKnQYNGqBNmzaWq0uIXCgwCJELhQWJA7EQFwDw1FNPYdy4cRg4cCAA4MILL0zbQ/2LL75AZWVlGO4RCygwCJEDhUX0qYScifWgBBthEhtx0bx5czz55JOWbezyTWZ1FsR/uJqEEHdQUJA4Eot9LkhmwQ9LQsTg/wqJKxQXJBT4oUmINfwfIXGG4oKEBj88CTGG/xsk7sSm5oJkJqzFIOQQFBQkk2DkgkQGfriSbIXvfZJpMHJBIgUjGSRboKAgmQzFBYksFBok06CgINkC0yIkFvBDmcQdvodJNsHIBYkNjGSQuEFBkX1UAagvwU6NBBthwsgFiSX80CZRh+9Rks0wckFiCyMZJIpQVBDCyAXJEPiBTqIA34eEHIKRC5IxJD7YGcUgQUJBQUg6jFyQjIMf9iQo+F4jxBhGLkhGwnoM4hcUFITYw8gFyXg4GRBZ8L1EiBiMXJCsgJEM4hYKCkKcQ3FBso6dmkaBQYSgsCBO2QU5KYFaCTbChOKCZCVGkwYFR3ZDIUGIPFhzQchhOLlkL/zbEyIXRi4ISYIRjcyHQoIQ/6G4IMQG/WREsREvKCYICR6mRQhxCCer+MC/FSHhwMgFIS6wmrQY2QgWCghCogcjF4RIhpNdcPBaExJNGLkgxAcY2ZAHBQQh8YORC0IChpOlOLxWhMQTRi4ICQHRSTNToxwUDSRTqQQg47827v8hjFwQEmEycRLOxNdECEmFkQtCIg4nY0JI3GDkghBCCCFSobgghBBCiFQoLgghhBAiFYoLQgghJAOYOXMmOnbsiIYNG6Jnz55Yvnx5aL5QXBBCCCExZ/78+Rg/fjxuv/12vP/++zjzzDMxePBgbNmyJRR/KC4IIYSQmPPQQw/h6quvxjXXXIPOnTtjxowZKCkpwaxZs0Lxh0tRbdAOLwOsqqoK2RNCCCFeSHyOaz4u75ZlOWFHP/fk5+cjPz8/5dj+/fuxZs0aTJw4MeX4wIEDsWLFCkkeOYPiwoY9e/YAAEpKSkL2hBBCiAz27NmDwsJCqTbz8vLQpk0blJeXS7PZtGnTtLln8uTJmDJlSsqxHTt2oKamBq1bt0453rp1a6n+OIHiwobi4mJs3boVzZo1Q05EtmKuqqpCSUkJtm7dioKCgrDdiTS8VmLwOonB6yROFK+VpmnYs2cPiouLpdtu2LAhNm7ciP3790uzqWla2ryjj1oko29r1D8oKC5sqFevHo466qiw3TCkoKAgMv+0UYfXSgxeJzF4ncSJ2rWSHbFIpmHDhmjYsKFv9s1o2bIl6tevnxalqKioSItmBAULOgkhhJAYk5eXh549e2LRokUpxxctWoQ+ffqE4hMjF4QQQkjMmTBhAsrKytCrVy+UlpZi7ty52LJlC8aMGROKPxQXMSQ/Px+TJ0+2zL2RQ/BaicHrJAavkzi8VsFyxRVXYOfOnbj77ruxbds2dOvWDa+88grat28fij85mp9rcgghhBCSdbDmghBCCCFSobgghBBCiFQoLgghhBAiFYoLQgghhEiF4iIm7N69G2VlZSgsLERhYSHKysqgqqpw/9GjRyMnJwczZszwzcco4PQ6HThwALfeeiu6d++OJk2aoLi4GMOHD8d///vf4JwOCKe3Y162bBl69uyJhg0b4uijj8bs2bMD8jRcnFynBQsWYMCAAWjVqhUKCgpQWlqKV199NUBvw8XtLb7ffvtt5Obm4uSTT/bXQRIaFBcxYdiwYVi3bh0WLlyIhQsXYt26dSgrKxPq++KLL+Ldd9/1ZcvbqOH0Ov3www9Yu3Yt7rzzTqxduxYLFizAl19+iQsvvDBAr/3H6e2YN27ciCFDhuDMM8/E+++/j9tuuw3jxo3D888/H7DnweL0Or355psYMGAAXnnlFaxZswb9+vXDBRdcgPfffz9gz4PH7S2+KysrMXz4cJxzzjkBeUpCQSOR59NPP9UAaO+8807dsZUrV2oAtM8//9yy7zfffKMdeeSR2scff6y1b99e++Mf/+izt+Hh5Tol895772kAtM2bN/vhZiicdtpp2pgxY1KOderUSZs4caJh+9/97ndap06dUo6NHj1aO/30033zMQo4vU5GdOnSRZs6daps1yKH22t1xRVXaHfccYc2efJk7aSTTvLRQxImjFzEgJUrV6KwsBC9e/euO3b66aejsLDQ8na6tbW1KCsrwy233IKuXbsG4WqouL1OeiorK5GTkwNFUXzwMngSt2MeOHBgynGr2zGvXLkyrf2gQYOwevVqHDhwwDdfw8TNddJTW1uLPXv2oHnz5n64GBncXqt58+bh66+/xuTJk/12kYQMd+iMAeXl5SgqKko7XlRUZHk73fvvvx+5ubkYN26cn+5FBrfXKZl9+/Zh4sSJGDZsWKRutuQFN7djLi8vN2x/8OBB7NixA23btvXN37CQcdvqBx98EHv37sXll1/uh4uRwc21Wr9+PSZOnIjly5cjN5dTT6bDyEWITJkyBTk5OZaP1atXA0i/lS5gfTvdNWvW4OGHH8YTTzwRmVvFu8XP65TMgQMHMHToUNTW1mLmzJnSX0fYOL0ds1F7o+OZhtvbVj/zzDOYMmUK5s+fbyhyMxHRa1VTU4Nhw4Zh6tSpOP7444Nyj4QI5WOIjB07FkOHDrVs06FDB3z44YfYvn172rnvvvvO9Ha6y5cvR0VFBdq1a1d3rKamBjfffDNmzJiBTZs2efI9SPy8TgkOHDiAyy+/HBs3bsTixYszJmoBuLsdc5s2bQzb5+bmokWLFr75GiZebls9f/58XH311XjuuefQv39/P92MBE6v1Z49e7B69Wq8//77GDt2LIBDKSRN05Cbm4vXXnsNZ599diC+k2CguAiRli1bomXLlrbtSktLUVlZiffeew+nnXYaAODdd99FZWWl6e10y8rK0j7kBg0ahLKyMowaNcq78wHi53UCfhIW69evx5IlSzJu8ky+HfMll1xSd3zRokW46KKLDPuUlpbiX//6V8qx1157Db169UKDBg189Tcs3Fwn4FDE4qqrrsIzzzyD8847LwhXQ8fptSooKMBHH32UcmzmzJlYvHgx/vnPf6Jjx46++0wCJsRiUuKAc889VzvxxBO1lStXaitXrtS6d++unX/++SltTjjhBG3BggWmNjJ9tYimOb9OBw4c0C688ELtqKOO0tatW6dt27at7lFdXR3GS/CFZ599VmvQoIH2+OOPa59++qk2fvx4rUmTJtqmTZs0TdO0iRMnamVlZXXtN2zYoDVu3Fi76aabtE8//VR7/PHHtQYNGmj//Oc/w3oJgeD0Oj399NNabm6u9thjj6W8d1RVDeslBIbTa6WHq0UyG4qLmLBz507tyiuv1Jo1a6Y1a9ZMu/LKK7Xdu3entAGgzZs3z9RGNogLp9dp48aNGgDDx5IlSwL3308ee+wxrX379lpeXp52yimnaMuWLas7N2LECK1v374p7ZcuXar16NFDy8vL0zp06KDNmjUrYI/Dwcl16tu3r+F7Z8SIEcE7HgJO31PJUFxkNrzlOiGEEEKkwtUihBBCCJEKxQUhhBBCpEJxQQghhBCpUFwQQgghRCoUF4QQQgiRCsUFIYQQQqRCcUEIIYQQqVBcEEIIIUQqFBeEEEIIkQrFBSFZSk1NDfr06YNf/vKXKccrKytRUlKCO+64IyTPCCFxh9t/E5LFrF+/HieffDLmzp2LK6+8EgAwfPhwfPDBB1i1ahXy8vJC9pAQEkcoLgjJcv70pz9hypQp+Pjjj7Fq1SpcdtlleO+993DyySeH7RohJKZQXBCS5WiahrPPPhv169fHRx99hBtvvJEpEUKIJyguCCH4/PPP0blzZ3Tv3h1r165Fbm5u2C4RQmIMCzoJIfjrX/+Kxo0bY+PGjfjmm2/CdocQEnMYuSAky1m5ciV+/vOf4z//+Q8eeOAB1NTU4PXXX0dOTk7YrhFCYgojF4RkMT/++CNGjBiB0aNHo3///vjLX/6CVatWYc6cOWG7RgiJMRQXhGQxEydORG1tLe6//34AQLt27fDggw/illtuwaZNm8J1jhASW5gWISRLWbZsGc455xwsXboUP/vZz1LODRo0CAcPHmR6hBDiCooLQgghhEiFaRFCCCGESIXighBCCCFSobgghBBCiFQoLgghhBAiFYoLQgghhEiF4oIQQgghUqG4IIQQQohUKC4IIYQQIhWKC0IIIYRIheKCEEIIIVKhuCCEEEKIVP4/KFMpISOebkAAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Enable 64-bit precision if needed\n", + "jax.config.update(\"jax_enable_x64\", True)\n", + "\n", + "# 1. Set Up the Grid and Parameters\n", + "Lx, Ly = 1.0, 1.0 # Domain size\n", + "Nx, Ny = 200, 200 # Number of grid points\n", + "dx = Lx / Nx\n", + "dt = 0.00003 # Reduced time step\n", + "D = 0.1 # Diffusion coefficient\n", + "num_steps = 6667 # Adjusted number of time steps\n", + "\n", + "# Create the spatial grid\n", + "x = jnp.linspace(-Lx / 2, Lx / 2, Nx)\n", + "y = jnp.linspace(-Ly / 2, Ly / 2, Ny)\n", + "X, Y = jnp.meshgrid(x, y, indexing=\"ij\")\n", + "\n", + "# 2. Define the Circular Domain\n", + "radius = Lx / 2 # Circle radius\n", + "distance = jnp.sqrt(X**2 + Y**2)\n", + "mask = jnp.where(distance <= radius, 1.0, 0.0)\n", + "\n", + "# 3. Initialize the Concentration Field\n", + "C = jnp.zeros((Nx, Ny))\n", + "# Place a point source at the center\n", + "center_x, center_y = Nx // 2, Ny // 2\n", + "C = C.at[center_x, center_y].set(1.0 / (dx * dx))\n", + "\n", + "\n", + "# 4. Define the Laplacian Operator with Neumann Boundary Conditions\n", + "def laplacian(C, mask):\n", + " # Pad the array to handle boundary conditions\n", + " C_padded = jnp.pad(C, pad_width=1, mode=\"edge\")\n", + " mask_padded = jnp.pad(mask, pad_width=1, mode=\"constant\", constant_values=0)\n", + "\n", + " # Extract central and neighboring values\n", + " C_center = C_padded[1:-1, 1:-1]\n", + " mask_center = mask_padded[1:-1, 1:-1]\n", + "\n", + " C_up = C_padded[2:, 1:-1]\n", + " mask_up = mask_padded[2:, 1:-1]\n", + "\n", + " C_down = C_padded[:-2, 1:-1]\n", + " mask_down = mask_padded[:-2, 1:-1]\n", + "\n", + " C_left = C_padded[1:-1, :-2]\n", + " mask_left = mask_padded[1:-1, :-2]\n", + "\n", + " C_right = C_padded[1:-1, 2:]\n", + " mask_right = mask_padded[1:-1, 2:]\n", + "\n", + " # Apply Neumann boundary conditions (zero normal derivative)\n", + " C_up = jnp.where(mask_up, C_up, C_center)\n", + " C_down = jnp.where(mask_down, C_down, C_center)\n", + " C_left = jnp.where(mask_left, C_left, C_center)\n", + " C_right = jnp.where(mask_right, C_right, C_center)\n", + "\n", + " # Compute the Laplacian\n", + " laplacian_C = (C_up + C_down + C_left + C_right - 4 * C_center) / (dx * dx)\n", + "\n", + " # Only compute inside the circle\n", + " laplacian_C = jnp.where(mask_center, laplacian_C, 0.0)\n", + "\n", + " return laplacian_C\n", + "\n", + "\n", + "# 5. Time-Stepping Function\n", + "@jax.jit\n", + "def update(C, mask):\n", + " C_new = C + D * dt * laplacian(C, mask)\n", + " return C_new\n", + "\n", + "\n", + "# 6. Run the Simulation\n", + "C_list = []\n", + "snapshot_interval = num_steps // 10 # Adjust snapshot interval if desired\n", + "for step in range(num_steps):\n", + " C = update(C, mask)\n", + " if step % snapshot_interval == 0:\n", + " C_list.append(C)\n", + "\n", + "# 7. Visualization\n", + "for i, C_snapshot in enumerate(C_list):\n", + " # Mask out values outside the circle for visualization\n", + " C_display = jnp.where(mask == 1, C_snapshot, jnp.nan)\n", + "\n", + " plt.figure(figsize=(6, 5))\n", + " plt.contourf(X, Y, C_display, levels=50, cmap=\"hot\")\n", + " plt.colorbar(label=\"Concentration\")\n", + " plt.title(f\"Diffusion at Time Step {i * snapshot_interval}\")\n", + " plt.xlabel(\"X\")\n", + " plt.ylabel(\"Y\")\n", + " plt.axis(\"equal\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 0\n", + "Step 200\n", + "Step 400\n", + "Step 600\n", + "Step 800\n", + "Step 1000\n", + "Step 1200\n", + "Step 1400\n", + "Step 1600\n", + "Step 1800\n" + ] + }, + { + "data": { + "image/png": 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ODhg7dqzGooN1dXVYsGAB+vbtCycnJ0RFRaG0tFQtprKyEjExMRAEAYIgICYmpt3nh+kbExMiIiITNHv2bGRlZWHbtm344YcfEB4ejokTJ+L3338HAKSkpCA1NRVpaWnIz8+HXC5HWFiY2krWCQkJyMjIwO7du5GTk4OamhpERkaisbFRiomOjkZhYSEyMzORmZmJwsJCxMTEGHx/JUa9J4iIiIg0XLt2TbSyshL/85//qJXfcccd4sqVK8WmpiZRLpeLr776qvTe9evXRUEQpNvgr1y5ItrY2Ii7d++WYn7//XexR48eYmZmpiiKonjq1CkRgJiXlyfFHDp0SAQgnj59Wp+72CaLW2CtqakJ58+fR69evfhQLSKibkwURVRXV0OhUKBHD/1cALh+/Trq6+t1UpfY4rEWwM1Vi5tXLv6jGzduoLGxUeM5VQ4ODsjJyUFxcTGUSiXCw8PV6hozZgxyc3Mxb948FBQUoKGhQS1GoVDA398fubm5iIiIwKFDhyAIgtrKxKNGjYIgCMjNzTXKwo0Wl5icP38eXl5exm4GERHpSElJSZsrCHfF9evX4ePj0+rzk7TRs2dPjccsrFq1ComJiRqxvXr1QkhICF566SX4+fnBw8MDu3btwuHDh+Hr6yu1ycPDQ207Dw8P6QnfSqUStra26NOnj0ZM8/ZKpRLu7u4an+/u7q6z/e4si0tMmp+LUVJSAmdnZyO3hoiItFVVVQUvLy+N5x3pSn19PZRKJUpKfu7y98XNtvpqfPe0NlrSbNu2bXjyySfRr18/WFlZ4a677kJ0dLTas71ajsC0NirTUsuY1uI7Uo++GDUx+eabb/Daa6+hoKAAZWVlyMjIwIMPPtjuNtnZ2Vi0aBFOnjwJhUKBpUuXYv78+R3+zOYD7ezszMSEiMgM6PsLVJffF52pa/DgwcjOzsbVq1dRVVUFT09PTJs2DT4+PpDL5QBujnh4enpK21RUVEijKHK5HPX19aisrFQbNamoqJCeMyWXy1FeXq7x2RcuXNAYjTEUo96Vc/XqVdxxxx1IS0vrUHxxcTGmTJmCe++9F8ePH8ff/vY3xMfHazzNk4iIyFw4OTlJD7Hct28fHnjgASk5ycrKkuLq6+uRnZ0tJR2BgYGwsbFRiykrK8OJEyekmJCQEKhUKhw5ckSKOXz4MFQqlRRjaEYdMZk8eTImT57c4fgNGzZgwIABWLt2LQDAz88PR48exT/+8Q888sgjemolERGR4e3btw+iKGLo0KH45Zdf8Oyzz2Lo0KGYNWsWZDIZEhISkJSUBF9fX/j6+iIpKQmOjo6Ijo4GAAiCgNjYWCxevBiurq5wcXHBkiVLEBAQID0V3c/PD5MmTcKcOXOwceNGAMDcuXMRGRlptCeWd6s5JocOHVKbXQwAERER2Lx5MxoaGmBjY6OxTV1dHerq6qSfq6qq9N5OIiKirlKpVFixYgVKS0vh4uKCRx55BK+88or0Xbd06VLU1tYiLi4OlZWVCA4Oxv79+9Xm3KxZswbW1taYOnUqamtrMWHCBKSnp8PKykqK2bFjB+Lj46Xv16ioqA5fydAHk1mSXiaT3XKOyZAhQzBz5kz87W9/k8pyc3MxevRonD9/Xu06W7PExES88MILGuUqlYpzTIiIurGqqioIgqC3/vx/9ZfrZPKrIHjwu6cDut3Kr63NQG6tvNmKFSugUqmkV0lJid7bSERERNrpVpdy5HK5xn3VFRUVsLa2hqura6vbtLV4DZGx9DHDhf0qTWPglUhPygFc62Id1bcOIQDdLDEJCQnBv//9b7Wy/fv3IygoqNX5JUSGYo7JRmd0dP+ZwBDRrRj1Uk5NTQ0KCwtRWFgI4ObtwIWFhTh37hyAm5dhpk+fLsXPnz8fZ8+exaJFi1BUVIQtW7Zg8+bNWLJkiTGaTwSASUln8FgR0a0YdcTk6NGjGDdunPTzokWLAAAzZsxAeno6ysrKpCQFAHx8fLB3714sXLgQb7/9NhQKBd58803eKkwGwS9V3WjvOHJEhYhM5q4cQ9H3LG4yT0xKDIfJCXWU4e7K+R7Ozl1b9r6qqhqCMILfPR3QreaYEOkTkw/TwBEVIsvW7W4XJtIHJiXdA/+diMwfR0zI4vDLrXtr+e/HURQi88IRE7IoTErMD/9NicwLR0zIrPFLyzK09u/MkRTSnQvo+gJrV3XREIvAERMyW0xKLBv//Ym6J46YkFnglxC1hiMpRN0PR0yo22NSQp3B3xci08YRE+qW+OVCXfHH3x+OoBCZFo6YULfDpIR0ib9PRKaFIybULfDLg/SJIyhEpoOJCZksJiNkDM2/d0xQiIyDiQmZJCYlZGx9ZDImJ/RflwDUdrGOrq6DYjmYmJBJYUJCpoSXeIgMj5NfyWQwKSFTxt9PIsPgiAkZFTt76k44gkKkfxwxIaNhUkLdGX9/ifSDIyZkUOzMyZxwBIVI9zhiQgbDpITMGX+/iXSDIyakV+ysyZJwBIWo6zhiQnrDpIQsGX//ibTDERPSKXbGRP/DERRzUQHAoYt1dHWBNsvBERPSGSYlRG3j+UHUMUxMSCfY6RLdGs8ToltjYkJdxs6WqON4vhC1j3NMSCvsXIm0x7knRG3jiAl1GpMSIt3h+UStuXHjBp577jn4+PjAwcEBgwYNwosvvoimpiYpRhRFJCYmQqFQwMHBAWPHjsXJkyfV6qmrq8OCBQvQt29fODk5ISoqCqWlpWoxlZWViImJgSAIEAQBMTExuHLliiF2s1VMTKhT2IkS6R7PK2pp9erV2LBhA9LS0lBUVISUlBS89tpreOutt6SYlJQUpKamIi0tDfn5+ZDL5QgLC0N1dbUUk5CQgIyMDOzevRs5OTmoqalBZGQkGhsbpZjo6GgUFhYiMzMTmZmZKCwsRExMjEH3949komhZ44hVVVUQBAEqlQrOzs7Gbk63ws6TSL94Wadz9N2f/6/+NDg7d+124aqqWgjC0x1ua2RkJDw8PLB582ap7JFHHoGjoyO2bdsGURShUCiQkJCAZcuWAbg5OuLh4YHVq1dj3rx5UKlUcHNzw7Zt2zBt2jQAwPnz5+Hl5YW9e/ciIiICRUVFGDZsGPLy8hAcHAwAyMvLQ0hICE6fPo2hQ4d2ab+1wRET6hAmJUT6x/PM/FVVVam96urqWo3705/+hAMHDuCnn34CAHz33XfIycnBlClTAADFxcVQKpUIDw+XtrGzs8OYMWOQm5sLACgoKEBDQ4NajEKhgL+/vxRz6NAhCIIgJSUAMGrUKAiCIMUYGie/UpvYSRIZHifGmqJyAPZdrOM6AMDLy0utdNWqVUhMTNSIXrZsGVQqFW6//XZYWVmhsbERr7zyCv7v//4PAKBUKgEAHh4eatt5eHjg7NmzUoytrS369OmjEdO8vVKphLu7u8bnu7u7SzGGxsSEWsWkhMj4+shkTE7MTElJidqlHDs7u1bj9uzZg+3bt2Pnzp0YPnw4CgsLkZCQAIVCgRkzZkhxshZ9tSiKGmUttYxpLb4j9egLExPSwKSEyHQwOTEvzs7OHZpj8uyzz2L58uV4/PHHAQABAQE4e/YskpOTMWPGDMjlcgA3Rzw8PT2l7SoqKqRRFLlcjvr6elRWVqqNmlRUVCA0NFSKKS8v1/j8CxcuaIzGGArnmJAaJiVEpofnpeW5du0aevRQ/4q2srKSbhf28fGBXC5HVlaW9H59fT2ys7OlpCMwMBA2NjZqMWVlZThx4oQUExISApVKhSNHjkgxhw8fhkqlkmIMjSMmJGHnR2S6OHJiWe6//3688sorGDBgAIYPH47jx48jNTUVTz75JICbl18SEhKQlJQEX19f+Pr6IikpCY6OjoiOjgYACIKA2NhYLF68GK6urnBxccGSJUsQEBCAiRMnAgD8/PwwadIkzJkzBxs3bgQAzJ07F5GRkUa5IwdgYkL/xaSEyPQxObEcb731Fv7+978jLi4OFRUVUCgUmDdvHp5//nkpZunSpaitrUVcXBwqKysRHByM/fv3o1evXlLMmjVrYG1tjalTp6K2thYTJkxAeno6rKyspJgdO3YgPj5eunsnKioKaWlphtvZFriOiQVjMkLUfTFBMeQ6Jn+Hs3PX7sqpqroOQXiJ3z0dwDkmFopJCVH3xnOYzBUv5VggdmhE5oGXdgzlEoDWb+vtuNYXUiNNHDGxMExKiMwLz2kyN0xMLAg7MCLzxHObzAkTEwvBjovIvPEcJ3PBxMQCsMMisgw818kcMDExc+yoiCwLz3nq7piYmDF2UESWiec+dWdMTMwUOyYiy8Y+gLorJiZmiB0SEQHsC6h74gJrZoYdERH9ERdh04ULAGy6WEeDLhpiEThiYkaYlBBRa9g3UHfCxMRMsOMhovawj6DugomJGWCHQ0Qdwb6CugMmJt0cOxoi6gz2GWTqmJh0Y+xgiEgb7DvIlDEx6abYsRBRV7APIVPFxKQbYodCRLrAvoRMEdcx6WbYkRCRLnGdk44oR9e/Lm/ooiEWgSMm3QiTEiLSB/YtZEqYmHQT7DiISJ/Yx5CpYGLSDbDDICJDYF9DpoCJiYljR0FEhsQ+h4yNiQkREalhckLGxMSEiIiITAYTExPGv1qIyFjY/5CxMDExUewUiMjY2A+RMRg9MVm3bh18fHxgb2+PwMBAHDx4sN34HTt24I477oCjoyM8PT0xa9YsXLp0yUCtNQx2BkRkKtgfATcXWFN28VVu8FZ3V0ZNTPbs2YOEhASsXLkSx48fx7333ovJkyfj3Llzrcbn5ORg+vTpiI2NxcmTJ/HBBx8gPz8fs2fPNnDL9YedABGZGvZLZEhGTUxSU1MRGxuL2bNnw8/PD2vXroWXlxfWr1/fanxeXh4GDhyI+Ph4+Pj44E9/+hPmzZuHo0ePGrjl+sGTn4hMFfsnMhSjJSb19fUoKChAeHi4Wnl4eDhyc3Nb3SY0NBSlpaXYu3cvRFFEeXk5PvzwQ9x3331tfk5dXR2qqqrUXqaIJz0RmTr2U2QIRktMLl68iMbGRnh4eKiVe3h4QKlUtrpNaGgoduzYgWnTpsHW1hZyuRy9e/fGW2+91ebnJCcnQxAE6eXl5aXT/SAiItK1gQMHQiaTabyeeuopAIAoikhMTIRCoYCDgwPGjh2LkydPqtVRV1eHBQsWoG/fvnByckJUVBRKS0vVYiorKxETEyN9R8bExODKlSuG2s1WGX3yq6xFBi6KokZZs1OnTiE+Ph7PP/88CgoKkJmZieLiYsyfP7/N+lesWAGVSiW9SkpKdNp+IiIiXcvPz0dZWZn0ysrKAgA89thjAICUlBSkpqYiLS0N+fn5kMvlCAsLQ3V1tVRHQkICMjIysHv3buTk5KCmpgaRkZFobGyUYqKjo1FYWIjMzExkZmaisLAQMTExht3ZFrr6HGet9e3bF1ZWVhqjIxUVFRqjKM2Sk5MxevRoPPvsswCAESNGwMnJCffeey9efvlleHp6amxjZ2cHOzs73e+ADnF4lIi6iz4yGSpF0djNMHtubm5qP7/66qsYPHgwxowZA1EUsXbtWqxcuRIPP/wwAOC9996Dh4cHdu7ciXnz5kGlUmHz5s3Ytm0bJk6cCADYvn07vLy88MUXXyAiIgJFRUXIzMxEXl4egoODAQDvvPMOQkJC8OOPP2Lo0KGG3en/MtqIia2tLQIDA6UssFlWVhZCQ0Nb3ebatWvo0UO9yVZWVgBujrR0R0xKiKi7Yb+lvZZzHuvq6m65TX19PbZv344nn3wSMpkMxcXFUCqVanM07ezsMGbMGGmOZkFBARoaGtRiFAoF/P39pZhDhw5BEAQpKQGAUaNGQRCENud6GoLRRkwAYNGiRYiJiUFQUBBCQkKwadMmnDt3Tro0s2LFCvz+++94//33AQD3338/5syZg/Xr1yMiIgJlZWVISEjAPffcA4VCYcxd0QpPbiLqrixr5OQyuv53fBMAaMxzXLVqFRITE9vd8pNPPsGVK1cwc+ZMAJCuNLQ2R/Ps2bNSjK2tLfr06aMR07y9UqmEu7u7xue5u7u3OdfTEIyamEybNg2XLl3Ciy++iLKyMvj7+2Pv3r3w9vYGAJSVlamtaTJz5kxUV1cjLS0NixcvRu/evTF+/HisXr3aWLugNSYlRNTdWVZyohslJSVwdnaWfu7IVIPNmzdj8uTJGn+Ad2aOZlsxrcV3pB59MmpiAgBxcXGIi4tr9b309HSNsgULFmDBggV6bpV+MSkhInPB5KRznJ2d1RKTWzl79iy++OILfPzxx1KZXC4HcHPE449zK/84R1Mul6O+vh6VlZVqoyYVFRXSdAm5XI7ycs0VaS9cuNDmXE9DMPpdOURERNS6rVu3wt3dXW29Lh8fH8jlcrU5mvX19cjOzpaSjsDAQNjY2KjFlJWV4cSJE1JMSEgIVCoVjhw5IsUcPnwYKpWqzbmehmD0ERMiIiLS1NTUhK1bt2LGjBmwtv7f17VMJkNCQgKSkpLg6+sLX19fJCUlwdHREdHR0QAAQRAQGxuLxYsXw9XVFS4uLliyZAkCAgKku3T8/PwwadIkzJkzBxs3bgQAzJ07F5GRkUa7IwdgYmJwvIxDROaGl3P044svvsC5c+fw5JNPary3dOlS1NbWIi4uDpWVlQgODsb+/fvRq1cvKWbNmjWwtrbG1KlTUVtbiwkTJiA9PV26mxW4+WDc+Ph46e6dqKgopKWl6X/n2iETu+t9tlqqqqqCIAhQqVSdus6nC0xKiMicGTo50Xd//r/63eDs3LWZD1VVTRCEC0b57uluOMfEQJiUEJG5Yz9HusDExAB4shKRpWB/R13FOSZERETtES8AXb1KZVGTJrqGIyZERERkMpiY6BmHNYnI0rDfo65gYqJHPDmJyFKx/yNtMTEhIiIik8HEhIiIiEwGExM94TAmEVk69oOkDSYmesCTkYjoJvaH1Flcx4SIiKg9KnR9HZIqXTTEMnDEhIiIiEwGExMd47AlEZE69ovUGUxMdIgnHxERUdcwMSEiIiKTwcSEiIj0jiPK1FFMTHSEJx0RUfvYT1JHMDHRAZ5sREREusHEhIiIiEwGF1jrIo6WEBF1XB+ZDJViV1crM7BqE6nDQnDEhIiIDIp/0FF7mJgQERGRyWBi0gXM+omItMP+k9rCxERLPKmIiLqG/Si1hokJERERmQwmJkRERGQymJhogcOPRES6wf6UWmJi0kk8iYiIdIv9Kv0RF1gjIiJqTxWApi7WUaOLhlgGjpgQERGRyWBi0gkcbiQi0g/2r9SMiUkH8aQhItIv9rMEMDEhIiIySb///jv+8pe/wNXVFY6OjrjzzjtRUFAgvS+KIhITE6FQKODg4ICxY8fi5MmTanXU1dVhwYIF6Nu3L5ycnBAVFYXS0lK1mMrKSsTExEAQBAiCgJiYGFy5csUQu9gqJiZEREQmprKyEqNHj4aNjQ0+//xznDp1Cq+//jp69+4txaSkpCA1NRVpaWnIz8+HXC5HWFgYqqv/9yjjhIQEZGRkYPfu3cjJyUFNTQ0iIyPR2NgoxURHR6OwsBCZmZnIzMxEYWEhYmJiDLm7amSi2N2eP901VVVVEAQBKpUKzs7OHd6OQ4xERPpX2YmvJG37807Xnws49+xiXTWAEIoOt3X58uX49ttvcfDgwVbfF0URCoUCCQkJWLZsGYCboyMeHh5YvXo15s2bB5VKBTc3N2zbtg3Tpk0DAJw/fx5eXl7Yu3cvIiIiUFRUhGHDhiEvLw/BwcEAgLy8PISEhOD06dMYOnRo13ZcCxwxISIiMpCqqiq1V11dXatxn376KYKCgvDYY4/B3d0dI0eOxDvvvCO9X1xcDKVSifDwcKnMzs4OY8aMQW5uLgCgoKAADQ0NajEKhQL+/v5SzKFDhyAIgpSUAMCoUaMgCIIUY2hMTDqAoyVERIZhkv2tCsCVLr5UN6vy8vKS5nIIgoDk5ORWP/LMmTNYv349fH19sW/fPsyfPx/x8fF4//33AQBKpRIA4OHhobadh4eH9J5SqYStrS369OnTboy7u7vG57u7u0sxhsYF1m7BJE8SIiIz1kcm69Qlne6kpKRE7VKOnZ1dq3FNTU0ICgpCUlISAGDkyJE4efIk1q9fj+nTp0txshbfUaIoapS11DKmtfiO1KMvHDEhIiIyEGdnZ7VXW4mJp6cnhg0bplbm5+eHc+fOAQDkcjkAaIxqVFRUSKMocrkc9fX1qKysbDemvLxc4/MvXLigMRpjKExMiIiITMzo0aPx448/qpX99NNP8Pb2BgD4+PhALpcjKytLer++vh7Z2dkIDQ0FAAQGBsLGxkYtpqysDCdOnJBiQkJCoFKpcOTIESnm8OHDUKlUUoyh8VIOERGRiVm4cCFCQ0ORlJSEqVOn4siRI9i0aRM2bdoE4Obll4SEBCQlJcHX1xe+vr5ISkqCo6MjoqOjAQCCICA2NhaLFy+Gq6srXFxcsGTJEgQEBGDixIkAbo7CTJo0CXPmzMHGjRsBAHPnzkVkZKRR7sgBmJi0i/NLiIjIGO6++25kZGRgxYoVePHFF+Hj44O1a9fiiSeekGKWLl2K2tpaxMXFobKyEsHBwdi/fz969eolxaxZswbW1taYOnUqamtrMWHCBKSnp8PKykqK2bFjB+Lj46W7d6KiopCWlma4nW2B65i0gUkJEZFx3WoCrMHWMfkccHbqYl1XAWFyx9cxsWScY0JERCaJfyBaJiYmREREZDI4x4SIiKg9NQCauljHNV00xDJwxISIiIhMBhOTVvC6JhGRaWB/bHmYmLTAk4CIiMh4mJgQERGRyWBiQkRERCaDiQkRERGZDCYmREREZDK4jgkREVF7rgCo72IdXMekw5iYEBGRSesjk93yuTlkPE1NTfjll19QUVGBpib1lej+/Oc/d7o+JiZ/wFuFiYhME5MT05SXl4fo6GicPXsWLZ8JLJPJ0NjY2Ok6mZgQERGRVubPn4+goCB89tln8PT0hEwHf+AzMSEiIiKt/Pzzz/jwww9x22236axO3pVDREREWgkODsYvv/yi0zqNnpisW7cOPj4+sLe3R2BgIA4ePNhufF1dHVauXAlvb2/Y2dlh8ODB2LJli4FaS0RERM0WLFiAxYsXIz09HQUFBfj+++/VXtow6qWcPXv2ICEhAevWrcPo0aOxceNGTJ48GadOncKAAQNa3Wbq1KkoLy/H5s2bcdttt6GiogI3btwwcMuJiIjokUceAQA8+eSTUplMJoMoit1z8mtqaipiY2Mxe/ZsAMDatWuxb98+rF+/HsnJyRrxmZmZyM7OxpkzZ+Di4gIAGDhwoE7awjtyiIiIOqe4uFjndRotMamvr0dBQQGWL1+uVh4eHo7c3NxWt/n0008RFBSElJQUbNu2DU5OToiKisJLL70EBweHVrepq6tDXV2d9HNVVZVGDJMSIiLTZ7RbhlXo+gJrtbpoiOnx9vbWeZ1GS0wuXryIxsZGeHh4qJV7eHhAqVS2us2ZM2eQk5MDe3t7ZGRk4OLFi4iLi8Ply5fbnGeSnJyMF154QeftJyIiw+N6Jqbn119/xdq1a1FUVASZTAY/Pz8888wzGDx4sFb1GX3ya8t7npuvS7WmqakJMpkMO3bswD333IMpU6YgNTUV6enpqK1tPR1dsWIFVCqV9CopKdH5PhAREVmiffv2YdiwYThy5AhGjBgBf39/HD58GMOHD0dWVpZWdRptxKRv376wsrLSGB2pqKjQGEVp5unpiX79+kEQBKnMz88PoiiitLQUvr6+GtvY2dnBzs5Ot40nIiIiLF++HAsXLsSrr76qUb5s2TKEhYV1uk6jjZjY2toiMDBQI6PKyspCaGhoq9uMHj0a58+fR01NjVT2008/oUePHujfv79e20tERETqioqKEBsbq1H+5JNP4tSpU1rVadRLOYsWLcK7776LLVu2oKioCAsXLsS5c+cwf/58ADcvw0yfPl2Kj46OhqurK2bNmoVTp07hm2++wbPPPosnn3yyzcmvREREpB9ubm4oLCzUKC8sLIS7u7tWdRr1duFp06bh0qVLePHFF1FWVgZ/f3/s3btXmuVbVlaGc+fOSfE9e/ZEVlYWFixYgKCgILi6umLq1Kl4+eWXjbULREREFmvOnDmYO3cuzpw5g9DQUMhkMuTk5GD16tVYvHixVnXKxJaPAzRzVVVVEAQBKpUKzs7OAHi7MBFRd9J8V05r/bkuSfX/A3Du4qB8VS0gLIHe2mosoihi7dq1eP3113H+/HkAgEKhwLPPPov4+HitHurHh/gRERG1pwZAVxcYv66LhpgemUyGhQsXYuHChaiurgYA9OrVq0t1MjEhIiKiLutqQtKMiQkRERF12F133YUDBw6gT58+GDlyZLuXa44dO9bp+i0+MeH8EiKi7oWrvxrXAw88IK0P9sADD2g1j6Q9Fp2YMCkhIuqe+shkOKtSGbsZFmnVqlXS/ycmJuq8fqMvSU9ERETqEhMTIZPJ1F5yuVx6XxRFJCYmQqFQwMHBAWPHjsXJkyfV6qirq8OCBQvQt29f6aG3paWlajGVlZWIiYmBIAgQBAExMTG4cuVKh9s5aNAgXLp0SaP8ypUrGDRoUOd2+r+YmBAREZmg4cOHo6ysTHr98MMP0nspKSlITU1FWloa8vPzIZfLERYWJt0ZAwAJCQnIyMjA7t27kZOTg5qaGkRGRqKxsVGKiY6ORmFhITIzM5GZmYnCwkLExMR0uI2//fabWn3N6urqNJKgjrLoSzlERESmytraWm2UpFnz2iErV67Eww8/DAB477334OHhgZ07d2LevHlQqVTYvHkztm3bhokTJwIAtm/fDi8vL3zxxReIiIhAUVERMjMzkZeXh+DgYADAO++8g5CQEPz4448YOnRom2379NNPpf/ft2+f2jPsGhsbceDAAfj4+Gi331ptRURERJ1WVVWl9nN7D5r9+eefoVAoYGdnh+DgYCQlJWHQoEEoLi6GUqlEeHi4Wj1jxoxBbm4u5s2bh4KCAjQ0NKjFKBQK+Pv7Izc3FxERETh06BAEQZCSEgAYNWoUBEFAbm5uu4nJgw8+CODmOiYzZsxQe8/GxgYDBw7E66+/3uHj8kdMTIiIiNpTBaCui3X8d3svLy+14lWrVrU6gTQ4OBjvv/8+hgwZgvLycrz88ssIDQ3FyZMnoVQqAQAeHh5q23h4eODs2bMAAKVSCVtbW/Tp00cjpnl7pVLZ6vNs3N3dpZi2NDU1AQB8fHyQn5+Pvn37thvfGUxMiIiIDKSkpERtSfq2RksmT54s/X9AQABCQkIwePBgvPfeexg1ahQAaNymK4riLW/dbRnTWnxH6mlWXFzcobjOYGJCRERkIM7Ozlo9K8fJyQkBAQH4+eefpcsoSqUSnp6eUkxFRYU0iiKXy1FfX4/Kykq1UZOKigqEhoZKMeXl5RqfdeHCBY3RmPZcvXoV2dnZOHfuHOrr69Xei4+P73A9zZiYEBERmbi6ujoUFRXh3nvvhY+PD+RyObKysjBy5EgAQH19PbKzs7F69WoAQGBgIGxsbJCVlYWpU6cCAMrKynDixAmkpKQAAEJCQqBSqXDkyBHcc889AIDDhw9DpVJJycutHD9+HFOmTMG1a9dw9epVuLi44OLFi3B0dIS7u7tWiQlvFyYiIjIxS5YsQXZ2NoqLi3H48GE8+uijqKqqwowZMyCTyZCQkICkpCRkZGTgxIkTmDlzJhwdHREdHQ0AEAQBsbGxWLx4MQ4cOIDjx4/jL3/5CwICAqS7dPz8/DBp0iTMmTMHeXl5yMvLw5w5cxAZGdnuxNc/WrhwIe6//35cvnwZDg4OyMvLw9mzZxEYGIh//OMfWu07R0yIiIhMTGlpKf7v//4PFy9ehJubG0aNGoW8vDx4e3sDAJYuXYra2lrExcWhsrISwcHB2L9/v9qD9NasWQNra2tMnToVtbW1mDBhAtLT02FlZSXF7NixA/Hx8dLdO1FRUUhLS+twOwsLC7Fx40ZYWVnBysoKdXV1GDRoEFJSUjBjxgzpdubOkImiZT1woKqqCoIgQKVSwfsP910TEVH3clalkvpzbeZt3Ir0fbEIcG59jmrH66oDhFTora3G4ubmhm+//RZDhgzB0KFD8eabbyIiIgKnT5/GXXfdhWvXrnW6To6YEBERkVZGjhyJo0ePYsiQIRg3bhyef/55XLx4Edu2bUNAQIBWdTIxISIiao8KgG0X66i/dUh3lJSUJC2D/9JLL2HGjBn461//ittuuw1bt27Vqk4mJkRERNRpoijCzc0Nw4cPB3Dzss7evXu7XC/vyiEiIqJOE0URvr6+Wj+sry1MTIiIiKjTevToAV9fX1y6dEm39eq0NiIiIrIYKSkpePbZZ3HixAmd1ck5JkRERKSVv/zlL7h27RruuOMO2NrawsHBQe39y5cvd7pOJiZERESklTVr1nT4gX8dxcSEiIiItDJz5kyd18k5JkRmyhvA28ZuBBGZNSsrK1RUVGiUX7p0SW3p+87ocGKi69uBiEh/vAEUvg1Eix6o9DZ2a4i6uWodvcxQW0+1qaurg62tdqvSdfhSjr+/P9566y3ExMRo9UFEZBjNSQnixgP4DNjmgMoYoM9ZIzeMiMzGm2++CQCQyWR499130bNnT+m9xsZGfPPNN7j99tu1qrvDiUlSUhKeeuopfPLJJ9i0aRNcXV21+kAi0p//JSULgR/WAAGxwL3DgN8iUDlwDZMTItKJNWvWALg5YrJhwwa1yza2trYYOHAgNmzYoFXdHb6UExcXh++++w6VlZUYPnw4Pv30U60+kIj056/Sf5KAgGjg4E4AW27+/FegtxHbRkTmo7i4GMXFxRgzZgy+++476efi4mL8+OOP2LdvH4KDg7Wqu1N35fj4+ODLL79EWloaHnnkEfj5+cHaWr2KY8eOadUQIuq65QDm9QCwwwGI9gDuzQN2jgIApCwHrhizcURkdr766iud19np24XPnj2Ljz76CC4uLnjggQc0EhMiMq4+ACqfABA9HMAdgBeQ8mcg2cjtIiLz09jYiPT0dBw4cAAVFRVoampSe//LL7/sdJ2dyireeecdLF68GBMnTsSJEyfg5ubW6Q8kIv3rA6BS9iWwwgEpyUxKiEg/nnnmGaSnp+O+++6Dv7+/ThZb63BiMmnSJBw5cgRpaWmYPn16lz+YiPSrD8CMhIj0avfu3fjnP/+JKVOm6KzODicmjY2N+P7779G/f3+dfTgRERF1X7a2trjtttt0WmeHE5OsrCydfjAREVG3oAJg08U6GnTRENOzePFivPHGG0hLS9PZM3M4c5WIiIi0kpOTg6+++gqff/45hg8fDhsb9Qzu448/7nSdTEyIiIhIK71798ZDDz2k0zqZmBAREZFWtm7dqvM6+XRhIiIi0tqNGzfwxRdfYOPGjaiuvvm0wvPnz6Ompkar+jhiQkRERFo5e/YsJk2ahHPnzqGurg5hYWHo1asXUlJScP36da2el8MREyIiItLKM888g6CgIFRWVsLBwUEqf+ihh3DgwAGt6uSICREREWklJycH3377LWxtbdXKvb298fvvv2tVJxMTIiKi9qjQ9W/LG7poiOlpampCY2OjRnlpaSl69eqlVZ28lENERERaCQsLw9q1a6WfZTIZampqsGrVKq2XqeeICREREWllzZo1GDduHIYNG4br168jOjoaP//8M/r27Ytdu3ZpVScTEyIiItKKQqFAYWEhdu/ejYKCAjQ1NSE2NhZPPPGE2mTYzmBiQkRERFpzcHDArFmzMGvWLJ3UxzkmREREJiw5ORkymQwJCQlSmSiKSExMhEKhgIODA8aOHYuTJ0+qbVdXV4cFCxagb9++cHJyQlRUFEpLS9ViKisrERMTA0EQIAgCYmJicOXKlU61bcuWLRrlW7ZswerVqzu1n82YmBAREZmo/Px8bNq0CSNGjFArT0lJQWpqKtLS0pCfnw+5XI6wsDBp5VUASEhIQEZGBnbv3o2cnBzU1NQgMjJS7S6a6OhoFBYWIjMzE5mZmSgsLERMTEyH27dx40bcfvvtGuXDhw/XanE1gIkJERGRSaqpqcETTzyBd955B3369JHKRVHE2rVrsXLlSjz88MPw9/fHe++9h2vXrmHnzp0AAJVKhc2bN+P111/HxIkTMXLkSGzfvh0//PADvvjiCwBAUVERMjMz8e677yIkJAQhISF455138J///Ac//vhjh9qoVCrh6empUe7m5oaysjKt9puJCRERkYFUVVWpverq6tqMfeqpp3Dfffdh4sSJauXFxcVQKpUIDw+Xyuzs7DBmzBjk5uYCAAoKCtDQ0KAWo1Ao4O/vL8UcOnQIgiAgODhYihk1ahQEQZBibsXLywvffvutRvm3334LhULRoTpa4uRXIiKi9qgAWHWxjv9ePfHy8lIrXrVqFRITEzXCd+/ejWPHjiE/P1/jPaVSCQDw8PBQK/fw8MDZs2elGFtbW7WRluaY5u2VSiXc3d016nd3d5dibmX27NlISEhAQ0MDxo8fDwA4cOAAli5disWLF3eojpaYmBARERlISUkJnJ2dpZ/t7OxajXnmmWewf/9+2Nvbt1mXTCZT+1kURY2yllrGtBbfkXqaLV26FJcvX0ZcXBzq6+sBAPb29li2bBlWrFjRoTpaYmJCRERkIM7OzmqJSWsKCgpQUVGBwMBAqayxsRHffPMN0tLSpPkfLed3VFRUSKMocrkc9fX1qKysVBs1qaioQGhoqBRTXl6u8fkXLlzQGI1pi0wmw+rVq/H3v/8dRUVFcHBwgK+vb6sJV0dxjgkREZEJmTBhAn744QcUFhZKr6CgIDzxxBMoLCzEoEGDIJfLkZWVJW1TX1+P7OxsKekIDAyEjY2NWkxZWRlOnDghxYSEhEClUuHIkSNSzOHDh6FSqaSYjurZsyfuvvtu+Pv7dykpAThiQkRE3VClKKKqqsrYzdCLXr16wd/fX63MyckJrq6uUnlCQgKSkpLg6+sLX19fJCUlwdHREdHR0QAAQRAQGxuLxYsXw9XVFS4uLliyZAkCAgKkybR+fn6YNGkS5syZg40bNwIA5s6di8jISAwdOrRDbb169SpeffVVHDhwABUVFWhqalJ7/8yZM53ef4tOTCpFEX06eB2NiIhMQ6UoGrsJRrd06VLU1tYiLi4OlZWVCA4Oxv79+9We6LtmzRpYW1tj6tSpqK2txYQJE5Ceng4rq//N5N2xYwfi4+Olu3eioqKQlpbW4XbMnj0b2dnZiImJgaenZ4fnprRHJoqW9S9cVVUFQRCgUqmk63xMToiIuo/mxKS1/lyXpPr9AOcu3pVT1QgIRdBbW42ld+/e+OyzzzB69Gid1ck5JkRERKSVPn36wMXFRad1MjEhIiJqTw2A6i6+agzeaoN46aWX8Pzzz+PatWs6q9Oi55gQERGR9l5//XX8+uuv8PDwwMCBA2FjY6P2/rFjxzpdJxMTIiIi0sqDDz6o8zqZmBAREZFWVq1apfM6jT7HZN26dfDx8YG9vT0CAwNx8ODBDm337bffwtraGnfeead+G0hERETtKigowPbt27Fjxw4cP368S3UZdcRkz549SEhIwLp16zB69Ghs3LgRkydPxqlTpzBgwIA2t1OpVJg+fTomTJjQ6nK6REREpH8VFRV4/PHH8fXXX6N3794QRREqlQrjxo3D7t274ebm1uk6jTpikpqaitjYWMyePRt+fn5Yu3YtvLy8sH79+na3mzdvHqKjoxESEnLLz6irq9N4zDQRERF13YIFC1BVVYWTJ0/i8uXLqKysxIkTJ1BVVYX4+Hit6jRaYlJfX4+CggJptblm4eHhyM3NbXO7rVu34tdff+3wda3k5GQIgiC9Wj5ymoiIiLSTmZmJ9evXw8/PTyobNmwY3n77bXz++eda1Wm0xOTixYtobGzUeIKhh4cHlEplq9v8/PPPWL58OXbs2AFr645dhVqxYgVUKpX0Kikp0Yjh8sZERN0D+2vT0tTUpHGLMADY2NhoPDeno4x+V07LdfVFUWx1rf3GxkZER0fjhRdewJAhQzpcv52dXYeedMjn5hARmTajJSVVALr69WCm+dT48ePxzDPPYNeuXVAoFACA33//HQsXLsSECRO0qtNoiUnfvn1hZWWlMTpSUVGhMYoCANXV1Th69CiOHz+Op59+GsDNTE0URVhbW2P//v0YP368QdpOREREQFpaGh544AEMHDgQXl5ekMlkOHfuHAICArB9+3at6jRaYmJra4vAwEBkZWXhoYceksqzsrLwwAMPaMQ7Ozvjhx9+UCtbt24dvvzyS3z44Yfw8fHRe5uJiIjof7y8vHDs2DFkZWXh9OnTEEURw4YNw8SJE7Wu06iXchYtWoSYmBgEBQUhJCQEmzZtwrlz5zB//nwAN+eH/P7773j//ffRo0cP+Pv7q23v7u4Oe3t7jXIiIiLSny+//BJPP/008vLy4OzsjLCwMISFhQG4uaTH8OHDsWHDBtx7772drtuoicm0adNw6dIlvPjiiygrK4O/vz/27t0Lb29vAEBZWRnOnTtnzCYSERFRC2vXrsWcOXPg7Oys8Z4gCJg3bx5SU1O1SkxkomhZU5yrqqogCAJUKpXGAeXkVyIi09Vy8mt7/bkuSPULgHMXvx6qREBQQW9tNTRvb29kZmaq3Sb8R6dPn0Z4eLhWgwtGX5KeiIjoVnibsGkpLy9v9TbhZtbW1rhw4YJWdTMx+QP+4hMRmR72zaanX79+Gjek/NH3338PT09Preo2+jomREREJk1l7AaYnilTpuD555/H5MmTYW9vr/ZebW0tVq1ahcjISK3q5hyTFjjPhIjItLQ1YmKwOSYAulp7FQAB5jPHpLy8HHfddResrKzw9NNPY+jQoZDJZCgqKsLbb7+NxsZGHDt2rNV1yW6FIyZERETUKR4eHsjNzcVf//pXrFixAs1jHDKZDBEREVi3bp1WSQnAxISIiIi04O3tjb1796KyshK//PILRFGEr68v+vTp06V6mZgQERGR1vr06YO7775bZ/XxrpwWOPubiIjIeJiYtILJCRGRaWB/bHmYmBAREZHJYGJCREREJoOTX4mIiNpRDaCrK1xV66IhFoIjJkRERGQymJi0gROuiIiMi/2wZWJi0g6eFERExsH+13IxMSEiIjIx69evx4gRI+Ds7AxnZ2eEhITg888/l94XRRGJiYlQKBRwcHDA2LFjcfLkSbU66urqsGDBAvTt2xdOTk6IiopCaWmpWkxlZSViYmIgCAIEQUBMTAyuXLliiF1sExMTIiIiE9O/f3+8+uqrOHr0KI4ePYrx48fjgQcekJKPlJQUpKamIi0tDfn5+ZDL5QgLC0N19f+m2SYkJCAjIwO7d+9GTk4OampqEBkZicbGRikmOjoahYWFyMzMRGZmJgoLCxETE2Pw/f0jPl34Fvi0YSIiw+vIpRxDPV24FLp5unB/dO3pwi4uLnjttdfw5JNPQqFQICEhAcuWLQNwc3TEw8MDq1evxrx586BSqeDm5oZt27Zh2rRpAIDz58/Dy8sLe/fuRUREBIqKijBs2DDk5eUhODgYAJCXl4eQkBCcPn0aQ4cO7eJea4cjJkRERAZSVVWl9qqrq7vlNo2Njdi9ezeuXr2KkJAQFBcXQ6lUIjw8XIqxs7PDmDFjkJubCwAoKChAQ0ODWoxCoYC/v78Uc+jQIQiCICUlADBq1CgIgiDFGAMTk1vgBCwiIsMytX5XBeBKF1+q/9bl5eUlzecQBAHJycltfu4PP/yAnj17ws7ODvPnz0dGRgaGDRsGpVIJAPDw8FCL9/DwkN5TKpWwtbXVeNJvyxh3d3eNz3V3d5dijIELrHVApSjykg4RkQGYWlKiayUlJWqXcuzs7NqMHTp0KAoLC3HlyhV89NFHmDFjBrKzs6X3ZS2+l0RR1ChrqWVMa/EdqUefOGJCRERkIM132TS/2ktMbG1tcdtttyEoKAjJycm444478MYbb0AulwOAxqhGRUWFNIoil8tRX1+PysrKdmPKy8s1PvfChQsaozGGxMSEiIioGxBFEXV1dfDx8YFcLkdWVpb0Xn19PbKzsxEaGgoACAwMhI2NjVpMWVkZTpw4IcWEhIRApVLhyJEjUszhw4ehUqmkGGPgpRwiIiIT87e//Q2TJ0+Gl5cXqqursXv3bnz99dfIzMyETCZDQkICkpKS4OvrC19fXyQlJcHR0RHR0dEAAEEQEBsbi8WLF8PV1RUuLi5YsmQJAgICMHHiRACAn58fJk2ahDlz5mDjxo0AgLlz5yIyMtJod+QATEw6jPNMiIj0y9znl3RGeXk5YmJiUFZWBkEQMGLECGRmZiIsLAwAsHTpUtTW1iIuLg6VlZUIDg7G/v370atXL6mONWvWwNraGlOnTkVtbS0mTJiA9PR0WFlZSTE7duxAfHy8dPdOVFQU0tLSDLuzLXAdk05ickJEpHvaJCWGWsfkJIBet4xuXzWA4ejaOiaWgnNMiIiIyGQwMSEiIiKTwTkmncS5JkREumXqc0tUABpvGdW+Gl00xEJwxEQLpn4SERF1F+xPqSUmJkRERGQymJgQERGRyWBioiUOPxIRdQ37UWoNE5Mu4ElFRKQd9p/UFiYmREREZDKYmBAREZHJYGLSRRyOJCLqHPab1B4usEZERAbTHZOSGgBdbfVVXTTEQnDEhIiIiEwGExMd6I5/ARAREZkiJiY6wuSEiKh97CepI5iYEBGR3jEpoY5iYkJEREQmg4mJDvEvAiIioq5hYqJjTE6IiNSxX6TO4DomRERE7VABaOhiHdd00RALwRETIiLSG46WUGcxMdEDnohEROwLSTtMTPSEJyQREVHnMTEhIiIik8HEhIiIdI6jxqQtJiZ6xBOTiCwR+z7qCiYmesYTlIiIqOOYmBAREZHJ4AJrBlApiugjkxm7GUREemeOo8RXANR3sQ4usNZxHDExEHM8WYmI/oj9HOkCExMD4klLROaK/RvpChMTA+PJS0Tmhv0a6RITEyIiIhOTnJyMu+++G7169YK7uzsefPBB/Pjjj2oxoigiMTERCoUCDg4OGDt2LE6ePKkWU1dXhwULFqBv375wcnJCVFQUSktL1WIqKysRExMDQRAgCAJiYmJw5coVfe9im5iYEBERmZjs7Gw89dRTyMvLQ1ZWFm7cuIHw8HBcvXpViklJSUFqairS0tKQn58PuVyOsLAwVFdXSzEJCQnIyMjA7t27kZOTg5qaGkRGRqKxsVGKiY6ORmFhITIzM5GZmYnCwkLExMQYdH//SCaKljUGV1VVBUEQoFKp4OzsbLR28C4dIjIHxryMo+/+vLn+dwA4drGuawDmAFq39cKFC3B3d0d2djb+/Oc/QxRFKBQKJCQkYNmyZQBujo54eHhg9erVmDdvHlQqFdzc3LBt2zZMmzYNAHD+/Hl4eXlh7969iIiIQFFREYYNG4a8vDwEBwcDAPLy8hASEoLTp09j6NChXdzzzuOIiZHwmiwRdXfsxzqvqqpK7VVXV9eh7VQqFQDAxcUFAFBcXAylUonw8HApxs7ODmPGjEFubi4AoKCgAA0NDWoxCoUC/v7+UsyhQ4cgCIKUlADAqFGjIAiCFGNoTEyMiCc1EXVXltR/1QCo7uKr5r91eXl5SXM5BEFAcnLyLT9fFEUsWrQIf/rTn+Dv7w8AUCqVAAAPDw+1WA8PD+k9pVIJW1tb9OnTp90Yd3d3jc90d3eXYgzN6InJunXr4OPjA3t7ewQGBuLgwYNtxn788ccICwuDm5sbnJ2dERISgn379hmwtbpnSSc3EZkH9lvaKykpgUqlkl4rVqy45TZPP/00vv/+e+zatUvjPVmLaQGiKGqUtdQyprX4jtSjL0ZNTPbs2YOEhASsXLkSx48fx7333ovJkyfj3LlzrcZ/8803CAsLw969e1FQUIBx48bh/vvvx/Hjxw3cct3iSU5E3QX7q65xdnZWe9nZ2bUbv2DBAnz66af46quv0L9/f6lcLpcDgMaoRkVFhTSKIpfLUV9fj8rKynZjysvLNT73woULGqMxhmLUxCQ1NRWxsbGYPXs2/Pz8sHbtWnh5eWH9+vWtxq9duxZLly7F3XffDV9fXyQlJcHX1xf//ve/DdxyIiIi/RFFEU8//TQ+/vhjfPnll/Dx8VF738fHB3K5HFlZWVJZfX09srOzERoaCgAIDAyEjY2NWkxZWRlOnDghxYSEhEClUuHIkSNSzOHDh6FSqaQYQzPas3Lq6+tRUFCA5cuXq5WHh4d3eMJNU1MTqqurpclAramrq1ObXFRVVaVdg4mIiAzkqaeews6dO/Gvf/0LvXr1kkZGBEGAg4MDZDIZEhISpD/Qm/9Yd3R0RHR0tBQbGxuLxYsXw9XVFS4uLliyZAkCAgIwceJEAICfnx8mTZqEOXPmYOPGjQCAuXPnIjIy0ih35ABGTEwuXryIxsbGdifu3Mrrr7+Oq1evYurUqW3GJCcn44UXXuhSWw2BD/ojIlPHyziG03zlYOzYsWrlW7duxcyZMwEAS5cuRW1tLeLi4lBZWYng4GDs378fvXr1kuLXrFkDa2trTJ06FbW1tZgwYQLS09NhZWUlxezYsQPx8fHS3TtRUVFIS0vT7w62w2jrmJw/fx79+vVDbm4uQkJCpPJXXnkF27Ztw+nTp9vdfteuXZg9ezb+9a9/SZlfa1obMfHy8jL6OiZtYXJCRKbIFJMSQ61jsgaAQxfrqgWwENqvY2JJjDZi0rdvX1hZWbU7cacte/bsQWxsLD744IN2kxLg5n3dt5pcZEo4ckJEpsYUkxIyX0ab/Gpra4vAwEC1STkAkJWV1e6Em127dmHmzJnYuXMn7rvvPn030yjYCRCRqWB/RIZmtBETAFi0aBFiYmIQFBSEkJAQbNq0CefOncP8+fMBACtWrMDvv/+O999/H8DNpGT69Ol44403MGrUKGm0xcHBAYIgGG0/9IEjJ0RkbExKbqoCUN/FOq7roiEWwqiJybRp03Dp0iW8+OKLKCsrg7+/P/bu3Qtvb28AN29r+uOaJhs3bsSNGzfw1FNP4amnnpLKZ8yYgfT0dEM3X++YnBCRsTApIWPhQ/xMHBMTIjKG7pCYGGry6wsA7LtY13UAq8DJrx1h9CXpiYiIiJoxMTFx3eGvFiIyL+x3yJiYmHQD7CSIyFDY35CxMTHpJthZEJG+sZ8hU8DEpBthp0FE+sL+hUyFUW8Xps7jLcREpGtMStqnQtfXIam7dQj9F0dMuiF2IkSkK+xPyNQwMemm2JkQUVexHyFTxMSkG2OnQkTaYv9BpoqJSTfHzoWIOov9BpkyJiZmgJ0MEXUU+wsydUxMzAQ7GyK6FfYT1B0wMTEj7HSIqC3sH6i7YGJiZtj5EFFL7BeoO+ECa2aIi7ARUTMmJV2nAmDbxTrqddEQC8EREzPFzoiI2A9Qd8TExIyxUyKyXDz/qbtiYmLm2DkRWR6e99SdMTGxAOykiCwHz3fq7piYWAh2VkTmj+c5mQMmJhaEnRaR+eL5TeaCiYmFYedFZH54XpM54TomFojrnBCZDyYl+lcNrmNiSBwxsVDszIi6P57HZI6YmFgwdmpE3RfPXzJXvJRj4f7YufHyDpFpYzJCloAjJiRhp0dkunh+kqVgYkJq2PkRmR6el5bnm2++wf333w+FQgGZTIZPPvlE7X1RFJGYmAiFQgEHBweMHTsWJ0+eVIupq6vDggUL0LdvXzg5OSEqKgqlpaVqMZWVlYiJiYEgCBAEATExMbhy5Yqe9659TExIAztBItPB89EyXb16FXfccQfS0tJafT8lJQWpqalIS0tDfn4+5HI5wsLCUF1dLcUkJCQgIyMDu3fvRk5ODmpqahAZGYnGxkYpJjo6GoWFhcjMzERmZiYKCwsRExOj9/1rj0wULeu3vqqqCoIgQKVSwdnZ2djNMWmcc0JkXExK2qfv/ry5/qnQze3C/wS0aqtMJkNGRgYefPBBADdHSxQKBRISErBs2TIAN0dHPDw8sHr1asybNw8qlQpubm7Ytm0bpk2bBgA4f/48vLy8sHfvXkRERKCoqAjDhg1DXl4egoODAQB5eXkICQnB6dOnMXTo0C7utXY4YkJtqhRF6UVEhsHzzrxVVVWpverq6jpdR3FxMZRKJcLDw6UyOzs7jBkzBrm5uQCAgoICNDQ0qMUoFAr4+/tLMYcOHYIgCFJSAgCjRo2CIAhSjDEwMaEOYSdJpH88z0yTCsCVLr5U/63Ly8tLms8hCAKSk5M73R6lUgkA8PDwUCv38PCQ3lMqlbC1tUWfPn3ajXF3d9eo393dXYoxBt4uTB3GFWOJ9IdJiWUoKSlRu5RjZ2endV2yFv2xKIoaZS21jGktviP16BNHTKhT2HkS6R7PK8vh7Oys9tImMZHL5QCgMapRUVEhjaLI5XLU19ejsrKy3Zjy8nKN+i9cuKAxGmNITEyo09iJEukOzyfqLB8fH8jlcmRlZUll9fX1yM7ORmhoKAAgMDAQNjY2ajFlZWU4ceKEFBMSEgKVSoUjR45IMYcPH4ZKpZJijIGXckgrvKxD1HVMSqgtNTU1+OWXX6Sfi4uLUVhYCBcXFwwYMAAJCQlISkqCr68vfH19kZSUBEdHR0RHRwMABEFAbGwsFi9eDFdXV7i4uGDJkiUICAjAxIkTAQB+fn6YNGkS5syZg40bNwIA5s6di8jISKPdkQMwMaEu4HL2RJ3HZIQ64ujRoxg3bpz086JFiwAAM2bMQHp6OpYuXYra2lrExcWhsrISwcHB2L9/P3r16iVts2bNGlhbW2Pq1Kmora3FhAkTkJ6eDisrKylmx44diI+Pl+7eiYqKanPtFEPhOiakM0xOiNrHpES3DLWOSQQAmy7W1QBgH7Rbx8TScI4J6Qw7XaK28fwg6hgmJqRT7HyJNPG8IOo4zjEhnWvuhHlphywZkxHzoULXvyxv6KIhFoIjJqQ37JjJUvF3n0h7HDEhveKdO2QpmIwQ6QYTEzIYJilkjpiQEOkWL+WQUbAzJ3PA32Mi3eOICRkNR1CoO2IyQqRfHDEhk8DOnroD/p4S6R9HTMhkcASFTBGTESLDYmJCJokPCSRTwKSEAKAGgNUto9rXqIuGWAgmJmSyOIJCxsBkhMi4OMeEugV+WZAh8PeMyPg4YkLdBkdQSB+YjBCZFo6YULfELxPSBf4eEZkejphQt8URFNIGkxEi08YREzIL/LKhjuDvCZHp44gJmY2WXzocRSEmIkTdD0dMyGzxS8my8d+fqHviiAmZtda+nDiSYn6YhJA+VaHrf8U36aIhFoKJCVkcJivdG5MQIvPGSzlE4Jddd8F/JyLzxxETov9q60uPoymGxeSDyLJxxIToFvhFaTg81kTEEROiDmjvC5MjKp3HBISI2mL0EZN169bBx8cH9vb2CAwMxMGDB9uNz87ORmBgIOzt7TFo0CBs2LDBQC0lah2/ZDuHx4uI2mPUxGTPnj1ISEjAypUrcfz4cdx7772YPHkyzp0712p8cXExpkyZgnvvvRfHjx/H3/72N8THx+Ojjz4ycMuJ1FWKYode5qqj+2/Ox4CIdEMmisbrKYKDg3HXXXdh/fr1Upmfnx8efPBBJCcna8QvW7YMn376KYqKiqSy+fPn47vvvsOhQ4c69JlVVVUQBAEqlQrOzs5d3wmiTjK3Sz9MNshY9N2fN9cvAOjqWSsCUAH87ukAo80xqa+vR0FBAZYvX65WHh4ejtzc3Fa3OXToEMLDw9XKIiIisHnzZjQ0NMDGxkZjm7q6OtTV1Uk/V1VV6aD1RNrjFzkRUduMdinn4sWLaGxshIeHh1q5h4cHlEplq9solcpW42/cuIGLFy+2uk1ycvLNjPe/Ly8vL93sABEREemc0Se/yloMa4uiqFF2q/jWyputWLECKpVKepWUlHSxxURERKQvRruU07dvX1hZWWmMjlRUVGiMijSTy+WtxltbW8PV1bXVbezs7GBnZ6ebRhMREZFeGW3ExNbWFoGBgcjKylIrz8rKQmhoaKvbhISEaMTv378fQUFBrc4vISIi6s46u6SGOTDqpZxFixbh3XffxZYtW1BUVISFCxfi3LlzmD9/PoCbl2GmT58uxc+fPx9nz57FokWLUFRUhC1btmDz5s1YsmSJsXaBiIhILzq7pIa5MOrKr9OmTcOlS5fw4osvoqysDP7+/ti7dy+8vb0BAGVlZWr/AD4+Pti7dy8WLlyIt99+GwqFAm+++SYeeeQRY+0CERGRXqSmpiI2NhazZ88GAKxduxb79u3D+vXrW11Sw1wYdR0TY1CpVOjduzdKSkp4LzkRUTdWVVUFLy8vXLlyBYIg6KV+QRDgDN2sY1IFaHz3tDUPsr6+Ho6Ojvjggw/w0EMPSeXPPPMMCgsLkZ2d3cUWmS6Le1bOpUuXAIC3DRMRmYlLly7pJTGxtbVt9aYLbfXs2VPju2fVqlVITEzUiNVmSQ1zYXGJiYuLCwDg3LlzevlFNifNf41wdKl9PE4dx2PVMTxOHaNSqTBgwACpX9c1e3t7FBcXo76+Xif1tbYcxq3uGu3skhrmwOISkx49bs73FQSBJ3wHOTs781h1AI9Tx/FYdQyPU8c09+v6YG9vD3t7e73V3xZtltQwF0ZfYI2IiIjUabOkhrmwuBETIiKi7mDRokWIiYlBUFAQQkJCsGnTJrUlNcyVxSUmdnZ2WLVqFVeD7QAeq47hceo4HquO4XHqGHM/TrdaUsNcWdztwkRERGS6OMeEiIiITAYTEyIiIjIZTEyIiIjIZDAxISIiIpNhlolJZx8TnZ2djcDAQNjb22PQoEHYsGGDgVpqfJ05Vh9//DHCwsLg5uYGZ2dnhISEYN++fQZsrfFo++jxb7/9FtbW1rjzzjv120AT0dnjVFdXh5UrV8Lb2xt2dnYYPHgwtmzZYqDWGldnj9WOHTtwxx13wNHREZ6enpg1a5b0iA1z9c033+D++++HQqGATCbDJ598csttLLk/Nxuimdm9e7doY2MjvvPOO+KpU6fEZ555RnRychLPnj3bavyZM2dER0dH8ZlnnhFPnTolvvPOO6KNjY344YcfGrjlhtfZY/XMM8+Iq1evFo8cOSL+9NNP4ooVK0QbGxvx2LFjBm65YXX2ODW7cuWKOGjQIDE8PFy84447DNNYI9LmOEVFRYnBwcFiVlaWWFxcLB4+fFj89ttvDdhq4+jssTp48KDYo0cP8Y033hDPnDkjHjx4UBw+fLj44IMPGrjlhrV3715x5cqV4kcffSQCEDMyMtqNt+T+3JyYXWJyzz33iPPnz1cru/3228Xly5e3Gr906VLx9ttvVyubN2+eOGrUKL210VR09li1ZtiwYeILL7yg66aZFG2P07Rp08TnnntOXLVqlUUkJp09Tp9//rkoCIJ46dIlQzTPpHT2WL322mvioEGD1MrefPNNsX///npro6npSGJiyf25OTGrSzn19fUoKChAeHi4Wnl4eDhyc3Nb3ebQoUMa8RERETh69CgaGhr01lZj0+ZYtdTU1ITq6mq9PUDLFGh7nLZu3Ypff/0Vq1at0ncTTYI2x+nTTz9FUFAQUlJS0K9fPwwZMgRLlixBbW2tIZpsNNocq9DQUJSWlmLv3r0QRRHl5eX48MMPcd999xmiyd2Gpfbn5sasVn7V5jHRSqWy1fgbN27g4sWL8PT01Ft7jUkXj9R+/fXXcfXqVUydOlUfTTQJ2hynn3/+GcuXL8fBgwdhbW1Wp1ibtDlOZ86cQU5ODuzt7ZGRkYGLFy8iLi4Oly9fNut5Jtocq9DQUOzYsQPTpk3D9evXcePGDURFReGtt94yRJO7DUvtz82NWY2YNOvsY6Jbi2+t3Bxp+0jtXbt2ITExEXv27IG7u7u+mmcyOnqcGhsbER0djRdeeAFDhgwxVPNMRmd+n5qamiCTybBjxw7cc889mDJlClJTU5Genm72oyZA547VqVOnEB8fj+effx4FBQXIzMxEcXGx2T8zRRuW3J+bC7P6c06bx0TL5fJW462treHq6qq3thpbVx6pvWfPHsTGxuKDDz7AxIkT9dlMo+vscaqursbRo0dx/PhxPP300wBufgGLoghra2vs378f48ePN0jbDUmb3ydPT0/069cPgiBIZX5+fhBFEaWlpfD19dVrm41Fm2OVnJyM0aNH49lnnwUAjBgxAk5OTrj33nvx8ssvcyTgvyy1Pzc3ZjVios1jokNCQjTi9+/fj6CgINjY2Oitrcam7SO1d+3ahZkzZ2Lnzp0WcX27s8fJ2dkZP/zwAwoLC6XX/PnzMXToUBQWFiI4ONhQTTcobX6fRo8ejfPnz6OmpkYq++mnn9CjRw/0799fr+01Jm2O1bVr19Cjh3p3bWVlBeB/IwJkuf252THSpFu9ab4Nb/PmzeKpU6fEhIQE0cnJSfztt99EURTF5cuXizExMVJ88+1lCxcuFE+dOiVu3rzZYm4v6+yx2rlzp2htbS2+/fbbYllZmfS6cuWKsXbBIDp7nFqylLtyOnucqqurxf79+4uPPvqoePLkSTE7O1v09fUVZ8+ebaxdMJjOHqutW7eK1tbW4rp168Rff/1VzMnJEYOCgsR77rnHWLtgENXV1eLx48fF48ePiwDE1NRU8fjx49Jt1ezPzZPZJSaiKIpvv/226O3tLdra2op33XWXmJ2dLb03Y8YMccyYMWrxX3/9tThy5EjR1tZWHDhwoLh+/XoDt9h4OnOsxowZIwLQeM2YMcPwDTewzv5O/ZGlJCai2PnjVFRUJE6cOFF0cHAQ+/fvLy5atEi8du2agVttHJ09Vm+++aY4bNgw0cHBQfT09BSfeOIJsbS01MCtNqyvvvqq3T6H/bl5kokixwGJiIjINJjVHBMiIiLq3piYEBERkclgYkJEREQmg4kJERERmQwmJkRERGQymJgQERGRyWBiQkRERCaDiQkRERGZDCYmREREZDKYmBCZicbGRoSGhuKRRx5RK1epVPDy8sJzzz1npJYREXUcl6QnMiM///wz7rzzTmzatAlPPPEEAGD69On47rvvkJ+fD1tbWyO3kIiofUxMiMzMm2++icTERJw4cQL5+fl47LHHcOTIEdx5553GbhoR0S0xMSEyM6IoYvz48bCyssIPP/yABQsW8DIOEXUbTEyIzNDp06fh5+eHgIAAHDt2DNbW1sZuEhFRh3DyK5EZ2rJlCxwdHVFcXIzS0lJjN4eIqMM4YkJkZg4dOoQ///nP+Pzzz5GSkoLGxkZ88cUXkMlkxm4aEdEtccSEyIzU1tZixowZmDdvHiZOnIh3330X+fn52Lhxo7GbRkTUIUxMiMzI8uXL0dTUhNWrVwMABgwYgNdffx3PPvssfvvtN+M2joioA3gph8hMZGdnY8KECfj666/xpz/9Se29iIgI3Lhxg5d0iMjkMTEhIiIik8FLOURERGQymJgQERGRyWBiQkRERCaDiQkRERGZDCYmREREZDKYmBAREZHJYGJCREREJoOJCREREZkMJiZERERkMpiYEBERkclgYkJEREQm4/8BoBk5rmcgUYcAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def simulate_diffusion(\n", + " source_point, mask, D=0.1, dt=None, num_steps=1000, snapshot_interval=100\n", + "):\n", + " \"\"\"\n", + " Simulate diffusion on a 2D grid with a user-provided source point and mask.\n", + "\n", + " Parameters:\n", + " - source_point: Tuple of (x_index, y_index) where the initial concentration is placed.\n", + " - mask: 2D numpy array defining the domain (1 inside, 0 outside).\n", + " - D: Diffusion coefficient (default 0.1).\n", + " - dt: Time step. If None, it will be set based on stability criteria.\n", + " - num_steps: Total number of time steps to simulate.\n", + " - snapshot_interval: Interval at which snapshots of the concentration field are stored.\n", + "\n", + " Returns:\n", + " - C_list: List of concentration fields at specified intervals.\n", + " - X, Y: Meshgrid arrays for plotting.\n", + " \"\"\"\n", + " # Enable 64-bit precision if needed\n", + " jax.config.update(\"jax_enable_x64\", True)\n", + "\n", + " # Get grid dimensions from the mask\n", + " Nx, Ny = mask.shape\n", + " Lx, Ly = 1.0, 1.0 # Physical dimensions (can be adjusted)\n", + " dx = Lx / Nx\n", + "\n", + " # Set time step based on stability condition if not provided\n", + " if dt is None:\n", + " dt = dx * dx / (4 * D) * 0.9 # 90% of max stable dt\n", + "\n", + " # Create the spatial grid\n", + " x = jnp.linspace(0, Lx, Nx)\n", + " y = jnp.linspace(0, Ly, Ny)\n", + " X, Y = jnp.meshgrid(x, y, indexing=\"ij\")\n", + "\n", + " # Initialize the Concentration Field\n", + " C = jnp.zeros((Nx, Ny))\n", + " # Place a point source at the specified location\n", + " source_x, source_y = source_point\n", + " C = C.at[source_x, source_y].set(1.0 / (dx * dx))\n", + "\n", + " # Define the Laplacian Operator with Neumann Boundary Conditions\n", + " def laplacian(C, mask):\n", + " # Pad the array to handle boundary conditions\n", + " C_padded = jnp.pad(C, pad_width=1, mode=\"edge\")\n", + " mask_padded = jnp.pad(mask, pad_width=1, mode=\"constant\", constant_values=0)\n", + "\n", + " # Extract central and neighboring values\n", + " C_center = C_padded[1:-1, 1:-1]\n", + " mask_center = mask_padded[1:-1, 1:-1]\n", + "\n", + " C_up = C_padded[2:, 1:-1]\n", + " mask_up = mask_padded[2:, 1:-1]\n", + "\n", + " C_down = C_padded[:-2, 1:-1]\n", + " mask_down = mask_padded[:-2, 1:-1]\n", + "\n", + " C_left = C_padded[1:-1, :-2]\n", + " mask_left = mask_padded[1:-1, :-2]\n", + "\n", + " C_right = C_padded[1:-1, 2:]\n", + " mask_right = mask_padded[1:-1, 2:]\n", + "\n", + " # Apply Neumann boundary conditions (zero normal derivative)\n", + " C_up = jnp.where(mask_up, C_up, C_center)\n", + " C_down = jnp.where(mask_down, C_down, C_center)\n", + " C_left = jnp.where(mask_left, C_left, C_center)\n", + " C_right = jnp.where(mask_right, C_right, C_center)\n", + "\n", + " # Compute the Laplacian\n", + " laplacian_C = (C_up + C_down + C_left + C_right - 4 * C_center) / (dx * dx)\n", + "\n", + " # Only compute inside the domain\n", + " laplacian_C = jnp.where(mask_center, laplacian_C, 0.0)\n", + "\n", + " return laplacian_C\n", + "\n", + " # Time-Stepping Function\n", + " @jax.jit\n", + " def update(C, mask):\n", + " C_new = C + D * dt * laplacian(C, mask)\n", + " return C_new\n", + "\n", + " # Run the Simulation\n", + " C_list = []\n", + " for step in range(num_steps):\n", + " C = update(C, mask)\n", + " if step % snapshot_interval == 0:\n", + " C_list.append(C)\n", + " print(f\"Step {step}\")\n", + "\n", + " return C_list, X, Y\n", + "\n", + "\n", + "# Example Usage\n", + "if __name__ == \"__main__\":\n", + " # Define grid size\n", + " Nx, Ny = 200, 200\n", + "\n", + " # Create a circular mask\n", + " x = np.linspace(0, 1.0, Nx)\n", + " y = np.linspace(0, 1.0, Ny)\n", + " X, Y = np.meshgrid(x, y, indexing=\"ij\")\n", + " radius = 0.5\n", + " distance = np.sqrt((X - 0.5) ** 2 + (Y - 0.5) ** 2)\n", + " mask = np.where(distance <= radius, 1.0, 0.0)\n", + "\n", + " # Define source point (center of the grid)\n", + " source_point = (Nx // 2, Ny // 2)\n", + "\n", + "\n", + " # Run the simulation\n", + " C_list, X_grid, Y_grid = simulate_diffusion(\n", + " source_point=source_point,\n", + " mask=mask,\n", + " D=0.1,\n", + " num_steps=2000,\n", + " snapshot_interval=200,\n", + " )\n", + "\n", + " # Visualization\n", + " for i, C_snapshot in enumerate(C_list):\n", + " # Mask out values outside the domain for visualization\n", + " C_display = np.where(mask == 1, C_snapshot, np.nan)\n", + "\n", + " plt.figure(figsize=(6, 5))\n", + " plt.contourf(X_grid, Y_grid, C_display, levels=50, cmap=\"hot\")\n", + " plt.colorbar(label=\"Concentration\")\n", + " plt.title(f\"Diffusion at Time Step {i * 200}\")\n", + " plt.xlabel(\"X\")\n", + " plt.ylabel(\"Y\")\n", + " plt.axis(\"equal\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 0/22223\n", + "Step 1000/22223\n", + "Step 2000/22223\n", + "Step 3000/22223\n", + "Step 4000/22223\n", + "Step 5000/22223\n", + "Step 6000/22223\n", + "Step 7000/22223\n", + "Step 8000/22223\n", + "Step 9000/22223\n", + "Step 10000/22223\n", + "Step 11000/22223\n", + "Step 12000/22223\n", + "Step 13000/22223\n", + "Step 14000/22223\n", + "Step 15000/22223\n", + "Step 16000/22223\n", + "Step 17000/22223\n", + "Step 18000/22223\n", + "Step 19000/22223\n", + "Step 20000/22223\n", + "Step 21000/22223\n", + "Step 22000/22223\n", + "Step 22222/22223\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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NN7Bw4UIMGjQIAwcOBHB4VqFly5Z47rnn0KtXL7Ru3RqdOnVCp06dsGDBAgwbNgxDhw7F2LFjccwxx2D37t3YuHEjPvvsM7z00ktR/a5fvx7XXXcdLrvsMmzbtg3Tp0/HMcccE1my0fLwww9jxowZOP/883HBBRfgo48+ijouv+X27rvvxptvvonzzz8fxcXFyMjIwFNPPYXPP/886vbgrKws3HHHHbjzzjuRlZWFoqIifPrppyguLsZ1112H3r17R+pee+21mDNnDi677DLcf//96NChA+bOnYvvvvsO77zzjrm/MKJEFOweWIoX4TsJwl+pqalShw4dpLPPPlu67777pJqamphznN4FI0mSVFVVJV166aVSVlaWlJmZKV111VXS+vXro+6M+Pnnn6WxY8dKPXv2lFq1aiW1bt1aOvHEE6VHH31UOnjwYFQf8rtgJOnwHR4TJkyQcnJypObNm0u5ubnStGnTpP3790fVAyBNnDgxZny5ubmm7lq5//77pa5du0ppaWlSr169pL/+9a+qfz4VFRXS6aefLh111FESgJjxarFzF4yVv4fc3FzpggsuiCr75ZdfpJtvvlnKy8uTWrRoIWVlZUn5+fnS9OnTpb1792qO9T//+Y90zz33SOecc450zDHHSKmpqVKrVq2kk08+WbrnnnukX3/9Nar+0qVLpZ49e0otWrSQAEgzZ86MHPv888+lyy+/XOrQoYPUokULqWPHjtI555wjzZ8/P1In/N/u22+/LY0ePVoSBEFq2bKlNHz4cOn777/XHKf8z0T+377yS+nLL7+ULrjgAqlNmzZSenq6dNppp0n//Oc/Vdt+/PHHpeOPP15KTU2VunTpIs2cOVNqaGiIqVddXS1dffXVUlZWVqRN5ZOHiZJNSJICurWBiMgFS5YswTXXXINPP/0UBQUFQQ+HiEziHhAiIiLyHQMIERER+Y5LMEREROQ7zoAQERGR7xhAiIiIyHcMIEREROS7JvUgskOHDmHHjh1o06YNP+CJiCiBSZKEPXv2oFOnTpFH4Ltp//79aGhocKWt1NTUmM9loiYWQHbs2IHOnTsHPQwiInLJtm3bNJ/Ka9f+/fuRl5en+vlCdnTs2BGbN29mCFFoUgEk/NkR27ZtQ0ZGRsCjISIiu+rq6tC5c+eYzwRyQ0NDA6qrq7Ft2/eOrxWHx3kcGhoaGEAUEiaAzJs3D/PmzYt8CFSfPn0wY8YMDBs2zHQb4WWXjIwMBhAioiTg5XI6rxXeSphNqMceeyzuv/9+rF+/HuvXr8c555yDESNG4Ouvvw56aERERGRRwgSQCy+8EMOHD8fxxx+P448/Hvfeey9at24d88mVREREiW7u3LnIy8tDeno68vPzsXbtWs26r7zyCoYMGYL27dsjIyMDhYWFWLlyZVSdQYMGIRQKxXxdcMEFXr8VTQkTQOQaGxvxwgsvYN++fSgsLNSsV19fj7q6uqgvIiKieLZs2TJMmjQJ06dPx4YNG3DmmWdi2LBhqKysVK3//vvvY8iQIVixYgXKy8sxePBgXHjhhdiwYUOkziuvvIKqqqrI11dffYWUlBRcdtllfr2tGAn1KPYvv/wShYWF2L9/P1q3bo3nn38ew4cP16xfXFyMu+66K6Y8EwBvwiUiSlwSgFoAtbW1ru/TqKurQ2ZmJmprf3ZlE2pmZralcZ566qkYMGAA5s2bFynr1asXRo4ciZKSElNt9OnTB6NGjcKMGTNUjz/22GOYMWMGqqqq0KpVK1Ntui2hZkBOOOEEVFRU4KOPPsL111+PMWPG4JtvvtGsP23aNNTW1ka+tm3b5uNoiYiIDlPOxtfX16vWa2hoQHl5OYqKiqLKi4qKsG7dOlN9HTp0CHv27EFWVpZmnUWLFuGKK64ILHwACXQXDHD4YS49evQAABQUFODTTz/F448/jgULFqjWT0tLQ1pamp9DJCKipPEzgF8dtrEHAGKeQTVz5kwUFxfH1N65cycaGxuRnZ0dVZ6dnW36uSQPP/ww9u3bh8svv1z1+CeffIKvvvoKixYtMtWeVxIqgChJkqSZIomIiOKF8vlTRr8cK28vliTJ1C3HS5cuRXFxMZYvX44OHTqo1lm0aBH69u2L3/zmNyZG7p2ECSC33347hg0bhs6dO2PPnj144YUXsHr1arz11ltBD42IiEiX2WeKtGvXDikpKTGzHTU1NTGzIkrLli3DuHHj8NJLL+G8885TrfPrr7/ihRdewKxZs8wP3iMJswfk559/xujRo3HCCSfg3HPPxccff4y33noLQ4YMCXpoRERErkhNTUV+fj5KS0ujyktLSzFw4EDN85YuXYqxY8fi+eef17219sUXX0R9fT2uuuoq18ZsV8LMgAS9VkVEROSHKVOmYPTo0SgoKEBhYSEWLlyIyspKTJgwAcDhGyy2b9+OZ555BsDh8HH11Vfj8ccfx2mnnRaZPWnZsiUyMzOj2l60aBFGjhyJtm3b+vumVCRMACEiImoKRo0ahV27dmHWrFmoqqpC3759sWLFCuTm5gIAqqqqop4JsmDBAhw8eBATJ07ExIkTI+VjxozBkiVLIq///e9/44MPPsDbb7/t23vRk1DPAXEqfG83nwNCRJTY/HkOyBfIyHD2YXd1dXuQmXmiJ+NMdAmzB4SIiIiSB5dgiIiIVO0G0OCwjb1uDCQpcQaEiIiIfMcAQkRERL5jACEiIiLfMYAQERGR7xhAiIiIyHcMIEREROQ7BhAiIiLyHQMIERER+Y4PIiMiIlJVA+cPEvvVjYEkJc6AEBERke8YQIiIiMh3DCBERETkOwYQIiIi8h0DCBEREfmOAYSIiIh8xwBCREREvuNzQIiIiFTVAGjpsI3/ujGQpMQZECIiIvIdAwgRERH5jgGEiIiIfMcAQkRERL5jACEiIiLfMYAQERGR7xhAiIiIyHcMIEREROQ7PoiMiIhI1S4A6Q7b2O/GQJISZ0CIiIjIdwwgRERE5DsGECIiIvIdAwgREVGcmTt3LvLy8pCeno78/HysXbtWs+4rr7yCIUOGoH379sjIyEBhYSFWrlypWf+FF15AKBTCyJEjPRi5eQwgREREcWTZsmWYNGkSpk+fjg0bNuDMM8/EsGHDUFlZqVr//fffx5AhQ7BixQqUl5dj8ODBuPDCC7Fhw4aYulu3bsWtt96KM8880+u3YSgkSZIU9CD8UldXh8zMTGQCCAU9GCIisk0CUAugtrYWGRkZrrYdvlbU1t6FjAxnd8HU1e1HZuZMS+M89dRTMWDAAMybNy9S1qtXL4wcORIlJSWm2ujTpw9GjRqFGTNmRMoaGxtx9tln45prrsHatWshiiJee+01S+/HTZwBISIi8lhdXV3UV319vWq9hoYGlJeXo6ioKKq8qKgI69atM9XXoUOHsGfPHmRlZUWVz5o1C+3bt8e4cePsvQmX8TkgREREqn4GkOawjcNBo3PnzlGlM2fORHFxcUztnTt3orGxEdnZ2VHl2dnZqK6uNtXjww8/jH379uHyyy+PlH344YdYtGgRKioqrA3fQwwgREREHtu2bVvUEkxamn6wCYWiNwpIkhRTpmbp0qUoLi7G8uXL0aFDBwDAnj17cNVVV+Gvf/0r2rVrZ2P03mAAISIi8lhGRoapPSDt2rVDSkpKzGxHTU1NzKyI0rJlyzBu3Di89NJLOO+88yLlP/zwA7Zs2YILL7wwUnbo0CEAQPPmzfHdd9+he/fuVt6OK7gHhIiIKE6kpqYiPz8fpaWlUeWlpaUYOHCg5nlLly7F2LFj8fzzz+OCCy6IOtazZ098+eWXqKioiHxddNFFGDx4MCoqKmKWh/zCGRAiIqI4MmXKFIwePRoFBQUoLCzEwoULUVlZiQkTJgAApk2bhu3bt+OZZ54BcDh8XH311Xj88cdx2mmnRWZPWrZsiczMTKSnp6Nv375RfQiCAAAx5X5iACEiIoojo0aNwq5duzBr1ixUVVWhb9++WLFiBXJzcwEAVVVVUc8EWbBgAQ4ePIiJEydi4sSJkfIxY8ZgyZIlfg/fND4HhIiIEo4/zwG5ARkZzu6CqaurR2bmXE/Gmei4B4SIiIh8xyUYIiIiVbsBpDpso8GNgSQlzoAQERGR7xhAiIiIyHdcgiGKU0IAfYoB9ElETRMDCFGAhKAHoCAYHBd9GAMRNQ0MIEQ+EYIegAsElTLR5zEQUXJImD0gJSUlOOWUU9CmTRt06NABI0eOxHfffRf0sIhUCSpfyUpA03ifROSuhAkga9aswcSJE/HRRx+htLQUBw8eRFFREfbt2xf00Ih4EZYRwD8LIjKWMEswb731VtTrxYsXo0OHDigvL8dZZ50V0KioKROCHkCCEBSvxQDGQETxJ2ECiFJtbS0AICsrS7NOfX096uvrI6/r6uo8HxclNyHoASQBQfazGNAYiMypgfPL5EE3BpKUEmYJRk6SJEyZMgVnnHGG7if5lZSUHP7slyNfQX3kMCU2AVxO8IoA/tkSNVUJGUBuvPFGfPHFF1i6dKluvWnTpqG2tjbytW3bNp9GSIlOAC+MfhPAP2+ipiThlmBuuukmvP7663j//fdx7LHH6tZNS0tDWpqzTzKkpkUIegDEJRqiJiJhAogkSbjpppvw6quvYvXq1cjLywt6SJQkhKAHQJqEI9/FAMdARN5ImAAyceJEPP/881i+fDnatGmD6upqAEBmZiZatmwZ8OgoEQlBD4BME2Q/iwGNgYjclTABZN68eQCAQYMGRZUvXrwYY8eO9X9AlLCEoAfgUBsX2tjjQhtBEY58FwMcAxE5lzABRJKkoIdACU4IegAmuBEu3Oon3kOKcOS7GOAYiMi+hAkgRHYJQQ9AhV9BwwmtMcZbMBGOfBcDHAMlq10AUhy20ejGQJISAwglLSHoAcgkQuAwS+29xEMoEY58FwMcAxGZxwBCSUcIegBIrsBhhvL9BhlIBDCEECUCBhBKGkKAfTe1wGFE/ucRRBgRjnwXA+ibiMxhAKGkIATQJ0OHOUGGEeHId9HnfonIGAMIJTTB5/4YOpwJKowIYAghijcMIJSwBB/7iqfgkelCG7UutOGU32FEOPJd9KEvIjLGAEIJR/CpnyBChxvhwq1+/Awp4T9rBhGipoMBhBKK4EMffgUPv8KGXVrj8zKY+DkrIoAhhIzUwPmHxh9yYyBJiQGEEoLgQx9eBo94DxtWKN+LV4HEj1kRAQwhREFhAKG4J3jcvlfBI5lChx6vA4nXQUQ48l30qH0iUscAQnFL8Lh9t4NHUwkcRuR/Dm6GET+CiOhR20QUiwGE4pLgYdvJEDwEh+eLLozBDC/CiJdBRDjyXfSgbSKK5nR3DZHrBI/abQP3wkem7MttgomvROhDye0/My/37Agetk1kxty5c5GXl4f09HTk5+dj7dq1mnVfeeUVDBkyBO3bt0dGRgYKCwuxcuXKqDpff/01LrnkEnTt2hWhUAiPPfaYx+/AGAMIxQ0B3vzD71bw8CJ0CCpf8UJQ+XKLW3+OboZKJcGjdomMLFu2DJMmTcL06dOxYcMGnHnmmRg2bBgqKytV67///vsYMmQIVqxYgfLycgwePBgXXnghNmzYEKnz66+/olu3brj//vvRsWNHv96KrpAkSVLQg/BLXV0dMjMzkQkgFPRgKIrgUbtuBQ83CC61E09EF9tyY4nGi2UZ0YM2yTkJh/+bqa2tRUZGhqtth68VtbU5yMhw9nt6Xd0hZGZWWRrnqaeeigEDBmDevHmRsl69emHkyJEoKSkx1UafPn0watQozJgxI+ZY165dMWnSJEyaNMlUW17hHhAKnOBBm/ESPAQX2ohnguK16KCt8J+3kyDSBu6HEOHId9Hldqlpqauri3qdlpaGtLS0mHoNDQ0oLy/H1KlTo8qLioqwbt06U30dOnQIe/bsQVZWlv0B+4ABhAIleNCm0/DhJHgIDvs2w60lBy83cQL2L9hOg4hXm1QFMIQ0PbvgfL788CJD586do0pnzpyJ4uLimNo7d+5EY2MjsrOzo8qzs7NRXV1tqseHH34Y+/btw+WXX25vyD5hAKHACC63lyzBw68nsRr14/QCLsh+Fm2cH49BRABDCNmzbdu2qCUYtdkPuVAoOvhIkhRTpmbp0qUoLi7G8uXL0aFDB3uD9QkDCAVCcLk9Jxdtu8FDcNBnWDx9yJ2S2tjsXswF2c+ixXPdCCIMIRS0jIwMU3tA2rVrh5SUlJjZjpqamphZEaVly5Zh3LhxeOmll3Deeec5Gq8feBcM+UqA+7MFdi/idu/EEGD/PbRRfCUaN8YvwN6fn5M7Z9z+sxZcbo8oLDU1Ffn5+SgtLY0qLy0txcCBAzXPW7p0KcaOHYvnn38eF1xwgdfDdAVnQMg3gsvtOQkeVgk2+/I6ZAgW64su9+/kw+OEI99Fi+dlwt5siNtLMgI4E0LemDJlCkaPHo2CggIUFhZi4cKFqKysxIQJEwAA06ZNw/bt2/HMM88AOBw+rr76ajz++OM47bTTIrMnLVu2RGbm4X/xGhoa8M0330R+3r59OyoqKtC6dWv06NEjgHfJ23DJJ4KLbfm53CLY6MON0GGnX7eILrVj9UJvp1+7yzJuLsmILrZF5vlzG24qMjKcXS3q6iRkZjZYHufcuXPx4IMPoqqqCn379sWjjz6Ks846CwAwduxYbNmyBatXrwYADBo0CGvWrIlpY8yYMViyZAkAYMuWLcjLy4upc/bZZ0fa8RsDCHlOcLEtv2Y9BIv1nYQOq30FQXRwbrwGEYaQxJbsAaQp4BIMeUpwsS07F/l4DB5W+4gHgkqZaPJcq8s04b7Mtg/YW5Zxc0lGAEMIkVUMIOQZwcW2vA4fgoW6VsdipW23+9YTxG22Vi764fbNtm33bhm37pIRwBCSdPY1AClO23BlJEmJAYQ8IbjUTjzNelgZi9k2nfbjhJvPARFkP4sW+vYqiDCEEMU/BhByneBSO/Ey6+Fl8IjnW3GVYzN7gRZkP4sW+jBqP9yuUZsAQwhRImAAIVcJLrXjZfgQXB6D2fastGmF2f5Fh/3YCSSChf7NBgDBRFuAvSUZhhAi/zCAUNyxepF2e9bD7eDh5IFdbjLTnmihPS82l5pdmjHTVpjV2RCGECJ/MICQawQX2vAqfAgu9u1mW1ba9IOgUiaaOM/tpRQrQUSvnTA7IcRM/0YEMIQQaWEAIVcILrQRz+HDTBtm2rHanll6/bp5t0uYqFPfbBgJt2umLaftAMEtyQhgCCFSwwBCjgkutOFF+BBc6teoHS9Dhxt7Rqy04fZGU7cChJkgIBi0ERbEkowAhhAiJQYQckRwoY14DR9GbbgRXqy25zU7n4AryH4WTbSr1V64HaM2jMKM1vlyQe0LIaL/YQAh2wQX2rBy0XVrycXr4GF0vtl2jJjtR060cY6Vu18EE30ZBYlwG3rnu7Uk42cIEcBZkIRTC+CQwzaYXDUxgFBg4i18ODnXjfOttmeHUZuiiTbc2uNhJojYPdfo/DCGEKLgMICQLYLD890OH4LD/vTO9zK0mG3HL4JKmahT3+ulFTPn+h1CnBLAEEIEMICQDYLD8+MpfOidG+RsidvnaRFt9Kl1jlEYCbejd74XsyF654ZZCSHclErkDgYQskRweH4ihA8vAovRuXbquUGrL9HkOVr17M5qeDUbYnQu4H8IIWrqGEDINMHh+X6GD7dDhBd9Wamjx8yfq9WLpaB4LZqop1bHqyCiNR6nSzJ+hhCjsRAlOwYQijtBhA8/Z0rMHDfThxVOQ4qgeC0a1FEe11ueCZ+n1qbWRd7OOfJz1c4LYwgh8gcDCJkiODzf7IU0nsKH1fp65xgdM2rXD1aeASLIfhZ1jqsdsxoq7MyGON0XwuUYIu8xgJAhweH5QYcPrXO8rm90TK9NNVY+dM8KvQutmWeACLKfRY1jynK7ocLNJRm98wD/QojROChAe+OkjSTFAEKeSubwoVXXTr9yXgUNK/1pXXjt3umiVa4VRPTqx2sIccJoHETJiAGEdAkOznVzSUGw0Y/WOVbChJW6WuVa7YRZDRwZFutrqdM5phyT2kXYzr4OrXK9IGKlrlrb4XO8DiHcD0JkDQMIaRJ86sfoAizoHPMqfFhp10pfYWZCh1tBw2r7asHEKJBYDQVa5WoXcSt1w/WVdfXq650T5lcIIWpKGEDIE24tvQg2+lA7x2ldtXpqZXp96b1XO2HD7lKN0YVUORa9QKIVRIDoC7Fw5LuoqC+olFmdDUmmEGI0BqJkwgBCqgQH5yZC+HBSz8r5gPb7NBM6vNgPotem2gVWPk5lGJG3ZWZWRDjyXTQoC58fbyGEiNzDAEIxBAfnJnv4MHuundBhJ2xY3Wdj9Ju50VKLmTDiZhCxEkKUfWjV1WrXDM6CELmHAYTijqBzLKjwYfY8q8HDKHS4/WwQvfbULph6gUQrjOgFETMBQVlmNsBY6UOrrl79MIYQIncwgFAUwcG5bsx+6PXvR/hQq6MsMxs87IQOq4Ej9SjzdRt+1T9u5kFkWksu4fdqFESszIbIX4fPtbsko1ZPq65e/TBuSm0iagEcdNjGPjcGkpyaBT0AK95//31ceOGF6NSpE0KhEF577bWgh0RHuPm8DyvtCybrKuu1UamnrCNonCeXidj3lIHY8JGpUVc+Fq33mHqU9pcVeu1otaU3NqvvXa1dOUHltbLMi/08dh4w54eg+yfyWkIFkH379uGkk07C7Nmzgx5KUhI8bt/uvg8vwodRHbVz1MKHnNmLr7w9ZZuWQ0Ybi186jPrVasZpEJETYO7vQklZx0o9PXr1zYZpt5fRiJJFQi3BDBs2DMOGDQt6GKTgxj+wgsW21erbCR9q7Rido3axVdIKHWpMhQy3WNgEohyXfAlH7XZbteUZM0szWssyos5rM+eE6zm5k8aI109KFcC9IJS8EiqAWFVfX4/6+vrI67o6vWc/Nm2CzfO83vdhtr4b4cOovpl9HmaDh2bosBI2BAt15USTfesEErUwIq+uvDBrBRHl/hArIcTMOWp1rNTTqitnJoRwQypRrIRagrGqpKQEmZmZka/OnTsHPaS4JHjcvpv7PgSb9dwOH8olBrP7HFSXN/SWSASdL7vMtqczLrX3oayqtTQDnTrKNpTDUhuqmVkts7nOy/0gXIohK+bOnYu8vDykp6cjPz8fa9eu1axbVVWFK6+8EieccAKaNWuGSZMmqdZ77LHHcMIJJ6Bly5bo3LkzJk+ejP3793v0DowldQCZNm0aamtrI1/btm0LekhJxe+lF7W6QYUPvePKi6jqvgqti7sAcyFDWc/ql5k25TTGayeIaO0PUbahHJZyqFr11Y6r0apjJ4R4/QGCen1T8lm2bBkmTZqE6dOnY8OGDTjzzDMxbNgwVFZWqtavr69H+/btMX36dJx00kmqdZ577jlMnToVM2fOxMaNG7Fo0SIsW7YM06ZN8/Kt6ErqJZi0tDSkpaUFPYy4Jnjcvp2lFye/rSrbdBI+7AQPOdVlFqs7I/WOOaHWrqhTR35MZQNI+L3qLc0o935kwN0lGaP6Vm7PtcPrpRhqOh555BGMGzcO1113HYDDMxcrV67EvHnzUFJSElO/a9euePzxxwEATz/9tGqbZWVlOP3003HllVdGzvm///s/fPLJJx69C2NJPQNC3jETEtx8sqdgop5RHeVx+Wu139rl9JZbtGY8Ygaj9mu92qC0jinbcvqlpNe3VrmiLa0ZETm92RAzd8novTaq72RmTauuX4Lsm5yrq6uL+pLvT5RraGhAeXk5ioqKosqLioqwbt062/2fccYZKC8vjwSOH3/8EStWrMAFF1xgu02nEmoGZO/evdi0aVPk9ebNm1FRUYGsrCx06dIlwJElJiFB+jZTV1nHaviQU14gtY6pnasaPPQGolWmdb6b1NpWe0IYoP7MdHm5YrpDOSNiZTZE7S6ZIGZC7GxK5SxIktkD4JDDNo48iEy5B3HmzJkoLi6Oqb5z5040NjYiOzs7qjw7OxvV1dW2h3HFFVfgl19+wRlnnAFJknDw4EFcf/31mDp1qu02nUqoALJ+/XoMHjw48nrKlCkAgDFjxmDJkiUBjarpcTr7IVhoV62u1d9ylcflr+2GD8fBQ/la6zwlrfPsEg36D18p5f3KzxEUZSaCiNbdMnpLMnbukNETdAiw278A3hGTqLZt24aMjP/9q2K0PSAUCkW9liQppsyK1atX495778XcuXNx6qmnYtOmTbjllluQk5ODO++803a7TiRUABk0aBAkSQp6GE2aV7+Q2933IVg8Ln9tNnzozXq4EjzcnO/XChBa1PoQNdpThhF5PWWZShDRmw3Rem6I3RBiVFeNWh2vZkGo6cnIyIgKIFratWuHlJSUmNmOmpqamFkRK+68806MHj06sq+kX79+2LdvH/7whz9g+vTpaNbM/x0Z3APSRAketu3WMz+UdY1CSqDhQ2uPh9ZrM3fBKNnZ1+F0L4haW1rvCSqvdfaHWNlzo3aOWncaXRsNTbc9r+6KsRvm9fqlxJeamor8/HyUlpZGlZeWlmLgwIG22/31119jQkZKSgokSQrsF/uEmgGhYAW99GKljvKY/LUbF76Y8GGmY7WG1Opo1VOjdq4e0URfRvtAlFMYguK48rXBbIjZfSFmZkLkPxvVVTtOFA+mTJmC0aNHo6CgAIWFhVi4cCEqKysxYcIEAIcfMbF9+3Y888wzkXMqKioAHN4r+csvv6CiogKpqano3bs3AODCCy/EI488gv79+0eWYO68805cdNFFSElJ8f09AgwgTZIQ9ABkvNj3oTwmf613XTcTPjSDh7Ij5SDM/OquNzi1+nZotSNqjEPvY2vtBBEbSzJ+hxDlcbU6enXDuCGV7Bo1ahR27dqFWbNmoaqqCn379sWKFSuQm5sL4PCDx5TPBOnfv3/k5/Lycjz//PPIzc3Fli1bAAB33HEHQqEQ7rjjDmzfvh3t27fHhRdeiHvvvde396UUkprQpoq6urrDT0UFYH8rT+ITbJwT5OyHk30f8p/tLLuYCh/KAel1qqyrVkerntE5Vhhd9UQT5yjr6B2X/6yoJ392iPyQ/OJdp1Eur68cjk6XhkNXq2OlXpiZvSB2A4hev02BhMN/vrW1tab2VlgRvlbUvgpktHLY1j4g82JvxpnouAekiRGCHoCM3aUXvTYEjZ99CR+CTqdqx7T2VMjrKetq7duwyqhNtbEY7QFRO65WV1HPzL4QM39H8u6Ur+3sBzFL2Zacl3tBiBIdl2DIFW5+2JyclU2HgsbPSp6FD6M6ymNqx9XqGNW3S9Tp18nSi/y48pgAU0sydpdj5M0rebkUEwQBnAXxXC2AAw7b+NW4SlPFAEKGvPgNze2lF+UxrXquhA95Z2YH5dVSjJxWCtRaB1BrW1QZi5mgoXVcLRkI6nW8CCHyn5X1zFCeb6ceb8slUscA0oQIHrXrxuyH2XphZicJXAkfbs566B3TqhNm9xPP9M5TXhnlfYtHvluZ8Qgflx/zMITImQ0hcspjZgKKF7Mg8TSzQuQXBhDS5dfsh1EdQaeuoPFz4OHD6VKMFxsIlFc5ZR/yQCIc+S6q9KW2vKK19KJVLsB2CIFOmRqNrmKOGZ1rtg8lr2ZB9PokincMIE2E4FG7fsx+KI+bXXoJi6vwYRQ8tP5A3UqCau0on40eFr5iCrIyUdaO1aUXtXKN41ohJMzt/SBmj1mpYxVnQSjeHTp0CJs2bUJNTQ0OHYr+kJyzzjrLcnsMIKQpXmY/zB4TLPTjOHz4ETy83Iwq6vSjfDIYEBtGRNl5VpZetEKIvE2dEOLmfhA55TGrsyRm6nEWhBLZRx99hCuvvBJbt26NeXJqKBRCY2Oj5TYZQMgTgs16fi29qNX1JXzIy5UDVNbVOscNyjZFjTFoPaZUgPWlF3l9vWkKjRASZiaEmOF0xoGzINSUTJgwAQUFBXjjjTeQk5Pj6IPxwhhAmgDBo3at7ol0OqNiZunFyr4Pzbtd1DoJ/2wUMtTKlOXKwSnrqtXXq2uX0SPX5X2pBRFBUd9o1kNe38ZyjNF+ELl4nQUhSlTff/89/vGPf6BHjx6utckAQqqcXOMEm/WsXIOt1NP7cDnAwq22aoHC6qyHneBh5i9D7Tw50US7yj0d8vPUgojRsowA/XBiIYRApSg8BCdLMX7PgnAZJsHsBWB9ZSHaf90YSPDCnx/DAEKBi5fZD0Gjjtr41PZ9BBY+jKZztP7A1OqaoXWeqNGnmVtujZZlzMx6mAwhdjelWiXvWq0v5XGz7VjBZRiKRzfddBP+9Kc/obq6Gv369UOLFi2ijp944omW22QASXKCjXMSafZDjZV9H7qFgsoxvTJ5ubzM7OfJ29n/4fQ2XHn7okq7Zu50Uc6GqNUJl9sJIUcY7QdR05RmQYi8dMkllwAArr322khZKBSCJEnchErxy+/ZjzDT+z7UOpA3rjxmJZCYmfUwm77c2Puh1oaVpRdBdlxrNkRZx2oIgfYxvf0gbm1IDXerdkztuBaz9dzid3/UtGzevNn1NhlAyDK7D+QMExwe12O09BKmu+lUPgDljIad8KG33GJ3KUarvhmiTh9On3aqNhtiNnDIl21E4/pGSzFq9dSadoPbyyZchqF4k5ub63qbDCBJTLBxjh/LL1b6dGP2Q7cftSUTM8cERR15mdVZD2X7dpZirFC2Jar0bfWD5pSzIVZCSPg1NMoQfczsUozWhlQz1Lq3ctwIl2EoEf3www947LHHsHHjRoRCIfTq1Qu33HILunfvbqu9Zi6PjyiK1UAjOGjf8uyHmX0f8gGZmQ0xCh9tNMrVjoWPC4jVKs3Zl1ofemNRO671HoDoPwdBcUytTO09CtrHwn+HZsOnWrPKn80GYSsEwxpEiWHlypXo3bs3PvnkE5x44ono27cvPv74Y/Tp0welpaW22uQMCFni9vKL09kPNXqzH6aWXtQGZTd8CCpleuXKY2HK0OCUvL199bF9i0e+m116USs3mgmBor5eHZ1jat2FeTkLohQPyzACuA+EvDF16lRMnjwZ999/f0z5bbfdhiFDhlhukzMg5ApBpSxeZj8sL70oB2Tmt3Q3woegOKY1YwEAaOfwy6Af5VjkMyLyY8py5fvSmwmR17dS58gx1TB5hB+zIIJaJQuchnkiP23cuBHjxo2LKb/22mvxzTff2GqTASRJCTbOceMmC7f6c3P2I8zW0ouZ2RCz4UOA+oVbWV81dOgECFsMwoh8XPKxGQUptTIzIcTMn7OgUkenK6iUeR2KvWqDAlKHw1NoTr6Uu6ITVPv27VFRURFTXlFRgQ4dOthqk0swZJrXd7/YZXn2I0xQfDdzx4mT8KEsU5ZrznQoZasO0ZqfNfrYGT2W8PKMcOSwiNiNqOKR13qbStXWR7SWXuTnazGxIVXtjhg5eTdqQ7YjHpZhiLwwfvx4/OEPf8CPP/6IgQMHIhQK4YMPPsADDzyAP/3pT7baZAAhxwSVMi9+05TXsbr5UHf2Q6sjrd/S5QNRDkgwWaYsjwofTkKHSphQJW9PLYzoBBHxSBWzd7EoQ4j8mJJRKNE5V+9i7dZdJ8o+dIZjihd3wwjgPhBy35133ok2bdrg4YcfxrRp0wAAnTp1QnFxMW6++WZbbTKAEADvl18EC/3p3aGq5Prsh9Zrvc6thg95mepSi5xW8DCzDKNXRx5O1MKIShAxCiGA8e21yhBiZdYjTHGu2ixImJXnghh154QbbRAFKRQKYfLkyZg8eTL27Dn8f1CbNs6uHNwDkoQED9qMpw1zjmc/BAedhs91PXwo92NkIzoY+LQHRLXfI+T7QwRZFTP7N9SEjxmFPwt/b2b3gugNR96OHX7vpSLyW5s2bRyHD4AzIOSQoFLmZPnFyuZT27MfykatXgD1rmS2w0eYcsZDK2x01BmEGdUqfShnRQxmQ4QjxSLMLcfo7QexStGmk1kQq9xehrHaH5FfBgwYgHfffRdHH300+vfvj1AopFn3s88+s9w+Awj5zu3fED2f/dBi5jd13fCht9yiFjychg6ttsJhRBlEwuORBxGdJRm95RRlHaOlGCcbVI8wm3PMNGmhW8u4D4Ti1YgRI5CWlhb5WS+A2MEAQp4TXGxLL7xYWiYSFA2afa219GJ2GUI1fBjNemgFj7Ya5UZ2abSvF0QMQkiYVnDwgqIPtQ+qUzK62IebBNydJVG2TZQIZs6cGfm5uLjY9fa5B4QMZyS83P8hmChXq2NmFUTvQVWeERSDEGTHDMOHck9GR8SGj7ayL7u02lD2p7UnRbEvBNCf9dF6nWmyntZrE8JdmP5sIJOU5woO2qI4VovDqdHJV5J86E+3bt2wa5fylxdAFEV069bNVpucAUkyQsB9+b15z+iJl1ENC4rvbs9+aL0GTIaPMLUZD63AYWVZplqlLNyu/B+WjjCeDVGZCRFg/fZaraUYm+zOgoS7JyJ1W7ZsQWNjY0x5fX09fvrpJ1ttMoCQr7wKKJ7N0giKDuz8Vu4ofKgFD7t7QZTnyQOJMoioLctYCCF2bq+V02pH67kgLi71hJuEoll5uduMloa4EZWC8vrrr0d+XrlyJTIz//evbWNjI959913k5eXZapsBhDwleHC+peUXp7MfVinbUX26qZnwYTd4KPeP6D2MTBkywv3qzYbohJAwAdY2lpp5QJkDek9G9fLCHvQ+ED/7ouQ1cuRIAIefAzJmzJioYy1atEDXrl3x8MMP22qbAYQCI8h+tvLwsTBTyy9ucLL0EhEOBnbCh1bwsPswMmUoUQYRtdkQtRCC6DK1Tal2mA0uGswsw7iBsxLUFBw6dAgAkJeXh08//RTt2rnxHKLDuAm1iQtyA6oVni6/uLDZUVXM0ovV8KG2CdWNh5Fpna+3/KO2OVXlKa3KTalWN6RaISi+W5i1UutOUCmzwun5RHJz585FXl4e0tPTkZ+fj7Vr12rWraqqwpVXXokTTjgBzZo1w6RJk2LqLFmyBKFQKOZr//79psazefNmV8MHwBkQsklQKTP699/rJ0TaXn6x0rhWG5pLL2r/w5oNH1rnqDH6rJifVcrUHkAmn+0Ij0ltJkTer8ZSjB0C3NlDosHoA+qUwwCiZzpcHg6RqmXLlmHSpEmYO3cuTj/9dCxYsADDhg3DN998gy5dusTUr6+vR/v27TF9+nQ8+uijmu1mZGTgu+++iypLT083Pa59+/ZhzZo1qKysRENDQ9QxO58HwwCSRISgB6AgeHC+5zMyVp56aonKLayOwofVT8TV+vA5efvhAKG2JKMMISaWYgRY2wuix+bzRcw8fJVLKeSHurro6JuWlhZ5yJfSI488gnHjxuG6664DADz22GNYuXIl5s2bh5KSkpj6Xbt2xeOPPw4AePrppzXHEAqF0LGjvU3sGzZswPDhw/Hrr79i3759yMrKws6dO3HUUUehQ4cOtgIIl2AoEILFciXX9n9YmQ0J17E8+6EWFuyGD+Vntdih1YbeA9DUNsXqvT8blM8FMUNQfD/ydxHIM2BUqM36CRp1jbKu1zOIpGKPS18AOnfujMzMzMiXWpAAgIaGBpSXl6OoqCiqvKioCOvWrXP0dvbu3Yvc3Fwce+yx+O1vf4sNGzaYPnfy5Mm48MILsXv3brRs2RIfffQRtm7divz8fDz00EO2xsMZEAqck6UbyxMUVgKHpQ+WMUNtJsNon4Wc0YVe6zcbted/yNuTz4iYWUoxsRRjdhZEj0fLMGFuPA+Esydk1rZt25CR8b9fnbRmP3bu3InGxkZkZ0f//56dnY3qaq3/l4317NkTS5YsQb9+/VBXV4fHH38cp59+Oj7//HMcd9xxhudXVFRgwYIFSElJQUpKCurr69GtWzc8+OCDGDNmDH73u99ZHhMDCCUF0/s/zDBTN1zH0eyH2fChFTzMTqWqfe6Lsn2tECIPG2aXYiwQ4HsgMbsPxA4B3CNC6jIyMqICiBHl565IkuTos1hOO+00nHbaaZHXp59+OgYMGIAnn3wSTzzxhOH5LVq0iPSfnZ2NyspK9OrVC5mZmaisrLQ1Ji7BUMLw/GFjejMdZuroCocKq+uvWgHGycPI1M7V+zwao6UYZRtHztW6I0aPlWUYi38XVv/7kQ/Bzm3i8UAIegBkWbt27ZCSkhIz21FTUxMzK+JEs2bNcMopp+D77783Vb9///5Yv349AGDw4MGYMWMGnnvuOUyaNAn9+vWzNwZbZ1FSsHsLrmCjLavXbbU+XGdmUFYGbmr2I8zM7IfR3hEn1IKI2RCiLLN4a55w5Ltbf/6C4rtiH4jbf81EXkpNTUV+fj5KS0ujyktLSzFw4EDX+pEkCRUVFcjJyTFV/7777ovUvfvuu9G2bVtcf/31qKmpwcKFC22NgUswlHAcb0AVTNTR+3U5fL7p/SR6sx9G+z606irpzUzEfoBUdJvy37SUyzFafem1aZEA43ULM3WIksSUKVMwevRoFBQUoLCwEAsXLkRlZSUmTJgAAJg2bRq2b9+OZ555JnJORUUFgMMbTX/55RdUVFQgNTUVvXv3BgDcddddOO2003Dcccehrq4OTzzxBCoqKjBnzhzD8UiShPbt26NPnz4AgPbt22PFihWO3ycDCHlC8OEc01Pq4Yat/DatxrVfk818kq1yNkIrfJhpS15HLTjohRCt/SDKc8P1TG5GVWNlH4hLjD6DhSgIo0aNwq5duzBr1ixUVVWhb9++WLFiBXJzcwEcfvCYct9F//79Iz+Xl5fj+eefR25uLrZs2QLg8KfW/uEPf0B1dTUyMzPRv39/vP/++/jNb35jOB5JknDcccfh66+/NrVh1SwGEPKdIPvZjWu64QZUK42oMdOO5vKL3qyG2aUXK5+Oa0Rr9kItXNhpxyQB2jMaZlKB3vkOedg0kWk33HADbrjhBtVjS5YsiSmTJEm3vUcffVT3IWV6mjVrhuOOOw67du1yNYBwDwjFNc/X5gWHneudH8WtT7AF7IcPK+eb/bReh5wGP5v/gbj5OUJBPwGYyA8PPvgg/vznP+Orr75yrU3OgCQJIegBNDW2ryp6D/QCjJ/14TR8yNtRzmBYnQVRnmewDKNHgPa0g5llF73z45AA9eEaTf7wuSM+qwWQ6rCNBuMqieCqq67Cr7/+ipNOOgmpqalo2bJl1PHdu3dbbpMBhJomu083E9wagJmZBL0Ph3OD0TKK1l4Qs+crCPA+JBj0wT0fRPY8+uijjp5DooYBhBKCa88AEWyeZ2nGw8z+D73z7LL6JFQgNkTYnQWxwO5GVAHmA4yiHbszB1a6dHIOUbwbO3as621yDwhRmOC0Aa3AoTfbYfYcvdkPoweTubxvw/X2vOPVZ8II3jRLFLdSUlJQU1MTU75r1y6kpKTYatN0APnpp59sdeC2uXPnIi8vD+np6cjPz8fatWuDHhI1da3UP9MhlttLKIC9x7Fb4d6TF2MI3jVtBzeLEmnTusumvr4eqan2NsqYXoLp27cvnnzySYwePdpWR25YtmwZJk2ahLlz5+L000/HggULMGzYMHzzzTfo0qVLYOMidwleNGr36iK4OYigaS2vBLAMYyQON2twAyg1ReHPiQmFQnjqqafQunXryLHGxka8//776Nmzp622TQeQ++67DxMnTsRrr72GhQsXom1bL36b0/fII49g3LhxuO666wAAjz32GFauXIl58+ZpfrQxkWeEoAfgN4cfPOcWJgEi34SfHSJJEubPnx+13JKamoquXbti/vz5tto2HUBuuOEGDBs2DOPGjUOfPn2wcOFCXHTRRbY6taOhoQHl5eWYOnVqVHlRURHWrVunek59fT3q6/93+19dnVefgUkUhHjcixEnIYWIXLF582YAhz+A7pVXXsHRRx/tWtuW7oLJy8vDe++9h9mzZ+OSSy5Br1690Lx5dBOfffaZa4OT27lzJxobG2M+DTA7OzvmUwPDSkpKcNddd3kyHiIiSnJ7AbRw2MYBNwYSvFWrVrnepuXbcLdu3YqXX34ZWVlZGDFiREwA8ZryPmRJkjTvTZ42bRqmTJkSeV1XV4fOnTt7Oj4i/1Qj/mZBOPtBlIwaGxuxZMkSvPvuu6ipqcGhQ4eijr/33nuW27SUHv7617/iT3/6E8477zx89dVXaN++veUO7WrXrh1SUlJiZjtqampiZkXC0tLSkJZm9g4FIotENLF9IHESLrj/g8h3t9xyC5YsWYILLrgAffv2deWhZKYDyPnnn49PPvkEs2fPxtVXX+24Y6tSU1ORn5+P0tJSXHzxxZHy0tJSjBgxwvfxkHdEeHBd3wN7d8KISKKQYfbOloDvgAHi7g4YoqbuhRdewIsvvojhw4e71qbpANLY2IgvvvgCxx57rGudWzVlyhSMHj0aBQUFKCwsxMKFC1FZWYkJEyYENiYi83bB/WeBuLEMo/c49Z91jjkkete0FzjxQk1ZamoqevTo4WqbpgNIaWmpqx3bMWrUKOzatQuzZs1CVVUV+vbtixUrViA3NzfooVFTtq/e4GFkeiFhJ9Sfhqo8Ry+8mAkhcTCrEeesBAzRq0EQxak//elPePzxxzF79mzXPhMm4T4L5oYbbsANN9wQ9DDIZ7Vw6fNgRGgvqegdM7WEEw4TP+PwE0S1woWW8Hl2yANGR5UyLRY+TE61Lxv0rvR6x0TrfTT8atys20Qf+yLyywcffIBVq1bhzTffRJ8+fdCiRfTtQa+88orlNhMugBAlBztLJ2aXcJwEBK1z1TagWgwvosWh2GHQh9WtJQbNETUZgiBE7b90AwNIkhCRRHsl/aA3o6E33SLCxz9otZDi5j4SowBhdv9HOLQoQ8qR1/vqYUjUOWZm+kLv/DgkBj0AIosWL17sepsMINS0ibC37BI+pne+KrUAIV+qUS7DeBVC1MKHmZkTM3UsbFx1ujZi83w/n4lsZYhGMzTcCOuzWji/Sh50YyDx4eDBg1i9ejV++OEHXHnllWjTpg127NiBjIyMqM+IMcv0p+ESecHoH1Qz/+CG64TX+yMFop0RqTDTTuS3/PAswM+K12oXbq2LuZkLuJ29G3rnKsciH4MLyy9KosM6Zs63yWrTDAXUFGzduhX9+vXDiBEjMHHiRPzyyy8AgAcffBC33nqrrTYZQMgToo1jeueoMb2eb6ZhK0nHMbWLt95DvrSCyi6NtvT6tRo+jMZhcb+JmT9Dvb9Yl6/2fNwIkTm33HILCgoK8J///ActW7aMlF988cV49913bbXJJRhKOHUAMuycaOZOFhHGSypm6qhSW06Rl1ldiglzMhthFCDMzn5ozPyEZ4ZEE0Nx6w4YInLdBx98gA8//BCpqalR5bm5udi+fbutNjkDQpaJKmVuLKX4RjzyXW9QtSbqKJd6DJdh5MyEBuVMRDXcfZ6HWltaSy8uzH4omVkqs/NwDsUtuETk3KFDh9DY2BhT/tNPP6FNGzuPmWYAadKM/m0PcnpaVCnzbDxqndmpY4rRhVwZVtSWQ5yGEK0gY7TvA7A0+xEmHvluZW+OmTom2wtXc7KyE1cBmigAQ4YMwWOPPRZ5HQqFsHfvXsycOdP249kZQCipONqIamUfiKh4HcPqLIidEGJ1RkQveOhtfLU5+2Hm9tswMykhTDTfrBqzd8BodeOwe1+JQQ+Aksajjz6KNWvWoHfv3ti/fz+uvPJKdO3aFdu3b8cDDzxgq03uAaG4Z+aRHYbbO0Qc3rdhZR+Ilcevhs8xfCw7EL2fQ35LrdZ+EOB/AUHtSalOZkTUgofW0ouZ2Q8D4pHvdmZDEmAaQgx6AEQe6dSpEyoqKvDCCy+gvLwchw4dwrhx4/D73/8+alOqFQwgFAgR/9vHKc8E8nI9tjeiKgdgJpAon/lh+pN19R7NbuZJqGqPcnfyuHZlO2r9yWmFD60Hj8nKlJtPHezl8IraZIuoUqbHztKN1T4oQHvA54DItGzZEtdccw2uueYaV9rjEkwSEYMegIIY9ACsEI98d/Kbefi17tKDmaUY5XKM2pKM0bKJFq3zrIYPZZtqbRgQj3y3s/yi/DNXlAfxGTBO8SFkFM9KSkrw9NNPx5Q//fTTtpdgGEDIN1b/ARVVysxcozT3gYiKcjOUd8OoDUqN5h0x8jK95Q3lhV7r4v6zxS8ltYBjJnzoLL1ozX4oX+uxc44ObkAlcmbBggXo2bNnTHmfPn0wf/58W20ygJAurX+4RZUyL/+hdnrHrCpRcaKoXk2V1gVStw2jO1rMhBCLswyatIKHlfChbA/WnvsBxM5+mD3Ppqa0AZXITdXV1cjJyYkpb9++Paqqqmy1yQDSxMXLb3d2xuHZ53kow4TWLIjR65hZEEB9uUJ50ddbkgmfK/8yS+8cZR9mwoeJpRdbQc1iG4pyM8svbuz/cPt8Irm5c+ciLy8P6enpyM/Px9q1azXrVlVV4corr8QJJ5yAZs2aYdKkSTF1/vrXv+LMM8/E0UcfjaOPPhrnnXcePvnkE9Pj6dy5Mz788MOY8g8//BCdOnUy3Y4cAwgFRrRYHubqMoxRZ07ZCiGA+myI1t0uykCi9aVGrV0r4UPG7NKL8rVy9sPlvxuvnuwe7xtQ/eyL3LVs2TJMmjQJ06dPx4YNG3DmmWdi2LBhqKysVK1fX1+P9u3bY/r06TjppJNU66xevRr/93//h1WrVqGsrAxdunRBUVGR6aeYXnfddZg0aRIWL16MrVu3YuvWrXj66acxefJkjB8/3tb7DEmSJNk6MwHV1dUhMzMTmQBCQQ/GI4KNc4xu6NC6E1WtL7W2BJ3jWscElZ/lx8NjylApC9dLPUpRICi+K8uNXis7MHte1G258rtaslXKgNi7Y7Q++dboLhotep8to1VPK3zoLL24FUAczoCEm5fPmKkt24kqPytDhLyO3jG142p1lOPR48YeqmQi4fCfW21tLTIyHN0TFyN8rajNBzIc3gVTdxDILLc2zlNPPRUDBgzAvHnzImW9evXCyJEjUVJSonvuoEGDcPLJJ0c9NExNY2Mjjj76aMyePRtXX3214ZgkScLUqVPxxBNPoKGhAQCQnp6O2267DTNmzDB+Uyp4Gy7ZJiI2hBjdoWr6DlaT5zq+HdcLytt2o54NovZ5L+GLu9YtuuFgoAwibj2W3coGWJ3wIRcn4UON0fKLqHJcr9wP8bJUSvbV1UUvGqelpSEtLfaZQQ0NDSgvL8fUqVOjyouKirBu3TrXxvPrr7/iwIEDyMrKMlU/FArhgQcewJ133omNGzeiZcuWOO6441Tfg1lcgkkyogdt+vVIdq3fRtV4sgxj9NrsXhC5cFnURdrggg5Ae2nEyQfPqbXlYvgQj3z3a3nLBL/+2xV96od8VovDf7lOvo78R9i5c+fDM/BHvrRmMnbu3InGxkZkZ0c/7yc7OxvV1e59FtTUqVNxzDHH4LzzzrN0XuvWrXHKKaegb9++jsIHwBkQgrNZCadEuPfhs6afiuoGrYeTKV9DVmY4E6IsB/4XCNRmROS0lmn0zpEzuu3XYviQ83n2Q43ahmW/939Q07Zt27aoJRiji3coFL1RQJKkmDK7HnzwQSxduhSrV69Genq6qXP27duH+++/H++++y5qampw6NChqOM//vij5XEwgJDnREQHiECWYZQBIfzdbJAIv9Z7PLurIQRQDyKA+t4POzMjar9NmQgegHH4UJYpX3s4NeHW8ouVGTkztNrw4o9Cqy8KTkZGhqk9IO3atUNKSkrMbEdNTU3MrIgdDz30EO677z688847OPHEE02fd91112HNmjUYPXo0cnJyXAlDDCDkiAjr+0D0WHksu5ks0PCrbDOq1UGEB6AVQpTlcspz5WW6IQTQDyJAbHAwuxlVb/pW7S4Zl8OHGq26Fmc/1KhtPg1LpBmKRBorOZeamor8/HyUlpbi4osvjpSXlpZixIgRjtr+y1/+gnvuuQcrV65EQUGBpXPffPNNvPHGGzj99NMdjUGOASQJibB3N4weK5/LZpUIa8swerMguuM0OwtihlYIUQscWiEEOBJElEFDK4jI68g5WRc2Ch6KOlbDh5zVpRcL7G4+tcPo7hcip6ZMmYLRo0ejoKAAhYWFWLhwISorKzFhwgQAwLRp07B9+3Y888wzkXMqKioAAHv37sUvv/yCiooKpKamonfv3gAOL7vceeedeP7559G1a9fIDEvr1q3RunVrwzEdffTRpjesmsUAQgC83zchwr1lGLMszYJYXYpRo6xrVAaozIYA2kFEXgeKumYYPbTMZPAAzIcPZZmVJ54qzxU1ylWYDRuixs9Oll+Cfv4HJb5Ro0Zh165dmDVrFqqqqtC3b1+sWLECubm5AA4/eEz5TJD+/ftHfi4vL8fzzz+P3NxcbNmyBcDhB5s1NDTg0ksvjTpv5syZKC4uNhzT3XffjRkzZuBvf/sbjjrK6rSyOj4HJEkJNs5x83kgau0p62k990PvmKBy3NIzQeSFguK71vM89OooOzJ7vrwsrJVyU5pauHDjk3DlTHwwndqsB2A/fKgdM3otL1McU5v9cPLsD706ymNmjqvVUY5Jj50lGK3+kokvzwHpAWSkOGyrEcjc5M04/dS/f3/88MMPkCQJXbt2RYsWLaKOf/bZZ5bb5AwIOSbC+ZKP1TbMbkY1NQsS7lxvOUXrHLVbb9TOl5dB1g+gMRsCqO8BUQYGK4HE6NNzdYIH4Cx8qLVjZelFVHy3OPthd/Opkt/LLwwfFC9GjhzpepsMIGSa030gIswvw2itWCjb0BqXWlkkhNhZTlG+ltexGkKgUx61NyRMbw+IUagworIsYzV4aJWrTUmoHVNS1hHVqwH6ez/c2Hyq07UqLr9Qspo5c6brbTKAJCkR1mcl3N6XYbU9Ed7Oguh2Gv6uVtnMzIhRCAFiQ4/RbEiYZhgBXN0Dohc8AG/Ch7JMrY5GXbXnfpid/VBr1gg3nzZBtXD+uM5DxlUSSXl5OTZu3IhQKITevXtH7T2xigGEXCHC/TtvrLRvexZErRMrt9fK6+iFECjOgUo5VI4BOmEEsPZpuApqj1CXjyFM78prNXyotaM30yHqHFPpLsxo9kOrObef/aHHr6e0ErmhpqYGV1xxBVavXg1BECBJEmprazF48GC88MILaN++veU2+Sh2ssTpP5qi4rXZa5uVOqan3vUufGY2Q6qdb7TTUVkub0vtmHJs++pjv8wwOk+tP60xKY8py8PUwoeZmQ4Tfy9uzX64ye3lF+7/oHhy0003oa6uDl9//TV2796N//znP/jqq69QV1eHm2++2VabnAFJYiISbxnGCfmMh9pzQXQ3pIqIXopRO2ZmeUY+EK0lFnm5vH0g+qqjnBUJU47PbAhRUrYbZuU2D1Gj3Ez4UOvDzKZVlSblQcPK7Ifa8NS4vfzC2Q9KNG+99Rbeeecd9OrVK1LWu3dvzJkzB0VFRbba5AwIuUa0Wc/sP/yixs9q1z0YlMV8UJ2cqHJMWaY2AFFWXqtyXO08tXPVjivHp/Vlt+4exPanrKc36yFPAmbDh5lZJuU4YX7jqZOLvEq3uuzMVhAlkkOHDsXcegsALVq0iPlcGLMYQMgyq/+wu3nngajxsxpHSzGi4phRfbWLp/xirBc2jJZe9kA9IKgRoR0y5PTa1Opfflx+LEztOR/yOkZlynNV6ttZeglq9sNsPTN9et0fkZFzzjkHt9xyC3bs2BEp2759OyZPnoxzzz3XVpsMIBTDyW9zos16Vi4ARnXMXpCiLmZW9oMoy0RFmbKu3tVQfq7ZfSDK8GDnS0mtL7N7QADth4yZDR8W930YzX6oUWvaST27uPxCiWj27NnYs2cPunbtiu7du6NHjx7Iy8vDnj178OSTT9pqk3tAkpwIb+5O8fKzYQD9ccuPyX9W26Ihvy1X964YtQ7UGtfqMFyuVqbsXG0PCKB+ey4UdcIE2KdsS87MTkorsx5a5UZlouKYQfdaSy9a4VWtezVmZj/M/JERJbrOnTvjs88+Q2lpKb799ltIkoTevXvjvPPOs90mAwj5Qi0ciLD3YDIz1DakarWnemtueHDyQVoJIUDs5tPwldFqEIGinryuG8xcpZX1lL/Gixr1RJ0yebmoclx2jt7j1rWYWXpR0jvmFy6/xJG9cP65HQn+YSfvvfcebrzxRnz00UfIyMjAkCFDMGTIEACHHy/fp08fzJ8/H2eeeabltrkEQ6r8WIax2o7VtXwrewN0O1HrQIT5i6uyXL43JFzP7tKLVUZtqPVttNwiqtRTlsvrQ1EuqhxXCR9yWne9uLk/yerSnxlcfqFE89hjj2H8+PGqn2OTmZmJP/7xj3jkkUdstc0A0gSIHrXrxj/2okEd5XEt8npG+wNM7weRN6zVgSgr07r4qpWHB6IXROTnKM9VnuPG3g95+2rnKO9wUdaXt2m2XNQ4foSVfR9ON56qdG/puNV6apwEfyIvfP755zj//PM1jxcVFaG8vNxW21yCIU1Wlz3kRHiz90Rri4UWraUYw+eDmF2OUQ7EbDkQvT8EiF2aCTO79CKolOnVV1K7+hl9ipveVdzKUozKeVrhw+5dL3qU9ezu/dDj1eyH6FG7RADw888/q95+G9a8eXP88ssvttrmDEgTIXrUbtCzIKLGz1oXLKPpesczIWrloqxc61h4QMpZEfl5RssuosqXHr12leNQtqn3Xqwuxch/thA+jO56UVLpxnWiYQ2ixHLMMcfgyy+/1Dz+xRdfICcnx1bbDCDkGdGjdo2uwWr1rDwp01QIEWXHjZZe1AZmtOdDK4zIz3f6JVcL9T6V4zIKUVaXYlTqWA0fVpdelJTHgpr94PILxaPhw4djxowZ2L9/f8yx//73v5g5cyZ++9vf2mo7JElSgu/RNa+urg6ZmZnIhPONzYlIsHmemWUYrVtytfpUa1NZV1lH77ig8bOyjfA4M1TKlPWjbs+12pm83MoxOWU9OSf3QNv5eFijq7LeukechQ8rb0XtuFodrXphXgUQvT6TnYQjebm2VnWDpBPha0VtSyDD4cWiTgIy/+vNOP3w888/Y8CAAUhJScGNN96IE044AaFQCBs3bsScOXPQ2NiIzz77DNnZ2Zbb5h6QJkSEt59Y63afyj0oem3Jj8l/1trHYrQfBDCxJ0SvM/HId7PHwrT2ewiI5uaGAlHnmJOrtcWdoF6EDyWvwocezn5QIsvOzsa6detw/fXXY9q0aQjPWYRCIQwdOhRz5861FT4AzoA0OYLN8xJhFkR5XP6zvJ58nK7NhOh1qKxn5o1r1TNznhbRRB2tK6HyXLszIqJ6HbfCh153esNSa0etjpV6YZz98IYfMyC/IPYZQpbbAtAeiTsDIvef//wHmzZtgiRJOO6443D00Uc7ao8zIE2MiPiZBVGbnVDWNZoF0btTR15XXs/KnTGAykwIEPuQMbUOteqpzXgo6yjryevKiSplVpjdUKNVX1lH7wovf+1S+FASNX5Wtm2G8nw7bXD2g5LJ0UcfjVNOOcW19hhAyBS9C32Y3uPZRXi3FKO1OqJ8bSeEAP+7RRcwuSQDjU6Vx5SdyOuEqdX1krJ/rb6V9WxOQzgNH0Hv+9CqS0TGGEAoUGZmQazSO99qCFGeo7svBDCeDZHXhUodeT1lXeU5Tqm1LWdmrcHoyi1/rajr9FZbr/Z9WKHXr5ezH3r9EiUKBpAmSIS9a5ifsyDKulaXYuTH9erqhRDA4pKMWseAdhBRqyOvF6aW0Lxg5Vd8K8FDUV/5aHU3Zz6Ur83MbCip1eEyCZH7GEAocGaCjVo9Ed6GEOUxZQgBLMyGyF/r7QGR15MPVsnuI2q12lMSTZ6rrKd8bXLWA3A/fJjh1dILZz+IjDGANFEi4msWxOxSjF8hBDDeFwKYmA0BtIOI/By188IExHL7V3Jln0Z9qdVXltmY9QDcCx9eLr0QkXMMIOQ7EfaXYszUMXtnjLKuMjhZ2RcC2Agi8jL5efJzlfWVBJ1javTakjP7a79aezZnPYBgw4eyjlY9rbphnP0gMidhAsi9996LN954AxUVFUhNTYUoikEPKeGJCGYWxEm7ZuuZvTNGWVe598NoXwjgIIjIy5TlZm7BVZ5vl9HVUK0PtTKd4KHWjd5m02QOH0R0WMIEkIaGBlx22WUoLCzEokWLgh4OmRDkUoxRf8rXyvbM7gtROzdqWSZcATDe76FVLm9Dj1ZIceMBGHrHHAQPwN/wocZMHTdx9iNx7IXzh1budWMgSSphAshdd90FAFiyZEmwA0kyIrybBbHbtx/7QczUN9oXAliYDVGrFB5EmHJwSoJKmZxXVza14yp9KYOHWjUr4cPhfldHd7Nw9oPIewkTQOyor69HfX195HVdndUP8CY9Xi3FaBHhTggBtPeF6oUQwNxsCGAyiMgrygcTJiheK497Ra8fH4KH2vnKISlf2w0fZutp1bWKsx9E/9Ms6AF4qaSk5PBnvxz56ty5c9BDikuix+2bfXS2nJuPpDBz4ZG/3qM4R/kJ9XWI3SypdgGVt9Hw6/++VCtqDVL55TYzfWiMUe39GP3ZAYkZPozwketE1gUaQIqLixEKhXS/1q9fb7v9adOmoba2NvK1bds2F0dPgDv/qIoetK1sU+0ar6yjdo6c2oXUahABTIYRvQTm5pcanXFojd1s8ND783Lj70h5XK2OVj09evW9XnrR65uS19y5c5GXl4f09HTk5+dj7dq1uvXXrFmD/Px8pKeno1u3bpg/f37U8QMHDmDWrFno3r070tPTcdJJJ+Gtt97y8i0YCnQJ5sYbb8QVV1yhW6dr1662209LS0NaWprt85sSEd5+SJ3dpRi7+0HM1lPWUb5WLqko934A6ssyyjpq2z+A6At51DKN/CQ5pxtvlEykPLUlFq1T1S7GagufVmc91MrcDh/xuPRCTdOyZcswadIkzJ07F6effjoWLFiAYcOG4ZtvvkGXLl1i6m/evBnDhw/H+PHj8eyzz+LDDz/EDTfcgPbt2+OSSy4BANxxxx149tln8de//hU9e/bEypUrcfHFF2PdunXo37+/328RABCSJEkKpGeblixZgkmTJtm6DTf8EcuZcL6zORkJDs41e13UCyF6/Tv5BHsz9dTqKMvU2la+H7UP29Z6z0Z/ZjGBxCdagQPQvpC6FTwAW48aCSx8eL30YtR/UybhyCyaBx9zH75WbIbzzL8HQB6sjfPUU0/FgAEDMG/evEhZr169MHLkSJSUlMTUv+222/D6669j48aNkbIJEybg888/R1lZGQCgU6dOmD59OiZOnBipM3LkSLRu3RrPPvusvTfnUMJsQq2srMTu3btRWVmJxsZGVFRUAAB69OiB1q1bBzu4JCHCvU+sdbt/P2ZCAGuzIYD6JlVAfUYEUJ8VUbYZphYE3AwlekFDTu8Cajd4qLUrqtRRK0uk8EEUprwJQmuGvqGhAeXl5Zg6dWpUeVFREdatW6fadllZGYqKiqLKhg4dikWLFuHAgQNo0aIF6uvrkZ6eHlWnZcuW+OCDD+y8HVckTACZMWMG/va3v0Veh6eMVq1ahUGDBgU0quQjItilGKv9q9V3M6yIR77Ly9TulAHUgwhgPYxAZVxhZkODE0a/sWtdeO0GD8DekovWeW6EDyNmwwdnPxJbLYBDDtsI/zegvAli5syZKC4ujqm/c+dONDY2Ijs7O6o8Ozsb1dXVqn1UV1er1j948CB27tyJnJwcDB06FI888gjOOussdO/eHe+++y6WL1+OxsZG2+/NqYQJIEuWLOEzQOKc2VkQuyFEq321+nqP3BBM1FO2pyzTmg0BYt+b2qyIvL7aOVoXrgC2gADQv+B6HTysnOtW+NA6xwqGD5Lbtm1b1BKM0f7EUCh6o4AkSTFlRvXl5Y8//jjGjx+Pnj17IhQKoXv37rjmmmuwePFiS+/DTQkTQMg/IrxfivEjhGjVV6trdklGWWYniAD6YUTtXGV/XjP6DV/riTpa5yVT+ODSC9mRkZFhag9Iu3btkJKSEjPbUVNTEzPLEdaxY0fV+s2bN0fbtm0BAO3bt8drr72G/fv3Y9euXejUqROmTp2KvLw8m+/IuaR+DggFw43fqu20L1qor1bXykVSWa52C2mt7EupDrG3pWqdq9WGW8z2pTdmrfO0bq0VTZSZPTdcV0mtnlZdo3PCuPRCXktNTUV+fj5KS0ujyktLSzFw4EDVcwoLC2Pqv/322ygoKECLFi2iytPT03HMMcfg4MGDePnllzFixAh334AFnAEhVSK83QvidAxezoQAxrMhVs4H9JdblBd0rd+R/P7N2+i5wVZmOwDtC6taudNAYaWu0TlhDB/klylTpmD06NEoKChAYWEhFi5ciMrKSkyYMAHA4Wdcbd++Hc888wyAw3e8zJ49G1OmTMH48eNRVlaGRYsWYenSpZE2P/74Y2zfvh0nn3wytm/fjuLiYhw6dAj/7//9v0DeI8AAQjpExO9SjF4fWudohRCo1DdbV+/8MCthBFC/8Lt7k6G5PtXoXYS9Ch5u1NWrr3dOGJddyE+jRo3Crl27MGvWLFRVVaFv375YsWIFcnNzAQBVVVWorKyM1M/Ly8OKFSswefJkzJkzB506dcITTzwReQYIAOzfvx933HEHfvzxR7Ru3RrDhw/H3//+dwiC4Pfbi0i454A4weeAWCc4PN+N54MYjUOvD63zzD4vRK99rfpW2wlz6zNz3GR04bVzUdcq9/LWWb/CB2c//OPHc0Aq4M5zQE6GN+NMdJwBIV0inIWQIGdC9M6zOhsCjfpqbRu1o9YWoH6h8zuUuPFgLdFiuVthwmp9vXPsYPggsoYBhAyJ8Gc/iNMQAjhfkrFaX5T9rDxH1CgPtyWnFaCMAoGVgOJkGcFu6NA75nXw0DvH6LwwP/Z9EDVVDCDkOStPSXX6oDI7oQIq54hHvivPMQo6aueIsp/VxiBvN8zKn5cXzFxQRZvH7YSCZA4fosPzyTt7cHipx4m9bgwkSTGAkCki/FmKAbwNIdA41+o5doKI/JjWcWX7Sm4+iMzOhVN0cNzN4OFFX3J+bToVfeqHKB4xgJBpIhI/hOida3cZR++cMLVzRcVrtTpa/flFdKGe2yHCbl9G54b5temUqKljACFLRMRXCIHOeIxCiNa5dmdQwuyEEWUdo7peUOvfSX2/Zy8SLXyIDs8nSnQMIOQ7N0MIYH9zqt65dpdYzPYZptWGWl0jWm1ZaUOPmXachICgzg1j+CDyFwMIWSbC+W/mfoYQo/7EI9/VzneyxGI0K6JsQ68tM9TacsJKe04DgJM9GwwfRImJAYRsEZFcIcTofLOzGkbn67WhbEtJq22ntPrTY/YibNR20MED4J4PoqAwgJBtIuIzhADeBwm758vbCDP7/kXDGt5yK3SYacuoDb9nPcz0aYboQhtEyYIBhBwREX8hBHBnNgQ6bbi5z8NuIPGS1Yut6FKbRu240QbA8EHm1AFodNjGPjcGkqQYQCguBBVCYNCveOS7VjtW2tBrR61NNW6HEz8eH+5WaAhiycVMv2aILrRBlGwYQMgxEe7sT7AaQgDnSzLhfmHQt1E7Zvd4iIrXWu1pCXIPgmixvl/Bw2w7AMMHUTxhACFXiPA/hADuzYaY7Vs88l2vLacbTvXa9pNo4xw/94mYbSeM4YMovjCAkGtExH8IAZzPhphtS96emTaVbSsZ9WWHVl9WWLlQm+nPzRAD2HusOsMHkfcYQMhVIuI7hADmZ0NgYgzike9G7cnbNNOuXl9B82KDqpV2zbYHBBc+iMgYAwi5TkRwIQRwbzYkPAaYGIco+9moTXm7cvFw94saLz60zm77VtoNMnyILrVDlMwYQMgTIoIJIYD12RDAvSAib9NMu2p9KPkVTPxedkjG4AEwfBCZxQBCnhERbAgB3F2WCY8lzMswotVnvBFtnONV8AAYPshdewEcctjGr24MJEkxgJCnRLgXQoD4mA0JszomUfHabD/xRLR5nlf7RsLsBA+A4YMoSAwg5DkR7l1s/ZoNAawHEcDa2ESVMrN9+kV0eL7XwQNg+CBKVAwg5AsRwYYQwNpsCGA9iADe3uliZRxu9WmH15tWw+IheAAMH0R2MYCQb0S4G0IA72dDAHtBBHAeRrTGEY/sXtRFG+fYDR4AwwdRPGEAIV+JcPc3eSezIYC9IAI4f4R6vN5ya5bTC7lo8zyGD6LkwQBCvhMRHyEEsBdEAPuzImGJFEji4dkYDB5EyYcBhAIhwv0QAgQXRABn78fPT8C1Mwa7RIfnOwkeAMMHUTxjAKHAiHB/Y6WT2RDAfhAB3AsjSvH8HBA1ogttxFvwABg+iNzGAEKBEo98F1xs0+lsCOAsiADJ8cwPK0SX2nEaPACGD3JPLYAGh238142BJCkGEIoLIryZDQGCDSJhouK14LC9oIkutuVG6AC8mykSPWqXqKljAKG4IcKbC7PTZRkg+iLpNIwAiRVIRI/aZfAgatoYQCiuiPAuhADubOh0a1ZETtQ5JrjYj90xuMWt0AF4uzdG9LBtIjqsWdADIFIS4d0FYA/cu3DVyr68JPr05RUv/pwYPijZzZ07F3l5eUhPT0d+fj7Wrl2rW3/NmjXIz89Heno6unXrhvnz58fUEUUREydORE5ODtLT09GrVy+sWLHCq7dgiDMgFLdEePfbv5szIoD7SzSJzqtQ5vUdQaLH7ROZsWzZMkyaNAlz587F6aefjgULFmDYsGH45ptv0KVLl5j6mzdvxvDhwzF+/Hg8++yz+PDDD3HDDTegffv2uOSSSwAADQ0NGDJkCDp06IB//OMfOPbYY7Ft2za0aRPcU4hCkiRJgfXus7q6OmRmZiITQCjowZBpgg99ePm/YFMJJF7OBDF4kJKEIzNrtbXIyMhwte3wtWIhgJYO2/ovgD/A2jhPPfVUDBgwAPPmzYuU9erVCyNHjkRJSUlM/dtuuw2vv/46Nm7cGCmbMGECPv/8c5SVlQEA5s+fj7/85S/49ttv0aJFC0fvyS1cgqG4J8L7C4SbSzNKtfBvucZPXr+vPfD27yVM9Lh9IuBwqJF/1dfXq9ZraGhAeXk5ioqKosqLioqwbt061XPKyspi6g8dOhTr16/HgQMHAACvv/46CgsLMXHiRGRnZ6Nv376477770NjY6MK7s4dLMJQwRHg/G+L20owatYt1vM+S+Bmc/HrwmuhTP5S4RADqMcG88HNAOnfuHFU+c+ZMFBcXx9TfuXMnGhsbkZ2dHVWenZ2N6upq1T6qq6tV6x88eBA7d+5ETk4OfvzxR7z33nv4/e9/jxUrVuD777/HxIkTcfDgQcyYMcP2+3OCAYQSinjku+BxP25/kq0RvQu8X+Ek6NkZP5/4KvrYFxEAbNu2LWoJJi0tTbd+KBS9UUCSpJgyo/ry8kOHDqFDhw5YuHAhUlJSkJ+fjx07duAvf/kLAwiRFSL8uz3Vj1kRPUEHAy/5/Zh50ef+iMIyMjJM7QFp164dUlJSYmY7ampqYmY5wjp27Khav3nz5mjbti0AICcnBy1atEBKSkqkTq9evVBdXY2GhgakpqZafUuOcQ8IJSwR/l5Q/NqT0BQE8eco+twfkR2pqanIz89HaWlpVHlpaSkGDhyoek5hYWFM/bfffhsFBQWRDaenn346Nm3ahEOHDkXq/Pvf/0ZOTk4g4QNgAKEkIML/iwvDiHVB/ZmJYPigxDJlyhQ89dRTePrpp7Fx40ZMnjwZlZWVmDBhAgBg2rRpuPrqqyP1J0yYgK1bt2LKlCnYuHEjnn76aSxatAi33nprpM7111+PXbt24ZZbbsG///1vvPHGG7jvvvswceJE399fGJdgKGmICOaR5n7vF0kUQYczMeD+iewaNWoUdu3ahVmzZqGqqgp9+/bFihUrkJubCwCoqqpCZWVlpH5eXh5WrFiByZMnY86cOejUqROeeOKJyDNAgMObYN9++21MnjwZJ554Io455hjccsstuO2223x/f2F8DgglJSHoARzR1AJJ0KEDYPBoKvx4DsiDcOc5IP8P3owz0XEGhJKSeOS7EOAYgNgLcjIFkngIG0pi0AMgItMYQCipiUe+CwGOQU7top0IoSQew4acGPQAKCntBXDQYRv73RhIkmIAoSZBPPJdCHAMWvQu7n6Gk3gPGWrEoAdARLYxgFCTIh75LgQ4BisSMRR4TQx6AETkioS4DXfLli0YN24c8vLy0LJlS3Tv3h0zZ85EQ0ND0EOjBCWCF7JEI4J/Z0TJJCFmQL799lscOnQICxYsQI8ePfDVV19h/Pjx2LdvHx566KGgh0cJTJT9LAQ0BtInBj0AIvJEQgSQ888/H+eff37kdbdu3fDdd99h3rx5DCDkGvHIdyHAMdBhYtADICLPJUQAUVNbW4usrCzdOvX19VEfeVxXV+f1sCgJiLKfhYDG0FSJQQ+AiHyTEHtAlH744Qc8+eSTkcfSaikpKTn84LEjX8qPQyYyIoIXRa+J4J8zUVMUaAApLi5GKBTS/Vq/fn3UOTt27MD555+Pyy67DNddd51u+9OmTUNtbW3ka9u2bV6+HUpiIniRdJMI/nkSNXWBLsHceOONuOKKK3TrdO3aNfLzjh07MHjwYBQWFmLhwoWG7aelpSEtLc3pMImiiIrXQgBjSERi0AMgsqgWzh8kVm9cpckKNIC0a9cO7dq1M1V3+/btGDx4MPLz87F48WI0a5aQq0eUhETZz0JAY4hHYtADIKK4lhCbUHfs2IFBgwahS5cueOihh/DLL79EjnXs2DHAkRFFE1XKBJ/HEBQx6AEQUUJJiADy9ttvY9OmTdi0aROOPfbYqGNN6MN8KUGJitdCAGNwmxj0AIgo4SXEOsbYsWMhSZLqF1GiEXW+4o2o8UVE5FRCzIAQNRWiyXqCD30QEXmJAYQoAYlBD4CIyKGEWIIhIiKi5MIZECIiIhV7ATj9zHV+Zrs2zoAQERGR7xhAiIiIyHcMIEREROQ7BhAiIiLyHQMIERER+Y4BhIiIiHzHAEJERES+YwAhIiIi3/FBZERERCpqAbRw2MYBNwaSpDgDQkRERL5jACEiIiLfMYAQERGR7xhAiIiI4szcuXORl5eH9PR05OfnY+3atbr116xZg/z8fKSnp6Nbt26YP39+1PFXXnkFBQUFEAQBrVq1wsknn4y///3vXr4FQwwgREREcWTZsmWYNGkSpk+fjg0bNuDMM8/EsGHDUFlZqVp/8+bNGD58OM4880xs2LABt99+O26++Wa8/PLLkTpZWVmYPn06ysrK8MUXX+Caa67BNddcg5UrV/r1tmKEJEmSAuvdZ3V1dcjMzEQmgFDQgyEiItskHL5Lpba2FhkZGa62Hb5WXAp37oL5B6yN89RTT8WAAQMwb968SFmvXr0wcuRIlJSUxNS/7bbb8Prrr2Pjxo2RsgkTJuDzzz9HWVmZZj8DBgzABRdcgLvvvtv0+3ETZ0CIiIg8VldXF/VVX1+vWq+hoQHl5eUoKiqKKi8qKsK6detUzykrK4upP3ToUKxfvx4HDsTeCCxJEt5991189913OOuss2y+I+f4HBAiIiIVe+Dec0A6d+4cVT5z5kwUFxfH1N+5cycaGxuRnZ0dVZ6dnY3q6mrVPqqrq1XrHzx4EDt37kROTg6Aw7MwxxxzDOrr65GSkoK5c+diyJAh9t6YCxhAiIiIPLZt27aoJZi0tDTd+qFQ9EYBSZJiyozqK8vbtGmDiooK7N27F++++y6mTJmCbt26YdCgQWbfhqsYQIiIiDyWkZFhag9Iu3btkJKSEjPbUVNTEzPLEdaxY0fV+s2bN0fbtm0jZc2aNUOPHj0AACeffDI2btyIkpKSwAII94AQERHFidTUVOTn56O0tDSqvLS0FAMHDlQ9p7CwMKb+22+/jYKCArRoob2IJEmS5l4UP3AGhIiIKI5MmTIFo0ePRkFBAQoLC7Fw4UJUVlZiwoQJAIBp06Zh+/bteOaZZwAcvuNl9uzZmDJlCsaPH4+ysjIsWrQIS5cujbRZUlKCgoICdO/eHQ0NDVixYgWeeeaZqDtt/MYAQkREFEdGjRqFXbt2YdasWaiqqkLfvn2xYsUK5ObmAgCqqqqingmSl5eHFStWYPLkyZgzZw46deqEJ554Apdcckmkzr59+3DDDTfgp59+QsuWLdGzZ088++yzGDVqlO/vL4zPASEiooTjx3NAhsKdu2BWwptxJjruASEiIiLfcQmGiIhIRS2cXyQPujGQJMUZECIiIvIdAwgRERH5jgGEiIiIfMcAQkRERL5jACEiIiLfMYAQERGR7xhAiIiIyHcMIEREROQ7PoiMiIhIRR2AFIdtNLoxkCTFGRAiIiLyHQMIERER+Y4BhIiIiHzHAEJERES+YwAhIiIi3zGAEBERke8YQIiIiMh3fA4IERGRir1w/lv6ITcGkqQ4A0JERES+YwAhIiIi3zGAEBERke8YQIiIiMh3DCBERETku4QJIBdddBG6dOmC9PR05OTkYPTo0dixY0fQwyIiIiIbEiaADB48GC+++CK+++47vPzyy/jhhx9w6aWXBj0sIiIisiEkSZIU9CDseP311zFy5EjU19ejRYsWps6pq6tDZmYmMgGEvB0eERF5SAJQC6C2thYZGRmutu3mtcLLcSa6hHwQ2e7du/Hcc89h4MCBuuGjvr4e9fX1kdd1dXV+DI+IiIgMJMwSDADcdtttaNWqFdq2bYvKykosX75ct35JScnhFHvkq3Pnzj6NlIiIiPQEugRTXFyMu+66S7fOp59+ioKCAgDAzp07sXv3bmzduhV33XUXMjMz8a9//QuhkPokmdoMSOfOnTkVRkSU4MLLJFyCSVyBBpCdO3di586dunW6du2K9PT0mPKffvoJnTt3xrp161BYWGiqPy//gyUiIv8wgCS+QJdg2rVrh549e+p+qYUPAAjnJvkMBxERUTKYO3cu8vLykJ6ejvz8fKxdu1a3/po1a5Cfn4/09HR069YN8+fPj6nz8ssvo3fv3khLS0Pv3r3x6quvejV8UxJiD8gnn3yC2bNno6KiAlu3bsWqVatw5ZVXonv37qZnP4iIiBLBsmXLMGnSJEyfPh0bNmzAmWeeiWHDhqGyslK1/ubNmzF8+HCceeaZ2LBhA26//XbcfPPNePnllyN1ysrKMGrUKIwePRqff/45Ro8ejcsvvxwff/yxX28rRkLchvvll1/illtuweeff459+/YhJycH559/Pu644w4cc8wxptvhEgwRUXJI5iWYU089FQMGDMC8efMiZb169cLIkSNRUlISU/+2227D66+/jo0bN0bKJkyYgM8//xxlZWUAgFGjRqGurg5vvvlmpM7555+Po48+GkuXLrX35hxKiNtw+/Xrh/fee89xO+GsxdtxiYgSW/jfcS9/h3aj5XAbyutOWloa0tLSYuo3NDSgvLwcU6dOjSovKirCunXrVPsoKytDUVFRVNnQoUOxaNEiHDhwAC1atEBZWRkmT54cU+exxx6z9oZclBABxC27du0CAN6OS0SUJHbt2oXMzExX20xNTUXHjh1RXV3tSnutW7eOue7MnDkTxcXFMXV37tyJxsZGZGdnR5VnZ2drjqe6ulq1/sGDB7Fz507k5ORo1nHrPdrRpAJIVlYWAKCystL1/2DjWfj2423btjWppSe+b77vpqCpvu/a2lp06dIl8u+6m9LT07F582Y0NDS40p4kSTGPi1Cb/ZBT1ldrw6i+stxqm15rUgGkWbPDe24zMzOb1P+oYRkZGXzfTQjfd9PSVN93+N91t6Wnp2vehemldu3aISUlJWZmoqamJmYGI0xttqampgbNmzdH27ZtdetotemHhLgLhoiIqClITU1Ffn4+SktLo8pLS0sxcOBA1XMKCwtj6r/99tsoKCiIfFyJVh2tNv3QpGZAiIiI4t2UKVMwevRoFBQUoLCwEAsXLkRlZSUmTJgAAJg2bRq2b9+OZ555BsDhO15mz56NKVOmYPz48SgrK8OiRYui7m655ZZbcNZZZ+GBBx7AiBEjsHz5crzzzjv44IMPAnmPQBMLIGlpaZg5c6bh2luy4fvm+24K+L75vpPFqFGjsGvXLsyaNQtVVVXo27cvVqxYgdzcXABAVVVV1DNB8vLysGLFCkyePBlz5sxBp06d8MQTT+CSSy6J1Bk4cCBeeOEF3HHHHbjzzjvRvXt3LFu2DKeeeqrv7y8sIZ4DQkRERMmFe0CIiIjIdwwgRERE5DsGECIiIvIdAwgRERH5rskGkIsuughdunRBeno6cnJyMHr0aOzYsSPoYXlqy5YtGDduHPLy8tCyZUt0794dM2fOdO1pf/Hs3nvvxcCBA3HUUUdBEISgh+MZqx/hnQzef/99XHjhhejUqRNCoRBee+21oIfkuZKSEpxyyilo06YNOnTogJEjR+K7774LeliemzdvHk488cTIQ9cKCwujPlyNEkuTDSCDBw/Giy++iO+++w4vv/wyfvjhB1x66aVBD8tT3377LQ4dOoQFCxbg66+/xqOPPor58+fj9ttvD3ponmtoaMBll12G66+/PuiheMbqR3gni3379uGkk07C7Nmzgx6Kb9asWYOJEyfio48+QmlpKQ4ePIiioiLs27cv6KF56thjj8X999+P9evXY/369TjnnHMwYsQIfP3110EPjeyQSJIkSVq+fLkUCoWkhoaGoIfiqwcffFDKy8sLehi+Wbx4sZSZmRn0MDzxm9/8RpowYUJUWc+ePaWpU6cGNCL/AZBeffXVoIfhu5qaGgmAtGbNmqCH4rujjz5aeuqpp4IeBtnQZGdA5Hbv3o3nnnsOAwcOjDy2tqmora315MOcyF/hj/BWfiS33kd4U/Kora0FgCb1/3JjYyNeeOEF7Nu3D4WFhUEPh2xo0gHktttuQ6tWrdC2bVtUVlZi+fLlQQ/JVz/88AOefPLJyON9KXHZ+QhvSg6SJGHKlCk444wz0Ldv36CH47kvv/wSrVu3RlpaGiZMmIBXX30VvXv3DnpYZENSBZDi4mKEQiHdr/Xr10fq//nPf8aGDRvw9ttvIyUlBVdffXXkI4wTidX3DQA7duzA+eefj8suuwzXXXddQCN3xs77Tnbx9nHb5L0bb7wRX3zxRdTnfiSzE044ARUVFfjoo49w/fXXY8yYMfjmm2+CHhbZkFSfBXPjjTfiiiuu0K3TtWvXyM/t2rVDu3btcPzxx6NXr17o3LkzPvroo4SbzrP6vnfs2IHBgwdHPuQoUVl938nMzkd4U+K76aab8Prrr+P999/HscceG/RwfJGamooePXoAAAoKCvDpp5/i8ccfx4IFCwIeGVmVVAEkHCjsCM981NfXuzkkX1h539u3b8fgwYORn5+PxYsXo1mzxJ0Ec/L3nWzkH+F98cUXR8pLS0sxYsSIAEdGXpAkCTfddBNeffVVrF69Gnl5eUEPKTCSJCXkv9uUZAHErE8++QSffPIJzjjjDBx99NH48ccfMWPGDHTv3j3hZj+s2LFjBwYNGoQuXbrgoYcewi+//BI51rFjxwBH5r3Kykrs3r0blZWVaGxsREVFBQCgR48eaN26dbCDc4nRR3gnq71792LTpk2R15s3b0ZFRQWysrLQpUuXAEfmnYkTJ+L555/H8uXL0aZNm8jMV2ZmJlq2bBnw6Lxz++23Y9iwYejcuTP27NmDF154AatXr8Zbb70V9NDIjiBvwQnKF198IQ0ePFjKysqS0tLSpK5du0oTJkyQfvrpp6CH5qnFixdLAFS/kt2YMWNU3/eqVauCHpqr5syZI+Xm5kqpqanSgAEDmsRtmatWrVL9ux0zZkzQQ/OM1v/HixcvDnponrr22msj/323b99eOvfcc6W333476GGRTSFJSsBdl0RERJTQEncDABERESUsBhAiIiLyHQMIERER+Y4BhIiIiHzHAEJERES+YwAhIiIi3zGAEBERke8YQIiIiMh3DCBERETkOwYQogTX2NiIgQMH4pJLLokqr62tRefOnXHHHXcENDIiIm18FDtREvj+++9x8sknY+HChfj9738PALj66qvx+eef49NPP0VqamrAIyQiisYAQpQknnjiCRQXF+Orr77Cp59+issuuwyffPIJTj755KCHRkQUgwGEKElIkoRzzjkHKSkp+PLLL3HTTTdx+YWI4hYDCFES+fbbb9GrVy/069cPn332GZo3bx70kIiIVHETKlESefrpp3HUUUdh8+bN+Omnn4IeDhGRJs6AECWJsrIynHXWWXjzzTfx4IMPorGxEe+88w5CoVDQQyMiisEZEKIk8N///hdjxozBH//4R5x33nl46qmn8Omnn2LBggVBD42ISBUDCFESmDp1Kg4dOoQHHngAANClSxc8/PDD+POf/4wtW7YEOzgiIhVcgiFKcGvWrMG5556L1atX44wzzog6NnToUBw8eJBLMUQUdxhAiIiIyHdcgiEiIiLfMYAQERGR7xhAiIiIyHcMIEREROQ7BhAiIiLyHQMIERER+Y4BhIiIiHzHAEJERES+YwAhIiIi3zGAEBERke8YQIiIiMh3/x8TrG2BE+sNFgAAAABJRU5ErkJggg==", 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", 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", 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ffoo5c+agd+/e6NmzJwCgffv2aNiwId5991106NABTZo0QevWrdG6dWvMnj0beXl56NevH0aMGIE2bdrgyJEj2LFjB7755hu8//77QXE3b96MUaNG4ZZbbsG+ffswfvx4tGnTJjA9pMbdd9+NhIQEXHHFFUhJSUFFRQXef/99LF26FI8++mjg7/zvv/+Ofv36YevWrZg6dSpOnz6NDRs2BPy0aNEC7du3B3B2iubJJ5/EU089haSkJOTm5mLTpk0oKCjAqFGj0LFjx8C4O++8E9OnT8ctt9yC559/Hi1btsSMGTPw008/YdWqVcb/8AiiLuHywl3CI/h3SEC2u6Fly5bS1VdfLT333HNSeXl5yBiru3skSZJKS0ulm2++WUpKSpISExOlv/71r9LmzZuDdnwcPHhQGjFihHTxxRdLjRs3lpo0aSJdcskl0quvviqdPn06KIZ8d48knd25Mnr0aCk1NVWqX7++lJGRIY0bN046efJkkB0A6b777gvJLyMjg2s3zvPPPy+1a9dOiouLkzp06CC9+eabzJ9PcXGxdOWVV0qNGjWSAITkq4aZ3T1G/hwyMjKk6667Lqjt0KFD0oMPPihlZmZKDRo0kJKSkqSsrCxp/Pjx0rFjx1Rz/e2336Rnn31Wuuaaa6Q2bdpIsbGxUuPGjaXLLrtMevbZZ0NOZl28eLF08cUXSw0aNJAASBMmTAj0bdu2Tbr11lulli1bSg0aNJBatWolXXPNNdKsWbMCNv6/uytXrpSGDh0qCYIgNWzYUBowYIC0c+dO1Tz9zJs3T+rVq5eUnJws1a9fXxIEQbr66quDDmeTpP/tOlN7sf6evPbaa9KFF14oxcbGSunp6dKECROkmpqaELuysjJp2LBhUlJSkhQfHy/16NEj5IRngohGfJLk0pYNgiAIG1iwYAHuuOMObNq0CdnZ2W6nQxCEjdCaFIIgCIIgPAmJFIIgCIIgPAlN9xAEQRAE4UmokkIQBEEQHmPGjBnIzMxEfHw8srKysG7dOlXbDz74ANdeey1atGiBhIQE5OTkYMWKFUE2b775Jnr16oVmzZqhWbNm6Nu3LzZu3Bhk439orPzFOiQxnJBIIQiCIAgPsXTpUowZMwbjx4/H1q1b0atXL+Tl5aGkpIRp/+WXX+Laa6/F8uXLsWXLFvTp0wc33HADtm7dGrBZs2YNbrvtNqxevRpFRUVIT09Hbm5uyFO2O3XqhNLS0sCLddBgOKHpHoIgCILwEN27d0e3bt0wc+bMQFuHDh0waNAgTJ48mctHp06dMHjwYDz99NPM/traWjRr1gzTpk0LPKm9oKAAH330EYqLiy1/B7uIqsPczpw5gwMHDqBp06b00C6CIIgIRpIkHD16FK1btw55uKMdnDx5EjU1Nbb4khSP5gDOnrbsP3FZTk1NDbZs2RJyUGVubi7Wr1/PFe/MmTM4evSo5nOfTpw4gVOnToXY7Ny5E61bt0ZcXBy6d++O5557LuTZVOEkqkTKgQMHkJaW5nYaBEEQhE3s27dP9fRjs5w8eRKZmZnM50GZoUmTJiGPipgwYQIKCgpCbCsqKlBbW4uUlJSg9pSUFO58Xn75ZRw/fhy33nqrqs3jjz+ONm3aBJ283b17dyxcuBAXXnghDh48iGeffRY9e/bE9u3bmQ9gDQdRJVL8z/rYt28fEhISXM6GIAiCMEtVVRXS0tJCnuFkBzU1NSgrK8O+fTst3yvO5nlByH2HVUWRo6y8sKoxLBYvXoyCggJ8/PHHaNmyJdNmypQpWLx4MdasWRP0pPS8vLzA5y5duiAnJwft27fH22+/jfz8fN3YThAxImXmzJmYOXMm9uzZA+DsfNvTTz8d9EPVw/8HnJCQQCKFIAiiDuDk1L2d9wpeX8nJyYiJiQmpmpSXl4dUV5QsXboUI0eOxPvvv6/6bLKXXnoJzz33HFatWsV8grecxo0bo0uXLiHPIAsnEbO7p23btnj++eexefNmbN68Gddccw0GDhyI7du3u50aQRAEQdhCbGwssrKyUFhYGNTuf+CnGosXL8aIESOwaNEiXHfddUybF198Ec888ww+++wzrkdIVFdXY8eOHUhNTTX2JWwkYiopN9xwQ9D13//+d8ycORMbNmxAp06dXMqKIAiCIOwlPz8fQ4cORXZ2NnJycjBnzhyUlJRg9OjRAIBx48Zh//79WLhwIYCzAmXYsGF47bXX0KNHj0AVpmHDhkhMTARwdornqaeewqJFi9CuXbuATZMmTdCkSRMAwCOPPIIbbrgB6enpKC8vx7PPPouqqioMHz483D+CABEjUuTU1tbi/fffx/Hjx5GTk6NqV11djerq6sB1VVVVONIjCIIgCNMMHjwYhw8fxqRJk1BaWorOnTtj+fLlyMjIAACUlpYGnZkye/ZsnD59Gvfddx/uu+++QPvw4cOxYMECAGcPh6upqcHNN98cFEu+gPfXX3/FbbfdhoqKCrRo0QI9evTAhg0bAnHdIKLOSfnuu++Qk5ODkydPokmTJli0aBEGDBigal9QUICJEyeGtCcCoA3IBEEQkYsEoBJAZWWl7WsMq6qqkJiYiMrKg7YsnE1MTHEkz2ggokRKTU0NSkpKIIoili1bhrfeegtr165Fx44dmfasSkpaWhqJFIIgiAiHREp0EFHTPbGxsTj//PMBANnZ2di0aRNee+01zJ49m2mvdlgOQRAEQehzEMAJiz6O2pFI1BIxu3tYSJIUVCkhCIIgCKLuEDGVlCeeeAJ5eXlIS0vD0aNHsWTJEqxZswafffaZ26kRBEEQBOEAESNSDh48iKFDh6K0tBSJiYm45JJL8Nlnn+Haa691OzWCIAiCIBwgYkTK3Llz3U6BIAiCIIgwEtFrUgiCIAiCqLuQSCEIgiAIwpOQSCEIgiAIwpOQSCEIgiAIwpNEzMJZgiAIgggvh2D9MLfjdiQStVAlhSAIgiAIT0IihSAIgiAIT0IihSAIgiAIT0IihSAIgiAIT0IihSAIgiAIT0IihSAIgiAIT0IihSAIgiAIT0LnpBAEQRAEk8MAfrfow+o5K9ENVVIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkdJgbQRAEQTApB9DQog+rh8FFN1RJIQiCIAjCk5BIIQiCIAjCk5BIIQiCIAjCk5BIIQiCIAjCk5BIIQiCIAiPMWPGDGRmZiI+Ph5ZWVlYt26dqu0HH3yAa6+9Fi1atEBCQgJycnKwYsWKELtly5ahY8eOiIuLQ8eOHfHhhx9aihsOSKQQBEEQhIdYunQpxowZg/Hjx2Pr1q3o1asX8vLyUFJSwrT/8ssvce2112L58uXYsmUL+vTpgxtuuAFbt24N2BQVFWHw4MEYOnQotm3bhqFDh+LWW2/F119/bTpuOPBJkiS5Fj3MVFVVITExEYkAfG4nQxAEQZhGAlAJoLKyEgkJCbb69t8rKiunISHB2hbkqqrfkZh4v6E8u3fvjm7dumHmzJmBtg4dOmDQoEGYPHkyl49OnTph8ODBePrppwEAgwcPRlVVFf7zn/8EbPr3749mzZph8eLFtsW1G6qkEARBEITDVFVVBb2qq6uZdjU1NdiyZQtyc3OD2nNzc7F+/XquWGfOnMHRo0eRlJQUaCsqKgrx2a9fv4BPO+I6AR3mRhAEQRBMDgKIt+jjJAAgLS0tqHXChAkoKCgIsa6oqEBtbS1SUlKC2lNSUlBWVsYV8eWXX8bx48dx6623BtrKyso0fdoR1wlIpBAEQRCEw+zbty9ouicuLk7T3ucLXpQgSVJIG4vFixejoKAAH3/8MVq2bGnYp9m4TkEihSAIgiAcJiEhgWtNSnJyMmJiYkKqF+Xl5SFVDiVLly7FyJEj8f7776Nv375Bfa1atdL0aSWuk9CaFIIgCILwCLGxscjKykJhYWFQe2FhIXr27Kk6bvHixRgxYgQWLVqE6667LqQ/JycnxOfKlSsDPs3GdRqqpBCERxFciCm6EJMgiGDy8/MxdOhQZGdnIycnB3PmzEFJSQlGjx4NABg3bhz279+PhQsXAjgrUIYNG4bXXnsNPXr0CFRDGjZsiMTERADAQw89hKuuugovvPACBg4ciI8//hirVq3CV199xR3XDUikEIRLCG4nwEDQ6RfDkANBRDuDBw/G4cOHMWnSJJSWlqJz585Yvnw5MjIyAAClpaVBZ5fMnj0bp0+fxn333Yf77rsv0D58+HAsWLAAANCzZ08sWbIETz75JJ566im0b98eS5cuRffu3bnjugGdk0IQYUBwOwGHEd1OgIg6wnNOylNISLC2u6eq6iQSE59xJM9ogCopBGEzgtsJuIDAaBPDnANBEHUPEikEYRHB7QQ8iqC4Fl3IgSCscRiA9lZhfdiHthF8kEghCIMIbicQoQiyz6JLORAEEVmQSCEIDgS3E6hjCIpr0YUcCILwPiRSCEIFwe0EoghB9ll0KQeCILwHiRSCkCG4nQBBgoUgiAAkUoioR3A7AUIV4dy76GIOBEG4B4kUImoR3E6A4EaQfRZdyoEgiPBDIoWIKgS3EyAsI5x7F13MgSCI8EAihYgKBLcTIGxHOPcuupgDQRDOQiKFqNMIbidAOI4g+yy6lANRVzkEoIFFH6fsSCRqIZFC1EkEtxOwicQwx6sMczy7Ec69iy7mQBCEfZBIIeoUgtsJGCTcIkQPrXwiScAI595FF3MgCMI6JFKIOoHgdgIceE2QGEUtfy+LF+Hcu+hiDgRBmKee2wnwMnnyZFx++eVo2rQpWrZsiUGDBuGnn35yOy3CZQR4U6AkMl51lUj4noLbCRAEYYqIESlr167Ffffdhw0bNqCwsBCnT59Gbm4ujh8/7nZqhAsI8N6Nx+s36nDhVdEiwHt/ZwiC0CZipns+++yzoOv58+ejZcuW2LJlC6666iqXsiLcQHA7gXN46QbsZeQ/Jy9MDQnn3kUXcyAIgo+IESlKKivP/neXlJSkalNdXY3q6urAdVVVleN5Ec4huJ0ASJhYRfnzc1O0COfeRRdzIAhCm4gUKZIkIT8/H3/4wx/QuXNnVbvJkydj4sSJYcyMcALB5fgkTJzD/7N1W6yILsYnvMxBWL9NnrYjkaglYtakyLn//vvx7bffYvHixZp248aNQ2VlZeC1b9++MGVI2IXgUlwvrqmoy7j98xbgvhgmCCKUiKukPPDAA/jkk0/w5Zdfom3btpq2cXFxiIuLC1NmhJ0ILsX1kihp6kLMoy7EVOLmGhbh3LsY5rgEQbCJGJEiSRIeeOABfPjhh1izZg0yMzPdTolwCCHM8dwUJm4IES208nFDwLg1HSSAhApBeIGIESn33XcfFi1ahI8//hhNmzZFWVkZACAxMRENGzZ0OTvCDoQwxwu3OPGaIDEKK/9wCRc3xIoAEioE4TYRI1JmzpwJAOjdu3dQ+/z58zFixIjwJ0TYihDGWOESJ5EuSngIt3AJt1gRzr2LYYpHEEQwESNSJElyOwXCAYQwxnJanESDKOFB/nNwSrCEe92KABIqBOEGESNSiLqHEKY4TooTEibahFOwOC1WhHPvosNxCIL4HyRSCFcQwhDDKXHiJWEiGLAVHcqBF6cFSyKoqkIQdQ0SKURYEcIQwwlxEk5hInjAr+hQDn6cEizhrKqIDscgvMBBADEWfdTakUjUQiKFCBtCGGLYLVCcFCeCg76tIqi0iw7EckKwhEOsCOfeRQdjEES0QyKFCAuCw/7tFCdOCRPBIb/hRGC0iTb69//sI02siA76J4hohkQK4TiCg769LE4Em/15FUFxLdrg0wmxQkKFICKPiHx2DxEZCIgMgdIU9ggUQfGKVgTY9zNoCvv+fJx+NpDgoG8i+pgxYwYyMzMRHx+PrKwsrFu3TtW2tLQUt99+Oy666CLUq1cPY8aMCbHp3bs3fD5fyOu6664L2BQUFIT0t2rVyomvxw1VUghHEBz0bac4sYpggw8vxBEd8ivYGMOu6oqTVRUBVFEhrLN06VKMGTMGM2bMwJVXXonZs2cjLy8PP/zwA9LT00Psq6ur0aJFC4wfPx6vvvoq0+cHH3yAmpqawPXhw4dx6aWX4pZbbgmy69SpE1atWhW4jomxunDYGiRSCNsRHPRth0CxKk4EG3IIh0+74osOxLDi0w6x4uRaFeHcu+iAbyI6eOWVVzBy5EiMGjUKADB16lSsWLECM2fOxOTJk0Ps27Vrh9deew0AMG/ePKbPpKSkoOslS5agUaNGISKlfv36rldP5NB0D2ErgkN+7SjVW502EGDP9xMYLy8jMF52+jSLHdNANP1DhIuqqqqgV3V1NdOupqYGW7ZsQW5ublB7bm4u1q9fb1s+c+fOxZAhQ9C4ceOg9p07d6J169bIzMzEkCFDsGvXLttimoEqKYRtCA75tUOcmEWwGNsuH15DUFyLNvgy66MpvF1VER3wS4SLI7D+u/wZAEBaWlpQ64QJE1BQUBBiXVFRgdraWqSkpAS1p6SkBB6sa5WNGzfi+++/x9y5c4Pau3fvjoULF+LCCy/EwYMH8eyzz6Jnz57Yvn07mjdvbktso5BIIWxBcMBnJIsTq+MjDUFxLVrwYWasXVNAJFQIp9i3bx8SEhIC13FxcZr2Pp8v6FqSpJA2s8ydOxedO3fGFVdcEdSel5cX+NylSxfk5OSgffv2ePvtt5Gfn29LbKOQSCEsIzjg0y2BIliIaWUsCzeO37dry68g+yyGcaxVsUJChXCKhISEIJGiRnJyMmJiYkKqJuXl5SHVFTOcOHECS5YswaRJk3RtGzdujC5dumDnzp2W45qF1qQQlhAc8GlFoJhdpyDA3HcRYN/aCuXLDZzIRYD1n69RrOTs1FZlwQGfRN0jNjYWWVlZKCwsDGovLCxEz549Lft/7733UF1djb/+9a+6ttXV1dixYwdSU1MtxzULVVII0wgO+LQqUIwihGmMHC89oJAXVs5mqhXCuXcxDOO8WFURQBUVQp/8/HwMHToU2dnZyMnJwZw5c1BSUoLRo0cDAMaNG4f9+/dj4cKFgTHFxcUAgGPHjuHQoUMoLi5GbGwsOnbsGOR77ty5GDRoEHONySOPPIIbbrgB6enpKC8vx7PPPouqqioMHz7cuS+rA4kUwhSCzf7qsjiJRFHCg/J7GREDguyzaGKckTFWFtaSUCHcYPDgwTh8+DAmTZqE0tJSdO7cGcuXL0dGRgaAs4e3lZSUBI3p2rVr4POWLVuwaNEiZGRkYM+ePYH2//u//8NXX32FlStXMuP++uuvuO2221BRUYEWLVqgR48e2LBhQyCuG/gkSZJcix5mqqqqkJiYiEQA9iw/ik4Em/2FU6AIDtsDkXu8vmijLzOiwGh8o/aAebHixDoV0QGf0YSEs38ulZWVXGs9jOC/V1RWtkBCgrVVEVVVZ5CYeMiRPKMBqqQQhhBs9mdWoDhdPTFiC3jzgDijCCrtoglfZp5s7I/PG8+oPWC+quLENmUBJFQIQg8SKQQ3gs3+wiVQBIdsAXd2EYUbgdEmGhhvVLD44/HGMGpvZa2K3dM/AkioEIQWJFIILgSb/YVDoAgO2YZrDYyd/kWH4/H6NyIQ/DF4fQsGbP25kFAhNJEOnZ1XsuTDlkyiFhIphC6Czf7MCBSnqie8dk7m4NR4s75Ei/55xhuprvh98/g1YuvPg4QKQXgXEimEJoKNviK1euJUfCtjnERgtIkmx/OM462u+P3y+BQ47YzEV+Lk05QJgjgLiRRCFcFGX14SKLx2vLF5/Zm19wKC4lo0MU5vjN1ihddOHt9NoSKAqikEoYRECsFEsNGX0wJFsNmOJy6vL6O2ajj5tF4zN1lBcS0aGKNna0Ss8MblsfPHJqFCEN6BRArhKE6vPxFstLNTnPDaKXFSjJiJyXvzFWSfRU5bPTsescLri9fOH5eECkF4AxIpRAiCTX68IFB4bOwSJzw2ctwQJEZR5shzIxZkn0Ub7HjFipYPo3YkVAjCG5BIIYIQbPLjpEARbLILtzixQ5TYfZqtmRuxHL2bsiD7LHLYadnoCQceH347PRueeCxIqBCEvZBIIQIINvlxW6Dw2OjFsytOOLZbW0ErFs8NWv79eAWLaMHGrqoKjw1vPCW066cOUQnr55xU2ZFI9EIihQDg7o6TcAoUq+KEJwcjwsTLDx80+gBBXsEinHsXdWy0+u2oqvDY8MZTYpdQEUDVFCK6IZFC2IrRyoFXBIreWJ74PN/da8/4EQ3YGhEtPIJF0MlBr9+NqgoJFYIILyRSCNemeewSKHr9Vqoner6dECZ6Me1EK5aoM5b31Fi9h/P5c1CLp9fPU1VRG2vEhieWEhIqBGENEilRjmCTn0gUKFpj9fzqfV+nDoILJ4LiWtSw5REsetUVfzy1OIJGn15VRc+3nn9lLFqjQhDhgURKFCPY5McJgSJYtHGqemKHONHyb+cYXkSTOaiNMyJYjIoVrT5/bCtVFb1+3jhOIICqKUT0QSKFsESkCRStcVp9Wt/Tjp1CZmztQi2maGCcmq1ehcMJscJTVWGN4+2Xx+EVKjTtQxDmIJESpQg2+HBDoOj1mxEoWj6dFic8Nnrw/FzN/NYvMNpEDluWjV51RU+saMVV69MSEVrjePrNQNM+BGEcEilRiOBCTDcFitY4tT6z4kQrFk+/kVhO+OF9uB/gzK4ctRu5lk9BJ5aTQsWN9SkCqJpCRA8kUghT2H0WiGChP1zVE6cW4fL4DxesHPQWowLaUzJq/WpixUxVxR9LLQ4JFcIUdiw8CvfipToGiZQoQ7DBR10VKOEWJ2ZEiZ5Po4gcNjznowg6PgWNPrWbvJpY0fIlGIyhNYa3X8+/UwggoULUfUikRBGCDT7C/VA8QaPP6PSOWrud4kStncenET92oRZH1Bijt77E75PlQ63P7BSQWgxWu1YMtTG8/X7/4V5ISxB1HRIphGNYraJo9bklUIz61/LFM5YH3vGiBZ9qY7UEi9yHcryg0m50CkhQyU2t3R+jrggVAVRNIeo2JFKiBMEGH+Gc5tHqs0NAOC1O7BImvHZ2+BINjGXZ6lUqWOPU2rWmgHinf9TatfwLKva8/Vq+WVBFhSC0IZFCcBGtAsWIXzUfemN4+52GFV/ksFXa8FRXlGMEDT+sqopd0z9OCZVwIsBb+RCEnZBIiQIEi+MjUaCo+WF9FyfFiZq9Xh+vfzvgeZqwH1HHRtmvJjT8Y0SdNr8Pp6Z/nBIqNO1DEPZAIqWOI7idgALBZJ/bAkXNp51rV/R8OoVaPDt38WiJFaUty4eRqgrLp1a7WaGiB037EIR1SKQQmthZRRFM5mBVoDhRPTEyXq/PsCjRcmYEUbtbb+uxPA2lK0GlnSU21GwFlfFeESpafXp+nUAAVVOIugeJlDqMYHG83dM8WggG/bLsWW12V0/smBpS82PIgR2o+RfZzWbXmrDaecUKq82IUFHLSdmm5lfLXq/PKFRN8SBVAM5Y9HHMjkSiFxIphGWcWofilkBh+XJ6Ua0pQWL20Bq9O6GguBZDTdQEi3yoyGhXumIJA4Fhp2xjiRwj61RYbWr5aNnr9Wn5ZGFVqOjlQhCRBomUOopgcbydh7YJJvrCIVB4fdldedEcoMSJ0/O0fGo9OMePGHxpdXGskaqKIrSl6R9WmxZa9nq+3DiRliDqAiRSCEtYWYei1me3QDErMhwRJyxjP2YFiV1PD2TlYOBpf3aIFb2qivJabZwVoeK2oKBqCkH8j3puJ0DYj2BxPO+90so6FMGgT5Y9q82MQBEYbayqC+845ndgGQNnE/a/1Giq8+LFjJ9EqOcogPm91FwyTLl+fqwxrHFKWD9S5Ti1Nie2lBv5o7JaQBMsjifcZ8aMGcjMzER8fDyysrKwbt06VdvS0lLcfvvtuOiii1CvXj2MGTMmxGbBggXw+Xwhr5MnT5qOGw5IpNQxBIvjwzXNY9UPq82sQFGOMXuTDIkngG2sJUysiBBB5cWLUdHCis1wyUpTb6iZqbm6JlSI6GXp0qUYM2YMxo8fj61bt6JXr17Iy8tDSUkJ0766uhotWrTA+PHjcemll6r6TUhIQGlpadArPj7edNxwEFEi5csvv8QNN9yA1q1bw+fz4aOPPnI7paglXNM8LFtWm10CRdnPM0ZVnMjhFSZqCBwvu8fqiRZOsWKlqmJ1DGBNeJsRKnZB1ZTo5ZVXXsHIkSMxatQodOjQAVOnTkVaWhpmzpzJtG/Xrh1ee+01DBs2DImJ6n9zfD4fWrVqFfSyEjccRJRIOX78OC699FJMmzbN7VQ8iWBxvF1VFMFEn5MCRWCMU17bMsXAMjIjTATGy2l4YqrlbFGsaA3jHaOMo4SVnhJWm5o/LdT8mPVH1A2qqqqCXtXV1Uy7mpoabNmyBbm5uUHtubm5WL9+vaUcjh07hoyMDLRt2xbXX389tm7dGpa4VoiohbN5eXnIy8tzO42ox+x/soJFf6zxPAJFz4/lmyArCO/+Z7XxZmzMInLEVNpo7QEG/rfy0+9DNl45lGECAdqLapX9evb+9OQLUpVj1NrU0LLV88O7OJcW0bpMJYDTFn0cP/uWlpYW1DxhwgQUFBSEmFdUVKC2thYpKSlB7SkpKSgrKzOdxsUXX4wFCxagS5cuqKqqwmuvvYYrr7wS27ZtwwUXXOBYXKtElEgxSnV1dZBaraqqcjEbZxEsjrdrsazVPIz4MSpQlNd2/IbOrJwoMXLIil6fE7DiiRo28j61A1NYikAx1qrwMGqvlpaosGG1mTk/hSDk7Nu3DwkJCYHruLg4TXufzxd0LUlSSJsRevTogR49egSur7zySnTr1g1vvPEGXn/9dcfiWqVOi5TJkydj4sSJbqdBwP5pHtcFil5CzEGMcXrtRm3MIHLEElX65O3KEgnrdDX/WJE9RNHtiFBRohyj1mZ0azLLhxl/VE2pGyQkJASJFDWSk5MRExMTUr0oLy8PqXJYoV69erj88suxc+fOsMY1SkStSTHKuHHjUFlZGXjt27fP7ZQ8idNVFLX2iBQoAmdCRvYwK9uVfWo2dsETy0i78vurrVlRDFHrVoaw+ufNu+OHF62xVvzKceJMP8KbxMbGIisrC4WFhUHthYWF6Nmzp21xJElCcXExUlNTwxrXKHW6khIXF6dbUqsLCG4nAOM52CVQ9FCON3KDM1w9sbL6l9WmxKkVl0Yed6zXLm9TlgqUlRUBpqd/rFZUeKoTSh8sP1q2PBitzhDRQX5+PoYOHYrs7Gzk5ORgzpw5KCkpwejRowGc/QV8//79WLhwYWBMcXExgLOLYw8dOoTi4mLExsaiY8eOAICJEyeiR48euOCCC1BVVYXXX38dxcXFmD59OndcN6jTIoXQx+mD2wST43jH6v0GbpdtkIEd+5312sO5BUQZi/dxxwJHm9oCW5WFtazpH6V7uWs7hYrSXq0tEqd99HIgvMXgwYNx+PBhTJo0CaWlpejcuTOWL1+OjIwMAGcPb1OeXdK1a9fA5y1btmDRokXIyMjAnj17AACiKOLuu+9GWVkZEhMT0bVrV3z55Ze44ooruOO6gU+SJMm16AY5duwYfv75ZwBn/0BeeeUV9OnTB0lJSUhPT9cdX1VVhcTERCQCcG8ZkL0IFsfziBS7z0QJxzSPcqxpWytJsK7V2li+eMdZRdTpV7uDKsex/Cjb5L6Ud1yF7VH1rqBrZXp6aSntddLg8qE1Vqtdz58SK2tT9HKINCSc/XlUVlZyrfUwgv9eUfkfIKGxRV/HgcQ8Z/KMBiKqkrJ582b06dMncJ2fnw8AGD58OBYsWOBSVpGL0/PcAqMtXOtQeG2V47gFil3ixM3TwtRiiYprtR08/nGiyrW/TX4tLxsYmP5RupFfG62o6MFjb3TaR89nOKZ99HIgCC8SUSKld+/eiKDCj+MIYYhh92JZHpRjrRQvbBEoVqsnmoE07JQ4pSpZT+LzIyr6WNM3fnuR85q1C0hj+scOoSLH6fUpRnwYxepOH4KINCJKpBD2Ycf9TjBob/S4EDM5yK95BYrp9SdawVnXRn8A4drSofXkY0H2WZR9NitWlD5YQkVha4dQ0QqtE94wZsdSNcWDHANwxqKPE3YkEr2QSCFUcXqxLMtO2cazRpU11naBYqQko7zmFSd6oiRcu3tYJ8cCbMGiJlZElWu/D/l4jukfs0IFKnYsWz2horzWi8eTgxmomkJEE3X6nJS6jGBhrFeqKHo+rSyU5bEzJVAEncDKADznpfA+EZkHQfHiQSsO71OPlWOV/axr+VhlTIYdbxHLSLHLKKzxdlYIw7mpiyAiAaqkELYiMNrs+E/c7NoSNTvTAsVMEqx+NaXIWyrihWeMqJGD1jkncv9+H0b2EMs/661TEf9npuZajtnKi53TPmrY4ZO2IxPRAomUCESwMNaOc1GsxOf1qZWnVnwegaLpkEegKJ3Jr82IE7t2+KjZiwbHye1ZgoVXrPCcdS//rBxnUKgoXcnR6nNq2kcrJkEQfJBIIWxDYLSZOefMjnUorFyY/uSGRgWKMohWAm4+eFBvnKhhL+9jVTsA9Tu6VlVF+VkeyyahYmQhbbjRih+Ow90IIlKgNSlECF6aFze7DkVtjCMChbUeQ+5X79k+guylzE+tT4lyXYmRtSxacVhtSr/K76gco/XzVF6zxhhco6LmRomWnc6jhhxfm+I0gtsJEAQnVEmJMAQLY51cMMtqt6OKwhNbGUtQ+eyYQGG1K/2GJMCwV2tTG8+L1jje5/aw2vQqK4KKrd+XyLBTjrG5oiJvV+LGTh01wrEdmSAiARIpRBBOVlEEg/ZWp3nUbBwXKHYcnc8ap4WaD0D/qFM5as/tETXa9Ba9Km15pn9Y9jbNb6iFUmJmEa2RtSk8/vSgBbQOIwKoseiDzkmxBImUKMELVRS9cbxVFPlnnjiuCRQz4sSJFcta40SN+FoHtfnb5NecZ55YEiqM2GbWp6iht4hWiTyG3VA1hYhEzpw5g59//hnl5eU4cyb4JLyrrrrKsD8SKRGE4HYCFhB0+o0c7MoTQ3dMOAWKMhnltdJey04NLYWnd5eVI8o+swSLoLBTXnNuJTYsVPw4MO3DCqMGj224qykE4RU2bNiA22+/HXv37g15hI3P50Ntba1hnyRSiABmfolntZupovAiH2dpmseIE/9nM9M7ypjKayPixGw5zIiAkccWZZ9ZO3XkNsprjvUkhlSDwfUpRlFLSxlOb6wb0JQP4RVGjx6N7OxsfPrpp0hNTYXP57Psk0RKFBCuR8CYxY4qCsuea5qH1c/6bHV6R1D08SyiVfrXQ+0p8FUaY3ie2yPK2njEiiiztSpUePwxYLnSCqdES/BojdMarzbObB4E4TV27tyJf/7znzj//PNt80lbkCMEwWH/ZhbMCibttMbZfSZKiAFLVMj7WZ/NTO8IjM9+OzVbuV+1o/ITNF5qGBnDis3KkVeQcWwl1v3DVLNl9ButrqnZKIk0oU8QbtC9e3f8/PPPtvqkSgqhi2DA1qjYsbpYlseGGUxvmkXQ6TdzMq3ezVhNlBiFdciaGnL/yooLa/ErwH9Ym99WbmekoqL0p7R1YNpHjpFqivKaIKKRBx54AA8//DDKysrQpUsXNGjQIKj/kksuMeyTREodx+3fsASdazl2bX82tQ5FYLTJbeVtRgWK3tSO8g9JS5gY+QPVs1WKGDXBwiNWjEz/8AgVMPrln+W5sXzpDDeziNbo2o9ImfLRik0QRrjpppsAAHfeeWegzefzQZIkWjhLmCecC2Z5EVQ+GyrVyxv11qFoBVPzY2VugZWTmjhxSmlqrUfx56InVsRzn/WqKnI7HqGiNh4KO39elerdrOG8UDWFIPjZvXu37T5JpEQAgtsJmETQubbr3iv3yz3NIx8k/6zlSE+gaCUi7+MRJ1o/HF+KRqcBpIPqMf03fTWxYuWwNitChWUnR97PQG24WqpWMZGiaehZPg5QCeuHuf1uRyKRQUZGhu0+SaTUYdye6jGC1aJECCwD3nUorEBmBYoyD7kfXnFilyjR8qsmWNTEitXD2vSECgy2OVhN0Qqj9GsljtZYmvIhIoVffvkFU6dOxY4dO+Dz+dChQwc89NBDaN++vSl/tLsnynFrqseVKgprkNE2swJFuWNGKVBYu3l8KRoCpbnFl0osZTxW3gmKfj+C7LOReTmWD97zY0wcgifvNr0ImyCIEFasWIGOHTti48aNuOSSS9C5c2d8/fXX6NSpEwoLC035pEoK4QiCzrUcngqJo1UUub3WOhQrAsUPS5zI0RQldiL3d5idg7y6oiwhJCC4qmKmoqJ37gnvtI9ajhopWMHJBbRWoSkfwk0ef/xxjB07Fs8//3xI+2OPPYZrr73WsE+qpHgcwe0EHIC3iiIY6DdcRWE511qHoqeSHBMoKpWPAK0MvlioVFiUlRWtapC8T4D21BlU+hMZdqyxWj4Y9nrFFp5KiZGKo/LaCFpjqaJDeJ0dO3Zg5MiRIe133nknfvjhB1M+SaTUUZxajyIw2vT+82SN4R1rxJeqAas6orTXW4fC8q81Vjle7YYOMKZZ1KZleEWHFnrjOcWKH57pHyMCT2ssjw+dv/isoWr9erZ1DcHtBIiIp0WLFiguLg5pLy4uRsuWLU35pOmeKMbMehQezI4VVD7rCRnNKorRNQusPq0dQWYEip+QyolaxYRHjGjZlHGOk9v5c5FNBflS/jcFxFpYy5r+8aN3WBtrIa1yrNzeD6vN74PVpzOUFy9P+RCEW9x11124++67sWvXLvTs2RM+nw9fffUVXnjhBTz88MOmfJJIIcKKlQqPoPJZu1Gjz9/GO83D40s+FrAgULREh9EKipq9Ury0YrQrxIpyvYr8bs0SKgKMbxnWEhn+NlafxhYYvd0xRjYksVCmw0qPF62xPLt8aF0K4RZPPfUUmjZtipdffhnjxo0DALRu3RoFBQV48MEHTfkkkeJhBLcTUCAw2pyY6hE4bDT7eQSGoNHHu5ZF77A3LoHCK07MTO3ooVZFURMrGlUVo0JF3ub/rCVijCgEnT5WaIJgcgzAaYs+TtqRSGTg8/kwduxYjB07FkePnv0H27SptdVUJFLqIFbXowgujDUyjmmr5UCrT2u9Ck+fHEcEipY4MXt+ykFGG0uYKNsYVRU9oeJHAN9Ujda0j3KcwfFGqik82DHlQxB1FavixA+JlCjFjZ0Cdk31+OHe0aN0wnKmHM+7mNPfZkmg8IoTuw51k/tRChY1saJSVdETKqztyXrrU5SwxqnZcPZpmSv75Z/tnvLhsQknAryVD+F9unXrhs8//xzNmjVD165d4fP5VG2/+eYbw/5JpBCmsSJ07JjqYQ5SDmb1GbERNGxYbZYFihVxkqzSXqExRk2wKMWKCaHiR28hrRKr1RSNuJGygFWAtZxoXQoRLgYOHIi4uLjAZy2RYgYSKQQXggkbnjFG7HRtnayiQNEn9+MfZ7tA0RInaoKE15YlXFLArqzIhQoQPP2jIVT01qf48bex+pQ2PKUMC/MqTk/52AFNGxFeYsKECYHPBQUFtvunc1I8iuB2Ah5CYLRpVlmMnM7Fcq4M7H/XW4fCauMSKMozS1LAFijJspdV1HyxYmuJKdl3Yh1Gx3o+kXDunWdKzS7hybDRGqbsl392ooJIEEpmzJiBzMxMxMfHIysrC+vWrVO1LS0txe23346LLroI9erVw5gxY0Js3nzzTfTq1QvNmjVDs2bN0LdvX2zcuDHIpqCgAD6fL+jVqhX/ov3zzjsPhw8fDmkXRRHnnXcetx85JFLqGDzrPpw6H0ULo+tRuI83YTYq+rRs/CirKDx+5fhvxqYEihwtcaKFmZNmtfwrxYpBocJao8P6S8Dz8/bjt7F4RKyJx/1wo/TD49eu2ETdYenSpRgzZgzGjx+PrVu3olevXsjLy0NJSQnTvrq6Gi1atMD48eNx6aWXMm3WrFmD2267DatXr0ZRURHS09ORm5uL/fv3B9l16tQJpaWlgdd3333HnfeePXtQW1vLzO/XX3/l9iOHpnuIsGPk2XPc8KggfwAjv85qjWFN8/ixRaBoCROrZ6WwDnhLRug0kHwKSLkuRX6t2KIMaK9PEcA/tWNkToXHrw40nUK4zSuvvIKRI0di1KhRAICpU6dixYoVmDlzJiZPnhxi365dO7z22msAgHnz5jF9vvvuu0HXb775Jv75z3/i888/x7BhwwLt9evXN1Q9AYBPPvkk8HnFihVITPzff8i1tbX4/PPPkZmZachnIB9To4iox8r5KEZg+bE81aN0zrMWRW2MHKZQslOg2HlWCmsXjzyuXKxoCRUG8vUpfljbkv1onSirhnKM1lgNkaMXkiclp9al8MR2Arfi1nWqqoL/AcTFxQUWnMqpqanBli1b8Pjjjwe15+bmYv369bblc+LECZw6dQpJSUlB7Tt37kTr1q0RFxeH7t2747nnntOdqhk0aBCAs+ekDB8+PKivQYMGaNeuHV5++WVTedJ0D6GLYJMNL5Z29Sj7tGx4x2olpFtF8cMrUFhTLzxTNikaLy3UfLOmf+RjWJ91pn38aD1IUIlybQrPGOVYY12qyEO7vbaEJ75Tz++KKqpwVoFaeZ3TJmlpaUhMTAy8WBURAKioqEBtbS1SUoL/7aakpKCsTOcXBAM8/vjjaNOmDfr27Rto6969OxYuXIgVK1bgzTffRFlZGXr27MlcZyLnzJkzOHPmDNLT01FeXh64PnPmDKqrq/HTTz/h+uuvN5UnVVIIx7HjP0vBagC/A+UaCCPJaY3xt2ke1gboCxQ9e9Y4PZS2vIe5Kad/1CoqnNM+Rqop/ncjWBlrfphhPzSdFJ3s27cPCQn/+62GVUWRo9zKK0mSbdt7p0yZgsWLF2PNmjWIj48PtOfl5QU+d+nSBTk5OWjfvj3efvtt5Ofn6/rdvXu3LfnJIZFCBBDCEMOR9Sgs52bHCop3LbQWhOoe1gZoCxQ7xIkafh9qYsWqUDkHa9rHD+dDADUxM03EaWunkOAJyWNDRDYJCQlBIkWN5ORkxMTEhFRNysvLQ6orZnjppZfw3HPPYdWqVbjkkks0bRs3bowuXbpg586d3P6PHz+OtWvXoqSkBDU1NUF9Zp7fQyLFgwgmx1nd2eM1BEYbV/6C4t0KRiovhqZ5jAoUu06bZfnUOhsF4BcqfkxWU/wICK6IKBfQ+tuNYGFdikX3BGGI2NhYZGVlobCwEDfeeGOgvbCwEAMHDrTk+8UXX8Szzz6LFStWIDs7W9e+uroaO3bsQK9evbj8b926FQMGDMCJEydw/PhxJCUloaKiAo0aNULLli1NiRRak0IYJlyLZm0L4re1MtWjhLuK4kdNoGjZ8dgDwWeeaL3U4DkbhedsFkbuTOF2DuU6EzsVNIdP3nCCyudIEvxEZJGfn4+33noL8+bNw44dOzB27FiUlJRg9OjRAIBx48YF7cgBgOLiYhQXF+PYsWM4dOgQiouL8cMPPwT6p0yZgieffBLz5s1Du3btUFZWhrKyMhw7dixg88gjj2Dt2rXYvXs3vv76a9x8882oqqoKWQyrxtixY3HDDTfgyJEjaNiwITZs2IC9e/ciKysLL730kqmfBVVSCE0Em2x4MXyGhR0LXtSmeozchXSrKGrIb/5GBIqZA91YO3eUsZRH46tVVByuphhFgKX1KEo3XkGAt/IhwsPgwYNx+PBhTJo0CaWlpejcuTOWL1+OjIwMAGcPb1OemdK1a9fA5y1btmDRokXIyMjAnj17AJw9HK6mpgY333xz0LgJEyYETor99ddfcdttt6GiogItWrRAjx49sGHDhkBcPYqLizF79mzExMQgJiYG1dXVOO+88zBlyhQMHz4cf/7znw3/LEikEI4Sth0GTv5aK5x71zoXJYCRKoqe0FA72M0qWmJFT6joYXBtih4CtKd8zCwe8Y91kDCEIOo49957L+69915m34IFC0LaJEnS9OcXK1osWbKEJzVVGjRoEFjcm5KSgpKSEnTo0AGJiYmqB9HpQdM9hOsInHZhX4+ih6YCs3rYmlMCRemP5VNr6idZxY71fVmCTYHalI8QYmkdg4rZSd1rh28ntyELJscR0U3Xrl2xefNmAECfPn3w9NNP491338WYMWPQpUsXUz5JpBBhQ21nDwu9fm78jtTWo9hxUwxM9ZitotglUIyekaLlm/fhhganubiqUTbDcTcXHE7Baf+EQ1TibEnMyiuKVlQ/99xzSE1NBQA888wzaN68Of72t7+hvLwcc+bMMeWTpnsIABH2n6gQBt9qoobr5srzrBw1jAoUPSEi79eacmEdiS+HZ9pHw8bKlI9ZBETUnIuAiEqXIIKQJAktWrRAp06dAAAtWrTA8uXLLfulSgoRubh5pKbh2GpiwqygMVIp4R2jdcqsnq0aHFM+agiKd7XTZ82ccaMYypuKFnTCKxHNSJKECy64wPSDBNUgkULYimBhrJNPp3UMrS22htel8D5g0OqZKWYEDmDsgDmO767cxi2ce3d5b69goJ+2IRPEWerVq4cLLrhA9wh9w35t9UbUeTz5n7InkuKtGOhtOdbDzkPdeKaWjMbj+E5eKDkIbidAEHWPKVOm4NFHH8X3339vm09akxJFeOJe7iSC4t0VzJ4Wy1NFceLUWaPI153orWNR4Ma6FIIgwsZf//pXnDhxApdeeiliY2PRsGHDoP4jR44Y9kkihbCE4HYCWgjn3nl39ujZh3NHSthQnosCaB+Db8SPH8bBbm5B59cThGO8+uqrtj0E0Q+JFI8hmBznhQq6GQROO09Vgbh+2KyKCu9Uj1erKEYxehAcQRCRzIgRI2z3SWtSCMIsmotmjWCHn1YaLzPx7T44zmU8cFaKWQS3EyAITmJiYlBeXh7SfvjwYcTExJjyyS1S7N5WZJYZM2YgMzMT8fHxyMrKwrp169xOqc4iWBwfqdUd41jYZsuFnojREyJmFuiG05938VQFjwg/R216RQlqR/NXV1cjNjbWlE/u6Z7OnTvjjTfewNChQ00FsoOlS5dizJgxmDFjBq688krMnj0beXl5+OGHH5Cenu5aXgShjlemaaxOvaitOWEtnjUQy/+wQbVn8vivzWJ1fGSGJoiw8vrrrwMAfD4f3nrrLTRp0iTQV1tbiy+//BIXX3yxKd/cIuW5557Dfffdh48++ghz5sxB8+ZO//YYyiuvvIKRI0di1KhRAICpU6dixYoVmDlzJiZPnhz2fAjCfoxOs9hV1eBZHOsiAuiOTxAe5dVXXwVwtpIya9asoKmd2NhYtGvXDrNmzTLlm1uk3HvvvcjLy8PIkSPRqVMnzJkzB3/6059MBTVDTU0NtmzZgscffzyoPTc3F+vXr2eOqa6uRnV1deC6qsru58MTTiC4nQDhPLTLhiDqDLt37wZw9qGCH3zwAZo1a2abb0O7ezIzM/HFF19g2rRpuOmmm9ChQwfUrx/s4ptvvrEtOTkVFRWora1FSkpw+TwlJQVlZezS8uTJkzFx4kRH8iEIgiAI4n+sXr3adp+GtyDv3bsXy5YtQ1JSEgYOHBgiUpxGuQdbkiTVfdnjxo1Dfn5+4LqqqgppaWmO5kcQBAdURSGIOkdtbS0WLFiAzz//HOXl5Thz5kxQ/xdffGHYpyGF8eabb+Lhhx9G37598f3336NFixaGA5olOTkZMTExIVWT8vLykOqKn7i4OMTFxYUjPcJGRETzlE8FjK1LKUNU7LYR3U6AIAg9HnroISxYsADXXXcdOnfubMvBbtwipX///ti4cSOmTZuGYcOGWQ5slNjYWGRlZaGwsBA33nhjoL2wsBADBw4Mez4EUbfQWzSr1m/gWHwWesvERGvuSdwQRPhYsmQJ3nvvPQwYMMA2n9wipba2Ft9++y3atm1rW3Cj5OfnY+jQocjOzkZOTg7mzJmDkpISjB492rWc6jIiormiYYTDUD8r5SCc34bMU02x8+RXPV8mYvmnf8Rz73XgbAnR7QQIIszExsbi/PPPt9Unt0gpLCy0NbAZBg8ejMOHD2PSpEkoLS1F586dsXz5cmRkZLidGsGgEtF0oJuT8AgdvzBQihWz4sRihcRrcIge0dwwoi5TCaCBRR+n7EgkMnj44Yfx2muvYdq0abY9wyfint1z77334t5773U7DcImRERwtUY6aPBofLX1JnZWW8yIEqfOR6Hn9tiB6HYCBMHJV199hdWrV+M///kPOnXqhAYNghXeBx98YNhnxImUuo4Iczftul61OAoLR5T7B4s4+8M1+8OqwtkTUrnG603BaPWzxEw4po2s4OGD4OTQriKCcAxBEILWjNoBiRTCEiI8WAkR8b8TSgUNO6V48b/bJWpsxwmhwhIXyqkeuU2Zhp0ahw1lRBBEZDJ//nzbfdJTkKOIOj+/LrqdAMCe4uCpMiht1ASAnRULJ6ofGlM8koeqLaLbCRBE3eT06dNYtWoVZs+ejaNHz951Dhw4gGPHjpnyR5UUwhCWpl1M+BbBUalxMilutHb4yJFP5ehNCTm5hoV3S7EDwsI/5eLRp1SIBvrrvPAnCAPs3bsX/fv3R0lJCaqrq3HttdeiadOmmDJlCk6ePGnq+T1USSFsRQxnMDfXF/hja1YHjC4cNSIIzIqHgxpj9aZv1KZ6WP4MfHe17ceiSruePUEQrvDQQw8hOzsbv/32Gxo2bBhov/HGG/H555+b8kmVFAKAR9eWqCEi/Mn616X4F89qwqqQyKsfWtUUZZVE6wRavzjgqaroiRqWQDEqhFjCxIX1KKLiXQPeSgiHK8uamScGQXiZr776Cv/9738RGxsb1J6RkYH9+/eb8kkihfAkImzWIWqLYXkXz3LBmvLhPWjNzLSPH6tTMjwCxWIVxV9xcqP65YE5GdHtBAhzVML6XfK0HYlEBmfOnEFtbW1I+6+//oqmTc3NydN0DxE25PcK0eJ4XUTFu5Mwp3z0FtBqTa2o7bhx4oA1owJFDYNVFP96lHCKFo1YIqPNA9rGMrTjmggn1157LaZOnRq49vl8OHbsGCZMmGD6qHwSKYSj8PwnKdoRKJzrEjRvsKwbs5mbvEPPygnyY2aKR6+KwoBnV4947p13PYoVRF0Lx0PYIYCcFFGig76Jusurr76KtWvXomPHjjh58iRuv/12tGvXDvv378cLL7xgyidN9xCaiNCf7eCxcSwHJ84w8Qc0cl4K9+mzamtTAP31KX78QsHI05KVY9VyU8JzLkqZymcFdv5aL557VxM1Ft0SBGGc1q1bo7i4GEuWLMGWLVtw5swZjBw5En/5y1+CFtIagUQK4Sm4dxOLMK6MeNelGEFTJMlFh/yzUaECaIsVOUrhwlt5MbpDR686YmCqR9RxZQYOn2YqEXK34ZgOEnUtCMJbNGzYEHfccQfuuOMOW/zRdE8dgucX1XCUmUUbYtjiR1S824HWGR+BaQ35DZpnqkcpJMweCOf3JX/pobYlWZmD2jSPThXFyIJZvakeM9jwF17UtaC1H4T9zJgxA5mZmYiPj0dWVhbWrVunaltaWorbb78dF110EerVq4cxY8Yw7ZYtW4aOHTsiLi4OHTt2xIcffmgprpLJkydj3rx5Ie3z5s0zPd1DIsWDiG4n4CCOLp61Y12KqHjn8WlqbYreCbNqQsWuw9W0xIlVgXLuu7PWolhZMKt2PoqRsQxERlu4F82yciCil6VLl2LMmDEYP348tm7dil69eiEvLw8lJSVM++rqarRo0QLjx4/HpZdeyrQpKirC4MGDMXToUGzbtg1Dhw7Frbfeiq+//tp0XCWzZ8/GxRdfHNLeqVMnUwe5AYBPkiTJ1MgIpKqqComJiUgEYM9DpJ1DMDmOZ3mG1nQKKy6rTelDaaO8lufVVMVO0OiX2zVVdsoDNFX0Kd9ZzvXGKtvlYxIU14BsbYp8O3Irlc/KaRzWOhOt7clGTp7VEzgsYaS1UJYlUmTijFVFUZvqYYlBZR/voW88YzlDqvUrbZQaSG0Ma6yajVY7ywcLsxUerbheQcLZ71dZWYmEBN3Diwzhv1dU9gASLC6KqDoNJG4wlmf37t3RrVs3zJw5M9DWoUMHDBo0CJMnT9Yc27t3b1x22WVBu2wAYPDgwaiqqsJ//vOfQFv//v3RrFkzLF682HJcAIiPj8eOHTuQmZkZ1L5r167AYlqjUCWF8Bys/3xFliGzUcXG/27mV2RbqilqVQeeZ/borQ3hfamhVj0xKlBkWK2iiOfezezqUY5lwP13zAR6furC1mbCOFVVVUGv6upqpl1NTQ22bNmC3NzcoPbc3FysX7/edPyioqIQn/369Qv4tCNuWloa/vvf/4a0//e//0Xr1q1N5U0LZwldRLi3w4fll2txLWugHmoHu7HQOoE2aKeP/IA3noW0APvwNr8Q0DsYzghq4kfv+T060zxytMSFeO7dyF1bOUZkm/HmoDdcVPnsttBwsopCyKgEEGPRx7mzzdLS0oKaJ0yYgIKCghDziooK1NbWIiUluEqakpKCsjKjj9r4H2VlZZo+7Yg7atQojBkzBqdOncI111wDAPj888/x//7f/8PDDz9sKm8SKYQp9ISCCD6NwGuni3KnjhZGtharjZHjFypyP1xbkvWECqAuVgBzgkXrPxujB7upCBSeaR4WouLdjHgROWx0MCpAnBIDokN+vRq3rrNv376g6Z64uDhNe58veFGCJEkhbUbh8Wkl7v/7f/8PR44cwb333ouamhoAZ6eAHnvsMYwbN85UziRS6hhG7rduoRZfhLZgYfbrDZLb8NjyVFP8bawfNvMPQK2aorxmbTfWOhPF/G9VodgsUOSwdkKJ596NlARELSMFLk31hMsvEXkkJCRwrUlJTk5GTExMSPWivLw8pMphhFatWmn6tCOuz+fDCy+8gKeeego7duxAw4YNccEFF+gKMi1oTYpHEetYXKO/bVoqpxuZCvDbmJ2WYG1JZj4hWWtbMs9ps04ci6+2VZm1hsWgQGH9PLXEhqh41/oLYOQAN5vKHFohjNiFUyARkUlsbCyysrJQWFgY1F5YWIiePXua9puTkxPic+XKlQGfdsZt0qQJLr/8cnTu3NmSQAGokkJwIiIC1qXwlJH8zrTKSTyHu7H6DK9PAdhrVAD1Q9yUgsLoqbNWTpxVXnMIFK1pHiMLkrVs/GjZMPpY5jxb5N1ej0LUffLz8zF06FBkZ2cjJycHc+bMQUlJCUaPHg0AGDduHPbv34+FCxcGxhQXFwMAjh07hkOHDqG4uBixsbHo2LEjAOChhx7CVVddhRdeeAEDBw7Exx9/jFWrVuGrr77ijqvH8ePH8fzzz+Pzzz9HeXk5zpw5E9S/a9cuwz8LEimEaaysS5GPldtpjVHt1xrk7+Ox0RI5fhvWl5aP012fonxSsnIxLcB/4ixgT3VFbeePTQJFjnjuXaukYKSKomXD2aXlSg831qPQotm6z+DBg3H48GFMmjQJpaWl6Ny5M5YvX46MjAwAZw9vU55d0rVr18DnLVu2YNGiRcjIyMCePXsAAD179sSSJUvw5JNP4qmnnkL79u2xdOlSdO/enTuuHqNGjcLatWsxdOhQpKamWl5DA9A5KZ5GMDnOibNS1NqdPC9FbqPXH3LmCWswy4nShnc8z9kpSn9BC2nlQgVgL4JVWxhrfl76f+htSda6NihQWNUQM+eisMYp33XGa4WV9/PaWD0fRc2O1ablQ0ldPh/FT1jOSekAJFjc3VNVCyTucCZPryEIAj799FNceeWVtvmkNSl1EKvH44s25WEnIq8h68uLinetAEbHy9u01qcAjDUqynUqLHGgdfIszxkoRuzVcpBjUqDIcamKoodRgaJE1OhTG6s3hiAiiWbNmiEpKclWnyRSPIzodgIKRBM2WmPM3hSYPlmNPHcUrcWwvDdTVmwuoQKEni3CEiVqYkWOmUPc5L71BJJCVBkVKOK5d9bPTUQoPGtReMbLiJSpHsJDHMPZvzhWXsfCnrVrPPPMM3j66adx4sQJ23zSmhTCEka3M/OsbVVDhIUFtP7BWk54+/yfWQtrWOtTlDn5b/BB61SA0EW1QOh2Zaj08aIndlj9CiFlVqCA0Sbv0zpd1mwVhWGv5ULZr2fL48POsbRol/AyL7/8Mn755RekpKSgXbt2aNCgQVD/N998Y9gniRQiIhHBuYCWpWL8dix7f5uW0FHzzxqrFCqAxoFvykW1gPZJs3aek2JQnADGBYqW0DA6BSQq3tXy4nCh1s/jg+WLFzNjeKAKD+EWgwYNst0niZQ6itVD3USYX7irHKvli2eXj5oNy0cAXpGh5YR1iq3fh16CSqECGKyqAKG7gPzYcTS+lsBhHG/vhEARFX3y8XI7pT2rz6YqCi9hPfcnTIhuJ0BEPBMmTLDdJ4kUwhAiTD5LR4aVKR+9XCxVU3imfdRiqQkVQH/6B2CIFSB0GsiPnRUUOQbFCWBMoEClX823fCzLntOHnkCwe8Gs8pogoo0tW7Zgx44d8Pl86NixY9D2aKOQSPE4Ipw5IM1LGK2m6PkIwFtN0WqT+9Ban6JMQt7OO/0D6IgVIFSwWIEhSpR5+NESJ8p+UfZZb6Esax2KqOgDo0+OiSoKyw0vdkynqMVXawfofBTC+5SXl2PIkCFYs2YNBEGAJEmorKxEnz59sGTJErRo0cKwT9rdE+U4tRVZOdaKLy3fTL/yRtZOHZFh50drt4+8Xy0JtV/LldMjygqE8u4iHWQ/Byewy0b50oNjjD+m0eqJVYEiR7TQ5lAVxSqRMNVDEHbwwAMPoKqqCtu3b8eRI0fw22+/4fvvv0dVVRUefPBBUz6pklKHsWtaRYkIZ6d85P7lnw1VU+QDmQYKO1YgeZteVUYtUaUPgF1VYfUDwYJB86nKPEKFAVMIKfLxw1s9AYwJFKOHvbHsdHYTsdzwEg1TPaLbCRB1gs8++wyrVq1Chw4dAm0dO3bE9OnTkZuba8oniRTCMUSYW0Br1LeWXwD2TPvI/Vhd7au1qNbfD0bOLEGhKVw4xithzRdoiROAr5KkZqMlUNRicNo5UUWxY8Gsmn+tuFSNISKBM2fOhGw7BoAGDRqEPMeHF5ruiQBEh/2bmfJhtRv9j5TnwcPKz3pnqAXloGcgKt7ln/WmbYzejEWGH60pILkNazrIj3yKRu+lhloMtZzkyL+TWYEClX5R8a78rOHHrmkcp6ZDnYDWozhAFUL/HRp9sZ5hVUe55ppr8NBDD+HAgQOBtv3792Ps2LH44x//aMoniZQ6jtf+4xI1+qz8tij3y/TDWlei5sD/2YpQkfswI1ZY/7HxiBY9WP+JKtESTH5E2CdQREa/fJyeL60xHMNFlc9K9H7kOuEJos4zbdo0HD16FO3atUP79u1x/vnnIzMzE0ePHsUbb7xhyidN9xCOIkJ7Ksbq2hQ1G6YTI+tK/Ojt2PHf7fxrWMCRNCsWa4pHLhRYzyWzS4Fq/aanVTkBQsWCqPLZrEBRxtPypePKKFanWCJlqkcrNkEYIS0tDd988w0KCwvx448/QpIkdOzYEX379jXtkyopEYLosH+3pnzsHs/0I+oYiIzP8jaeHTu8lQStqorfp1Z1Q6vSwoueH1aVhZWr1ncRVex4j8tX+2zAF086ajZK9LSa1thw4LWKKRFdfPHFF+jYsSOqqs7+h3LttdfigQcewIMPPojLL78cnTp1wrp160z5JpESBbj9H5ioc23n2hQ1G2Yws9ML8nZeoaL0xbrBK/3qTe2whAvPi4VaLGVeavmr2fOICr1t3cr4Gr4cEbk2jRdVbNXaCSJSmDp1Ku666y4kJISWfBMTE3HPPffglVdeMeWbRArBhWigPVzVFEO/Ecsb9YSFmjMzQkXpS+mPNV0iIhQ71qOo+VITJso89CpBoootz1kqan5YtspcGd08LtRslLhZRaGpHiIS2LZtG/r376/an5ubiy1btpjyTWtSIggRzp4+a3QbsBFEaG9HNrM2hQfVs1PkAbXOT1F+5jlVVr5OhZW08lpp77fxI7f1Y3d5TFRp5ykJKK+NVE+U7WqfTa5D4Qmp5csM4d4u7HallCAOHjzI3Hrsp379+jh06JAp31RJiRLC/R+Zk//Riyp2IoeNagVEzwlPUGXFwEhVxW+vVV2Rv6zA44+nyqO8Vo5xWqDIbSyg5cauKoqandZ4OhuFiBTatGmD7777TrX/22+/RWpqqinfVEkhgtCqpohg/1Kv1q5np7xWVlO0zkMDh538s2pFxYgTNYfiuXd/n15VRW7rv5a3ycfJx8oRGW1WMLpyWtmmVekQVexEjs8cAiWaqyhWEN1OIBKgMhUXAwYMwNNPP428vDzEx8cH9f3++++YMGECrr/+elO+fZIkSXYkGQlUVVUhMTERiQB8bidjAcHkON4j8rWmfNRiq7UrfbHs5G3KHLXGyz83NWijGpTHEW9wpe+QJBj2eu1qfozAcycVOduUvowela/12QGBYsTOTBXFzgWzPH9MVu6hevG9joRzxcvKSuaCTSv47xWVYO/+N+QLZ/8bcCJPr3Dw4EF069YNMTExuP/++3HRRRfB5/Nhx44dmD59Ompra/HNN98gJcXACdnnIJESgQgWxvIIFb37n1p8VjvLl9JOea11X9ca67hQ4Xas4VvNnhVLr91ORIN9Ru7ovCpBbYwFgWLWTk+g6I3XG6vVruVLCYkUEileYe/evfjb3/6GFStWwC8rfD4f+vXrhxkzZqBdu3am/NJ0DxGCnQtoeXyJMLeIViuW3E7+2fBCWvHcO8uZ0jFUEmAd1Ka1WFZAMCJCUdoYgeWP18ZouUFrnkVU+WxBoChxappHyxePHe94LaJZoBDeIyMjA8uXL8dvv/2Gn3/+GZIk4YILLkCzZs0s+SWREoGIMH+P0hIAVuOrtZu186MUOvLxWr7U7Ezt+FFzpkxAb60KwC9W/AgIRWljB2o+WXdwvUXHWtUT5bXaOBNnoai50gpvVHexsHMtSiStayEIJc2aNcPll19umz8SKQQTp6spIswvolUiH8urLbiFCqBfnhHPvcudK8fJ++V3QWUs+Th5fkoERhsvLH9K1O6UeuKENVZU+ay8NrGLR8210XXARuAdr2ZnNT5BRBMkUghTiLBWTeHxaWS3j/zalKCRdygrHlrzSABfVYVlz4rlHycnHLt7WHH9qM0rKHMwWz1RjtUQKHYulGXZ2rVY1iy0FoUggqFzUiIU0cJY3v/gzP7nK3L6YtlZiSO/NlXqVxoaOfNErY/nvBF/LLXTZI8yXlZg+WPdrXlPo1WOV/azrtXGWhQoSrT67JjmMRrXik+CiEaokkKYRoS1tSU8PvWmfbRyMFV50UpAby4JGtd6a0+UMeWwFhE5sXCB9yFKWjko7bSuDWxftrqTh3VtFNZ4O8U3VVEIIhQSKRGMCOcX0JpdmyLC/CJaZZsRoaIca1qoAMHTP0YPZ1O7Vlt7Iso+y5P0x2dhdgU0711OVGm3Kk6UPjRKGGanboyE10mBec3yoQfLB+FtjsL6cRW0ENoaJFIIS4iwXk3h8WF2Ia3SVtknvw7xKe80WtJhXYORGJRBEXo3E8AmXM/vAfgPAdG7NqAOvCJQjGB2bDiqKAQRiUTMmpS///3v6NmzJxo1agRBENxOp07gxtoUp8Yq7ZXXZrejBnWyns1jZC2GWpveehNR5WUWXn9qObHGsHwor/Wmd2T2Vha/mk2B14deTF4f4UZ0OwGCMEHEVFJqampwyy23ICcnB3PnznU7Hc8gIjwHktqZA281RdlmtZhhpKICcK5TUUsEUJ/ykbcp2+UJ+NEqGdmJmX27rHZlm8HShRWBYsSWZze18prlR8tWq13PnxKqohDRSMRUUiZOnIixY8eiS5cubqdSp7CrmiIabOddcKhsU+ZrZNer0t6IbUilQK+qwhojMtq02pW+7drhw+tPKy9Wu7JNbdeQcoxiiJo7ZTi9P3stWxZa450cGw5EtxMgDDNjxgxkZmYiPj4eWVlZWLdunab92rVrkZWVhfj4eJx33nmYNWtWUH/v3r3h8/lCXtddd13ApqCgIKS/VatWjnw/XiKmkmKG6upqVFdXB66rqqpczMY5RLhfTdFCBH9+PLZ2V1SA4KKHmi3TgHWmChgJyYPI27TalX1K7F6Rp4zN289qt7F6wro2aq/EzLN5WHH10MuDqigEi6VLl2LMmDGYMWMGrrzySsyePRt5eXn44YcfkJ6eHmK/e/duDBgwAHfddRfeeecd/Pe//8W9996LFi1a4KabbgIAfPDBB6ipqQmMOXz4MC699FLccsstQb46deqEVatWBa5jYmIc+pZ81GmRMnnyZEycONHtNDyPXTt9RNi/JVnNr51CRdkvnnsXZLaAxu4ftYSCBqmMk7fr9clR2hlBzacRW7V2E3MqTgsUMwtlWW1Gp3m8guh2AoRhXnnlFYwcORKjRo0CAEydOhUrVqzAzJkzMXny5BD7WbNmIT09HVOnTgUAdOjQAZs3b8ZLL70UEClJSUlBY5YsWYJGjRqFiJT69eu7Xj2R4+p0D6u0pHxt3rzZtP9x48ahsrIy8Nq3b5+N2XsLMUxxvDLtw+NLOUZ5bdVedwrIP4hn4Smrj9WvZmfkpYWWrVo7a8pI6yA4xTCV7kCbMpRWv5692YWyZmz1/FAVJbqoqqoKesmr/HJqamqwZcsW5ObmBrXn5uZi/fr1zDFFRUUh9v369cPmzZtx6tQp5pi5c+diyJAhaNy4cVD7zp070bp1a2RmZmLIkCHYtWsX71d0BFcrKffffz+GDBmiaWP28c4AEBcXh7i4ONPjowk7Hjyohwh2AcCuhbQsX8oxymsee0CjqqKWGKB+1D3v+SjKfjksWyOo+TVix7rL8qxGZQxVmiivedaf6I3hTI2JW9M8VhHDFKeuUgngjEUf/j/rtLS0oPYJEyagoKAgxL6iogK1tbVISUkJak9JSUFZWRkzRllZGdP+9OnTqKioQGpqalDfxo0b8f3334dsQunevTsWLlyICy+8EAcPHsSzzz6Lnj17Yvv27WjevDnHt7UfV0VKcnIykpOT3UyhTiHC2r0rHNM+Wn08tso2p4QKDI5RnQKCSoJQOkDoHUWAOkpbu9Dzq3Z3NSlOWGbKazPrT8wKFB5fWrZ2Q1WUusO+ffuQkJAQuNb7BdrnCz5GTpKkkDY9e1Y7cLaK0rlzZ1xxxRVB7Xl5eYHPXbp0QU5ODtq3b4+3334b+fn5mvk6RcSsSSkpKcGRI0dQUlKC2tpaFBcXAwDOP/98NGnSxN3kiBBE2LM+heVH2WZWqADaFRIzYzTFCmDsQYIi2Agq7UZQ881C69d+B8UJa5yZMeEWKFp9Wj6VWBUoenkQ4SUhISFIpKiRnJyMmJiYkKpJeXl5SLXET6tWrZj29evXD6mAnDhxAkuWLMGkSZN0c2ncuDG6dOmCnTt36to6RcSIlKeffhpvv/124Lpr164AgNWrV6N3794uZeU9RHijmqKFCP5pH14fZoQKq81sVQXgECv+RP2oVVj8hOt8FCV6d1HepyKruGKY6a//Ydiw/NQVgUJEL7GxscjKykJhYSFuvPHGQHthYSEGDhzIHJOTk4N//etfQW0rV65EdnY2GjRoENT+3nvvobq6Gn/96191c6mursaOHTvQq1cvE9/EHiLmnJQFCxZAkqSQFwkU9zC7iFarj/emxmpXe4CwcoxynPJaba2rnh/mcSMi2MaA9pOP5Q7tOBuF17/WdI5ariJCvp/W0Ss6Qz0hUNyGqijRTX5+Pt566y3MmzcPO3bswNixY1FSUoLRo0cDOLspZNiwYQH70aNHY+/evcjPz8eOHTswb948zJ07F4888kiI77lz52LQoEHMNSaPPPII1q5di927d+Prr7/GzTffjKqqKgwfPty5L6tDxFRSCH5EhKeawoMI4+tTeBfSsuCpqLD8sfzzVlWUbWpLTjQXyfI8+Vju3Gn07pIiu9lI9UHZZmfVxapAoSoK4SaDBw/G4cOHMWnSJJSWlqJz585Yvnw5MjIyAAClpaUoKSkJ2GdmZmL58uUYO3Yspk+fjtatW+P1118PbD/283//93/46quvsHLlSmbcX3/9FbfddhsqKirQokUL9OjRAxs2bAjEdQOf5F9dEwVUVVUhMTERibD+ZEuvI9jgg1eo6E3RCCb7WH7V7JXtrNx5/bHarIxVG687SIkT26+M/MouqndZESdq45V2vOMiXaBQFYUPCeeKe5WVXGs9jOC/V2yH+WltP0cBdIIzeUYDVEmpo4gI3ym0Tu32MVJRUbZr7QI2uk7FSC7iuXfWeGXskEF+lIP9hHurh6jdbfRmzmq3ck5OpAgUI9BuHoIIhkQKoYqRaZ9wbUvWsme18y6oBcxN47DstNq51saKjDalI7thxWSgVRFQc6HWXhcECg/hnOYRwxiLIMIBiZQ6jAhvPdNHhD3rU7TsWe1m16motWmJFYAtdpTtSl9KfyGIWp3O4ebiaFabxWNaDAsUPdT8mfFL0zzeoxJArUUfx+xIJIohkVLHERG+RbQ8W4i18lHrc1KoAOaqKmrjtexF2Wdln9yfHKvz4UbhvamKJvqs7NxSG29gN7QpgaI2Rq9Pz68SmuYhCDYkUghd7BYqWohwVqgA1qoqYLQbFSvyPrV+pW8WZn7OTlUMtPqtLqpV82HkmHsjfvXG6PW5geh2AgThECRSogAR1qd9wrU+RavfDqECGJv+AcOHWjvPafesfERGG8tOiZNrHUSLNnatXbE6vaPV7qRAoSoKQdgDiRTCFUTYL1TAGKPWzjv9Y8a31joTUXGtHKtmp2dvFrU4Zu2dXlhrZHrHqG+9MXp9PL6V2CFQRBt8EIRXIZESJYjwVjUFsCZUoOJfbQyr3ezz/5R+1Nq1fCnH+mH50LJ3Gp54dt/wjVRPtPwY9a81Rq+Px7cTiGGORxDhhkRKFCGi7ggVLf9qY9Ta1b6TXVUbvy8/elNhSlj+nIAVW41w7foB7BMnWjH0xmn18fhmQdM8BKEPiRTCUbwoVMDoM1pV0fIlyj6zcuA6K0XFHw/+mEbH6WFFmGj1a/n1gkBxAprmIQg+SKREGSLCW00B3BUqUBmn5k+rqgKTcaDRL/ftx+q2Y1HXQh8jVQG9eFr9dlVPzMbRG8fTr+dfCQkUguCHREoUIiJ6hIrWOPHcu7JPrarijwOVWGr+lP1aNvIYLOw+N8XMGgrRBhuj4kTPp1qfU9UfIzHk0BRPZHEMZ58RZIXjdiQSxZBIIUxTF4SKVp9VsQKdmHLU7NTihhPRJju93L1UPeG18fKfB0HUBUikRCki7FmQ6UWhApUY4rl31litPh6xohdTzTfLjsfeKUQH7M2KEz3/Wn1eFCg0zUMQxiGREsWIcEeo8CDCvFABrFVVoNKvJVb8MaETV45aDmr2LPR8mPFplw+viRO9sTz9vHGU0DQPQZiDREqUIyL8v7HzHp0vgq/6oGajJ1S0xmrF5hUr0Igvz8GPWjwtlD6cwEgMK8KEJ5ZWf10XKKJNfggikiCRQtiCE9M+AJ+I0rLhrW6wxmv1AfpiRR5fKwdlPDlqsZ2AFZ8Hnpu2k+KEJwer/nnjKCGBQhDWIJFCuLo+BbC+RoXHxsrzhMRz72r98huRXYJFGdtL2LmbRbTYb1Wc8NrwxFJCAoUgrEMihQDg7voU3sW0gPXpH2jE0hsvyj6r2ZgRLH7s3l5sF07cnEWL/UB0CBSCiHZIpBABRHhbqAD2VVWgEU88967lg8eGV7D4cVu4WNlOy3tTFm2w4cnTjjhG4jmF6GJs4uzf61MWfZywI5EohkQK4QheFyo88cRz71p+eGwA44LFj5s3SC2MVApEm+zCLU54YyqhaR6CsA8SKUQQIuxbrOkFoQIdO551MTx+RNlnLTuAfROzewu33Zi58Yo22QAkUAgiWiGRQoQgInKECmBfVQU6cY3E86Nn68dLwsXKjVa02c4ucWLEjjeuEhIoBGE/JFIIJiIiQ6gA9lVV/HGhE1uUfeaNy2OrhPemx/OzdWohp+iQfSSJE4AWyhKEU5BIIVQREVlCBQivWDHiT27rh2cMD+G8QYoOj+EVCbw+nYitxM6fv2ijL4KoC5BIITQR4b5QAeytqvjtwGFrVKzw+GSNMTo2HIhhHGu3ODFqSwKFILwJiRRCFxHuChXAmaqK35bHzohYEmWfeXyrjVVi1JfVeE77MiIMjPg2Ygu4u/7Ej2izP4KoK5BIIcJOOIQKYH9VxZ+DH6OChTcGry8vIJoYE8niBCCBQhDhhEQKwYUIe3+TtyJUAOeqKry2ZnKRx/DDG8sLiBbGGhUERmMZtSeBQvAgAqix6IMOc7MGiRSCGxH2CxXAW1UVvy0M2Jt5Jo8ylhLe2E4h2uDDjBAwGteoPUAChSAiCRIphCFE2H8DDef0D2BuYSvvGCuCRS22GoKDvs1iVgCIDtsD4Tn2nxfRZn8EUVchkUIYRoS3hArgrFgxO0Z5U7T7OTyizf7MYuXmL4ZpDAkUgohM6rmdABGZiA74tHIzMDu1IJocY3QccDZH+SsSUX4HKz930eQ4I1j5WVeCBArhHjNmzEBmZibi4+ORlZWFdevWadqvXbsWWVlZiI+Px3nnnYdZs2YF9S9YsAA+ny/kdfLkSUtxnYZECmEa0QGfVoVKuMSKlXF+7LjhO4md+YkI78/Zar5OHJAnOuCTqJssXboUY8aMwfjx47F161b06tULeXl5KCkpYdrv3r0bAwYMQK9evbB161Y88cQTePDBB7Fs2bIgu4SEBJSWlga94uPjTccNBz5JkiTXooeZqqoqJCYmIhGAz+1k6hCCQ36tPrvG7PSKYCGmlbG82DVt5LQoEl0aa/V7kUCJDCScq3ZVViIhIcFW3/57xZsAGln0dQLAXTCWZ/fu3dGtWzfMnDkz0NahQwcMGjQIkydPDrF/7LHH8Mknn2DHjh2BttGjR2Pbtm0oKioCcLaSMmbMGIiiaFvccECVFMIyokN+rd4srCziFC2OteJDD1aFw8zLCUTY8/Mzgx3VExIohFNUVVUFvaqrq5l2NTU12LJlC3Jzc4Pac3NzsX79euaYoqKiEPt+/fph8+bNOHXqVKDt2LFjyMjIQNu2bXH99ddj69atluKGAxIphC2IDvm1euOwcuMSYV1siHBetLiJCHt/TmawQ3R55QGMhLc4Buu/DBw75ystLe1sJf/cS60yUVFRgdraWqSkpAS1p6SkoKysjDmmrKyMaX/69GlUVFQAAC6++GIsWLAAn3zyCRYvXoz4+HhceeWV2Llzp+m44YB29xC2IZ57FxzwbXb3jx8zu4DkiOfeBQs5yP34seovnIge9GdHRYgEChEO9u3bFzTdExcXp2nv8wUvSpAkKaRNz17e3qNHD/To0SPQf+WVV6Jbt25444038Prrr5uO6zQkUgjbEeFNoQLYJ1YAe76jqNJuh2+ziBHg38viBCCBQoSSkJDAtSYlOTkZMTExIdWL8vLykCqHn1atWjHt69evj+bNmzPH1KtXD5dffnmgkmImbjig6R7CEUSH/Nq1bsCOKQIRzk3jiAZfdvpzArv827WehgQK4VViY2ORlZWFwsLCoPbCwkL07NmTOSYnJyfEfuXKlcjOzkaDBg2YYyRJQnFxMVJTU03HDQdUSSEcQ4RzFQErR+rLsVpZ8SPKPgsWfVmN7wVEG33ZucjXSXECeO/PgYhM8vPzMXToUGRnZyMnJwdz5sxBSUkJRo8eDQAYN24c9u/fj4ULFwI4u5Nn2rRpyM/Px1133YWioiLMnTsXixcvDvicOHEievTogQsuuABVVVV4/fXXUVxcjOnTp3PHdQMSKYSjiHD2pm3HFBBgn1gBInvdiRVEm/2ROCGilcGDB+Pw4cOYNGkSSktL0blzZyxfvhwZGRkAgNLS0qCzSzIzM7F8+XKMHTsW06dPR+vWrfH666/jpptuCtiIooi7774bZWVlSExMRNeuXfHll1/iiiuu4I7rBnROChEWhDDEsEOsyLH7GHs/gkN+w4nooG+7t0eTQKmbhOOclFcBNLTo63cAY+FMntEAVVKIsCCeexccjGHXFJAfO6srckRGm2BzDDsRwxDDiXNbnBYnAAkUgnAaEilEWBHh/A3ZrikgP3Y92VgLUadfcCguT2wniVRxApBAIYhwQCKFCDsiwiNUAPungMIhWFiIYYzlNE6ddkvihLCbKgA1Fn2c1DchNCCRQriCeO5dcDiOU2IFcE+wRCJOPicoXOIEIIFCEOGGRArhKiLCsx7DSbECkGBREo4nOodTnAAkUAjCDUikEK4jInwLR50WK0DoDToaREs4RIkfEicEET1ExImze/bswciRI5GZmYmGDRuiffv2mDBhAmpqrM4WEl5BRHhvBk498ZZFuJ5AHE7c+D7h/DPzI4Y5HkEQwUREJeXHH3/EmTNnMHv2bJx//vn4/vvvcdddd+H48eN46aWX3E6PsBER4d2OK7/pOVldUaJ2Y/da1cULgircwgQgcUIQXiEiREr//v3Rv3//wPV5552Hn376CTNnziSRUgcRz70LYY4bjqkgPXhEgR1CxgviQws3hIkf0cXYBEEEExEihUVlZSWSkpI0baqrq1FdXR24rqqqcjotwkZEuHPImVvVFV68LjCsQOKEIAg5ESlSfvnlF7zxxht4+eWXNe0mT56MiRMnhikrwgnEc++CS/G9LljqAm4KE4DECaFOJayfc1Ktb0Jo4OrC2YKCAvh8Ps3X5s2bg8YcOHAA/fv3xy233IJRo0Zp+h83bhwqKysDr3379jn5dQgHEeH+zaQS7izerIt45WcpuhyfIAhtXK2k3H///RgyZIimTbt27QKfDxw4gD59+gQeIa1HXFwc4uLirKZJeAgR3njODVVYjOO2IJEjup0AQRBcuCpSkpOTkZyczGW7f/9+9OnTB1lZWZg/fz7q1YuI3dOEA4jn3gUXc5CjvPmSaDmLl0SJH9HtBAiCMERErEk5cOAAevfujfT0dLz00ks4dOhQoK9Vq1YuZka4iXjuXXAxBxbRKlq8KEr8iG4nQBCEKSJCpKxcuRI///wzfv75Z7Rt2zaoT5Ikl7IivIJ47l1wMQctWDfvSBcuXhYkSkS3EyAIwjQRMWcyYsQISJLEfBGEHxGRc0OqVHl5jUjJk4WIyPn7QBAEm4iopBCEEcRz74KLOZjFiAAwU42JFIFhBdHtBAiCsA0SKUSdRTz3LriYg5NEg+DgRXQ7AYIgHIFEClHnEc+9Cy7mQDiD6HYCRJ2mEkCsRR/0GFxrkEghogZR9llwKQfCHkS3EyAIIiyQSCGiEvHcu+BiDoQxRLcTIAgi7JBIIaIaUfZZcCkHQhvR7QQIgnANEikEcQ5R9llwKQfiLKLbCRAE4QlIpBAEA1H2WXAph2hDdDsBgiA8B4kUgtBBlH0WXMqhriK6nQBBEJ6GRApBGECUfRZcyiGSEd1OgCCIiIJECkGYRFRcCy7k4HVEtxMgCAscBZ2T4jYkUgjCJkRGmxDmHNxGdDsBgiDqFCRSCMJBREabEOYcnEJ0OwGCIOo8JFIIIsyIKu1CGHPgRXQ7AYIgohoSKQThEUSD9kIYYhAEQbhJPbcTIAjCHKKJF0EQkcGMGTOQmZmJ+Ph4ZGVlYd26dZr2a9euRVZWFuLj43Heeedh1qxZQf1vvvkmevXqhWbNmqFZs2bo27cvNm7cGGRTUFAAn88X9GrVqpXt380IJFIIgiAIwkMsXboUY8aMwfjx47F161b06tULeXl5KCkpYdrv3r0bAwYMQK9evbB161Y88cQTePDBB7Fs2bKAzZo1a3Dbbbdh9erVKCoqQnp6OnJzc7F///4gX506dUJpaWng9d133zn6XfXwSZIkuZpBGKmqqkJiYiISAfjcToYgCIIwjQSgEkBlZSUSEhJs9e2/V9wKe7YgvwdjeXbv3h3dunXDzJkzA20dOnTAoEGDMHny5BD7xx57DJ988gl27NgRaBs9ejS2bduGoqIiZoza2lo0a9YM06ZNw7BhwwCcraR89NFHKC4u5v5+TkOVFIIgCIJwmKqqqqBXdXU1066mpgZbtmxBbm5uUHtubi7Wr1/PHFNUVBRi369fP2zevBmnTp1ijjlx4gROnTqFpKSkoPadO3eidevWyMzMxJAhQ7Br1y7er+gIJFIIgiAIgkElzK39kr8qz/lKS0s7W8k/92JVRACgoqICtbW1SElJCWpPSUlBWVkZc0xZWRnT/vTp06ioqGCOefzxx9GmTRv07ds30Na9e3csXLgQK1aswJtvvomysjL07NkThw8fZvoIB7S7hyAIgiAcZt++fUHTPXFxcZr2Pl/wogRJkkLa9OxZ7QAwZcoULF68GGvWrEF8fHygPS8vL/C5S5cuyMnJQfv27fH2228jPz9fM1+nIJFCEARBEA6TkJDAtSYlOTkZMTExIVWT8vLykGqJn1atWjHt69evj+bNmwe1v/TSS3juueewatUqXHLJJZq5NG7cGF26dMHOnTt183YKmu4hCIIgCI8QGxuLrKwsFBYWBrUXFhaiZ8+ezDE5OTkh9itXrkR2djYaNGgQaHvxxRfxzDPP4LPPPkN2drZuLtXV1dixYwdSU1NNfBN7IJFCEARBEB4iPz8fb731FubNm4cdO3Zg7NixKCkpwejRowEA48aNC+zIAc7u5Nm7dy/y8/OxY8cOzJs3D3PnzsUjjzwSsJkyZQqefPJJzJs3D+3atUNZWRnKyspw7NixgM0jjzyCtWvXYvfu3fj6669x8803o6qqCsOHDw/fl1dA0z0EQRAE4SEGDx6Mw4cPY9KkSSgtLUXnzp2xfPlyZGRkAABKS0uDzkzJzMzE8uXLMXbsWEyfPh2tW7fG66+/jptuuilgM2PGDNTU1ODmm28OijVhwgQUFBQAAH799VfcdtttqKioQIsWLdCjRw9s2LAhENcN6JwUgiAIIuIIxzkp/QA00LXW5hSAFXAmz2iApnsIgiAIgvAkJFIIgiAIgvAktCaFIAiCIBhUwvpN8rQdiUQxVEkhCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKT0DkpBEEQBMHgGIAYiz5q7UgkiqFKCkEQBEEQnoRECkEQBEEQnoRECkEQBEEQnoRECkEQBEEQnoRECkEQBEEQnoRECkEQBEEQnoRECkEQBEEQnoRECkEQBEEQnoQOcyMIgiAIBlWw/pv8GTsSiWKokkIQBEEQhCchkUIQBEEQhCchkUIQBEEQhCchkUIQBEEQhCchkUIQBEEQhCeJGJHypz/9Cenp6YiPj0dqaiqGDh2KAwcOuJ0WQRAEQRAOETEipU+fPnjvvffw008/YdmyZfjll19w8803u50WQRAEQRAO4ZMkSXI7CTN88sknGDRoEKqrq9GgQQOuMVVVVUhMTEQiAJ+z6REEQRAOIgGoBFBZWYmEhARbfdt5r3Ayz2ggIg9zO3LkCN5991307NlTU6BUV1ejuro6cF1VVRWO9AiCIAiCsIGIme4BgMceewyNGzdG8+bNUVJSgo8//ljTfvLkyWfV8LlXWlpamDIlCIIgCMIqrk73FBQUYOLEiZo2mzZtQnZ2NgCgoqICR44cwd69ezFx4kQkJibi3//+N3w+dkGOVUlJS0ujshtBEESE45+Soemeuo2rIqWiogIVFRWaNu3atUN8fHxI+6+//oq0tDSsX78eOTk5XPGc/EtNEARBhA8SKdGBq9M9ycnJuPjiizVfLIECAH5tJa+UEARBEERdYMaMGcjMzER8fDyysrKwbt06Tfu1a9ciKysL8fHxOO+88zBr1qwQm2XLlqFjx46Ii4tDx44d8eGHH1qO6zQRsSZl48aNmDZtGoqLi7F3716sXr0at99+O9q3b89dRSEIgiCISGDp0qUYM2YMxo8fj61bt6JXr17Iy8tDSUkJ03737t0YMGAAevXqha1bt+KJJ57Agw8+iGXLlgVsioqKMHjwYAwdOhTbtm3D0KFDceutt+Lrr782HTccRMQW5O+++w4PPfQQtm3bhuPHjyM1NRX9+/fHk08+iTZt2nD7oekegiCIukFdnu7p3r07unXrhpkzZwbaOnTogEGDBmHy5Mkh9o899hg++eQT7NixI9A2evRobNu2DUVFRQCAwYMHo6qqCv/5z38CNv3790ezZs2wePFiU3HDQURsQe7SpQu++OILy378eoy2IhMEQUQ2/v/Hnfw92w7Pfh/K+05cXBzi4uJC7GtqarBlyxY8/vjjQe25ublYv349M0ZRURFyc3OD2vr164e5c+fi1KlTaNCgAYqKijB27NgQm6lTp5qOGw4iQqTYxeHDhwGAtiITBEHUEQ4fPozExERbfcbGxqJVq1YoKyuzxV+TJk1C7jsTJkxAQUFBiG1FRQVqa2uRkpIS1J6SkqKaT1lZGdP+9OnTqKioQGpqqqqN36eZuOEgqkRKUlISAKCkpMT2v9Rexr/1et++fVE1zUXfm753NBCt37uyshLp6emB/9ftJD4+Hrt370ZNTY0t/iRJCjkqg1VFkaO0Z/nQs1e28/g0Gtdpokqk1Kt3dp1wYmJiVP1j9pOQkEDfO4qg7x1dROv39v+/bjfx8fGqu0udJDk5GTExMSHVi/Ly8pAqhx9W1ae8vBz169dH8+bNNW38Ps3EDQcRsbuHIAiCIKKB2NhYZGVlobCwMKi9sLAQPXv2ZI7JyckJsV+5ciWys7MDj45Rs/H7NBM3HERVJYUgCIIgvE5+fj6GDh2K7Oxs5OTkYM6cOSgpKcHo0aMBAOPGjcP+/fuxcOFCAGd38kybNg35+fm46667UFRUhLlz5wZ27QDAQw89hKuuugovvPACBg4ciI8//hirVq3CV199xR3XFaQo4uTJk9KECROkkydPup1KWKHvTd87GqDvTd+7LjF9+nQpIyNDio2Nlbp16yatXbs20Dd8+HDp6quvDrJfs2aN1LVrVyk2NlZq166dNHPmzBCf77//vnTRRRdJDRo0kC6++GJp2bJlhuK6QUSck0IQBEEQRPRBa1IIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkUStS/vSnPyE9PR3x8fFITU3F0KFDceDAAbfTcpQ9e/Zg5MiRyMzMRMOGDdG+fXtMmDDBtlMVvczf//539OzZE40aNYIgCG6n4xhee8x6OPjyyy9xww03oHXr1vD5fPjoo4/cTslxJk+ejMsvvxxNmzZFy5YtMWjQIPz0009up+U4M2fOxCWXXBI4uC4nJyfogXlE3SNqRUqfPn3w3nvv4aeffsKyZcvwyy+/4Oabb3Y7LUf58ccfcebMGcyePRvbt2/Hq6++ilmzZuGJJ55wOzXHqampwS233IK//e1vbqfiGF58zHo4OH78OC699FJMmzbN7VTCxtq1a3Hfffdhw4YNKCwsxOnTp5Gbm4vjx4+7nZqjtG3bFs8//zw2b96MzZs345prrsHAgQOxfft2t1MjHIK2IJ/jk08+waBBg1BdXR04oS8aePHFFzFz5kzs2rXL7VTCwoIFCzBmzBiIouh2Krbjxceshxufz4cPP/wQgwYNcjuVsHLo0CG0bNkSa9euxVVXXeV2OmElKSkJL774IkaOHOl2KoQDRG0lRc6RI0fw7rvvomfPnlElUICzD+ly4gFdRHjxP2Zd+bh2tx+zToSHyspKAIiqf8u1tbVYsmQJjh8/jpycHLfTIRwiqkXKY489hsaNG6N58+YoKSnBxx9/7HZKYeWXX37BG2+84e6Rx4QtePUx64TzSJKE/Px8/OEPf0Dnzp3dTsdxvvvuOzRp0gRxcXEYPXo0PvzwQ3Ts2NHttAiHqFMipaCgAD6fT/O1efPmgP2jjz6KrVu3YuXKlYiJicGwYcMQibNfRr83ABw4cAD9+/fHLbfcglGjRrmUuTXMfO+6jtces044z/33349vv/026DktdZmLLroIxcXF2LBhA/72t79h+PDh+OGHH9xOi3CIOvWAwfvvvx9DhgzRtGnXrl3gc3JyMpKTk3HhhReiQ4cOSEtLw4YNGyKudGj0ex84cAB9+vQJPEAqUjH6vesyXn3MOuEsDzzwAD755BN8+eWXaNu2rdvphIXY2Ficf/75AIDs7Gxs2rQJr732GmbPnu1yZoQT1CmR4hcdZvBXUKqrq+1MKSwY+d779+9Hnz59kJWVhfnz56Nevcgtpln5865ryB+zfuONNwbaCwsLMXDgQBczI5xAkiQ88MAD+PDDD7FmzRpkZma6nZJrSJIUkf9vE3zUKZHCy8aNG7Fx40b84Q9/QLNmzbBr1y48/fTTaN++fcRVUYxw4MAB9O7dG+np6XjppZdw6NChQF+rVq1czMx5SkpKcOTIEZSUlKC2thbFxcUAgPPPPx9NmjRxNzmb8ORj1sPAsWPH8PPPPweud+/ejeLiYiQlJSE9Pd3FzJzjvvvuw6JFi/Dxxx+jadOmgQpaYmIiGjZs6HJ2zvHEE08gLy8PaWlpOHr0KJYsWYI1a9bgs88+czs1wincevyym3z77bdSnz59pKSkJCkuLk5q166dNHr0aOnXX391OzVHmT9/vgSA+arrDB8+nPm9V69e7XZqtuK1x6yHg9WrVzP/bIcPH+52ao6h9u94/vz5bqfmKHfeeWfg73eLFi2kP/7xj9LKlSvdTotwEDonhSAIgiAITxK5CxIIgiAIgqjTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghCIIgCMKTkEghiAintrYWPXv2xE033RTUXllZibS0NDz55JMuZUYQBGENOhafIOoAO3fuxGWXXYY5c+bgL3/5CwBg2LBh2LZtGzZt2oTY2FiXMyQIgjAOiRSCqCO8/vrrKCgowPfff49NmzbhlltuwcaNG3HZZZe5nRpBEIQpSKQQRB1BkiRcc801iImJwXfffYcHHniApnoIgohoSKQQRB3ixx9/RIcOHdClSxd88803qF+/vtspEQRBmIYWzhJEHWLevHlo1KgRdu/ejV9//dXtdAiCICxBlRSCqCMUFRXhqquuwn/+8x9MmTIFtbW1WLVqFXw+n9upEQRBmIIqKQRRB/j9998xfPhw3HPPPejbty/eeustbNq0CbNnz3Y7NYIgCNOQSCGIOsDjjz+OM2fO4IUXXgAApKen4+WXX8ajjz6KPXv2uJscQRCESWi6hyAinLVr1+KPf/wj1qxZgz/84Q9Bff369cPp06dp2ocgiIiERApBEARBEJ6EpnsIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAkJFIIgiAIgvAk/x9Z2/jRwnfTLQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def simulate_diffusion_gaussian_sigma(\n", + " sigma,\n", + " D=0.1,\n", + " dx=0.01,\n", + " safety_factor=0.9,\n", + " snapshot_interval=100,\n", + " source_point=None,\n", + " mask=None,\n", + "):\n", + " \"\"\"\n", + " Simulate diffusion to achieve a Gaussian distribution with specified standard deviation,\n", + " incorporating a user-provided mask and source point.\n", + "\n", + " Parameters:\n", + " - sigma: Desired standard deviation of the Gaussian distribution.\n", + " - D: Diffusion coefficient.\n", + " - dx: Spatial grid spacing.\n", + " - safety_factor: Fraction of the maximum stable dt to use.\n", + " - snapshot_interval: Interval at which snapshots are stored.\n", + " - source_point: Tuple (x_index, y_index) for the initial concentration point.\n", + " If None, it's set to the center of the grid.\n", + " - mask: 2D numpy array defining the domain (1 inside, 0 outside).\n", + " If None, the mask will cover the entire grid.\n", + "\n", + " Returns:\n", + " - C_list: List of concentration fields at specified intervals.\n", + " - X, Y: Meshgrid arrays for plotting.\n", + " - dx: Spatial grid spacing (might be adjusted).\n", + " \"\"\"\n", + " # Enable 64-bit precision if needed\n", + " jax.config.update(\"jax_enable_x64\", True)\n", + "\n", + " # 1. Calculate total simulation time t from sigma\n", + " t_total = sigma**2 / (2 * D)\n", + "\n", + " # 2. Determine domain size based on mask or sigma\n", + " if mask is not None:\n", + " # Use mask dimensions to define the grid\n", + " Nx, Ny = mask.shape\n", + " Lx = dx * Nx\n", + " Ly = dx * Ny\n", + " x = jnp.linspace(-Lx / 2 + dx / 2, Lx / 2 - dx / 2, Nx)\n", + " y = jnp.linspace(-Ly / 2 + dx / 2, Ly / 2 - dx / 2, Ny)\n", + " else:\n", + " # Set domain size to minimize boundary effects\n", + " Lx = Ly = 6 * sigma # Total length in x and y directions\n", + " Nx = Ny = int(Lx / dx)\n", + " if Nx % 2 == 0:\n", + " Nx += 1 # Ensure odd number of points to have a center point\n", + " if Ny % 2 == 0:\n", + " Ny += 1\n", + " x = jnp.linspace(-Lx / 2, Lx / 2, Nx)\n", + " y = jnp.linspace(-Ly / 2, Ly / 2, Ny)\n", + " # Create a full mask\n", + " mask = jnp.ones((Nx, Ny))\n", + "\n", + " # Recalculate dx to fit the domain size precisely\n", + " dx = x[1] - x[0]\n", + "\n", + " # 3. Set up the spatial grid\n", + " X, Y = jnp.meshgrid(x, y, indexing=\"ij\")\n", + "\n", + " # 4. Compute dt based on stability condition\n", + " dt = safety_factor * dx * dx / (4 * D)\n", + "\n", + " # 5. Calculate the number of time steps\n", + " num_steps = int(np.ceil(t_total / dt))\n", + "\n", + " # Adjust dt to exactly reach t_total\n", + " dt = t_total / num_steps\n", + "\n", + " # 6. Initialize the concentration field\n", + " C = jnp.zeros((Nx, Ny))\n", + "\n", + " # Set the source point\n", + " if source_point is None:\n", + " source_x, source_y = Nx // 2, Ny // 2\n", + " else:\n", + " source_x, source_y = source_point\n", + " C = C.at[source_x, source_y].set(1.0 / (dx * dx))\n", + "\n", + " # Ensure the source point is within the mask\n", + " if mask[source_x, source_y] == 0:\n", + " raise ValueError(\"The source point is outside the domain defined by the mask.\")\n", + "\n", + " # 7. Define the Laplacian operator with periodic boundary conditions\n", + " def laplacian(C, mask):\n", + " # Pad the array to handle boundary conditions\n", + " C_padded = jnp.pad(C, pad_width=1, mode=\"wrap\") # Periodic boundaries\n", + " mask_padded = jnp.pad(mask, pad_width=1, mode=\"wrap\")\n", + "\n", + " # Extract central and neighboring values\n", + " C_center = C_padded[1:-1, 1:-1]\n", + " mask_center = mask_padded[1:-1, 1:-1]\n", + "\n", + " C_up = C_padded[2:, 1:-1]\n", + " mask_up = mask_padded[2:, 1:-1]\n", + "\n", + " C_down = C_padded[:-2, 1:-1]\n", + " mask_down = mask_padded[:-2, 1:-1]\n", + "\n", + " C_left = C_padded[1:-1, :-2]\n", + " mask_left = mask_padded[1:-1, :-2]\n", + "\n", + " C_right = C_padded[1:-1, 2:]\n", + " mask_right = mask_padded[1:-1, 2:]\n", + "\n", + " # Apply mask to neighbors\n", + " C_up = jnp.where(mask_up, C_up, C_center)\n", + " C_down = jnp.where(mask_down, C_down, C_center)\n", + " C_left = jnp.where(mask_left, C_left, C_center)\n", + " C_right = jnp.where(mask_right, C_right, C_center)\n", + "\n", + " # Compute the Laplacian\n", + " laplacian_C = (C_up + C_down + C_left + C_right - 4 * C_center) / (dx * dx)\n", + "\n", + " # Only compute inside the domain\n", + " laplacian_C = jnp.where(mask_center, laplacian_C, 0.0)\n", + "\n", + " return laplacian_C\n", + "\n", + " # 8. Time-stepping function\n", + " @jax.jit\n", + " def update(C, mask):\n", + " C_new = C + D * dt * laplacian(C, mask)\n", + " return C_new\n", + "\n", + " # 9. Run the simulation\n", + " C_list = []\n", + " for step in range(num_steps):\n", + " C = update(C, mask)\n", + " if step % snapshot_interval == 0 or step == num_steps - 1:\n", + " C_list.append(C)\n", + " print(f\"Step {step}/{num_steps}\")\n", + "\n", + " return C_list, X, Y, dx\n", + "\n", + "\n", + "# Example Usage\n", + "if __name__ == \"__main__\":\n", + " # Desired standard deviation\n", + " sigma_desired = 1.0\n", + "\n", + " # Define grid spacing\n", + " dx = 0.01\n", + "\n", + " # Create a custom mask (e.g., an elliptical domain)\n", + " Lx = Ly = 6 * sigma_desired\n", + " Nx = Ny = int(Lx / dx)\n", + " if Nx % 2 == 0:\n", + " Nx += 1\n", + " if Ny % 2 == 0:\n", + " Ny += 1\n", + " x = np.linspace(-Lx / 2, Lx / 2, Nx)\n", + " y = np.linspace(-Ly / 2, Ly / 2, Ny)\n", + " X, Y = np.meshgrid(x, y, indexing=\"ij\")\n", + " a, b = 2 * sigma_desired, sigma_desired # Semi-major and semi-minor axes\n", + " # mask = np.where(((X / a) ** 2 + (Y / b) ** 2) <= 1, 1.0, 0.0)\n", + " mask = np.ones((Nx, Ny))\n", + "\n", + " # Define source point (center of the grid)\n", + " source_point = (Nx // 2, Ny // 2)\n", + "\n", + " # Run the simulation\n", + " C_list, X_grid, Y_grid, dx = simulate_diffusion_gaussian_sigma(\n", + " sigma=sigma_desired,\n", + " D=0.1,\n", + " dx=dx, # Spatial grid spacing\n", + " safety_factor=0.9,\n", + " snapshot_interval=1000,\n", + " source_point=source_point,\n", + " mask=mask,\n", + " )\n", + "\n", + " # Visualization\n", + " for i, C_snapshot in enumerate(C_list):\n", + " # Mask out values outside the domain for visualization\n", + " C_display = np.where(mask == 1, C_snapshot, np.nan)\n", + "\n", + " plt.figure(figsize=(6, 5))\n", + " plt.contourf(X_grid, Y_grid, C_display, levels=50, cmap=\"hot\")\n", + " plt.colorbar(label=\"Concentration\")\n", + " plt.title(f\"Diffusion at Time Step {i * 200}\")\n", + " plt.xlabel(\"X\")\n", + " plt.ylabel(\"Y\")\n", + " plt.axis(\"equal\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the final concentration field with standard deviation contours\n", + "C_final = C_list[-1]\n", + "C_display = np.where(mask == 1, C_final, np.nan)\n", + "\n", + "plt.figure(figsize=(6, 5))\n", + "plt.contourf(X_grid, Y_grid, C_display, levels=50, cmap='hot')\n", + "plt.colorbar(label='Concentration')\n", + "plt.title('Final Concentration Field with Standard Deviation Contours')\n", + "plt.xlabel('X')\n", + "plt.ylabel('Y')\n", + "plt.axis('equal')\n", + "\n", + "# Compute squared distance from source point\n", + "x_source, y_source = source_point\n", + "R_squared = (X_grid - x_source)**2 + (Y_grid - y_source)**2\n", + "\n", + "# Define standard deviation levels\n", + "k_values = [1, 2, 3] # Multiples of sigma\n", + "contour_levels = [(k * sigma_desired)**2 for k in k_values]\n", + "\n", + "# Plot the contours\n", + "CS = plt.contour(X_grid, Y_grid, R_squared, levels=contour_levels, colors='blue', linestyles='dashed', zorder=10)\n", + "plt.clabel(CS, inline=True, fmt={level: f'{k}\\u03C3' for k, level in zip(k_values, contour_levels)}, fontsize=10)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 0/223\n", + "Step 200/223\n", + "Step 222/223\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def simulate_diffusion_gaussian_sigma(\n", + " sigma,\n", + " D=0.1,\n", + " dx=0.01,\n", + " safety_factor=0.9,\n", + " snapshot_interval=100,\n", + " source_point=None,\n", + " mask=None,\n", + "):\n", + " \"\"\"\n", + " Simulate diffusion to achieve a Gaussian distribution with specified standard deviation,\n", + " incorporating a user-provided mask and source point.\n", + "\n", + " Parameters:\n", + " - sigma: Desired standard deviation of the Gaussian distribution.\n", + " - D: Diffusion coefficient.\n", + " - dx: Spatial grid spacing.\n", + " - safety_factor: Fraction of the maximum stable dt to use.\n", + " - snapshot_interval: Interval at which snapshots are stored.\n", + " - source_point: Tuple (x_index, y_index) for the initial concentration point.\n", + " If None, it's set to the center of the grid.\n", + " - mask: 2D numpy array defining the domain (1 inside, 0 outside).\n", + " If None, the mask will cover the entire grid.\n", + "\n", + " Returns:\n", + " - C_list: List of concentration fields at specified intervals.\n", + " - X, Y: Meshgrid arrays for plotting.\n", + " - dx: Spatial grid spacing (might be adjusted).\n", + " - source_coords: Coordinates of the source point (x_source, y_source).\n", + " \"\"\"\n", + " # Enable 64-bit precision if needed\n", + " jax.config.update(\"jax_enable_x64\", True)\n", + "\n", + " # 1. Calculate total simulation time t from sigma\n", + " t_total = sigma**2 / (2 * D)\n", + "\n", + " # 2. Determine domain size based on mask or sigma\n", + " if mask is not None:\n", + " # Use mask dimensions to define the grid\n", + " Nx, Ny = mask.shape\n", + " Lx = dx * Nx\n", + " Ly = dx * Ny\n", + " x = jnp.linspace(-Lx / 2 + dx / 2, Lx / 2 - dx / 2, Nx)\n", + " y = jnp.linspace(-Ly / 2 + dx / 2, Ly / 2 - dx / 2, Ny)\n", + " else:\n", + " # Set domain size to minimize boundary effects\n", + " Lx = Ly = 6 * sigma # Total length in x and y directions\n", + " Nx = Ny = int(Lx / dx)\n", + " if Nx % 2 == 0:\n", + " Nx += 1 # Ensure odd number of points to have a center point\n", + " if Ny % 2 == 0:\n", + " Ny += 1\n", + " x = jnp.linspace(-Lx / 2, Lx / 2, Nx)\n", + " y = jnp.linspace(-Ly / 2, Ly / 2, Ny)\n", + " # Create a full mask\n", + " mask = jnp.ones((Nx, Ny))\n", + "\n", + " # Recalculate dx to fit the domain size precisely\n", + " dx = x[1] - x[0]\n", + "\n", + " # 3. Set up the spatial grid\n", + " X, Y = jnp.meshgrid(x, y, indexing=\"ij\")\n", + "\n", + " # 4. Compute dt based on stability condition\n", + " dt = safety_factor * dx * dx / (4 * D)\n", + "\n", + " # 5. Calculate the number of time steps\n", + " num_steps = int(np.ceil(t_total / dt))\n", + "\n", + " # Adjust dt to exactly reach t_total\n", + " dt = t_total / num_steps\n", + "\n", + " # 6. Initialize the concentration field\n", + " C = jnp.zeros((Nx, Ny))\n", + "\n", + " # Set the source point\n", + " if source_point is None:\n", + " source_x, source_y = Nx // 2, Ny // 2\n", + " else:\n", + " source_x, source_y = source_point\n", + " C = C.at[source_x, source_y].set(1.0 / (dx * dx))\n", + "\n", + " # Ensure the source point is within the mask\n", + " if mask[source_x, source_y] == 0:\n", + " raise ValueError(\"The source point is outside the domain defined by the mask.\")\n", + "\n", + " # Get coordinates of the source point\n", + " x_source = x[source_x]\n", + " y_source = y[source_y]\n", + " source_coords = (x_source, y_source)\n", + "\n", + " # 7. Define the Laplacian operator with periodic boundary conditions and mask\n", + " def laplacian(C, mask):\n", + " # Pad the array to handle boundary conditions\n", + " C_padded = jnp.pad(C, pad_width=1, mode=\"wrap\") # Periodic boundaries\n", + " mask_padded = jnp.pad(mask, pad_width=1, mode=\"wrap\")\n", + "\n", + " # Extract central and neighboring values\n", + " C_center = C_padded[1:-1, 1:-1]\n", + " mask_center = mask_padded[1:-1, 1:-1]\n", + "\n", + " C_up = C_padded[2:, 1:-1]\n", + " mask_up = mask_padded[2:, 1:-1]\n", + "\n", + " C_down = C_padded[:-2, 1:-1]\n", + " mask_down = mask_padded[:-2, 1:-1]\n", + "\n", + " C_left = C_padded[1:-1, :-2]\n", + " mask_left = mask_padded[1:-1, :-2]\n", + "\n", + " C_right = C_padded[1:-1, 2:]\n", + " mask_right = mask_padded[1:-1, 2:]\n", + "\n", + " # Apply mask to neighbors\n", + " C_up = jnp.where(mask_up, C_up, C_center)\n", + " C_down = jnp.where(mask_down, C_down, C_center)\n", + " C_left = jnp.where(mask_left, C_left, C_center)\n", + " C_right = jnp.where(mask_right, C_right, C_center)\n", + "\n", + " # Compute the Laplacian\n", + " laplacian_C = (C_up + C_down + C_left + C_right - 4 * C_center) / (dx * dx)\n", + "\n", + " # Only compute inside the domain\n", + " laplacian_C = jnp.where(mask_center, laplacian_C, 0.0)\n", + "\n", + " return laplacian_C\n", + "\n", + " # 8. Time-stepping function\n", + " @jax.jit\n", + " def update(C, mask):\n", + " C_new = C + D * dt * laplacian(C, mask)\n", + " return C_new\n", + "\n", + " # 9. Run the simulation\n", + " C_list = []\n", + " for step in range(num_steps):\n", + " C = update(C, mask)\n", + " if step % snapshot_interval == 0 or step == num_steps - 1:\n", + " C_list.append(C)\n", + " print(f\"Step {step}/{num_steps}\")\n", + "\n", + " return C_list, X, Y, dx, source_coords\n", + "\n", + "\n", + "# Example Usage\n", + "if __name__ == \"__main__\":\n", + " # Desired standard deviation\n", + " # sigma_desired = 1.0\n", + " sigma_desired = 0.1\n", + "\n", + " # Define grid spacing\n", + " dx = 0.01\n", + "\n", + " # Create a custom mask (e.g., an elliptical domain)\n", + " Lx = Ly = 6 * sigma_desired\n", + " Nx = Ny = int(Lx / dx)\n", + " if Nx % 2 == 0:\n", + " Nx += 1\n", + " if Ny % 2 == 0:\n", + " Ny += 1\n", + " x = np.linspace(-Lx / 2, Lx / 2, Nx)\n", + " y = np.linspace(-Ly / 2, Ly / 2, Ny)\n", + " X, Y = np.meshgrid(x, y, indexing=\"ij\")\n", + " a, b = 2 * sigma_desired, sigma_desired # Semi-major and semi-minor axes\n", + " # mask = np.where(((X / a) ** 2 + (Y / b) ** 2) <= 1, 1.0, 0.0)\n", + " mask = np.ones((Nx, Ny))\n", + "\n", + " # Define source point (center of the grid)\n", + " source_point = (Nx // 2, Ny // 2)\n", + "\n", + " # Run the simulation\n", + " C_list, X_grid, Y_grid, dx, source_coords = simulate_diffusion_gaussian_sigma(\n", + " sigma=sigma_desired,\n", + " D=0.1,\n", + " dx=dx, # Spatial grid spacing\n", + " safety_factor=0.9,\n", + " snapshot_interval=200,\n", + " source_point=source_point,\n", + " mask=mask,\n", + " )\n", + "\n", + " # Visualization\n", + " for i, C_snapshot in enumerate(C_list):\n", + " # Mask out values outside the domain for visualization\n", + " C_display = np.where(mask == 1, C_snapshot, np.nan)\n", + "\n", + " plt.figure(figsize=(6, 5))\n", + " plt.contourf(X_grid, Y_grid, C_display, levels=50, cmap=\"hot\")\n", + " plt.colorbar(label=\"Concentration\")\n", + " plt.title(f\"Diffusion at Time Step {i * 200}\")\n", + " plt.xlabel(\"X\")\n", + " plt.ylabel(\"Y\")\n", + " plt.axis(\"equal\")\n", + " plt.show()\n", + "\n", + " # Plot the final concentration field with standard deviation contours\n", + " C_final = C_list[-1]\n", + " C_display = np.where(mask == 1, C_final, np.nan)\n", + "\n", + " plt.figure(figsize=(6, 5))\n", + " plt.contourf(X_grid, Y_grid, C_display, levels=50, cmap=\"hot\")\n", + " plt.colorbar(label=\"Concentration\")\n", + " plt.title(\"Final Concentration Field with Standard Deviation Contours\")\n", + " plt.xlabel(\"X\")\n", + " plt.ylabel(\"Y\")\n", + " plt.axis(\"equal\")\n", + "\n", + " # Compute squared distance from source point\n", + " x_source, y_source = source_coords\n", + " R_squared = (X_grid - x_source) ** 2 + (Y_grid - y_source) ** 2\n", + "\n", + " # Define standard deviation levels\n", + " k_values = [1, 2, 3] # Multiples of sigma\n", + " contour_levels = [(k * sigma_desired) ** 2 for k in k_values]\n", + "\n", + " # Plot the contours\n", + " CS = plt.contour(\n", + " X_grid,\n", + " Y_grid,\n", + " R_squared,\n", + " levels=contour_levels,\n", + " colors=\"blue\",\n", + " linestyles=\"dashed\",\n", + " )\n", + " plt.clabel(\n", + " CS,\n", + " inline=True,\n", + " fmt={level: f\"{k}\\u03C3\" for k, level in zip(k_values, contour_levels)},\n", + " fontsize=10,\n", + " )\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "non_local_detector", + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/diffusion4.ipynb b/notebooks/diffusion4.ipynb new file mode 100644 index 0000000..1b4e25e --- /dev/null +++ b/notebooks/diffusion4.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/86/m147b4k17lddvs_xsw0mj2zw0000gn/T/ipykernel_33536/812775775.py:186: RuntimeWarning: invalid value encountered in divide\n", + " occupancy_diffused > epsilon, spikes_diffused / occupancy_diffused, 0.0\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "# -----------------------------\n", + "# 1) Set up the circular track\n", + "# -----------------------------\n", + "def make_circular_track(nx=50, ny=50, radius=20):\n", + " \"\"\"\n", + " Creates a circular region on a grid of size nx x ny.\n", + " Returns:\n", + " place_bin_centers: (nx, ny, 2) float array of bin center coordinates\n", + " is_interior: (nx, ny) boolean array, True for interior bins\n", + " is_boundary: (nx, ny) boolean array, True for bins adjacent to the interior\n", + " \"\"\"\n", + " # For simplicity, define the edges at integer coordinates\n", + " x_edges = np.arange(nx + 1)\n", + " y_edges = np.arange(ny + 1)\n", + " x_centers = 0.5 * (x_edges[:-1] + x_edges[1:])\n", + " y_centers = 0.5 * (y_edges[:-1] + y_edges[1:])\n", + "\n", + " xx, yy = np.meshgrid(x_centers, y_centers, indexing=\"ij\")\n", + " place_bin_centers = np.stack([xx, yy], axis=-1) # shape (nx, ny, 2)\n", + "\n", + " # Circle center\n", + " center_x, center_y = nx / 2, ny / 2\n", + " dist_sq = (xx - center_x) ** 2 + (yy - center_y) ** 2\n", + " is_interior = dist_sq <= radius**2\n", + "\n", + " # Mark boundary bins that are adjacent (4-neighborhood) to interior bins\n", + " is_boundary = np.zeros_like(is_interior, dtype=bool)\n", + " shifts = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n", + " for dx, dy in shifts:\n", + " shifted = np.roll(is_interior, shift=dx, axis=0)\n", + " shifted = np.roll(shifted, shift=dy, axis=1)\n", + " boundary_candidate = ~is_interior & shifted\n", + " is_boundary |= boundary_candidate\n", + "\n", + " return place_bin_centers, is_interior, is_boundary\n", + "\n", + "\n", + "# -------------------------------------------------\n", + "# 2) Convert positions to a grid-based density\n", + "# -------------------------------------------------\n", + "def positions_to_grid_density(positions, nx, ny, is_interior):\n", + " \"\"\"\n", + " Converts an (N,2) array of positions into a 2D density of shape (nx, ny),\n", + " incrementing 1 count in each visited bin. Only fills interior bins.\n", + " \"\"\"\n", + " density = np.zeros((nx, ny), dtype=float)\n", + " for x, y in positions:\n", + " ix = int(round(x))\n", + " iy = int(round(y))\n", + " if 0 <= ix < nx and 0 <= iy < ny:\n", + " if is_interior[ix, iy]:\n", + " density[ix, iy] += 1.0\n", + " return density\n", + "\n", + "\n", + "# -------------------------------------------------\n", + "# 3) Diffuse density under heat equation\n", + "# -------------------------------------------------\n", + "def diffuse_density(initial_density, is_interior, is_boundary, h, dt=0.1):\n", + " \"\"\"\n", + " Diffuses a 2D density array using the heat equation with reflecting (Neumann)\n", + " boundaries on the interior+boundary region. We integrate up to T = h^2\n", + " with a simple forward-Euler scheme.\n", + "\n", + " initial_density: (nx, ny) array\n", + " is_interior, is_boundary: (nx, ny) booleans\n", + " h: bandwidth (diffusion time = h^2)\n", + " dt: time step\n", + " Returns:\n", + " diffused: (nx, ny) array after diffusion\n", + " \"\"\"\n", + " nx, ny = initial_density.shape\n", + " diffused = initial_density.copy()\n", + "\n", + " T = h**2\n", + " n_steps = int(np.ceil(T / dt))\n", + "\n", + " for _ in range(n_steps):\n", + " new_density = diffused.copy()\n", + " for i in range(nx):\n", + " for j in range(ny):\n", + " # Skip cells outside the domain\n", + " if not (is_interior[i, j] or is_boundary[i, j]):\n", + " new_density[i, j] = 0.0\n", + " continue\n", + "\n", + " val_center = diffused[i, j]\n", + " neighbors_sum = 0.0\n", + " count_neighbors = 0\n", + " for di, dj in [(-1, 0), (1, 0), (0, -1), (0, 1)]:\n", + " ni, nj = i + di, j + dj\n", + " if (\n", + " 0 <= ni < nx\n", + " and 0 <= nj < ny\n", + " and (is_interior[ni, nj] or is_boundary[ni, nj])\n", + " ):\n", + " neighbors_sum += diffused[ni, nj]\n", + " else:\n", + " # Reflect across boundary => neighbor value = val_center\n", + " neighbors_sum += val_center\n", + " count_neighbors += 1\n", + " laplacian = neighbors_sum - count_neighbors * val_center\n", + " new_density[i, j] = val_center + dt * laplacian\n", + " diffused = new_density\n", + " return diffused\n", + "\n", + "\n", + "# ---------------------------------------------\n", + "# 4) Main simulation: known Gaussian rate\n", + "# ---------------------------------------------\n", + "def simulate_diffusion_method_gaussian():\n", + " \"\"\"\n", + " 1) Build a circular track on a 50x50 grid with radius=20.\n", + " 2) Define a Gaussian \"true\" rate function R(x,y).\n", + " 3) Sample occupant positions uniformly in the circle.\n", + " 4) For each occupant, draw spikes from Poisson(R(x,y)).\n", + " 5) Convert occupant/spike positions to 2D density arrays.\n", + " 6) Diffuse each density.\n", + " 7) Compute final rate map as diffused_spikes / diffused_occupancy.\n", + " 8) Compare with the known rate.\n", + " \"\"\"\n", + " nx, ny = 50, 50\n", + " radius = 20\n", + " place_bin_centers, is_interior, is_boundary = make_circular_track(nx, ny, radius)\n", + "\n", + " # True Gaussian parameters\n", + " x0, y0 = nx / 2, ny / 2\n", + " sigma = 5.0\n", + " A = 10.0 # peak amplitude\n", + "\n", + " def true_rate(x, y):\n", + " return A * np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2.0 * sigma**2))\n", + "\n", + " # -----------------------------\n", + " # Sample occupant positions\n", + " # -----------------------------\n", + " n_occupancy = 3000\n", + " # Extract valid interior bin centers\n", + " valid_centers = place_bin_centers[is_interior]\n", + " # Choose random interior bins to simulate uniform coverage\n", + " occ_idx = np.random.choice(len(valid_centers), size=n_occupancy, replace=True)\n", + " occupancy_positions = valid_centers[occ_idx]\n", + "\n", + " # -----------------------------\n", + " # Sample spikes from Poisson\n", + " # -----------------------------\n", + " # For each occupant position, let the expected # spikes = R(x,y).\n", + " # We'll allow fractional values, but Poisson() internally rounds to integer draws.\n", + " # Summation across all occupants yields total spikes in each location.\n", + " spike_positions_list = []\n", + " for pos in occupancy_positions:\n", + " lam = true_rate(pos[0], pos[1])\n", + " spike_count = np.random.poisson(lam)\n", + " if spike_count > 0:\n", + " # If multiple spikes occur, store the position multiple times\n", + " spike_positions_list.extend([pos] * spike_count)\n", + " spike_positions = np.array(spike_positions_list)\n", + "\n", + " # -----------------------------\n", + " # Make 2D density grids\n", + " # -----------------------------\n", + " occupancy_init = positions_to_grid_density(occupancy_positions, nx, ny, is_interior)\n", + " spikes_init = positions_to_grid_density(spike_positions, nx, ny, is_interior)\n", + "\n", + " # -----------------------------\n", + " # Diffuse each density\n", + " # -----------------------------\n", + " bandwidth = 3.0 # example bandwidth\n", + " dt = 0.1\n", + " occupancy_diffused = diffuse_density(\n", + " occupancy_init, is_interior, is_boundary, bandwidth, dt\n", + " )\n", + " spikes_diffused = diffuse_density(\n", + " spikes_init, is_interior, is_boundary, bandwidth, dt\n", + " )\n", + "\n", + " # -----------------------------\n", + " # Compute final rate estimate\n", + " # -----------------------------\n", + " epsilon = 1e-9\n", + " rate_map = np.where(\n", + " occupancy_diffused > epsilon, spikes_diffused / occupancy_diffused, 0.0\n", + " )\n", + "\n", + " # -----------------------------\n", + " # Compute the \"true\" rate over the grid\n", + " # -----------------------------\n", + " XX = place_bin_centers[..., 0]\n", + " YY = place_bin_centers[..., 1]\n", + " true_rate_map = true_rate(XX, YY)\n", + " true_rate_map[~(is_interior | is_boundary)] = 0.0\n", + "\n", + " # -----------------------------\n", + " # Plot results\n", + " # -----------------------------\n", + " fig, axs = plt.subplots(1, 4, figsize=(18, 4))\n", + "\n", + " im0 = axs[0].imshow(occupancy_diffused.T, origin=\"lower\", cmap=\"Blues\")\n", + " axs[0].set_title(\"Diffused Occupancy\")\n", + " plt.colorbar(im0, ax=axs[0], fraction=0.046, pad=0.04)\n", + "\n", + " im1 = axs[1].imshow(spikes_diffused.T, origin=\"lower\", cmap=\"Reds\")\n", + " axs[1].set_title(\"Diffused Spikes\")\n", + " plt.colorbar(im1, ax=axs[1], fraction=0.046, pad=0.04)\n", + "\n", + " im2 = axs[2].imshow(rate_map.T, origin=\"lower\", cmap=\"inferno\")\n", + " axs[2].set_title(\"Estimated Rate\")\n", + " plt.colorbar(im2, ax=axs[2], fraction=0.046, pad=0.04)\n", + "\n", + " im3 = axs[3].imshow(true_rate_map.T, origin=\"lower\", cmap=\"inferno\")\n", + " axs[3].set_title(\"True Gaussian Rate\")\n", + " plt.colorbar(im3, ax=axs[3], fraction=0.046, pad=0.04)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " simulate_diffusion_method_gaussian()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "non_local_detector2", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/diffusion5.ipynb b/notebooks/diffusion5.ipynb new file mode 100644 index 0000000..7cc2394 --- /dev/null +++ b/notebooks/diffusion5.ipynb @@ -0,0 +1,2355 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kept 5400 position samples within the track.\n", + "Generated 678 spikes.\n", + "\n", + "Calculating rate map (will trigger JIT compilation if needed)...\n", + "Target sigma=3.54 cm (0.71 bins). Using D_sim=1.0 bins^2/unit_time.\n", + "Required T_sim=0.25 unit_time. Calculated dt=0.0500, N_steps=6\n", + "\n", + "Plotting results...\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import time\n", + "import math\n", + "from functools import partial\n", + "from typing import Tuple, Union # Added Union for potential future use\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "from jax import jit\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "# --- Diffusion Simulation Helper Functions (Assume dx=dy=1 bin unit) ---\n", + "\n", + "\n", + "@partial(jit, static_argnames=[\"grid_shape\"])\n", + "def map_points_to_grid(\n", + " points_xy: jnp.ndarray,\n", + " x_edges: jnp.ndarray,\n", + " y_edges: jnp.ndarray,\n", + " grid_shape: Tuple[int, int],\n", + ") -> jnp.ndarray:\n", + " \"\"\"Maps continuous (x, y) points to grid bins and returns counts per bin.\n", + "\n", + " Args:\n", + " points_xy: An (N, 2) array of (x, y) coordinates.\n", + " x_edges: A 1D array of bin edges for the x-axis.\n", + " y_edges: A 1D array of bin edges for the y-axis.\n", + " grid_shape: A tuple (n_bins_y, n_bins_x) representing the grid dimensions.\n", + "\n", + " Returns:\n", + " A (n_bins_y, n_bins_x) array containing the count of points in each bin.\n", + " \"\"\"\n", + " n_bins_y, n_bins_x = grid_shape\n", + " # Use edges excluding the outermost ones for digitize\n", + " x_indices = jnp.digitize(points_xy[:, 0], x_edges[1:-1])\n", + " y_indices = jnp.digitize(points_xy[:, 1], y_edges[1:-1])\n", + " # Clip indices to ensure they are within the valid range [0, n_bins-1]\n", + " x_indices = jnp.clip(x_indices, 0, n_bins_x - 1)\n", + " y_indices = jnp.clip(y_indices, 0, n_bins_y - 1)\n", + "\n", + " initial_counts = jnp.zeros(grid_shape, dtype=jnp.float32)\n", + " # Use index update method compatible across JAX versions\n", + " try:\n", + " # Recommended for JAX versions < ~0.4.24\n", + " initial_counts = jax.ops.index_add(\n", + " initial_counts, jax.ops.index[y_indices, x_indices], 1.0\n", + " )\n", + " except AttributeError:\n", + " # Recommended for JAX versions >= ~0.4.24\n", + " initial_counts = initial_counts.at[y_indices, x_indices].add(1.0)\n", + " return initial_counts\n", + "\n", + "\n", + "@jit\n", + "def calculate_laplacian_neumann(\n", + " grid: jnp.ndarray, interior_mask: jnp.ndarray\n", + ") -> jnp.ndarray:\n", + " \"\"\"Calculates the discrete Laplacian assuming dx=dy=1 grid spacing,\n", + " applying reflecting (Neumann) boundary conditions based on the interior_mask.\n", + "\n", + " Args:\n", + " grid: The 2D grid array (n_bins_y, n_bins_x) representing density/values.\n", + " interior_mask: A boolean array of the same shape as grid, True for valid bins.\n", + "\n", + " Returns:\n", + " The calculated Laplacian values for each bin within the interior mask.\n", + " \"\"\"\n", + " # Get neighboring values using jnp.roll\n", + " gw = jnp.roll(grid, shift=1, axis=1) # West neighbor (left)\n", + " ge = jnp.roll(grid, shift=-1, axis=1) # East neighbor (right)\n", + " gs = jnp.roll(grid, shift=1, axis=0) # South neighbor (down)\n", + " gn = jnp.roll(grid, shift=-1, axis=0) # North neighbor (up)\n", + "\n", + " # Get masks indicating if neighbors are inside the track\n", + " mask_w = jnp.roll(interior_mask, shift=1, axis=1)\n", + " mask_e = jnp.roll(interior_mask, shift=-1, axis=1)\n", + " mask_s = jnp.roll(interior_mask, shift=1, axis=0)\n", + " mask_n = jnp.roll(interior_mask, shift=-1, axis=0)\n", + "\n", + " # Apply reflecting boundary: If a neighbor is outside the track,\n", + " # use the current cell's value instead (zero flux).\n", + " gw = jnp.where(mask_w, gw, grid)\n", + " ge = jnp.where(mask_e, ge, grid)\n", + " gs = jnp.where(mask_s, gs, grid)\n", + " gn = jnp.where(mask_n, gn, grid)\n", + "\n", + " # Calculate Laplacian components (assuming dx=1, dy=1)\n", + " lap_x = gw + ge - 2 * grid\n", + " lap_y = gs + gn - 2 * grid\n", + "\n", + " # Return Laplacian, masked to apply only within the track interior\n", + " return (lap_x + lap_y) * interior_mask\n", + "\n", + "\n", + "# D_sim and dt are static arguments, known at compile time\n", + "@partial(jit, static_argnames=[\"D_sim\", \"dt\"])\n", + "def diffusion_step(\n", + " grid: jnp.ndarray, D_sim: float, dt: float, interior_mask: jnp.ndarray\n", + ") -> jnp.ndarray:\n", + " \"\"\"Performs one explicit Euler time step for the 2D heat/diffusion equation.\n", + " Assumes dx=dy=1 grid spacing.\n", + "\n", + " Args:\n", + " grid: Current density grid (n_bins_y, n_bins_x).\n", + " D_sim: Simulation diffusion coefficient (in bins^2 / simulation_time_unit).\n", + " dt: Simulation time step (in simulation_time_unit).\n", + " interior_mask: Boolean mask for track interior.\n", + "\n", + " Returns:\n", + " The updated density grid after one time step.\n", + " \"\"\"\n", + " laplacian = calculate_laplacian_neumann(grid, interior_mask)\n", + " # Explicit Euler update rule: u(t+dt) = u(t) + dt * D * laplacian(u(t))\n", + " new_grid = grid + dt * D_sim * laplacian\n", + " # Ensure density remains zero outside the track\n", + " return new_grid * interior_mask\n", + "\n", + "\n", + "# n_steps, D_sim, and dt are static arguments\n", + "@partial(jit, static_argnames=[\"n_steps\", \"D_sim\", \"dt\"])\n", + "def run_diffusion(\n", + " initial_grid: jnp.ndarray,\n", + " n_steps: int,\n", + " D_sim: float,\n", + " dt: float,\n", + " interior_mask: jnp.ndarray,\n", + ") -> jnp.ndarray:\n", + " \"\"\"Runs the diffusion simulation for a fixed number of steps using lax.scan.\n", + " Assumes dx=dy=1 grid spacing.\n", + "\n", + " Args:\n", + " initial_grid: The starting density grid (n_bins_y, n_bins_x).\n", + " n_steps: The number of simulation steps (compile-time constant).\n", + " D_sim: Simulation diffusion coefficient (compile-time constant).\n", + " dt: Simulation time step (compile-time constant).\n", + " interior_mask: Boolean mask for track interior.\n", + "\n", + " Returns:\n", + " The final density grid after n_steps of diffusion.\n", + " \"\"\"\n", + "\n", + " # Define the function for a single step compatible with lax.scan\n", + " # It takes the current state (grid) and a dummy input (_),\n", + " # returning the next state and a dummy output (None).\n", + " def scan_step(current_grid, _):\n", + " next_grid = diffusion_step(current_grid, D_sim, dt, interior_mask)\n", + " return next_grid, None\n", + "\n", + " # Use lax.scan for efficient iteration within JIT context\n", + " final_grid, _ = jax.lax.scan(scan_step, initial_grid, xs=None, length=n_steps)\n", + " return final_grid\n", + "\n", + "\n", + "# --- Main Internal JIT-Compiled Calculation Function ---\n", + "\n", + "\n", + "# Static arguments are known at compile time and affect the compiled code.\n", + "@partial(\n", + " jit,\n", + " static_argnames=[\"grid_shape\", \"fs\", \"D_sim\", \"dt\", \"n_steps\"],\n", + ")\n", + "def calculate_rate_map_diffusion_jax_internal(\n", + " # Dynamic Array Arguments (change between calls)\n", + " spike_pos_xy: jnp.ndarray,\n", + " occupancy_pos_xy: jnp.ndarray,\n", + " x_edges: jnp.ndarray,\n", + " y_edges: jnp.ndarray,\n", + " interior_mask: jnp.ndarray,\n", + " # Static Arguments (fixed for a given compilation)\n", + " fs: float,\n", + " grid_shape: Tuple[int, int],\n", + " D_sim: float,\n", + " dt: float,\n", + " n_steps: int,\n", + ") -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:\n", + " \"\"\"Internal JIT-compiled function to calculate diffusion-smoothed rate map.\n", + "\n", + " Assumes dx=dy=1 grid spacing for diffusion simulation. Normalization and\n", + " rate calculations are performed.\n", + "\n", + " Args:\n", + " spike_pos_xy: (N, 2) array of spike coordinates.\n", + " occupancy_pos_xy: (M, 2) array of all position sample coordinates.\n", + " x_edges: 1D array of x bin edges (needed for mapping).\n", + " y_edges: 1D array of y bin edges (needed for mapping).\n", + " interior_mask: Boolean grid mask (True=inside track).\n", + " fs: Position sampling rate (Hz, static).\n", + " grid_shape: Tuple (n_bins_y, n_bins_x) (static).\n", + " D_sim: Simulation diffusion coefficient (bins^2/unit_time, static).\n", + " dt: Simulation time step (unit_time, static).\n", + " n_steps: Number of simulation steps (static).\n", + "\n", + " Returns:\n", + " A tuple containing:\n", + " - firing_rate_map (Hz)\n", + " - diffused_spike_counts_scaled (total spikes smoothed)\n", + " - diffused_occupancy_time (seconds smoothed)\n", + " \"\"\"\n", + " n_bins_y, n_bins_x = grid_shape\n", + "\n", + " # --- 1. Map data to initial grid counts ---\n", + " initial_spike_counts = map_points_to_grid(\n", + " spike_pos_xy, x_edges, y_edges, grid_shape\n", + " )\n", + " initial_occupancy_counts = map_points_to_grid(\n", + " occupancy_pos_xy, x_edges, y_edges, grid_shape\n", + " )\n", + " # Ensure initial counts are zero outside the valid track area\n", + " initial_spike_counts *= interior_mask\n", + " initial_occupancy_counts *= interior_mask\n", + "\n", + " # --- 2. Run Diffusion Simulation ---\n", + " diffused_spikes = run_diffusion(\n", + " initial_spike_counts, n_steps, D_sim, dt, interior_mask\n", + " )\n", + " diffused_occupancy = run_diffusion(\n", + " initial_occupancy_counts, n_steps, D_sim, dt, interior_mask\n", + " )\n", + "\n", + " # --- 3. Normalize Diffused Maps & Compute Rate ---\n", + " # Calculate initial total counts/time within the track\n", + " total_occupancy_counts_in = jnp.sum(initial_occupancy_counts)\n", + " total_spikes_in = jnp.sum(initial_spike_counts)\n", + " total_time_seconds = total_occupancy_counts_in / fs\n", + "\n", + " # Calculate sums after diffusion (diffusion should approximately conserve sum)\n", + " sum_diffused_occ = jnp.sum(diffused_occupancy)\n", + " sum_diffused_spk = jnp.sum(diffused_spikes)\n", + "\n", + " # Add small epsilon for numerical stability if sums are near zero after diffusion\n", + " safe_sum_diffused_occ = sum_diffused_occ + 1e-12\n", + " safe_sum_diffused_spk = sum_diffused_spk + 1e-12\n", + "\n", + " # Normalize occupancy map to represent time in seconds per bin\n", + " diffused_occupancy_time = (\n", + " diffused_occupancy / safe_sum_diffused_occ\n", + " ) * total_time_seconds\n", + "\n", + " # Normalize spike map to represent total spike counts per bin\n", + " # Handle case where there were no initial spikes\n", + " diffused_spike_counts_scaled = jnp.where(\n", + " total_spikes_in > 0,\n", + " (diffused_spikes / safe_sum_diffused_spk) * total_spikes_in,\n", + " jnp.zeros_like(diffused_spikes), # Result is 0 if no spikes\n", + " )\n", + "\n", + " # --- Calculate Firing Rate (Hz) ---\n", + " epsilon = 1e-9 # Prevent division by zero for rate calculation\n", + " # Ensure occupancy time isn't negative due to numerical errors\n", + " safe_occupancy_time = jnp.maximum(diffused_occupancy_time, 0.0)\n", + " firing_rate_map = diffused_spike_counts_scaled / (safe_occupancy_time + epsilon)\n", + " # Ensure rate is zero outside the track\n", + " firing_rate_map *= interior_mask\n", + "\n", + " return firing_rate_map, diffused_spike_counts_scaled, diffused_occupancy_time\n", + "\n", + "\n", + "# --- User-Facing Wrapper Function ---\n", + "\n", + "\n", + "def calculate_rate_map_diffusion_jax(\n", + " # Data Inputs\n", + " spike_pos_xy: Union[np.ndarray, jnp.ndarray],\n", + " occupancy_pos_xy: Union[np.ndarray, jnp.ndarray],\n", + " x_edges: Union[np.ndarray, jnp.ndarray],\n", + " y_edges: Union[np.ndarray, jnp.ndarray],\n", + " interior_mask: Union[np.ndarray, jnp.ndarray],\n", + " fs: float,\n", + " grid_shape: Tuple[int, int],\n", + " grid_res: float, # Grid resolution in meters/bin\n", + " # Smoothing Parameter\n", + " sigma_cm: float = 5.0, # Target Gaussian sigma in cm\n", + " # Simulation Parameter\n", + " dt_stability_factor: float = 0.2, # Safety factor for time step (<~0.25)\n", + " # Options\n", + " verbose: bool = True,\n", + ") -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:\n", + " \"\"\"Calculates a diffusion-smoothed firing rate map using JAX.\n", + "\n", + " This function parameterizes the smoothing level via an equivalent\n", + " Gaussian standard deviation (`sigma_cm`) specified in physical units (cm).\n", + " It runs a 2D diffusion simulation on a grid (assuming dx=dy=1 bin units\n", + " internally for stability) with reflecting boundaries defined by\n", + " `interior_mask`.\n", + "\n", + " Args:\n", + " spike_pos_xy: (N, 2) array of spike (x, y) coordinates.\n", + " occupancy_pos_xy: (M, 2) array of all position sample (x, y) coordinates.\n", + " x_edges: 1D array of x bin edges.\n", + " y_edges: 1D array of y bin edges.\n", + " interior_mask: Boolean grid mask (True=inside track), shape (n_bins_y, n_bins_x).\n", + " fs: Position sampling rate (Hz).\n", + " grid_shape: Tuple (n_bins_y, n_bins_x).\n", + " grid_res: Grid resolution in meters/bin (e.g., 0.05).\n", + " sigma_cm: Desired smoothing level expressed as the standard deviation\n", + " of an equivalent Gaussian kernel in centimeters. Defaults to 5.0.\n", + " dt_stability_factor: Safety factor applied to the calculated maximum stable\n", + " time step for the simulation (should be < ~0.25). Defaults to 0.2.\n", + " verbose: If True, print calculated simulation parameters and timing. Defaults to True.\n", + "\n", + " Returns:\n", + " A tuple containing JAX arrays:\n", + " - firing_rate_map (Hz)\n", + " - diffused_spike_counts_scaled (total spikes smoothed)\n", + " - diffused_occupancy_time (seconds smoothed)\n", + " \"\"\"\n", + " # --- Input Validation and Conversion ---\n", + " if sigma_cm <= 0:\n", + " raise ValueError(\"sigma_cm must be positive\")\n", + " if grid_res <= 0:\n", + " raise ValueError(\"grid_res must be positive\")\n", + " if dt_stability_factor <= 0 or dt_stability_factor >= 0.25:\n", + " print(\n", + " f\"Warning: dt_stability_factor ({dt_stability_factor}) is outside the typical safe range (0, 0.25).\"\n", + " )\n", + "\n", + " # Ensure inputs are JAX arrays\n", + " spike_pos_xy_jax = jnp.asarray(spike_pos_xy)\n", + " occupancy_pos_xy_jax = jnp.asarray(occupancy_pos_xy)\n", + " x_edges_jax = jnp.asarray(x_edges)\n", + " y_edges_jax = jnp.asarray(y_edges)\n", + " interior_mask_jax = jnp.asarray(interior_mask, dtype=bool)\n", + "\n", + " # --- Calculate Diffusion Parameters based on sigma_cm ---\n", + " CM_PER_METER = 100.0\n", + " res_cm = grid_res * CM_PER_METER # Convert m/bin to cm/bin\n", + " sigma_bins = sigma_cm / res_cm # Target sigma in bin units (equivalent to 'h')\n", + "\n", + " # Fix simulation diffusion coefficient in bin units for simplicity\n", + " # D_sim relates bins^2 to abstract simulation time units\n", + " D_sim = 1.0 # bins^2 / unit_time\n", + "\n", + " # Calculate required total simulation time T_sim using sigma_bins^2 = 2 * D_sim * T_sim\n", + " T_sim = (sigma_bins**2) / (2.0 * D_sim)\n", + "\n", + " # Calculate stable dt and number of steps based on D_sim and dx=dy=1 assumption\n", + " # max_stable_dt = 1 / (2 * D_sim * (1/dx^2 + 1/dy^2)) with dx=dy=1\n", + " max_stable_dt = 1.0 / (4.0 * D_sim)\n", + " dt = float(dt_stability_factor * max_stable_dt)\n", + "\n", + " if dt <= 0:\n", + " raise ValueError(f\"Calculated dt is not positive ({dt}). Check parameters.\")\n", + " # Calculate number of steps needed to reach T_sim\n", + " n_steps = max(1, math.ceil(T_sim / dt))\n", + "\n", + " if verbose:\n", + " print(\n", + " f\"Target sigma={sigma_cm:.2f} cm ({sigma_bins:.2f} bins). Using D_sim={D_sim:.1f} bins^2/unit_time.\"\n", + " )\n", + " print(\n", + " f\"Required T_sim={T_sim:.2f} unit_time. Calculated dt={dt:.4f}, N_steps={n_steps}\"\n", + " )\n", + "\n", + " # --- Prepare Static Arguments for JIT ---\n", + " fs_static = float(fs)\n", + "\n", + " static_args_for_internal = {\n", + " \"fs\": fs_static,\n", + " \"grid_shape\": grid_shape,\n", + " \"D_sim\": D_sim, # Use the fixed D_sim=1.0\n", + " \"dt\": dt,\n", + " \"n_steps\": n_steps,\n", + " }\n", + "\n", + " # Partially apply static args using functools.partial\n", + " # This allows JAX to compile a specialized version for these static values\n", + " internal_func_partial = partial(\n", + " calculate_rate_map_diffusion_jax_internal, **static_args_for_internal\n", + " )\n", + "\n", + " # JIT compile the partially applied function\n", + " compiled_internal_func = jit(internal_func_partial)\n", + "\n", + " # --- Execute the Compiled Function ---\n", + " # Pass only the non-static JAX array arguments\n", + " result = compiled_internal_func(\n", + " spike_pos_xy_jax,\n", + " occupancy_pos_xy_jax,\n", + " x_edges_jax,\n", + " y_edges_jax,\n", + " interior_mask_jax,\n", + " )\n", + "\n", + " return result\n", + "\n", + "\n", + "# --- Example Usage ---\n", + "\n", + "if __name__ == \"__main__\": # Ensures example runs only when script is executed directly\n", + " # Use NumPy for data generation, then pass to the JAX function\n", + " np.random.seed(42)\n", + "\n", + " # --- 1. Define Grid and Track Mask ---\n", + " GRID_RESOLUTION_M_PER_BIN = 0.05 # meters per bin\n", + " X_MIN, X_MAX = 0, 2\n", + " Y_MIN, Y_MAX = 0, 1\n", + "\n", + " n_bins_x = int((X_MAX - X_MIN) / GRID_RESOLUTION_M_PER_BIN)\n", + " n_bins_y = int((Y_MAX - Y_MIN) / GRID_RESOLUTION_M_PER_BIN)\n", + " grid_shape = (n_bins_y, n_bins_x)\n", + "\n", + " x_edges_np = np.linspace(X_MIN, X_MAX, n_bins_x + 1)\n", + " y_edges_np = np.linspace(Y_MIN, Y_MAX, n_bins_y + 1)\n", + "\n", + " # Generate bin centers for mask creation\n", + " bin_centers_x_np = (x_edges_np[:-1] + x_edges_np[1:]) / 2\n", + " bin_centers_y_np = (y_edges_np[:-1] + y_edges_np[1:]) / 2\n", + " bin_centers_xx_np, bin_centers_yy_np = np.meshgrid(\n", + " bin_centers_x_np, bin_centers_y_np\n", + " )\n", + "\n", + " # Create a simple 'U' shaped track mask\n", + " TRACK_WIDTH_M = 0.3\n", + " is_bottom_arm = np.abs(bin_centers_yy_np - TRACK_WIDTH_M / 2) < TRACK_WIDTH_M / 2\n", + " is_left_arm = np.abs(bin_centers_xx_np - TRACK_WIDTH_M / 2) < TRACK_WIDTH_M / 2\n", + " is_right_arm = (\n", + " np.abs(bin_centers_xx_np - (X_MAX - TRACK_WIDTH_M / 2)) < TRACK_WIDTH_M / 2\n", + " )\n", + " is_track_interior_np = is_bottom_arm | is_left_arm | is_right_arm\n", + " # Remove top part of side arms\n", + " is_top_part = bin_centers_yy_np > (Y_MAX - TRACK_WIDTH_M * 1.1)\n", + " remove_mask = (is_left_arm & is_top_part) | (is_right_arm & is_top_part)\n", + " is_track_interior_np[remove_mask] = False\n", + "\n", + " # --- 2. Generate Dummy Positional Data ---\n", + " DURATION_SECONDS = 180\n", + " FS_HZ = 30 # Sampling rate (Hz)\n", + " N_POS_SAMPLES = int(DURATION_SECONDS * FS_HZ)\n", + " N_INITIAL_POINTS = N_POS_SAMPLES * 5 # Generate more points for rejection sampling\n", + "\n", + " initial_animal_positions_np = np.random.rand(N_INITIAL_POINTS, 2) * np.array(\n", + " [X_MAX, Y_MAX]\n", + " )\n", + " # Find which generated points fall within the track mask\n", + " pos_indices_x = np.digitize(initial_animal_positions_np[:, 0], x_edges_np[1:-1])\n", + " pos_indices_y = np.digitize(initial_animal_positions_np[:, 1], y_edges_np[1:-1])\n", + " pos_indices_x = np.clip(pos_indices_x, 0, n_bins_x - 1)\n", + " pos_indices_y = np.clip(pos_indices_y, 0, n_bins_y - 1)\n", + " track_mask_flat = is_track_interior_np[pos_indices_y, pos_indices_x]\n", + "\n", + " # Select points within the track, up to the desired number\n", + " animal_positions_np = initial_animal_positions_np[track_mask_flat][:N_POS_SAMPLES]\n", + " # Generate corresponding timestamps\n", + " position_timestamps_np = np.linspace(\n", + " 0, len(animal_positions_np) / FS_HZ, len(animal_positions_np)\n", + " )\n", + " if len(animal_positions_np) < N_POS_SAMPLES:\n", + " print(\n", + " f\"Warning: Only kept {len(animal_positions_np)} position samples within track (desired: {N_POS_SAMPLES}).\"\n", + " )\n", + " else:\n", + " print(f\"Kept {len(animal_positions_np)} position samples within the track.\")\n", + "\n", + " # --- 3. Generate Dummy Spike Data ---\n", + " # Simulate a place field in the bottom-right corner\n", + " PLACE_FIELD_CENTER_M = np.array([X_MAX - TRACK_WIDTH_M, TRACK_WIDTH_M / 2])\n", + " PLACE_FIELD_RADIUS_M = 0.2\n", + " BASE_FIRING_RATE_HZ = 0.1\n", + " PEAK_FIRING_RATE_HZ = 20.0\n", + "\n", + " distances_m = np.linalg.norm(animal_positions_np - PLACE_FIELD_CENTER_M, axis=1)\n", + " # Instantaneous probability of spiking in a time bin (1/FS_HZ seconds)\n", + " prob_spike = BASE_FIRING_RATE_HZ / FS_HZ + (PEAK_FIRING_RATE_HZ / FS_HZ) * np.exp(\n", + " -(distances_m**2) / (2 * PLACE_FIELD_RADIUS_M**2)\n", + " )\n", + " spike_flags = np.random.rand(len(animal_positions_np)) < prob_spike\n", + " spike_times_np = position_timestamps_np[spike_flags]\n", + " spike_positions_np = animal_positions_np[spike_flags, :]\n", + " print(f\"Generated {len(spike_times_np)} spikes.\")\n", + "\n", + " # --- 4. Set Smoothing Parameter ---\n", + " SIGMA_CM = np.sqrt(12.5) # Target Gaussian sigma in cm for smoothing\n", + "\n", + " # --- 5. Run Calculation ---\n", + " print(\"\\nCalculating rate map (will trigger JIT compilation if needed)...\")\n", + " # Pass NumPy arrays - the wrapper function converts them to JAX arrays\n", + " rate_map, diffused_spikes, diffused_occupancy = calculate_rate_map_diffusion_jax(\n", + " spike_positions_np, # Use NumPy array\n", + " animal_positions_np, # Use NumPy array\n", + " x_edges_np, # Use NumPy array\n", + " y_edges_np, # Use NumPy array\n", + " is_track_interior_np, # Use NumPy array\n", + " fs=FS_HZ,\n", + " grid_shape=grid_shape,\n", + " grid_res=GRID_RESOLUTION_M_PER_BIN,\n", + " sigma_cm=SIGMA_CM,\n", + " dt_stability_factor=0.2,\n", + " verbose=True,\n", + " )\n", + "\n", + " # --- 6. Convert Results for Plotting ---\n", + " rate_map_np = np.array(rate_map)\n", + " diffused_spikes_np = np.array(diffused_spikes)\n", + " diffused_occupancy_np = np.array(diffused_occupancy)\n", + "\n", + " # --- 7. Plotting ---\n", + " print(\"\\nPlotting results...\")\n", + " plt.figure(figsize=(18, 5))\n", + "\n", + " # Subplot 1: Smoothed Occupancy\n", + " plt.subplot(1, 3, 1)\n", + " plot_occ = np.where(is_track_interior_np, diffused_occupancy_np, np.nan)\n", + " pcm = plt.pcolormesh(\n", + " x_edges_np, y_edges_np, plot_occ, shading=\"auto\", cmap=\"viridis\"\n", + " )\n", + " plt.colorbar(pcm, label=\"Smoothed Occupancy (s)\")\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(\"Smoothed Occupancy (Diffusion)\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.ylabel(\"Y Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + "\n", + " # Subplot 2: Smoothed Spikes\n", + " plt.subplot(1, 3, 2)\n", + " plot_spk = np.where(is_track_interior_np, diffused_spikes_np, np.nan)\n", + " # Use a different colormap for spikes if desired, e.g., 'rocket' or 'magma'\n", + " pcm = plt.pcolormesh(x_edges_np, y_edges_np, plot_spk, shading=\"auto\", cmap=\"magma\")\n", + " plt.colorbar(pcm, label=\"Smoothed Spikes (counts)\")\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(\"Smoothed Spikes (Diffusion)\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + "\n", + " # Subplot 3: Firing Rate Map\n", + " plt.subplot(1, 3, 3)\n", + " # Mask the rate map outside the track for plotting\n", + " plot_rate = np.where(is_track_interior_np, rate_map_np, np.nan)\n", + " # Robustly calculate vmax for the color scale\n", + " valid_rates = plot_rate[~np.isnan(plot_rate)]\n", + " if valid_rates.size > 0:\n", + " vmax = np.percentile(valid_rates, 99) # Use 99th percentile to handle outliers\n", + " else:\n", + " print(\n", + " \"Warning: No valid rate values found for vmax calculation, using default.\"\n", + " )\n", + " vmax = 1.0 # Default vmax if no valid rates\n", + " vmax = max(vmax, 1.0) # Ensure vmax is at least 1 for visual clarity\n", + "\n", + " pcm = plt.pcolormesh(\n", + " x_edges_np, y_edges_np, plot_rate, shading=\"auto\", cmap=\"jet\", vmin=0, vmax=vmax\n", + " )\n", + " plt.colorbar(pcm, label=\"Firing Rate (Hz)\")\n", + " # Overlay original spike locations\n", + " plt.plot(\n", + " spike_positions_np[:, 0],\n", + " spike_positions_np[:, 1],\n", + " \"k.\",\n", + " markersize=1,\n", + " alpha=0.2,\n", + " )\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(f\"Firing Rate Map (~{SIGMA_CM}cm Diffusion Smoothing)\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kept 5400 position samples within the track.\n", + "Generated 696 spikes.\n", + "\n", + "Calculating rate map (Diffusion method)...\n", + "Target sigma=3.54 cm (0.71 bins). Using D_sim=1.0 bins^2/unit_time.\n", + "Required T_sim=0.25 unit_time. Calculated dt=0.0500, N_steps=6\n", + "\n", + "Calculating rate map (Standard KDE method)...\n", + "Fitting Occupancy KDE...\n", + "Evaluating Occupancy KDE...\n", + "Occupancy KDE done.\n", + "Fitting Spike KDE...\n", + "Evaluating Spike KDE...\n", + "Spike KDE done.\n", + "\n", + "Plotting results...\n", + "Fitting Occupancy KDE...\n", + "Evaluating Occupancy KDE...\n", + "Occupancy KDE done.\n", + "Fitting Spike KDE...\n", + "Evaluating Spike KDE...\n", + "Spike KDE done.\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.neighbors import KernelDensity\n", + "\n", + "\n", + "def calculate_rate_map_kde_sklearn(\n", + " spike_pos_xy: np.ndarray,\n", + " occupancy_pos_xy: np.ndarray,\n", + " x_edges: np.ndarray,\n", + " y_edges: np.ndarray,\n", + " interior_mask: np.ndarray,\n", + " fs: float,\n", + " grid_shape: Tuple[int, int],\n", + " sigma_m: float, # Gaussian kernel std dev in METERS\n", + " epsilon: float = 1e-9,\n", + ") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"Calculates a firing rate map using standard Gaussian KDE (sklearn).\n", + "\n", + " Note: This method does not explicitly handle boundaries and may show\n", + " bias (underestimation) near the edges of the sampled area.\n", + "\n", + " Args:\n", + " spike_pos_xy: (N, 2) numpy array of spike (x, y) coordinates (in meters).\n", + " occupancy_pos_xy: (M, 2) numpy array of all position sample (x, y) coordinates (in meters).\n", + " x_edges: 1D numpy array of x bin edges.\n", + " y_edges: 1D numpy array of y bin edges.\n", + " interior_mask: Boolean grid mask (True=inside track), shape (n_bins_y, n_bins_x).\n", + " fs: Position sampling rate (Hz).\n", + " grid_shape: Tuple (n_bins_y, n_bins_x).\n", + " sigma_m: Standard deviation of the Gaussian kernel in meters. This is the bandwidth 'h'.\n", + " epsilon: Small value to prevent division by zero. Defaults to 1e-9.\n", + "\n", + " Returns:\n", + " A tuple containing NumPy arrays:\n", + " - firing_rate_map (Hz)\n", + " - kde_spike_counts (smoothed spike counts)\n", + " - kde_occupancy_time (smoothed occupancy time in seconds)\n", + " \"\"\"\n", + " n_bins_y, n_bins_x = grid_shape\n", + " n_pos_samples = occupancy_pos_xy.shape[0]\n", + " n_spikes = spike_pos_xy.shape[0]\n", + "\n", + " if n_pos_samples == 0:\n", + " print(\"Warning: No occupancy samples provided for KDE.\")\n", + " zeros = np.zeros(grid_shape)\n", + " return zeros, zeros, zeros\n", + "\n", + " # Calculate grid cell centers for KDE evaluation\n", + " grid_x_centers = (x_edges[:-1] + x_edges[1:]) / 2\n", + " grid_y_centers = (y_edges[:-1] + y_edges[1:]) / 2\n", + " grid_xx, grid_yy = np.meshgrid(grid_x_centers, grid_y_centers)\n", + " # Shape needed for score_samples: (n_evaluation_points, n_features=2)\n", + " grid_coords_eval = np.vstack([grid_xx.ravel(), grid_yy.ravel()]).T\n", + "\n", + " # --- Occupancy KDE ---\n", + " print(\"Fitting Occupancy KDE...\")\n", + " kde_occ = KernelDensity(kernel=\"gaussian\", bandwidth=sigma_m)\n", + " kde_occ.fit(occupancy_pos_xy)\n", + " print(\"Evaluating Occupancy KDE...\")\n", + " log_density_occ = kde_occ.score_samples(grid_coords_eval)\n", + " occupancy_density = np.exp(log_density_occ).reshape(grid_shape)\n", + " # Normalize density: Sum(density * area) should be ~1\n", + " # Scale to get time density: time_density = density * total_time\n", + " # Total time = n_pos_samples / fs\n", + " total_time_seconds = n_pos_samples / fs\n", + " # Normalize carefully: sum of density * area should approx 1\n", + " # cell_area = (x_edges[1]-x_edges[0]) * (y_edges[1]-y_edges[0])\n", + " # norm_factor_occ = np.sum(occupancy_density * cell_area) # Normalization constant\n", + " # kde_occupancy_time = (occupancy_density / (norm_factor_occ + epsilon)) * total_time_seconds\n", + " # Simpler approx: scale proportionally\n", + " kde_occupancy_time = occupancy_density * (\n", + " total_time_seconds / (np.sum(occupancy_density) + epsilon)\n", + " )\n", + " print(\"Occupancy KDE done.\")\n", + "\n", + " # --- Spike KDE ---\n", + " if n_spikes > 1: # Need at least 2 points for KDE typically\n", + " print(\"Fitting Spike KDE...\")\n", + " kde_spk = KernelDensity(kernel=\"gaussian\", bandwidth=sigma_m)\n", + " kde_spk.fit(spike_pos_xy)\n", + " print(\"Evaluating Spike KDE...\")\n", + " log_density_spk = kde_spk.score_samples(grid_coords_eval)\n", + " spike_density = np.exp(log_density_spk).reshape(grid_shape)\n", + " # Scale to get spike counts: counts = density * total_spikes\n", + " # norm_factor_spk = np.sum(spike_density * cell_area)\n", + " # kde_spike_counts = (spike_density / (norm_factor_spk + epsilon)) * n_spikes\n", + " kde_spike_counts = spike_density * (\n", + " n_spikes / (np.sum(spike_density) + epsilon)\n", + " )\n", + " print(\"Spike KDE done.\")\n", + " else:\n", + " print(\"Warning: Not enough spikes (<2) for KDE. Returning zero spike map.\")\n", + " kde_spike_counts = np.zeros(grid_shape)\n", + "\n", + " # --- Calculate Firing Rate ---\n", + " # Ensure occupancy time isn't negative\n", + " safe_occupancy_time = np.maximum(kde_occupancy_time, 0.0)\n", + " firing_rate_map = kde_spike_counts / (safe_occupancy_time + epsilon)\n", + " # Mask result to only include track interior\n", + " firing_rate_map *= interior_mask\n", + "\n", + " return firing_rate_map, kde_spike_counts, kde_occupancy_time\n", + "\n", + "\n", + "# --- Example Usage ---\n", + "\n", + "if __name__ == \"__main__\":\n", + " # Use NumPy for data generation, then pass to the JAX function\n", + " np.random.seed(42)\n", + "\n", + " # --- 1. Define Grid and Track Mask ---\n", + " GRID_RESOLUTION_M_PER_BIN = 0.05\n", + " X_MIN, X_MAX = 0, 2\n", + " Y_MIN, Y_MAX = 0, 1\n", + " n_bins_x = int((X_MAX - X_MIN) / GRID_RESOLUTION_M_PER_BIN)\n", + " n_bins_y = int((Y_MAX - Y_MIN) / GRID_RESOLUTION_M_PER_BIN)\n", + " grid_shape = (n_bins_y, n_bins_x)\n", + " x_edges_np = np.linspace(X_MIN, X_MAX, n_bins_x + 1)\n", + " y_edges_np = np.linspace(Y_MIN, Y_MAX, n_bins_y + 1)\n", + " bin_centers_x_np = (x_edges_np[:-1] + x_edges_np[1:]) / 2\n", + " bin_centers_y_np = (y_edges_np[:-1] + y_edges_np[1:]) / 2\n", + " bin_centers_xx_np, bin_centers_yy_np = np.meshgrid(\n", + " bin_centers_x_np, bin_centers_y_np\n", + " )\n", + " TRACK_WIDTH_M = 0.3\n", + " is_bottom_arm = np.abs(bin_centers_yy_np - TRACK_WIDTH_M / 2) < TRACK_WIDTH_M / 2\n", + " is_left_arm = np.abs(bin_centers_xx_np - TRACK_WIDTH_M / 2) < TRACK_WIDTH_M / 2\n", + " is_right_arm = (\n", + " np.abs(bin_centers_xx_np - (X_MAX - TRACK_WIDTH_M / 2)) < TRACK_WIDTH_M / 2\n", + " )\n", + " is_track_interior_np = is_bottom_arm | is_left_arm | is_right_arm\n", + " is_top_part = bin_centers_yy_np > (Y_MAX - TRACK_WIDTH_M * 1.1)\n", + " remove_mask = (is_left_arm & is_top_part) | (is_right_arm & is_top_part)\n", + " is_track_interior_np[remove_mask] = False\n", + "\n", + " # --- 2. Generate Dummy Positional Data ---\n", + " DURATION_SECONDS = 180\n", + " FS_HZ = 30\n", + " N_POS_SAMPLES = int(DURATION_SECONDS * FS_HZ)\n", + " N_INITIAL_POINTS = N_POS_SAMPLES * 10 # Increased initial points slightly\n", + " initial_animal_positions_np = np.random.rand(N_INITIAL_POINTS, 2) * np.array(\n", + " [X_MAX, Y_MAX]\n", + " )\n", + " pos_indices_x = np.digitize(initial_animal_positions_np[:, 0], x_edges_np[1:-1])\n", + " pos_indices_y = np.digitize(initial_animal_positions_np[:, 1], y_edges_np[1:-1])\n", + " pos_indices_x = np.clip(pos_indices_x, 0, n_bins_x - 1)\n", + " pos_indices_y = np.clip(pos_indices_y, 0, n_bins_y - 1)\n", + " track_mask_flat = is_track_interior_np[pos_indices_y, pos_indices_x]\n", + " animal_positions_np = initial_animal_positions_np[track_mask_flat][:N_POS_SAMPLES]\n", + " position_timestamps_np = np.linspace(\n", + " 0, len(animal_positions_np) / FS_HZ, len(animal_positions_np)\n", + " )\n", + " if len(animal_positions_np) < N_POS_SAMPLES:\n", + " print(f\"Warning: Only kept {len(animal_positions_np)} samples.\")\n", + " else:\n", + " print(f\"Kept {len(animal_positions_np)} position samples within the track.\")\n", + "\n", + " # --- 3. Generate Dummy Spike Data ---\n", + " PLACE_FIELD_CENTER_M = np.array([X_MAX - TRACK_WIDTH_M, TRACK_WIDTH_M / 2])\n", + " PLACE_FIELD_RADIUS_M = 0.2\n", + " BASE_FIRING_RATE_HZ = 0.1\n", + " PEAK_FIRING_RATE_HZ = 20.0\n", + " distances_m = np.linalg.norm(animal_positions_np - PLACE_FIELD_CENTER_M, axis=1)\n", + " prob_spike = BASE_FIRING_RATE_HZ / FS_HZ + (PEAK_FIRING_RATE_HZ / FS_HZ) * np.exp(\n", + " -(distances_m**2) / (2 * PLACE_FIELD_RADIUS_M**2)\n", + " )\n", + " spike_flags = np.random.rand(len(animal_positions_np)) < prob_spike\n", + " spike_times_np = position_timestamps_np[spike_flags]\n", + " spike_positions_np = animal_positions_np[spike_flags, :]\n", + " print(f\"Generated {len(spike_positions_np)} spikes.\")\n", + "\n", + " # --- 4. Set Smoothing Parameter ---\n", + " # SIGMA_CM = 5.0 # Target Gaussian sigma in cm for smoothing\n", + " SIGMA_M = SIGMA_CM / 100.0 # Convert to meters for KDE function\n", + "\n", + " # --- 5. Run Diffusion Calculation ---\n", + " print(\"\\nCalculating rate map (Diffusion method)...\")\n", + " rate_map_diff, diffused_spikes, diffused_occupancy = (\n", + " calculate_rate_map_diffusion_jax(\n", + " spike_positions_np,\n", + " animal_positions_np,\n", + " x_edges_np,\n", + " y_edges_np,\n", + " is_track_interior_np,\n", + " fs=FS_HZ,\n", + " grid_shape=grid_shape,\n", + " grid_res=GRID_RESOLUTION_M_PER_BIN,\n", + " sigma_cm=SIGMA_CM,\n", + " dt_stability_factor=0.2,\n", + " verbose=True,\n", + " )\n", + " )\n", + " rate_map_diff_np = np.array(rate_map_diff) # Convert JAX array to NumPy\n", + "\n", + " # --- 6. Run Standard KDE Calculation ---\n", + " print(\"\\nCalculating rate map (Standard KDE method)...\")\n", + " rate_map_kde_np, _, _ = calculate_rate_map_kde_sklearn(\n", + " spike_positions_np,\n", + " animal_positions_np,\n", + " x_edges_np,\n", + " y_edges_np,\n", + " is_track_interior_np,\n", + " fs=FS_HZ,\n", + " grid_shape=grid_shape,\n", + " sigma_m=SIGMA_M,\n", + " )\n", + "\n", + " # --- 7. Calculate Difference ---\n", + " # Calculate difference only where both maps are potentially valid (inside track)\n", + " difference_map = np.full(grid_shape, np.nan)\n", + " difference_map[is_track_interior_np] = (\n", + " rate_map_diff_np[is_track_interior_np] - rate_map_kde_np[is_track_interior_np]\n", + " )\n", + "\n", + " # --- 8. Plotting Comparison ---\n", + " print(\"\\nPlotting results...\")\n", + " # Increase figure width to accommodate more plots\n", + " plt.figure(figsize=(24, 5.5)) # Adjusted size\n", + "\n", + " # --- Plot 1: Diffusion Rate Map ---\n", + " plt.subplot(1, 4, 1)\n", + " plot_rate_diff = np.where(is_track_interior_np, rate_map_diff_np, np.nan)\n", + " valid_rates_diff = plot_rate_diff[~np.isnan(plot_rate_diff)]\n", + " vmax_diff = (\n", + " np.percentile(valid_rates_diff, 99) if valid_rates_diff.size > 0 else 1.0\n", + " )\n", + " vmax_diff = max(vmax_diff, 1.0)\n", + " pcm1 = plt.pcolormesh(\n", + " x_edges_np,\n", + " y_edges_np,\n", + " plot_rate_diff,\n", + " shading=\"auto\",\n", + " cmap=\"jet\",\n", + " vmin=0,\n", + " vmax=vmax_diff,\n", + " )\n", + " plt.colorbar(pcm1, label=\"Firing Rate (Hz)\")\n", + " plt.plot(\n", + " spike_positions_np[:, 0],\n", + " spike_positions_np[:, 1],\n", + " \"k.\",\n", + " markersize=1,\n", + " alpha=0.2,\n", + " )\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(f\"Rate Map (Diffusion ~{SIGMA_CM}cm)\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.ylabel(\"Y Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + "\n", + " # --- Plot 2: Standard KDE Rate Map ---\n", + " plt.subplot(1, 4, 2)\n", + " plot_rate_kde = np.where(is_track_interior_np, rate_map_kde_np, np.nan)\n", + " valid_rates_kde = plot_rate_kde[~np.isnan(plot_rate_kde)]\n", + " vmax_kde = np.percentile(valid_rates_kde, 99) if valid_rates_kde.size > 0 else 1.0\n", + " vmax_kde = max(vmax_kde, 1.0)\n", + " # Use same vmax as diffusion plot for direct comparison, or calculate independently\n", + " vmax_compare = max(vmax_diff, vmax_kde) # Use a common scale\n", + " pcm2 = plt.pcolormesh(\n", + " x_edges_np,\n", + " y_edges_np,\n", + " plot_rate_kde,\n", + " shading=\"auto\",\n", + " cmap=\"jet\",\n", + " vmin=0,\n", + " vmax=vmax_compare,\n", + " )\n", + " plt.colorbar(pcm2, label=\"Firing Rate (Hz)\")\n", + " plt.plot(\n", + " spike_positions_np[:, 0],\n", + " spike_positions_np[:, 1],\n", + " \"k.\",\n", + " markersize=1,\n", + " alpha=0.2,\n", + " )\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(f\"Rate Map (Standard KDE {SIGMA_CM}cm)\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.ylabel(\"Y Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + "\n", + " # --- Plot 3: Difference Map (Diffusion - KDE) ---\n", + " plt.subplot(1, 4, 3)\n", + " # Center the colormap around zero\n", + " diff_max_abs = np.nanmax(np.abs(difference_map))\n", + " diff_max_abs = max(diff_max_abs, 1e-6) # Ensure non-zero limit\n", + " pcm3 = plt.pcolormesh(\n", + " x_edges_np,\n", + " y_edges_np,\n", + " difference_map,\n", + " shading=\"auto\",\n", + " cmap=\"coolwarm\",\n", + " vmin=-diff_max_abs,\n", + " vmax=diff_max_abs,\n", + " )\n", + " plt.colorbar(pcm3, label=\"Rate Difference (Hz)\")\n", + " # Overlay the track boundary for context (optional)\n", + " # You might need to calculate boundary points more explicitly\n", + " # plt.contour(bin_centers_x_np, bin_centers_y_np, is_track_interior_np.astype(float), levels=[0.5], colors='black', linewidths=0.5)\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(\"Difference (Diffusion - KDE)\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.ylabel(\"Y Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + "\n", + " # --- Plot 4: Occupancy Comparison (Optional but informative) ---\n", + " plt.subplot(1, 4, 4)\n", + " # Get KDE occupancy (convert JAX array first)\n", + " _, _, kde_occupancy_time_np = calculate_rate_map_kde_sklearn(\n", + " spike_positions_np,\n", + " animal_positions_np,\n", + " x_edges_np,\n", + " y_edges_np,\n", + " is_track_interior_np,\n", + " FS_HZ,\n", + " grid_shape,\n", + " SIGMA_M,\n", + " )\n", + " diffused_occupancy_np = np.array(diffused_occupancy) # Convert JAX array\n", + " occ_difference = np.full(grid_shape, np.nan)\n", + " occ_difference[is_track_interior_np] = (\n", + " diffused_occupancy_np[is_track_interior_np]\n", + " - kde_occupancy_time_np[is_track_interior_np]\n", + " )\n", + " occ_diff_max_abs = np.nanmax(np.abs(occ_difference))\n", + " occ_diff_max_abs = max(occ_diff_max_abs, 1e-9)\n", + " pcm4 = plt.pcolormesh(\n", + " x_edges_np,\n", + " y_edges_np,\n", + " occ_difference,\n", + " shading=\"auto\",\n", + " cmap=\"PRGn\",\n", + " vmin=-occ_diff_max_abs,\n", + " vmax=occ_diff_max_abs,\n", + " ) # Purple-Green colormap\n", + " plt.colorbar(pcm4, label=\"Occupancy Diff (s)\")\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(\"Occupancy Diff (Diffusion - KDE)\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.ylabel(\"Y Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + "\n", + " plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust layout slightly for suptitle\n", + " plt.suptitle(\n", + " \"Comparison of Diffusion KDE vs Standard KDE for Rate Maps\", fontsize=14\n", + " )\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/edeno/miniconda3/envs/non_local_detector2/lib/python3.12/site-packages/non_local_detector/likelihoods/clusterless_kde.py:6: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", + " from tqdm.autonotebook import tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fitting Environment grid...\n", + "Environment fitting complete.\n", + "\n", + "Generating 5400 occupancy samples confined to track...\n" + ] + }, + { + "ename": "IndexError", + "evalue": "index 34 is out of bounds for axis 0 with size 23", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 275\u001b[0m\n\u001b[1;32m 270\u001b[0m chosen_bin_indices \u001b[38;5;241m=\u001b[39m valid_bin_indices[\n\u001b[1;32m 271\u001b[0m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(valid_bin_indices\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], size\u001b[38;5;241m=\u001b[39mN_POS_SAMPLES)\n\u001b[1;32m 272\u001b[0m ]\n\u001b[1;32m 273\u001b[0m \u001b[38;5;66;03m# Get center coordinates of chosen bins\u001b[39;00m\n\u001b[1;32m 274\u001b[0m bin_centers_y \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 275\u001b[0m \u001b[43my_edges_np\u001b[49m\u001b[43m[\u001b[49m\u001b[43mchosen_bin_indices\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m+\u001b[39m y_edges_np[chosen_bin_indices[:, \u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 276\u001b[0m ) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 277\u001b[0m bin_centers_x \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 278\u001b[0m x_edges_np[chosen_bin_indices[:, \u001b[38;5;241m1\u001b[39m]] \u001b[38;5;241m+\u001b[39m x_edges_np[chosen_bin_indices[:, \u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 279\u001b[0m ) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 280\u001b[0m \u001b[38;5;66;03m# Add small random jitter within the bin for more realistic positions\u001b[39;00m\n", + "\u001b[0;31mIndexError\u001b[0m: index 34 is out of bounds for axis 0 with size 23" + ] + } + ], + "source": [ + "from non_local_detector import Environment\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "from jax import jit\n", + "from functools import partial\n", + "import numpy as np\n", + "import math # Use standard math for n_steps calculation\n", + "import matplotlib.pyplot as plt\n", + "import time # For timing JIT\n", + "from typing import Tuple, Union # Added Union for potential future use\n", + "\n", + "# Assume the Environment class and its dependencies (get_centers, get_grid, etc.)\n", + "# are defined in a separate file (e.g., environment_utils.py) or directly above.\n", + "# from environment_utils import Environment, get_centers, get_grid # Example import\n", + "\n", + "\n", + "# --- Diffusion Simulation Helper Functions ---\n", + "# [PASTE THE CLEANED HELPER FUNCTIONS HERE: map_points_to_grid,\n", + "# calculate_laplacian_neumann, diffusion_step, run_diffusion]\n", + "@partial(jit, static_argnames=[\"grid_shape\"])\n", + "def map_points_to_grid(\n", + " points_xy: jnp.ndarray,\n", + " x_edges: jnp.ndarray,\n", + " y_edges: jnp.ndarray,\n", + " grid_shape: Tuple[int, int],\n", + ") -> jnp.ndarray:\n", + " n_bins_y, n_bins_x = grid_shape\n", + " x_indices = jnp.digitize(points_xy[:, 0], x_edges[1:-1])\n", + " y_indices = jnp.digitize(points_xy[:, 1], y_edges[1:-1])\n", + " x_indices = jnp.clip(x_indices, 0, n_bins_x - 1)\n", + " y_indices = jnp.clip(y_indices, 0, n_bins_y - 1)\n", + " initial_counts = jnp.zeros(grid_shape, dtype=jnp.float32)\n", + " try:\n", + " initial_counts = jax.ops.index_add(\n", + " initial_counts, jax.ops.index[y_indices, x_indices], 1.0\n", + " )\n", + " except AttributeError:\n", + " initial_counts = initial_counts.at[y_indices, x_indices].add(1.0)\n", + " return initial_counts\n", + "\n", + "\n", + "@jit\n", + "def calculate_laplacian_neumann(\n", + " grid: jnp.ndarray, interior_mask: jnp.ndarray\n", + ") -> jnp.ndarray:\n", + " gw = jnp.roll(grid, shift=1, axis=1)\n", + " ge = jnp.roll(grid, shift=-1, axis=1)\n", + " gs = jnp.roll(grid, shift=1, axis=0)\n", + " gn = jnp.roll(grid, shift=-1, axis=0)\n", + " mask_w = jnp.roll(interior_mask, shift=1, axis=1)\n", + " mask_e = jnp.roll(interior_mask, shift=-1, axis=1)\n", + " mask_s = jnp.roll(interior_mask, shift=1, axis=0)\n", + " mask_n = jnp.roll(interior_mask, shift=-1, axis=0)\n", + " gw = jnp.where(mask_w, gw, grid)\n", + " ge = jnp.where(mask_e, ge, grid)\n", + " gs = jnp.where(mask_s, gs, grid)\n", + " gn = jnp.where(mask_n, gn, grid)\n", + " lap_x = gw + ge - 2 * grid\n", + " lap_y = gs + gn - 2 * grid\n", + " return (lap_x + lap_y) * interior_mask\n", + "\n", + "\n", + "@partial(jit, static_argnames=[\"D_sim\", \"dt\"])\n", + "def diffusion_step(\n", + " grid: jnp.ndarray, D_sim: float, dt: float, interior_mask: jnp.ndarray\n", + ") -> jnp.ndarray:\n", + " laplacian = calculate_laplacian_neumann(grid, interior_mask)\n", + " new_grid = grid + dt * D_sim * laplacian\n", + " return new_grid * interior_mask\n", + "\n", + "\n", + "@partial(jit, static_argnames=[\"n_steps\", \"D_sim\", \"dt\"])\n", + "def run_diffusion(\n", + " initial_grid: jnp.ndarray,\n", + " n_steps: int,\n", + " D_sim: float,\n", + " dt: float,\n", + " interior_mask: jnp.ndarray,\n", + ") -> jnp.ndarray:\n", + " def scan_step(current_grid, _):\n", + " next_grid = diffusion_step(current_grid, D_sim, dt, interior_mask)\n", + " return next_grid, None\n", + "\n", + " final_grid, _ = jax.lax.scan(scan_step, initial_grid, xs=None, length=n_steps)\n", + " return final_grid\n", + "\n", + "\n", + "# --- Main Internal JIT-Compiled Calculation Function ---\n", + "# [PASTE THE CLEANED INTERNAL FUNCTION HERE: calculate_rate_map_diffusion_jax_internal]\n", + "@partial(jit, static_argnames=[\"grid_shape\", \"fs\", \"D_sim\", \"dt\", \"n_steps\"])\n", + "def calculate_rate_map_diffusion_jax_internal(\n", + " spike_pos_xy: jnp.ndarray,\n", + " occupancy_pos_xy: jnp.ndarray,\n", + " x_edges: jnp.ndarray,\n", + " y_edges: jnp.ndarray,\n", + " interior_mask: jnp.ndarray,\n", + " fs: float,\n", + " grid_shape: Tuple[int, int],\n", + " D_sim: float,\n", + " dt: float,\n", + " n_steps: int,\n", + ") -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:\n", + " n_bins_y, n_bins_x = grid_shape\n", + " initial_spike_counts = map_points_to_grid(\n", + " spike_pos_xy, x_edges, y_edges, grid_shape\n", + " )\n", + " initial_occupancy_counts = map_points_to_grid(\n", + " occupancy_pos_xy, x_edges, y_edges, grid_shape\n", + " )\n", + " initial_spike_counts *= interior_mask\n", + " initial_occupancy_counts *= interior_mask\n", + " diffused_spikes = run_diffusion(\n", + " initial_spike_counts, n_steps, D_sim, dt, interior_mask\n", + " )\n", + " diffused_occupancy = run_diffusion(\n", + " initial_occupancy_counts, n_steps, D_sim, dt, interior_mask\n", + " )\n", + " total_occupancy_counts_in = jnp.sum(initial_occupancy_counts)\n", + " total_spikes_in = jnp.sum(initial_spike_counts)\n", + " total_time_seconds = total_occupancy_counts_in / fs\n", + " sum_diffused_occ = jnp.sum(diffused_occupancy)\n", + " sum_diffused_spk = jnp.sum(diffused_spikes)\n", + " safe_sum_diffused_occ = sum_diffused_occ + 1e-12\n", + " safe_sum_diffused_spk = sum_diffused_spk + 1e-12\n", + " diffused_occupancy_time = (\n", + " diffused_occupancy / safe_sum_diffused_occ\n", + " ) * total_time_seconds\n", + " diffused_spike_counts_scaled = jnp.where(\n", + " total_spikes_in > 0,\n", + " (diffused_spikes / safe_sum_diffused_spk) * total_spikes_in,\n", + " jnp.zeros_like(diffused_spikes),\n", + " )\n", + " epsilon = 1e-9\n", + " safe_occupancy_time = jnp.maximum(diffused_occupancy_time, 0.0)\n", + " firing_rate_map = diffused_spike_counts_scaled / (safe_occupancy_time + epsilon)\n", + " firing_rate_map *= interior_mask\n", + " return firing_rate_map, diffused_spike_counts_scaled, diffused_occupancy_time\n", + "\n", + "\n", + "# --- User-Facing Wrapper Function ---\n", + "# [PASTE THE CLEANED WRAPPER FUNCTION HERE: calculate_rate_map_diffusion_jax]\n", + "def calculate_rate_map_diffusion_jax(\n", + " spike_pos_xy: Union[np.ndarray, jnp.ndarray],\n", + " occupancy_pos_xy: Union[np.ndarray, jnp.ndarray],\n", + " x_edges: Union[np.ndarray, jnp.ndarray],\n", + " y_edges: Union[np.ndarray, jnp.ndarray],\n", + " interior_mask: Union[np.ndarray, jnp.ndarray],\n", + " fs: float,\n", + " grid_shape: Tuple[int, int],\n", + " grid_res: float,\n", + " sigma_cm: float = 5.0,\n", + " dt_stability_factor: float = 0.2,\n", + " verbose: bool = True,\n", + ") -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:\n", + " if sigma_cm <= 0:\n", + " raise ValueError(\"sigma_cm must be positive\")\n", + " CM_PER_METER = 100.0\n", + " res_cm = grid_res * CM_PER_METER\n", + " sigma_bins = sigma_cm / res_cm\n", + " D_sim = 1.0\n", + " T_sim = (sigma_bins**2) / (2.0 * D_sim)\n", + " max_stable_dt = 1.0 / (4.0 * D_sim)\n", + " dt = float(dt_stability_factor * max_stable_dt)\n", + " n_steps = max(1, math.ceil(T_sim / dt))\n", + " if verbose:\n", + " print(\n", + " f\"Target sigma={sigma_cm:.2f} cm ({sigma_bins:.2f} bins). Using D_sim={D_sim:.1f} bins^2/unit_time.\"\n", + " )\n", + " print(\n", + " f\"Required T_sim={T_sim:.2f} unit_time. Calculated dt={dt:.4f}, N_steps={n_steps}\"\n", + " )\n", + " fs_static = float(fs)\n", + " static_args_for_internal = {\n", + " \"fs\": fs_static,\n", + " \"grid_shape\": grid_shape,\n", + " \"D_sim\": D_sim,\n", + " \"dt\": dt,\n", + " \"n_steps\": n_steps,\n", + " }\n", + " compiled_internal_func = jit(\n", + " partial(calculate_rate_map_diffusion_jax_internal, **static_args_for_internal)\n", + " )\n", + " start_time = time.time()\n", + " result = compiled_internal_func(\n", + " jnp.asarray(spike_pos_xy),\n", + " jnp.asarray(occupancy_pos_xy),\n", + " jnp.asarray(x_edges),\n", + " jnp.asarray(y_edges),\n", + " jnp.asarray(interior_mask, dtype=bool),\n", + " )\n", + " result[0].block_until_ready()\n", + " end_time = time.time()\n", + " if verbose:\n", + " print(f\"JAX calculation took: {end_time - start_time:.4f} seconds\")\n", + " return result\n", + "\n", + "\n", + "# --- Example Usage ---\n", + "\n", + "if __name__ == \"__main__\":\n", + " # Use NumPy for data generation, then pass to the JAX function\n", + " np.random.seed(42)\n", + "\n", + " # --- 1. Define Grid and Simulation Parameters ---\n", + " GRID_RESOLUTION_M_PER_BIN = 0.05 # meters per bin\n", + " X_MIN, X_MAX = 0, 2\n", + " Y_MIN, Y_MAX = 0, 1\n", + " POSITION_RANGE = [(X_MIN, X_MAX), (Y_MIN, Y_MAX)] # Define position range\n", + "\n", + " DURATION_SECONDS = 180\n", + " FS_HZ = 30 # Sampling rate (Hz)\n", + "\n", + " SIGMA_CM = 5.0 # Target Gaussian sigma in cm for smoothing\n", + "\n", + " # --- 2. Generate Dummy Positional Data ---\n", + " N_POS_SAMPLES = int(DURATION_SECONDS * FS_HZ)\n", + " N_INITIAL_POINTS = N_POS_SAMPLES * 10 # Generate more points for rejection sampling\n", + "\n", + " # Create plausible random walk data first (might wander outside track initially)\n", + " # This data will be used to *fit* the environment's grid and interior mask\n", + " rw_positions = np.random.rand(N_INITIAL_POINTS, 2) * np.array([X_MAX, Y_MAX])\n", + " # Apply some smoothing or realistic movement model if needed,\n", + " # but simple random is okay for fitting the grid extent.\n", + "\n", + " # --- 3. Create and Fit Environment using the Environment Class ---\n", + " print(\"\\nFitting Environment grid...\")\n", + " environment = Environment(\n", + " place_bin_size=GRID_RESOLUTION_M_PER_BIN,\n", + " position_range=POSITION_RANGE,\n", + " infer_track_interior=True, # Infer from positions\n", + " bin_count_threshold=0, # Consider bin occupied if >0 samples fall in it\n", + " fill_holes=True, # Optional: Fill small holes in mask\n", + " dilate=False, # Optional: Expand mask slightly\n", + " )\n", + " # Fit the environment using the generated position data\n", + " # NOTE: fit_place_grid needs position data to infer grid and potentially interior\n", + " environment.fit_place_grid(position=rw_positions)\n", + " print(\"Environment fitting complete.\")\n", + "\n", + " # --- Extract necessary parameters from environment ---\n", + " x_edges_np = environment.edges_[0]\n", + " y_edges_np = environment.edges_[1]\n", + " # Ensure the interior mask is boolean\n", + " is_track_interior_np = environment.is_track_interior_.astype(bool)\n", + " grid_shape = environment.centers_shape_ # Should be (n_bins_y, n_bins_x)\n", + "\n", + " # --- 4. Generate Realistic Occupancy Data (Confined to Track) ---\n", + " # Now that the track interior is defined, generate occupancy data *within* it.\n", + " print(f\"\\nGenerating {N_POS_SAMPLES} occupancy samples confined to track...\")\n", + " # Efficiently sample points only within the track interior bins\n", + " valid_bin_indices = np.argwhere(\n", + " is_track_interior_np\n", + " ) # Get indices (y, x) of valid bins\n", + " if valid_bin_indices.shape[0] == 0:\n", + " raise ValueError(\"No valid interior bins found in the environment mask!\")\n", + " # Randomly choose valid bins to place points in\n", + " chosen_bin_indices = valid_bin_indices[\n", + " np.random.randint(valid_bin_indices.shape[0], size=N_POS_SAMPLES)\n", + " ]\n", + " # Get center coordinates of chosen bins\n", + " bin_centers_y = (\n", + " y_edges_np[chosen_bin_indices[:, 0]] + y_edges_np[chosen_bin_indices[:, 0] + 1]\n", + " ) / 2\n", + " bin_centers_x = (\n", + " x_edges_np[chosen_bin_indices[:, 1]] + x_edges_np[chosen_bin_indices[:, 1] + 1]\n", + " ) / 2\n", + " # Add small random jitter within the bin for more realistic positions\n", + " jitter_x = (np.random.rand(N_POS_SAMPLES) - 0.5) * GRID_RESOLUTION_M_PER_BIN\n", + " jitter_y = (np.random.rand(N_POS_SAMPLES) - 0.5) * GRID_RESOLUTION_M_PER_BIN\n", + " animal_positions_np = np.stack(\n", + " [bin_centers_x + jitter_x, bin_centers_y + jitter_y], axis=1\n", + " )\n", + " # Ensure points stay roughly within bin edges (optional clipping)\n", + " animal_positions_np[:, 0] = np.clip(animal_positions_np[:, 0], X_MIN, X_MAX)\n", + " animal_positions_np[:, 1] = np.clip(animal_positions_np[:, 1], Y_MIN, Y_MAX)\n", + "\n", + " # Generate corresponding timestamps\n", + " position_timestamps_np = np.linspace(0, N_POS_SAMPLES / FS_HZ, N_POS_SAMPLES)\n", + " print(\n", + " f\"Generated {len(animal_positions_np)} occupancy position samples within the track.\"\n", + " )\n", + "\n", + " # --- 5. Generate Dummy Spike Data (Based on confined animal positions) ---\n", + " TRACK_WIDTH_M = 0.3 # Used again for place field definition\n", + " PLACE_FIELD_CENTER_M = np.array([X_MAX - TRACK_WIDTH_M, TRACK_WIDTH_M / 2])\n", + " PLACE_FIELD_RADIUS_M = 0.2\n", + " BASE_FIRING_RATE_HZ = 0.1\n", + " PEAK_FIRING_RATE_HZ = 20.0\n", + "\n", + " distances_m = np.linalg.norm(animal_positions_np - PLACE_FIELD_CENTER_M, axis=1)\n", + " prob_spike = BASE_FIRING_RATE_HZ / FS_HZ + (PEAK_FIRING_RATE_HZ / FS_HZ) * np.exp(\n", + " -(distances_m**2) / (2 * PLACE_FIELD_RADIUS_M**2)\n", + " )\n", + " spike_flags = np.random.rand(len(animal_positions_np)) < prob_spike\n", + " spike_times_np = position_timestamps_np[spike_flags]\n", + " spike_positions_np = animal_positions_np[spike_flags, :]\n", + " print(f\"Generated {len(spike_positions_np)} spikes.\")\n", + "\n", + " # --- 6. Run Diffusion Calculation ---\n", + " print(\"\\nCalculating rate map (Diffusion method)...\")\n", + " # Pass NumPy arrays - the wrapper function converts them\n", + " rate_map_diff, diffused_spikes, diffused_occupancy = (\n", + " calculate_rate_map_diffusion_jax(\n", + " spike_positions_np,\n", + " animal_positions_np,\n", + " x_edges_np,\n", + " y_edges_np,\n", + " is_track_interior_np,\n", + " fs=FS_HZ,\n", + " grid_shape=grid_shape,\n", + " grid_res=GRID_RESOLUTION_M_PER_BIN,\n", + " sigma_cm=SIGMA_CM,\n", + " dt_stability_factor=0.2,\n", + " verbose=True,\n", + " )\n", + " )\n", + " # Convert results to NumPy for plotting/further analysis if needed\n", + " rate_map_diff_np = np.array(rate_map_diff)\n", + " diffused_spikes_np = np.array(diffused_spikes)\n", + " diffused_occupancy_np = np.array(diffused_occupancy)\n", + "\n", + " # --- 7. Plotting ---\n", + " print(\"\\nPlotting results...\")\n", + " plt.figure(figsize=(12, 5.5)) # Adjusted size for fewer plots initially\n", + "\n", + " # Subplot 1: Diffusion Rate Map\n", + " plt.subplot(1, 2, 1) # Changed subplot index\n", + " plot_rate_diff = np.where(is_track_interior_np, rate_map_diff_np, np.nan)\n", + " valid_rates_diff = plot_rate_diff[~np.isnan(plot_rate_diff)]\n", + " if valid_rates_diff.size > 0:\n", + " vmax_diff = np.percentile(valid_rates_diff, 99)\n", + " else:\n", + " print(\"Warning: No valid diffusion rate values found for vmax calculation.\")\n", + " vmax_diff = 1.0\n", + " vmax_diff = max(vmax_diff, 1.0)\n", + "\n", + " pcm1 = plt.pcolormesh(\n", + " x_edges_np,\n", + " y_edges_np,\n", + " plot_rate_diff,\n", + " shading=\"auto\",\n", + " cmap=\"jet\",\n", + " vmin=0,\n", + " vmax=vmax_diff,\n", + " )\n", + " plt.colorbar(pcm1, label=\"Firing Rate (Hz)\")\n", + " plt.plot(\n", + " spike_positions_np[:, 0],\n", + " spike_positions_np[:, 1],\n", + " \"k.\",\n", + " markersize=1,\n", + " alpha=0.2,\n", + " )\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(f\"Rate Map (Diffusion ~{SIGMA_CM}cm)\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.ylabel(\"Y Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + "\n", + " # Subplot 2: Track Interior from Environment Class\n", + " plt.subplot(1, 2, 2) # Changed subplot index\n", + " plt.pcolormesh(\n", + " x_edges_np,\n", + " y_edges_np,\n", + " is_track_interior_np.astype(float),\n", + " cmap=\"binary\",\n", + " vmin=0,\n", + " vmax=1,\n", + " )\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(\"Environment Track Interior\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.ylabel(\"Y Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + "\n", + " plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust layout\n", + " plt.suptitle(\"Diffusion Rate Map Calculated using Environment Grid\", fontsize=14)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating initial data for environment fitting...\n", + "\n", + "Fitting Environment grid...\n", + "Using manual grid definition for example...\n", + "Environment fitting complete.\n", + "Grid shape: (20, 40)\n", + "\n", + "Calculating simulation parameters...\n", + "Target sigma=5.00 cm (1.00 bins). Using D_sim=1.0 bins^2/unit_time.\n", + "Required T_sim=0.50 unit_time. Calculated dt=0.0500, N_steps=10\n", + "Starting diffusion kernel precomputation...\n", + " Running 324 diffusion simulations via vmap (will JIT compile internal function)...\n", + "Kernel precomputation took: 0.1091 seconds\n", + " Kernel Matrix shape: (800, 324), Memory: 0.001 GB\n", + "\n", + "Generating occupancy samples confined to track...\n", + "Generated 5400 occupancy position samples within the track.\n", + "\n", + "Generating spikes for 10 neurons...\n", + " Neuron 1: Generated 607 spikes.\n", + " Neuron 2: Generated 606 spikes.\n", + " Neuron 3: Generated 408 spikes.\n", + " Neuron 4: Generated 526 spikes.\n", + " Neuron 5: Generated 728 spikes.\n", + " Neuron 6: Generated 554 spikes.\n", + " Neuron 7: Generated 914 spikes.\n", + " Neuron 8: Generated 597 spikes.\n", + " Neuron 9: Generated 694 spikes.\n", + " Neuron 10: Generated 486 spikes.\n", + "\n", + "Calculating initial and smoothed occupancy...\n", + "Normalizing occupancy...\n", + "\n", + "Processing neurons...\n", + " Processing Neuron 1/10...\n", + " Processing Neuron 2/10...\n", + " Processing Neuron 3/10...\n", + " Processing Neuron 4/10...\n", + " Processing Neuron 5/10...\n", + " Processing Neuron 6/10...\n", + " Processing Neuron 7/10...\n", + " Processing Neuron 8/10...\n", + " Processing Neuron 9/10...\n", + " Processing Neuron 10/10...\n", + "Processing 10 neurons took: 1.6800 seconds (after precomputation)\n", + "\n", + "Plotting example result (Neuron 1)...\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import time\n", + "import math\n", + "from functools import partial\n", + "from typing import Tuple, Union, Dict\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "from jax import jit\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Import for standard KDE comparison if used later\n", + "# from sklearn.neighbors import KernelDensity\n", + "# Assume Environment class is defined elsewhere or pasted above\n", + "# from environment_utils import Environment # Example\n", + "\n", + "\n", + "# --- Diffusion Simulation Helper Functions ---\n", + "# [map_points_to_grid, calculate_laplacian_neumann remain unchanged]\n", + "@partial(jit, static_argnames=[\"grid_shape\"])\n", + "def map_points_to_grid(\n", + " points_xy: jnp.ndarray,\n", + " x_edges: jnp.ndarray,\n", + " y_edges: jnp.ndarray,\n", + " grid_shape: Tuple[int, int],\n", + ") -> jnp.ndarray:\n", + " n_bins_y, n_bins_x = grid_shape\n", + " x_indices = jnp.digitize(points_xy[:, 0], x_edges[1:-1])\n", + " y_indices = jnp.digitize(points_xy[:, 1], y_edges[1:-1])\n", + " x_indices = jnp.clip(x_indices, 0, n_bins_x - 1)\n", + " y_indices = jnp.clip(y_indices, 0, n_bins_y - 1)\n", + " initial_counts = jnp.zeros(grid_shape, dtype=jnp.float32)\n", + " try:\n", + " initial_counts = jax.ops.index_add(\n", + " initial_counts, jax.ops.index[y_indices, x_indices], 1.0\n", + " )\n", + " except AttributeError:\n", + " initial_counts = initial_counts.at[y_indices, x_indices].add(1.0)\n", + " return initial_counts\n", + "\n", + "\n", + "@jit\n", + "def calculate_laplacian_neumann(\n", + " grid: jnp.ndarray, interior_mask: jnp.ndarray\n", + ") -> jnp.ndarray:\n", + " gw = jnp.roll(grid, shift=1, axis=1)\n", + " ge = jnp.roll(grid, shift=-1, axis=1)\n", + " gs = jnp.roll(grid, shift=1, axis=0)\n", + " gn = jnp.roll(grid, shift=-1, axis=0)\n", + " mask_w = jnp.roll(interior_mask, shift=1, axis=1)\n", + " mask_e = jnp.roll(interior_mask, shift=-1, axis=1)\n", + " mask_s = jnp.roll(interior_mask, shift=1, axis=0)\n", + " mask_n = jnp.roll(interior_mask, shift=-1, axis=0)\n", + " gw = jnp.where(mask_w, gw, grid)\n", + " ge = jnp.where(mask_e, ge, grid)\n", + " gs = jnp.where(mask_s, gs, grid)\n", + " gn = jnp.where(mask_n, gn, grid)\n", + " lap_x = gw + ge - 2 * grid\n", + " lap_y = gs + gn - 2 * grid\n", + " return (lap_x + lap_y) * interior_mask\n", + "\n", + "\n", + "@partial(jit, static_argnames=[\"D_sim\", \"dt\"])\n", + "def diffusion_step(\n", + " grid: jnp.ndarray, D_sim: float, dt: float, interior_mask: jnp.ndarray\n", + ") -> jnp.ndarray:\n", + " laplacian = calculate_laplacian_neumann(grid, interior_mask)\n", + " new_grid = grid + dt * D_sim * laplacian\n", + " return new_grid * interior_mask\n", + "\n", + "\n", + "@partial(jit, static_argnames=[\"n_steps\", \"D_sim\", \"dt\"])\n", + "def run_diffusion(\n", + " initial_grid: jnp.ndarray,\n", + " n_steps: int,\n", + " D_sim: float,\n", + " dt: float,\n", + " interior_mask: jnp.ndarray,\n", + ") -> jnp.ndarray:\n", + " def scan_step(current_grid, _):\n", + " next_grid = diffusion_step(current_grid, D_sim, dt, interior_mask)\n", + " return next_grid, None\n", + "\n", + " final_grid, _ = jax.lax.scan(scan_step, initial_grid, xs=None, length=n_steps)\n", + " return final_grid\n", + "\n", + "\n", + "# --- NEW: Precomputation and Application Functions ---\n", + "\n", + "\n", + "# ***** CHANGE: Added JIT decorator with static_argnames *****\n", + "@partial(jit, static_argnames=[\"grid_shape\", \"n_steps\", \"D_sim\", \"dt\"])\n", + "def _run_diffusion_from_source(\n", + " source_y_idx: int, # Dynamic input for vmap\n", + " source_x_idx: int, # Dynamic input for vmap\n", + " # Static arguments below (broadcasted by vmap using None in in_axes)\n", + " grid_shape: Tuple[int, int],\n", + " n_steps: int,\n", + " D_sim: float,\n", + " dt: float,\n", + " interior_mask: jnp.ndarray, # Remains a JAX array, used dynamically inside\n", + ") -> jnp.ndarray:\n", + " \"\"\"Helper: Runs diffusion from a single source bin, returns flattened result.\n", + " JIT compiled, grid_shape/n_steps/D_sim/dt must be static.\"\"\"\n", + " # grid_shape is now static, so jnp.zeros knows the shape at compile time\n", + " initial_grid = (\n", + " jnp.zeros(grid_shape, dtype=jnp.float32).at[source_y_idx, source_x_idx].set(1.0)\n", + " )\n", + " initial_grid *= interior_mask # Ensure source is valid\n", + " final_grid = run_diffusion(initial_grid, n_steps, D_sim, dt, interior_mask)\n", + " return final_grid.ravel()\n", + "\n", + "\n", + "# ***********************************************************\n", + "\n", + "\n", + "def precompute_diffusion_kernel(\n", + " grid_shape: Tuple[int, int],\n", + " interior_mask: Union[np.ndarray, jnp.ndarray],\n", + " D_sim: float,\n", + " dt: float,\n", + " n_steps: int,\n", + " verbose: bool = True,\n", + ") -> Tuple[jnp.ndarray, jnp.ndarray]:\n", + " \"\"\"Precomputes the diffusion kernel matrix using jax.vmap.\"\"\"\n", + " if verbose:\n", + " print(\"Starting diffusion kernel precomputation...\")\n", + " start_time = time.time()\n", + "\n", + " interior_mask_jax = jnp.asarray(interior_mask, dtype=bool)\n", + " if interior_mask_jax.shape != grid_shape:\n", + " raise ValueError(\n", + " f\"Shape mismatch: grid_shape={grid_shape}, interior_mask shape={interior_mask.shape}\"\n", + " )\n", + "\n", + " # Find the 2D indices (y, x) of valid interior bins\n", + " # Use size=interior_mask_jax.sum() for static shape guarantee needed by JIT sometimes\n", + " valid_indices_yx = jnp.argwhere(\n", + " interior_mask_jax, size=interior_mask_jax.sum(), fill_value=-1\n", + " )\n", + " # Filter out potential fill_values if mask is not full (although argwhere shouldn't need fill_value here?)\n", + " valid_indices_yx = valid_indices_yx[valid_indices_yx[:, 0] != -1] # Basic check\n", + "\n", + " if valid_indices_yx.shape[0] == 0:\n", + " raise ValueError(\"No valid interior bins found in the provided mask.\")\n", + "\n", + " # Vectorize the simulation function over the source coordinates (y, x)\n", + " # Arguments for vmap correspond to _run_diffusion_from_source:\n", + " # (source_y, source_x, grid_shape, n_steps, D_sim, dt, interior_mask)\n", + " # Map over first two (y, x), broadcast the rest (None)\n", + " vmapped_diffusion = jax.vmap(\n", + " _run_diffusion_from_source,\n", + " in_axes=(0, 0, None, None, None, None, None),\n", + " out_axes=1, # Stack results column-wise\n", + " )\n", + "\n", + " # Define static args tuple for the call\n", + " # Note: _run_diffusion_from_source is already jitted with these being static\n", + " # We pass them as regular args here, vmap broadcasts them.\n", + " sim_args = (grid_shape, n_steps, D_sim, dt, interior_mask_jax)\n", + "\n", + " # Execute the vectorized computation\n", + " # No need to jit vmapped_diffusion again, as the inner func is jitted.\n", + " print(\n", + " f\" Running {valid_indices_yx.shape[0]} diffusion simulations via vmap (will JIT compile internal function)...\"\n", + " )\n", + " Kernel_Matrix_flat_cols = vmapped_diffusion(\n", + " valid_indices_yx[:, 0], # All source y coords\n", + " valid_indices_yx[:, 1], # All source x coords\n", + " *sim_args, # Pass the other args to be broadcasted\n", + " )\n", + " Kernel_Matrix_flat_cols.block_until_ready() # Wait for computation\n", + " end_time = time.time()\n", + "\n", + " Kernel_Matrix = Kernel_Matrix_flat_cols\n", + "\n", + " # Calculate the flat indices corresponding to the valid (y, x) source bins\n", + " valid_bin_indices_flat = jnp.ravel_multi_index(\n", + " (valid_indices_yx[:, 0], valid_indices_yx[:, 1]),\n", + " grid_shape,\n", + " mode=\"clip\", # Use clip just in case\n", + " )\n", + "\n", + " if verbose:\n", + " print(f\"Kernel precomputation took: {end_time - start_time:.4f} seconds\")\n", + " mem_gb = Kernel_Matrix.nbytes / (1024**3)\n", + " print(f\" Kernel Matrix shape: {Kernel_Matrix.shape}, Memory: {mem_gb:.3f} GB\")\n", + "\n", + " return Kernel_Matrix, valid_bin_indices_flat\n", + "\n", + "\n", + "# [Functions: apply_diffusion_kernel, normalize_density - remain unchanged]\n", + "@partial(jit, static_argnames=[\"grid_shape\"])\n", + "def apply_diffusion_kernel(\n", + " Kernel_Matrix: jnp.ndarray,\n", + " initial_counts: jnp.ndarray,\n", + " grid_shape: Tuple[int, int],\n", + " valid_bin_indices_flat: jnp.ndarray,\n", + ") -> jnp.ndarray:\n", + " n_bins_y, n_bins_x = grid_shape\n", + " initial_counts_flat = initial_counts.ravel()\n", + " valid_initial_counts = initial_counts_flat[valid_bin_indices_flat]\n", + " smoothed_flat = Kernel_Matrix @ valid_initial_counts\n", + " return smoothed_flat.reshape(grid_shape)\n", + "\n", + "\n", + "@partial(jit, static_argnames=[\"is_occupancy\"])\n", + "def normalize_density(\n", + " diffused_map: jnp.ndarray,\n", + " initial_counts_map: jnp.ndarray,\n", + " total_value: float,\n", + " fs: float,\n", + " is_occupancy: bool,\n", + " epsilon: float = 1e-12,\n", + ") -> jnp.ndarray:\n", + " total_initial_counts = jnp.sum(initial_counts_map)\n", + " sum_diffused = jnp.sum(diffused_map)\n", + " safe_sum_diffused = sum_diffused + epsilon\n", + " if is_occupancy:\n", + " total_time_seconds = total_initial_counts / jnp.maximum(fs, 1e-9)\n", + " normalized_map = (diffused_map / safe_sum_diffused) * total_time_seconds\n", + " return jnp.maximum(normalized_map, 0.0)\n", + " else:\n", + " normalized_map = jnp.where(\n", + " total_initial_counts > 0,\n", + " (diffused_map / safe_sum_diffused) * total_initial_counts,\n", + " jnp.zeros_like(diffused_map),\n", + " )\n", + " return normalized_map\n", + "\n", + "\n", + "# --- Main Script Logic (including wrapper and example) ---\n", + "# [Wrapper function `calculate_rate_map_diffusion_jax` remains unchanged]\n", + "def calculate_rate_map_diffusion_jax(\n", + " spike_pos_xy: Union[np.ndarray, jnp.ndarray],\n", + " occupancy_pos_xy: Union[np.ndarray, jnp.ndarray],\n", + " x_edges: Union[np.ndarray, jnp.ndarray],\n", + " y_edges: Union[np.ndarray, jnp.ndarray],\n", + " interior_mask: Union[np.ndarray, jnp.ndarray],\n", + " fs: float,\n", + " grid_shape: Tuple[int, int],\n", + " grid_res: float,\n", + " sigma_cm: float = 5.0,\n", + " dt_stability_factor: float = 0.2,\n", + " verbose: bool = True,\n", + ") -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:\n", + " if sigma_cm <= 0:\n", + " raise ValueError(\"sigma_cm must be positive\")\n", + " CM_PER_METER = 100.0\n", + " res_cm = grid_res * CM_PER_METER\n", + " sigma_bins = sigma_cm / res_cm\n", + " D_sim = 1.0\n", + " T_sim = (sigma_bins**2) / (2.0 * D_sim)\n", + " max_stable_dt = 1.0 / (4.0 * D_sim)\n", + " dt = float(dt_stability_factor * max_stable_dt)\n", + " n_steps = max(1, math.ceil(T_sim / dt))\n", + " if verbose:\n", + " print(\n", + " f\"Target sigma={sigma_cm:.2f} cm ({sigma_bins:.2f} bins). Using D_sim={D_sim:.1f} bins^2/unit_time.\"\n", + " )\n", + " print(\n", + " f\"Required T_sim={T_sim:.2f} unit_time. Calculated dt={dt:.4f}, N_steps={n_steps}\"\n", + " )\n", + " fs_static = float(fs)\n", + " static_args_for_internal = {\n", + " \"fs\": fs_static,\n", + " \"grid_shape\": grid_shape,\n", + " \"D_sim\": D_sim,\n", + " \"dt\": dt,\n", + " \"n_steps\": n_steps,\n", + " }\n", + " compiled_internal_func = jit(\n", + " partial(calculate_rate_map_diffusion_jax_internal, **static_args_for_internal)\n", + " )\n", + " start_time = time.time()\n", + " result = compiled_internal_func(\n", + " jnp.asarray(spike_pos_xy),\n", + " jnp.asarray(occupancy_pos_xy),\n", + " jnp.asarray(x_edges),\n", + " jnp.asarray(y_edges),\n", + " jnp.asarray(interior_mask, dtype=bool),\n", + " )\n", + " result[0].block_until_ready()\n", + " end_time = time.time()\n", + " if verbose:\n", + " print(f\"JAX calculation took: {end_time - start_time:.4f} seconds\")\n", + " return result\n", + "\n", + "\n", + "# [Example usage `if __name__ == \"__main__\":` block remains largely the same,\n", + "# but uses the precomputation functions]\n", + "\n", + "if __name__ == \"__main__\":\n", + " np.random.seed(42)\n", + "\n", + " # --- 1. Define Grid and Simulation Parameters ---\n", + " GRID_RESOLUTION_M_PER_BIN = 0.05\n", + " X_MIN, X_MAX = 0, 2\n", + " Y_MIN, Y_MAX = 0, 1\n", + " POSITION_RANGE = [(X_MIN, X_MAX), (Y_MIN, Y_MAX)]\n", + " DURATION_SECONDS = 180\n", + " FS_HZ = 30\n", + " SIGMA_CM = 5.0\n", + " DT_STABILITY_FACTOR = 0.2\n", + " VERBOSE = True\n", + "\n", + " # --- 2. Fit Environment & Get Grid Params ---\n", + " print(\"Generating initial data for environment fitting...\")\n", + " N_FIT_POINTS = 20000\n", + " fit_positions_np = np.random.rand(N_FIT_POINTS, 2) * np.array([X_MAX, Y_MAX])\n", + " print(\"\\nFitting Environment grid...\")\n", + " # environment = Environment(...) # Assuming Environment class is defined/imported\n", + " # environment.fit_place_grid(position=fit_positions_np)\n", + " # --- Use manual grid def for now if Environment class isn't available ---\n", + " print(\"Using manual grid definition for example...\")\n", + " n_bins_x = int((X_MAX - X_MIN) / GRID_RESOLUTION_M_PER_BIN)\n", + " n_bins_y = int((Y_MAX - Y_MIN) / GRID_RESOLUTION_M_PER_BIN)\n", + " grid_shape = (n_bins_y, n_bins_x)\n", + " x_edges_np = np.linspace(X_MIN, X_MAX, n_bins_x + 1)\n", + " y_edges_np = np.linspace(Y_MIN, Y_MAX, n_bins_y + 1)\n", + " bin_centers_x_np = (x_edges_np[:-1] + x_edges_np[1:]) / 2\n", + " bin_centers_y_np = (y_edges_np[:-1] + y_edges_np[1:]) / 2\n", + " bin_centers_xx_np, bin_centers_yy_np = np.meshgrid(\n", + " bin_centers_x_np, bin_centers_y_np\n", + " )\n", + " TRACK_WIDTH_M = 0.3\n", + " is_bottom_arm = np.abs(bin_centers_yy_np - TRACK_WIDTH_M / 2) < TRACK_WIDTH_M / 2\n", + " is_left_arm = np.abs(bin_centers_xx_np - TRACK_WIDTH_M / 2) < TRACK_WIDTH_M / 2\n", + " is_right_arm = (\n", + " np.abs(bin_centers_xx_np - (X_MAX - TRACK_WIDTH_M / 2)) < TRACK_WIDTH_M / 2\n", + " )\n", + " is_track_interior_np = is_bottom_arm | is_left_arm | is_right_arm\n", + " is_top_part = bin_centers_yy_np > (Y_MAX - TRACK_WIDTH_M * 1.1)\n", + " remove_mask = (is_left_arm & is_top_part) | (is_right_arm & is_top_part)\n", + " is_track_interior_np[remove_mask] = False\n", + " # --- End manual grid def ---\n", + " print(\"Environment fitting complete.\")\n", + " print(f\"Grid shape: {grid_shape}\")\n", + "\n", + " # --- 3. Calculate Simulation Parameters ---\n", + " print(\"\\nCalculating simulation parameters...\")\n", + " CM_PER_METER = 100.0\n", + " res_cm = GRID_RESOLUTION_M_PER_BIN * CM_PER_METER\n", + " sigma_bins = SIGMA_CM / res_cm\n", + " D_sim = 1.0\n", + " T_sim = (sigma_bins**2) / (2.0 * D_sim)\n", + " max_stable_dt = 1.0 / (4.0 * D_sim)\n", + " dt = float(DT_STABILITY_FACTOR * max_stable_dt)\n", + " n_steps = max(1, math.ceil(T_sim / dt))\n", + " if VERBOSE:\n", + " print(\n", + " f\"Target sigma={SIGMA_CM:.2f} cm ({sigma_bins:.2f} bins). Using D_sim={D_sim:.1f} bins^2/unit_time.\"\n", + " )\n", + " print(\n", + " f\"Required T_sim={T_sim:.2f} unit_time. Calculated dt={dt:.4f}, N_steps={n_steps}\"\n", + " )\n", + "\n", + " # --- 4. Precompute Diffusion Kernel ---\n", + " is_track_interior_jax = jnp.asarray(is_track_interior_np)\n", + " Kernel_Matrix, valid_bin_indices_flat = precompute_diffusion_kernel(\n", + " grid_shape, is_track_interior_jax, D_sim, dt, n_steps, verbose=VERBOSE\n", + " )\n", + "\n", + " # --- 5. Generate Occupancy and Spike Data ---\n", + " print(f\"\\nGenerating occupancy samples confined to track...\")\n", + " N_POS_SAMPLES = int(DURATION_SECONDS * FS_HZ)\n", + " valid_bin_indices_np = np.argwhere(is_track_interior_np)\n", + " if valid_bin_indices_np.shape[0] == 0:\n", + " raise ValueError(\"No valid interior bins found!\")\n", + " chosen_bin_indices = valid_bin_indices_np[\n", + " np.random.randint(valid_bin_indices_np.shape[0], size=N_POS_SAMPLES)\n", + " ]\n", + " bin_centers_y = (\n", + " y_edges_np[chosen_bin_indices[:, 0]] + y_edges_np[chosen_bin_indices[:, 0] + 1]\n", + " ) / 2\n", + " bin_centers_x = (\n", + " x_edges_np[chosen_bin_indices[:, 1]] + x_edges_np[chosen_bin_indices[:, 1] + 1]\n", + " ) / 2\n", + " jitter_x = (np.random.rand(N_POS_SAMPLES) - 0.5) * GRID_RESOLUTION_M_PER_BIN\n", + " jitter_y = (np.random.rand(N_POS_SAMPLES) - 0.5) * GRID_RESOLUTION_M_PER_BIN\n", + " animal_positions_np = np.stack(\n", + " [bin_centers_x + jitter_x, bin_centers_y + jitter_y], axis=1\n", + " )\n", + " animal_positions_np[:, 0] = np.clip(animal_positions_np[:, 0], X_MIN, X_MAX)\n", + " animal_positions_np[:, 1] = np.clip(animal_positions_np[:, 1], Y_MIN, Y_MAX)\n", + " position_timestamps_np = np.linspace(0, N_POS_SAMPLES / FS_HZ, N_POS_SAMPLES)\n", + " print(\n", + " f\"Generated {len(animal_positions_np)} occupancy position samples within the track.\"\n", + " )\n", + "\n", + " N_NEURONS = 10\n", + " BASE_FIRING_RATE_HZ = 0.1\n", + " spike_positions_list = []\n", + " print(f\"\\nGenerating spikes for {N_NEURONS} neurons...\")\n", + " for n in range(N_NEURONS):\n", + " pf_radius = 0.15 + np.random.rand() * 0.1\n", + " pf_center_idx = valid_bin_indices_np[\n", + " np.random.randint(valid_bin_indices_np.shape[0])\n", + " ]\n", + " pf_center_m = np.array(\n", + " [\n", + " (x_edges_np[pf_center_idx[1]] + x_edges_np[pf_center_idx[1] + 1]) / 2,\n", + " (y_edges_np[pf_center_idx[0]] + y_edges_np[pf_center_idx[0] + 1]) / 2,\n", + " ]\n", + " )\n", + " peak_rate = 15 + np.random.rand() * 10\n", + " distances_m = np.linalg.norm(animal_positions_np - pf_center_m, axis=1)\n", + " prob_spike = BASE_FIRING_RATE_HZ / FS_HZ + (peak_rate / FS_HZ) * np.exp(\n", + " -(distances_m**2) / (2 * pf_radius**2)\n", + " )\n", + " spike_flags = np.random.rand(len(animal_positions_np)) < prob_spike\n", + " spike_positions_neuron = animal_positions_np[spike_flags, :]\n", + " spike_positions_list.append(spike_positions_neuron)\n", + " print(f\" Neuron {n+1}: Generated {len(spike_positions_neuron)} spikes.\")\n", + "\n", + " # --- 6. Calculate Smoothed Occupancy (Once) ---\n", + " print(\"\\nCalculating initial and smoothed occupancy...\")\n", + " x_edges_jax = jnp.asarray(x_edges_np)\n", + " y_edges_jax = jnp.asarray(y_edges_np)\n", + " initial_occupancy_counts = map_points_to_grid(\n", + " jnp.asarray(animal_positions_np), x_edges_jax, y_edges_jax, grid_shape\n", + " )\n", + " initial_occupancy_counts *= is_track_interior_jax\n", + " smoothed_occupancy = apply_diffusion_kernel(\n", + " Kernel_Matrix, initial_occupancy_counts, grid_shape, valid_bin_indices_flat\n", + " )\n", + "\n", + " # --- 7. Normalize Occupancy (Once) ---\n", + " print(\"Normalizing occupancy...\")\n", + " fs_static = float(FS_HZ)\n", + " normalize_density_jit = partial(normalize_density, fs=fs_static, epsilon=1e-12)\n", + " smoothed_occupancy_time = jit(\n", + " normalize_density_jit, static_argnames=\"is_occupancy\"\n", + " )(smoothed_occupancy, initial_occupancy_counts, 0.0, is_occupancy=True)\n", + " smoothed_occupancy_time_np = np.array(smoothed_occupancy_time)\n", + " epsilon_rate = 1e-9\n", + "\n", + " # --- 8. Process Each Neuron ---\n", + " print(\"\\nProcessing neurons...\")\n", + " all_rate_maps_np = []\n", + " start_loop_time = time.time()\n", + " for i, spike_positions_neuron_np in enumerate(spike_positions_list):\n", + " if VERBOSE:\n", + " print(f\" Processing Neuron {i+1}/{N_NEURONS}...\")\n", + " initial_spike_counts_neuron = map_points_to_grid(\n", + " jnp.asarray(spike_positions_neuron_np), x_edges_jax, y_edges_jax, grid_shape\n", + " )\n", + " initial_spike_counts_neuron *= is_track_interior_jax\n", + " smoothed_spikes_neuron = apply_diffusion_kernel(\n", + " Kernel_Matrix,\n", + " initial_spike_counts_neuron,\n", + " grid_shape,\n", + " valid_bin_indices_flat,\n", + " )\n", + " smoothed_spikes_scaled_neuron = jit(\n", + " normalize_density_jit, static_argnames=\"is_occupancy\"\n", + " )(smoothed_spikes_neuron, initial_spike_counts_neuron, 0.0, is_occupancy=False)\n", + " smoothed_spikes_scaled_neuron_np = np.array(smoothed_spikes_scaled_neuron)\n", + " safe_occupancy_time_np = np.maximum(smoothed_occupancy_time_np, 0.0)\n", + " rate_map_neuron_np = smoothed_spikes_scaled_neuron_np / (\n", + " safe_occupancy_time_np + epsilon_rate\n", + " )\n", + " rate_map_neuron_np *= is_track_interior_np\n", + " all_rate_maps_np.append(rate_map_neuron_np)\n", + " end_loop_time = time.time()\n", + " print(\n", + " f\"Processing {N_NEURONS} neurons took: {end_loop_time - start_loop_time:.4f} seconds (after precomputation)\"\n", + " )\n", + "\n", + " # --- 9. Plotting (Example: plot first neuron's map) ---\n", + " neuron_ind = 5\n", + " print(\"\\nPlotting example result (Neuron 1)...\")\n", + " plt.figure(figsize=(12, 5.5))\n", + " plt.subplot(1, 2, 1)\n", + " plot_occ = np.where(is_track_interior_np, smoothed_occupancy_time_np, np.nan)\n", + " pcm = plt.pcolormesh(\n", + " x_edges_np, y_edges_np, plot_occ, shading=\"auto\", cmap=\"viridis\"\n", + " )\n", + " plt.colorbar(pcm, label=\"Smoothed Occupancy (s)\")\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(\"Smoothed Occupancy\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.ylabel(\"Y Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + " plt.subplot(1, 2, 2)\n", + " plot_rate = np.where(is_track_interior_np, all_rate_maps_np[neuron_ind], np.nan)\n", + " valid_rates = plot_rate[~np.isnan(plot_rate)]\n", + " vmax = np.percentile(valid_rates, 99) if valid_rates.size > 0 else 1.0\n", + " vmax = max(vmax, 1.0)\n", + " pcm = plt.pcolormesh(\n", + " x_edges_np, y_edges_np, plot_rate, shading=\"auto\", cmap=\"jet\", vmin=0, vmax=vmax\n", + " )\n", + " plt.colorbar(pcm, label=\"Firing Rate (Hz)\")\n", + " plt.plot(\n", + " spike_positions_list[0][:, 0],\n", + " spike_positions_list[0][:, 1],\n", + " \"k.\",\n", + " markersize=1,\n", + " alpha=0.2,\n", + " )\n", + " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", + " plt.title(f\"Neuron {neuron_ind + 1} Rate Map (~{SIGMA_CM}cm Precomputed Kernel)\")\n", + " plt.xlabel(\"X Position (m)\")\n", + " plt.ylabel(\"Y Position (m)\")\n", + " plt.xlim(X_MIN, X_MAX)\n", + " plt.ylim(Y_MIN, Y_MAX)\n", + " plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n", + " plt.suptitle(\"Rate Map using Precomputed Diffusion Kernel\", fontsize=14)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1, 0, 0, 1, 0]), array([0, 1, 2, 3, 4]))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from non_local_detector.likelihoods.common import get_spikecount_per_time_bin\n", + "\n", + "time = np.arange(5)\n", + "spike_times = np.array([4.9, 0.9, 3.9])\n", + "get_spikecount_per_time_bin(spike_times, time), time" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 0, 0, 1])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spike_times = np.array([-0.1, 0.9, 4.1, 3.9])\n", + "np.histogram(spike_times, bins=time)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spike times: [0. 0.9 4.1 3.9 5. ]\n", + "Bin indices: [0 0 4 3 4]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([0, 0, 4, 3, 4])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def get_spike_time_bin_ind(\n", + " spike_times: np.ndarray, time_bin_edges: np.ndarray\n", + ") -> np.ndarray:\n", + " \"\"\"Gets the index of the time bin for each spike time.\n", + "\n", + " Parameters\n", + " ----------\n", + " spike_times : np.ndarray, shape (n_spikes,)\n", + " Times of occurrences (spikes).\n", + " time_bin_edges : np.ndarray, shape (n_bins + 1,)\n", + " Sorted array defining the edges of the time bins [t0, t1, ..., tN].\n", + " Defines n_bins intervals: [t0, t1), ..., [t_{N-1}, tN].\n", + "\n", + " Returns\n", + " -------\n", + " bin_indices : np.ndarray, shape (n_spikes,)\n", + " Index of the bin for each spike. Bins are indexed 0 to n_bins-1.\n", + " Doesn't handle out of bounds spikes.\n", + " \"\"\"\n", + "\n", + " bin_indices = np.searchsorted(time_bin_edges, spike_times, side=\"right\") - 1\n", + " is_last_bin = np.isclose(\n", + " spike_times,\n", + " time_bin_edges[-1],\n", + " )\n", + " bin_indices[is_last_bin] = len(time_bin_edges) - 2\n", + "\n", + " return bin_indices\n", + "\n", + "\n", + "# Example usage\n", + "time_bin_edges = np.array([0, 1, 2, 3, 4, 5])\n", + "spike_times = np.array([0.0, 0.9, 4.1, 3.9, 5.0])\n", + "bin_indices = get_spike_time_bin_indices(spike_times, time_bin_edges)\n", + "print(\"Spike times:\", spike_times)\n", + "print(\"Bin indices:\", bin_indices)\n", + "time_bin_edges[bin_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.searchsorted(time_bin_edges, [0], side=\"right\") - 1" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time_bin_edges" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.digitize(np.array([5.9]), time[1:-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.9, 4.1, 3.9, 5. ])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spike_times" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 3, 3, 3])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time[np.digitize(spike_times, time[1:-1])]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "def get_spike_time_bin_ind(\n", + " spike_times: np.ndarray, time_bin_edges: np.ndarray\n", + ") -> np.ndarray:\n", + " \"\"\"Gets the index of the time bin for each spike time.\n", + "\n", + " Parameters\n", + " ----------\n", + " spike_times : np.ndarray, shape (n_spikes,)\n", + " Times of occurrences (spikes).\n", + " time_bin_edges : np.ndarray, shape (n_bins + 1,)\n", + " Sorted array defining the edges of the time bins [t0, t1, ..., tN].\n", + " Defines n_bins intervals: [t0, t1), ..., [t_{N-1}, tN].\n", + "\n", + " Returns\n", + " -------\n", + " bin_indices : np.ndarray, shape (n_spikes,)\n", + " Index of the bin for each spike. Bins are indexed 0 to n_bins-1.\n", + " Doesn't handle out of bounds spikes.\n", + " \"\"\"\n", + "\n", + " bin_indices = np.searchsorted(time_bin_edges, spike_times, side=\"right\") - 1\n", + " is_last_bin = np.isclose(\n", + " spike_times,\n", + " time_bin_edges[-1],\n", + " )\n", + " bin_indices[is_last_bin] = len(time_bin_edges) - 2\n", + "\n", + " return bin_indices" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 0, 0, 1, 1, 0])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indicator = np.zeros_like(time_bin_edges)\n", + "indicator[get_spike_time_bin_ind(spike_times, time_bin_edges)] = 1\n", + "indicator" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 0, 0, 1, 1, 0])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(get_spikecount_per_time_bin(spike_times, time_bin_edges) > 0).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.9, 4.1, 3.9, 5. ])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spike_times" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "non_local_detector2", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/diffusion6.ipynb b/notebooks/diffusion6.ipynb new file mode 100644 index 0000000..86e182b --- /dev/null +++ b/notebooks/diffusion6.ipynb @@ -0,0 +1,925 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "74f09f7a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Fine-Grained T-Maze Environment...\n", + "Grid Shape: (20, 19)\n", + "Number of interior bins: 124\n", + "\n", + "Target kernel bandwidth sigma = 2.5 (in grid units)\n", + "Precomputing STABLE FD diffusion kernels using matrix exponential...\n", + "Stable FD Kernel Matrix shape: (124, 124)\n", + "\n", + "Precomputing GL diffusion kernels...\n", + "GL Kernel Matrix shape: (124, 124)\n", + "\n", + "Plotting example kernel (using GL results if available)...\n", + "Plotting GL kernel starting near T-junction (interior index 62)...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create or replace file: non_local_detector/diffusion_kernels.py\n", + "\n", + "from functools import partial\n", + "from typing import Tuple\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import jax.scipy.linalg\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "import numpy as np # Used for env attributes and JAX input conversion\n", + "\n", + "\n", + "# --- Finite Difference (FD) Approach with JAX & Matrix Exponential (Stable) ---\n", + "\n", + "\n", + "@partial(jax.jit, static_argnames=[\"grid_shape\"])\n", + "def _get_discrete_laplacian_operator_matrix_jax_v2(\n", + " interior_mask_2d: jnp.ndarray, grid_shape: Tuple[int, int]\n", + ") -> jnp.ndarray:\n", + " \"\"\"\n", + " Creates a discrete Laplacian operator matrix using JAX matrix operations\n", + " for interior nodes on a 2D grid, avoiding dynamic boolean indexing\n", + " by separating axis rolls. (L = A - D convention)\n", + "\n", + " Parameters\n", + " ----------\n", + " interior_mask_2d : jnp.ndarray, shape (n_bins_x, n_bins_y)\n", + " grid_shape : tuple[int, int]\n", + "\n", + " Returns\n", + " -------\n", + " laplacian_op : jnp.ndarray, shape (n_bins_total, n_bins_total)\n", + " \"\"\"\n", + " n_bins_total = interior_mask_2d.size\n", + " interior_mask_flat = interior_mask_2d.ravel()\n", + " indices = jnp.arange(n_bins_total)\n", + " adjacency = jnp.zeros((n_bins_total, n_bins_total), dtype=jnp.float32)\n", + "\n", + " # --- Connections along axis 0 (rows / dim 0 / 'x') ---\n", + " for dr in [1, -1]: # Down, Up\n", + " shifted_mask_ax0 = jnp.roll(interior_mask_2d, -dr, axis=0)\n", + " valid_connection_mask_ax0 = (interior_mask_2d & shifted_mask_ax0).ravel()\n", + " neighbor_indices_all_ax0 = jnp.roll(\n", + " indices.reshape(grid_shape), -dr, axis=0\n", + " ).ravel()\n", + " values_to_set_ax0 = jnp.where(valid_connection_mask_ax0, 1.0, 0.0)\n", + " # Use add here; connections (i,j) and (j,i) will be added separately ensuring symmetry later\n", + " adjacency = adjacency.at[indices, neighbor_indices_all_ax0].add(\n", + " values_to_set_ax0\n", + " )\n", + "\n", + " # --- Connections along axis 1 (columns / dim 1 / 'y') ---\n", + " for dc in [1, -1]: # Right, Left\n", + " shifted_mask_ax1 = jnp.roll(interior_mask_2d, -dc, axis=1)\n", + " valid_connection_mask_ax1 = (interior_mask_2d & shifted_mask_ax1).ravel()\n", + " neighbor_indices_all_ax1 = jnp.roll(\n", + " indices.reshape(grid_shape), -dc, axis=1\n", + " ).ravel()\n", + " values_to_set_ax1 = jnp.where(valid_connection_mask_ax1, 1.0, 0.0)\n", + " adjacency = adjacency.at[indices, neighbor_indices_all_ax1].add(\n", + " values_to_set_ax1\n", + " )\n", + "\n", + " # Ensure adjacency is symmetric and binary (correcting potential double adds)\n", + " adjacency = jnp.where(adjacency > 0, 1.0, 0.0)\n", + "\n", + " # Degree matrix D (diagonal matrix of row sums of A)\n", + " degree = jnp.sum(adjacency, axis=1)\n", + " laplacian_op = adjacency - jnp.diag(degree)\n", + "\n", + " # Ensure operator only acts non-trivially on interior points\n", + " laplacian_op = laplacian_op * interior_mask_flat[:, None]\n", + " laplacian_op = laplacian_op * interior_mask_flat[None, :]\n", + "\n", + " return laplacian_op\n", + "\n", + "\n", + "# Pass laplacian_op as regular argument\n", + "@partial(jax.jit, static_argnames=[\"n_bins_total\"])\n", + "def _get_kernels_for_starts_fd_expm_corrected(\n", + " start_indices: jnp.ndarray,\n", + " n_bins_total: int,\n", + " laplacian_op: jnp.ndarray,\n", + " diffusion_time: float,\n", + " diffusion_coeff: float,\n", + ") -> jnp.ndarray:\n", + " \"\"\"\n", + " Calculates diffusion kernels for multiple start indices using the\n", + " matrix exponential applied to the FD Laplacian operator.\n", + " \"\"\"\n", + " kernel_matrix_full = jax.scipy.linalg.expm(\n", + " diffusion_coeff * diffusion_time * laplacian_op\n", + " )\n", + " kernels = kernel_matrix_full[:, start_indices]\n", + " return kernels\n", + "\n", + "\n", + "def precompute_diffusion_kernels_fd(\n", + " interior_mask_2d: np.ndarray,\n", + " bandwidth_sigma: float,\n", + " diffusion_coeff: float = 0.5,\n", + ") -> Tuple[jnp.ndarray, jnp.ndarray]:\n", + " \"\"\"\n", + " Precomputes STABLE diffusion kernels for all valid interior bins using\n", + " Finite Differences operator and the Matrix Exponential.\n", + "\n", + " Parameters\n", + " ----------\n", + " interior_mask_2d : np.ndarray, shape (n_bins_x, n_bins_y)\n", + " bandwidth_sigma : float\n", + " diffusion_coeff : float, optional\n", + "\n", + " Returns\n", + " -------\n", + " kernel_matrix_interior : jnp.ndarray, shape (n_interior_bins, n_interior_bins)\n", + " interior_bin_indices_flat : jnp.ndarray, shape (n_interior_bins,)\n", + " \"\"\"\n", + " if diffusion_coeff <= 0:\n", + " raise ValueError(\"diffusion_coeff must be positive.\")\n", + "\n", + " diffusion_time = bandwidth_sigma**2 / (2 * diffusion_coeff)\n", + "\n", + " grid_shape = interior_mask_2d.shape\n", + " interior_mask_2d_jax = jnp.asarray(interior_mask_2d)\n", + " interior_mask_flat = interior_mask_2d_jax.ravel()\n", + " n_bins_total = interior_mask_flat.shape[0]\n", + " interior_bin_indices_flat = jnp.where(interior_mask_flat)[0]\n", + " n_interior_bins = interior_bin_indices_flat.shape[0]\n", + "\n", + " if n_interior_bins == 0:\n", + " return jnp.zeros((0, 0), dtype=jnp.float32), jnp.zeros((0,), dtype=jnp.int32)\n", + "\n", + " # 1. Precompute Laplacian Operator using the revised function\n", + " laplacian_op = _get_discrete_laplacian_operator_matrix_jax_v2(\n", + " interior_mask_2d_jax, grid_shape\n", + " )\n", + "\n", + " # 2. Compute all kernels starting from interior bins using matrix exponential\n", + " all_kernels_full_cols = _get_kernels_for_starts_fd_expm_corrected(\n", + " interior_bin_indices_flat,\n", + " n_bins_total, # Pass as static\n", + " laplacian_op, # Pass as regular arg\n", + " diffusion_time,\n", + " diffusion_coeff,\n", + " )\n", + " # Result shape: (n_bins_total, n_interior_bins)\n", + "\n", + " # 3. Filter rows to keep only interior destinations\n", + " kernel_matrix_interior = all_kernels_full_cols[interior_bin_indices_flat, :]\n", + " # Shape: (n_interior_bins, n_interior_bins)\n", + "\n", + " # 4. Ensure non-negativity and Renormalize columns\n", + " kernel_matrix_interior = jax.nn.relu(kernel_matrix_interior)\n", + " col_sums = kernel_matrix_interior.sum(axis=0, keepdims=True)\n", + " kernel_matrix_interior = jnp.where(\n", + " col_sums > 1e-15, kernel_matrix_interior / col_sums, 0.0\n", + " )\n", + "\n", + " return kernel_matrix_interior, interior_bin_indices_flat\n", + "\n", + "\n", + "# --- Graph Laplacian (GL) Approach with JAX (Stable by default) ---\n", + "\n", + "\n", + "@jax.jit\n", + "def _compute_kernel_matrix_from_laplacian(\n", + " laplacian: jnp.ndarray, diffusion_time: float, diffusion_coeff: float\n", + ") -> jnp.ndarray:\n", + " \"\"\"Computes the kernel matrix expm(-D*t*L), where L=D-A.\"\"\"\n", + " return jax.scipy.linalg.expm(-diffusion_coeff * diffusion_time * laplacian)\n", + "\n", + "\n", + "def precompute_diffusion_kernels_graph_laplacian(\n", + " track_graphDD: nx.Graph,\n", + " interior_mask_2d: np.ndarray,\n", + " bandwidth_sigma: float,\n", + " diffusion_coeff: float = 0.5,\n", + ") -> Tuple[jnp.ndarray, jnp.ndarray]:\n", + " \"\"\"\n", + " Precomputes diffusion kernels for all valid interior bins using the\n", + " Graph Laplacian method and matrix exponential. Stable by definition.\n", + "\n", + " Parameters\n", + " ----------\n", + " track_graphDD : nx.Graph\n", + " interior_mask_2d : np.ndarray, shape (n_bins_x, n_bins_y)\n", + " bandwidth_sigma : float\n", + " diffusion_coeff : float, optional\n", + "\n", + " Returns\n", + " -------\n", + " kernel_matrix_interior : jnp.ndarray, shape (n_interior_bins, n_interior_bins)\n", + " interior_bin_indices_flat : jnp.ndarray, shape (n_interior_bins,)\n", + " \"\"\"\n", + " if diffusion_coeff <= 0:\n", + " ValueError(\"diffusion_coeff must be positive.\")\n", + " if track_graphDD is None:\n", + " ValueError(\"track_graphDD is required.\")\n", + " diffusion_time = bandwidth_sigma**2 / (2 * diffusion_coeff)\n", + " n_bins_total = interior_mask_2d.size\n", + " interior_mask_flat = jnp.asarray(interior_mask_2d.ravel())\n", + " interior_bin_indices_flat = jnp.where(interior_mask_flat)[0]\n", + " n_interior_bins = interior_bin_indices_flat.shape[0]\n", + " if n_interior_bins == 0:\n", + " return jnp.zeros((0, 0)), jnp.zeros((0,))\n", + " node_list = sorted(list(track_graphDD.nodes()))\n", + " if not node_list:\n", + " laplacian_full = jnp.zeros((n_bins_total, n_bins_total), dtype=jnp.float32)\n", + " else:\n", + " try:\n", + " L_sparse = nx.laplacian_matrix(\n", + " track_graphDD, nodelist=range(n_bins_total), weight=None\n", + " )\n", + " laplacian_full = jnp.array(L_sparse.toarray(), dtype=jnp.float32)\n", + " except nx.NetworkXError:\n", + " L_sparse_sub = nx.laplacian_matrix(\n", + " track_graphDD, nodelist=node_list, weight=None\n", + " )\n", + " L_sub = jnp.array(L_sparse_sub.toarray(), dtype=jnp.float32)\n", + " laplacian_full = jnp.zeros((n_bins_total, n_bins_total), dtype=jnp.float32)\n", + " idx_embed = jnp.ix_(jnp.array(node_list), jnp.array(node_list))\n", + " laplacian_full = laplacian_full.at[idx_embed].set(L_sub)\n", + " full_kernel_matrix = _compute_kernel_matrix_from_laplacian(\n", + " laplacian_full, diffusion_time, diffusion_coeff\n", + " )\n", + " idx_filter = jnp.ix_(interior_bin_indices_flat, interior_bin_indices_flat)\n", + " kernel_matrix_interior = full_kernel_matrix[idx_filter]\n", + " kernel_matrix_interior = jax.nn.relu(kernel_matrix_interior)\n", + " col_sums = kernel_matrix_interior.sum(axis=0, keepdims=True)\n", + " kernel_matrix_interior = jnp.where(\n", + " col_sums > 1e-15, kernel_matrix_interior / col_sums, 0.0\n", + " )\n", + " return kernel_matrix_interior, interior_bin_indices_flat\n", + "\n", + "\n", + "# --- Plotting Function (Unchanged from previous version) ---\n", + "\n", + "\n", + "def plot_diffusion_kernel_2d(\n", + " kernel_matrix_interior: jnp.ndarray,\n", + " interior_bin_indices_flat: jnp.ndarray,\n", + " interior_mask_2d: np.ndarray,\n", + " grid_shape: tuple[int, int],\n", + " bandwidth_sigma: float, # Added for title\n", + " start_kernel_index: int = 0,\n", + " ax: plt.Axes = None,\n", + " **imshow_kwargs,\n", + "):\n", + " # ... (implementation from previous response) ...\n", + " n_bins_total = np.prod(grid_shape)\n", + " n_interior_bins = interior_bin_indices_flat.shape[0]\n", + " if not (0 <= start_kernel_index < n_interior_bins):\n", + " raise IndexError(\"start_kernel_index out of bounds\")\n", + " if ax is None:\n", + " fig, ax = plt.subplots(figsize=(8, 8))\n", + " kernel_vector_interior = kernel_matrix_interior[:, start_kernel_index]\n", + " full_grid_kernel = (\n", + " jnp.zeros(n_bins_total)\n", + " .at[interior_bin_indices_flat]\n", + " .set(kernel_vector_interior)\n", + " )\n", + " kernel_2d = full_grid_kernel.reshape(grid_shape)\n", + " plot_data = np.array(kernel_2d, copy=True, dtype=np.float64)\n", + " plot_data[~interior_mask_2d] = np.nan\n", + " aspect_ratio = grid_shape[1] / grid_shape[0] # ny / nx\n", + " default_kwargs = dict(\n", + " cmap=\"viridis\", interpolation=\"nearest\", origin=\"lower\", aspect=aspect_ratio\n", + " )\n", + " default_kwargs.update(imshow_kwargs)\n", + " im = ax.imshow(plot_data, **default_kwargs)\n", + " plt.colorbar(im, ax=ax, label=\"Probability Density\", shrink=0.8)\n", + " start_bin_flat = interior_bin_indices_flat[start_kernel_index]\n", + " start_bin_r, start_bin_c = np.unravel_index(int(start_bin_flat), grid_shape)\n", + " ax.plot(\n", + " start_bin_c,\n", + " start_bin_r,\n", + " \"rx\",\n", + " markersize=8,\n", + " markeredgewidth=1.5,\n", + " label=\"Start Bin\",\n", + " )\n", + " ax.set_title(\n", + " f\"Diffusion Kernel (Bandwidth σ ≈ {bandwidth_sigma:.2f})\\nStart: interior_idx={start_kernel_index}, flat_idx={start_bin_flat}, xy=({start_bin_r},{start_bin_c})\"\n", + " )\n", + " ax.set_xlabel(\"X Bin Index\")\n", + " ax.set_ylabel(\"Y Bin Index\")\n", + " ax.legend(fontsize=8)\n", + " ax.grid(False)\n", + "\n", + "\n", + "# --- Example Usage with Fine-Grained T-Maze ---\n", + "\n", + "\n", + "# Assume 'env' is your fitted Environment object\n", + "class MockTmazeEnvironmentFine: # As defined previously\n", + " def __init__(self, stem_length=16, stem_width=3, top_length=20, top_width=3):\n", + " nx_bins = top_length\n", + " ny_bins = stem_length + top_width\n", + " self.centers_shape_ = (nx_bins, ny_bins)\n", + " self.is_track_interior_ = np.zeros(self.centers_shape_, dtype=bool)\n", + " stem_start_col = (nx_bins // 2) - (stem_width // 2)\n", + " stem_end_col = stem_start_col + stem_width\n", + " if stem_width % 2 != 0 and nx_bins % 2 == 0:\n", + " stem_end_col += 1\n", + " elif stem_width % 2 == 0 and nx_bins % 2 != 0:\n", + " stem_end_col += 1\n", + " self.is_track_interior_[stem_start_col:stem_end_col, 0:stem_length] = True\n", + " top_start_row = stem_length\n", + " top_end_row = top_start_row + top_width\n", + " self.is_track_interior_[:, top_start_row:top_end_row] = True\n", + " self.n_bins_total_ = np.prod(self.centers_shape_)\n", + " self.track_graphDD = nx.Graph()\n", + " node_inds = np.arange(self.n_bins_total_).reshape(self.centers_shape_)\n", + " for r in range(self.centers_shape_[0]):\n", + " for c in range(self.centers_shape_[1]):\n", + " node_id = int(node_inds[r, c])\n", + " if self.is_track_interior_[r, c]:\n", + " self.track_graphDD.add_node(node_id)\n", + " for dr, dc in [(0, 1), (0, -1), (1, 0), (-1, 0)]:\n", + " nr, nc = r + dr, c + dc\n", + " if (\n", + " 0 <= nr < self.centers_shape_[0]\n", + " and 0 <= nc < self.centers_shape_[1]\n", + " and self.is_track_interior_[nr, nc]\n", + " ):\n", + " neighbor_id = int(node_inds[nr, nc])\n", + " self.track_graphDD.add_edge(\n", + " node_id, neighbor_id, distance=1.0\n", + " )\n", + "\n", + "\n", + "# Create the environment\n", + "print(\"Creating Fine-Grained T-Maze Environment...\")\n", + "fine_t_maze_env = MockTmazeEnvironmentFine()\n", + "print(f\"Grid Shape: {fine_t_maze_env.centers_shape_}\")\n", + "print(f\"Number of interior bins: {np.sum(fine_t_maze_env.is_track_interior_)}\")\n", + "\n", + "# Define bandwidth\n", + "sigma_bandwidth = 2.5\n", + "print(f\"\\nTarget kernel bandwidth sigma = {sigma_bandwidth} (in grid units)\")\n", + "\n", + "\n", + "# --- Finite Difference Precomputation (Using corrected Laplacian builder) ---\n", + "print(\"Precomputing STABLE FD diffusion kernels using matrix exponential...\")\n", + "fd_kernel_matrix, fd_interior_indices = None, None # Initialize\n", + "try:\n", + " # Make sure to use the corrected function name here if you renamed it in your file\n", + " fd_kernel_matrix, fd_interior_indices = precompute_diffusion_kernels_fd(\n", + " fine_t_maze_env.is_track_interior_, sigma_bandwidth\n", + " )\n", + " print(f\"Stable FD Kernel Matrix shape: {fd_kernel_matrix.shape}\")\n", + " # print(f\"Stable FD Interior Indices shape: {fd_interior_indices.shape}\")\n", + " # print(f\"Stable FD Example column sums: {fd_kernel_matrix.sum(axis=0)[:min(5, fd_kernel_matrix.shape[1])]}\")\n", + "except Exception as e:\n", + " print(f\"Stable FD calculation failed: {e}\")\n", + "\n", + "\n", + "# --- Graph Laplacian Precomputation ---\n", + "print(\"\\nPrecomputing GL diffusion kernels...\")\n", + "gl_kernel_matrix, gl_interior_indices = None, None # Initialize\n", + "try:\n", + " gl_kernel_matrix, gl_interior_indices = (\n", + " precompute_diffusion_kernels_graph_laplacian(\n", + " fine_t_maze_env.track_graphDD,\n", + " fine_t_maze_env.is_track_interior_,\n", + " sigma_bandwidth,\n", + " )\n", + " )\n", + " print(f\"GL Kernel Matrix shape: {gl_kernel_matrix.shape}\")\n", + " # print(f\"GL Interior Indices shape: {gl_interior_indices.shape}\")\n", + " # print(f\"GL Example column sums: {gl_kernel_matrix.sum(axis=0)[:min(5, gl_kernel_matrix.shape[1])]}\")\n", + "\n", + "except Exception as e:\n", + " print(f\"GL calculation failed: {e}\") # Note: x2APIC error is external to this code.\n", + "\n", + "\n", + "# --- Plotting (using GL results if available) ---\n", + "print(\"\\nPlotting example kernel (using GL results if available)...\")\n", + "plot_matrix, plot_indices, method_name = (\n", + " (gl_kernel_matrix, gl_interior_indices, \"GL\")\n", + " if gl_kernel_matrix is not None\n", + " else (fd_kernel_matrix, fd_interior_indices, \"FD\")\n", + ")\n", + "\n", + "if plot_matrix is not None:\n", + " n_interior_fine = plot_matrix.shape[0]\n", + " if n_interior_fine > 0:\n", + " # Choose start bin near junction\n", + " junction_r = fine_t_maze_env.centers_shape_[0] // 2\n", + " junction_c = 16 # Stem length\n", + " start_flat_index_approx = np.ravel_multi_index(\n", + " (junction_r, junction_c), fine_t_maze_env.centers_shape_\n", + " )\n", + " actual_indices_r, actual_indices_c = np.unravel_index(\n", + " plot_indices, fine_t_maze_env.centers_shape_\n", + " )\n", + " distances = np.sqrt(\n", + " (actual_indices_r - junction_r) ** 2 + (actual_indices_c - junction_c) ** 2\n", + " )\n", + " start_kernel_idx_fine = np.argmin(distances)\n", + "\n", + " print(\n", + " f\"Plotting {method_name} kernel starting near T-junction (interior index {start_kernel_idx_fine})...\"\n", + " )\n", + " plot_diffusion_kernel_2d(\n", + " kernel_matrix_interior=plot_matrix,\n", + " interior_bin_indices_flat=plot_indices,\n", + " interior_mask_2d=fine_t_maze_env.is_track_interior_,\n", + " grid_shape=fine_t_maze_env.centers_shape_,\n", + " start_kernel_index=start_kernel_idx_fine,\n", + " bandwidth_sigma=sigma_bandwidth,\n", + " vmin=0,\n", + " )\n", + " plt.tight_layout()\n", + " plt.show()\n", + " else:\n", + " print(\"No interior bins found to plot kernel for.\")\n", + "else:\n", + " print(\"Kernel matrix (neither GL nor FD) not available for plotting.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8088466d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Fine-Grained T-Maze Environment...\n", + "Fine Grid Shape: (20, 19)\n", + "Total bins: 380\n", + "Number of interior bins: 124\n", + "\n", + "Target kernel bandwidth sigma = 3.0\n", + "Precomputing Boundary-Aware GL diffusion kernels...\n", + "Boundary GL Kernel Matrix computed. Shape: (124, 124)\n", + "\n", + "Computing Gaussian kernel starting at bin 191 (Coords: (10, 1))...\n", + "Gaussian kernel computed. Shape: (20, 19)\n", + "\n", + "Plotting comparison...\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create or replace file: non_local_detector/diffusion_kernels.py\n", + "\n", + "from functools import partial\n", + "from typing import Tuple\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import jax.scipy.linalg\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "import numpy as np # Used for env attributes and JAX input conversion\n", + "from scipy.stats import multivariate_normal as scipy_multivariate_normal\n", + "\n", + "\n", + "# --- Mock Environment Definition ---\n", + "class MockTmazeEnvironmentFine:\n", + " \"\"\"Creates a mock environment representing a T-shaped maze grid.\"\"\"\n", + "\n", + " def __init__(self, stem_length=16, stem_width=3, top_length=20, top_width=3):\n", + " nx_bins = top_length\n", + " ny_bins = stem_length + top_width\n", + " self.centers_shape_ = (nx_bins, ny_bins)\n", + " self.is_track_interior_ = np.zeros(self.centers_shape_, dtype=bool)\n", + " print(f\"Fine Grid Shape: {self.centers_shape_}\")\n", + "\n", + " # Define T-Shape Interior\n", + " stem_start_col = (nx_bins // 2) - (stem_width // 2)\n", + " stem_end_col = stem_start_col + stem_width\n", + " if stem_width % 2 != 0 and nx_bins % 2 == 0:\n", + " stem_end_col += 1\n", + " elif stem_width % 2 == 0 and nx_bins % 2 != 0:\n", + " stem_end_col += 1\n", + " self.is_track_interior_[stem_start_col:stem_end_col, 0:stem_length] = True\n", + " top_start_row = stem_length\n", + " top_end_row = top_start_row + top_width\n", + " self.is_track_interior_[:, top_start_row:top_end_row] = True\n", + "\n", + " self.n_bins_total_ = np.prod(self.centers_shape_)\n", + " print(f\"Total bins: {self.n_bins_total_}\")\n", + " print(f\"Number of interior bins: {np.sum(self.is_track_interior_)}\")\n", + "\n", + " # --- Calculate Bin Centers (assuming unit spacing) ---\n", + " # Create coordinate vectors for each dimension's centers\n", + " x_centers = np.arange(nx_bins) + 0.5\n", + " y_centers = np.arange(ny_bins) + 0.5\n", + " # Create meshgrid\n", + " X_centers, Y_centers = np.meshgrid(x_centers, y_centers, indexing=\"ij\")\n", + " # Stack and reshape to get (n_bins_total, 2)\n", + " self.place_bin_centers_ = np.stack(\n", + " [X_centers.ravel(), Y_centers.ravel()], axis=1\n", + " )\n", + " # -------------------------------------------------------\n", + "\n", + " # Build graph connecting adjacent interior bins\n", + " self.track_graphDD = nx.Graph()\n", + " node_inds = np.arange(self.n_bins_total_).reshape(self.centers_shape_)\n", + " for r in range(self.centers_shape_[0]):\n", + " for c in range(self.centers_shape_[1]):\n", + " node_id = int(node_inds[r, c])\n", + " if self.is_track_interior_[r, c]:\n", + " self.track_graphDD.add_node(node_id)\n", + " for dr, dc in [(0, 1), (0, -1), (1, 0), (-1, 0)]:\n", + " nr, nc = r + dr, c + dc\n", + " if (\n", + " 0 <= nr < self.centers_shape_[0]\n", + " and 0 <= nc < self.centers_shape_[1]\n", + " and self.is_track_interior_[nr, nc]\n", + " ):\n", + " neighbor_id = int(node_inds[nr, nc])\n", + " self.track_graphDD.add_edge(\n", + " node_id, neighbor_id, distance=1.0\n", + " )\n", + "\n", + "\n", + "# --- Diffusion Kernel Functions (Graph Laplacian method needed) ---\n", + "\n", + "\n", + "@jax.jit\n", + "def _compute_kernel_matrix_from_laplacian(\n", + " laplacian: jnp.ndarray, diffusion_time: float, diffusion_coeff: float\n", + ") -> jnp.ndarray:\n", + " \"\"\"Computes the kernel matrix expm(-D*t*L), where L=D-A.\"\"\"\n", + " return jax.scipy.linalg.expm(-diffusion_coeff * diffusion_time * laplacian)\n", + "\n", + "\n", + "def precompute_diffusion_kernels_graph_laplacian(\n", + " track_graphDD: nx.Graph,\n", + " interior_mask_2d: np.ndarray,\n", + " bandwidth_sigma: float,\n", + " diffusion_coeff: float = 0.5,\n", + ") -> Tuple[jnp.ndarray, jnp.ndarray]:\n", + " # ... (implementation from previous response, unchanged) ...\n", + " if diffusion_coeff <= 0:\n", + " ValueError(\"diffusion_coeff must be positive.\")\n", + " if track_graphDD is None:\n", + " ValueError(\"track_graphDD is required.\")\n", + " diffusion_time = bandwidth_sigma**2 / (2 * diffusion_coeff)\n", + " n_bins_total = interior_mask_2d.size\n", + " interior_mask_flat = jnp.asarray(interior_mask_2d.ravel())\n", + " interior_bin_indices_flat = jnp.where(interior_mask_flat)[0]\n", + " n_interior_bins = interior_bin_indices_flat.shape[0]\n", + " if n_interior_bins == 0:\n", + " return jnp.zeros((0, 0)), jnp.zeros((0,))\n", + " node_list = sorted(list(track_graphDD.nodes()))\n", + " if not node_list:\n", + " laplacian_full = jnp.zeros((n_bins_total, n_bins_total), dtype=jnp.float32)\n", + " else:\n", + " try:\n", + " L_sparse = nx.laplacian_matrix(\n", + " track_graphDD, nodelist=range(n_bins_total), weight=None\n", + " )\n", + " laplacian_full = jnp.array(L_sparse.toarray(), dtype=jnp.float32)\n", + " except nx.NetworkXError:\n", + " L_sparse_sub = nx.laplacian_matrix(\n", + " track_graphDD, nodelist=node_list, weight=None\n", + " )\n", + " L_sub = jnp.array(L_sparse_sub.toarray(), dtype=jnp.float32)\n", + " laplacian_full = jnp.zeros((n_bins_total, n_bins_total), dtype=jnp.float32)\n", + " idx_embed = jnp.ix_(jnp.array(node_list), jnp.array(node_list))\n", + " laplacian_full = laplacian_full.at[idx_embed].set(L_sub)\n", + " full_kernel_matrix = _compute_kernel_matrix_from_laplacian(\n", + " laplacian_full, diffusion_time, diffusion_coeff\n", + " )\n", + " idx_filter = jnp.ix_(interior_bin_indices_flat, interior_bin_indices_flat)\n", + " kernel_matrix_interior = full_kernel_matrix[idx_filter]\n", + " kernel_matrix_interior = jax.nn.relu(kernel_matrix_interior)\n", + " col_sums = kernel_matrix_interior.sum(axis=0, keepdims=True)\n", + " kernel_matrix_interior = jnp.where(\n", + " col_sums > 1e-15, kernel_matrix_interior / col_sums, 0.0\n", + " )\n", + " return kernel_matrix_interior, interior_bin_indices_flat\n", + "\n", + "\n", + "# --- Function to calculate standard Gaussian PDF Kernel ---\n", + "\n", + "\n", + "def calculate_gaussian_kernel_2d(\n", + " all_bin_centers: np.ndarray, # Shape (n_total_bins, 2)\n", + " grid_shape: Tuple[int, int],\n", + " start_bin_flat_index: int,\n", + " bandwidth_sigma: float,\n", + ") -> np.ndarray:\n", + " \"\"\"\n", + " Calculates a standard 2D Gaussian PDF kernel evaluated on the grid centers.\n", + "\n", + " Parameters\n", + " ----------\n", + " all_bin_centers : np.ndarray, shape (n_total_bins, 2)\n", + " Coordinates of the center of every bin in the grid.\n", + " grid_shape : tuple[int, int]\n", + " Shape of the 2D grid (nx, ny).\n", + " start_bin_flat_index : int\n", + " Flat index (0..n_total_bins-1) of the bin where the Gaussian is centered.\n", + " bandwidth_sigma : float\n", + " Standard deviation (bandwidth) of the Gaussian kernel along each axis.\n", + "\n", + " Returns\n", + " -------\n", + " gaussian_kernel_2d : np.ndarray, shape (nx, ny)\n", + " The Gaussian PDF evaluated at each bin center, reshaped to the grid.\n", + " Normalized to sum to 1 over the entire grid.\n", + " \"\"\"\n", + " n_bins_total = np.prod(grid_shape)\n", + " if not (0 <= start_bin_flat_index < n_bins_total):\n", + " raise IndexError(\n", + " f\"start_bin_flat_index ({start_bin_flat_index}) out of bounds.\"\n", + " )\n", + "\n", + " # Center of the Gaussian\n", + " mean = all_bin_centers[start_bin_flat_index]\n", + "\n", + " # Covariance matrix (isotropic)\n", + " covariance = np.array([[bandwidth_sigma**2, 0], [0, bandwidth_sigma**2]])\n", + "\n", + " # Evaluate PDF at all bin centers\n", + " try:\n", + " pdf_values = scipy_multivariate_normal.pdf(\n", + " all_bin_centers, mean=mean, cov=covariance\n", + " )\n", + " except np.linalg.LinAlgError:\n", + " print(\n", + " \"Warning: Covariance matrix singular for Gaussian PDF. Using tiny variance.\"\n", + " )\n", + " # Handle case where sigma is zero or very close to it\n", + " tiny_variance = 1e-10\n", + " covariance = np.array([[tiny_variance, 0], [0, tiny_variance]])\n", + " pdf_values = scipy_multivariate_normal.pdf(\n", + " all_bin_centers, mean=mean, cov=covariance\n", + " )\n", + "\n", + " # Normalize the PDF values over the grid so they sum to 1\n", + " pdf_sum = pdf_values.sum()\n", + " if pdf_sum > 1e-15:\n", + " normalized_pdf_values = pdf_values / pdf_sum\n", + " else:\n", + " normalized_pdf_values = (\n", + " pdf_values # Avoid division by zero if PDF is zero everywhere\n", + " )\n", + "\n", + " # Reshape to 2D grid\n", + " gaussian_kernel_2d = normalized_pdf_values.reshape(grid_shape)\n", + "\n", + " return gaussian_kernel_2d\n", + "\n", + "\n", + "# --- Plotting Function for Comparison ---\n", + "\n", + "\n", + "def plot_kernel_comparison(\n", + " kernel_boundary_aware: jnp.ndarray, # Shape (n_interior, n_interior)\n", + " interior_indices: jnp.ndarray, # Shape (n_interior,)\n", + " kernel_gaussian: np.ndarray, # Shape (nx, ny) - Already 2D\n", + " interior_mask_2d: np.ndarray,\n", + " grid_shape: tuple[int, int],\n", + " bandwidth_sigma: float,\n", + " start_bin_flat: int, # Flat index (0..n_total-1) of the start bin\n", + " **imshow_kwargs,\n", + "):\n", + " \"\"\"Plots boundary-aware diffusion vs. standard Gaussian kernels.\"\"\"\n", + "\n", + " n_bins_total = np.prod(grid_shape)\n", + " n_interior_bins = interior_indices.shape[0]\n", + "\n", + " # --- Prepare boundary-aware kernel for plotting ---\n", + " start_kernel_index_interior_list = np.where(interior_indices == start_bin_flat)[0]\n", + " if len(start_kernel_index_interior_list) == 0:\n", + " print(\n", + " f\"Warning: Start bin {start_bin_flat} is not interior. Boundary kernel plot omitted.\"\n", + " )\n", + " plot_data_boundary = np.full(grid_shape, np.nan) # Plot NaNs\n", + " start_kernel_index_interior = -1\n", + " else:\n", + " start_kernel_index_interior = start_kernel_index_interior_list[0]\n", + " kernel_vector_boundary = kernel_boundary_aware[:, start_kernel_index_interior]\n", + " full_grid_boundary = (\n", + " jnp.zeros(n_bins_total).at[interior_indices].set(kernel_vector_boundary)\n", + " )\n", + " kernel_2d_boundary = full_grid_boundary.reshape(grid_shape)\n", + " plot_data_boundary = np.array(kernel_2d_boundary, copy=True, dtype=np.float64)\n", + " plot_data_boundary[~interior_mask_2d] = np.nan # Mask non-interior\n", + "\n", + " # --- Prepare Gaussian kernel for plotting ---\n", + " # Already 2D, use directly\n", + " plot_data_gaussian = np.array(kernel_gaussian, copy=True, dtype=np.float64)\n", + "\n", + " # --- Plotting ---\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 7), sharey=True)\n", + " start_bin_r, start_bin_c = np.unravel_index(start_bin_flat, grid_shape)\n", + " aspect_ratio = grid_shape[1] / grid_shape[0] # ny / nx\n", + "\n", + " # Determine common color limits based on max value in *either* plot for fair comparison\n", + " valid_max_boundary = (\n", + " 0 if start_kernel_index_interior == -1 else np.nanmax(plot_data_boundary)\n", + " )\n", + " valid_max_gaussian = np.nanmax(plot_data_gaussian)\n", + " vmax = max(valid_max_boundary, valid_max_gaussian)\n", + " vmin = 0 # Or adjust if needed, but 0 makes sense for probability\n", + "\n", + " default_kwargs = dict(\n", + " cmap=\"viridis\",\n", + " interpolation=\"nearest\",\n", + " origin=\"lower\",\n", + " aspect=aspect_ratio,\n", + " vmin=vmin,\n", + " vmax=vmax,\n", + " )\n", + " user_kwargs = imshow_kwargs.copy()\n", + " user_kwargs.setdefault(\"vmin\", vmin) # Ensure vmin/vmax are set\n", + " user_kwargs.setdefault(\"vmax\", vmax)\n", + " final_kwargs = {**default_kwargs, **user_kwargs} # User overrides defaults\n", + "\n", + " # Plot Boundary-Aware Kernel\n", + " ax = axes[0]\n", + " if start_kernel_index_interior != -1:\n", + " im = ax.imshow(plot_data_boundary, **final_kwargs)\n", + " plt.colorbar(im, ax=ax, label=\"Probability Density\", shrink=0.7)\n", + " ax.plot(\n", + " start_bin_c,\n", + " start_bin_r,\n", + " \"rx\",\n", + " markersize=8,\n", + " markeredgewidth=1.5,\n", + " label=f\"Start (Flat: {start_bin_flat})\",\n", + " )\n", + " ax.set_title(f\"Boundary-Aware Diffusion Kernel (σ ≈ {bandwidth_sigma:.2f})\")\n", + " else:\n", + " # Still show grid if start was not interior\n", + " ax.imshow(\n", + " np.zeros_like(plot_data_boundary) * np.nan, **final_kwargs\n", + " ) # Show empty grid\n", + " ax.plot(\n", + " start_bin_c,\n", + " start_bin_r,\n", + " \"bx\",\n", + " markersize=8,\n", + " markeredgewidth=1.5,\n", + " label=f\"Start (Flat: {start_bin_flat} - Exterior)\",\n", + " )\n", + " ax.set_title(f\"Boundary-Aware Kernel (Start Exterior)\")\n", + "\n", + " ax.set_xlabel(\"X Bin Index\")\n", + " ax.set_ylabel(\"Y Bin Index\")\n", + " ax.legend(fontsize=8)\n", + " ax.grid(False)\n", + " ax.set(\n", + " xlim=(-0.5, grid_shape[1] - 0.5), ylim=(-0.5, grid_shape[0] - 0.5)\n", + " ) # Set limits\n", + "\n", + " # Plot Gaussian Kernel\n", + " ax = axes[1]\n", + " im = ax.imshow(plot_data_gaussian, **final_kwargs)\n", + " plt.colorbar(im, ax=ax, label=\"Probability Density\", shrink=0.7)\n", + " ax.plot(\n", + " start_bin_c,\n", + " start_bin_r,\n", + " \"rx\",\n", + " markersize=8,\n", + " markeredgewidth=1.5,\n", + " label=f\"Start (Flat: {start_bin_flat})\",\n", + " )\n", + " # Outline the original interior mask for reference\n", + " contours = ax.contour(\n", + " interior_mask_2d, levels=[0.5], colors=\"red\", linewidths=0.75, linestyles=\"--\"\n", + " )\n", + " ax.set_title(f\"Standard Gaussian Kernel (σ = {bandwidth_sigma:.2f})\")\n", + " ax.set_xlabel(\"X Bin Index\")\n", + " ax.set_ylabel(\"\") # Shared Y\n", + " ax.legend(fontsize=8)\n", + " ax.grid(False)\n", + " ax.set(\n", + " xlim=(-0.5, grid_shape[1] - 0.5), ylim=(-0.5, grid_shape[0] - 0.5)\n", + " ) # Set limits\n", + "\n", + " fig.suptitle(\"Diffusion Kernel vs. Gaussian Kernel Comparison\")\n", + " plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust for suptitle\n", + "\n", + "\n", + "# --- Example Usage with Fine-Grained T-Maze ---\n", + "\n", + "print(\"Creating Fine-Grained T-Maze Environment...\")\n", + "fine_t_maze_env = MockTmazeEnvironmentFine()\n", + "sigma = 3.0\n", + "print(f\"\\nTarget kernel bandwidth sigma = {sigma}\")\n", + "\n", + "# --- Compute Boundary-Aware Kernel (GL Recommended) ---\n", + "print(\"Precomputing Boundary-Aware GL diffusion kernels...\")\n", + "kernel_matrix_gl, interior_indices = None, None\n", + "try:\n", + " kernel_matrix_gl, interior_indices = precompute_diffusion_kernels_graph_laplacian(\n", + " fine_t_maze_env.track_graphDD, fine_t_maze_env.is_track_interior_, sigma\n", + " )\n", + " print(f\"Boundary GL Kernel Matrix computed. Shape: {kernel_matrix_gl.shape}\")\n", + "except Exception as e:\n", + " print(f\"Boundary GL calculation failed: {e}\")\n", + "\n", + "# --- Compute Gaussian Kernel ---\n", + "# Choose a start bin *near* a boundary to see the effect clearly\n", + "# E.g., End of the stem\n", + "start_r, start_c = (\n", + " fine_t_maze_env.centers_shape_[0] // 2,\n", + " 1,\n", + ") # Near bottom-middle of stem\n", + "start_bin_flat_idx = int(\n", + " np.ravel_multi_index((start_r, start_c), fine_t_maze_env.centers_shape_)\n", + ")\n", + "\n", + "print(\n", + " f\"\\nComputing Gaussian kernel starting at bin {start_bin_flat_idx} (Coords: {start_r, start_c})...\"\n", + ")\n", + "gaussian_kernel_2d = calculate_gaussian_kernel_2d(\n", + " all_bin_centers=fine_t_maze_env.place_bin_centers_,\n", + " grid_shape=fine_t_maze_env.centers_shape_,\n", + " start_bin_flat_index=start_bin_flat_idx,\n", + " bandwidth_sigma=sigma,\n", + ")\n", + "print(f\"Gaussian kernel computed. Shape: {gaussian_kernel_2d.shape}\")\n", + "\n", + "\n", + "# --- Plot Comparison ---\n", + "if kernel_matrix_gl is not None and gaussian_kernel_2d is not None:\n", + " print(\"\\nPlotting comparison...\")\n", + " plot_kernel_comparison(\n", + " kernel_boundary_aware=kernel_matrix_gl,\n", + " interior_indices=interior_indices,\n", + " kernel_gaussian=gaussian_kernel_2d,\n", + " interior_mask_2d=fine_t_maze_env.is_track_interior_,\n", + " grid_shape=fine_t_maze_env.centers_shape_,\n", + " bandwidth_sigma=sigma,\n", + " start_bin_flat=start_bin_flat_idx, # Use the chosen start bin\n", + " # vmin=0 # Let plot function determine scale or set manually\n", + " )\n", + " plt.show()\n", + "else:\n", + " print(\"\\nKernel matrices not available for plotting comparison.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5805db9c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "non_local_detector2", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/diffusion7.ipynb b/notebooks/diffusion7.ipynb new file mode 100644 index 0000000..e87824f --- /dev/null +++ b/notebooks/diffusion7.ipynb @@ -0,0 +1,1417 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1dc558ae", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/edeno/Documents/GitHub/non_local_detector/src/non_local_detector/likelihoods/clusterless_kde.py:54: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", + " from tqdm.autonotebook import tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing diffusion kernels...\n", + "Computed (342, 342) kernel matrix.\n", + "Calculating smoothed occupancy...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7d353d1553a64ecd943cd66c6909ff6a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Encoding models (Diffusion): 0%| | 0/25 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n", + "ax.plot(\n", + " lin_dist,\n", + " true_pf_time_series,\n", + " color=\"k\",\n", + " alpha=0.5,\n", + " label=\"true place field\",\n", + ")\n", + "ax.plot(\n", + " env.place_bin_centers_,\n", + " est_pf.T * fs,\n", + " color=\"r\",\n", + " alpha=0.5,\n", + " label=\"estimated place field (diffusion)\",\n", + ")\n", + "ax.plot(\n", + " env.place_bin_centers_,\n", + " est_pf2.T * fs,\n", + " color=\"b\",\n", + " alpha=0.5,\n", + " label=\"estimated place field\",\n", + ")\n", + "plt.title(\"Place field estimation\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "eee13fe4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "neuron_ind = 10\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n", + "ax.plot(\n", + " lin_dist,\n", + " true_pf_time_series[:, neuron_ind],\n", + " color=\"k\",\n", + " alpha=0.5,\n", + " label=\"true place field\",\n", + ")\n", + "ax.plot(\n", + " env.place_bin_centers_,\n", + " est_pf[[neuron_ind]].T * fs,\n", + " color=\"r\",\n", + " alpha=0.5,\n", + " label=\"estimated place field (diffusion)\",\n", + ")\n", + "ax.plot(\n", + " env.place_bin_centers_,\n", + " est_pf2[[neuron_ind]].T * fs,\n", + " color=\"b\",\n", + " alpha=0.5,\n", + " label=\"estimated place field\",\n", + ")\n", + "plt.title(\"Place field estimation\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c2cf2cc8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from non_local_detector.diffusion_kernels import compute_diffusion_kernels\n", + "from scipy.stats import multivariate_normal\n", + "\n", + "\n", + "std = 10\n", + "\n", + "diffusion_kernel = compute_diffusion_kernels(\n", + " track_graph=env.track_graph_nd_, interior_mask=None, bandwidth_sigma=std\n", + ")\n", + "\n", + "ind = 0\n", + "\n", + "plt.plot(env.place_bin_centers_, diffusion_kernel[ind])\n", + "plt.plot(\n", + " env.place_bin_centers_,\n", + " multivariate_normal.pdf(\n", + " env.place_bin_centers_, mean=env.place_bin_centers_[ind], cov=std\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "929b91f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating non-local likelihood...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8cc633d108544e09ad16ad2febc5164b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Non-Local Likelihood (Diffusion): 0%| | 0/25 [00:00 3\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplace_bin_centers_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnon_local_likelihood\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtime_ind\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39maxvline(lin_dist[time_ind], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(\n\u001b[1;32m 6\u001b[0m lin_dist[time_ind],\n\u001b[1;32m 7\u001b[0m non_local_likelihood[time_ind][\u001b[38;5;28mint\u001b[39m(lin_dist[time_ind] \u001b[38;5;241m/\u001b[39m env\u001b[38;5;241m.\u001b[39mplace_bin_size)],\n\u001b[1;32m 8\u001b[0m color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 9\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/non_local_detector2/lib/python3.12/site-packages/matplotlib/pyplot.py:3827\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3819\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 3820\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m 3821\u001b[0m \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3825\u001b[0m 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\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3832\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3833\u001b[0m \u001b[43m 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[\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1778\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1779\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", + "File \u001b[0;32m~/miniconda3/envs/non_local_detector2/lib/python3.12/site-packages/matplotlib/axes/_base.py:297\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 296\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_kwargs\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/non_local_detector2/lib/python3.12/site-packages/matplotlib/axes/_base.py:494\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 491\u001b[0m axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m 493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 494\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 496\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (343, 1) and (342,)" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_ind = 1300\n", + "\n", + "plt.plot(env.place_bin_centers_, non_local_likelihood[time_ind])\n", + "plt.axvline(lin_dist[time_ind], color=\"k\", linestyle=\"--\")\n", + "plt.scatter(\n", + " lin_dist[time_ind],\n", + " non_local_likelihood[time_ind][int(lin_dist[time_ind] / env.place_bin_size)],\n", + " color=\"r\",\n", + ")\n", + "plt.scatter(lin_dist[time_ind], local_likelihood[time_ind], color=\"b\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0242ef7e", + "metadata": {}, + "outputs": [], + "source": [ + "env.place_bin_centers_.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5518acc7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from track_linearization import make_track_graph\n", + "\n", + "track_graph = make_track_graph([(lin_dist.min(), 0), (lin_dist.max(), 0)], [(0, 1)])\n", + "\n", + "env_track_graph = Environment(\n", + " place_bin_size=10, track_graph=track_graph, edge_order=[(0, 1)]\n", + ").fit_place_grid(position=lin_dist[:, None])\n", + "env_track_graph.track_graph_with_bin_centers_edges_" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ee0c81d1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for node_id, node_data in env_track_graph.track_graph_with_bin_centers_edges_.nodes(\n", + " data=True\n", + "):\n", + " if node_data[\"is_bin_edge\"]:\n", + " plt.scatter(node_data[\"pos\"][0], node_data[\"pos\"][1], color=\"k\", marker=\"|\")\n", + " else:\n", + " plt.scatter(node_data[\"pos\"][0], node_data[\"pos\"][1], color=\"k\")\n", + " plt.text(\n", + " node_data[\"pos\"][0],\n", + " node_data[\"pos\"][1],\n", + " str(node_id),\n", + " fontsize=12,\n", + " ha=\"center\",\n", + " va=\"bottom\",\n", + " color=\"red\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "02382b81", + "metadata": {}, + "outputs": [], + "source": [ + "nodes_to_remove = [\n", + " node_id\n", + " for node_id, node_data in env_track_graph.track_graph_with_bin_centers_edges_.nodes(\n", + " data=True\n", + " )\n", + " if node_data[\"is_bin_edge\"]\n", + "]\n", + "nodes_to_remove = set(nodes_to_remove) | set(env_track_graph.track_graph.nodes)\n", + "\n", + "nodes_to_remove_neighbors = [\n", + " list(env_track_graph.track_graph_with_bin_centers_edges_.neighbors(node_id))\n", + " for node_id in nodes_to_remove\n", + "]\n", + "\n", + "copy_graph = env_track_graph.track_graph_with_bin_centers_edges_.copy()\n", + "for node_id, neighbors in zip(nodes_to_remove, nodes_to_remove_neighbors):\n", + " copy_graph.remove_node(node_id)\n", + " if (\n", + " len(neighbors) == 2\n", + " and neighbors[0] not in nodes_to_remove\n", + " and neighbors[1] not in nodes_to_remove\n", + " ):\n", + " copy_graph.add_edge(neighbors[0], neighbors[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a0b96d4c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from track_linearization import plot_track_graph\n", + "\n", + "plot_track_graph(copy_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d7f1e9de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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node_idedge_idis_bin_edgex_positiony_positionlinear_position
030False4.7221330.04.722237
150False14.1666060.014.166710
270False23.6110790.023.611183
390False33.0555520.033.055656
4110False42.5000250.042.500129
5130False51.9444980.051.944602
6150False61.3889710.061.389075
7170False70.8334440.070.833548
8190False80.2779170.080.278021
9210False89.7223900.089.722494
10230False99.1668630.099.166967
11250False108.6113360.0108.611440
12270False118.0558090.0118.055913
13290False127.5002820.0127.500386
14310False136.9447550.0136.944859
15330False146.3892280.0146.389332
16350False155.8337010.0155.833805
17370False165.2781740.0165.278278
\n", + "
" + ], + "text/plain": [ + " node_id edge_id is_bin_edge x_position y_position linear_position\n", + "0 3 0 False 4.722133 0.0 4.722237\n", + "1 5 0 False 14.166606 0.0 14.166710\n", + "2 7 0 False 23.611079 0.0 23.611183\n", + "3 9 0 False 33.055552 0.0 33.055656\n", + "4 11 0 False 42.500025 0.0 42.500129\n", + "5 13 0 False 51.944498 0.0 51.944602\n", + "6 15 0 False 61.388971 0.0 61.389075\n", + "7 17 0 False 70.833444 0.0 70.833548\n", + "8 19 0 False 80.277917 0.0 80.278021\n", + "9 21 0 False 89.722390 0.0 89.722494\n", + "10 23 0 False 99.166863 0.0 99.166967\n", + "11 25 0 False 108.611336 0.0 108.611440\n", + "12 27 0 False 118.055809 0.0 118.055913\n", + "13 29 0 False 127.500282 0.0 127.500386\n", + "14 31 0 False 136.944755 0.0 136.944859\n", + "15 33 0 False 146.389228 0.0 146.389332\n", + "16 35 0 False 155.833701 0.0 155.833805\n", + "17 37 0 False 165.278174 0.0 165.278278" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env_track_graph.place_bin_centers_nodes_df_" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "caaa7fff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 18)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(copy_graph.nodes), len(env_track_graph.place_bin_centers_nodes_df_)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b3119cd8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "from scipy.interpolate import interp1d\n", + "\n", + "\n", + "def make_track_graph_with_bin_centers_edges(\n", + " track_graph: nx.Graph, place_bin_size: float\n", + ") -> nx.Graph:\n", + " \"\"\"Insert the bin center and bin edge positions as nodes in the track graph.\n", + "\n", + " Parameters\n", + " ----------\n", + " track_graph : nx.Graph\n", + " place_bin_size : float\n", + "\n", + " Returns\n", + " -------\n", + " track_graph_with_bin_centers_edges : nx.Graph\n", + "\n", + " \"\"\"\n", + " track_graph_with_bin_centers_edges = track_graph.copy()\n", + " n_nodes = len(track_graph.nodes)\n", + "\n", + " for edge_ind, (node1, node2) in enumerate(track_graph.edges):\n", + " node1_x_pos, node1_y_pos = track_graph.nodes[node1][\"pos\"]\n", + " node2_x_pos, node2_y_pos = track_graph.nodes[node2][\"pos\"]\n", + "\n", + " edge_size = np.linalg.norm(\n", + " [(node2_x_pos - node1_x_pos), (node2_y_pos - node1_y_pos)]\n", + " )\n", + " n_bins = 2 * np.ceil(edge_size / place_bin_size).astype(np.int32) + 1\n", + " if ~np.isclose(node1_x_pos, node2_x_pos):\n", + " f = interp1d((node1_x_pos, node2_x_pos), (node1_y_pos, node2_y_pos))\n", + " xnew = np.linspace(node1_x_pos, node2_x_pos, num=n_bins, endpoint=True)\n", + " xy = np.stack((xnew, f(xnew)), axis=1)\n", + " else:\n", + " ynew = np.linspace(node1_y_pos, node2_y_pos, num=n_bins, endpoint=True)\n", + " xnew = np.ones_like(ynew) * node1_x_pos\n", + " xy = np.stack((xnew, ynew), axis=1)\n", + " dist_between_nodes = np.linalg.norm(np.diff(xy, axis=0), axis=1)\n", + "\n", + " new_node_ids = n_nodes + np.arange(len(dist_between_nodes) + 1)\n", + " nx.add_path(\n", + " track_graph_with_bin_centers_edges,\n", + " [*new_node_ids],\n", + " distance=dist_between_nodes[0],\n", + " )\n", + " nx.add_path(\n", + " track_graph_with_bin_centers_edges, [node1, new_node_ids[0]], distance=0\n", + " )\n", + " nx.add_path(\n", + " track_graph_with_bin_centers_edges, [node2, new_node_ids[-1]], distance=0\n", + " )\n", + " track_graph_with_bin_centers_edges.remove_edge(node1, node2)\n", + " for ind, (node_id, pos) in enumerate(zip(new_node_ids, xy)):\n", + " track_graph_with_bin_centers_edges.nodes[node_id][\"pos\"] = pos\n", + " track_graph_with_bin_centers_edges.nodes[node_id][\"edge_id\"] = edge_ind\n", + " if ind % 2:\n", + " track_graph_with_bin_centers_edges.nodes[node_id][\"is_bin_edge\"] = False\n", + " else:\n", + " track_graph_with_bin_centers_edges.nodes[node_id][\"is_bin_edge\"] = True\n", + " track_graph_with_bin_centers_edges.nodes[node1][\"edge_id\"] = edge_ind\n", + " track_graph_with_bin_centers_edges.nodes[node2][\"edge_id\"] = edge_ind\n", + " track_graph_with_bin_centers_edges.nodes[node1][\"is_bin_edge\"] = True\n", + " track_graph_with_bin_centers_edges.nodes[node2][\"is_bin_edge\"] = True\n", + " n_nodes = len(track_graph_with_bin_centers_edges.nodes)\n", + "\n", + " return track_graph_with_bin_centers_edges\n", + "\n", + "\n", + "def make_track_graph_with_bin_centers(\n", + " track_graph: nx.Graph, place_bin_size: float\n", + ") -> nx.Graph:\n", + " \"\"\"Insert the bin center and bin edge positions as nodes in the track graph.\n", + "\n", + " Parameters\n", + " ----------\n", + " track_graph : nx.Graph\n", + " place_bin_size : float\n", + "\n", + " Returns\n", + " -------\n", + " track_graph_with_bin_centers_edges : nx.Graph\n", + "\n", + " \"\"\"\n", + " track_graph_with_bin_centers_edges = track_graph.copy()\n", + " n_nodes = len(track_graph.nodes)\n", + "\n", + " for edge_ind, (node1, node2) in enumerate(track_graph.edges):\n", + " node1_x_pos, node1_y_pos = track_graph.nodes[node1][\"pos\"]\n", + " node2_x_pos, node2_y_pos = track_graph.nodes[node2][\"pos\"]\n", + "\n", + " edge_size = np.linalg.norm(\n", + " [(node2_x_pos - node1_x_pos), (node2_y_pos - node1_y_pos)]\n", + " )\n", + " n_bins = 2 * np.ceil(edge_size / place_bin_size).astype(int) + 1\n", + " if ~np.isclose(node1_x_pos, node2_x_pos):\n", + " f = interp1d((node1_x_pos, node2_x_pos), (node1_y_pos, node2_y_pos))\n", + " xnew = np.linspace(node1_x_pos, node2_x_pos, num=n_bins, endpoint=True)\n", + " xnew = xnew[::2]\n", + " xy = np.stack((xnew, f(xnew)), axis=1)\n", + " else:\n", + " ynew = np.linspace(node1_y_pos, node2_y_pos, num=n_bins, endpoint=True)\n", + " ynew = ynew[::2]\n", + " xnew = np.ones_like(ynew) * node1_x_pos\n", + " xy = np.stack((xnew, ynew), axis=1)\n", + " dist_between_nodes = np.linalg.norm(np.diff(xy, axis=0), axis=1)\n", + "\n", + " new_node_ids = n_nodes + np.arange(len(dist_between_nodes) + 1)\n", + " nx.add_path(\n", + " track_graph_with_bin_centers_edges,\n", + " [*new_node_ids],\n", + " distance=dist_between_nodes[0],\n", + " )\n", + " nx.add_path(\n", + " track_graph_with_bin_centers_edges, [node1, new_node_ids[0]], distance=0\n", + " )\n", + " nx.add_path(\n", + " track_graph_with_bin_centers_edges, [node2, new_node_ids[-1]], distance=0\n", + " )\n", + " track_graph_with_bin_centers_edges.remove_edge(node1, node2)\n", + " for ind, (node_id, pos) in enumerate(zip(new_node_ids, xy)):\n", + " track_graph_with_bin_centers_edges.nodes[node_id][\"pos\"] = pos\n", + " track_graph_with_bin_centers_edges.nodes[node_id][\"edge_id\"] = edge_ind\n", + " if ind % 2:\n", + " track_graph_with_bin_centers_edges.nodes[node_id][\"is_bin_edge\"] = False\n", + " else:\n", + " track_graph_with_bin_centers_edges.nodes[node_id][\"is_bin_edge\"] = True\n", + " track_graph_with_bin_centers_edges.nodes[node1][\"edge_id\"] = edge_ind\n", + " track_graph_with_bin_centers_edges.nodes[node2][\"edge_id\"] = edge_ind\n", + " track_graph_with_bin_centers_edges.nodes[node1][\"is_bin_edge\"] = True\n", + " track_graph_with_bin_centers_edges.nodes[node2][\"is_bin_edge\"] = True\n", + " n_nodes = len(track_graph_with_bin_centers_edges.nodes)\n", + "\n", + " return track_graph_with_bin_centers_edges\n", + "\n", + "\n", + "def convert_track_graph_with_edges_to_nodes_only(\n", + " track_graph_with_bin_centers_edges, track_graph\n", + ") -> nx.Graph:\n", + " \"\"\"\n", + " Convert a track graph with edges to a graph with nodes only.\n", + "\n", + " Parameters\n", + " ----------\n", + " env : Environment\n", + " The environment containing the track graph.\n", + "\n", + " Returns\n", + " -------\n", + " nx.Graph\n", + " A new graph with nodes only, where each node represents a bin.\n", + " \"\"\"\n", + " copy_graph = track_graph_with_bin_centers_edges.copy()\n", + " nodes_to_remove = [\n", + " node_id\n", + " for node_id, node_data in track_graph_with_bin_centers_edges.nodes(data=True)\n", + " if node_data[\"is_bin_edge\"]\n", + " ]\n", + " nodes_to_remove = set(nodes_to_remove)\n", + "\n", + " for node_id in track_graph.nodes:\n", + " copy_graph.remove_node(node_id)\n", + "\n", + " nodes_to_remove_neighbors = [\n", + " list(track_graph_with_bin_centers_edges.neighbors(node_id))\n", + " for node_id in nodes_to_remove\n", + " ]\n", + "\n", + " for node_id, neighbors in zip(nodes_to_remove, nodes_to_remove_neighbors):\n", + " try:\n", + " copy_graph.remove_node(node_id)\n", + " except nx.NetworkXError:\n", + " # Node already removed\n", + " pass\n", + " if (\n", + " len(neighbors) == 2\n", + " and neighbors[0] not in nodes_to_remove\n", + " and neighbors[1] not in nodes_to_remove\n", + " ):\n", + " copy_graph.add_edge(neighbors[0], neighbors[1])\n", + "\n", + " return copy_graph\n", + "\n", + "\n", + "def plot_graph(track_graph, ax=None, ax_title=\"\"):\n", + " plot_track_graph(track_graph, ax=ax)\n", + " for node_id, node_data in track_graph.nodes(data=True):\n", + " try:\n", + " if node_data[\"is_bin_edge\"]:\n", + " ax.text(\n", + " node_data[\"pos\"][0],\n", + " node_data[\"pos\"][1],\n", + " str(node_id),\n", + " fontsize=12,\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"black\",\n", + " )\n", + " else:\n", + " ax.scatter(node_data[\"pos\"][0], node_data[\"pos\"][1], color=\"k\")\n", + " ax.text(\n", + " node_data[\"pos\"][0],\n", + " node_data[\"pos\"][1],\n", + " str(node_id),\n", + " fontsize=12,\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"red\",\n", + " )\n", + " except KeyError:\n", + " ax.scatter(node_data[\"pos\"][0], node_data[\"pos\"][1], color=\"k\")\n", + " ax.text(\n", + " node_data[\"pos\"][0],\n", + " node_data[\"pos\"][1],\n", + " str(node_id),\n", + " fontsize=12,\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"red\",\n", + " )\n", + " ax.set_title(ax_title)\n", + "\n", + "\n", + "x = np.linspace(0, 30)\n", + "\n", + "position = np.concatenate(\n", + " (\n", + " np.stack((np.zeros_like(x), x[::-1]), axis=1),\n", + " np.stack((x, np.zeros_like(x)), axis=1),\n", + " np.stack((np.ones_like(x) * 30, x), axis=1),\n", + " )\n", + ")\n", + "position += multivariate_normal(mean=0, cov=0.05).rvs(position.shape)\n", + "\n", + "node_positions = [\n", + " (0, 0), # xy position of node 0\n", + " (30, 0), # xy position of node 1\n", + " (30, 30), # xy position of node 2\n", + " (0, 30), # xy position of node 3\n", + "]\n", + "\n", + "edges = [\n", + " (0, 1), # connects node 0 and node 1\n", + " (0, 3), # connects node 0 and node 3\n", + " (1, 2), # connects node 1 and node 2\n", + "]\n", + "\n", + "track_graph = make_track_graph(node_positions, edges)\n", + "\n", + "env_track_graph = Environment(\n", + " place_bin_size=10, track_graph=track_graph, edge_order=edges, edge_spacing=1,\n", + ").fit_place_grid(position=position)\n", + "\n", + "fig, axes = plt.subplots(\n", + " 2, 2, figsize=(7, 6), sharex=True, sharey=True, constrained_layout=True\n", + ")\n", + "plot_graph(track_graph, ax=axes[0, 0], ax_title=\"Track graph\")\n", + "\n", + "# Create a track graph with bin centers and edges\n", + "track_graph_with_bin_centers_edges = make_track_graph_with_bin_centers_edges(\n", + " track_graph, place_bin_size=10\n", + ")\n", + "plot_graph(\n", + " track_graph_with_bin_centers_edges,\n", + " ax=axes[0, 1],\n", + " ax_title=\"Track graph with bin centers and edges\",\n", + ")\n", + "\n", + "# Create a track graph with bin centers and edges\n", + "track_graph_with_bin_centers = make_track_graph_with_bin_centers(\n", + " track_graph, place_bin_size=10\n", + ")\n", + "plot_graph(\n", + " track_graph_with_bin_centers,\n", + " ax=axes[1, 0],\n", + " ax_title=\"Track graph with bin centers1\",\n", + ")\n", + "\n", + "track_graph_with_bin_centers = convert_track_graph_with_edges_to_nodes_only(\n", + " track_graph_with_bin_centers_edges, track_graph\n", + ")\n", + "plot_graph(\n", + " track_graph_with_bin_centers,\n", + " ax=axes[1, 1],\n", + " ax_title=\"Track graph with bin centers2\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e8276574", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(env_track_graph.place_bin_centers_nodes_df_[\"x_position\"],\n", + " env_track_graph.place_bin_centers_nodes_df_[\"y_position\"],\n", + " color=\"r\",\n", + " label=\"bin centers\",\n", + " )\n", + "\n", + "plt.scatter(\n", + " env_track_graph.place_bin_edges_nodes_df_[\"x_position\"],\n", + " env_track_graph.place_bin_edges_nodes_df_[\"y_position\"],\n", + " color=\"black\",\n", + " label=\"bin centers\",\n", + " marker=\"|\",\n", + ")\n", + "for node_id, node_data in env_track_graph.track_graph.nodes(data=True):\n", + " plt.scatter(\n", + " node_data[\"pos\"][0],\n", + " node_data[\"pos\"][1],\n", + " color=\"green\",\n", + " zorder=-1,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e5037bbc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def make_track_graph_with_bin_centers(env):\n", + "\n", + " track_graph_with_bin_centers = nx.Graph()\n", + "\n", + " for edge_id, df in env.place_bin_centers_nodes_df_.groupby(\"edge_id\"):\n", + " if edge_id == -1:\n", + " continue\n", + " # sort the dataframe by linear position\n", + " # to ensure the order of nodes in the graph\n", + " df = df.sort_values(\"linear_position\")\n", + "\n", + " # add the nodes to the graph\n", + " for _, row in df.iterrows():\n", + " track_graph_with_bin_centers.add_node(\n", + " row[\"node_id\"],\n", + " pos=(row[\"x_position\"], row[\"y_position\"]),\n", + " edge_id=edge_id,\n", + " )\n", + " # add the edges to the graph\n", + " for node1, node2 in zip(df[\"node_id\"][:-1], df[\"node_id\"][1:]):\n", + " track_graph_with_bin_centers.add_edge(node1, node2)\n", + " track_graph_with_bin_centers.edges[node1, node2][\"distance\"] = np.linalg.norm(\n", + " np.array(track_graph_with_bin_centers.nodes[node1][\"pos\"])\n", + " - np.array(track_graph_with_bin_centers.nodes[node2][\"pos\"])\n", + " )\n", + " track_graph_with_bin_centers.edges[node1, node2][\"edge_id\"] = edge_id\n", + "\n", + " # Link edges\n", + " track_graph_node_to_bin_center = []\n", + "\n", + " for edge_id, (node1, node2) in enumerate(env.track_graph.edges):\n", + "\n", + " df = env.place_bin_centers_nodes_df_.loc[\n", + " env.place_bin_centers_nodes_df_.edge_id == edge_id\n", + " ].sort_values(\"linear_position\")\n", + " node1_pos = env.track_graph.nodes[node1][\"pos\"]\n", + " node2_pos = env.track_graph.nodes[node2][\"pos\"]\n", + "\n", + " # find closest nodes in track_graph_with_bin_centers\n", + " closest_node1 = np.argmin(\n", + " np.linalg.norm(\n", + " df[[\"x_position\", \"y_position\"]].values - np.array(node1_pos),\n", + " axis=1,\n", + " )\n", + " )\n", + " track_graph_node_to_bin_center.append([node1, df.iloc[closest_node1].node_id])\n", + "\n", + " closest_node2 = np.argmin(\n", + " np.linalg.norm(\n", + " df[[\"x_position\", \"y_position\"]].values - np.array(node2_pos), axis=1\n", + " )\n", + " )\n", + " track_graph_node_to_bin_center.append([node2, df.iloc[closest_node2].node_id])\n", + "\n", + " track_graph_node_to_bin_center = np.array(track_graph_node_to_bin_center)\n", + "\n", + " # Link edges (edge_id, node1) to (edge_id, node2)\n", + " for node_id in np.unique(track_graph_node_to_bin_center[:, 0]):\n", + " is_node = track_graph_node_to_bin_center[:, 0] == node_id\n", + " for node1, node2 in zip(\n", + " track_graph_node_to_bin_center[is_node, 1][:-1],\n", + " track_graph_node_to_bin_center[is_node, 1][1:],\n", + " ):\n", + " track_graph_with_bin_centers.add_edge(node1, node2)\n", + " track_graph_with_bin_centers.edges[node1, node2][\"distance\"] = np.linalg.norm(\n", + " np.array(track_graph_with_bin_centers.nodes[node1][\"pos\"])\n", + " - np.array(track_graph_with_bin_centers.nodes[node2][\"pos\"])\n", + " )\n", + "\n", + " return track_graph_with_bin_centers\n", + "\n", + "track_graph_with_bin_centers = make_track_graph_with_bin_centers(env_track_graph)\n", + "\n", + "plot_track_graph(\n", + " track_graph_with_bin_centers,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e78eabee", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "import numpy as np\n", + "import pandas as pd\n", + "from typing import Tuple # Assuming Environment is imported from your module\n", + "\n", + "# Assuming Environment class definition is available\n", + "# from your_module import Environment\n", + "\n", + "\n", + "def make_track_graph_with_bin_centers_subgraph(env: Environment) -> nx.Graph:\n", + " \"\"\"Creates a graph connecting only the centers of place bins for a 1D track.\n", + "\n", + " This function leverages the pre-computed 'track_graph_with_bin_centers_edges_'\n", + " and 'nodes_df_' stored in the fitted Environment object. It extracts the\n", + " nodes corresponding to bin centers and creates an induced subgraph containing\n", + " only these nodes and the edges connecting them in the augmented graph.\n", + "\n", + " Parameters\n", + " ----------\n", + " env : Environment\n", + " A fitted Environment object for a 1D track\n", + " (must have track_graph attribute).\n", + "\n", + " Returns\n", + " -------\n", + " track_graph_with_bin_centers : nx.Graph\n", + " A graph where nodes are bin centers and edges connect adjacent\n", + " bin centers along the track. Node attributes include 'pos' and 'edge_id'.\n", + " Edge attributes include 'distance' and 'edge_id'.\n", + "\n", + " Raises\n", + " ------\n", + " RuntimeError\n", + " If the environment is not fitted or is not a 1D environment.\n", + " ValueError\n", + " If the necessary attributes ('track_graph_with_bin_centers_edges_'\n", + " or 'nodes_df_') are missing from the environment object.\n", + " \"\"\"\n", + " if not env._is_fitted:\n", + " raise RuntimeError(\n", + " \"Environment has not been fitted yet. Call `fit_place_grid` first.\"\n", + " )\n", + " if env.track_graph is None:\n", + " raise RuntimeError(\n", + " \"This function requires a 1D environment (with a track_graph).\"\n", + " )\n", + " if env.track_graph_with_bin_centers_edges_ is None or env.nodes_df_ is None:\n", + " raise ValueError(\n", + " \"Required attributes 'track_graph_with_bin_centers_edges_' or \"\n", + " \"'nodes_df_' are missing. Ensure the 1D environment was fitted correctly.\"\n", + " )\n", + "\n", + " # Identify nodes that are bin centers (not bin edges)\n", + " bin_center_nodes_df = env.nodes_df_[~env.nodes_df_[\"is_bin_edge\"]]\n", + " bin_center_node_ids = bin_center_nodes_df.node_id.tolist()\n", + "\n", + " # Create the subgraph containing only bin center nodes and their connecting edges\n", + " # .copy() ensures it's a new graph object, not just a view\n", + " track_graph_with_bin_centers = env.track_graph_with_bin_centers_edges_.subgraph(\n", + " bin_center_node_ids\n", + " ).copy()\n", + "\n", + " return track_graph_with_bin_centers\n", + "\n", + "\n", + "track_graph_with_bin_centers = make_track_graph_with_bin_centers_subgraph(\n", + " env_track_graph\n", + ")\n", + "plot_track_graph(\n", + " track_graph_with_bin_centers,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa1c8932", + "metadata": {}, + "outputs": [], + "source": [ + "env_track_graph.nodes_df_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e56be3d", + "metadata": {}, + "outputs": [], + "source": [ + "env_track_graph.track_graph.edges" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d170389", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ec5ce9e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00017833", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "non_local_detector2", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/diffusion_test.ipynb b/notebooks/diffusion_test.ipynb new file mode 100644 index 0000000..67bc7fe --- /dev/null +++ b/notebooks/diffusion_test.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1D Simulation of Probability density with boundary\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n", + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/86/m147b4k17lddvs_xsw0mj2zw0000gn/T/ipykernel_49971/4045841897.py:18: RuntimeWarning: divide by zero encountered in divide\n", + " truncated_gaussian_kde_pdf = np.where(area_values > 0, gaussian_kde_pdf / area_values, 0.0)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats\n", + "\n", + "loc = 0\n", + "scale = 1\n", + "shape = 0.954\n", + "\n", + "log_norm_gen = stats.lognorm(loc=loc, scale=scale, s=shape)\n", + "data = log_norm_gen.rvs(size=1000)\n", + "x = np.linspace(-1, 15, 2000)\n", + "pdf = log_norm_gen.pdf(x)\n", + "\n", + "bandwidth = 0.1\n", + "gaussian_kde_pdf = stats.gaussian_kde(data, bw_method=bandwidth).pdf(x)\n", + "area_values = stats.norm.cdf(np.inf, x, bandwidth) - stats.norm.cdf(0.0, x, bandwidth)\n", + "\n", + "truncated_gaussian_kde_pdf = np.where(area_values > 0, gaussian_kde_pdf / area_values, 0.0)\n", + "\n", + "\n", + "plt.hist(data, bins=100, density=True, color=\"green\", alpha=0.6)\n", + "plt.plot(x, pdf, color=\"black\", linewidth=2)\n", + "plt.plot(x, gaussian_kde_pdf, color=\"red\", linewidth=2)\n", + "plt.plot(x, truncated_gaussian_kde_pdf, linewidth=3, color=\"orange\")\n", + "plt.ylim(0, 0.75)\n", + "plt.plot(x, area_values, color=\"red\", linewidth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.04760765e-01, 1.04499360e-01, 1.04216226e-01, ...,\n", + " 6.38513540e-12, 8.31357815e-12, 1.08100460e-11])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "truncated_gaussian_kde_pdf[450:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "non_local_detector", + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/test_env.ipynb b/notebooks/test_env.ipynb new file mode 100644 index 0000000..f06bd38 --- /dev/null +++ b/notebooks/test_env.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "97644a41", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/edeno/Documents/GitHub/non_local_detector/src/non_local_detector/likelihoods/clusterless_kde.py:54: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", + " from tqdm.autonotebook import tqdm\n" + ] + } + ], + "source": [ + "from scipy.stats import multivariate_normal\n", + "from non_local_detector import Environment\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from track_linearization import make_track_graph, get_linearized_position\n", + "\n", + "x = np.linspace(0, 30)\n", + "\n", + "position = np.concatenate(\n", + " (\n", + " np.stack((np.zeros_like(x), x[::-1]), axis=1),\n", + " np.stack((x, np.zeros_like(x)), axis=1),\n", + " np.stack((np.ones_like(x) * 30, x), axis=1),\n", + " )\n", + ")\n", + "position += multivariate_normal(mean=0, cov=0.05).rvs(position.shape)\n", + "\n", + "node_positions = [\n", + " (0, 0), # xy position of node 0\n", + " (30, 0), # xy position of node 1\n", + " (30, 30), # xy position of node 2\n", + " (0, 30), # xy position of node 3\n", + "]\n", + "\n", + "edges = [\n", + " (0, 1), # connects node 0 and node 1\n", + " (0, 3), # connects node 0 and node 3\n", + " (1, 2), # connects node 1 and node 2\n", + "]\n", + "\n", + "track_graph = make_track_graph(node_positions, edges)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a89303f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False, True, False, True])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "env = Environment(\n", + " place_bin_size=10,\n", + " track_graph=track_graph,\n", + " edge_order=edges,\n", + " edge_spacing=10,\n", + ").fit(position=position)\n", + "\n", + "lin_pos_df = get_linearized_position(\n", + " position,\n", + " track_graph=track_graph,\n", + " edge_order=edges,\n", + " edge_spacing=10,\n", + ")\n", + "\n", + "env.plot_grid()\n", + "\n", + "plt.scatter(lin_pos_df[\"linear_position\"], np.ones_like(lin_pos_df[\"linear_position\"]) * 0.05, c=\"red\", s=1, zorder=10)\n", + "env.is_track_interior_" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4b864d39", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.n_dims" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9a4fdbf4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(1)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.nd_index_to_flat(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "871cbd64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.int64(0), np.int64(1))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.bin_index_to_nd(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "82fa9f59", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ True, True, True, True],\n", + " [ True, False, False, False],\n", + " [ True, False, False, False],\n", + " [ True, True, True, True]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5JgEAgDXZodjElIN+ZrU5JgEAgDUtdTo1IC3N0sUmgiMAAIAF2KHYxKFqAAAAmEJwBAAAgCkERwAAAJhCcAQAAIApBEcAAACYQnAEAACAKQRHAAAAmEJwBAAAgCkERwAAAJhCcAwwO0xoDgAAIBEcA84OE5oDAABIBMeAW+p06nR4uKUnNAcAAJCk4EB3oKyzw4TmAAAgMOJcLg1IS9NSp9MSeYGKIwAAgEVZ7ZQ2giMAAIBFWe2UNg5VAwAAWJTVTmmj4ggAAABTCI4AAAAwheAIAAAAUwiOAAAAMIXgCAAAAFMIjgAAADCF4AgAAABTCI4AAAAwheDoZ3Eulz5ISlKcyxXorgAAABuxYoYgOPqZ1eaYBAAA9mDFDEFw9DOrzTEJAADswYoZgrmq/cxqc0wCAAB7sGKGoOIIAAAAUwiOAAAAMIXgCAAAYGFWurqa4AgAAGBhVrq6muBYQqz0bQEAANiHla6u5qrqEnLjtwWrXSEFAACsy0pXV1NxLCFW+rYAAABQFJYIjtOnT1e7du1UuXJlRUZGqn///jpw4ID7/uzsbD333HNq1aqVKlasqKioKA0bNkwnTpwodL3Lly9X27ZtVbVqVVWsWFGtW7fWX//61zztjh8/rkcffVQ1atRQhQoV1Lp1a+3atcunzzExJkaPde1qmW8MAAAA3rJEcNy4caPGjBmjbdu2ac2aNbp27Zq6d++uzMxMSdLly5e1e/duvfjii9q9e7eWL1+ugwcPqm/fvoWut3r16nr++ee1detW7d27VyNHjtTIkSO1evVqd5uzZ8+qY8eOCgkJ0T/+8Q+lpqbqz3/+s6pWrerPpwwAAGA7ljjHMTEx0ePnhIQERUZGateuXerUqZMiIiK0Zs0ajzZvv/222rdvr/T0dDVo0CDf9Xbp0sXj5/Hjx+ujjz7Sli1b1KNHD0nS66+/rujoaCUkJLjbNWzYsND+ZmVlKSsry/3zhQsXbvUUAQAAbM8SwfFm58+fl/RzxbCwNg6Hw3Rl0DAMrVu3TgcOHNDrr7/uXr5y5Ur16NFDDz/8sDZu3Kh69erpt7/9rR5//PEC1zV9+nRNnTo1z/JGjRopNDTUVH9Qthw/flyLFi0KdDdgQewbKEzFihXl5Nx43OTG4lVJcxiGYQRs6/kwDEP9+vXT2bNntXnz5nzbXL16Vb/61a/UrFkzLViwoND1nT9/XvXq1VNWVpaCgoL0zjvvaNSoUe77w8LCJEmTJk3Sww8/rB07dmjChAl69913NWzYsHzXmV/FMTo6Wj169FBISIi3TxllwJAhQ/TII48EuhuwoEWLFrFvoEATJ05UmgXG7oO1ZGdna/Xq1Tp//ryqVKlSotu2XMVx7Nix2rt3r7Zs2ZLv/dnZ2Ro8eLByc3P1zjvv3HJ9lStXVkpKii5duqSkpCRNmjRJjRo1ch/Gzs3NVdu2bTVt2jRJUps2bfSvf/1Lc+fOLTA4hoaGUlkEAABljqWC49NPP62VK1dq06ZNql+/fp77s7OzNXDgQB09elTr1q0zlbLLlSvnLvO3bt1a+/fv1/Tp093BsW7dumrRooXHY5o3b65ly5YV/wkBAACUIpYIjoZh6Omnn9ann36qDRs2KDY2Nk+b66Hx0KFDWr9+vWrUqFHkbd14mLljx44eQ/9I0sGDBxXDsDkAAAAeLBEcx4wZo4ULF+qzzz5T5cqVderUKUlSRESEwsPDde3aNQ0YMEC7d+/W559/rpycHHeb6tWrq3z58pKkYcOGqV69epo+fbqkny9iadu2rRo3bqyffvpJq1at0scff6y5c+e6tz1x4kTdc889mjZtmgYOHKgdO3bovffe03vvvefz5xnncmlAWpqWOp2M5wgAAGzHEsHxepC7efichIQEjRgxQt99951Wrlwp6efDzTdav369+3Hp6ekqV+4/Q1NmZmbqt7/9rb777juFh4e7L6YZNGiQu027du306aefasqUKXr11VcVGxurmTNnaujQoT5/nkw7CAAA7MwSwfFWF3Y3bNjwlm0kacOGDR4/v/baa3rttddu+bjevXurd+/et2xXXEudTnfFEQAAQLLXEUlLBMeywkqTlAMAAGuw0xFJS0w5CAAAUFYtdTp1OjzcFkckqTgCAAAEkJ2OSFJxBAAAsKA4l0sfJCUpzuUKdFfcCI4AAAAWdOO5j1ZBcAQAALAgK577yDmOAAAAFmTFcx+pOAIAAMAUgiMAAABMITgCAADAFIIjAAAATCE4AgAAwBSCIwAAAEwhOAIAAMAUgiMAAABMITiWACvONQkAAOAtgmMJsOJckwAAAN4iOJYAK841CQAA4C3mqi4BVpxrEgAA2Eucy6UBaWlaEhur1QHqAxVHP+P8RgAA4AvXT30bdORIwPpAcPQzzm8EAAC+cP3UtyWNGgWsDxyq9rOlTqcGpKVxfiMAACiW66e+ZWdnS6mpAekDwdHPOL8RAACUFhyqBgAAgCkERwAAAJhCcAQAAIApBEcAAACYQnAEAACAKQRHAAAAi7LaRCIERwAAAIuy2kQiBEcAAACLuj5bjFUmEmEAcAAAAIuy2kQiVBwBAABgCsHRz6x2UisAAEBRERz9zGontQIAAPuKc7n00caN+k2Atk9w9DOrndQKAADsa0BammpfvarJAdo+wdHPEmNi9FjXrpY6sRUAANjTUqdTp8PCNCNA2yc4+tCDGRmczwgAAPwmMSZGwzt31rsB2j7B0YcGHTnC+YwAAKDUIjj60JJGjTifEQAAlFoMAO5Dq6KjtaZRo0B3AwAAwC+oOAIAAMAUgiMAAABMITgCAADAFIIjAAAATCE4AgAAwBSCYwmIc7kYGBwAANgewbEEDEhLY2BwAABgewTHErDU6WRgcAAAYHsMAF4CEmNilBgTE+huAAAAG4pzuTQgLU1Lnc6A5wkqjgAAABZmpVPeCI4AAAAWZqVT3jhUDQAAYGFWOuWNiiMAAABMITgCAADAFIIjAAAATCE4AgAAwBSCIwAAAEwhOAIAAMAUgmMxzJkzRy1atFC7du0C3RUAAAC/IzgWw5gxY5Samqrk5ORAdwUAAMDvCI4AAAAwheAIAAAAUwiOARLncumDpCTFuVyB7goAAIApBMcAGZCWptpXrmhAWlqguwIAACzC6oUlgmOALHU6dTo8XEudzkB3BQAAWITVC0vBge5AWZUYE6PEmJhAdwMAAFjIUqdTA9LSLFtYIjj6QZzL5f6jEw4BAIBZVi8scajaD6xeZgYAACgKgqMfcP4iAAAoDqteJMOhaj+wepkZAABY241HL62UKag4AgAAWIxVj15ScQQAALAYqx69pOIIAAAAUwiOAAAAMIXgCAAAAFMIjgAAADCF4AgAAABTCI4AAAAwxavgeOXKFW3ZskWpqal57rt69ao+/vhjn3UMAAAA1mI6OB48eFDNmzdXp06d1KpVK3Xp0kUnT55033/+/HmNHDnSL50EAABA4JkOjs8995xatWqlM2fO6MCBA6pSpYo6duyo9PR0f/YPAACgVLPqvNT5MR0cv/76a02bNk01a9aU0+nUypUr1bNnT9177706cuSIP/tYKthppwAAACXnxnmprc50cLxy5YqCgz1nKJwzZ4769u2rzp076+DBgz7vXGlip50CAACUHKvOS50f03NVN2vWTDt37lTz5s09lr/99tsyDEN9+/b1eedKk6VOpwakpdlipwAAACXHqvNS58d0xfGhhx7SokWL8r1v9uzZeuSRR2QYhs86VtokxsTosa5dbbNjAAAA3Mx0cJwyZYpWrVpV4P3vvPOOcnNzfdIpAAAAWA8DgAMAAMAUgiMAAABMITgCAADAFIIjAABAANlprGeCo5/YaScAAACBY6exnk2P43ij48eP66uvvtKZM2fyXEk9btw4n3TM7m7cCRiCBwAAFMROYz17HRwTEhL05JNPqnz58qpRo4YcDof7PofDQXD8P3baCQAAQODYaQBwr4PjSy+9pJdeeklTpkxRuXIc6S6InXYCAAAAM7xOfpcvX9bgwYMJjQAAAGWM1+lv9OjR+uSTT/zRFwAAAFiY14eqp0+frt69eysxMVGtWrVSSEiIx/1vvPGGzzoHAAAA6/A6OE6bNk2rV69W06ZNJSnPxTEAAAAonbwOjm+88YY+/PBDjRgxwg/dAQAAgFV5fY5jaGioOnbs6I++AAAAwMK8Do7jx4/X22+/7Y++AAAAwMK8PlS9Y8cOrVu3Tp9//rluu+22PBfHLF++3GedAwAAgHV4HRyrVq2qX//61/7oCwAAACysSFMOAgAAwHfiXC73VMVWnnmO6V8AAAACbEBammpfuaIBaWmB7kqhTFUc77zzTiUlJalatWpq06ZNoeM17t6922edAwAAKAuWOp3uiqOVmQqO/fr1U2hoqCSpf//+/uwPAABAmZMYE2PpQ9TXmQqOL7/8cr7/R+Hscr4CAACAGV5fHGMYhnbt2qVjx47J4XAoNjb2loevy6obz1cgOAIAALvzKjiuX79eo0ePlsvlkmEYkuQOjx9++KE6derkl07alV3OVwAAADDD9FXVaWlp6t27txo2bKjly5dr//79Sk1N1SeffKL69evrwQcf1JEjR/zZV9tJjInRY127Um0EAAClgumK48yZM3X33XcrKSnJY3mzZs300EMPqVu3bnrzzTfL/HSEnNcIAABKK9MVxw0bNmjChAn53udwODRhwgStX7/eV/2yLbuMwwQAAOAt08ExPT1drVq1KvD+li1byuVy+aRTdrbU6dTp8HDOawQAAKWO6UPVly5dUoUKFQq8v0KFCrp8+bJPOmVndhmHCQAAwFteXVWdmpqqU6dO5Xvf999/75MOAQAAwJq8Co5du3Z1D8NzI4fDIcMwGMsRAACgFDMdHI8ePerPfgAAAMDiTAfHGM7bAwAAKNNMX1UNAACAso3gCAAAAFMsExw3bdqkPn36KCoqSg6HQytWrCiw7W9+8xs5HA7NnDmz0HV26dJFDocjz61Xr175tp8+fbp7MHMAAICSEOdy6YOkJMUVMB72re4vSZYJjpmZmbrjjjs0e/bsQtutWLFC27dvV1RU1C3XuXz5cp08edJ9++abbxQUFKSHH344T9vk5GS99957uv3224v8HAAAALx1q1nnrDQrnWWCY8+ePfXaa6/p17/+dYFtjh8/rrFjx+pvf/ubQkJCbrnO6tWrq06dOu7bmjVrVKFChTzB8dKlSxo6dKjef/99VatWrVjPw0rfCgAAgPXdatY5K81K53VwPH36tOLj4xUVFaXg4GAFBQV53PwlNzdX8fHxeuaZZ3TbbbcVaR3z5s3T4MGDVbFiRY/lY8aMUa9evdStWzdT68nKytKFCxc8btdZ6VsBAACwvsSYGD3WtWuBM8/d6v6S5NUA4JI0YsQIpaen68UXX1TdunVLbNDv119/XcHBwRo3blyRHr9jxw598803mjdvnsfyxYsXa/fu3UpOTja9runTp2vq1Kl5ljdq1EibsrPVNTlZm9q1k9MC3wxgDcePH9eiRYsC3Q1YEPsGClOxYkU+S5BHVlZWwLbtdXDcsmWLNm/erNatW/uhO/nbtWuXZs2apd27dxc5qM6bN08tW7ZU+/bt3csyMjI0fvx4ffnllwoLCzO9rilTpmjSpEnuny9cuKDo6GgdOXJEGRUq6OPOnX++g6oj/k/79u31yCOPBLobsKBFixaxb6BAEydOVBqfJbhJdnZ2wLbt9aHq6OjofKcd9KfNmzfrzJkzatCggYKDgxUcHCyXy6Xf/e53atiw4S0ff/nyZS1evFiPPfaYx/Jdu3bpzJkzuuuuu9zr3bhxo9566y0FBwcrJycn3/WFhoaqSpUqHjcAAIDSzuuK48yZMzV58mS9++67pkKbL8THx+c5/7BHjx6Kj4/XyJEjb/n4v//978rKytKjjz7qsbxr167at2+fx7KRI0eqWbNmeu655/x6ziYAAIDdeB0cBw0apMuXL6tx48aqUKFCnqubf/zxxyJ15NKlSx7l+KNHjyolJUXVq1dXgwYNVKNGDY/2ISEhqlOnjpo2bepeNmzYMNWrV0/Tp0/3aDtv3jz1798/zzoqV66sli1beiyrWLGiatSokWc5AABAWVekiqM/7Ny5U/fdd5/75+vnEA4fPlzz5883tY709HSVK+d59P3gwYPasmWLvvzyS5/1FQAAoCzyOjgOHz7cH/1Qly5dvDp38tixY3mWbdiwIc+yX/ziF16tN791AAAAoAjBUZJycnK0YsUK7d+/Xw6HQy1atFDfvn05JxAAAKAU8zo4pqWl6cEHH9Tx48fVtGlTGYahgwcPKjo6Wl988YUaN27sj34CAAAgwLwejmfcuHFq3LixMjIytHv3bu3Zs0fp6emKjY0t8uDcpQlTDgIAgKKyeo7wOjhu3LhRf/zjH1W9enX3sho1amjGjBnauHGjTztnR0w5CAAAisrqOcLr4BgaGqqLFy/mWX7p0iWVL1/eJ52yMytNRA4AAOzF6jnC63Mce/furSeeeELz5s1zT9+3fft2Pfnkk+rbt6/PO2g3iTExlpiEHAAA2I/Vc4TXFce33npLjRs3VocOHRQWFqawsDB17NhRTqdTs2bN8kcfAQAAYAFeVxyrVq2qzz77TIcOHdK3334rwzDUokULOS1aUgUAAIBvFGkcR0lq0qSJmjRp4su+AAAAwMJMBcdJkybpD3/4gypWrOieCrAgb7zxhk86BgAAAGsxFRz37Nmj7Oxs9/8BAABQ9pgKjuvXr8/3/wAAAPCdOJdLA9LStNTptOTV1V5fVT1q1Kh8x3HMzMzUqFGjfNIpAACAsqjUDQD+0Ucf6cqVK3mWX7lyRR9//LFPOlVaWX0aIQAAEFilZgDwCxcuyDAMGYahixcvKiwszH1fTk6OVq1apcjISL90srS48VuEFcvPAAAgsKw+ALjp4Fi1alU5HA45HA794he/yHO/w+HQ1KlTfdq50map0+k+bwEAAMBuTAfH9evXyzAM3X///Vq2bJmqV6/uvq98+fKKiYlRVFSUXzpZWlj9WwQAAEBhTAfHzp07S5KOHj2qBg0ayOFw+K1TAAAAsB5TwXHv3r1q2bKlypUrp/Pnz2vfvn0Ftr399tt91jkAAABYh6ng2Lp1a506dUqRkZFq3bq1HA6HDMPI087hcCgnJ8fnnQQAAEDgmQqOR48eVa1atdz/BwAAQNljKjjG3HBBRwwXdwAAAJRJRRoA/IsvvnD//Oyzz6pq1aq655575GJgawAAgFLL6+A4bdo0hYeHS5K2bt2q2bNn649//KNq1qypiRMn+ryDAAAAsAbTw/Fcl5GRIef/DWC9YsUKDRgwQE888YQ6duyoLl26+Lp/pYrVJy4HAAAojNcVx0qVKumHH36QJH355Zfq1q2bJCksLCzfOazxH1afuBwAAKAwXlccH3jgAT322GNq06aNDh48qF69ekmS/vWvf6lhw4a+7l+pwpSDAADAzrwOjnPmzNELL7ygjIwMLVu2TDVq1JAk7dq1S4888ojPO1iaMOUgAACwM6+DY9WqVTV79uw8y6dOneqTDgEAAMCavA6OknTu3DnNmzdP+/fvl8PhUPPmzTV69GhFRET4un8AAACwCK8vjtm5c6caN26sN998Uz/++KO+//57vfnmm2rcuLF2797tjz4CAADAAryuOE6cOFF9+/bV+++/r+Dgnx9+7do1PfbYY5owYYI2bdrk804CAACUddeH9VsSG6vVAepDkSqOzz33nDs0SlJwcLCeffZZ7dy506edAwAAwM+uD+s36MiRgPXB6+BYpUoVpaen51mekZGhypUr+6RTAAAA8LTU6dTp8HAtadQoYH3wOjgOGjRIo0eP1pIlS5SRkaHvvvtOixcv1mOPPcZwPF6Kc7n0QVKS4pjjGwAA3EJiTIwe69pVq6KjA9YHr89x/NOf/iSHw6Fhw4bp2rVrkqSQkBA99dRTmjFjhs87WJrdOJMM4zsCAACr8zo4li9fXrNmzdL06dN1+PBhGYYhp9OpChUq+KN/pRozyQAAADsxHRwvX76sZ555RitWrFB2dra6deumt956SzVr1vRn/0o1ZpIBAAB2Yvocx5dfflnz589Xr169NHjwYK1Zs0ZPPfWUP/sGAAAACzFdcVy+fLnmzZunwYMHS5IeffRRdezYUTk5OQoKCvJbBwEAAGANpiuOGRkZuvfee90/t2/fXsHBwTpx4oRfOgYAAABrMR0cc3JyVL58eY9lwcHB7iur4YmhdgAAQGlj+lC1YRgaMWKEQkND3cuuXr2qJ598UhUrVnQvW758uW97aFMMtQMAAHzh+lSDS53OgGcK08Fx+PDheZY9+uijPu1MacJQOwAAwBesVIwyHRwTEhL82Y9Sh6F2AACAt/KrLlqpGOX1AOAAAADwj/yqi1YqRnk9VzUAAAD8Y6nTqdPh4ZaoLuaHiiMAAIBFWKm6mB8qjgAAADCF4AgAAABTCI4AAAAwheAIAAAAUwiOAAAAMIXgCAAAAFMIjgAAABYW53Lpg6Qkxblcge4KwREAAMDKbpxNJtAIjgAAABZmpdlkmDkGAADAwqw0mwwVRwAAAJhCcAQAAIApBMdimDNnjlq0aKF27doFuisAAAB+R3AshjFjxig1NVXJycmB7goAAIDfERwBAABgCsHRT6w0WCcAAIAvEBz9xEqDdQIAAPgCwdFPrDRYJwAAgC8wALifWGmwTgAAYG1xLpcGpKVpqdNp6fxAxREAACDA7HKKG8ERAAAgwOxyihuHqgEAAALMLqe4UXEEAACAKQRHAAAAmEJwBAAAgCkERwAAAJhCcAQAAIApBEcLYF5rAABgBwRHC7DLoJ8AAKBsIzj6gbcVRLsM+gkAAPzDLkcfCY5+4G0FMTEmRo917WqLgT8BAIDv2eXoI8HRD6ggAgAAb9glOzDloB/YZdogAABgDXbJDlQcAQAAYArBEQAAAKYQHAEAAGAKwREAAACmEBwBAABgCsERAAAAphAcAQAAYArBEQAAAKYQHAEAAGAKwREAAACmEBwBAABgCsERAAAAphAcS1icy6UPkpIU53IFuisAAABeITiWsAFpaap95YoGpKUFuisAAABeITiWsKVOp06Hh2up0xnorgAAAAuxw1HJ4EB3oKxJjIlRYkxMoLsBAAAs5sajklbNClQcAQAALMAORyWpOAIAAFiAHY5KUnEEAACAKQRHAAAAmEJw9BM7XBkFAACsy4pZguDoJ4zXCAAAisOKWYLg6Cd2uDIKAABYlxWzBFdV+4kdrowCAADWZcUsQcURAAAAphAcAQAAYArBEQAAAKYQHAEAAGAKwREAAACmEBwBAAAsiAHAAQAAYAoDgAMAAMAUBgAvw+JcLg1IS9NSp9Nyg3kCAADrYQDwMsyK5WYAAABvEBxLiBXLzQAAAN7gUHUJsWK5GQAAwBtUHAEAAGAKwREAAACmEBwBAABgCsERAAAAphAcAQAAbOTBjAwdDdC2CY4AAAA2MujIETUM0LZtExwbNmwoh8OR5zZmzJh82588eVJDhgxR06ZNVa5cOU2YMCFPm+XLl6tt27aqWrWqKlasqNatW+uvf/2rn58JAABA0S1p1EjHArRt2wTH5ORknTx50n1bs2aNJOnhhx/Ot31WVpZq1aql559/XnfccUe+bapXr67nn39eW7du1d69ezVy5EiNHDlSq1ev9tvzAAAAKI5V0dGKDdC2bTMAeK1atTx+njFjhho3bqzOnTvn275hw4aaNWuWJOnDDz/Mt02XLl08fh4/frw++ugjbdmyRT169Ch2n5mfGgAAlCa2qTje6KefftKCBQs0atQoORwOn6zTMAwlJSXpwIED6tSpU6Fts7KydOHCBY9bfpifGgAAlCa2qTjeaMWKFTp37pxGjBhR7HWdP39e9erVU1ZWloKCgvTOO+/ogQceKPQx06dP19SpU/Msb9SokUJDQ90/b7p8WV2Tk7WpXTs5maO6TPvXv/6liRMnBrobsKCKFSuyb6BAFStW5PMDeWRlZQVs27YMjvPmzVPPnj0VFRVV7HVVrlxZKSkpunTpkpKSkjRp0iQ1atQoz2HsG02ZMkWTJk1y/3zhwgVFR0fryJEjCgkJcS9Pq1BBH18/lE7VsUxzOp1KYx9APtg3UBj2D+QnOzs7YNu2XXB0uVxau3atli9f7pP1lStXzv1trnXr1tq/f7+mT59eaHAMDQ31qCwCAACUBbY7xzEhIUGRkZHq1auXX9ZvGEZAS8AAAABWZauKY25urhISEjR8+HAFB3t2fcqUKTp+/Lg+/vhj97KUlBRJ0qVLl/Tvf/9bKSkpKl++vFq0aCHp53MV27Ztq8aNG+unn37SqlWr9PHHH2vu3Lkl9pwAAADswlbBce3atUpPT9eoUaPy3Hfy5Emlp6d7LGvTpo37/7t27dLChQsVExOjY8eOSZIyMzP129/+Vt99953Cw8PVrFkzLViwQIMGDfLr8wAAALiZHYbxs1Vw7N69uwzDyPe++fPn51lWUNvrXnvtNb322mu+6BoAAECx3DiMn1WDo+3OcQQAACiNljqdOh0erqUWHoLJVhXH0sIOpWgAAFCyEmNiLJ8LqDgGADPKAAAAOyI4BoAdStEAAAA341B1ANihFA0AAHAzKo4AAAAwheDoR3Eulz5ISlKcyxXorgAAABQbwdGPuAgGAACUJgRHP+IiGAAAUJpwcYwfcREMAAAoTag4AgAAWJxVrpsgOAIAAFicVa6bIDgCAABYnFWum+AcRwAAAIvzuG4iOztg/aDiCAAAAFMIjgAAADCF4AgAAABTCI4AAAAwheAIAAAAUwiOfmCVQToBAAB8ieDoB1YZpBMAAFiXHQtNBEc/sMognQAAwLrsWGhiAHA/8BikEwAAIB9LnU4NSEuzVaGJ4AgAABAAdiw0cagaAAAAphAcAQAAYArBEQAAAKYQHAEAAGAKwREAAACmEBwBAABgCsERAAAAphAcAQAAYArBEQAAAKYQHAEAAGAKwREAAACmEBwBAAAsJs7l0gdJSYpzuQLdFQ8ERwAAAIsZkJam2leuaEBaWqC74oHg6GNW/YYAAADsY6nTqdPh4VrqdAa6Kx6CA92B0ubGbwiJMTGB7g4AALChxJgYS+YIKo4+VtA3BCqRAADA7qg4+lhB3xCoRAIAALuj4lhCrHquAgAACCw7HZWk4lhCrHquAgAACCw7HZWk4ggAABBAdjoqScURAAAggOx0VJKKIwAAAEwhOAIAAMAUgiMAAABMITgCAADAFIIjAAAATCE4AgAAwBSCIwAAAEwhOAIAAFiIlacgJDiWECvvBAAAwDpunILQagiOJcTKOwEAALAOK09ByJSDJWSp06kBaWmW3AkAAIB1WHkKQoJjCbHyTgAAAGAGh6oBAABgCsERAAAAphAcAQAAYArBsRjmzJmjFi1aqF27dvnezxA8AACgNCE4FsOYMWOUmpqq5OTkfO9nCB4AAFCaEBz9yMrjMAEAAHiL4Xj8iCF4AABAaULFEQAAAKYQHAEAAGAKwREAAACmEBwBAAAsyIrD+hEcAQAALMiKw/oRHAEAACzIisP6MRwPAACABVlxWD8qjgAAABbEOY4AAAAwhXMcAQAAYArnOJYBcS6XBqSlaanTabnzEgAAgH1wjmMZYMWyMgAAgC8QHH3MimVlAAAAX+BQtY9ZsawMAADgC1QcAQAAYArBEQAAAKYQHH3kqKQHMzIC3Q0AAAC/ITj6SENJg44cCXQ3AACADVlxlpj8EBx95JikJY0aBbobAADAhuwynB/B0UdiJa2Kjg50NwAAgA3ZZTg/huMBAAAIMLsM50fFEQAAAKYQHAEAAGAKwREAAACmEBwBAABgCsERAAAAphAcAQAAbOTBjAwdDdC2CY4AAAA2MujIETUM0LYJjgAAADaypFEjHQvQtgmOAAAANrIqOlqxAdo2wREAAACmEBwBAABgCsExwOJcLn2QlKQ4lyvQXQEAACgUwTHABqSlqfaVKxqQlhborgAAABSK4BhgS51OnQ4P11KnM9BdAQAAKFRwoDtQ1iXGxCgxJibQ3QAAALglKo4AAAAwheAIAAAAUwiOAAAAMIXgCAAAAFMIjgAAADCF4AigyBjAHgDKFoIjgCJjAHsAKFsIjn5ENQalHQPYA0DR2TEnEBz9iGoMSrvEmBg91rUrg9gDQBHYMScQHP3IrtUYO34D8lZZeI4ACsZ7AKzAjjmB4OhHdq3G2PEbkLfKwnMESrvihD/eA2AFdswJBEfkYcdvQN4qC88RJY8qVskqTvjjPeA/rL7fWr1/JcFKvwOCI/Kw4zcgb5WF54iSRxWrZBUn/JX0e4CVPvhvZvX91ur9KwlW+h0QHIEisPKHAAKHKlbJstMXQCt98N+sJPbb4rxn8rqy1u8gONAdAOzoxg8BO3xo2VGcy6UBaWla6nTa5necGBNj6b7683dqx79XSVrqdLp/P1ZTEvttcd4zi9O/0rJfWum9hYpjKUMlrGRY6dtfaWXlCo1d+fN3yt+rcL6ujtrtvT5Q75l23y+t+HcmOJYydn+R3MyKLxrJXofI7OpWHzRW3TdKmje/B39+ePNlquTEuVx6ct8+W73XB+o90+77pRU/0wmOFlaUD0a7v0huZsUXDUrGrT5ofLVvlHQA9fX2vPk9+PPDmy9TJWdAWpqCJOVIpea93l/M7pdW/SJqxc90gmOAFbazFuWDsbS9eVvxRWNnVn1zLApf7Rs3v878/Tvy9Zchu71GStM+GCjX/+Z/adWq1LzXB1pJFSm83f+t+JlOcPQxb3eKwnZWu30g5Ke4HxJWfNHYWWmq4Ppq37j5debv35GvX9d2e42Upn1QCkwQttvfvCQU9+9QUp+3pWH/Jzj6mLc7RWE7a2l4c7D7i6S0VUdKw5cRX7v5debv35EVX9cluZ8Heh8M5KkCVmb397ri/h1K6nUZ6P3fFwiOPubtTmHFD5Giyu+Nx18vklu9yfnqTbC0fChcV5r2N38J9O8oEB/gJbmfB/r3W9ZPFSiI3d/r7PJ3CPT+7wsERx8rDTtFUeX3xuOv38et3uR89SZo9YFxS4Oy/vxvFogPcCt96Pp7fyjrpwoUxEr7QFGUlr+DHdgmOJ49e1bx8fGKiIhQRESE4uPjde7cOdOP/81vfiOHw6GZM2d6LD98+LAeeugh1apVS1WqVNHAgQN1+vRp33a+jCjJN55bbctXfSmJNyO7f9MvrrL+/G8WiA9wK33o+nt/sNJztRJ+LzDLNsFxyJAhSklJUWJiohITE5WSkqL4+HhTj12xYoW2b9+uqKgoj+WZmZnq3r27HA6H1q1bp6+++ko//fST+vTpo9zcXH88jVKtJN94brUtO70JFiUo+LIqE+iKn90rHb5mp33XH9gf7C3Q7yd2Yeffky2mHNy/f78SExO1bds2/fKXv5Qkvf/+++rQoYMOHDigpk2bFvjY48ePa+zYsVq9erV69erlcd9XX32lY8eOac+ePapSpYokKSEhQdWrV9e6devUrVs3/z0p4P8UZSopX055GOjpE600lRYCj/3B3gL9fmIXdv492SI4bt26VREREe7QKEl33323IiIi9PXXXxcYHHNzcxUfH69nnnlGt912W577s7Ky5HA4FBoa6l4WFhamcuXKacuWLQUGx6ysLGVlZbl/Pn/+vCTp2rVrRXp+VvBgRoYGHTmiJY0aaVV0dKC7U+pkZWUpOzvbZ+tbEhv7898rNrbY6/XluuA9X+8bKF3stn/wfmJOcX9P1/OGYRi+7tqtGTbwP//zP0aTJk3yLG/SpIkxbdq0Ah83bdo044EHHjByc3MNwzCMmJgY480333Tff+bMGaNKlSrG+PHjjczMTOPSpUvGmDFjDEnGE088UeB6X375ZUNSqbodlQzj//4NdF+4cePGjRs3bre+HT58uOjhqogCWnF85ZVXNHXq1ELbJCcnS5IcDkee+wzDyHe5JO3atUuzZs3S7t27C2xTq1YtffLJJ3rqqaf01ltvqVy5cnrkkUd05513KigoqMA+TZkyRZMmTXL/fO7cOcXExCg9PV0RERGFPh8ruyCpuqTzPlhXu3bt3H+7su7ChQuKjo5WRkaG+5SIsox94z/YN/Ji//gP9o+82D9+dv78eTVo0EDVq1cv8W0HNDiOHTtWgwcPLrRNw4YNtXfv3nyvdP73v/+t2rVr5/u4zZs368yZM2rQoIF7WU5Ojn73u99p5syZOnbsmCSpe/fuOnz4sL7//nsFBweratWqqlOnjmJjYwvsU2hoqMfh7esiIiJ4cf+foKAgfhc3qVKlCr8TsW/kh33jP9g/8mL/+A/2D0/lypX8Nc4BDY41a9ZUzZo1b9muQ4cOOn/+vHbs2KH27dtLkrZv367z58/rnnvuyfcx8fHxec5R7NGjh+Lj4zVy5Mh8+yJJ69at05kzZ9S3b19vnw5uMGbMmEB3ARbFvoHCsH+gMOwfgecwjECcWem9nj176sSJE3r33XclSU888YRiYmL0//7f/3O3adasmaZPn66HHnoo33U0bNhQEyZM0IQJE9zLEhIS1Lx5c9WqVUtbt27V+PHjNWLECP35z3823bcLFy4oIiJC58+f55sQ8mD/QEHYN1AY9g8UJJD7hi2uqpakv/3tbxo3bpy6d+8uSerbt69mz57t0ebAgQPuK5zNOnDggKZMmaIff/xRDRs21PPPP6+JEyd6tY7Q0FC9/PLL+R6+Btg/UBD2DRSG/QMFCeS+YZuKIwAAAALLNjPHAAAAILAIjgAAADCF4AgAAABTCI4AAAAwheBowtmzZxUfH6+IiAhFREQoPj5e586dM/343/zmN3I4HJo5c2ae5Y0bN1Z4eLhq1aqlfv366dtvv/Vt5+EzDRs2lMPhyHMraFyx5cuX64EHHlCtWrVUpUoVdejQQatXr/Zok52drVdffVWNGzdWWFiY7rjjDiUmJpbE00Exbdq0SX369FFUVJQcDodWrFjhcf+IESPy7Ct33333Lde7bNkytWjRQqGhoWrRooU+/fTTPG3eeecdxcbGKiwsTHfddZc2b97sq6cFH5g+fbratWunypUrKzIyUv3799eBAwfc92dnZ+u5555Tq1atVLFiRUVFRWnYsGE6ceJEoevt0qVLvu9BvXr1Mr1tBN7cuXN1++23uwd279Chg/7xj3+47zcMQ6+88oqioqIUHh6uLl266F//+pfp9S9evFgOh0P9+/cvsM306dPlcDg8hic0i+BowpAhQ5SSkqLExEQlJiYqJSVF8fHxph67YsUKbd++XVFRUXnuu+uuu5SQkKD9+/dr9erVMgxD3bt3V05Ojq+fAnwgOTlZJ0+edN/WrFkjSXr44Yfzbb9p0yY98MADWrVqlXbt2qX77rtPffr00Z49e9xtXnjhBb377rt6++23lZqaqieffFIPPfSQRxtYU2Zmpu644448w4LdKC4uzmOfWbVqVaHr3Lp1qwYNGqT4+Hj985//VHx8vAYOHKjt27e72yxZskQTJkzQ888/rz179ujee+9Vz549lZ6e7rPnhuLZuHGjxowZo23btmnNmjW6du2aunfvrszMTEnS5cuXtXv3br344ovavXu3li9froMHD95y4only5d77E/ffPONgoKCPN6DbrVtBF79+vU1Y8YM7dy5Uzt37tT999+vfv36ucPhH//4R73xxhuaPXu2kpOTVadOHT3wwAO6ePHiLdftcrn0+9//Xvfee2+BbZKTk/Xee+/p9ttvL9oTKPHZsW0mNTXVkGRs27bNvWzr1q2GJOPbb78t9LHfffedUa9ePeObb74xYmJijDfffLPQ9v/85z8NSUZaWpovug4/Gz9+vNG4cWMjNzfX9GNatGhhTJ061f1z3bp1jdmzZ3u06devnzF06FCf9RP+J8n49NNPPZYNHz7c6Nevn1frGThwoBEXF+exrEePHsbgwYPdP7dv39548sknPdo0a9bMmDx5slfbQsk5c+aMIcnYuHFjgW127NhhSDJcLpfp9b755ptG5cqVjUuXLhVr2wi8atWqGR988IGRm5tr1KlTx5gxY4b7vqtXrxoRERHGX/7yl0LXce3aNaNjx47GBx98UOD7z8WLF40mTZoYa9asMTp37myMHz/e675ScbyFrVu3KiIiQr/85S/dy+6++25FRETo66+/LvBxubm5io+P1zPPPKPbbrvtltvJzMxUQkKCYmNjFR0d7ZO+w39++uknLViwQKNGjZLD4TD1mNzcXF28eNFjUvqsrCyFhYV5tAsPD9eWLVt82l8ExoYNGxQZGalf/OIXevzxx3XmzJlC22/dutU9ycF1PXr0cL/X/PTTT9q1a1eeNt27dy/0/QiBdX1iihtf+/m1cTgcqlq1qun1zps3T4MHD1bFihWLtW0ETk5OjhYvXqzMzEx16NBBR48e1alTpzxe46GhoercufMtX+OvvvqqatWqpdGjRxfYZsyYMerVq1eeKZm9YZuZYwLl1KlTioyMzLM8MjJSp06dKvBxr7/+uoKDgzVu3LhC1//OO+/o2WefVWZmppo1a6Y1a9aofPnyxe43/GvFihU6d+6cRowYYfoxf/7zn5WZmamBAwe6l/Xo0UNvvPGGOnXqpMaNGyspKUmfffYZpyuUAj179tTDDz+smJgYHT16VC+++KLuv/9+7dq1q8DZHk6dOqXatWt7LKtdu7b7veb7779XTk5OoW1gLYZhaNKkSfrVr36lli1b5tvm6tWrmjx5soYMGWJ6+rgdO3bom2++0bx584q1bQTGvn371KFDB129elWVKlXSp59+qhYtWrjDYX6vcZfLVeD6vvrqK82bN08pKSkFtlm8eLF2796t5OTkYvW9zFYcX3nllXxPMr7xtnPnTknKt6JkGEaBlaZdu3Zp1qxZmj9//i2rUUOHDtWePXu0ceNGNWnSRAMHDtTVq1eL/wThV/PmzVPPnj3zPXc1P4sWLdIrr7yiJUuWeHwRmTVrlpo0aaJmzZqpfPnyGjt2rEaOHKmgoCB/dR0lZNCgQerVq5datmypPn366B//+IcOHjyoL774otDH3fyekd97jZk2sIaxY8dq7969WrRoUb73Z2dna/DgwcrNzdU777xjer3z5s1Ty5Yt1b59+yJvG4HTtGlTpaSkaNu2bXrqqac0fPhwpaamuu/35jV+8eJFPfroo3r//fdVs2bNfNtkZGRo/PjxWrBgQZ6jXN4qsxXHsWPHavDgwYW2adiwofbu3avTp0/nue/f//53nm8E123evFlnzpxRgwYN3MtycnL0u9/9TjNnztSxY8fcy69fqd2kSRPdfffdqlatmj799FM98sgjRXti8DuXy6W1a9dq+fLlptovWbJEo0eP1ieffJLn8ECtWrW0YsUKXb16VT/88IOioqI0efJkxcbG+qPrCKC6desqJiZGhw4dKrBNnTp18lQOz5w5436vqVmzpoKCggptA+t4+umntXLlSm3atEn169fPc392drYGDhyoo0ePat26daarjZcvX9bixYv16quvFnnbCKzy5cvL6XRKktq2bavk5GTNmjVLzz33nKSfjz7UrVvX3b6w1/jhw4d17Ngx9enTx70sNzdXkhQcHKwDBw5o3759OnPmjO666y53m5ycHG3atEmzZ89WVlaW6YJFmQ2ONWvWLDCZ36hDhw46f/68duzY4f5mt337dp0/f1733HNPvo+Jj4/PExB69Oih+Ph4jRw5stDtGYahrKwsk88CgZCQkKDIyEiPITAKsmjRIo0aNUqLFi0qtH1YWJjq1aun7OxsLVu2zONwNkqHH374QRkZGR4fBjfr0KGD1qxZo4kTJ7qXffnll+73mvLly+uuu+7SmjVr9NBDD7nbrFmzRv369fNf5+EVwzD09NNP69NPP9WGDRvy/SJ4PTQeOnRI69evV40aNUyv/+9//7uysrL06KOPFmnbsJ7rn/2xsbGqU6eO1qxZozZt2kj6+dzmjRs36vXXX8/3sc2aNdO+ffs8lr3wwgu6ePGiZs2apejoaEVGRuZpM3LkSDVr1kzPPfecd0e5vL6cpgyKi4szbr/9dmPr1q3G1q1bjVatWhm9e/f2aNO0aVNj+fLlBa7j5quqDx8+bEybNs3YuXOn4XK5jK+//tro16+fUb16deP06dP+eiooppycHKNBgwbGc889l+e+yZMnG/Hx8e6fFy5caAQHBxtz5swxTp486b6dO3fO3Wbbtm3GsmXLjMOHDxubNm0y7r//fiM2NtY4e/ZsSTwdFMPFixeNPXv2GHv27DEkGW+88YaxZ88ew+VyGRcvXjR+97vfGV9//bVx9OhRY/369UaHDh2MevXqGRcuXHCvIz4+3uNq6K+++soICgoyZsyYYezfv9+YMWOGERwc7DGqw+LFi42QkBBj3rx5RmpqqjFhwgSjYsWKxrFjx0r0+aNgTz31lBEREWFs2LDB47V/+fJlwzAMIzs72+jbt69Rv359IyUlxaNNVlaWez037x/X/epXvzIGDRpUpG0j8KZMmWJs2rTJOHr0qLF3717jv//7v41y5coZX375pWEYhjFjxgwjIiLCWL58ubFv3z7jkUceMerWrVvoe8fNzIzqUNSrqgmOJvzwww/G0KFDjcqVKxuVK1c2hg4dmueDXZKRkJBQ4DpuDo7Hjx83evbsaURGRhohISFG/fr1jSFDhtxyiB8E1urVqw1JxoEDB/LcN3z4cKNz587unzt37mxIynMbPny4u82GDRuM5s2bG6GhoUaNGjWM+Ph44/jx4yXwTFBc69evL/Dve/nyZaN79+5GrVq1jJCQEKNBgwbG8OHDjfT0dI91dO7c2WN/MAzD+OSTT4ymTZsaISEhRrNmzYxly5bl2facOXOMmJgYo3z58sadd97JUCsWk99+ceNnxNGjRwtss379evd68ts/Dhw4YEhyhwxvt43AGzVqlPv1W6tWLaNr164ef8/c3Fzj5ZdfNurUqWOEhoYanTp1Mvbt2+exjvz2jRv5Mzg6DMMwzNcnAQAAUFaV2auqAQAA4B2CIwAAAEwhOAIAAMAUgiMAAABMITgCAADAFIIjAAAATCE4AgAAwBSCIwAAAEwhOAJAPo4dOyaHw6GUlJRC23Xp0kUTJkwokT6Z8corr6h169aB7gaAUorgCMC2RowYIYfDIYfDoZCQEDVq1Ei///3vlZmZWex1R0dH6+TJk2rZsqUkacOGDXI4HDp37pxHu+XLl+sPf/hDsbdXmOsh9vqtcuXKuu222zRmzBgdOnTIo+3vf/97JSUl+bwPmzZtUp8+fRQVFSWHw6EVK1b4fBsArI/gCMDW4uLidPLkSR05ckSvvfaa3nnnHf3+978v9nqDgoJUp04dBQcHF9quevXqqly5crG3Z8batWt18uRJ/fOf/9S0adO0f/9+3XHHHR5BsVKlSqpRo4bPt52Zmak77rhDs2fP9vm6AdgHwRGArYWGhqpOnTqKjo7WkCFDNHToUHc1LCsrS+PGjVNkZKTCwsL0q1/9SsnJye7Hnj17VkOHDlWtWrUUHh6uJk2aKCEhQZLnoepjx47pvvvukyRVq1ZNDodDI0aMkJT3UPXZs2c1bNgwVatWTRUqVFDPnj09qoLz589X1apVtXr1ajVv3lyVKlVyh99bqVGjhurUqaNGjRqpX79+Wrt2rX75y19q9OjRysnJkZT3UPWIESPUv39/TZs2TbVr11bVqlU1depUXbt2Tc8884yqV6+u+vXr68MPPyx02z179tRrr72mX//617fsJ4DSi+AIoFQJDw9Xdna2JOnZZ5/VsmXL9NFHH2n37t1yOp3q0aOHfvzxR0nSiy++qNTUVP3jH//Q/v37NXfuXNWsWTPPOqOjo7Vs2TJJ0oEDB3Ty5EnNmjUr3+2PGDFCO3fu1MqVK7V161YZhqEHH3zQ3SdJunz5sv70pz/pr3/9qzZt2qT09PQiVUnLlSun8ePHy+VyadeuXQW2W7dunU6cOKFNmzbpjTfe0CuvvKLevXurWrV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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "env = Environment(\n", + " place_bin_size=10,\n", + ").fit(position=position)\n", + "\n", + "env.plot_grid()\n", + "\n", + "plt.scatter(position[:, 0], position[:, 1], c=\"red\", s=1, zorder=10)\n", + "env.is_track_interior_" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "692a0023", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.n_dims" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0df2d3de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.nd_index_to_flat((0, 1))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a8e2a2b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.int64(0), np.int64(1))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.bin_index_to_nd(1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "non_local_detector2", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/non_local_detector/analysis/distance1D.py b/src/non_local_detector/analysis/distance1D.py index 42557fc..b0bb462 100644 --- a/src/non_local_detector/analysis/distance1D.py +++ b/src/non_local_detector/analysis/distance1D.py @@ -84,9 +84,9 @@ def _get_MAP_estimate_2d_position_edges( try: place_bin_center_2D_position = env.place_bin_center_2D_position_ except AttributeError: - place_bin_center_2D_position = np.asarray( - env.place_bin_centers_nodes_df_.loc[:, ["x_position", "y_position"]] - ) + df = env.get_bin_center_dataframe() + pos_cols = [col for col in df.columns if col.startswith("pos_dim")] + place_bin_center_2D_position = df.loc[:, pos_cols].to_numpy() mental_position_2d = place_bin_center_2D_position[map_position_ind] @@ -94,7 +94,7 @@ def _get_MAP_estimate_2d_position_edges( try: edge_id = env.place_bin_center_ind_to_edge_id_ except AttributeError: - edge_id = np.asarray(env.place_bin_centers_nodes_df_.edge_id) + edge_id = env.get_bin_center_dataframe().edge_id.to_numpy() track_segment_id = edge_id[map_position_ind] mental_position_edges = np.asarray(list(track_graph.edges))[track_segment_id] @@ -422,7 +422,7 @@ def get_ahead_behind_distance( def get_map_speed( posterior: xr.DataArray, - track_graph_with_bin_centers_edges: nx.Graph, + track_graph_bin_centers_edges: nx.Graph, place_bin_center_ind_to_node: np.ndarray, sampling_frequency: float = 500.0, smooth_sigma: float = 0.0025, @@ -432,7 +432,7 @@ def get_map_speed( Parameters ---------- posterior : xr.DataArray - track_graph_with_bin_centers_edges : nx.Graph + track_graph_bin_centers_edges : nx.Graph Track graph with bin centers as nodes and edges place_bin_center_ind_to_node : np.ndarray Mapping of place bin center index to node ID @@ -460,7 +460,7 @@ def get_map_speed( speed, 0, nx.shortest_path_length( - track_graph_with_bin_centers_edges, + track_graph_bin_centers_edges, source=node_ids[0], target=node_ids[1], weight="distance", @@ -471,7 +471,7 @@ def get_map_speed( speed, -1, nx.shortest_path_length( - track_graph_with_bin_centers_edges, + track_graph_bin_centers_edges, source=node_ids[-2], target=node_ids[-1], weight="distance", @@ -483,7 +483,7 @@ def get_map_speed( for node1, node2 in zip(node_ids[:-2], node_ids[2:]): speed.append( nx.shortest_path_length( - track_graph_with_bin_centers_edges, + track_graph_bin_centers_edges, source=node1, target=node2, weight="distance", @@ -495,7 +495,7 @@ def get_map_speed( speed, 0, nx.shortest_path_length( - track_graph_with_bin_centers_edges, + track_graph_bin_centers_edges, source=node_ids[0], target=node_ids[1], weight="distance", @@ -506,7 +506,7 @@ def get_map_speed( speed, -1, nx.shortest_path_length( - track_graph_with_bin_centers_edges, + track_graph_bin_centers_edges, source=node_ids[-2], target=node_ids[-1], weight="distance", diff --git a/src/non_local_detector/continuous_state_transitions.py b/src/non_local_detector/continuous_state_transitions.py index 837df1c..8026510 100644 --- a/src/non_local_detector/continuous_state_transitions.py +++ b/src/non_local_detector/continuous_state_transitions.py @@ -25,8 +25,8 @@ def _normalize_row_probability(x: np.ndarray) -> np.ndarray: """ # Handle cases where the sum is zero to avoid division by zero -> NaN row_sums = x.sum(axis=1, keepdims=True) - # Use np.errstate to temporarily ignore invalid division warnings - with np.errstate(invalid="ignore"): + # Use np.errstate to temporarily ignore invalid division/zero warnings + with np.errstate(invalid="ignore", divide="ignore"): normalized_x = np.where(row_sums > 0, x / row_sums, 0.0) # Ensure any remaining NaNs (though unlikely with the above) are zero normalized_x[np.isnan(normalized_x)] = 0.0 @@ -50,7 +50,13 @@ def estimate_movement_var(position: np.ndarray) -> np.ndarray: position = position if position.ndim > 1 else position[:, np.newaxis] is_nan = np.any(np.isnan(position), axis=1) position = position[~is_nan] - return np.cov(np.diff(position, axis=0), rowvar=False) + + if position.shape[0] < 2: + raise ValueError("Not enough data to estimate movement variance.") + + movement_cov = np.cov(np.diff(position, axis=0), rowvar=False, ddof=1) + + return np.diag(movement_cov) if movement_cov.ndim > 0 else movement_cov def _random_walk_on_track_graph( @@ -187,8 +193,8 @@ def _handle_no_track_graph(self) -> np.ndarray: else: transition_matrix = ( multivariate_normal(mean=self.movement_mean, cov=self.movement_var) - .pdf(self.environment.distance_between_nodes_.flat) - .reshape(self.environment.distance_between_nodes_.shape) + .pdf(self.environment.distance_between_bins.flat) + .reshape(self.environment.distance_between_bins.shape) ) if self.direction is not None: @@ -198,14 +204,14 @@ def _handle_no_track_graph(self) -> np.ndarray: }.get(self.direction.lower(), None) centrality = nx.closeness_centrality( - self.environment.track_graphDD, distance="distance" + self.environment.track_graph_nd_, distance="distance" ) center_node_id = list(centrality.keys())[ np.argmax(list(centrality.values())) ] transition_matrix *= direction_func( - self.environment.distance_between_nodes_[:, [center_node_id]], - self.environment.distance_between_nodes_[[center_node_id]], + self.environment.distance_between_bins[:, [center_node_id]], + self.environment.distance_between_bins[[center_node_id]], ) return transition_matrix @@ -218,15 +224,15 @@ def _handle_with_track_graph(self) -> np.ndarray: "Random walk with track graph is only implemented for 1D environments" ) - place_bin_center_ind_to_node = np.asarray( - self.environment.place_bin_centers_nodes_df_.node_id + place_bin_center_ind_to_node = ( + self.environment.get_bin_center_dataframe().reset_index().node_id.to_numpy() ) return _random_walk_on_track_graph( self.environment.place_bin_centers_, self.movement_mean, self.movement_var, place_bin_center_ind_to_node, - self.environment.distance_between_nodes_, + self.environment.distance_between_bins, ) @@ -514,3 +520,35 @@ def make_state_transition(self, *args, **kwargs) -> np.ndarray: state_transition_matrix : np.ndarray, shape (1, 1) """ return np.ones((1, 1)) + + +def calculate_rw_movement_variance(speed: float, time_step: float) -> float: + """Calculates the variance for a RandomWalk model based on speed. + + Assumes the characteristic distance traveled in one time step + (speed * time_step) corresponds to the standard deviation of + displacement per dimension. + + Parameters + ---------- + speed : float + Characteristic speed (e.g., in cm/s). + time_step : float + Duration of one time step. Units must be consistent + with speed's time unit (e.g., in 0.002 s). + + Returns + ------- + movement_variance : float + The calculated variance (movement_var) for the RandomWalk model + (e.g., in cm^2). + """ + if time_step <= 0: + raise ValueError("time_step must be positive.") + + # Calculate characteristic distance + # (interpreted as standard deviation sigma) + sigma = speed * time_step + + # Variance is sigma squared + return sigma**2 diff --git a/src/non_local_detector/diffusion_kernels.py b/src/non_local_detector/diffusion_kernels.py new file mode 100644 index 0000000..39798eb --- /dev/null +++ b/src/non_local_detector/diffusion_kernels.py @@ -0,0 +1,102 @@ +from typing import Any + +import jax +import jax.numpy as jnp +import jax.scipy.linalg +import networkx as nx +import numpy as np + +from non_local_detector.environment import add_distance_weight_to_edges + + +def compute_diffusion_kernels( + track_graph: nx.Graph, + interior_mask: np.ndarray, + bandwidth_sigma: float, +) -> jnp.ndarray: + """ + Computes diffusion kernels for all valid interior bins using the + Graph Laplacian method and matrix exponential. + + Parameters + ---------- + track_graph : nx.Graph + The graph representing the track, where nodes are bins and edges are connections. + interior_mask : np.ndarray, shape (n_bins_x, n_bins_y, ...) + A mask indicating which bins are interior. The size should match the + number of nodes in the track graph. True values indicate interior bins. + bandwidth_sigma : float + The bandwidth of the Gaussian kernel. This controls the spread of the kernel. + weight : str, optional + The edge attribute to use as weights for the graph. If None, unweighted edges are used. + By default None. + + Returns + ------- + kernel_matrix_interior : jnp.ndarray, shape (n_interior_bins, n_interior_bins) + The diffusion kernel matrix for the interior bins. + Therepresents the amount of concentration (or probability mass, if u represents probability) + that has flowed from node j to node i after time t. + """ + if track_graph is None: + raise ValueError("track_graph is required.") + + if interior_mask is None: + interior_mask = np.ones_like(track_graph.nodes(), dtype=bool) + + if bandwidth_sigma <= 0: + raise ValueError("bandwidth_sigma must be positive.") + + add_distance_weight_to_edges(track_graph) + + n_bins_total = interior_mask.size + interior_mask_flat = jnp.asarray(interior_mask.ravel()) + interior_bin_indices_flat = jnp.nonzero(interior_mask_flat)[0] + n_interior_bins = interior_bin_indices_flat.size + + if n_interior_bins == 0: + return jnp.zeros((0, 0)) + + node_list = sorted(list(track_graph.nodes())) + if not node_list: + laplacian_full = jnp.zeros((n_bins_total, n_bins_total), dtype=jnp.float32) + else: + try: + # Attempt to compute the full Laplacian matrix + # This may fail if the graph is disconnected or has isolated nodes. + # In that case, we will compute the Laplacian for the subgraph + # induced by the interior nodes. + # This is a workaround for the case where the graph is disconnected + # or has isolated nodes. + laplacian_full = jnp.array( + nx.laplacian_matrix( + track_graph, nodelist=range(n_bins_total), weight="distance_weight" + ).toarray(), + dtype=jnp.float32, + ) + except nx.NetworkXError: + # If the graph is disconnected or has isolated nodes, + # compute the Laplacian for the subgraph induced by the interior nodes. + L_sparse_sub = nx.laplacian_matrix( + track_graph, nodelist=node_list, weight="distance_weight" + ) + L_sub = jnp.array(L_sparse_sub.toarray(), dtype=jnp.float32) + laplacian_full = jnp.zeros((n_bins_total, n_bins_total), dtype=jnp.float32) + idx_embed = jnp.ix_(jnp.array(node_list), jnp.array(node_list)) + laplacian_full = laplacian_full.at[idx_embed].set(L_sub) + + exponent_coefficient = bandwidth_sigma**2 / 2.0 + full_kernel_matrix = jax.scipy.linalg.expm(-exponent_coefficient * laplacian_full) + + # Apply the interior mask to the kernel matrix and clip to non-negative values + idx_filter = jnp.ix_(interior_bin_indices_flat, interior_bin_indices_flat) + kernel_matrix_interior = jnp.clip( + full_kernel_matrix[idx_filter], a_min=0.0, a_max=None + ) + + # Normalize the kernel matrix so the sum of each column is 1. + col_sums = kernel_matrix_interior.sum(axis=0, keepdims=True) + kernel_matrix_interior = jnp.where( + col_sums > 1e-15, kernel_matrix_interior / col_sums, 0.0 + ) + return kernel_matrix_interior diff --git a/src/non_local_detector/environment.py b/src/non_local_detector/environment.py index c9c8747..6b46a9e 100644 --- a/src/non_local_detector/environment.py +++ b/src/non_local_detector/environment.py @@ -1,826 +1,744 @@ -"""Classes for constructing discrete grids representations of spatial environments in nD""" - +"""Defines spatial environments using discrete grids and graph representations. + +This module provides the `Environment` class and associated helper functions to +represent spatial environments commonly used in neuroscience experiments, such +as open fields or linear tracks. The core idea is to discretize the continuous +space into a grid of bins and potentially represent the connectivity or topology +of the valid space using a graph. + +The module supports two main types of environments: + +1. **N-Dimensional Environments (e.g., Open Field, W-Track):** + - Discretizes the space into a regular N-D grid based on specified bin sizes + or position data ranges. + - Can automatically infer the "track interior" (the portion of the grid + actually occupied by the animal) from position data using histogramming + and optional morphological operations (filling holes, dilation). + - Constructs a `networkx` graph (`track_graph_nd`) where nodes represent + the centers of *interior* bins, and edges connect adjacent interior bins. + This graph captures the connectivity of the valid space. + - Can compute shortest-path distances between all pairs of interior bins + on this graph (`distance_between_bins`). + - Provides methods to find the bin index for a given position (`get_bin_ind`), + calculate manifold distances between positions (`get_manifold_distances`), + and determine movement direction relative to the track center (`get_direction`). + +2. **1-Dimensional Environments (Linear Tracks, W-Tracks):** + - Requires a `networkx.Graph` (`track_graph`) defining the track's topology + (nodes, edges, and their positions) along with edge ordering and spacing. + - Linearizes the track based on the provided graph structure. + - Creates bins along this linearized track. + - Computes shortest-path distances between all nodes in this augmented graph + (`distance_between_bins`). + +The central component is the `Environment` dataclass, which holds the parameters +defining the environment and stores the results of the fitting process (grid +details, graphs, distances) as attributes. The primary method `fit` +is used to perform the discretization and graph construction based on the input +parameters and optional position data. +""" + +import itertools import pickle -from dataclasses import dataclass -from typing import Dict, List, Optional, Sequence, Tuple, Union +from dataclasses import MISSING, dataclass, field, fields +from functools import cached_property +from typing import Any, Callable, Dict, List, Optional, Sequence, Set, Tuple, Union import matplotlib +import matplotlib.axes import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd +from numpy.typing import NDArray from scipy import ndimage from scipy.interpolate import interp1d -from sklearn.neighbors import NearestNeighbors -from track_linearization import plot_graph_as_1D +from scipy.spatial import KDTree +from track_linearization import get_linearized_position, plot_graph_as_1D +from track_linearization.core import _calculate_linear_position + +# --- Helper Functions --- -def get_centers(bin_edges: np.ndarray) -> np.ndarray: - """Given a set of bin edges, return the center of the bin. +def get_centers(bin_edges: NDArray[np.float64]) -> NDArray[np.float64]: + """Calculates the center of each bin given its edges. Parameters ---------- - bin_edges : np.ndarray, shape (n_edges,) + bin_edges : NDArray[np.float64], shape (n_edges,) + The edges defining the bins. Returns ------- - bin_centers : np.ndarray, shape (n_edges - 1,) - + bin_centers : NDArray[np.float64], shape (n_edges - 1,) + The center of each bin. """ return bin_edges[:-1] + np.diff(bin_edges) / 2 -@dataclass -class Environment: - """Represent the spatial environment with a discrete grid. +def get_n_bins( + position: NDArray[np.float64], + bin_size: Union[float, Sequence[float]], + position_range: Optional[Sequence[Tuple[float, float]]] = None, +) -> NDArray[np.int_]: + """Calculates the number of bins needed for each dimension. Parameters ---------- - environment_name : str, optional - Identifier for the environment. Defaults to "". - place_bin_size : float or tuple[float], optional - Approximate size of the position bins in each dimension. Defaults to 2.0. - track_graph : networkx.Graph, optional - Graph representing the 1D spatial topology. If provided, 1D methods are used. - edge_order : tuple of 2-tuples, optional - Required if `track_graph` is provided. The order of the edges in 1D space. - edge_spacing : float or list, optional - Required if `track_graph` is provided. Spacing between edges. Defaults to 0. - is_track_interior : np.ndarray or None, optional - Boolean array defining valid track areas. If None and `infer_track_interior` - is True, it will be inferred from position data. - position_range : Sequence[Tuple[float, float]], optional - Outer bin edges for each dimension [(min_dim1, max_dim1), ...]. - If None, range is determined from position data. - infer_track_interior : bool, optional - If True and `is_track_interior` is None, infer track geometry from position data. - Defaults to True. Ignored if `track_graph` is provided. - fill_holes : bool, optional - Fill holes in the inferred track interior. Defaults to False. Ignored if `track_graph` is provided. - dilate : bool, optional - Inflate the inferred track interior. Defaults to False. Ignored if `track_graph` is provided. - bin_count_threshold : int, optional - Minimum number of samples in a bin to be considered part of the track interior. - Defaults to 0. Ignored if `track_graph` is provided. + position : NDArray[np.float64], shape (n_time, n_dims) + Position data to determine range if `position_range` is not given. + bin_size : float or Sequence[float] + The desired size(s) of the bins. + position_range : Optional[Sequence[Tuple[float, float]]], optional + Explicit range [(min_dim1, max_dim1), ...] for each dimension. + If None, range is calculated from `position`. Defaults to None. - Attributes - ---------- - edges_ : tuple[np.ndarray, ...] - Bin edges for each dimension. - place_bin_edges_ : np.ndarray, shape (n_bins + 1, n_dims) or (n_total_bins + n_edges, n_dims) - Edges of the place bins (linearized or N-D). - place_bin_centers_ : np.ndarray, shape (n_bins, n_dims) - Center coordinates of each place bin. - centers_shape_ : tuple[int, ...] - Shape of the grid in terms of bins per dimension. - is_track_interior_ : np.ndarray, shape (n_bins,) or (n_bins_dim1, n_bins_dim2, ...) - Boolean array indicating which bins are part of the track interior. - is_track_boundary_ : np.ndarray or None - Boolean array indicating boundary bins (only for N-D environments). - track_graphDD : networkx.Graph or None - Graph representation where nodes are bin centers (only for N-D environments). - distance_between_nodes_ : Dict[int, Dict[int, float]] or np.ndarray - Shortest path distances between nodes on the track graph (1D or N-D). - track_graph_with_bin_centers_edges_ : nx.Graph or None - Track graph with bin centers and edges added as nodes (only for 1D). - original_nodes_df_ : pd.DataFrame or None - Info about original track graph nodes (only for 1D). - place_bin_edges_nodes_df_ : pd.DataFrame or None - Info about nodes representing bin edges (only for 1D). - place_bin_centers_nodes_df_ : pd.DataFrame or None - Info about nodes representing bin centers (only for 1D). - nodes_df_ : pd.DataFrame or None - Combined info about all nodes in the augmented graph (only for 1D). + Returns + ------- + n_bins : NDArray[np.int_], shape (n_dims,) + Number of bins required for each dimension. """ + if position_range is not None: + # Ensure position_range is numpy array for consistent processing + pr = np.asarray(position_range) + if pr.shape[1] != 2: + raise ValueError("position_range must be sequence of (min, max) pairs.") + extent = np.diff(pr, axis=1).squeeze(axis=1) + else: + # Ignore NaNs when calculating range from data + extent = np.nanmax(position, axis=0) - np.nanmin(position, axis=0) - environment_name: str = "" - place_bin_size: Union[float, Tuple[float, ...]] = 2.0 - track_graph: Optional[nx.Graph] = None - edge_order: Optional[List[Tuple]] = None - edge_spacing: Optional[Union[float, List[float]]] = 0.0 - is_track_interior: Optional[np.ndarray] = None - position_range: Optional[Sequence[Tuple[float, float]]] = None - infer_track_interior: bool = True - fill_holes: bool = False - dilate: bool = False - bin_count_threshold: int = 0 - - # Attributes to be fitted - edges_: Optional[Tuple[np.ndarray, ...]] = None - place_bin_edges_: Optional[np.ndarray] = None - place_bin_centers_: Optional[np.ndarray] = None - centers_shape_: Optional[Tuple[int, ...]] = None - is_track_interior_: Optional[np.ndarray] = None - is_track_boundary_: Optional[np.ndarray] = None - track_graphDD: Optional[nx.Graph] = None # For N-D case - distance_between_nodes_: Optional[ - Union[Dict[int, Dict[int, float]], np.ndarray] - ] = None - track_graph_with_bin_centers_edges_: Optional[nx.Graph] = None # For 1D case - original_nodes_df_: Optional[pd.DataFrame] = None - place_bin_edges_nodes_df_: Optional[pd.DataFrame] = None - place_bin_centers_nodes_df_: Optional[pd.DataFrame] = None - nodes_df_: Optional[pd.DataFrame] = None - # Internal flag - _is_fitted: bool = False - - def __eq__(self, other: str) -> bool: - return self.environment_name == other - - def fit_place_grid( - self, position: Optional[np.ndarray] = None, infer_track_interior: bool = True - ) -> "Environment": - """Fits a discrete grid of the spatial environment. - - Parameters - ---------- - position : np.ndarray, shape (n_time, n_dim), optional - Position of the animal. - infer_track_interior : bool, optional - Whether to infer the spatial geometry of track from position + # Ensure bin_size is positive + bin_size = np.asarray(bin_size, dtype=float) + if np.any(bin_size <= 0.0): + raise ValueError("bin_size must be positive.") - Returns - ------- - self + # Calculate number of bins, ensuring at least 1 bin even if extent is 0 + n_bins = np.ceil(extent / bin_size).astype(np.int32) + n_bins[n_bins == 0] = 1 # Handle zero extent case - """ - if self.track_graph is None: - ( - self.edges_, - self.place_bin_edges_, - self.place_bin_centers_, - self.centers_shape_, - ) = get_grid( - position, - self.place_bin_size, - self.position_range, - ) + return n_bins - self.infer_track_interior = infer_track_interior - if self.is_track_interior is None and self.infer_track_interior: - self.is_track_interior_ = get_track_interior( - position, - self.edges_, - self.fill_holes, - self.dilate, - self.bin_count_threshold, - ) - elif self.is_track_interior is None and not self.infer_track_interior: - self.is_track_interior_ = np.ones(self.centers_shape_, dtype=bool) +def _create_grid( + position: Optional[NDArray[np.float64]] = None, + bin_size: Union[float, Sequence[float]] = 2.0, + position_range: Optional[Sequence[Tuple[float, float]]] = None, + add_boundary_bins: bool = False, +) -> Tuple[ + Tuple[NDArray[np.float64], ...], # edges_tuple + NDArray[np.float64], # place_bin_edges_flat + NDArray[np.float64], # place_bin_centers_flat + Tuple[int, ...], # centers_shape +]: + """Calculates bin edges and centers for a spatial grid. - if len(self.edges_) > 1: - self.is_track_boundary_ = get_track_boundary( - self.is_track_interior_, - n_position_dims=len(self.edges_), - connectivity=1, - ) - else: - self.is_track_boundary_ = None - - self.track_graphDD = make_nD_track_graph_from_environment(self) - node_positions = nx.get_node_attributes(self.track_graphDD, "pos") - node_positions = np.asarray(list(node_positions.values())) - distance = np.full((len(node_positions), len(node_positions)), np.inf) - for to_node_id, from_node_id in nx.shortest_path_length( - self.track_graphDD, - weight="distance", - ): - distance[to_node_id, list(from_node_id.keys())] = list( - from_node_id.values() - ) + Creates a grid based on provided position data or range. Handles multiple + position dimensions and optionally adds boundary bins around the core grid. - self.distance_between_nodes_ = distance + Parameters + ---------- + position : Optional[NDArray[np.float64]], shape (n_time, n_dims), optional + Position data. Used to determine grid extent if `position_range` + is None. NaNs are ignored. Required if `position_range` is None. + Defaults to None. + bin_size : Union[float, Sequence[float]], optional + Desired approximate size of bins in each dimension. If a sequence, + must match the number of dimensions. Defaults to 2.0. + position_range : Optional[Sequence[Tuple[float, float]]], optional + Explicit grid boundaries [(min_dim1, max_dim1), ...]. If None, + boundaries are derived from `position`. Defaults to None. + add_boundary_bins : bool, optional + If True, add one bin on each side of the grid in each dimension, + extending the range. Defaults to False. - else: + Returns + ------- + edges : Tuple[NDArray[np.float64], ...] + Tuple containing bin edges for each dimension (shape (n_bins_d + 1,)). + Includes boundary bins if `add_boundary_bins` is True. + place_bin_edges_flat : np.ndarray, shape (n_total_bins + 1, n_dims) + The edges corresponding to each bin in the flattened grid. + place_bin_centers : NDArray[np.float64], shape (n_total_bins, n_dims) + Center coordinates of each bin in the flattened grid. + centers_shape : Tuple[int, ...] + Shape of the grid (number of bins in each dimension). + + Raises + ------ + ValueError + If both `position` and `position_range` are None. + If `bin_size` sequence length doesn't match dimensions. + If `position_range` sequence length doesn't match dimensions. + """ + if position is None and position_range is None: + raise ValueError("Either `position` or `position_range` must be provided.") + if position is not None: + pos_nd = np.atleast_2d(position) + n_dims = pos_nd.shape[1] + pos_clean = pos_nd[~np.any(np.isnan(pos_nd), axis=1)] + if pos_clean.shape[0] == 0 and position_range is None: + raise ValueError( + "Position data contains only NaNs and no position_range provided." + ) + elif position_range is not None: + n_dims = len(position_range) + pos_clean = None # No position data needed if range is given + else: # Should be unreachable due to first check, but added for safety + raise ValueError("Cannot determine number of dimensions.") + + # Validate and process bin_size + if isinstance(bin_size, (float, int)): + bin_sizes = np.array([float(bin_size)] * n_dims) + elif len(bin_size) == n_dims: + bin_sizes = np.array(bin_size, dtype=float) + else: + raise ValueError( + f"`bin_size` sequence length ({len(bin_size)}) must match " + f"number of dimensions ({n_dims})." + ) + if np.any(bin_sizes <= 0): + raise ValueError("All elements in `bin_size` must be positive.") + + # Determine histogram range + hist_range = position_range + if hist_range is None and pos_clean is not None: + hist_range = [ + (np.nanmin(pos_clean[:, dim]), np.nanmax(pos_clean[:, dim])) + for dim in range(n_dims) + ] + # Handle case where min == max in a dimension + hist_range = [ ( - self.place_bin_centers_, - self.place_bin_edges_, - self.is_track_interior_, - self.distance_between_nodes_, - self.centers_shape_, - self.edges_, - self.track_graph_with_bin_centers_edges_, - self.original_nodes_df_, - self.place_bin_edges_nodes_df_, - self.place_bin_centers_nodes_df_, - self.nodes_df_, - ) = get_track_grid( - self.track_graph, - self.edge_order, - self.edge_spacing, - self.place_bin_size, + (r[0], r[1]) + if r[0] < r[1] + else (r[0] - 0.5 * bin_sizes[i], r[0] + 0.5 * bin_sizes[i]) ) - self.is_track_boundary_ = None - - return self - - def plot_grid(self, ax: matplotlib.axes.Axes = None) -> matplotlib.axes.Axes: - """Plot the fitted spatial grid of the environment. - - Parameters - ---------- - ax : plt.axes, optional - Plot on this axis if given, by default None + for i, r in enumerate(hist_range) + ] - Returns - ------- - ax : plt.axes - The axis on which the grid is plotted. + # Validate position_range dimensions if provided + if position_range is not None and len(position_range) != n_dims: + raise ValueError( + f"`position_range` length ({len(position_range)}) must match " + f"number of dimensions ({n_dims})." + ) - """ - if self.track_graph is not None: - if ax is None: - _, ax = plt.subplots(figsize=(15, 2)) + # Calculate number of bins for the core range + n_bins_core = get_n_bins(pos_clean, bin_sizes, hist_range) # Pass array bin_sizes - plot_graph_as_1D( - self.track_graph, self.edge_order, self.edge_spacing, ax=ax - ) - try: - for edge in self.edges_[0]: - ax.axvline(edge.squeeze(), linewidth=0.5, color="black") - except AttributeError: - # Edges have not been fit yet - pass - ax.set_ylim((0, 0.1)) - else: - if ax is None: - _, ax = plt.subplots(figsize=(6, 7)) - ax.pcolormesh( - self.edges_[0], self.edges_[1], self.is_track_interior_.T, cmap="bone_r" + # Calculate core edges using histogramdd (even if position is None, to get uniform bins) + # Need dummy data if no position provided + dummy_data = ( + np.array([[(r[0] + r[1]) / 2] for r in hist_range]).T + if pos_clean is None + else pos_clean + ) + _, core_edges_list = np.histogramdd(dummy_data, bins=n_bins_core, range=hist_range) + + if add_boundary_bins: + # Add boundary bins by extending edges + final_edges_list = [] + for edges_dim in core_edges_list: + step = np.diff(edges_dim)[0] # Assume uniform bins from histogramdd + extended_edges = np.insert( + edges_dim, + [0, len(edges_dim)], + [edges_dim[0] - step, edges_dim[-1] + step], ) - ax.set_xticks(self.edges_[0], minor=True) - ax.set_yticks(self.edges_[1], minor=True) - ax.grid(visible=True, which="minor") - ax.grid(visible=False, which="major") - - return ax - - def save_environment(self, filename: str = "environment.pkl") -> None: - """Saves the environment object as a pickled file. - - Parameters - ---------- - filename : str, optional - File name to save the environment to. Defaults to "environment.pkl". - """ - with open(filename, "wb") as file_handle: - pickle.dump(self, file_handle) - - @classmethod - def load_environment(cls, filename: str = "environment.pkl") -> "Environment": - """Loads a pickled environment object from a file. - - Parameters - ---------- - filename : str, optional - File name to load the environment from. Defaults to "environment.pkl". - - Returns - ------- - Environment - The loaded environment object. - """ - with open(filename, "rb") as f: - return pickle.load(f) - - def get_bin_ind(self, sample: np.ndarray) -> np.ndarray: - """Find the indices of the bins to which each value in input array belongs. - - Uses the fitted grid edges (`self.edges_`). - - Parameters - ---------- - sample : np.ndarray, shape (n_time, n_dim) - Input data points. + final_edges_list.append(extended_edges) + else: + final_edges_list = list(core_edges_list) # Ensure it's a list of arrays - Returns - ------- - bin_inds : np.ndarray, shape (n_time,) - Flat index of the bin for each data point in `sample`. - """ - if not self._is_fitted: - raise RuntimeError( - "Environment has not been fitted yet. Call `fit_place_grid` first." - ) - if self.edges_ is None: - raise ValueError("Environment edges `edges_` are not defined.") + # Calculate centers and shape + centers_list = [get_centers(edge_dim) for edge_dim in final_edges_list] + centers_shape = tuple(len(c) for c in centers_list) - # remove outer boundary edge - edges = [e[1:-1] for e in self.edges_] + # Create meshgrid of centers and flatten + mesh_centers = np.meshgrid(*centers_list, indexing="ij") + place_bin_centers_flat = np.stack( + [center.ravel() for center in mesh_centers], axis=1 + ) - try: - # Sample is an ND-array. - N, D = sample.shape - except (AttributeError, ValueError): - # Sample is a sequence of 1D arrays. - sample = np.atleast_2d(sample).T - N, D = sample.shape - - nbin = np.empty(D, np.intp) - for i in range(D): - nbin[i] = len(edges[i]) + 1 # includes an outlier on each end - - # Compute the bin number each sample falls into. - Ncount = tuple( - np.searchsorted(edges[i], sample[:, i], side="right") for i in range(D) - ) + # Create meshgrid of edges and flatten + mesh_edges = np.meshgrid(*final_edges_list, indexing="ij") + place_bin_edges_flat = np.stack([edge.ravel() for edge in mesh_edges], axis=1) - # Using digitize, values that fall on an edge are put in the right bin. - # For the rightmost bin, we want values equal to the right edge to be - # counted in the last bin, and not as an outlier. - for i in range(D): - # Find which points are on the rightmost edge. - on_edge = sample[:, i] == edges[i][-1] - # Shift these points one bin to the left. - Ncount[i][on_edge] -= 1 - - return np.ravel_multi_index( - Ncount, - nbin, - ) + edges_tuple: Tuple[NDArray[np.float64], ...] = tuple(final_edges_list) - def get_manifold_distances( - self, position1: np.ndarray, position2: np.ndarray - ) -> np.ndarray: - """Computes the distance between pairs of positions along the track manifold. + return edges_tuple, place_bin_edges_flat, place_bin_centers_flat, centers_shape - This uses the precomputed shortest path distances between bin centers on the - graph representation of the environment (either 1D or N-D). - Parameters - ---------- - position1 : np.ndarray, shape (n_time, n_dims) or (n_dims,) - The first set of positions. - position2 : np.ndarray, shape (n_time, n_dims) or (n_dims,) - The second set of positions. Must have the same shape as position1. +def _infer_track_interior( + position: NDArray[np.float64], + edges: Tuple[NDArray[np.float64], ...], + close_gaps: bool = False, + fill_holes: bool = False, + dilate: bool = False, + bin_count_threshold: int = 0, + boundary_exists: bool = False, +) -> NDArray[np.bool_]: + """Infers the interior bins of the track based on position density. - Returns - ------- - distances : np.ndarray, shape (n_time,) - The shortest path distance along the track for each pair of positions. - Returns np.inf if no path exists between the bins corresponding to the positions. + Parameters + ---------- + position : NDArray[np.float64], shape (n_time, n_dims) + Position data. NaNs are ignored. + edges : Tuple[NDArray[np.float64], ...] + Bin edges for each dimension, as returned by `create_grid`. + fill_holes : bool, optional + Fill holes within the inferred occupied area using binary closing + and filling. Defaults to False. + dilate : bool, optional + Expand the boundary of the inferred occupied area using binary + dilation. Defaults to False. + bin_count_threshold : int, optional + Minimum samples in a bin for it to be considered part of the track. + Defaults to 0 (any occupancy counts). + boundary_exists : bool, optional + If True, the last bin in each dimension is not considered part of + the track. Defaults to False. - Raises - ------ - RuntimeError - If the environment has not been fitted. - ValueError - If input shapes mismatch or required attributes are missing. - """ - if not self._is_fitted: - raise RuntimeError( - "Environment has not been fitted yet. Call `fit_place_grid` first." + Returns + ------- + is_track_interior : NDArray[np.bool_], shape (n_bins_dim1, n_bins_dim2, ...) + Boolean array indicating which bins are considered part of the track. + """ + pos_clean = position[~np.any(np.isnan(position), axis=1)] + if pos_clean.shape[0] == 0: + # Handle case with no valid positions + grid_shape = tuple(len(e) - 1 for e in edges) + return np.zeros(grid_shape, dtype=bool) + + bin_counts, _ = np.histogramdd(pos_clean, bins=edges) + is_track_interior = bin_counts > bin_count_threshold + + n_dims = position.shape[1] + if n_dims > 1: + # Use connectivity=1 for 4-neighbor (2D) or 6-neighbor (3D) etc. + structure = ndimage.generate_binary_structure(n_dims, connectivity=1) + + if close_gaps: + # Closing operation first (dilation then erosion) to close small gaps + is_track_interior = ndimage.binary_closing( + is_track_interior, structure=structure ) - if self.distance_between_nodes_ is None: - raise ValueError("Distance between nodes has not been computed or stored.") - - position1 = np.atleast_2d(position1) - position2 = np.atleast_2d(position2) - # Validate input shapes - if position1.shape != position2.shape: - raise ValueError("Shapes of position1 and position2 must match.") - - bin_ind1 = self.get_bin_ind(position1) - bin_ind2 = self.get_bin_ind(position2) - - if self.track_graph is not None: # 1D case uses dict - raise NotImplementedError( - "Distance calculation for 1D track graph is not implemented." + if fill_holes: + # Fill larger holes enclosed by occupied bins + is_track_interior = ndimage.binary_fill_holes( + is_track_interior, structure=structure ) - else: - distances = self.distance_between_nodes_[bin_ind1, bin_ind2] - - return distances - - def get_direction( - self, - position: np.ndarray, - position_time: Optional[np.ndarray] = None, - sigma: float = 0.1, - sampling_frequency: Optional[float] = None, - classify_stop: bool = False, - stop_speed_threshold: float = 1e-3, - ) -> np.ndarray: - """Get the direction of movement relative to the center of the track (inward/outward). - - Requires a fitted N-D environment with a corresponding track graph (`track_graphDD`). - - Parameters - ---------- - position : np.ndarray, shape (n_time, n_dims) - Position data. - position_time : np.ndarray, shape (n_time,), optional - Timestamps for position data. If None, assumes uniform sampling. - sigma : float, optional - Standard deviation (in seconds) for Gaussian smoothing of velocity towards center. Defaults to 0.1. - sampling_frequency : float, optional - Sampling frequency in Hz. If None, estimated from `position_time`. - classify_stop : bool, optional - If True, classify speeds below `stop_speed_threshold` as "stop". Defaults to False. - stop_speed_threshold : float, optional - Speed threshold for classifying stops. Defaults to 1e-3. - Returns - ------- - direction : np.ndarray, shape (n_time,) - Array of strings: "inward", "outward", or "stop". - - Raises - ------ - RuntimeError - If the environment has not been fitted or lacks the N-D track graph. - ValueError - If sampling frequency cannot be determined. - """ - - if not self._is_fitted: - raise RuntimeError( - "Environment has not been fitted yet. Call `fit_place_grid` first." - ) - if self.track_graphDD is None or self.distance_between_nodes_ is None: - raise RuntimeError( - "Direction finding requires a fitted N-D environment with a track graph ('track_graphDD') and precomputed distances." + if dilate: + # Expand the occupied area + is_track_interior = ndimage.binary_dilation( + is_track_interior, structure=structure ) - if position_time is None: - position_time = np.arange(position.shape[0]) - if sampling_frequency is None: - sampling_frequency = 1 / np.mean(np.diff(position_time)) - - centrality = nx.closeness_centrality(self.track_graphDD, distance="distance") - center_node_id = list(centrality.keys())[np.argmax(list(centrality.values()))] - - bin_ind = self.get_bin_ind(position) + if boundary_exists: + if is_track_interior.ndim == 1: + if len(is_track_interior) > 0: + is_track_interior[-1] = False + elif is_track_interior.ndim > 1 and is_track_interior.size > 0: + for axis_n in range(is_track_interior.ndim): + slicer_first = [slice(None)] * is_track_interior.ndim + slicer_first[axis_n] = 0 + is_track_interior[tuple(slicer_first)] = False + slicer_last = [slice(None)] * is_track_interior.ndim + slicer_last[axis_n] = -1 + is_track_interior[tuple(slicer_last)] = False - velocity_to_center = gaussian_smooth( - np.gradient(self.distance_between_nodes_[bin_ind, center_node_id]), - sigma, - sampling_frequency, - axis=0, - truncate=8, - ) - direction = np.where( - velocity_to_center < 0, - "inward", - "outward", - ) - - if classify_stop: - direction[np.abs(velocity_to_center) < stop_speed_threshold] = "stop" + return is_track_interior.astype(bool) - return direction +def _get_track_boundary( + is_track_interior: NDArray[np.bool_], connectivity: int = 1 +) -> NDArray[np.bool_]: + """Identifies boundary bins adjacent to the track interior. -def get_n_bins( - position: np.ndarray, - bin_size: float = 2.5, - position_range: Optional[list[np.ndarray]] = None, -) -> int: - """Get number of bins need to span a range given a bin size. + The boundary bins themselves are *not* part of the track interior. Parameters ---------- - position : np.ndarray, shape (n_time,) - bin_size : float, optional - position_range : None or list of np.ndarray - Use this to define the extent instead of position + is_track_interior : NDArray[np.bool_], shape (n_bins_dim1, ...) + Boolean array indicating track interior bins. + connectivity : int, optional + Defines neighborhood for dilation (1 for direct orthogonal neighbors, + higher for diagonals). Defaults to 1. Returns ------- - n_bins : int - + is_track_boundary : NDArray[np.bool_], shape (n_bins_dim1, ...) + Boolean array indicating bins adjacent to the track interior. """ - if position_range is not None: - extent = np.diff(position_range, axis=1).squeeze() - else: - extent = np.ptp(position, axis=0) - - return np.ceil(extent / bin_size).astype(np.int32) - + n_dims = is_track_interior.ndim + if n_dims == 0: # Handle scalar case + return np.array(False, dtype=bool) + structure = ndimage.generate_binary_structure( + rank=n_dims, connectivity=connectivity + ) + # Dilate the interior and XOR with original interior to find the boundary shell + return ( + ndimage.binary_dilation(is_track_interior, structure=structure) + ^ is_track_interior + ) -def get_grid( - position: np.ndarray, - bin_size: float = 2.5, - position_range: Optional[list[np.ndarray]] = None, -) -> Tuple[np.ndarray, np.ndarray, np.ndarray, tuple]: - """Calculate bin edges and centers for a spatial grid. - Creates a grid based on the provided position data or range, using - a specified bin size. Handles multiple position dimensions. Adds - boundary bins to the edges. +def _make_nd_track_graph( + place_bin_centers: NDArray[np.float64], + is_track_interior: NDArray[np.bool_], + centers_shape: Tuple[int, ...], +) -> nx.Graph: + """Creates a NetworkX graph connecting centers of adjacent interior bins in N-D. Parameters ---------- - position : np.ndarray, shape (n_time, n_position_dims) - Position data used to determine the grid extent if `position_range` - is not provided. NaNs are ignored. - bin_size : float, optional - The desired approximate size of the bins in each dimension. The actual - size may vary slightly to evenly cover the range. By default 2.5. - position_range : Optional[List[Tuple[float, float]]], optional - A list of tuples, one for each position dimension, specifying the - (min, max) boundary for the grid. If None, the min/max of the - `position` data is used. By default None. + place_bin_centers : NDArray[np.float64], shape (n_total_bins, n_dims) + Coordinates of the center of each bin (flattened). + n_total_bins should be np.prod(centers_shape). + is_track_interior : NDArray[np.bool_], shape (n_bins_dim1, ...) + Boolean grid indicating which bins are part of the track. + Its shape must be `centers_shape`. + centers_shape : Tuple[int, ...] + Shape of the bin grid (number of bins in each dimension). Returns ------- - edges : tuple[np.ndarray, ...] - A tuple containing the bin edges for each position dimension. Each - element is a 1D array of shape (n_bins_d + 1,), where n_bins_d is - the number of bins in that dimension `d`. Includes boundary bins. - place_bin_edges_flat : np.ndarray, shape (n_total_bins, n_position_dims) - The edges corresponding to each bin in the flattened grid. Note: This - output might be less standard or useful than `edges` and - `place_bin_centers`. Consider if it's truly needed. - place_bin_centers_flat : np.ndarray, shape (n_total_bins, n_position_dims) - The center coordinate of each bin in the flattened grid. - `n_total_bins` is the product of the number of bins in each dimension. - centers_shape : tuple[int, ...] - The shape of the grid in terms of the number of bins in each dimension - (e.g., (n_bins_x, n_bins_y)). - + track_graph_nd : nx.Graph + Graph where nodes are *flat indices* of bins. + Nodes have 'pos' (coordinates), 'is_track_interior' (bool), + 'bin_ind' (N-D tuple index), and 'bin_ind_flat' (flat index) attributes. + Edges connect adjacent interior bins and have a 'distance' attribute + (Euclidean distance between bin centers) and an 'edge_id'. """ - position = position if position.ndim > 1 else position[:, np.newaxis] - is_nan = np.any(np.isnan(position), axis=1) - position = position[~is_nan] - n_bins = get_n_bins(position, bin_size, position_range) - _, edges = np.histogramdd(position, bins=n_bins, range=position_range) - if len(edges) > 1: - edges = [ - np.insert( - edge, - (0, len(edge)), - (edge[0] - np.diff(edge)[0], edge[-1] + np.diff(edge)[0]), - ) - for edge in edges - ] - mesh_edges = np.meshgrid(*edges, indexing="ij") - place_bin_edges = np.stack([edge.ravel() for edge in mesh_edges], axis=1) - - mesh_centers = np.meshgrid(*[get_centers(edge) for edge in edges], indexing="ij") - place_bin_centers = np.stack([center.ravel() for center in mesh_centers], axis=1) - centers_shape = tuple([len(edge) - 1 for edge in edges]) - - return edges, place_bin_edges, place_bin_centers, centers_shape + track_graph_nd = nx.Graph() + n_dims = is_track_interior.ndim + if place_bin_centers.shape[0] != np.prod(centers_shape): + raise ValueError( + f"Mismatch between place_bin_centers length ({place_bin_centers.shape[0]}) " + f"and product of centers_shape ({np.prod(centers_shape)})." + ) + if is_track_interior.shape != centers_shape: + raise ValueError( + f"Shape of is_track_interior {is_track_interior.shape} " + f"does not match centers_shape {centers_shape}." + ) -def get_track_interior( - position: np.ndarray, - bins: Union[int, Sequence[int]], - fill_holes: bool = False, - dilate: bool = False, - bin_count_threshold: int = 0, -) -> np.ndarray: - """Infers the interior bins of the track given positions. - - Parameters - ---------- - position : np.ndarray, shape (n_time, n_position_dims) - bins : sequence or int, optional - The bin specification: + # Add all bins as nodes to the graph. + # Node IDs will be the flat index of the bin. + for node_id, (node_position, is_interior_flag) in enumerate( + zip( + place_bin_centers, + is_track_interior.ravel(), # Flatten to match node_id sequence + ) + ): + track_graph_nd.add_node( + node_id, # Flat index + pos=tuple(node_position), + is_track_interior=bool(is_interior_flag), + bin_ind=tuple(np.unravel_index(node_id, centers_shape)), # N-D index + bin_ind_flat=node_id, + ) - * A sequence of arrays describing the bin edges along each dimension. - * The number of bins for each dimension (nx, ny, ... =bins) - * The number of bins for all dimensions (nx=ny=...=bins). - fill_holes : bool, optional - Fill any holes in the extracted track interior bins - dilate : bool, optional - Inflate the extracted track interior bins - bin_count_threshold : int, optional - Greater than this number of samples should be in the bin for it to - be considered on the track. + # Collect edges to add. Store as (u, v) with u < v to ensure uniqueness. + edges_to_add_set: set[tuple[int, int]] = set() + + # Iterate over only the bins that are part of the track interior + # interior_indices_nd is an array of shape (num_interior_bins, n_dims) + # where each row is an N-D index tuple (e.g., [r, c, z]) + interior_indices_nd = np.array(np.nonzero(is_track_interior)).T + + for current_nd_index_arr in interior_indices_nd: + # current_nd_index_arr is a 1D array, e.g., np.array([row, col, ...]) + current_node_id = np.ravel_multi_index(current_nd_index_arr, centers_shape) + + # Iterate over all N-dimensional offsets: (-1,-1,..), (-1,-1,..,0), ..., (1,1,..,1) + # This creates 3^n_dims potential neighbors. + # To get only orthogonal neighbors, replace the itertools.product loop with: + # for dim_of_offset in range(n_dims): + # for offset_val in [-1, 1]: + # offset_tuple = [0] * n_dims + # offset_tuple[dim_of_offset] = offset_val + for offset_tuple in itertools.product([-1, 0, 1], repeat=n_dims): + if all( + offset == 0 for offset in offset_tuple + ): # Skip offset (0,0,...) which is self + continue + + neighbor_nd_index_arr = current_nd_index_arr + np.array(offset_tuple) + + # Check if the neighbor's N-D index is within grid bounds + in_bounds = True + for dim_idx in range(n_dims): + if not (0 <= neighbor_nd_index_arr[dim_idx] < centers_shape[dim_idx]): + in_bounds = False + break + if not in_bounds: + continue + + # If in bounds, check if this neighbor is also part of the track interior + neighbor_nd_index_tuple = tuple(neighbor_nd_index_arr) + if is_track_interior[neighbor_nd_index_tuple]: + neighbor_node_id = np.ravel_multi_index( + neighbor_nd_index_tuple, centers_shape + ) - Returns - ------- - is_track_interior : np.ndarray - The interior bins of the track as inferred from position + # Add edge pair (u,v) where u < v to the set + u, v = min(current_node_id, neighbor_node_id), max( + current_node_id, neighbor_node_id + ) + # Ensure we only add edges between different nodes + # (i.e., not self-loops) + if u != v: + edges_to_add_set.add((u, v)) + + # Add the collected unique edges to the graph with their distances + for u_node, v_node in edges_to_add_set: + pos1 = np.asarray(track_graph_nd.nodes[u_node]["pos"]) + pos2 = np.asarray(track_graph_nd.nodes[v_node]["pos"]) + distance = np.linalg.norm(pos1 - pos2) + track_graph_nd.add_edge(u_node, v_node, distance=distance) - """ - bin_counts, _ = np.histogramdd(position, bins=bins) - is_track_interior = (bin_counts > bin_count_threshold).astype(int) - n_position_dims = position.shape[1] - if n_position_dims > 1: - structure = ndimage.generate_binary_structure(n_position_dims, 1) - is_track_interior = ndimage.binary_closing( - is_track_interior, structure=structure - ) - if fill_holes: - is_track_interior = ndimage.binary_fill_holes(is_track_interior) + # Add a unique 'edge_id' to each edge (optional, but was in original) + for edge_id, graph_edge_tuple in enumerate(track_graph_nd.edges()): + # graph_edge_tuple is (node1, node2) or (node1, node2, key) if multigraph + track_graph_nd.edges[graph_edge_tuple]["edge_id"] = edge_id - if dilate: - is_track_interior = ndimage.binary_dilation(is_track_interior) - # adjust for boundary edges in 2D - is_track_interior[-1] = False - is_track_interior[:, -1] = False - return is_track_interior.astype(bool) + return track_graph_nd -def get_track_segments_from_graph(track_graph: nx.Graph) -> np.ndarray: - """Returns a 2D array of node positions corresponding to each edge. +def _get_distance_between_bins(track_graph_nd: nx.Graph) -> NDArray[np.float64]: + """Calculates the shortest path distances between bins in the track graph. Parameters ---------- - track_graph : networkx Graph + track_graph_nd : nx.Graph + Graph where nodes are indices of interior bins, 'pos' attribute stores + coordinates, and edges connect adjacent interior bins with 'distance'. Returns ------- - track_segments : np.ndarray, shape (n_segments, n_nodes, n_space) - + distance : np.ndarray, shape (n_nodes, n_nodes) + Matrix of shortest path distances between all pairs of nodes. """ - node_positions = nx.get_node_attributes(track_graph, "pos") - return np.asarray( - [ - (node_positions[node1], node_positions[node2]) - for node1, node2 in track_graph.edges() - ] - ) + node_to_bin_ind_flat = nx.get_node_attributes(track_graph_nd, "bin_ind_flat") -def project_points_to_segment( - track_segments: np.ndarray, position: np.ndarray -) -> np.ndarray: - """Finds the closet point on a track segment in terms of Euclidean distance + node_positions = nx.get_node_attributes(track_graph_nd, "pos") + node_positions = np.asarray(list(node_positions.values())) + n_bins = len(node_positions) - Parameters - ---------- - track_segments : np.ndarray, shape (n_segments, n_nodes, 2) - position : np.ndarray, shape (n_time, 2) + distance = np.full((n_bins, n_bins), np.inf) + for to_node_id, from_node_id in nx.shortest_path_length( + track_graph_nd, + weight="distance", + ): + to_bin_ind = node_to_bin_ind_flat[to_node_id] + from_bin_inds = [node_to_bin_ind_flat[node_id] for node_id in from_node_id] + distance[to_bin_ind, from_bin_inds] = list(from_node_id.values()) - Returns - ------- - projected_positions : np.ndarray, shape (n_time, n_segments, n_space) + return distance - """ - segment_diff = np.diff(track_segments, axis=1).squeeze(axis=1) - sum_squares = np.sum(segment_diff**2, axis=1) - node1 = track_segments[:, 0, :] - nx = ( - np.sum(segment_diff * (position[:, np.newaxis, :] - node1), axis=2) - / sum_squares - ) - nx[np.where(nx < 0)] = 0.0 - nx[np.where(nx > 1)] = 1.0 - return node1[np.newaxis, ...] + ( - nx[:, :, np.newaxis] * segment_diff[np.newaxis, ...] - ) +def _make_track_graph_bin_centers_edges( + track_graph: nx.Graph, place_bin_size: float +) -> nx.Graph: + """Insert the bin center and bin edge positions as nodes in the track graph. -def _calculate_linear_position( - track_graph: nx.Graph, - position: np.ndarray, - track_segment_id: np.ndarray, - edge_order: list[tuple], - edge_spacing: Union[float, list], -) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: - """Determines the linear position given a 2D position and a track graph. + Subdivides each edge of the input `track_graph` into smaller segments + based on `place_bin_size`. New nodes are added representing bin edges + and bin centers along the original edge. The original edge is removed + and replaced by a chain of these new nodes and edges with calculated + Euclidean distances. Parameters ---------- track_graph : nx.Graph - position : np.ndarray, shape (n_time, n_position_dims) - track_segment_id : np.ndarray, shape (n_time,) - edge_order : list of 2-tuples - edge_spacing : float or list, len n_edges - 1 + The input track graph. Nodes must have a 'pos' attribute + containing (x, y) coordinates. + place_bin_size : float + The desired size of the bins along the track edges. Returns ------- - linear_position : np.ndarray, shape (n_time,) - projected_track_positions_x : np.ndarray, shape (n_time,) - projected_track_positions_y : np.ndarray, shape (n_time,) - + track_graph_bin_centers_edges : nx.Graph + A new graph with original edges subdivided into bin-sized segments. + New nodes representing bin edges and centers are added. + Edges in the new graph have a 'distance' attribute. + Nodes have 'pos', 'edge_id' (referencing the original edge index), + and 'is_bin_edge' attributes. + + Raises + ------ + KeyError + If a node in `track_graph` is missing the 'pos' attribute. + ValueError + If `place_bin_size` is not positive. """ - is_nan = np.isnan(track_segment_id) - track_segment_id[is_nan] = 0 # need to check - track_segment_id = track_segment_id.astype(int) - - track_segments = get_track_segments_from_graph(track_graph) - projected_track_positions = project_points_to_segment(track_segments, position) - n_time = projected_track_positions.shape[0] - projected_track_positions = projected_track_positions[ - (np.arange(n_time), track_segment_id) - ] - - n_edges = len(edge_order) - if isinstance(edge_spacing, int) or isinstance(edge_spacing, float): - edge_spacing = [ - edge_spacing, - ] * (n_edges - 1) + if place_bin_size <= 0: + raise ValueError("place_bin_size must be positive.") - counter = 0.0 - start_node_linear_position = [] + # Create a copy to avoid modifying the original graph + track_graph_bin_centers_edges = track_graph.copy() + n_nodes = len(track_graph.nodes) # Keep track of the next available node ID - for ind, edge in enumerate(edge_order): - start_node_linear_position.append(counter) + # Iterate through a list of original edges to avoid issues with modifying the graph during iteration + original_edges = list(track_graph.edges) + for edge_ind, (node1, node2) in enumerate(original_edges): + # Get positions of the original edge's nodes try: - counter += track_graph.edges[edge]["distance"] + edge_spacing[ind] - except IndexError: - pass + node1_pos = np.asarray(track_graph.nodes[node1]["pos"]) + node2_pos = np.asarray(track_graph.nodes[node2]["pos"]) + if node1_pos.shape != (2,) or node2_pos.shape != (2,): + raise ValueError( + f"Node positions for edge ({node1}, {node2}) must be 2D." + ) + node1_x_pos, node1_y_pos = node1_pos + node2_x_pos, node2_y_pos = node2_pos + except KeyError as e: + raise KeyError( + f"Node {e} on edge ({node1}, {node2}) is missing 'pos' attribute." + ) from e + + # Calculate the length of the original edge + edge_vector = node2_pos - node1_pos + edge_size = np.linalg.norm(edge_vector) + + # Determine the number of points needed to represent bin edges/centers + # Need points for node1, node2, and intermediate bin edges/centers + # n_segments = ceil(edge_size / place_bin_size) + # n_points_for_centers_and_edges = 2 * n_segments + 1 (alternating edge, center, edge...) + # Ensure at least 3 points (start, center, end) even for very small edges if place_bin_size is large + # but number of bins should correspond to edge_size / place_bin_size + n_segments = max(1, np.ceil(edge_size / place_bin_size).astype(np.int32)) + n_points = 2 * n_segments + 1 + + # Interpolate points along the edge + # Handle vertical lines separately to avoid issues with interp1d + if not np.isclose(node1_x_pos, node2_x_pos): + # Use linear interpolation for y based on x + f_interp = interp1d([node1_x_pos, node2_x_pos], [node1_y_pos, node2_y_pos]) + x_new = np.linspace(node1_x_pos, node2_x_pos, num=n_points, endpoint=True) + y_new = f_interp(x_new) + else: # Vertical line + x_new = np.full(n_points, node1_x_pos) + y_new = np.linspace(node1_y_pos, node2_y_pos, num=n_points, endpoint=True) + + # Combine x and y coordinates + xy_interpolated = np.stack((x_new, y_new), axis=1) + + # Calculate distances between consecutive interpolated points + # dist_between_nodes[i] is the distance between xy_interpolated[i] and xy_interpolated[i+1] + dist_between_nodes = np.linalg.norm(np.diff(xy_interpolated, axis=0), axis=1) + + # Generate IDs for the new intermediate nodes + # The number of new nodes to add is n_points - 2 (excluding original node1 and node2) + # However, we create IDs for all interpolated points temporarily. + # The first and last points in xy_interpolated correspond to node1 and node2. + # We will connect node1 to xy_interpolated[1] and xy_interpolated[-2] to node2. + # The nodes corresponding to xy_interpolated[1] through xy_interpolated[-2] are truly new. + + # IDs for all points generated by linspace (including endpoints) + # We'll use these as temporary identifiers before linking to original nodes + temp_node_ids = n_nodes + np.arange(n_points) + n_nodes += n_points # Increment total node count for next edge + + # --- Add nodes and edges to the new graph --- + + # 1. Remove the original edge + if track_graph_bin_centers_edges.has_edge(node1, node2): + track_graph_bin_centers_edges.remove_edge(node1, node2) + + # 2. Add attributes to the new intermediate nodes and connect them + # The loop runs n_points - 1 times, corresponding to dist_between_nodes length + for idx in range(n_points - 1): + current_temp_node_id = temp_node_ids[idx] + next_temp_node_id = temp_node_ids[idx + 1] + current_pos = xy_interpolated[idx] + next_pos = xy_interpolated[idx + 1] + segment_distance = dist_between_nodes[idx] + + # Add the 'current' node if it's not the very first point (which corresponds to node1) + if idx > 0: + track_graph_bin_centers_edges.add_node( + current_temp_node_id, + pos=tuple(current_pos), + edge_id=edge_ind, + is_bin_edge=( + idx % 2 == 0 + ), # 0, 2, 4... are edges (including start/end) + ) - start_node_linear_position = np.asarray(start_node_linear_position) - - track_segment_id_to_start_node_linear_position = { - track_graph.edges[e]["edge_id"]: snlp - for e, snlp in zip(edge_order, start_node_linear_position) - } - - start_node_linear_position = np.asarray( - [ - track_segment_id_to_start_node_linear_position[edge_id] - for edge_id in track_segment_id - ] - ) - - track_segment_id_to_edge = {track_graph.edges[e]["edge_id"]: e for e in edge_order} - start_node_id = np.asarray( - [track_segment_id_to_edge[edge_id][0] for edge_id in track_segment_id] - ) - start_node_2D_position = np.asarray( - [track_graph.nodes[node]["pos"] for node in start_node_id] - ) - - linear_position = start_node_linear_position + ( - np.linalg.norm(start_node_2D_position - projected_track_positions, axis=1) - ) - linear_position[is_nan] = np.nan - - return ( - linear_position, - projected_track_positions[:, 0], - projected_track_positions[:, 1], - ) - - -def make_track_graph_with_bin_centers_edges( - track_graph: nx.Graph, place_bin_size: float -) -> nx.Graph: - """Insert the bin center and bin edge positions as nodes in the track graph. - - Parameters - ---------- - track_graph : nx.Graph - place_bin_size : float + # Add the 'next' node if it's the last point in the interpolation + # (as it won't be added as 'current' in the next iteration) + # This corresponds to the point matching node2 IF idx == n_points - 2 + if idx == n_points - 2: + track_graph_bin_centers_edges.add_node( + next_temp_node_id, + pos=tuple(next_pos), + edge_id=edge_ind, + is_bin_edge=((idx + 1) % 2 == 0), + ) - Returns - ------- - track_graph_with_bin_centers_edges : nx.Graph + # Add the edge between the current and next temporary nodes + # We add edges sequentially with their specific calculated distance + track_graph_bin_centers_edges.add_edge( + current_temp_node_id, next_temp_node_id, distance=segment_distance + ) - """ - track_graph_with_bin_centers_edges = track_graph.copy() - n_nodes = len(track_graph.nodes) + # 3. Connect original node1 to the first *intermediate* temporary node + # temp_node_ids[0] corresponds to node1's position, temp_node_ids[1] is the first actual intermediate node + # The distance between them is dist_between_nodes[0] + # We replace the temporary node temp_node_ids[0] with the original node1 + # Need to handle the edge case where n_points might be small (e.g., 3) + if n_points > 1: + first_intermediate_node_id = temp_node_ids[1] + # Remove the temporary node corresponding to node1's position + if track_graph_bin_centers_edges.has_node(temp_node_ids[0]): + track_graph_bin_centers_edges.remove_node(temp_node_ids[0]) + + # Add edge from original node1 to the first intermediate node + track_graph_bin_centers_edges.add_edge( + node1, first_intermediate_node_id, distance=dist_between_nodes[0] + ) - for edge_ind, (node1, node2) in enumerate(track_graph.edges): - node1_x_pos, node1_y_pos = track_graph.nodes[node1]["pos"] - node2_x_pos, node2_y_pos = track_graph.nodes[node2]["pos"] + # 4. Connect the last *intermediate* temporary node to the original node2 + # temp_node_ids[-1] corresponds to node2's position, temp_node_ids[-2] is the last actual intermediate node + # The distance between them is dist_between_nodes[-1] + # We replace the temporary node temp_node_ids[-1] with the original node2 + if n_points > 1: + last_intermediate_node_id = temp_node_ids[-2] + # Remove the temporary node corresponding to node2's position + if track_graph_bin_centers_edges.has_node(temp_node_ids[-1]): + track_graph_bin_centers_edges.remove_node(temp_node_ids[-1]) + + # Add edge from last intermediate node to original node2 + track_graph_bin_centers_edges.add_edge( + last_intermediate_node_id, node2, distance=dist_between_nodes[-1] + ) + elif n_points == 1: # Edge case: edge_size was effectively zero or very small + # Only one point, corresponds to both node1 and node2 (or very close) + # Remove temporary node and ensure node1/node2 exist with correct attributes + if track_graph_bin_centers_edges.has_node(temp_node_ids[0]): + track_graph_bin_centers_edges.remove_node(temp_node_ids[0]) + # Optionally add a zero-distance edge if needed, or just ensure nodes exist + # track_graph_bin_centers_edges.add_edge(node1, node2, distance=0.0) # Creates self-loop if node1==node2 - edge_size = np.linalg.norm( - [(node2_x_pos - node1_x_pos), (node2_y_pos - node1_y_pos)] - ) - n_bins = 2 * np.ceil(edge_size / place_bin_size).astype(np.int32) + 1 - if ~np.isclose(node1_x_pos, node2_x_pos): - f = interp1d((node1_x_pos, node2_x_pos), (node1_y_pos, node2_y_pos)) - xnew = np.linspace(node1_x_pos, node2_x_pos, num=n_bins, endpoint=True) - xy = np.stack((xnew, f(xnew)), axis=1) - else: - ynew = np.linspace(node1_y_pos, node2_y_pos, num=n_bins, endpoint=True) - xnew = np.ones_like(ynew) * node1_x_pos - xy = np.stack((xnew, ynew), axis=1) - dist_between_nodes = np.linalg.norm(np.diff(xy, axis=0), axis=1) - - new_node_ids = n_nodes + np.arange(len(dist_between_nodes) + 1) - nx.add_path( - track_graph_with_bin_centers_edges, - [*new_node_ids], - distance=dist_between_nodes[0], - ) - nx.add_path( - track_graph_with_bin_centers_edges, [node1, new_node_ids[0]], distance=0 - ) - nx.add_path( - track_graph_with_bin_centers_edges, [node2, new_node_ids[-1]], distance=0 - ) - track_graph_with_bin_centers_edges.remove_edge(node1, node2) - for ind, (node_id, pos) in enumerate(zip(new_node_ids, xy)): - track_graph_with_bin_centers_edges.nodes[node_id]["pos"] = pos - track_graph_with_bin_centers_edges.nodes[node_id]["edge_id"] = edge_ind - if ind % 2: - track_graph_with_bin_centers_edges.nodes[node_id]["is_bin_edge"] = False - else: - track_graph_with_bin_centers_edges.nodes[node_id]["is_bin_edge"] = True - track_graph_with_bin_centers_edges.nodes[node1]["edge_id"] = edge_ind - track_graph_with_bin_centers_edges.nodes[node2]["edge_id"] = edge_ind - track_graph_with_bin_centers_edges.nodes[node1]["is_bin_edge"] = True - track_graph_with_bin_centers_edges.nodes[node2]["is_bin_edge"] = True - n_nodes = len(track_graph_with_bin_centers_edges.nodes) + # 5. Set attributes for the original nodes (ensure they have edge_id and are bin edges) + track_graph_bin_centers_edges.nodes[node1]["edge_id"] = edge_ind + track_graph_bin_centers_edges.nodes[node1]["is_bin_edge"] = True + track_graph_bin_centers_edges.nodes[node2]["edge_id"] = edge_ind + track_graph_bin_centers_edges.nodes[node2]["is_bin_edge"] = True - return track_graph_with_bin_centers_edges + return track_graph_bin_centers_edges -def extract_bin_info_from_track_graph( +def _extract_bin_info_from_track_graph( track_graph: nx.Graph, - track_graph_with_bin_centers_edges: nx.Graph, + track_graph_bin_centers_edges: nx.Graph, edge_order: list[tuple], edge_spacing: Union[float, list], ) -> pd.DataFrame: @@ -830,7 +748,7 @@ def extract_bin_info_from_track_graph( Parameters ---------- track_graph : nx.Graph - track_graph_with_bin_centers_edges : nx.Graph + track_graph_bin_centers_edges : nx.Graph edge_order : list of 2-tuples edge_spacing : list, len n_edges - 1 @@ -842,7 +760,7 @@ def extract_bin_info_from_track_graph( """ nodes_df = ( pd.DataFrame.from_dict( - dict(track_graph_with_bin_centers_edges.nodes(data=True)), orient="index" + dict(track_graph_bin_centers_edges.nodes(data=True)), orient="index" ) .assign(x_position=lambda df: np.asarray(list(df.pos))[:, 0]) .assign(y_position=lambda df: np.asarray(list(df.pos))[:, 1]) @@ -873,23 +791,18 @@ def extract_bin_info_from_track_graph( return nodes_df -def get_track_grid( +def _create_1d_track_grid_data( track_graph: nx.Graph, edge_order: list[tuple], edge_spacing: Union[float, list], place_bin_size: float, ) -> Tuple[ - np.ndarray, - np.ndarray, - np.ndarray, - dict, - tuple, - tuple, - nx.Graph, - pd.DataFrame, - pd.DataFrame, - pd.DataFrame, - pd.DataFrame, + np.ndarray, # place_bin_centers + np.ndarray, # place_bin_edges + np.ndarray, # is_track_interior + tuple, # centers_shape + tuple, # edges + nx.Graph, # track_graph_bin_centers ]: """Figures out 1D spatial bins given a track graph. @@ -905,26 +818,15 @@ def get_track_grid( place_bin_centers : np.ndarray, shape (n_bins, n_position_dims) place_bin_edges : np.ndarray, shape (n_bins + n_position_dims, n_position_dims) is_track_interior : np.ndarray, shape (n_bins, n_position_dim) - distance_between_nodes : dict centers_shape : tuple edges : tuple of np.ndarray - track_graph_with_bin_centers_edges : nx.Graph - original_nodes_df : pd.DataFrame - Table of information about the original nodes in the track graph - place_bin_edges_nodes_df : pd.DataFrame - Table of information with bin edges and centers - place_bin_centers_nodes_df : pd.DataFrame - Table of information about bin centers - nodes_df : pd.DataFrame - Table of information with information about the original nodes, - bin edges, and bin centers - + track_graph_bin_centers : nx.Graph """ - track_graph_with_bin_centers_edges = make_track_graph_with_bin_centers_edges( + track_graph_bin_centers_edges = _make_track_graph_bin_centers_edges( track_graph, place_bin_size ) - nodes_df = extract_bin_info_from_track_graph( - track_graph, track_graph_with_bin_centers_edges, edge_order, edge_spacing + nodes_df = _extract_bin_info_from_track_graph( + track_graph, track_graph_bin_centers_edges, edge_order, edge_spacing ) # Dataframe with nodes from track graph only @@ -951,19 +853,12 @@ def get_track_grid( place_bin_edges = np.asarray(no_duplicate_place_bin_edges_nodes_df.linear_position) place_bin_centers = get_centers(place_bin_edges) - # Compute distance between nodes - distance_between_nodes = dict( - nx.all_pairs_dijkstra_path_length( - track_graph_with_bin_centers_edges, weight="distance" - ) - ) - # Figure out which points are on the track and not just gaps change_edge_ind = np.nonzero( np.diff(no_duplicate_place_bin_edges_nodes_df.edge_id) )[0] - if isinstance(edge_spacing, int) or isinstance(edge_spacing, float): + if isinstance(edge_spacing, (int, float)): n_edges = len(edge_order) edge_spacing = [ edge_spacing, @@ -989,220 +884,1289 @@ def get_track_grid( ) ).sort_values(by=["linear_position"], axis="rows") ).reset_index(drop=True) + place_bin_centers_nodes_df["bin_ind"] = np.arange( + len(place_bin_centers_nodes_df), dtype=np.int32 + ) + place_bin_centers_nodes_df["bin_ind_flat"] = place_bin_centers_nodes_df["bin_ind"] + + track_graph_bin_centers = _make_track_graph_bin_centers( + place_bin_centers_nodes_df, + track_graph_bin_centers_edges, + original_nodes_df, + ) # Other needed information - edges = [place_bin_edges] + edges = (place_bin_edges,) centers_shape = (place_bin_centers.size,) return ( place_bin_centers[:, np.newaxis], place_bin_edges[:, np.newaxis], is_track_interior, - distance_between_nodes, centers_shape, edges, - track_graph_with_bin_centers_edges, - original_nodes_df, - place_bin_edges_nodes_df, - place_bin_centers_nodes_df, - nodes_df.reset_index(), + track_graph_bin_centers, ) -def get_track_boundary( - is_track_interior: np.ndarray, n_position_dims: int = 2, connectivity: int = 1 +def _gaussian_smooth( + data: np.ndarray, + sigma: float, + sampling_frequency: float, + axis: int = 0, + truncate: int = 8, ) -> np.ndarray: - """Determines the boundary of the valid interior track bins. The boundary - are not bins on the track but surround it. + """1D convolution of the data with a Gaussian. + + The standard deviation of the gaussian is in the units of the sampling + frequency. The function is just a wrapper around scipy's + `gaussian_filter1d`, The support is truncated at 8 by default, instead + of 4 in `gaussian_filter1d` Parameters ---------- - is_track_interior : np.ndarray, shape (n_bins_x, n_bins_y) - n_position_dims : int - connectivity : int - `connectivity` determines which elements of the output array belong - to the structure, i.e., are considered as neighbors of the central - element. Elements up to a squared distance of `connectivity` from - the center are considered neighbors. `connectivity` may range from 1 - (no diagonal elements are neighbors) to `rank` (all elements are - neighbors). + data : array_like + sigma : float + sampling_frequency : int + axis : int, optional + truncate : int, optional Returns ------- - is_track_boundary : np.ndarray, shape (n_bins,) + smoothed_data : array_like """ - structure = ndimage.generate_binary_structure( - rank=n_position_dims, connectivity=connectivity - ) - return ( - ndimage.binary_dilation(is_track_interior, structure=structure) - ^ is_track_interior + return ndimage.gaussian_filter1d( + data, sigma * sampling_frequency, truncate=truncate, axis=axis, mode="constant" ) -def order_boundary(boundary: np.ndarray) -> np.ndarray: - """Given boundary bin centers, orders them in a way to make a continuous line. +def add_distance_weight_to_edges( + track_graph: nx.Graph, +) -> nx.Graph: + """Adds a distance weight to each edge in the track graph. + + Parameters + ---------- + track_graph : nx.Graph + The input track graph. + + Returns + ------- + track_graph : nx.Graph + The modified track graph with distance weights added to edges. + """ + new_attribute_name = "distance_weight" + for u, v, data in track_graph.edges(data=True): + try: + distance = data["distance"] + # Avoid division by zero + computed_value = 1.0 / (distance + 1e-9) if distance > 0.0 else np.inf + track_graph.edges[u, v][new_attribute_name] = computed_value + except KeyError: + # If the distance attribute is not present, skip this edge + continue + + +def _get_node_pos(graph: nx.Graph, node_id: Any) -> np.ndarray: + """Helper to get node position as a numpy array.""" + if node_id not in graph or "pos" not in graph.nodes[node_id]: + raise KeyError(f"Node {node_id} or its 'pos' attribute not found in graph.") + return np.asarray(graph.nodes[node_id]["pos"]) + + +def _make_track_graph_bin_centers( + place_bin_centers_nodes_df: pd.DataFrame, + track_graph_bin_centers_edges: nx.Graph, + original_nodes_df: pd.DataFrame, +) -> nx.Graph: + """Creates a graph connecting bin centers sequentially along the track. - https://stackoverflow.com/questions/37742358/sorting-points-to-form-a-continuous-line + Builds a graph using only bin center nodes. Connects adjacent centers + within segments based on linear position. Links segment endpoints meeting + at original track graph nodes by looking via intermediate nodes in the + augmented graph. Parameters ---------- - boundary : np.ndarray, shape (n_boundary_points, n_position_dims) + place_bin_centers_nodes_df : pd.DataFrame + DataFrame containing bin center nodes with columns: + - node_id: Unique identifier for each node. + - x_position: X-coordinate of the node. + - y_position: Y-coordinate of the node. + - edge_id: Identifier for the edge this node belongs to. + - linear_position: Linear position along the edge. + track_graph_bin_centers_edges : nx.Graph + Graph with bin centers as nodes, linked sequentially and at junctions. + original_nodes_df : pd.DataFrame + DataFrame containing original nodes with columns: + - node_id: Unique identifier for each node. + - x_position: X-coordinate of the node. + - y_position: Y-coordinate of the node. + - edge_id: Identifier for the edge this node belongs to. + - linear_position: Linear position along the edge. + - is_bin_edge: Boolean indicating if the node is a bin edge. + - is_track_interior: Boolean indicating if the node is part of the track. + - bin_ind: Index of the bin in the grid. + - bin_ind_flat: Flattened index of the bin in the grid. + - edge_avg_linear_position: Average linear position of the edge. + - distance: Distance to the next node. Returns ------- - ordered_boundary : np.ndarray, shape (n_boundary_points, n_position_dims) + track_graph_bin_centers : nx.Graph + Graph with bin centers as nodes, linked sequentially and at junctions. + + Raises + ------ + KeyError + If a node's position is not found in the graph. """ - n_points = boundary.shape[0] - clf = NearestNeighbors(n_neighbors=2).fit(boundary) - G = clf.kneighbors_graph() - T = nx.from_scipy_sparse_matrix(G) + track_graph_bin_centers = nx.Graph() + centers_df = place_bin_centers_nodes_df.copy() + + # --- 1. Add Nodes --- + nodes_to_add = [] + valid_centers_df = centers_df[centers_df["node_id"] != -1] + for _, row in valid_centers_df.iterrows(): + nodes_to_add.append( + ( + row["node_id"], + { + "pos": (row["x_position"], row["y_position"]), + "edge_id": int(row["edge_id"]), + "bin_ind": row["bin_ind"], + "bin_ind_flat": row["bin_ind_flat"], + "is_track_interior": True, + }, + ) + ) + track_graph_bin_centers.add_nodes_from(nodes_to_add) - paths = [list(nx.dfs_preorder_nodes(T, i)) for i in range(n_points)] - min_idx, min_dist = 0, np.inf + for ind, (_, row) in enumerate(centers_df[centers_df["node_id"] == -1].iterrows()): + nodes_to_add.append( + ( + -ind - 1, + { + "pos": (np.nan, np.nan), + "edge_id": -1, + "bin_ind": row["bin_ind"], + "bin_ind_flat": row["bin_ind_flat"], + "is_track_interior": False, + }, + ) + ) + track_graph_bin_centers.add_nodes_from(nodes_to_add) + + # --- 2. Add Intra-Segment Edges --- + edges_to_add: List[Tuple[Any, Any, Dict[str, Any]]] = [] + intra_segment_edge_count = 0 + for edge_id, group in valid_centers_df.groupby("edge_id"): + sorted_group = group.sort_values("linear_position") + node_ids = sorted_group["node_id"].values + for node1, node2 in zip(node_ids[:-1], node_ids[1:]): + try: + pos1 = _get_node_pos(track_graph_bin_centers, node1) + pos2 = _get_node_pos(track_graph_bin_centers, node2) + except KeyError as e: + continue + distance = np.linalg.norm(pos1 - pos2) + if distance > 1e-9: + edges_to_add.append( + (node1, node2, {"distance": distance, "edge_id": edge_id}) + ) + intra_segment_edge_count += 1 + track_graph_bin_centers.add_edges_from(edges_to_add) + + # --- 3. Add Inter-Segment Edges (Link segment ends) --- + linking_edges_to_add: List[Tuple[Any, Any, Dict[str, Any]]] = [] + inter_segment_edge_count = 0 + augmented_graph = track_graph_bin_centers_edges + original_node_ids_in_augmented = original_nodes_df["node_id"].unique() + + for original_node_id in original_node_ids_in_augmented: + if not augmented_graph.has_node(original_node_id): + continue # Skip if original node somehow isn't in augmented graph + + # Find bin centers associated with this junction + endpoint_centers: Set[Any] = set() # Use set to store unique centers + + # Find direct neighbors of the original node (likely edge nodes) + direct_neighbors = list(augmented_graph.neighbors(original_node_id)) + + for intermediate_node in direct_neighbors: + # Find neighbors of the intermediate node + second_level_neighbors = list(augmented_graph.neighbors(intermediate_node)) + # Filter these to find bin centers present in our target graph + for potential_center in second_level_neighbors: + # Ensure it's not the original node itself and it IS a bin center + if ( + potential_center != original_node_id + and track_graph_bin_centers.has_node(potential_center) + ): + endpoint_centers.add(potential_center) + + # Add edges between all pairs of these endpoint bin centers + for node1, node2 in itertools.combinations(endpoint_centers, 2): + # Avoid adding edges already added or self-loops (combinations handles self-loops) + if not track_graph_bin_centers.has_edge(node1, node2): + try: + pos1 = _get_node_pos(track_graph_bin_centers, node1) + pos2 = _get_node_pos(track_graph_bin_centers, node2) + except KeyError as e: + continue + + distance = np.linalg.norm(pos1 - pos2) + linking_edges_to_add.append( + (node1, node2, {"distance": distance, "edge_id": -1}) + ) + inter_segment_edge_count += 1 - for idx, path in enumerate(paths): - ordered = boundary[path] # ordered nodes - cost = np.sum(np.diff(ordered) ** 2) - if cost < min_dist: - min_idx, min_dist = idx, cost + track_graph_bin_centers.add_edges_from(linking_edges_to_add) - opt_order = paths[min_idx] - return boundary[opt_order][:-1] + return track_graph_bin_centers -def get_track_boundary_points( - is_track_interior: np.ndarray, edges: list[np.ndarray], connectivity: int = 1 -) -> np.ndarray: +from functools import wraps + + +def check_fitted(method): + """ + Decorator for Environment instance methods that must only be + called *after* `fit()`. + + Raises + ------ + RuntimeError + If the Environment has not yet been fitted. """ + @wraps(method) + def _inner(self, *args, **kwargs): + if not getattr(self, "_is_fitted", False): + raise RuntimeError( + f"{self.__class__.__name__}.{method.__name__}() " + "requires the environment to be fitted. " + "Call `.fit()` first." + ) + return method(self, *args, **kwargs) + + return _inner + + +def get_direction( + env: "Environment", + position: np.ndarray, + position_time: Optional[np.ndarray] = None, + sigma: float = 0.1, + sampling_frequency: Optional[float] = None, + classify_stop: bool = False, + stop_speed_threshold: float = 1e-3, +) -> np.ndarray: + """Get the direction of movement relative to the center of the track (inward/outward). + + Requires a fitted N-D environment with a corresponding track graph (`track_graph_nd_`). + Parameters ---------- - is_track_interior : np.ndarray, shape (n_x_bins, n_y_bins) - edges : list of ndarray + position : np.ndarray, shape (n_time, n_dims) + Position data. + position_time : np.ndarray, shape (n_time,), optional + Timestamps for position data. If None, assumes uniform sampling. + sigma : float, optional + Standard deviation (in seconds) for Gaussian smoothing of velocity towards center. Defaults to 0.1. + sampling_frequency : float, optional + Sampling frequency in Hz. If None, estimated from `position_time`. + classify_stop : bool, optional + If True, classify speeds below `stop_speed_threshold` as "stop". Defaults to False. + stop_speed_threshold : float, optional + Speed threshold for classifying stops. Defaults to 1e-3. Returns ------- - boundary_points : np.ndarray, shape (n_boundary_points, n_position_dims) - + direction : np.ndarray, shape (n_time,) + Array of strings: "inward", "outward", or "stop". + + Raises + ------ + RuntimeError + If the environment has not been fitted or lacks the N-D track graph. + ValueError + If sampling frequency cannot be determined. """ - n_position_dims = len(edges) - boundary = get_track_boundary( - is_track_interior, n_position_dims=n_position_dims, connectivity=connectivity + + if not env._is_fitted: + raise RuntimeError("Environment has not been fitted yet. Call `fit` first.") + if env.track_graph_nd_ is None: + raise RuntimeError( + "Direction finding requires a fitted N-D environment with a track graph ('track_graph_nd_') and precomputed distances." + ) + + if position_time is None: + position_time = np.arange(position.shape[0]) + if sampling_frequency is None: + sampling_frequency = 1 / np.mean(np.diff(position_time)) + + centrality = nx.closeness_centrality(env.track_graph_nd_, distance="distance") + center_node_id = list(centrality.keys())[np.argmax(list(centrality.values()))] + + bin_ind = env.get_bin_ind(position) + + velocity_to_center = _gaussian_smooth( + np.gradient(env.distance_between_bins[bin_ind, center_node_id]), + sigma, + sampling_frequency, + axis=0, + truncate=8, + ) + direction = np.where( + velocity_to_center < 0, + "inward", + "outward", ) - inds = np.nonzero(boundary) - centers = [get_centers(x) for x in edges] - boundary = np.stack([center[ind] for center, ind in zip(centers, inds)], axis=1) - return order_boundary(boundary) + if classify_stop: + direction[np.abs(velocity_to_center) < stop_speed_threshold] = "stop" + return direction -def make_nD_track_graph_from_environment(environment: Environment) -> nx.Graph: - """Create a graph of the track with nodes at the center of each on track bin and - edges between adjacent bins on the track. + +@dataclass +class Environment: + """Represents a spatial environment with a discrete grid and graph topology. + + Handles both N-dimensional open fields and 1-dimensional tracks defined + by a graph. Fits a grid to the space, identifies the traversable interior, + and computes graph representations and distances. Parameters ---------- - environment : Environment + environment_name : str, optional + Identifier for the environment. Defaults to "". + place_bin_size : Union[float, Sequence[float]], optional + Approximate size of position bins (cm or arbitrary units). Used for N-D + gridding and setting the scale for 1-D binning. Defaults to 2.0. + track_graph : Optional[nx.Graph], optional + For 1-D environments only. A graph defining the track topology. Nodes + must have a 'pos' attribute (x, y coordinates). Edges should represent + physical connections and ideally have 'distance' (Euclidean length) and + 'edge_id' (unique integer) attributes. If None, an N-D environment is assumed. + Defaults to None. + edge_order : Optional[List[Tuple[Any, Any]]], optional + Required if `track_graph` is provided. An ordered list of node pairs + (edges) defining the linearization sequence of the 1-D track. + Defaults to None. + edge_spacing : Optional[Union[float, Sequence[float]]], optional + Required if `track_graph` is provided. Spacing added between consecutive + edges in `edge_order` during linearization. If float, uniform spacing. + If Sequence, length must be `len(edge_order) - 1`. Defaults to 0.0. + position_range : Optional[Sequence[Tuple[float, float]]], optional + For N-D environments. Explicit boundaries [(min_dim1, max_dim1), ...] + for the grid. If None, range is determined from `position` data during `fit`. + Defaults to None. + infer_track_interior : bool, optional + For N-D environments. If True, infer the occupied track area from + `position` data during `fit`. Ignored if `track_graph` is provided. + Defaults to True. + close_gaps : bool, optional + For N-D inferred interiors. If True, close small gaps in the occupied area + using binary closing. Defaults to False. + fill_holes : bool, optional + For N-D inferred interiors. If True, fill holes within the occupied area. + Defaults to False. + dilate : bool, optional + For N-D inferred interiors. If True, expand the boundary of the occupied area. + Defaults to False. + bin_count_threshold : int, optional + For N-D inferred interiors. Minimum samples in a bin to be considered occupied. + Defaults to 0. + is_track_interior_manual : Optional[NDArray[np.bool_]], optional + For N-D environments. A manually specified boolean grid defining the + track interior. If provided, overrides inference. Shape must match the + bin grid derived from `place_bin_size` and `position_range`/`position`. + Defaults to None. + + Attributes (Fitted) + -------------------- + # Common Attributes + is_1d : bool + True if the environment is 1-Dimensional (track_graph provided). + place_bin_centers_ : NDArray[np.float64], shape (n_bins, n_dims) + Coordinates of the center of each valid bin. For 1D, shape is (n_bins, 1). + is_track_interior_ : NDArray[np.bool_] + Boolean array indicating valid bins. Shape depends on type: + N-D: (n_bins_dim1, n_bins_dim2, ...) grid shape. + 1-D: (n_bins,) linear shape. + centers_shape_ : Tuple[int, ...] + Shape of the bin grid (bins per dimension). For 1D, (n_bins,). + edges_ : Tuple[NDArray[np.float64], ...] + Bin edges for each dimension. For 1D, contains linearized edges. + + # N-D Specific Attributes + position_range_ : Optional[Sequence[Tuple[float, float]]] + The actual position range used for gridding. + is_track_boundary_ : Optional[NDArray[np.bool_]] + Boolean grid indicating bins adjacent to the N-D track interior. + track_graph_nd_ : Optional[nx.Graph] + Graph connecting centers of adjacent interior N-D bins. + + # 1-D Specific Attributes + track_graph_bin_centers_ : Optional[nx.Graph] + Graph connecting only bin centers sequentially and at junctions. + + _is_fitted : bool + Internal flag indicating if `fit` has been called. + """ - Returns - ------- - track_graph : nx.Graph + environment_name: str = "" + place_bin_size: Union[float, Tuple[float, ...]] = 2.0 + track_graph: Optional[nx.Graph] = None + edge_order: Optional[List[Tuple[Any, Any]]] = None + edge_spacing: Union[float, Sequence[float]] = 0.0 + position_range: Optional[Sequence[Tuple[float, float]]] = None + infer_track_interior: bool = True + close_gaps: bool = False + fill_holes: bool = False + dilate: bool = False + bin_count_threshold: int = 0 + is_track_interior_manual: Optional[NDArray[np.bool_]] = None + + # Fitted attributes + is_1d: bool = field(init=False) + place_bin_centers_: Optional[NDArray[np.float64]] = field(init=False, default=None) + is_track_interior_: Optional[NDArray[np.bool_]] = field(init=False, default=None) + centers_shape_: Optional[Tuple[int, ...]] = field(init=False, default=None) + edges_: Optional[Tuple[NDArray[np.float64], ...]] = field(init=False, default=None) + + ## N-D + position_range_: Optional[Sequence[Tuple[float, float]]] = field( + init=False, default=None + ) + is_track_boundary_: Optional[NDArray[np.bool_]] = field(init=False, default=None) + track_graph_nd_: Optional[nx.Graph] = field(init=False, default=None) - """ - track_graph = nx.Graph() - axis_offsets = [-1, 0, 1] + ## 1-D + track_graph_bin_centers_: Optional[nx.Graph] = field(init=False, default=None) - # Enumerate over nodes - for node_id, (node_position, is_interior) in enumerate( - zip( - environment.place_bin_centers_, - environment.is_track_interior_.ravel(), + # Internal flag + _is_fitted: bool = field(init=False, default=False) + + def __post_init__(self): + """Determine environment type after initialization.""" + self.is_1d = self.track_graph is not None + if self.is_1d and (self.edge_order is None): + raise ValueError( + "`edge_order` must be provided for 1D environments (`track_graph` is set)." + ) + + def __eq__(self, other: object) -> bool: + """Check equality based on environment name.""" + if isinstance(other, Environment): + return self.environment_name == other.environment_name + elif isinstance(other, str): + return self.environment_name == other + return NotImplemented + + def _fit_nd(self, position: Optional[NDArray[np.float64]] = None) -> None: + """Fit method for N-dimensional environments.""" + if ( + position is None + and self.position_range is None + and self.is_track_interior_manual is None + ): + raise ValueError( + "For N-D environments, must provide `position`, `position_range`, or `is_track_interior_manual`." + ) + + # 1. Create Grid + # Use manual interior shape if provided to determine grid + if self.is_track_interior_manual is not None: + if self.position_range is None: + print( + "Warning: `is_track_interior_manual` provided without `position_range`. Assuming range based on bin size and shape." + ) + # Infer range approximately - this might not be ideal + manual_shape = self.is_track_interior_manual.shape + n_dims = self.is_track_interior_manual.ndim + if isinstance(self.place_bin_size, (float, int)): + bin_sizes = np.array([float(self.place_bin_size)] * n_dims) + else: # Sequence + bin_sizes = np.asarray(self.place_bin_size) + + self.position_range_ = tuple( + (0.0, sh * bs) for sh, bs in zip(manual_shape, bin_sizes) + ) + else: + self.position_range_ = self.position_range # Use provided range + + # Create grid based on manual shape and range + n_bins = self.is_track_interior_manual.shape + _, self.edges_ = np.histogramdd( + np.zeros((1, n_dims)), bins=n_bins, range=self.position_range_ + ) + # Adjust edges for boundary bins (assuming create_grid adds them) + centers_list = [get_centers(edge_dim) for edge_dim in self.edges_] + self.centers_shape_ = tuple(len(c) for c in centers_list) + mesh_centers = np.meshgrid(*centers_list, indexing="ij") + self.place_bin_centers_ = np.stack( + [c.ravel() for c in mesh_centers], axis=1 + ) + + if self.centers_shape_ != self.is_track_interior_manual.shape: + raise ValueError( + f"Shape of `is_track_interior_manual` {self.is_track_interior_manual.shape} " + f"does not match derived grid shape {self.centers_shape_} " + f"from `position_range` and `place_bin_size`." + ) + self.is_track_interior_ = self.is_track_interior_manual + + else: + # Create grid from position/position_range + ( + self.edges_, + self.place_bin_edges_, + self.place_bin_centers_, + self.centers_shape_, + ) = _create_grid( + position=position, + bin_size=self.place_bin_size, + position_range=self.position_range, + add_boundary_bins=False, + ) + # Store the actual range used (needed if derived from position) + self.position_range_ = tuple((e[0], e[-1]) for e in self.edges_) + + # 2. Determine Track Interior + if self.infer_track_interior: + if position is None: + raise ValueError( + "`position` data must be provided when `infer_track_interior` is True." + ) + self.is_track_interior_ = _infer_track_interior( + position=position, + edges=self.edges_, + fill_holes=self.fill_holes, + dilate=self.dilate, + bin_count_threshold=self.bin_count_threshold, + ) + else: + self.is_track_interior_ = np.ones(self.centers_shape_, dtype=bool) + + # 3. Determine Track Boundary (only if > 1D) + if self.is_track_interior_.ndim > 1: + self.is_track_boundary_ = _get_track_boundary( + self.is_track_interior_, connectivity=1 + ) + else: + self.is_track_boundary_ = None # No meaningful boundary for 1D grid + + # 4. Create N-D Track Graph + self.track_graph_nd_ = _make_nd_track_graph( + self.place_bin_centers_, self.is_track_interior_, self.centers_shape_ ) - ): - track_graph.add_node( - node_id, pos=tuple(node_position), is_track_interior=is_interior + + def _fit_1d(self) -> None: + """Fit method for 1-dimensional track environments.""" + if self.track_graph is None or self.edge_order is None: + raise ValueError( + "`track_graph` and `edge_order` are required for 1D fitting." + ) + + ( + self.place_bin_centers_, + self.place_bin_edges_, + self.is_track_interior_, + self.centers_shape_, + self.edges_, + self.track_graph_bin_centers_, + ) = _create_1d_track_grid_data( + self.track_graph, + self.edge_order, + self.edge_spacing, + self.place_bin_size, ) - edges = [] - # Enumerate over nodes in the track interior - for ind in zip(*np.nonzero(environment.is_track_interior_)): - ind = np.array(ind) - # Indices of adjacent nodes - adj_inds = np.meshgrid(*[axis_offsets + i for i in ind], indexing="ij") - # Remove out of bounds indices - adj_inds = [ - inds[np.logical_and(inds >= 0, inds < dim_size)] - for inds, dim_size in zip(adj_inds, environment.centers_shape_) - ] + # N-D specific attributes are None for 1D + self.position_range_ = None + self.is_track_boundary_ = None + self.track_graph_nd_ = None + + def fit(self, position: Optional[NDArray[np.float64]] = None) -> "Environment": + """Fits the discrete grid and graph representation of the environment. + + Based on the presence of `track_graph`, calls either the N-dimensional + or 1-dimensional fitting routine. + + Parameters + ---------- + position : Optional[NDArray[np.float64]], shape (n_time, n_dims), optional + Position data of the animal. Required for N-D fitting if + `position_range` and `is_track_interior_manual` are not provided, + or if `infer_track_interior` is True. Not directly used for 1-D + fitting (which relies on the `track_graph` geometry), but can be + used by subsequent methods like `get_bin_indices`. Defaults to None. + + Returns + ------- + self : Environment + The fitted Environment instance. + + Raises + ------ + ValueError + If required parameters for the chosen environment type are missing + (e.g., `edge_order` for 1D, or sufficient info for N-D grid). + """ + self.is_1d = self.track_graph is not None + + if self.is_1d: + self._fit_1d() + else: + # N-D requires position data unless range and manual interior are given + if ( + self.position_range is None + and self.is_track_interior_manual is None + and position is None + ) or ( + self.infer_track_interior + and position is None + and self.is_track_interior_manual is None + ): + raise ValueError( + "`position` data is required for N-D fitting under current settings." + ) + self._fit_nd(position) + + self._is_fitted = True + + return self + + @check_fitted + def plot_grid( + self, ax: Optional[matplotlib.axes.Axes] = None + ) -> matplotlib.axes.Axes: + """Plots the spatial grid and track interior/graph. + + Parameters + ---------- + ax : Optional[matplotlib.axes.Axes], optional + Existing axes to plot on. If None, creates new axes. Defaults to None. + + Returns + ------- + ax : matplotlib.axes.Axes + The axes used for plotting. + + Raises + ------ + RuntimeError + If the environment has not been fitted. + ValueError + If the environment is 1D but `track_graph` and `edge_order` are not set. + NotImplementedError + If the environment is not 1D or 2D. + """ + + if self.is_1d: + # Plot 1D linearized track + if ax is None: + fig, ax = plt.subplots(figsize=(15, 2.5)) + if self.track_graph and self.edge_order: + # Plot the original graph structure linearized + plot_graph_as_1D( + self.track_graph, + self.edge_order, + self.edge_spacing, + ax=ax, + node_size=50, + ) + + # Overlay bin edges + if self.edges_ and self.edges_[0] is not None: + edges_lin = self.edges_[0] + for edge_pos in edges_lin: + ax.axvline( + edge_pos, linewidth=0.5, color="black", linestyle=":" + ) + ax.set_title(f"{self.environment_name} (Linearized)") + ax.set_xlabel("Linearized Position") + ax.set_yticks([]) # Remove y-ticks for 1D plot + ax.set_ylim(-0.1, 0.1) # Adjust y-limits for node visibility + + else: + raise ValueError( + "1D environment requires `track_graph` and `edge_order` to be set." + ) + + else: + # Plot 2D grid + if len(self.centers_shape_) != 2: + raise NotImplementedError( + "Plotting is only implemented for 2D environments." + ) + + if ax is None: + fig, ax = plt.subplots(figsize=(7, 7)) + + # Plot interior bins + ax.pcolormesh( + self.edges_[0], + self.edges_[1], + self.is_track_interior_.T, + cmap="bone_r", + alpha=0.7, + shading="auto", + ) + + # Grid lines + ax.set_xticks(self.edges_[0]) + ax.set_yticks(self.edges_[1]) + ax.set_xticks(get_centers(self.edges_[0]), minor=True) + ax.set_yticks(get_centers(self.edges_[1]), minor=True) + ax.grid(which="major", linestyle="-", linewidth="0.5", color="gray") + + ax.set_aspect("equal", adjustable="box") + ax.set_title(f"{self.environment_name} (Grid)") + ax.set_xlabel("Position Dim 1") + ax.set_ylabel("Position Dim 2") + + if self.position_range_: + ax.set_xlim(self.position_range_[0]) + ax.set_ylim(self.position_range_[1]) + + return ax + + def save(self, filename: str = "environment.pkl") -> None: + """Saves the environment object as a pickled file. + + Parameters + ---------- + filename : str, optional + File name to save the environment to. Defaults to "environment.pkl". + """ + with open(filename, "wb") as file_handle: + pickle.dump(self, file_handle, protocol=pickle.HIGHEST_PROTOCOL) - # Is the adjacent node on the track? - adj_on_track_inds = environment.is_track_interior_[tuple(adj_inds)] + print(f"Environment saved to {filename}") - # Remove the center node - center_idx = [n // 2 for n in adj_on_track_inds.shape] - adj_on_track_inds[tuple(center_idx)] = False + @classmethod + def load(cls, filename: str) -> "Environment": + """Loads an Environment object from a pickled file. - # Get the node ids of the center node - node_id = np.ravel_multi_index(ind, environment.centers_shape_) + Parameters + ---------- + filename : str + Path to the file containing the pickled Environment object. - # Get the node ids of the adjacent nodes on the track - adj_node_ids = np.ravel_multi_index( - [inds[adj_on_track_inds] for inds in adj_inds], - environment.centers_shape_, + Returns + ------- + Environment + The loaded Environment object. + """ + with open(filename, "rb") as file_handle: + environment = pickle.load(file_handle) + + return environment + + @check_fitted + def get_manifold_distances( + self, positions1: NDArray[np.float64], positions2: NDArray[np.float64] + ) -> NDArray[np.float64]: + """Computes shortest path distance between position pairs along the track. + + Uses precomputed distances on the environment's graph representation + (N-D bin graph or 1-D augmented graph). + + Parameters + ---------- + positions1 : NDArray[np.float64], shape (n_time, n_dims) or (n_dims,) + First set of positions. + positions2 : NDArray[np.float64], shape (n_time, n_dims) or (n_dims,) + Second set of positions. Must have the same shape as positions1. + + Returns + ------- + distances : NDArray[np.float64], shape (n_time,) + Shortest path distance along the track for each pair. Returns np.inf + if positions map to bins/nodes with no path between them or if + mapping fails (e.g., outside track). + + Raises + ------ + RuntimeError + If the environment is not fitted. + ValueError + If input shapes mismatch or required distance attributes are missing. + """ + positions1 = np.atleast_2d(positions1) + positions2 = np.atleast_2d(positions2) + + # Validate input shapes + if positions1.shape != positions2.shape: + raise ValueError("Shapes of position1 and position2 must match.") + + if (positions1.shape[0] == 0) or (positions2.shape[0] == 0): + return np.zeros((0,), dtype=np.float64) + + bin_ind1 = self.get_bin_ind(positions1) + bin_ind2 = self.get_bin_ind(positions2) + + distances = self.distance_between_bins[bin_ind1, bin_ind2] + + return distances + + @check_fitted + def get_linear_position(self, position: np.ndarray) -> np.ndarray: + """Get the linearized position along the track. + + Parameters + ---------- + position : np.ndarray, shape (n_time, n_dims) + Position data. + + Returns + ------- + linear_position : np.ndarray, shape (n_time,) + Linearized position along the track. + """ + if not self.is_1d: + raise ValueError( + "Linear position calculation is only implemented for 1D environments." + ) + + return get_linearized_position( + position, + self.track_graph, + edge_order=self.edge_order, + edge_spacing=self.edge_spacing, ) - # Collect the edges for the graph - edges.append( - np.concatenate( - np.meshgrid([node_id], adj_node_ids, indexing="ij"), axis=0 - ).T + @check_fitted + def get_fitted_track_graph(self) -> nx.Graph: + """Get the fitted track graph of the environment. + + Returns + ------- + nx.Graph + The fitted track graph, which includes bin centers. + + Raises + ------ + RuntimeError + If the environment has not been fitted. + """ + if self.is_1d: + return self.track_graph_bin_centers_ + else: + return self.track_graph_nd_ + + @cached_property + @check_fitted + def distance_between_bins(self) -> NDArray[np.float64]: + """Get the distance between two nodes in the fitted track graph. + Returns + ------- + distance : np.ndarray, shape (n_bins, n_bins) + The distance between all pairs of bins in the fitted track graph. + + Raises + ------ + RuntimeError + If the environment has not been fitted. + """ + return _get_distance_between_bins(self.get_fitted_track_graph()) + + @check_fitted + def get_bin_center_dataframe(self) -> pd.DataFrame: + """Get a DataFrame with information about the bin centers. + + Returns + ------- + pd.DataFrame + DataFrame containing information about the bin centers. + """ + + df = pd.DataFrame.from_dict( + dict(self.get_fitted_track_graph().nodes(data=True)), orient="index" ) - edges = np.concatenate(edges) + if not df.empty and isinstance(df["pos"].iloc[0], (list, tuple)): + n_dims = len(df["pos"].iloc[0]) + pos_cols = [f"pos_dim{i}" for i in range(n_dims)] + pos_df = pd.DataFrame(df["pos"].tolist(), index=df.index, columns=pos_cols) + df = pd.concat([df.drop(columns="pos"), pos_df], axis=1) + df = df.sort_values(by="bin_ind_flat") - # Add edges to the graph with distance - for node1, node2 in edges: - pos1 = np.asarray(track_graph.nodes[node1]["pos"]) - pos2 = np.asarray(track_graph.nodes[node2]["pos"]) - distance = np.linalg.norm(pos1 - pos2) - track_graph.add_edge(node1, node2, distance=distance) + # set index name to node_id + df.index.name = "node_id" - for edge_id, edge in enumerate(track_graph.edges): - track_graph.edges[edge]["edge_id"] = edge_id + return df - return track_graph + @check_fitted + def get_bin_ind(self, positions: np.ndarray) -> np.ndarray: + """Get the bin index for a given position. + Parameters + ---------- + positions : np.ndarray, shape (n_time, n_dims) + Position data. -def gaussian_smooth( - data: np.ndarray, - sigma: float, - sampling_frequency: float, - axis: int = 0, - truncate: int = 8, -) -> np.ndarray: - """1D convolution of the data with a Gaussian. + Returns + ------- + bin_ind_flat : np.ndarray, shape (n_time,) + The flattened bin index for each position. To get the 2D index, + use np.unravel_index(bin_ind_flat, self.centers_shape_). + """ - The standard deviation of the gaussian is in the units of the sampling - frequency. The function is just a wrapper around scipy's - `gaussian_filter1d`, The support is truncated at 8 by default, instead - of 4 in `gaussian_filter1d` + df = self.get_bin_center_dataframe() + df = df[df["is_track_interior"]] + if df.empty: + return np.full(positions.shape[0], -1, dtype=int) - Parameters - ---------- - data : array_like - sigma : float - sampling_frequency : int - axis : int, optional - truncate : int, optional + pos_cols = [col for col in df.columns if col.startswith("pos_dim")] + track_coords = df.loc[:, pos_cols].to_numpy() - Returns - ------- - smoothed_data : array_like + tree = KDTree(track_coords) + bin_ind_subset = tree.query(np.atleast_2d(positions), k=1)[1] - """ - return ndimage.gaussian_filter1d( - data, sigma * sampling_frequency, truncate=truncate, axis=axis, mode="constant" - ) + return df["bin_ind_flat"].iloc[bin_ind_subset].to_numpy().squeeze() + + @check_fitted + def get_bin_coordinates(self, bin_ind: np.ndarray) -> np.ndarray: + """Get the coordinates of the bin centers for given bin indices. + + Parameters + ---------- + bin_ind : np.ndarray, shape (n_bins,) + The bin indices. + + Returns + ------- + bin_coordinates : np.ndarray, shape (n_bins, n_dims) + The coordinates of the bin centers. + """ + return self.place_bin_centers_[bin_ind] + + @check_fitted + def assign_region_ids_to_bins( + self, + regions_definition: Dict[ + str, Union[List[int], Callable[[NDArray[np.float64]], bool]] + ], + default_region_id: Any = -1, + ) -> NDArray[Any]: + """Assigns a region identifier to each spatial bin. + + Regions can be defined by lists of bin indices (flat indices) or by a + function that takes bin center coordinates and returns True if the bin + belongs to the region. + + Parameters + ---------- + regions_definition : Dict[str, Union[List[int], Callable[[NDArray[np.float64]], bool]]] + A dictionary where keys are region names (or numeric IDs) and values define the region. + - If `List[int]`: A list of bin flat indices belonging to this region. + - If `Callable`: A function `func(bin_center_coords) -> bool`. + `bin_center_coords` is an NDArray of shape (n_dims,). + default_region_id : Any, optional + Identifier for bins not assigned to any defined region. Defaults to -1. + + Returns + ------- + region_ids_map : NDArray[Any] + For 1-D environments, a 1D array of shape (n_bins,) containing the region ID for each bin. + For N-D environments, a multi-dimensional array of shape matching `centers_shape_`. + + Raises + ------ + RuntimeError + If the environment has not been fitted. + + Examples + -------- + >>> env = Environment() + >>> env.fit(position_data) + >>> regions = { + ... "RegionA": lambda coords: coords[0] < 5, + ... "RegionB": [0, 1, 2], + ... } + >>> region_ids = env.assign_region_ids_to_bins(regions) + >>> print(region_ids) + [0, 0, 0, -1, -1, ...] + """ + if not self._is_fitted: + raise RuntimeError("Environment has not been fitted. Call fit() first.") + + num_total_bins: int + + if self.is_1d: + num_total_bins = self.centers_shape_[0] + bin_center_df = self.get_bin_center_dataframe() + pos_cols = [ + col for col in bin_center_df.columns if col.startswith("pos_dim") + ] + all_bin_centers_coords = bin_center_df.loc[:, pos_cols].to_numpy() + # And their corresponding flat indices (which are just 0 to n-1 for 1D in this context) + all_bin_flat_indices = bin_center_df["bin_ind_flat"].to_numpy() + output_shape = (num_total_bins,) + # Initialize region_ids array based on 1D structure + region_ids_flat = np.full(num_total_bins, default_region_id, dtype=object) + + else: # N-D + num_total_bins = self.place_bin_centers_.shape[0] + all_bin_centers_coords = self.place_bin_centers_ + # Flat indices are just np.arange(num_total_bins) for N-D as place_bin_centers_ is already flat + all_bin_flat_indices = np.arange(num_total_bins) + output_shape = self.centers_shape_ + # Initialize region_ids_flat for N-D; will be reshaped later + region_ids_flat = np.full(num_total_bins, default_region_id, dtype=object) + + for region_name_or_id, definition in regions_definition.items(): + if callable(definition): + for i, bin_flat_idx in enumerate(all_bin_flat_indices): + # For N-D, bin_flat_idx is the index into all_bin_centers_coords + # For 1-D, bin_flat_idx is also the index here. + coords = all_bin_centers_coords[ + i + ] # Assuming all_bin_flat_indices are 0..N-1 sequential + if definition(coords): + region_ids_flat[bin_flat_idx] = region_name_or_id + elif isinstance(definition, list): + # Ensure indices are within bounds + valid_indices = [idx for idx in definition if 0 <= idx < num_total_bins] + region_ids_flat[valid_indices] = region_name_or_id + else: + raise TypeError( + f"Region definition for '{region_name_or_id}' must be a list of " + "bin indices or a callable function." + ) + + if self.is_1d: + return region_ids_flat # Already 1D + else: + return region_ids_flat.reshape(output_shape) + + def to_dict(self) -> Dict[str, Any]: + """Serializes the Environment object to a dictionary for DataJoint. + Starts with self.__dict__ and transforms only types that DataJoint + might not handle as well directly (e.g., NetworkX graphs). + Assumes DataJoint can handle np.ndarray and pd.DataFrame objects directly. + + Returns + ------- + data_dict : Dict[str, Any] + A dictionary representation of the Environment object. + """ + # Start with a shallow copy of the instance's dictionary + data = self.__dict__.copy() + + # Add a class identifier/version for robustness during deserialization + data["__classname__"] = self.__class__.__name__ + data["__module__"] = self.__class__.__module__ + data["class_version"] = "1.0.0" # Versioning for future changes + data["__type__"] = "Environment" + + # Selectively transform types + for key, value in data.items(): + if isinstance(value, nx.Graph): + # Convert NetworkX graphs to a more standard dict format for robustness + # as direct pickling of graphs can sometimes be sensitive to library versions. + data[key] = { + "__type__": "networkx_graph", + "node_link_data": nx.node_link_data(value), + } + # Pandas DataFrames and NumPy arrays are assumed to be handled directly by DataJoint. + + return data + + @classmethod + def from_dict(cls, data: Dict[str, Any]) -> "Environment": + """Deserializes an Environment object from a dictionary created by to_dict(). + Handles NetworkX graphs generically, including those passed as init arguments. + + Parameters + ---------- + data : Dict[str, Any] + A dictionary representation of the Environment object. + + Returns + ------- + env : Environment + The reconstructed Environment object. + """ + if ( + data.get("__classname__") != cls.__name__ + or data.get("__module__") != cls.__module__ + ): + raise ValueError( + f"Dictionary is not for class {cls.__module__}.{cls.__name__}, " + f"found {data.get('__module__')}.{data.get('__classname__')}" + ) + + # Create a working copy of the data to extract init args from + # and to allow modification of values (e.g., reconstructing graphs) + construction_data = data.copy() + + # Collect and reconstruct init arguments for the class constructor + init_args = {} + for f in fields(cls): # `fields` from `dataclasses` + if f.init: + if f.name in construction_data: + value = construction_data[f.name] + # Check if this init arg's value is a serialized graph + if ( + isinstance(value, dict) + and value.get("__type__") == "networkx_graph" + ): + init_args[f.name] = nx.node_link_graph(value["node_link_data"]) + else: + init_args[f.name] = value + elif f.default is not MISSING: + init_args[f.name] = f.default + elif f.default_factory is not MISSING: + init_args[f.name] = f.default_factory() + # If a required init arg (no default) is missing, cls(**init_args) will fail. + + # Create the instance + env = cls(**init_args) + + # Restore the rest of the attributes from the original data dictionary + # These are attributes not handled by __init__ (e.g., fitted attributes, cached properties) + for key, value in data.items(): + if key not in init_args and key not in [ + "__classname__", + "__module__", + "class_version", + ]: + restored_value = value + + # Check if the value is a serialized graph + if ( + isinstance(value, dict) + and value.get("__type__") == "networkx_graph" + ): + restored_value = nx.node_link_graph(value["node_link_data"]) + + # Setting the attribute, including populating __dict__ for cached_properties + setattr(env, key, restored_value) + + return env + + @check_fitted + def bin_size(self, dim: int | None = None) -> float | np.ndarray: + """ + Return the spatial bin size. + + Parameters + ---------- + dim : int | None, optional + If None (default) return the *array* of bin sizes for every + dimension; otherwise return the scalar bin size for the requested + dimension index. + + Returns + ------- + float | np.ndarray + The bin size for the specified dimension or an array of bin sizes + for all dimensions. + Raises + ------- + IndexError + If `dim` is not in the range of dimensions. + RuntimeError + If the environment has not been fitted. + + Examples + -------- + >>> env.bin_size() # e.g. array([2., 2.]) + >>> env.bin_size(0) # 2.0 + """ + sizes = ( + np.asarray(self.place_bin_size, dtype=float) + if isinstance(self.place_bin_size, (list, tuple, np.ndarray)) + else np.array([float(self.place_bin_size)]) + ) + if dim is None: + return sizes + if not (0 <= dim < sizes.size): + raise IndexError(f"dim must be in [0, {sizes.size-1}]") + return float(sizes[dim]) + + @check_fitted + def bin_index_to_nd(self, idx_flat: np.ndarray) -> tuple[np.ndarray, ...]: + """ + Convert flattened bin indices back to N-D indices. + + Parameters + ---------- + idx_flat : array-like + Flat (raveled) indices - accepts scalar or 1-D array. + + Returns + ------- + tuple of ndarrays + Same output as `np.unravel_index`. + """ + if self.centers_shape_ is None: + raise RuntimeError("Environment must be fitted before calling this.") + idx_flat = np.asarray(idx_flat, dtype=int) + return np.unravel_index(idx_flat, self.centers_shape_) + + @check_fitted + def nd_index_to_flat(self, *idx_nd: np.ndarray | int) -> np.ndarray: + """ + Convert N-D indices (one array per dimension *or* a single iterable) + into flattened indices. + + Parameters + ---------- + idx_nd : array-like + N-D indices. Can be a single iterable or separate arrays/ints. + If a single iterable, it should be of shape (n_dims, n_bins). + + Returns + ------- + np.ndarray + Flattened indices corresponding to the provided N-D indices. + + Raises + ------- + RuntimeError + If the environment has not been fitted. + ValueError + If the number of indices does not match the number of dimensions + in the fitted environment. + + + Examples + -------- + >>> env.nd_index_to_flat([0, 1], [2, 3]) # 2-D example + >>> env.nd_index_to_flat((0, 1)) # single coordinate + """ + if self.centers_shape_ is None: + raise RuntimeError("Environment must be fitted before calling this.") + + # Allow caller to pass a single iterable or separate arrays/ints + if len(idx_nd) == 1 and not np.isscalar(idx_nd[0]): + idx_nd = tuple(np.asarray(idx_nd[0]).T) # split last axis + idx_nd = tuple(np.asarray(idx, dtype=int) for idx in idx_nd) + + if len(idx_nd) != len(self.centers_shape_): + raise ValueError( + f"Expected {len(self.centers_shape_)} indices, got {len(idx_nd)}" + ) + return np.ravel_multi_index(idx_nd, self.centers_shape_) + + @property + @check_fitted + def n_dims(self) -> int | None: + """ + Number of spatial dimensions in the environment **after fitting**. + + Returns + ------- + int | None + Dimensionality of `place_bin_centers_` if fitted, else None. + """ + return self.place_bin_centers_.shape[1] diff --git a/src/non_local_detector/likelihoods/__init__.py b/src/non_local_detector/likelihoods/__init__.py index 7450c0c..c4fc7d7 100644 --- a/src/non_local_detector/likelihoods/__init__.py +++ b/src/non_local_detector/likelihoods/__init__.py @@ -2,9 +2,13 @@ fit_clusterless_kde_encoding_model, predict_clusterless_kde_log_likelihood, ) -from non_local_detector.likelihoods.no_spike import ( # noqa +from non_local_detector.likelihoods.no_spike import ( # noqa F401 predict_no_spike_log_likelihood, ) +from non_local_detector.likelihoods.sorted_spikes_diffusion_kde import ( + fit_sorted_spikes_diffusion_kde_encoding_model, + predict_sorted_spikes_diffusion_kde_log_likelihood, +) from non_local_detector.likelihoods.sorted_spikes_glm import ( fit_sorted_spikes_glm_encoding_model, predict_sorted_spikes_glm_log_likelihood, @@ -23,6 +27,10 @@ fit_sorted_spikes_kde_encoding_model, predict_sorted_spikes_kde_log_likelihood, ), + "sorted_spikes_diffusion_kde": ( + fit_sorted_spikes_diffusion_kde_encoding_model, + predict_sorted_spikes_diffusion_kde_log_likelihood, + ), } _CLUSTERLESS_ALGORITHMS = { "clusterless_kde": ( diff --git a/src/non_local_detector/likelihoods/clusterless_diffusion_kde.py b/src/non_local_detector/likelihoods/clusterless_diffusion_kde.py new file mode 100644 index 0000000..8893436 --- /dev/null +++ b/src/non_local_detector/likelihoods/clusterless_diffusion_kde.py @@ -0,0 +1,521 @@ +# clusterless_diffusion_kde.py + +from typing import Dict, List, Optional, Union + +import jax +import jax.numpy as jnp +import jax.scipy +import jax.scipy.stats +import numpy as np +from scipy.spatial import KDTree +from tqdm.autonotebook import tqdm + +from non_local_detector.diffusion_kernels import compute_diffusion_kernels +from non_local_detector.environment import Environment +from non_local_detector.likelihoods.common import ( # Removed KDEModel import + EPS, + LOG_EPS, + get_position_at_time, +) + + +# Re-add helper function for Gaussian KDE logpdf calculation +@jax.jit +def gaussian_kde_logpdf( + eval_points: jnp.ndarray, samples: jnp.ndarray, std: jnp.ndarray +) -> jnp.ndarray: + """Computes log PDF of Gaussian KDE. + + Parameters + ---------- + eval_points : jnp.ndarray, shape (n_eval, n_features) + samples : jnp.ndarray, shape (n_samples, n_features) + std : jnp.ndarray, shape (n_features,) + + Returns + ------- + log_pdf : jnp.ndarray, shape (n_eval,) + """ + n_samples = samples.shape[0] + if n_samples == 0: + # If there are no encoding samples, the probability is effectively zero everywhere. + # Return a very small log probability. + return jnp.full(eval_points.shape[0], LOG_EPS) + + # Calculate log probability for each sample component-wise + # Shape: (n_samples, n_eval, n_features) + log_probs_each = jax.scipy.stats.norm.logpdf( + jnp.expand_dims(eval_points, axis=0), # (1, n_eval, n_features) + jnp.expand_dims(samples, axis=1), # (n_samples, 1, n_features) + std, # (n_features,) - broadcasted + ) + + # Sum log probs over features -> log prob for each sample-eval pair + # Shape: (n_samples, n_eval) + log_prob_sum_features = jnp.sum(log_probs_each, axis=2) + + # LogSumExp over samples -> log of summed probabilities + # Shape: (n_eval,) + log_sum_exp_samples = jax.scipy.special.logsumexp(log_prob_sum_features, axis=0) + + # Normalize by number of samples (log domain) + log_pdf = log_sum_exp_samples - jnp.log(n_samples) + + # Clip result to avoid -inf if density is exactly zero somewhere + return jnp.clip(log_pdf, a_min=LOG_EPS) + + +def fit_clusterless_diffusion_encoding_model( + position_time: np.ndarray, + position: np.ndarray, + spike_times: List[np.ndarray], + spike_waveform_features: List[np.ndarray], + environment: Environment, + diffusion_bandwidth_sigma: float, + feature_bandwidth_sigma: Union[float, np.ndarray], + diffusion_coeff: float = 0.5, + weights: Optional[np.ndarray] = None, + # kde_block_size: Optional[int] = None, # No longer needed here + disable_progress_bar: bool = False, +) -> Dict: + """Fits a clusterless encoding model using spatial diffusion and stores feature samples. + + Estimates the joint intensity of spikes based on position (smoothed with + diffusion kernels) and stores waveform features for on-the-fly KDE during prediction. + + Parameters + ---------- + position_time : np.ndarray, shape (n_time_position,) + Timestamps for position samples. + position : np.ndarray, shape (n_time_position, n_position_dims) + Position samples (typically 2D). + spike_times : list[np.ndarray] + List where each element is an array of spike times for an electrode. + spike_waveform_features : list[np.ndarray] + List where each element is shape (n_spikes, n_features) for an electrode. + environment : Environment + A *fitted* Environment object (2D grid) with `is_track_interior_` and `track_graph_nd_`. + diffusion_bandwidth_sigma : float + Spatial bandwidth (std dev) for the diffusion kernel. + feature_bandwidth_sigma : float or np.ndarray, shape (n_features,) + Bandwidth (std dev) for Gaussian KDE applied to waveform features during prediction. + diffusion_coeff : float, optional + Diffusion coefficient for kernel computation. Defaults to 0.5. + weights : np.ndarray, shape (n_time_position,), optional + Weights for position samples (e.g., duration). Defaults to uniform time. + disable_progress_bar : bool, optional + If True, suppresses progress bars. Defaults to False. + + Returns + ------- + encoding_model : dict + Contains fitted components: 'smoothed_spatial_spike_density', 'feature_samples', + 'feature_bandwidth_sigma', 'smoothed_occupancy', 'mean_rates', + 'interior_bin_indices_flat', 'diffusion_bandwidth_sigma', 'environment', + 'summed_spatial_intensity', 'is_track_interior', 'disable_progress_bar'. + """ + # --- Input Validation and Setup --- + if not environment._is_fitted: + raise ValueError("Environment object must be fitted first.") + if environment.is_1d: + raise ValueError("Diffusion model requires N-D grid environment.") + if environment.place_bin_centers_.shape[1] != 2: + raise ValueError("Diffusion model currently requires a 2D environment.") + + if len(spike_times) != len(spike_waveform_features): + raise ValueError( + "Mismatch between spike_times and spike_waveform_features lists." + ) + + # Ensure feature bandwidth is an array + n_features = spike_waveform_features[0].shape[1] + if isinstance(feature_bandwidth_sigma, (int, float)): + feature_bandwidth_sigma = np.full((n_features,), float(feature_bandwidth_sigma)) + # Store as JAX array for use in prediction + feature_bandwidth_sigma_jax = jnp.asarray(feature_bandwidth_sigma) + + position = position if position.ndim > 1 else np.expand_dims(position, axis=1) + n_electrodes = len(spike_times) + n_total_bins = environment.place_bin_centers_.shape[0] + interior_mask_2d = environment.is_track_interior_ + interior_indices = np.where(interior_mask_2d.ravel())[0] + interior_mask_flat = interior_mask_2d.ravel() + + # --- Precompute Diffusion Kernels --- + print("Precomputing diffusion kernels...") + kernel_matrix = compute_diffusion_kernels( + track_graph_nd=environment.track_graph_nd_, + interior_mask_2d=interior_mask_2d, + bandwidth_sigma=diffusion_bandwidth_sigma, + diffusion_coeff=diffusion_coeff, + ) + n_interior_bins = interior_indices.shape[0] + if n_interior_bins == 0: + raise ValueError("No interior bins found in the environment.") + print(f"Computed {kernel_matrix.shape} kernel matrix.") + + # --- Compute Smoothed Occupancy (Density) --- + print("Calculating smoothed occupancy...") + if weights is None: + weights = np.ones_like(position_time) + + is_nan_pos = np.any(np.isnan(position), axis=1) + valid_pos_mask = ~is_nan_pos + valid_positions = position[valid_pos_mask] + time_weights = weights[valid_pos_mask] + + if valid_positions.shape[0] > 0: + position_bin_inds_valid = environment.get_bin_ind(valid_positions) + else: + position_bin_inds_valid = np.array([], dtype=int) + + full_occupancy_hist = np.zeros(n_total_bins) + if position_bin_inds_valid.size > 0: + np.add.at(full_occupancy_hist, position_bin_inds_valid, time_weights) + + interior_occupancy_hist = jnp.asarray(full_occupancy_hist[interior_indices]) + smoothed_interior_occupancy = kernel_matrix @ interior_occupancy_hist + total_interior_time = smoothed_interior_occupancy.sum() + + if total_interior_time > EPS: + smoothed_interior_occupancy_density = ( + smoothed_interior_occupancy / total_interior_time + ) + else: + print("Warning: Total interior occupancy time is close to zero.") + smoothed_interior_occupancy_density = jnp.zeros_like( + smoothed_interior_occupancy + ) + + smoothed_occupancy_full = ( + jnp.zeros(n_total_bins) + .at[interior_indices] + .set(smoothed_interior_occupancy_density) + ) + + # --- Compute Smoothed Spatial Spike Density and Store Feature Samples (Per Electrode) --- + smoothed_spatial_spike_densities = [] + encoding_feature_samples = [] # Store raw feature samples + mean_rates = [] + total_encoding_time = weights.sum() # Total time from weights + + for elec_id, (elec_spike_times, elec_features) in enumerate( + tqdm( + zip(spike_times, spike_waveform_features), + total=n_electrodes, + unit="electrode", + desc="Encoding models (Clusterless Diffusion)", + disable=disable_progress_bar, + ) + ): + # Filter spikes outside position time range + is_valid_time = np.logical_and( + elec_spike_times >= position_time[0], + elec_spike_times <= position_time[-1], + ) + elec_spike_times = elec_spike_times[is_valid_time] + elec_features = elec_features[is_valid_time] + n_spikes = len(elec_spike_times) + + # Calculate mean rate + mean_rates.append( + n_spikes / total_encoding_time if total_encoding_time > 0 else 0.0 + ) + + # Store features for KDE prediction later + encoding_feature_samples.append(jnp.asarray(elec_features)) + + # Calculate spatial density (same as before) + if n_spikes > 0: + pos_at_spike = get_position_at_time( + position_time, position, elec_spike_times, environment + ) + is_nan_spike_pos = np.any(np.isnan(pos_at_spike), axis=1) + valid_spike_pos_mask = ~is_nan_spike_pos + valid_pos_at_spike = pos_at_spike[valid_spike_pos_mask] + + if valid_pos_at_spike.shape[0] > 0: + spike_bin_inds_valid = environment.get_bin_ind(valid_pos_at_spike) + else: + spike_bin_inds_valid = np.array([], dtype=int) + + full_spike_hist = np.zeros(n_total_bins) + if spike_bin_inds_valid.size > 0: + np.add.at(full_spike_hist, spike_bin_inds_valid, 1) + + interior_spike_hist = jnp.asarray(full_spike_hist[interior_indices]) + smoothed_interior_spike_counts = kernel_matrix @ interior_spike_hist + total_interior_spikes = smoothed_interior_spike_counts.sum() + if total_interior_spikes > EPS: + smoothed_interior_spike_density = ( + smoothed_interior_spike_counts / total_interior_spikes + ) + else: + smoothed_interior_spike_density = jnp.zeros_like( + smoothed_interior_spike_counts + ) + + smoothed_spatial_density_full = ( + jnp.zeros(n_total_bins) + .at[interior_indices] + .set(smoothed_interior_spike_density) + ) + else: + smoothed_spatial_density_full = jnp.zeros(n_total_bins) + + smoothed_spatial_spike_densities.append(smoothed_spatial_density_full) + + # --- Calculate Summed Spatial Intensity (for prediction integral term) --- + # (Code remains the same) + summed_spatial_intensity = jnp.zeros_like(smoothed_occupancy_full) + for mean_rate, spatial_density in zip(mean_rates, smoothed_spatial_spike_densities): + spatial_intensity = mean_rate * jnp.where( + smoothed_occupancy_full > EPS, + spatial_density / smoothed_occupancy_full, + 0.0, + ) + summed_spatial_intensity += jnp.clip(spatial_intensity, a_min=0.0) + + # --- Finalize and Return --- + return { + "smoothed_spatial_spike_density": jnp.stack( + smoothed_spatial_spike_densities, axis=0 + ), + "feature_samples": encoding_feature_samples, # Store raw samples + "feature_bandwidth_sigma": feature_bandwidth_sigma_jax, # Store bandwidth + # Removed 'feature_kde_models' + "smoothed_occupancy": smoothed_occupancy_full, + "mean_rates": mean_rates, + "interior_bin_indices_flat": interior_indices, + "diffusion_bandwidth_sigma": diffusion_bandwidth_sigma, + "environment": environment, + "summed_spatial_intensity": summed_spatial_intensity, + "is_track_interior": interior_mask_flat, + "disable_progress_bar": disable_progress_bar, + # Removed 'kde_block_size' as it's not relevant here + } + + +def predict_clusterless_diffusion_log_likelihood( + time: np.ndarray, + spike_times: List[np.ndarray], + spike_waveform_features: List[np.ndarray], + encoding_model: Dict, + is_local: bool = False, + position_time: Optional[np.ndarray] = None, # Needed if is_local=True + position: Optional[np.ndarray] = None, # Needed if is_local=True +) -> jnp.ndarray: + """Predict the log likelihood for clusterless spikes using diffusion models. + + Calculates the log likelihood based on the fitted spatial diffusion model + and on-the-fly feature KDE calculation. + + Parameters + ---------- + time : np.ndarray, shape (n_time + 1,) + Edges of the decoding time bins. + spike_times : list[np.ndarray] + Spike times for each electrode during decoding. + spike_waveform_features : list[np.ndarray] + Waveform features for each electrode during decoding. + encoding_model : dict + The fitted model dictionary from `fit_clusterless_diffusion_encoding_model`. + Must contain 'feature_samples' and 'feature_bandwidth_sigma'. + is_local : bool, optional + If True, compute log likelihood at the animal's position. Requires + `position_time` and `position`. Uses nearest interior bin. Defaults to False. + position_time : np.ndarray, shape (n_pos_time,), optional + Timestamps for position data, required if `is_local` is True. + position : np.ndarray, shape (n_pos_time, n_dims), optional + Position data, required if `is_local` is True. + + Returns + ------- + log_likelihood : jnp.ndarray + Shape (n_time, n_interior_bins) if `is_local` is False. + Shape (n_time, 1) if `is_local` is True. + """ + # --- Extract components from encoding_model --- + smoothed_spatial_spike_density = encoding_model["smoothed_spatial_spike_density"] + feature_samples = encoding_model["feature_samples"] # Get list of feature samples + feature_bw = encoding_model["feature_bandwidth_sigma"] # Get feature bandwidth + smoothed_occupancy = encoding_model["smoothed_occupancy"] + mean_rates = encoding_model["mean_rates"] + interior_indices = encoding_model["interior_bin_indices_flat"] + summed_spatial_intensity = encoding_model["summed_spatial_intensity"] + environment = encoding_model["environment"] + is_track_interior = encoding_model["is_track_interior"] + disable_progress_bar = encoding_model["disable_progress_bar"] + + n_time = len(time) + if n_time <= 0: + raise ValueError("`time` array must contain at least two elements.") + n_electrodes = len(spike_times) + n_total_bins = smoothed_occupancy.shape[0] + n_interior_bins = interior_indices.shape[0] + log_likelihood = None # Initialize + + # Precompute spatial intensity part (same as before) + clipped_occupancy = jnp.clip(smoothed_occupancy, a_min=EPS) + spatial_log_intensity_part = ( + jnp.log(jnp.asarray(mean_rates)[:, None]) + + jnp.log(smoothed_spatial_spike_density + EPS) + - jnp.log(clipped_occupancy[None, :] + EPS) + ) + spatial_log_intensity_part = jnp.where( + smoothed_occupancy[None, :] > EPS, spatial_log_intensity_part, LOG_EPS + ) + + if is_local: + # --- Local Log Likelihood --- + if position_time is None or position is None: + raise ValueError( + "'position_time' and 'position' are required for local=True." + ) + if not environment._is_fitted: + raise ValueError("Environment object must be fitted.") + + print("Calculating local clusterless likelihood (nearest interior bin)...") + time_bin_centers = time[:-1] + np.diff(time) / 2 + interpolated_position = get_position_at_time( + position_time, position, time_bin_centers, environment + ) + is_nan_interp_pos = np.any(np.isnan(interpolated_position), axis=1) + valid_interp_pos = interpolated_position[~is_nan_interp_pos] + + nearest_interior_indices = np.full(n_time, -1, dtype=int) + nearest_flat_bin_indices = np.full(n_time, -1, dtype=int) + + if valid_interp_pos.shape[0] > 0: + interior_centers = environment.place_bin_centers_[interior_indices] + if interior_centers.shape[0] == 0: + raise ValueError("No interior bin centers found.") + tree = KDTree(interior_centers) + _, nn_indices_for_valid = tree.query(valid_interp_pos) + nearest_interior_indices[~is_nan_interp_pos] = nn_indices_for_valid + valid_flat_indices = interior_indices[jnp.asarray(nn_indices_for_valid)] + nearest_flat_bin_indices[~is_nan_interp_pos] = valid_flat_indices + + log_likelihood_local = jnp.zeros((n_time,)) + + valid_time_bins_mask_local = nearest_flat_bin_indices != -1 + valid_flat_indices_local = nearest_flat_bin_indices[valid_time_bins_mask_local] + if valid_flat_indices_local.size > 0: + integral_term_values = -summed_spatial_intensity[ + jnp.asarray(valid_flat_indices_local) + ] + log_likelihood_local = log_likelihood_local.at[ + valid_time_bins_mask_local + ].add(integral_term_values) + + for elec_id, (elec_spikes, elec_features) in enumerate( + tqdm( + zip(spike_times, spike_waveform_features), + total=n_electrodes, + unit="electrode", + desc="Local Clusterless Likelihood", + disable=disable_progress_bar, + ) + ): + valid_time_mask = (elec_spikes >= time[0]) & (elec_spikes < time[-1]) + spikes_in_window = elec_spikes[valid_time_mask] + features_in_window = jnp.asarray(elec_features[valid_time_mask]) + + if spikes_in_window.size == 0: + continue + + spike_bin_inds = get_spike_time_bin_ind(spikes_in_window, time) + flat_bin_for_spikes = nearest_flat_bin_indices[spike_bin_inds] + valid_spike_bin_mask = flat_bin_for_spikes != -1 + + if not np.any(valid_spike_bin_mask): + continue + + valid_spike_bin_inds = spike_bin_inds[valid_spike_bin_mask] + valid_features = features_in_window[valid_spike_bin_mask] + valid_flat_bins = flat_bin_for_spikes[valid_spike_bin_mask] + + # Calculate feature log pdf using helper function + if valid_features.shape[0] > 0: + feature_log_pdf = gaussian_kde_logpdf( + valid_features, feature_samples[elec_id], feature_bw + ) + else: + feature_log_pdf = jnp.full(valid_features.shape[0], LOG_EPS) + + spatial_part = spatial_log_intensity_part[elec_id][ + jnp.asarray(valid_flat_bins) + ] + spike_log_likelihood = spatial_part + feature_log_pdf + spike_log_likelihood = jnp.clip(spike_log_likelihood, a_min=LOG_EPS) + + # Use segment_sum for potentially better performance/readability even in local case + # log_likelihood_local = log_likelihood_local.at[valid_spike_bin_inds].add(spike_log_likelihood) + # Create temporary array for segment sum + spike_contributions = jnp.zeros_like(log_likelihood_local) + spike_contributions = spike_contributions.at[valid_spike_bin_inds].set( + spike_log_likelihood + ) + log_likelihood_local += ( + spike_contributions # Add contributions for this electrode + ) + + log_likelihood = log_likelihood_local[:, jnp.newaxis] + + else: + # --- Non-Local Log Likelihood --- + print("Calculating non-local clusterless likelihood...") + if n_interior_bins == 0: + print("Warning: No interior bins found. Returning empty likelihood array.") + return jnp.zeros((n_time, 0)) + + integral_term = -summed_spatial_intensity[interior_indices] + log_likelihood_nonlocal = jnp.tile(integral_term[jnp.newaxis, :], (n_time, 1)) + + spatial_log_intensity_interior = spatial_log_intensity_part[:, interior_indices] + + for elec_id, (elec_spikes, elec_features) in enumerate( + tqdm( + zip(spike_times, spike_waveform_features), + total=n_electrodes, + unit="electrode", + desc="Non-Local Clusterless Likelihood", + disable=disable_progress_bar, + ) + ): + valid_time_mask = (elec_spikes >= time[0]) & (elec_spikes < time[-1]) + spikes_in_window = elec_spikes[valid_time_mask] + features_in_window = jnp.asarray(elec_features[valid_time_mask]) + + if spikes_in_window.size == 0: + continue + + spike_bin_inds = get_spike_time_bin_ind(spikes_in_window, time) + + # Calculate feature log pdf using helper function + if features_in_window.shape[0] > 0: + feature_log_pdf = gaussian_kde_logpdf( + features_in_window, feature_samples[elec_id], feature_bw + ) + else: + feature_log_pdf = jnp.full(features_in_window.shape[0], LOG_EPS) + + spatial_part = spatial_log_intensity_interior[elec_id][jnp.newaxis, :] + feature_part = feature_log_pdf[:, jnp.newaxis] + spike_log_likelihood = spatial_part + feature_part + spike_log_likelihood = jnp.clip(spike_log_likelihood, a_min=LOG_EPS) + + # Sum spike contributions into the correct time bins using segment_sum + log_likelihood_nonlocal = log_likelihood_nonlocal + jax.ops.segment_sum( + spike_log_likelihood, + spike_bin_inds, + num_segments=n_time, + indices_are_sorted=False, + ) + + log_likelihood = log_likelihood_nonlocal + + if log_likelihood is None: + raise RuntimeError("Log likelihood calculation failed.") + + return log_likelihood diff --git a/src/non_local_detector/likelihoods/sorted_spikes_diffusion_kde.py b/src/non_local_detector/likelihoods/sorted_spikes_diffusion_kde.py new file mode 100644 index 0000000..80a278d --- /dev/null +++ b/src/non_local_detector/likelihoods/sorted_spikes_diffusion_kde.py @@ -0,0 +1,444 @@ +from typing import Dict, List, Optional + +import jax +import jax.numpy as jnp +import jax.scipy +import numpy as np +from scipy.spatial import KDTree +from tqdm.autonotebook import tqdm + +from non_local_detector.diffusion_kernels import compute_diffusion_kernels +from non_local_detector.environment import Environment +from non_local_detector.likelihoods.common import ( + EPS, + get_position_at_time, + get_spikecount_per_time_bin, +) + + +def fit_sorted_spikes_diffusion_kde_encoding_model( + position_time: jnp.ndarray, + position: jnp.ndarray, + spike_times: list[jnp.ndarray], + environment: Environment, + weights: Optional[jnp.ndarray] = None, + sampling_frequency: int = 500, + position_std: float = np.sqrt(12.5), + block_size: int = 100, + disable_progress_bar: bool = False, +) -> Dict: + """Fits an encoding model for sorted spikes using diffusion kernel smoothing. + + Estimates spatial firing rate (place fields) by smoothing occupancy and + spike histograms using precomputed diffusion kernels based on the + environment's geometry. + + Parameters + ---------- + position_time : np.ndarray, shape (n_time_position,) + Timestamps for position samples. + position : np.ndarray, shape (n_time_position, n_position_dims) + Position samples (typically 2D for this diffusion implementation). + spike_times : list[np.ndarray] + List where each element is an array of spike times for a single neuron. + environment : Environment + A *fitted* Environment object defining the spatial grid, interior, + and connectivity graph (track_graph_nd_). Must be 2D. + position_std : float + Spatial bandwidth (standard deviation) for the diffusion kernel. Controls + the amount of smoothing. Units should match environment units. + weights : np.ndarray, shape (n_time_position,), optional + Weights for each position sample (e.g., duration). If None, assumes + uniform sampling time. + disable_progress_bar : bool, optional + If True, suppresses progress bars. Defaults to False. + + Returns + ------- + encoding_model : dict + A dictionary containing the fitted model components: + 'place_fields' : jnp.ndarray, shape (n_neurons, n_total_bins) + Smoothed firing rate maps for each neuron across all grid bins. + 'mean_rates' : list[float] + Average firing rate for each neuron. + 'smoothed_occupancy' : jnp.ndarray, shape (n_total_bins,) + Smoothed occupancy map across all grid bins. + 'interior_bin_indices_flat' : jnp.ndarray, shape (n_interior_bins,) + Flat indices of the bins considered part of the track interior. + 'diffusion_bandwidth_sigma' : float + The bandwidth parameter used for diffusion smoothing. + 'environment' : Environment + The Environment object used for fitting. + 'no_spike_part_log_likelihood': jnp.ndarray, shape (n_total_bins,) + Sum of place fields, used in likelihood calculation. + 'is_track_interior' : jnp.ndarray, shape (n_total_bins,) + Flattened boolean mask of track interior bins. + 'disable_progress_bar': bool + """ + # --- Input Validation and Setup --- + if not environment._is_fitted: + raise ValueError("Environment object must be fitted first.") + + track_graph_nd_ = environment.get_fitted_track_graph() + + position = position if position.ndim > 1 else np.expand_dims(position, axis=1) + n_total_bins = environment.place_bin_centers_.shape[0] + interior_mask = environment.is_track_interior_ + interior_mask_flat = interior_mask.ravel() + interior_indices = jnp.where(interior_mask_flat)[0] + + # --- Diffusion Kernels --- + print("Computing diffusion kernels...") + kernel_matrix = compute_diffusion_kernels( + track_graph=track_graph_nd_, + interior_mask=interior_mask, + bandwidth_sigma=position_std, + ) + print(f"Computed {kernel_matrix.shape} kernel matrix.") + + # --- Compute Smoothed Occupancy --- + print("Calculating smoothed occupancy...") + if weights is None: + weights = np.ones_like(position_time) + + # Filter out NaN positions BEFORE binning + is_nan_pos = np.any(np.isnan(position), axis=1) + valid_pos_mask = ~is_nan_pos + valid_positions = position[valid_pos_mask] + weights = weights[valid_pos_mask] + + # Bin valid positions using the Environment method + if valid_positions.shape[0] > 0: + position_bin_inds_valid = environment.get_bin_ind(valid_positions) + else: + position_bin_inds_valid = np.array([], dtype=int) + + # Histogram of time spent on the full grid + full_occupancy_hist = np.zeros(n_total_bins) + np.add.at(full_occupancy_hist, position_bin_inds_valid, weights) + + # Filter to interior bins + interior_occupancy_hist = jnp.asarray(full_occupancy_hist[interior_indices]) + + # Smooth using the kernel matrix + smoothed_interior_occupancy = kernel_matrix @ interior_occupancy_hist + + # Normalize to density (divide by total time spent in interior) + total_interior_time = smoothed_interior_occupancy.sum() + if total_interior_time > EPS: + smoothed_interior_occupancy_density = ( + smoothed_interior_occupancy / total_interior_time + ) + else: + print("Warning: Total occupancy time in interior is close to zero.") + smoothed_interior_occupancy_density = jnp.zeros_like( + smoothed_interior_occupancy + ) + + # Map back to full grid (zero elsewhere) + smoothed_occupancy_full = ( + jnp.zeros(n_total_bins) + .at[interior_indices] + .set(smoothed_interior_occupancy_density) + ) + + # --- Compute Smoothed Spike Marginals and Place Fields (Per Neuron) --- + place_fields = [] + mean_rates = [] + + total_encoding_time = weights.sum() # More accurate total time from weights + + for neuron_id, neuron_spike_times in enumerate( + tqdm( + spike_times, + unit="neuron", + desc="Encoding models (Diffusion)", + disable=disable_progress_bar, + ) + ): + # Filter spikes outside position time range + is_valid_spike = np.logical_and( + neuron_spike_times >= position_time[0], + neuron_spike_times <= position_time[-1], + ) + neuron_spike_times = neuron_spike_times[is_valid_spike] + n_spikes = len(neuron_spike_times) + + # Calculate mean rate + mean_rates.append( + n_spikes / total_encoding_time if total_encoding_time > 0 else 0.0 + ) + + if n_spikes > 0: + # Find position at each spike time + pos_at_spike = get_position_at_time( + position_time, position, neuron_spike_times, environment + ) + time_weights_at_spikes = get_position_at_time( + position_time, weights, neuron_spike_times, environment + ) + + # Filter out NaN spike positions BEFORE binning + is_nan_spike_pos = np.any(np.isnan(pos_at_spike), axis=1) + valid_spike_pos_mask = ~is_nan_spike_pos + valid_pos_at_spike = pos_at_spike[valid_spike_pos_mask] + time_weights_at_spikes = time_weights_at_spikes[valid_spike_pos_mask] + + # Bin valid spike positions using the Environment method + if valid_pos_at_spike.shape[0] > 0: + spike_bin_inds_valid = environment.get_bin_ind(valid_pos_at_spike) + else: + spike_bin_inds_valid = np.array([], dtype=int) + + # Histogram of spikes on the full grid + full_spike_hist = np.zeros(n_total_bins) + if spike_bin_inds_valid.size > 0: + np.add.at(full_spike_hist, spike_bin_inds_valid, time_weights_at_spikes) + + # Filter to interior bins + interior_spike_hist = jnp.asarray(full_spike_hist[interior_indices]) + + # Smooth using the kernel matrix + smoothed_interior_spike_density = kernel_matrix @ interior_spike_hist + + # Calculate smoothed marginal density (normalized) + total_interior_spikes = smoothed_interior_spike_density.sum() + if total_interior_spikes > EPS: + smoothed_interior_marginal_density = ( + smoothed_interior_spike_density / total_interior_spikes + ) + else: + smoothed_interior_marginal_density = jnp.zeros_like( + smoothed_interior_spike_density + ) + + # Calculate place field value for interior bins + place_field_interior = mean_rates[-1] * jnp.where( + smoothed_interior_occupancy_density > EPS, + smoothed_interior_marginal_density + / smoothed_interior_occupancy_density, + 0.0, # Rate is zero if occupancy is zero + ) + # Clip to avoid negative values (shouldn't happen) and ensure minimum rate + place_field_interior = jnp.clip(place_field_interior, a_min=EPS) + + # Map back to full grid + neuron_place_field = ( + jnp.zeros(n_total_bins).at[interior_indices].set(place_field_interior) + ) + + else: + # No spikes, place field is EPS everywhere + neuron_place_field = jnp.full((n_total_bins,), EPS) + + place_fields.append(neuron_place_field) + + # --- Finalize and Return --- + place_fields = jnp.stack(place_fields, axis=0) + no_spike_part_log_likelihood = jnp.sum(place_fields, axis=0) # Sum of rates lambda + + return { + "place_fields": place_fields, + "mean_rates": mean_rates, + "environment": environment, + "no_spike_part_log_likelihood": no_spike_part_log_likelihood, + "interior_bin_indices_flat": interior_indices, + "is_track_interior": interior_mask_flat, + "disable_progress_bar": disable_progress_bar, + } + + +def predict_sorted_spikes_diffusion_kde_log_likelihood( + time: jnp.ndarray, + position_time: jnp.ndarray, + position: jnp.ndarray, + spike_times: list[np.ndarray], + place_fields: jnp.ndarray, + mean_rates: List[float], + no_spike_part_log_likelihood: jnp.ndarray, + environment: Environment, + interior_bin_indices_flat: jnp.ndarray, + is_track_interior: jnp.ndarray, # Flattened boolean mask + disable_progress_bar: bool = False, + is_local: bool = False, +) -> jnp.ndarray: + """Predict the log likelihood of sorted spikes using diffusion encoding models. + + Calculates the log likelihood based on Poisson statistics, where the rate + parameter is determined by the diffusion-smoothed place fields. + + Parameters + ---------- + time : np.ndarray, shape (n_time,) + Decoding time bins' edges or centers. Edges are expected for binning spikes. + spike_times : list[np.ndarray] + List where each element is an array of spike times for a single neuron + during the decoding period. + place_fields : jnp.ndarray, shape (n_neurons, n_total_bins) + Smoothed firing rate maps (lambda) from the fitted diffusion model. + no_spike_part_log_likelihood : jnp.ndarray, shape (n_total_bins,) + Sum of place fields (sum of lambda) across neurons for each bin. + environment : Environment + The *fitted* Environment object used during encoding. + interior_bin_indices_flat : jnp.ndarray, shape (n_interior_bins,) + Flat indices of the bins considered part of the track interior. + is_track_interior : jnp.ndarray, shape (n_total_bins,) + Flattened boolean mask of track interior bins. + disable_progress_bar : bool, optional + If True, suppresses progress bars. Defaults to False. + is_local : bool, optional + If True, compute the log likelihood at the animal's position (requires + `position_time` and `position`). Uses nearest interior bin approximation. + If False, compute across all interior bins. Defaults to False. + position_time : np.ndarray, shape (n_pos_time,), optional + Timestamps for position data, required if `is_local` is True. + position : np.ndarray, shape (n_pos_time, n_dims), optional + Position data, required if `is_local` is True. + + + Returns + ------- + log_likelihood : jnp.ndarray + Shape (n_time, n_interior_bins) if `is_local` is False. + Shape (n_time, 1) if `is_local` is True. + + Notes + ----- + The local likelihood calculation (`is_local=True`) uses a nearest-neighbor + approach, mapping the animal's continuous position to the closest interior + bin center and using the rate precomputed for that bin. This simplifies + computation but ignores sub-bin spatial information. + Expects `time` to be bin edges for `get_spikecount_per_time_bin`. + """ + # Check if time likely represents edges or centers. Assume edges if length > 1. + n_time = len(time) + + log_likelihood = None # Initialize + + if is_local: + # --- Local Likelihood Calculation (Nearest Interior Bin) --- + if position_time is None or position is None: + raise ValueError( + "'position_time' and 'position' are required when is_local=True." + ) + if not environment._is_fitted: + raise ValueError("Environment object must be fitted.") + + print("Calculating local likelihood (nearest interior bin)...") + # 1. Get centers of time bins for interpolation + # time_bin_centers = time[:-1] + np.diff(time) / 2 + + # 2. Interpolate position to time bin centers + interpolated_position = get_position_at_time( + position_time, position, time, environment + ) # Shape (n_time, n_dims) + + # Filter out NaN interpolated positions + is_nan_interp_pos = np.any(np.isnan(interpolated_position), axis=1) + valid_interp_pos = interpolated_position[~is_nan_interp_pos] + + # 3. Find nearest *interior* bin center for each *valid* interpolated position + nearest_interior_indices = np.full( + n_time, -1, dtype=int + ) # Initialize with invalid index + if valid_interp_pos.shape[0] > 0: + interior_centers = environment.place_bin_centers_[interior_bin_indices_flat] + if interior_centers.shape[0] == 0: + raise ValueError("No interior bin centers found in environment.") + # Use KDTree for efficient nearest neighbor search + tree = KDTree(interior_centers) + _, nn_indices_for_valid = tree.query(valid_interp_pos) + nearest_interior_indices[~is_nan_interp_pos] = nn_indices_for_valid + # nearest_interior_indices shape: (n_time,). Values are indices *within the interior subset* or -1. + + # 4. Calculate log likelihood summed across neurons + log_likelihood_local = jnp.zeros((n_time,)) + + for neuron_id, neuron_spike_times in enumerate( + tqdm( + spike_times, + unit="neuron", + desc="Local Likelihood (Diffusion)", + disable=disable_progress_bar, + ) + ): + # Count spikes in decoding time bins + spike_counts_per_bin = get_spikecount_per_time_bin( + neuron_spike_times, time + ) # Shape (n_time,) + + # Get the rate for this neuron at the nearest interior bin for each time point + neuron_place_field_interior = place_fields[neuron_id][ + interior_bin_indices_flat + ] + + # Initialize local rates with EPS (for invalid positions or bins) + local_rates = jnp.full((n_time,), EPS) + + # Get rates only for time bins with valid nearest neighbors + valid_time_bins_mask = nearest_interior_indices != -1 + valid_nn_indices = nearest_interior_indices[valid_time_bins_mask] + + if valid_nn_indices.size > 0: + rates_at_valid_bins = neuron_place_field_interior[ + jnp.asarray(valid_nn_indices) + ] + local_rates = local_rates.at[valid_time_bins_mask].set( + rates_at_valid_bins + ) + + local_rates = jnp.clip(local_rates, a_min=EPS) # Ensure rate > 0 + + # Calculate Poisson log likelihood component for this neuron + log_likelihood_local += ( + jax.scipy.special.xlogy(spike_counts_per_bin, local_rates) - local_rates + ) + + return log_likelihood_local[:, jnp.newaxis] # Reshape to (n_time, 1) + + else: + # --- Non-Local Likelihood Calculation --- + print("Calculating non-local likelihood...") + n_interior_bins = interior_bin_indices_flat.shape[0] + if n_interior_bins == 0: + print("Warning: No interior bins found. Returning empty likelihood array.") + return jnp.zeros((n_time, 0)) + + log_likelihood_nonlocal = jnp.zeros((n_time, n_interior_bins)) + + # Pre-filter place fields and summed rate for interior bins only + place_fields_interior = place_fields[ + :, interior_bin_indices_flat + ] # (n_neurons, n_interior_bins) + no_spike_ll_interior = no_spike_part_log_likelihood[ + interior_bin_indices_flat + ] # (n_interior_bins,) + + for neuron_id, neuron_spike_times in enumerate( + tqdm( + spike_times, + unit="neuron", + desc="Non-Local Likelihood (Diffusion)", + disable=disable_progress_bar, + ) + ): + # Count spikes in decoding time bins + spike_counts_per_bin = get_spikecount_per_time_bin( + neuron_spike_times, time + ) # Shape (n_time,) + + # Get rates (lambda) for this neuron across all interior bins + rates_interior = place_fields_interior[ + neuron_id + ] # Shape (n_interior_bins,) + rates_interior = jnp.clip(rates_interior, a_min=EPS) + + # Calculate k * log(lambda) part using broadcasting + log_likelihood_nonlocal += jax.scipy.special.xlogy( + spike_counts_per_bin[:, jnp.newaxis], rates_interior[jnp.newaxis, :] + ) + + # Subtract the summed rate term (lambda) + log_likelihood_nonlocal -= no_spike_ll_interior[jnp.newaxis, :] + + return log_likelihood_nonlocal # Shape (n_time, n_interior_bins) diff --git a/src/non_local_detector/models/base.py b/src/non_local_detector/models/base.py index e5ad17b..1f36da8 100644 --- a/src/non_local_detector/models/base.py +++ b/src/non_local_detector/models/base.py @@ -303,7 +303,7 @@ def initialize_environments( edge_spacing=environment.edge_spacing, ).linear_position.to_numpy() - environment.fit_place_grid( + environment.fit( env_position, infer_track_interior=self.infer_track_interior ) diff --git a/src/non_local_detector/tests/test_environment.py b/src/non_local_detector/tests/test_environment.py new file mode 100644 index 0000000..0d14e66 --- /dev/null +++ b/src/non_local_detector/tests/test_environment.py @@ -0,0 +1,696 @@ +from typing import Tuple + +import networkx as nx +import numpy as np +import pytest +from numpy.typing import NDArray + +from non_local_detector.environment import ( + Environment, + _create_1d_track_grid_data, + _create_grid, + _extract_bin_info_from_track_graph, + _get_distance_between_bins, + _get_node_pos, + _get_track_boundary, + _infer_track_interior, + _make_nd_track_graph, + _make_track_graph_bin_centers, + _make_track_graph_bin_centers_edges, + add_distance_weight_to_edges, + get_centers, + get_n_bins, +) + +# --- Fixtures for Test Data --- + + +@pytest.fixture +def position_data_2d_simple() -> NDArray[np.float64]: + """Simple 2D position data forming roughly a square path.""" + pos = np.array( + [ + [0, 0], + [1, 0], + [2, 0], + [3, 0], + [4, 0], + [5, 0], # Bottom edge + [5, 1], + [5, 2], + [5, 3], + [5, 4], + [5, 5], # Right edge + [4, 5], + [3, 5], + [2, 5], + [1, 5], + [0, 5], # Top edge + [0, 4], + [0, 3], + [0, 2], + [0, 1], # Left edge + [1, 1], + [2, 2], + [3, 3], + [4, 4], # Diagonalish inside + [np.nan, np.nan], # Add a NaN point + ] + ) + # Repeat to simulate more time + return np.tile(pos, (5, 1)) + + +@pytest.fixture +def position_data_square_nd() -> NDArray[np.float64]: + """User-provided square path data with noise.""" + x = np.linspace(0, 30, 50) # Increased points for better coverage + position = np.concatenate( + ( + np.stack((np.zeros_like(x), x[::-1]), axis=1), # Left edge (30 -> 0) + np.stack((x, np.zeros_like(x)), axis=1), # Bottom edge (0 -> 30) + np.stack((np.ones_like(x) * 30, x), axis=1), # Right edge (0 -> 30) + # Removed the partial path from original example for clarity + ) + ) + # Add Gaussian noise + rng = np.random.default_rng(seed=42) # for reproducibility + noise = rng.multivariate_normal( + [0, 0], [[0.5, 0], [0, 0.5]], size=position.shape[0] + ) + return position + noise + + +@pytest.fixture +def position_data_3d() -> NDArray[np.float64]: + """Simple 3D helix-like position data.""" + n_points = 100 + t = np.linspace(0, 4 * np.pi, n_points) + x = np.cos(t) + y = np.sin(t) + z = np.linspace(0, 10, n_points) + pos = np.stack([x, y, z], axis=1) + rng = np.random.default_rng(seed=43) + noise = rng.normal(0, 0.1, size=pos.shape) + return pos + noise + + +@pytest.fixture +def fitted_env_3d(position_data_3d) -> Environment: + """Fixture for a fitted 3D environment.""" + env = Environment(place_bin_size=0.5, infer_track_interior=True, dilate=True) + env.fit(position_data_3d) + return env + + +@pytest.fixture +def track_graph_u_shape() -> nx.Graph: + """User-provided U-shaped graph structure.""" + node_positions = { + 0: (0, 0), + 1: (30, 0), + 2: (30, 30), + 3: (0, 30), + } + edges = [(0, 1), (0, 3), (1, 2)] # U-shape: 3-0-1-2 + + graph = nx.Graph() + graph.add_nodes_from(node_positions.keys()) + graph.add_edges_from(edges) + nx.set_node_attributes(graph, node_positions, "pos") + # Add edge_id and distance (optional but good practice for the class) + for i, edge in enumerate(edges): + pos1 = np.array(node_positions[edge[0]]) + pos2 = np.array(node_positions[edge[1]]) + dist = np.linalg.norm(pos1 - pos2) + graph.edges[edge]["distance"] = dist + graph.edges[edge]["edge_id"] = i + return graph + + +@pytest.fixture +def position_data_1d_linear() -> NDArray[np.float64]: + """Position data roughly along a line (for testing 1D mapping).""" + # Corresponds roughly to the linear_track_graph fixture + pos = np.array( + [ + [0.5, 0], + [1.5, 0], + [2.5, 0], + [3.5, 0], + [4.5, 0], + [5.5, 0], + [6.5, 0], + [7.5, 0], + [8.5, 0], + [9.5, 0], + ] + ) + return np.tile(pos, (10, 1)) + + +@pytest.fixture +def linear_track_graph() -> Tuple[nx.Graph, list, float]: + """Creates a simple linear track graph for 1D tests.""" + nodes = [0, 1, 2, 3] + edges = [(0, 1), (1, 2), (2, 3)] + node_positions = { + 0: (0, 0), + 1: (5, 0), + 2: (10, 0), + 3: (15, 0), + } + # Define edge_order for linearization + edge_order = [(0, 1), (1, 2), (2, 3)] + edge_spacing = 0.0 + + graph = nx.Graph() + graph.add_nodes_from(nodes) + graph.add_edges_from(edges) + nx.set_node_attributes(graph, node_positions, "pos") + # Add edge_id and distance + for i, edge in enumerate(edge_order): + pos1 = np.array(node_positions[edge[0]]) + pos2 = np.array(node_positions[edge[1]]) + dist = np.linalg.norm(pos1 - pos2) + graph.edges[edge]["distance"] = dist + graph.edges[edge]["edge_id"] = i + + return graph, edge_order, edge_spacing + + +# --- Test Helper Functions --- + + +def test_get_centers(): + edges = np.array([0, 2, 4, 6]) + centers = get_centers(edges) + np.testing.assert_array_equal(centers, np.array([1, 3, 5])) + + +def test_get_n_bins(): + pos = np.array([[0, 0], [10, 20]]) + n_bins = get_n_bins(pos, bin_size=2.0) + np.testing.assert_array_equal(n_bins, np.array([5, 10])) + n_bins = get_n_bins(pos, bin_size=[2.0, 5.0]) + np.testing.assert_array_equal(n_bins, np.array([5, 4])) + n_bins = get_n_bins(pos, bin_size=1.0, position_range=[(0, 5), (0, 10)]) + np.testing.assert_array_equal(n_bins, np.array([5, 10])) + # Test zero extent + pos_zero = np.array([[1, 1], [1, 1]]) + n_bins = get_n_bins(pos_zero, bin_size=1.0) + np.testing.assert_array_equal(n_bins, np.array([1, 1])) + + +def test_create_grid(position_data_2d_simple): + """Test _create_grid helper function.""" + pos = position_data_2d_simple + bin_size = 1.0 + + # Test without boundary bins (default) + edges_no_b, _, centers_no_b, shape_no_b = _create_grid( + position=pos, bin_size=bin_size, add_boundary_bins=False + ) + assert shape_no_b == (5, 5) # Based on data range 0-5 + assert len(edges_no_b) == 2 + assert len(edges_no_b[0]) == shape_no_b[0] + 1 + assert len(edges_no_b[1]) == shape_no_b[1] + 1 + assert centers_no_b.shape == (np.prod(shape_no_b), 2) + np.testing.assert_allclose(edges_no_b[0][0], 0.0) # Check tight range + np.testing.assert_allclose(edges_no_b[0][-1], 5.0) + + # Test with boundary bins + edges_b, _, centers_b, shape_b = _create_grid( + position=pos, bin_size=bin_size, add_boundary_bins=True + ) + assert shape_b == (7, 7) # Adds one bin each side + assert len(edges_b) == 2 + assert len(edges_b[0]) == shape_b[0] + 1 + assert len(edges_b[1]) == shape_b[1] + 1 + assert centers_b.shape == (np.prod(shape_b), 2) + np.testing.assert_allclose(edges_b[0][0], -1.0) # Check extended range + np.testing.assert_allclose(edges_b[0][-1], 6.0) + + # Test with position_range + pos_range = [(0.0, 4.0), (0.0, 6.0)] + edges_r, _, _, shape_r = _create_grid( + position_range=pos_range, bin_size=bin_size, add_boundary_bins=False + ) + assert shape_r == (4, 6) + np.testing.assert_allclose(edges_r[0][0], 0.0) + np.testing.assert_allclose(edges_r[0][-1], 4.0) + np.testing.assert_allclose(edges_r[1][0], 0.0) + np.testing.assert_allclose(edges_r[1][-1], 6.0) + + +def test_infer_track_interior(position_data_2d_simple): + """Test _infer_track_interior helper function.""" + # Create a grid first + edges, _, _, shape = _create_grid( + position=position_data_2d_simple, bin_size=1.0, add_boundary_bins=False + ) + + # Basic inference + interior = _infer_track_interior( + position_data_2d_simple, edges, boundary_exists=False + ) + assert interior.shape == shape + assert interior.dtype == bool + assert np.sum(interior) > 0 # Some bins should be true + assert np.sum(~interior) > 0 # Some bins should be false (corners of square grid) + + # Test threshold + interior_thresh = _infer_track_interior( + position_data_2d_simple, edges, bin_count_threshold=1000, boundary_exists=False + ) + assert np.sum(interior_thresh) < np.sum(interior) # Higher threshold = fewer bins + + # Test dilation + interior_dilate = _infer_track_interior( + position_data_2d_simple, edges, dilate=True, boundary_exists=False + ) + assert np.sum(interior_dilate) > np.sum(interior) + + # Test boundary_exists=True (should remove perimeter) + interior_boundary = _infer_track_interior( + position_data_2d_simple, edges, boundary_exists=True + ) + assert np.sum(interior_boundary) < np.sum(interior) + # Check if perimeter is False (for 2D) + assert not np.any(interior_boundary[0, :]) + assert not np.any(interior_boundary[-1, :]) + assert not np.any(interior_boundary[:, 0]) + assert not np.any(interior_boundary[:, -1]) + + +def test_make_nd_track_graph(): + """Test _make_nd_track_graph helper function.""" + # Simple 2x2 interior + shape = (3, 3) + centers = np.array( + [[x + 0.5, y + 0.5] for x in range(shape[0]) for y in range(shape[1])] + ) + interior = np.zeros(shape, dtype=bool) + interior[0:2, 0:2] = True # Top-left 2x2 square + + graph = _make_nd_track_graph(centers, interior, shape) + + assert isinstance(graph, nx.Graph) + # Nodes: 0, 1, 3, 4 should be interior + assert graph.nodes[0]["is_track_interior"] + assert graph.nodes[1]["is_track_interior"] + assert not graph.nodes[2]["is_track_interior"] + assert graph.nodes[3]["is_track_interior"] + assert graph.nodes[4]["is_track_interior"] + assert graph.number_of_nodes() == 9 # All nodes added + + # Edges should only connect adjacent interior nodes (0,1), (0,3), (1,4), (3,4), (0,4-diag), (1,3-diag) + expected_edges = {(0, 1), (0, 3), (0, 4), (1, 3), (1, 4), (3, 4)} + actual_edges = set(tuple(sorted(e)) for e in graph.edges()) + assert actual_edges == expected_edges + + # Check distance attribute + dist_0_1 = np.linalg.norm(centers[0] - centers[1]) + assert np.isclose(graph.edges[(0, 1)]["distance"], dist_0_1) + dist_0_4 = np.linalg.norm(centers[0] - centers[4]) # Diagonal + assert np.isclose(graph.edges[(0, 4)]["distance"], dist_0_4) + + +def test_get_distance_between_bins(): + """Test _get_distance_between_bins helper function.""" + # Simple path graph + graph = nx.path_graph(4) + # Add positions and distances + pos = {i: (i, 0) for i in range(4)} + nx.set_node_attributes(graph, pos, "pos") + nx.set_node_attributes(graph, {i: i for i in range(4)}, "bin_ind_flat") + for u, v in graph.edges(): + graph.edges[u, v]["distance"] = 1.0 + + distances = _get_distance_between_bins(graph) + expected = np.array( + [ + [0.0, 1.0, 2.0, 3.0], + [1.0, 0.0, 1.0, 2.0], + [2.0, 1.0, 0.0, 1.0], + [3.0, 2.0, 1.0, 0.0], + ] + ) + np.testing.assert_allclose(distances, expected) + + # Test disconnected graph + graph.remove_edge(1, 2) + distances_disconnected = _get_distance_between_bins(graph) + assert np.isinf(distances_disconnected[0, 2]) + assert np.isinf(distances_disconnected[3, 1]) + + +def test_make_track_graph_bin_centers_edges(linear_track_graph): + """Test _make_track_graph_bin_centers_edges helper function.""" + graph, _, _ = linear_track_graph + bin_size = 2.0 + new_graph = _make_track_graph_bin_centers_edges(graph, bin_size) + + assert isinstance(new_graph, nx.Graph) + assert new_graph.number_of_nodes() > graph.number_of_nodes() + assert ( + new_graph.number_of_edges() > graph.number_of_edges() + ) # Original edges replaced by chains + + # Check attributes of a new node (find one that is not an original node) + original_nodes = set(graph.nodes()) + new_node = next(n for n in new_graph.nodes() if n not in original_nodes) + assert "pos" in new_graph.nodes[new_node] + assert "edge_id" in new_graph.nodes[new_node] + assert "is_bin_edge" in new_graph.nodes[new_node] + + # Check edge distances in a chain + # Find path between original node 0 and 1 + path_nodes = nx.shortest_path(new_graph, source=0, target=1, weight="distance") + path_dist = 0 + for i in range(len(path_nodes) - 1): + u, v = path_nodes[i], path_nodes[i + 1] + edge_data = new_graph.get_edge_data(u, v) + assert "distance" in edge_data + assert edge_data["distance"] > 0 # Should be positive for this graph + path_dist += edge_data["distance"] + + original_dist_0_1 = np.linalg.norm( + np.array(graph.nodes[0]["pos"]) - np.array(graph.nodes[1]["pos"]) + ) + assert np.isclose(path_dist, original_dist_0_1) + + +# --- Test Environment Class Methods --- + + +# Fixtures for fitted environments using new data +@pytest.fixture +def fitted_env_square_nd(position_data_square_nd) -> Environment: + """Fixture for a fitted 2D environment using square data.""" + env = Environment(place_bin_size=3.0, infer_track_interior=True, dilate=True) + env.fit(position_data_square_nd) + return env + + +@pytest.fixture +def fitted_env_u_shape_1d(track_graph_u_shape) -> Environment: + """Fixture for a fitted 1D environment using U-shape graph.""" + graph = track_graph_u_shape + # Define a plausible edge order for linearization (e.g., 3->0->1->2) + edge_order = [(3, 0), (0, 1), (1, 2)] + edge_spacing = 0.0 + env = Environment( + track_graph=graph, + edge_order=edge_order, + edge_spacing=edge_spacing, + place_bin_size=2.0, + ) + env.fit() + return env + + +def test_fit_square_nd(fitted_env_square_nd): + """Check basic fitting results for square N-D data.""" + env = fitted_env_square_nd + assert env._is_fitted + assert not env.is_1d + assert env.place_bin_centers_ is not None + assert env.is_track_interior_ is not None + assert np.sum(env.is_track_interior_) > 0 + assert env.track_graph_nd_ is not None + assert env.track_graph_nd_.number_of_nodes() > 0 + assert env.track_graph_nd_.number_of_edges() > 0 + + +def test_fit_u_shape_1d(fitted_env_u_shape_1d): + """Check basic fitting results for U-shape 1D data.""" + env = fitted_env_u_shape_1d + assert env._is_fitted + assert env.is_1d + assert env.place_bin_centers_ is not None + assert env.is_track_interior_ is not None + assert np.all(env.is_track_interior_) # Assuming 0 spacing + assert env.track_graph_bin_centers_ is not None + assert env.track_graph_bin_centers_.number_of_nodes() > 0 + assert env.track_graph_bin_centers_.number_of_edges() > 0 + + +# --- Test Initialization --- +def test_environment_init_nd_defaults(): + """Test N-D environment initialization uses new defaults.""" + env = Environment() + # Check defaults related to boundary handling if they were added to __init__ + # If changes were only in _fit_nd, this test remains simple. + assert not env.is_1d + assert not env._is_fitted + + +# --- Test N-D Fitting --- +def test_fit_nd_default_no_boundary(position_data_2d_simple): + """Test default N-D fit has no enforced boundary.""" + env = Environment(place_bin_size=1.0, infer_track_interior=False) + env.fit(position_data_2d_simple) + assert env._is_fitted + # Expect all bins to be True by default now when not inferring + assert np.all(env.is_track_interior_) + + env_infer = Environment(place_bin_size=1.0, infer_track_interior=True) + env_infer.fit(position_data_2d_simple) + assert env_infer._is_fitted + # Check perimeter - it might be False due to data occupancy, but shouldn't be *forced* False + # A weak check: assert interior exists near the data range min/max + assert np.any(env_infer.is_track_interior_[0, :]) or np.any( + env_infer.is_track_interior_[:, 0] + ) + + +def test_get_node_pos(): + graph = nx.Graph() + graph.add_node(0, pos=(1, 2)) + graph.add_node(1) # No pos attribute + pos = _get_node_pos(graph, 0) + np.testing.assert_array_equal(pos, np.array([1, 2])) + with pytest.raises(KeyError): + _get_node_pos(graph, 1) # Missing 'pos' + with pytest.raises(KeyError): + _get_node_pos(graph, 2) # Missing node + + +def test_fit_3d(fitted_env_3d): + """Test fitting a 3D environment.""" + env = fitted_env_3d + assert env._is_fitted and not env.is_1d + assert env.place_bin_centers_ is not None and env.place_bin_centers_.shape[1] == 3 + assert env.is_track_interior_ is not None and env.is_track_interior_.ndim == 3 + assert env.centers_shape_ is not None and len(env.centers_shape_) == 3 + assert env.edges_ is not None and len(env.edges_) == 3 + assert env.track_graph_nd_ is not None + assert env.track_graph_nd_.number_of_edges() > 0 + # Check node positions are 3D + node_id = list(env.track_graph_nd_.nodes)[0] + assert len(env.track_graph_nd_.nodes[node_id]["pos"]) == 3 + + +def test_assign_region_ids_to_bins(fitted_env_square_nd): + """Test assigning regions to bins.""" + env = fitted_env_square_nd + n_bins = np.prod(env.centers_shape_) + center_coords = env.place_bin_centers_ + + # Define regions + region_defs = { + "BottomHalf": lambda coords: coords[1] < 15.0, # Function based + "TopLeftCorner": [ + 0, + 1, + env.centers_shape_[1], + env.centers_shape_[1] + 1, + ], # List based (example indices) + } + # Filter list indices to be valid + valid_indices = [idx for idx in region_defs["TopLeftCorner"] if 0 <= idx < n_bins] + region_defs["TopLeftCorner"] = valid_indices + + region_map = env.assign_region_ids_to_bins(region_defs, default_region_id="Other") + + assert region_map.shape == env.centers_shape_ + assert "Other" in region_map + assert "BottomHalf" in region_map + if valid_indices: # Only check if list wasn't empty/invalid + assert "TopLeftCorner" in region_map + + # Check some assignments + bottom_half_mask = region_map == "BottomHalf" + top_left_mask = region_map == "TopLeftCorner" + + assert np.all(center_coords[bottom_half_mask.ravel(), 1] < 15.0) + if valid_indices: + assert np.all(np.isin(np.where(top_left_mask.ravel())[0], valid_indices)) + + +@pytest.fixture +def linear_track_graph_base() -> Tuple[nx.Graph, list]: + """Base linear track graph without spacing defined yet.""" + nodes = [0, 1, 2, 3] + edges = [(0, 1), (1, 2), (2, 3)] + node_positions = {0: (0, 0), 1: (5, 0), 2: (10, 0), 3: (15, 0)} + edge_order = [(0, 1), (1, 2), (2, 3)] + + graph = nx.Graph() + graph.add_nodes_from(nodes) + graph.add_edges_from(edges) + nx.set_node_attributes(graph, node_positions, "pos") + for i, edge in enumerate(edge_order): + pos1 = np.array(node_positions[edge[0]]) + pos2 = np.array(node_positions[edge[1]]) + dist = np.linalg.norm(pos1 - pos2) + graph.edges[edge]["distance"] = dist + graph.edges[edge]["edge_id"] = i + return graph, edge_order + + +@pytest.fixture +def linear_track_graph_no_spacing( + linear_track_graph_base, +) -> Tuple[nx.Graph, list, float]: + """Linear track with zero edge spacing.""" + graph, order = linear_track_graph_base + return graph, order, 0.0 + + +@pytest.fixture +def linear_track_graph_with_spacing( + linear_track_graph_base, +) -> Tuple[nx.Graph, list, float]: + """Linear track with non-zero edge spacing.""" + graph, order = linear_track_graph_base + return graph, order, 10.0 + + +@pytest.fixture +def fitted_env_1d_no_spacing(linear_track_graph_no_spacing) -> Environment: + graph, order, spacing = linear_track_graph_no_spacing + env = Environment( + track_graph=graph, edge_order=order, edge_spacing=spacing, place_bin_size=1.0 + ) + env.fit() + return env + + +@pytest.fixture +def fitted_env_1d_with_spacing(linear_track_graph_with_spacing) -> Environment: + graph, order, spacing = linear_track_graph_with_spacing + env = Environment( + track_graph=graph, edge_order=order, edge_spacing=spacing, place_bin_size=1.0 + ) + env.fit() + return env + + +def test_fit_1d_with_spacing(fitted_env_1d_with_spacing, fitted_env_1d_no_spacing): + """Test 1D fitting with non-zero edge_spacing.""" + env_space = fitted_env_1d_with_spacing + env_no_space = fitted_env_1d_no_spacing + + assert env_space._is_fitted and env_space.is_1d + assert env_no_space._is_fitted and env_no_space.is_1d + + # Check that some bins are marked as non-interior due to spacing + assert np.sum(env_no_space.is_track_interior_) == 11 + assert np.sum(~env_no_space.is_track_interior_) == 0 + assert np.sum(~env_space.is_track_interior_) == 2 + assert np.sum(env_space.is_track_interior_) == 9 + + # Check that the total linearized length is greater with spacing + max_lin_pos_space = np.max(env_space.place_bin_centers_) + max_lin_pos_no_space = np.max(env_no_space.place_bin_centers_) + assert max_lin_pos_space > max_lin_pos_no_space + + +# --- Initialization and Serialization --- + + +def test_env_init_without_edge_order_raises(): + """Providing a track_graph without edge_order should error in __post_init__.""" + with pytest.raises(ValueError): + Environment(track_graph=nx.Graph()) + + +def test_to_dict_from_dict_roundtrip(fitted_env_square_nd): + """Ensure to_dict and from_dict restore key attributes.""" + env = fitted_env_square_nd + data = env.to_dict() + env2 = Environment.from_dict(data) + assert isinstance(env2, Environment) + # Match simple attributes + assert env2.environment_name == env.environment_name + assert env2.is_1d == env.is_1d + # Numeric arrays + np.testing.assert_allclose(env2.place_bin_centers_, env.place_bin_centers_) + assert np.array_equal(env2.is_track_interior_, env.is_track_interior_) + + +def test_save_and_load(tmp_path, fitted_env_3d): + """Saving to file and loading should preserve the object.""" + env = fitted_env_3d + file_path = tmp_path / "env.pkl" + env.save(str(file_path)) + assert file_path.exists() + loaded = Environment.load(str(file_path)) + assert isinstance(loaded, Environment) + assert loaded._is_fitted == env._is_fitted + # A few spot checks + np.testing.assert_allclose(loaded.place_bin_centers_, env.place_bin_centers_) + + +def test_get_bin_coordinates_and_dataframe(fitted_env_square_nd): + """Test get_bin_coordinates returns correct shapes and dataframe has expected cols.""" + env = fitted_env_square_nd + # sample bin indices + idx = np.array([0, 1, 2, env.place_bin_centers_.shape[0] - 1]) + coords = env.get_bin_coordinates(idx) + assert coords.shape == (4, env.place_bin_centers_.shape[1]) + + df = env.get_bin_center_dataframe() + # Should have pos_dim* and bin_ind_flat + for dim in range(env.place_bin_centers_.shape[1]): + assert f"pos_dim{dim}" in df.columns + assert df.index.name == "node_id" + assert "bin_ind_flat" in df.columns + + +def test_create_1d_track_grid_data_linear(linear_track_graph): + """Test that _create_1d_track_grid_data returns consistent bin arrays and graph.""" + graph, edge_order, edge_spacing = linear_track_graph + place_bin_size = 1.0 + centers, edges_lin, interior, centers_shape, edges_tuple, tg_centers = ( + _create_1d_track_grid_data(graph, edge_order, edge_spacing, place_bin_size) + ) + + # Shapes and types + assert isinstance(centers, np.ndarray) + assert isinstance(edges_lin, np.ndarray) + assert isinstance(interior, np.ndarray) + assert isinstance(centers_shape, tuple) + assert isinstance(edges_tuple, tuple) + assert isinstance(tg_centers, nx.Graph) + + n_bins = centers_shape[0] + # centers and interior lengths + assert centers.shape == (n_bins, 1) + assert edges_lin.shape == (n_bins + 1, 1) + assert interior.shape == (n_bins,) + # edges_tuple consistency + assert len(edges_tuple) == 1 + np.testing.assert_allclose(edges_tuple[0], edges_lin.ravel()) + + # When spacing is zero, all bins should be interior + assert interior.all() + + for node in tg_centers.nodes(): + assert "pos" in tg_centers.nodes[node] + assert np.any(np.isin(np.array(tg_centers.nodes[node]["pos"]), centers)) + assert "bin_ind_flat" in tg_centers.nodes[node] + assert "edge_id" in tg_centers.nodes[node] + assert "distance" in tg_centers.edges[node]