From 410dbf352d9e1e2b08889d780f8595f3b25a5a9c Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Thu, 16 Oct 2025 23:16:54 -0400 Subject: [PATCH 01/14] added simulator and model config exgauss --- notebooks/test_exgauss_wald.ipynb | 155 ++++++++++++++++++++++++++++ src/cssm.pyx | 55 +++++++++- ssms/config/_modelconfig/exgauss.py | 21 ++++ 3 files changed, 230 insertions(+), 1 deletion(-) create mode 100644 notebooks/test_exgauss_wald.ipynb create mode 100644 ssms/config/_modelconfig/exgauss.py diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb new file mode 100644 index 00000000..4a982f3f --- /dev/null +++ b/notebooks/test_exgauss_wald.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import ssms" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ddm',\n", + " 'ddm_st',\n", + " 'ddm_truncnormt',\n", + " 'ddm_rayleight',\n", + " 'ddm_sdv',\n", + " 'ddm_par2',\n", + " 'ddm_par2_no_bias',\n", + " 'ddm_par2_conflict_gamma_no_bias',\n", + " 'ddm_par2_angle_no_bias',\n", + " 'ddm_par2_weibull_no_bias',\n", + " 'ddm_seq2',\n", + " 'ddm_seq2_no_bias',\n", + " 'ddm_seq2_conflict_gamma_no_bias',\n", + " 'ddm_seq2_angle_no_bias',\n", + " 'ddm_seq2_weibull_no_bias',\n", + " 'ddm_mic2_adj',\n", + " 'ddm_mic2_adj_no_bias',\n", + " 'ddm_mic2_adj_conflict_gamma_no_bias',\n", + " 'ddm_mic2_adj_angle_no_bias',\n", + " 'ddm_mic2_adj_weibull_no_bias',\n", + " 'ddm_mic2_ornstein',\n", + " 'ddm_mic2_ornstein_no_bias',\n", + " 'ddm_mic2_ornstein_no_bias_no_lowdim_noise',\n", + " 'ddm_mic2_ornstein_conflict_gamma_no_bias',\n", + " 'ddm_mic2_ornstein_conflict_gamma_no_bias_no_lowdim_noise',\n", + " 'ddm_mic2_ornstein_angle_no_bias',\n", + " 'ddm_mic2_ornstein_angle_no_bias_no_lowdim_noise',\n", + " 'ddm_mic2_ornstein_weibull_no_bias',\n", + " 'ddm_mic2_ornstein_weibull_no_bias_no_lowdim_noise',\n", + " 'ddm_mic2_leak',\n", + " 'ddm_mic2_leak_no_bias',\n", + " 'ddm_mic2_leak_no_bias_no_lowdim_noise',\n", + " 'ddm_mic2_leak_conflict_gamma_no_bias',\n", + " 'ddm_mic2_leak_conflict_gamma_no_bias_no_lowdim_noise',\n", + " 'ddm_mic2_leak_angle_no_bias',\n", + " 'ddm_mic2_leak_angle_no_bias_no_lowdim_noise',\n", + " 'ddm_mic2_leak_weibull_no_bias',\n", + " 'ddm_mic2_leak_weibull_no_bias_no_lowdim_noise',\n", + " 'ddm_mic2_multinoise_no_bias',\n", + " 'ddm_mic2_multinoise_conflict_gamma_no_bias',\n", + " 'ddm_mic2_multinoise_angle_no_bias',\n", + " 'ddm_mic2_multinoise_weibull_no_bias',\n", + " 'full_ddm',\n", + " 'full_ddm_rv',\n", + " 'levy',\n", + " 'levy_angle',\n", + " 'angle',\n", + " 'weibull',\n", + " 'gamma_drift',\n", + " 'shrink_spot',\n", + " 'shrink_spot_extended',\n", + " 'shrink_spot_simple',\n", + " 'shrink_spot_simple_extended',\n", + " 'gamma_drift_angle',\n", + " 'ds_conflict_drift',\n", + " 'ds_conflict_drift_angle',\n", + " 'ds_conflict_stimflexons_drift',\n", + " 'ds_conflict_stimflexons_drift_angle',\n", + " 'ornstein',\n", + " 'ornstein_angle',\n", + " 'race_2',\n", + " 'race_no_bias_2',\n", + " 'race_no_z_2',\n", + " 'race_no_bias_angle_2',\n", + " 'race_no_z_angle_2',\n", + " 'race_3',\n", + " 'race_no_bias_3',\n", + " 'race_no_z_3',\n", + " 'race_no_bias_angle_3',\n", + " 'race_no_z_angle_3',\n", + " 'race_4',\n", + " 'race_no_bias_4',\n", + " 'race_no_z_4',\n", + " 'race_no_bias_angle_4',\n", + " 'race_no_z_angle_4',\n", + " 'dev_rlwm_lba_pw_v1',\n", + " 'dev_rlwm_lba_race_v1',\n", + " 'dev_rlwm_lba_race_v2',\n", + " 'lba2',\n", + " 'lba3',\n", + " 'lba_3_vs_constraint',\n", + " 'lba_angle_3_vs_constraint',\n", + " 'lba_angle_3',\n", + " 'lca_3',\n", + " 'lca_no_bias_3',\n", + " 'lca_no_z_3',\n", + " 'lca_no_bias_angle_3',\n", + " 'lca_no_z_angle_3',\n", + " 'lca_4',\n", + " 'lca_no_bias_4',\n", + " 'lca_no_z_4',\n", + " 'lca_no_bias_angle_4',\n", + " 'lca_no_z_angle_4',\n", + " 'tradeoff_no_bias',\n", + " 'tradeoff_angle_no_bias',\n", + " 'tradeoff_weibull_no_bias',\n", + " 'tradeoff_conflict_gamma_no_bias',\n", + " 'weibull_cdf',\n", + " 'full_ddm2',\n", + " 'ddm_legacy']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(ssms.config.model_config.keys())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ssms-venv", + "language": "python", + "name": "ssms-venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/cssm.pyx b/src/cssm.pyx index 36a1b3dd..503f23fd 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -4870,4 +4870,57 @@ def ddm_flexbound_tradeoff(np.ndarray[float, ndim = 1] vh, }} else: raise ValueError('return_option must be either "full" or "minimal"') -# ----------------------------------------------------------------------------------------------- \ No newline at end of file +# ----------------------------------------------------------------------------------------------- + + + +# Simulate (rt, choice) tuples from: EX GAUSSIAN ------------------------------------ +# @cythonboundscheck(False) +# @cythonwraparound(False) +def exgauss(np.ndarray[float, ndim = 1] mu, + np.ndarray[float, ndim = 1] sigma, + np.ndarray[float, ndim = 1] tau, + float delta_t = 0.001, + float max_t = 20, + int n_samples = 20000, + int n_trials = 1, + random_state = None, + return_option = 'full', + **kwargs): + """ Uhhhhhh thing """ + + set_seed(random_state) + + cdef float[:] mu_view = mu + cdef float[:] sigma_view = sigma + cdef float[:] tau_view = tau + + rts = np.zeros((n_samples, n_trials), dtype = DTYPE) + cdef float[:, :] rts_view = rts + + for k in range(n_trials): + for n in range(n_samples): + # Draw normal + exponential + norm_sample = random_gaussian() + norm_sample = mu_view[k] + sigma_view[k] * norm_sample + + exp_sample = tau_view[k] * random_exponential() + + rt_val = norm_sample + exp_sample + + rts_view[n, k] = rt_val + + if return_option == 'full': + return { + 'rts': rts, + 'metadata': { + 'mu': mu, 'sigma': sigma, 'tau': tau, + 'n_samples': n_samples, + 'n_trials': n_trials, + 'simulator': 'exgauss' + } + } + elif return_option == 'minimal': + return {'rts': rts, 'metadata': {'simulator': 'exgauss', 'n_samples': n_samples, 'n_trials': n_trials}} + else: + raise ValueError("return_option must be 'full' or 'minimal'") diff --git a/ssms/config/_modelconfig/exgauss.py b/ssms/config/_modelconfig/exgauss.py new file mode 100644 index 00000000..381714f2 --- /dev/null +++ b/ssms/config/_modelconfig/exgauss.py @@ -0,0 +1,21 @@ +"""DDM model configuration.""" + +import cssm +from ssms.basic_simulators import boundary_functions as bf + +def get_exgauss_config(): + """Get the configuration of the Ex-Gaussian model""" + return { + "name": "exgauss", + "params": ["mu", "sigma", "tau"], + "param_bounds": [[-50.0, 0.0, 0.0], [50.0, 50.0, 50.0]], + "boundary_name": None, + "boundary": None, + "boundary_params": [], + "n_params": 3, + "default_params": [0.5, 0.05, 0.3], + "nchoices": 1, + "choices": [None], + "n_particles": 1, + "simulator": cssm.exgauss, + } \ No newline at end of file From cc5bb79d402e86c41bfc504636c2cddc6abb9d91 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Fri, 17 Oct 2025 00:19:21 -0400 Subject: [PATCH 02/14] testing exgaussian sim --- notebooks/test_exgauss_wald.ipynb | 45 +++++++++++++++++++++++++++- src/cssm.pyx | 14 +++++---- ssms/basic_simulators/simulator.py | 4 +-- ssms/config/_modelconfig/__init__.py | 4 +++ ssms/config/_modelconfig/exgauss.py | 8 ++--- 5 files changed, 63 insertions(+), 12 deletions(-) diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index 4a982f3f..82ad9dc4 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -118,7 +118,8 @@ " 'tradeoff_conflict_gamma_no_bias',\n", " 'weibull_cdf',\n", " 'full_ddm2',\n", - " 'ddm_legacy']" + " 'ddm_legacy',\n", + " 'exgauss']" ] }, "execution_count": 2, @@ -129,6 +130,48 @@ "source": [ "list(ssms.config.model_config.keys())" ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "this is simulator output {'rts': array([[0.9175714]], dtype=float32), 'metadata': {'mu': array([0.5], dtype=float32), 'sigma': array([0.01], dtype=float32), 'tau': array([0.3], dtype=float32), 'n_samples': 1, 'n_trials': 1, 'simulator': 'exgauss'}}\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'possible_choices'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 2\u001b[39m out_list = []\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[32m100\u001b[39m):\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m sim_out = \u001b[43msimulator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mexgauss\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmu\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msigma\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtau\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0.3\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_samples\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m out_list.append(sim_out)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/oscar/home/azhan378/ssm-simulators/ssms/basic_simulators/simulator.py:680\u001b[39m, in \u001b[36msimulator\u001b[39m\u001b[34m(theta, model, n_samples, delta_t, max_t, no_noise, sigma_noise, smooth_unif, random_state)\u001b[39m\n\u001b[32m 673\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 674\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExpected simulator to return a dictionary, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(x).\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 675\u001b[39m )\n\u001b[32m 677\u001b[39m \u001b[38;5;66;03m# Postprocess simulator output ----------------------------\u001b[39;00m\n\u001b[32m 678\u001b[39m \u001b[38;5;66;03m# Additional model outputs, easy to compute:\u001b[39;00m\n\u001b[32m 679\u001b[39m \u001b[38;5;66;03m# Choice probability\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m680\u001b[39m x[\u001b[33m\"\u001b[39m\u001b[33mchoice_p\u001b[39m\u001b[33m\"\u001b[39m] = np.zeros((n_trials, \u001b[38;5;28mlen\u001b[39m(\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpossible_choices\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m)))\n\u001b[32m 681\u001b[39m x[\u001b[33m\"\u001b[39m\u001b[33mchoice_p_no_omission\u001b[39m\u001b[33m\"\u001b[39m] = np.zeros(\n\u001b[32m 682\u001b[39m (n_trials, \u001b[38;5;28mlen\u001b[39m(x[\u001b[33m\"\u001b[39m\u001b[33mmetadata\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mpossible_choices\u001b[39m\u001b[33m\"\u001b[39m]))\n\u001b[32m 683\u001b[39m )\n\u001b[32m 684\u001b[39m x[\u001b[33m\"\u001b[39m\u001b[33momission_p\u001b[39m\u001b[33m\"\u001b[39m] = np.zeros((n_trials, \u001b[32m1\u001b[39m))\n", + "\u001b[31mKeyError\u001b[39m: 'possible_choices'" + ] + } + ], + "source": [ + "from ssms.basic_simulators.simulator import simulator\n", + "out_list = []\n", + "for i in range(100):\n", + " sim_out = simulator(\n", + " model=\"exgauss\", theta={\"mu\": 0.5, \"sigma\":0.01, \"tau\": 0.3}, n_samples=1, random_state = 100,\n", + " )\n", + " out_list.append(sim_out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/cssm.pyx b/src/cssm.pyx index 503f23fd..1d5ce757 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -4895,8 +4895,9 @@ def exgauss(np.ndarray[float, ndim = 1] mu, cdef float[:] sigma_view = sigma cdef float[:] tau_view = tau - rts = np.zeros((n_samples, n_trials), dtype = DTYPE) - cdef float[:, :] rts_view = rts + rts = np.zeros((n_samples, n_trials, 1), dtype = DTYPE) + cdef float[:, :, :], : rts_view = rts + cdef int[:, :, :] choices_view = choices for k in range(n_trials): for n in range(n_samples): @@ -4908,19 +4909,22 @@ def exgauss(np.ndarray[float, ndim = 1] mu, rt_val = norm_sample + exp_sample - rts_view[n, k] = rt_val + rts_view[n, k, 0] = rt_val + choices_view[n, k, 0] = 1 if return_option == 'full': return { 'rts': rts, + 'choices': choices, 'metadata': { 'mu': mu, 'sigma': sigma, 'tau': tau, 'n_samples': n_samples, 'n_trials': n_trials, - 'simulator': 'exgauss' + 'simulator': 'exgauss', + 'possible_choices': [1], } } elif return_option == 'minimal': - return {'rts': rts, 'metadata': {'simulator': 'exgauss', 'n_samples': n_samples, 'n_trials': n_trials}} + return {'rts': rts, 'choices': choices, 'metadata': {'simulator': 'exgauss', 'n_samples': n_samples, 'n_trials': n_trials, 'possible_choices': [1]}} else: raise ValueError("return_option must be 'full' or 'minimal'") diff --git a/ssms/basic_simulators/simulator.py b/ssms/basic_simulators/simulator.py index f2f60aa2..44274276 100755 --- a/ssms/basic_simulators/simulator.py +++ b/ssms/basic_simulators/simulator.py @@ -603,7 +603,7 @@ def simulator( deadline = False model_config_local = deepcopy(model_config[model]) - + if deadline: model_config_local["params"] += ["deadline"] @@ -667,7 +667,7 @@ def simulator( **drift_dict, **sim_param_dict, ) - + print("this is simulator output", x) # Ensure x is a dictionary if not isinstance(x, dict): raise TypeError( diff --git a/ssms/config/_modelconfig/__init__.py b/ssms/config/_modelconfig/__init__.py index 0f531838..27bd4faa 100644 --- a/ssms/config/_modelconfig/__init__.py +++ b/ssms/config/_modelconfig/__init__.py @@ -123,6 +123,9 @@ get_shrink_spot_simple_extended_config, ) +from .exgauss import ( + get_exgauss_config +) def get_model_config(): """Accessor for model configurations. @@ -234,6 +237,7 @@ def get_model_config(): "weibull_cdf": get_weibull_config(), "full_ddm2": get_full_ddm_config(), "ddm_legacy": get_ddm_legacy_config(), + "exgauss": get_exgauss_config(), } diff --git a/ssms/config/_modelconfig/exgauss.py b/ssms/config/_modelconfig/exgauss.py index 381714f2..94c4e446 100644 --- a/ssms/config/_modelconfig/exgauss.py +++ b/ssms/config/_modelconfig/exgauss.py @@ -1,4 +1,4 @@ -"""DDM model configuration.""" +"""Ex Gauss model configuration.""" import cssm from ssms.basic_simulators import boundary_functions as bf @@ -9,13 +9,13 @@ def get_exgauss_config(): "name": "exgauss", "params": ["mu", "sigma", "tau"], "param_bounds": [[-50.0, 0.0, 0.0], [50.0, 50.0, 50.0]], - "boundary_name": None, - "boundary": None, + "boundary_name": 'constant', + "boundary": bf.constant, "boundary_params": [], "n_params": 3, "default_params": [0.5, 0.05, 0.3], "nchoices": 1, - "choices": [None], + "choices": [1], "n_particles": 1, "simulator": cssm.exgauss, } \ No newline at end of file From 1099c736145c4c4e7b87b01580e7efdd024fce86 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Tue, 4 Nov 2025 20:17:08 -0500 Subject: [PATCH 03/14] added shifting wald; added probability parameter to determine choice --- notebooks/test_exgauss_wald.ipynb | 104 +++++++++++++---- src/cssm.pyx | 138 +++++++++++++++++++++-- ssms/basic_simulators/simulator.py | 2 +- ssms/config/_modelconfig/exgauss.py | 12 +- ssms/config/_modelconfig/shifted_wald.py | 21 ++++ 5 files changed, 244 insertions(+), 33 deletions(-) create mode 100644 ssms/config/_modelconfig/shifted_wald.py diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index 82ad9dc4..4619b767 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -140,30 +140,51 @@ "name": "stdout", "output_type": "stream", "text": [ - "this is simulator output {'rts': array([[0.9175714]], dtype=float32), 'metadata': {'mu': array([0.5], dtype=float32), 'sigma': array([0.01], dtype=float32), 'tau': array([0.3], dtype=float32), 'n_samples': 1, 'n_trials': 1, 'simulator': 'exgauss'}}\n" + "[[[1]]\n", + "\n", + " [[1]]\n", + "\n", + " [[1]]\n", + "\n", + " ...\n", + "\n", + " [[1]]\n", + "\n", + " [[1]]\n", + "\n", + " [[1]]]\n" ] - }, + } + ], + "source": [ + "from ssms.basic_simulators.simulator import simulator\n", + "\n", + "sim_out = simulator(\n", + " model=\"exgauss\", theta={\"mu\": 0, \"sigma\":0.05, \"tau\": 0.3}, n_samples=10000, random_state = 100,\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "ename": "KeyError", - "evalue": "'possible_choices'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 2\u001b[39m out_list = []\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[32m100\u001b[39m):\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m sim_out = \u001b[43msimulator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mexgauss\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmu\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msigma\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtau\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0.3\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_samples\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m out_list.append(sim_out)\n", - "\u001b[36mFile \u001b[39m\u001b[32m/oscar/home/azhan378/ssm-simulators/ssms/basic_simulators/simulator.py:680\u001b[39m, in \u001b[36msimulator\u001b[39m\u001b[34m(theta, model, n_samples, delta_t, max_t, no_noise, sigma_noise, smooth_unif, random_state)\u001b[39m\n\u001b[32m 673\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 674\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExpected simulator to return a dictionary, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(x).\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 675\u001b[39m )\n\u001b[32m 677\u001b[39m \u001b[38;5;66;03m# Postprocess simulator output ----------------------------\u001b[39;00m\n\u001b[32m 678\u001b[39m \u001b[38;5;66;03m# Additional model outputs, easy to compute:\u001b[39;00m\n\u001b[32m 679\u001b[39m \u001b[38;5;66;03m# Choice probability\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m680\u001b[39m x[\u001b[33m\"\u001b[39m\u001b[33mchoice_p\u001b[39m\u001b[33m\"\u001b[39m] = np.zeros((n_trials, \u001b[38;5;28mlen\u001b[39m(\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpossible_choices\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m)))\n\u001b[32m 681\u001b[39m x[\u001b[33m\"\u001b[39m\u001b[33mchoice_p_no_omission\u001b[39m\u001b[33m\"\u001b[39m] = np.zeros(\n\u001b[32m 682\u001b[39m (n_trials, \u001b[38;5;28mlen\u001b[39m(x[\u001b[33m\"\u001b[39m\u001b[33mmetadata\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mpossible_choices\u001b[39m\u001b[33m\"\u001b[39m]))\n\u001b[32m 683\u001b[39m )\n\u001b[32m 684\u001b[39m x[\u001b[33m\"\u001b[39m\u001b[33momission_p\u001b[39m\u001b[33m\"\u001b[39m] = np.zeros((n_trials, \u001b[32m1\u001b[39m))\n", - "\u001b[31mKeyError\u001b[39m: 'possible_choices'" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.37832052]\n", + " [0.8623714 ]\n", + " [0.20216388]\n", + " ...\n", + " [0.5310766 ]\n", + " [0.17981271]\n", + " [0.17616363]]\n" ] } ], "source": [ - "from ssms.basic_simulators.simulator import simulator\n", - "out_list = []\n", - "for i in range(100):\n", - " sim_out = simulator(\n", - " model=\"exgauss\", theta={\"mu\": 0.5, \"sigma\":0.01, \"tau\": 0.3}, n_samples=1, random_state = 100,\n", - " )\n", - " out_list.append(sim_out)" + "print(sim_out['rts'])\n" ] }, { @@ -171,7 +192,52 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Define analytical likelihood for ex-Gaussian\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Density')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.hist(\n", + " sim_out[\"rts\"][sim_out[\"rts\"] != -999] * sim_out[\"choices\"][sim_out[\"rts\"] != -999],\n", + " bins=100,\n", + " histtype=\"step\",\n", + " color=\"black\",\n", + " label=\"Weibull Deadline\",\n", + " density=True,\n", + " alpha=0.4,\n", + ")\n", + "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", + "plt.ylabel(\"Density\")\n", + "\n" + ] } ], "metadata": { diff --git a/src/cssm.pyx b/src/cssm.pyx index 1d5ce757..26d2820b 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -4880,6 +4880,7 @@ def ddm_flexbound_tradeoff(np.ndarray[float, ndim = 1] vh, def exgauss(np.ndarray[float, ndim = 1] mu, np.ndarray[float, ndim = 1] sigma, np.ndarray[float, ndim = 1] tau, + np.ndarray[float, ndim = 1] p, # choice probability float delta_t = 0.001, float max_t = 20, int n_samples = 20000, @@ -4896,21 +4897,27 @@ def exgauss(np.ndarray[float, ndim = 1] mu, cdef float[:] tau_view = tau rts = np.zeros((n_samples, n_trials, 1), dtype = DTYPE) - cdef float[:, :, :], : rts_view = rts + choices = np.zeros((n_samples, n_trials, 1), dtype = np.intc) + cdef float[:, :, :] rts_view = rts cdef int[:, :, :] choices_view = choices - for k in range(n_trials): + for k in range(n_trials): for n in range(n_samples): # Draw normal + exponential + + random_val = rand() / float(RAND_MAX) + if random_val <= p: + choices_view[n, k, 0] = 1 + else: + choices_view[n, k, 0] = -1 + norm_sample = random_gaussian() norm_sample = mu_view[k] + sigma_view[k] * norm_sample exp_sample = tau_view[k] * random_exponential() rt_val = norm_sample + exp_sample - rts_view[n, k, 0] = rt_val - choices_view[n, k, 0] = 1 if return_option == 'full': return { @@ -4921,10 +4928,127 @@ def exgauss(np.ndarray[float, ndim = 1] mu, 'n_samples': n_samples, 'n_trials': n_trials, 'simulator': 'exgauss', - 'possible_choices': [1], + 'possible_choices': [-1, 1], + 'delta_t': delta_t, + 'max_t': max_t, + } + } + elif return_option == 'minimal': + return {'rts': rts, 'choices': choices, 'metadata': {'simulator': 'exgauss', 'n_samples': n_samples, 'n_trials': n_trials, 'possible_choices': [-1, 1]}} + else: + raise ValueError("return_option must be 'full' or 'minimal'") +# ----------------------------------------------------------------------------------------------- + + +# Simulate (rt, choice) tuples from: SHIFTED WALD ------------------------------------ +# @cythonboundscheck(False) +# @cythonwraparound(False) +def shifted_wald(np.ndarray[float, ndim = 1] gamma, + np.ndarray[float, ndim = 1] alpha, + np.ndarray[float, ndim = 1] theta, + np.ndarray[float, ndim = 1] s, # noise sigma + float delta_t = 0.001, + float max_t = 20, + int n_samples = 20000, + int n_trials = 1, + random_state = None, + return_option = 'full', + **kwargs): + """ Uhhhhhh thing for shifted wald """ + + set_seed(random_state) + + cdef float[:] gamma_view = gamma + cdef float[:] alpha_view = alpha + cdef float[:] theta_view = theta + cdef float[:] s_view = s + + traj = np.zeros((int(max_t / delta_t) + 1, 1), dtype=DTYPE) + traj[:, :] = -999 + cdef float[:, :] traj_view = traj + + rts = np.zeros((n_samples, n_trials, 1), dtype = DTYPE) + choices = np.zeros((n_samples, n_trials, 1), dtype = np.intc) + cdef float[:, :, :] rts_view = rts + cdef int[:, :, :] choices_view = choices + + cdef float delta_t_sqrt = sqrt(delta_t) + cdef float y, t_particle, smooth_u, sqrt_st + + cdef Py_ssize_t n, ix, k + cdef int m = 0 + cdef int num_draws = int(max_t / delta_t) + 1 + cdef float[:] gaussian_values = draw_gaussian(num_draws) + + + for k in range(n_trials): + sqrt_st = delta_t_sqrt * s_view[k] + + for n in range(n_samples): + y = 0.0 + t_particle = 0.0 + ix = 0 + + if n == 0: + if k == 0: + traj_view[0, 0] = y + + random_val = rand() / float(RAND_MAX) + if random_val <= p: + choices_view[n, k, 0] = 1 + else: + choices_view[n, k, 0] = -1 + + + while (y < alpha_view[k]) and (t_particle <= max_t): + y += (gamma_view[k] * delta_t) + (sqrt_st * gaussian_values[m]) + t_particle += delta_t + ix += 1 + m += 1 + if m == num_draws: + gaussian_values = draw_gaussian(num_draws) + m = 0 + + if n == 0: + if k == 0: + traj_view[ix, 0] = y + + if smooth_unif: + if t_particle == 0.0: + smooth_u = random_uniform() * 0.5 * delta_t + elif t_particle < max_t: + smooth_u = (0.5 - random_uniform()) * delta_t + else: + smooth_u = 0.0 + else: + smooth_u = 0.0 + + rts_view[n, k, 0] = t_particle + theta_view[k] + smooth_u + + if return_option == 'full': + return { + 'rts': rts, + 'choices': choices, + 'metadata': { + 'gamma': gamma, 'alpha': alpha, 'theta': theta, 's': s, + 'n_samples': n_samples, + 'n_trials': n_trials, + 'simulator': 'shifted_wald', + 'possible_choices': [-1, 1], + 'delta_t': delta_t, + 'max_t': max_t, + 'trajectory': traj, } } elif return_option == 'minimal': - return {'rts': rts, 'choices': choices, 'metadata': {'simulator': 'exgauss', 'n_samples': n_samples, 'n_trials': n_trials, 'possible_choices': [1]}} + return {'rts': rts, 'choices': choices, 'metadata': + {'simulator': 'shifted_wald', + 'n_samples': n_samples, + 'n_trials': n, + 'possible_choices': [-1, 1]}} else: - raise ValueError("return_option must be 'full' or 'minimal'") + raise ValueError("return_option must be 'full' or 'minimal'") +# ----------------------------------------------------------------------------------------------- + + + diff --git a/ssms/basic_simulators/simulator.py b/ssms/basic_simulators/simulator.py index 44274276..ea54458d 100755 --- a/ssms/basic_simulators/simulator.py +++ b/ssms/basic_simulators/simulator.py @@ -667,7 +667,7 @@ def simulator( **drift_dict, **sim_param_dict, ) - print("this is simulator output", x) + # Ensure x is a dictionary if not isinstance(x, dict): raise TypeError( diff --git a/ssms/config/_modelconfig/exgauss.py b/ssms/config/_modelconfig/exgauss.py index 94c4e446..bed0a5dd 100644 --- a/ssms/config/_modelconfig/exgauss.py +++ b/ssms/config/_modelconfig/exgauss.py @@ -7,15 +7,15 @@ def get_exgauss_config(): """Get the configuration of the Ex-Gaussian model""" return { "name": "exgauss", - "params": ["mu", "sigma", "tau"], - "param_bounds": [[-50.0, 0.0, 0.0], [50.0, 50.0, 50.0]], + "params": ["mu", "sigma", "tau", "p"], + "param_bounds": [[-50.0, 0.0, 0.0, 0.0], [50.0, 50.0, 50.0, 1.0]], "boundary_name": 'constant', "boundary": bf.constant, "boundary_params": [], - "n_params": 3, - "default_params": [0.5, 0.05, 0.3], - "nchoices": 1, - "choices": [1], + "n_params": 4, + "default_params": [0.5, 0.05, 0.3, 0.5], + "nchoices": 2, + "choices": [-1, 1], "n_particles": 1, "simulator": cssm.exgauss, } \ No newline at end of file diff --git a/ssms/config/_modelconfig/shifted_wald.py b/ssms/config/_modelconfig/shifted_wald.py new file mode 100644 index 00000000..922c5906 --- /dev/null +++ b/ssms/config/_modelconfig/shifted_wald.py @@ -0,0 +1,21 @@ +"""Shifted Wald model configuration.""" + +import cssm +from ssms.basic_simulators import boundary_functions as bf + +def get_shifted_wald_config(): + """Get the configuration of the Shifted Wald model""" + return { + "name": "shifted_wald", + "params": ["gamma", "alpha", "theta", "p"], + "param_bounds": [[0.0, 0.3, 0.0, 0.0], [5, 2.5, 2.0, 1.0]], + "boundary_name": 'constant', + "boundary": bf.constant, + "boundary_params": [], + "n_params": 4, + "default_params": [2.0, 1, 0.0, 0.5], + "nchoices": 2, + "choices": [-1, 1], + "n_particles": 1, + "simulator": cssm.shifted_wald, + } \ No newline at end of file From f6d8b3eb204d45dc0b3a95af8fe0571f86746392 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Tue, 4 Nov 2025 21:48:54 -0500 Subject: [PATCH 04/14] simulators for both exgauss and shifted wald are working --- notebooks/test_exgauss_wald.ipynb | 108 +++++++++++++++++++-------- src/cssm.pyx | 14 ++-- ssms/config/_modelconfig/__init__.py | 5 ++ 3 files changed, 92 insertions(+), 35 deletions(-) diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index 4619b767..1dad2b0e 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -119,7 +119,8 @@ " 'weibull_cdf',\n", " 'full_ddm2',\n", " 'ddm_legacy',\n", - " 'exgauss']" + " 'exgauss',\n", + " 'shifted_wald']" ] }, "execution_count": 2, @@ -133,58 +134,65 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from ssms.basic_simulators.simulator import simulator\n", + "\n", + "sim_out = simulator(\n", + " model=\"exgauss\", theta={\"mu\": 0.5, \"sigma\":0.05, \"tau\": 0.3, \"p\": 0}, n_samples=10000, random_state = 100,\n", + " )\n", + "sim_out_2 = simulator(\n", + " model=\"shifted_wald\", theta={\"gamma\": 2.0, \"alpha\":1, \"theta\": 0.0, \"p\": 0.8}, n_samples=10000, random_state = 100,\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[[1]]\n", - "\n", - " [[1]]\n", - "\n", - " [[1]]\n", - "\n", + "[[0.6775269 ]\n", + " [0.47768405]\n", + " [0.99526304]\n", " ...\n", - "\n", - " [[1]]\n", - "\n", - " [[1]]\n", - "\n", - " [[1]]]\n" + " [0.70154023]\n", + " [0.4867602 ]\n", + " [1.0765221 ]]\n" ] } ], "source": [ - "from ssms.basic_simulators.simulator import simulator\n", - "\n", - "sim_out = simulator(\n", - " model=\"exgauss\", theta={\"mu\": 0, \"sigma\":0.05, \"tau\": 0.3}, n_samples=10000, random_state = 100,\n", - " )\n" + "print(sim_out['rts'])\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.37832052]\n", - " [0.8623714 ]\n", - " [0.20216388]\n", + "[[1.2415992 ]\n", + " [0.3367004 ]\n", + " [0.29839447]\n", " ...\n", - " [0.5310766 ]\n", - " [0.17981271]\n", - " [0.17616363]]\n" + " [0.25902444]\n", + " [0.37568384]\n", + " [0.4568465 ]]\n" ] } ], "source": [ - "print(sim_out['rts'])\n" + "print(sim_out_2['rts'])" ] }, { @@ -199,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -208,13 +216,13 @@ "Text(0, 0.5, 'Density')" ] }, - "execution_count": 5, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -238,6 +246,46 @@ "plt.ylabel(\"Density\")\n", "\n" ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Density')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAG2CAYAAACDLKdOAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAPeVJREFUeJzt3X1cVHXe//E3MDIjjkCmAhqGmpbdIelq6u6mRqIWaVa41ZVIane4mVy2RaVmXUV33tVl2ZY3a5uplVq7lkoQWcpmapbdWd5CJt6siwg5oHB+f/hzLgcGBAQOc3g9H4/zeDjf+Z4znzmMzJvv+Z5z/AzDMAQAAGAR/mYXAAAAUJcINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFIINwAAwFJMDTfr1q1TfHy82rVrJz8/P61cufKs6xQXF+uxxx7ThRdeKLvdrqioKM2fP7/+iwUAAD7BZuaLFxUVKTo6WnfddZdGjBhRrXUSEhJ04MABzZs3TxdddJH279+vsrKyeq4UAAD4ClPDzZAhQzRkyJBq91+9erU+/fRT7dq1S61atZIkRUVF1VN1AADAF5kabmrqgw8+UM+ePfX888/rzTffVIsWLXTjjTfqqaeeUvPmzb2uU1xcrOLiYvfjsrIyHTlyROeff778/PwaqnQAAHAODMPQsWPH1K5dO/n7Vz2rxqfCza5du/T555/L4XBoxYoVOnz4sO6//379+9//1oIFC7yuk5aWpmnTpjVwpQAAoD7k5ubqggsuqLKPn2EYRgPVUyU/Pz+tWLFCw4cPr7TPoEGD9NlnnykvL08hISGSpOXLl+uWW25RUVGR19Gb8iM3R48eVYcOHZSbm6vg4OA6fx8AAKDuFRQUKDIyUvn5+e4MUBmfGrmJiIhQ+/btPd5Ut27dZBiGfvnlF3Xp0qXCOna7XXa7vUJ7cHAw4QYAAB9TnSklPnWdm379+unXX39VYWGhu+2nn36Sv7//WYeoAABA02BquCksLNTWrVu1detWSdLu3bu1detW5eTkSJJSU1M1atQod//bb79d559/vpKSkvT9999r3bp1euihh3TXXXdVOqEYAAA0LaaGm02bNikmJkYxMTGSpJSUFMXExGjKlCmSpP3797uDjiQ5nU6lp6crPz9fPXv21B133KH4+Hi99NJLptQPAAAan0YzobihFBQUKCQkREePHmXODQAAPqIm398+NecGAADgbAg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUmxmFwCgaSosLJTL5XI/djgccjqdJlYEwCoINwAaXGFhoZYtW6aTJ0+622w2mxISEgg4AM4Z4QZAg3O5XDp58qQGDhyo0NBQ5efnKzMzUy6Xi3AD4JwRbgCYJjQ0VK1btza7DAAWw4RiAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKTazCwCA0/Lz8z0eOxwOOZ1Oc4oB4LMINwBM53A4ZLPZlJmZ6dFus9mUkJBAwAFQI4QbAKZzOp1KSEiQy+Vyt+Xn5yszM1Mul4twA6BGCDcAGgWn00mIAVAnTJ1QvG7dOsXHx6tdu3by8/PTypUrq73u+vXrZbPZ1L1793qrDwAA+B5Tw01RUZGio6M1Z86cGq2Xn5+vUaNG6dprr62nygAAgK8y9bDUkCFDNGTIkBqvd++99+r2229XQEBAjUZ7AACA9fncdW4WLFigXbt2aerUqdXqX1xcrIKCAo8FAABYl0+Fm59//lmPPPKI/v73v8tmq96gU1pamkJCQtxLZGRkPVcJAADM5DPhprS0VLfffrumTZumrl27Vnu91NRUHT161L3k5ubWY5UAAMBsPnMq+LFjx7Rp0yZ99dVXGj9+vCSprKxMhmHIZrNp7dq1GjhwYIX17Ha77HZ7Q5cLAABM4jPhJjg4WNu2bfNoe+WVV5SZmal3331XHTt2NKkyAADQmJgabgoLC7Vjxw734927d2vr1q1q1aqVOnTooNTUVO3bt0+LFi2Sv7+/Lr/8co/127ZtK4fDUaEdAAA0XaaGm02bNmnAgAHuxykpKZKkxMRELVy4UPv371dOTo5Z5QEAAB9karjp37+/DMOo9PmFCxdWuf4TTzyhJ554om6LAgAAPs1nzpYCAACoDsINAACwFMINAACwFMINAACwFMINAACwFMINAACwFJ+5QjGApik/P9/9b4fDIafTaV4xAHwC4QZAo+RwOGSz2ZSZmelus9lsSkhIIOAAqBLhBkCj5HQ6lZCQIJfLJenUCE5mZqZcLhfhBkCVCDcAGi2n00mQAVBjTCgGAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWYmq4WbduneLj49WuXTv5+flp5cqVVfZfvny5rrvuOrVp00bBwcHq06eP1qxZ0zDFAgAAn2BquCkqKlJ0dLTmzJlTrf7r1q3Tddddpw8//FCbN2/WgAEDFB8fr6+++qqeKwUAAL7CZuaLDxkyREOGDKl2/1mzZnk8fuaZZ/T+++/rH//4h2JiYuq4OgAA4ItMDTfnqqysTMeOHVOrVq3MLgVAFQoLC+VyudyP8/PzzSsGgOX5dLh58cUXVVhYqISEhEr7FBcXq7i42P24oKCgIUoD8P8VFhZq2bJlOnnypEe7zWaTw+EwqSoAVuaz4Wbx4sWaNm2a3n//fbVt27bSfmlpaZo2bVoDVgbgTC6XSydPntTAgQMVGhrqbnc4HHI6neYVBsCyfPJU8CVLlmjs2LFatmyZYmNjq+ybmpqqo0ePupfc3NwGqhLAmUJDQ9W6dWv3QrABUF98buTm7bff1l133aUlS5bo+uuvP2t/u90uu93eAJUBAIDGwNRwU1hYqB07drgf7969W1u3blWrVq3UoUMHpaamat++fVq0aJGkU4eiEhMTNXv2bPXu3Vt5eXmSpObNmyskJMSU9wAAABoXUw9Lbdq0STExMe7TuFNSUhQTE6MpU6ZIkvbv36+cnBx3/7/+9a86efKkkpOTFRER4V4mTJhgSv0AAKDxMXXkpn///jIMo9LnFy5c6PE4KyurfgsCAAA+zycnFAMAAFSGcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACzFZnYBAFAT+fn5Ho8dDoecTqc5xQBolAg3AHyCw+GQzWZTZmamR7vNZlNCQgIBB4Ab4QaAT3A6nUpISJDL5XK35efnKzMzUy6Xi3ADwI1wA8BnOJ1OQgyAs2JCMQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBRTw826desUHx+vdu3ayc/PTytXrjzrOllZWbrqqqtkt9t10UUXaeHChfVeJwAA8B2mhpuioiJFR0drzpw51eq/e/duXX/99RowYIC2bt2qBx98UGPHjtWaNWvquVIAAOArbGa++JAhQzRkyJBq9587d646duyo6dOnS5K6deumzz//XDNnzlRcXFx9lQkAAHyIT825yc7OVmxsrEdbXFycsrOzK12nuLhYBQUFHgsAALAunwo3eXl5CgsL82gLCwtTQUGBjh8/7nWdtLQ0hYSEuJfIyMiGKBUAAJjEp8JNbaSmpuro0aPuJTc31+ySAABAPTJ1zk1NhYeH68CBAx5tBw4cUHBwsJo3b+51HbvdLrvd3hDlAQCARsCnRm769OmjjIwMj7b09HT16dPHpIoAAEBjY2q4KSws1NatW7V161ZJp0713rp1q3JyciSdOqQ0atQod/97771Xu3bt0l/+8hf9+OOPeuWVV7Rs2TJNnDjRjPIBAEAjZGq42bRpk2JiYhQTEyNJSklJUUxMjKZMmSJJ2r9/vzvoSFLHjh21atUqpaenKzo6WtOnT9cbb7zBaeAAAMDN1Dk3/fv3l2EYlT7v7erD/fv311dffVWPVQEAAF/mU3NuAAAAzoZwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALMXUu4IDQF3Iz893/9vhcMjpdJpXDADTEW4A+CyHwyGbzabMzEx3m81mU0JCAgEHaMIINwB8ltPpVEJCglwul6RTIziZmZlyuVyEG6AJq1W42bVrlzp16lTXtQBAjTmdToIMAA+1mlB80UUXacCAAfr73//u/osJAACgMahVuNmyZYuuvPJKpaSkKDw8XPfcc482btxY17UBAADUWK3CTffu3TV79mz9+uuvmj9/vvbv36/f//73uvzyyzVjxgwdOnSorusEAAColnO6zo3NZtOIESP0zjvv6LnnntOOHTs0adIkRUZGatSoUdq/f39d1QkAAFAt5xRuNm3apPvvv18RERGaMWOGJk2apJ07dyo9PV2//vqrhg0bVld1AgAAVEutzpaaMWOGFixYoO3bt2vo0KFatGiRhg4dKn//U1mpY8eOWrhwoaKiouqyVgAAgLOqVbh59dVXddddd2n06NGKiIjw2qdt27aaN2/eORUHAABQU7UKN+np6erQoYN7pOY0wzCUm5urDh06KDAwUImJiXVSJAAAQHXVas5N586ddfjw4QrtR44cUceOHc+5KAAAgNqqVbgxDMNre2FhoRwOxzkVBAAAcC5qdFgqJSVFkuTn56cpU6YoKCjI/Vxpaam++OILde/evU4LBAAAqIkahZuvvvpK0qmRm23btikwMND9XGBgoKKjozVp0qS6rRAAAKAGahRuPvnkE0lSUlKSZs+ereDg4HopCgAAoLZqdbbUggUL6roOAACAOlHtcDNixAgtXLhQwcHBGjFiRJV9ly9ffs6FAQAA1Ea1w01ISIj8/Pzc/wYAAGiMqh1uzjwUxWEpAADQWNXqOjfHjx/Xb7/95n68d+9ezZo1S2vXrq2zwgAAAGqjVuFm2LBhWrRokSQpPz9fvXr10vTp0zVs2DC9+uqrNd7enDlzFBUVJYfDod69e2vjxo1V9p81a5YuvvhiNW/eXJGRkZo4caJcLldt3goAALCYWoWbLVu26A9/+IMk6d1331V4eLj27t2rRYsW6aWXXqrRtpYuXaqUlBRNnTpVW7ZsUXR0tOLi4nTw4EGv/RcvXqxHHnlEU6dO1Q8//KB58+Zp6dKlevTRR2vzVgAAgMXUKtz89ttvatmypSRp7dq1GjFihPz9/XX11Vdr7969NdrWjBkzNG7cOCUlJenSSy/V3LlzFRQUpPnz53vtv2HDBvXr10+33367oqKiNGjQIN12221nHe0BAABNQ63CzUUXXaSVK1cqNzdXa9as0aBBgyRJBw8erNGF/UpKSrR582bFxsb+X0H+/oqNjVV2drbXdfr27avNmze7w8yuXbv04YcfaujQoV77FxcXq6CgwGMBAADWVatwM2XKFE2aNElRUVHq3bu3+vTpI+nUKE5MTEy1t3P48GGVlpYqLCzMoz0sLEx5eXle17n99tv15JNP6ve//72aNWumzp07q3///pUelkpLS1NISIh7iYyMrHZ9AADA99Qq3Nxyyy3KycnRpk2btHr1anf7tddeq5kzZ9ZZcd5kZWXpmWee0SuvvKItW7Zo+fLlWrVqlZ566imv/VNTU3X06FH3kpubW6/1AQAAc9Xq9guSFB4ervDwcI+2Xr161WgbrVu3VkBAgA4cOODRfuDAgQrbPm3y5Mm68847NXbsWEnSFVdcoaKiIt1999167LHH5O/vmdfsdrvsdnuN6gIAAL6rViM3RUVFmjx5svr27auLLrpInTp18liqKzAwUD169FBGRoa7raysTBkZGe5DXeX99ttvFQJMQECApFN3KwcAAE1brUZuxo4dq08//VR33nmnIiIi3LdlqI2UlBQlJiaqZ8+e6tWrl2bNmqWioiIlJSVJkkaNGqX27dsrLS1NkhQfH68ZM2YoJiZGvXv31o4dOzR58mTFx8e7Qw4AAGi6ahVuPvroI61atUr9+vU75wJGjhypQ4cOacqUKcrLy1P37t21evVq9yTjnJwcj5Gaxx9/XH5+fnr88ce1b98+tWnTRvHx8Xr66afPuRYAAOD7ahVuzjvvPLVq1arOihg/frzGjx/v9bmsrCyPxzabTVOnTtXUqVPr7PUBAIB11GrOzVNPPaUpU6Z43F8KAACgMajVyM306dO1c+dOhYWFKSoqSs2aNfN4fsuWLXVSHAAAQE3VKtwMHz68jssAAACoG7UKN8x3AQAAjVWt5txIUn5+vt544w2lpqbqyJEjkk4djtq3b1+dFQcAAFBTtRq5+eabbxQbG6uQkBDt2bNH48aNU6tWrbR8+XLl5ORo0aJFdV0nAABAtdRq5CYlJUWjR4/Wzz//LIfD4W4fOnSo1q1bV2fFAQAA1FStws2XX36pe+65p0J7+/btK72bNwAAQEOoVbix2+0qKCio0P7TTz+pTZs251wUAABAbdUq3Nx444168skndeLECUmSn5+fcnJy9PDDD+vmm2+u0wIBoKby8/N1+PBh91JYWGh2SQAaUK0v4nfLLbeoTZs2On78uK655hrl5eWpT58+3OMJgGkcDodsNpsyMzM92m02mxISEuR0Ok2qDEBDqlW4CQkJUXp6utavX6+vv/5ahYWFuuqqqxQbG1vX9QFAtTmdTiUkJMjlcrnb8vPzlZmZKZfLRbgBmogah5uysjItXLhQy5cv1549e+Tn56eOHTsqPDxchmHIz8+vPuoEgGpxOp2EGKCJq9GcG8MwdOONN2rs2LHat2+frrjiCl122WXau3evRo8erZtuuqm+6gQAAKiWGo3cLFy4UOvWrVNGRoYGDBjg8VxmZqaGDx+uRYsWadSoUXVaJAAAQHXVaOTm7bff1qOPPloh2EjSwIED9cgjj+itt96qs+IAAABqqkbh5ptvvtHgwYMrfX7IkCH6+uuvz7koAACA2qpRuDly5IjCwsIqfT4sLEz/+c9/zrkoAACA2qpRuCktLZXNVvk0nYCAAJ08efKciwIAAKitGk0oNgxDo0ePlt1u9/p8cXFxnRQFAABQWzUKN4mJiWftw5lSAADATDUKNwsWLKivOgAAAOpErW6cCQAA0FgRbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKUQbgAAgKXU6CJ+AOCr8vPz3f92OBxyOp3mFQOgXhFuAFiaw+GQzWZTZmamu81msykhIYGAA1gU4QaApTmdTiUkJMjlckk6NYKTmZkpl8tFuAEsinADwPKcTidBBmhCmFAMAAAspVGEmzlz5igqKkoOh0O9e/fWxo0bq+yfn5+v5ORkRUREyG63q2vXrvrwww8bqFoAANCYmX5YaunSpUpJSdHcuXPVu3dvzZo1S3Fxcdq+fbvatm1boX9JSYmuu+46tW3bVu+++67at2+vvXv3KjQ0tOGLBwAAjY7p4WbGjBkaN26ckpKSJElz587VqlWrNH/+fD3yyCMV+s+fP19HjhzRhg0b1KxZM0lSVFRUQ5YMAAAaMVMPS5WUlGjz5s2KjY11t/n7+ys2NlbZ2dle1/nggw/Up08fJScnKywsTJdffrmeeeYZlZaWeu1fXFysgoICjwUAAFiXqeHm8OHDKi0tVVhYmEd7WFiY8vLyvK6za9cuvfvuuyotLdWHH36oyZMna/r06fqf//kfr/3T0tIUEhLiXiIjI+v8fQAAgMajUUworomysjK1bdtWf/3rX9WjRw+NHDlSjz32mObOneu1f2pqqo4ePepecnNzG7hiAADQkEydc9O6dWsFBATowIEDHu0HDhxQeHi413UiIiLUrFkzBQQEuNu6deumvLw8lZSUKDAw0KO/3W6X3W6v++IBAECjZOrITWBgoHr06KGMjAx3W1lZmTIyMtSnTx+v6/Tr1087duxQWVmZu+2nn35SREREhWADAACaHtMPS6WkpOj111/X3/72N/3www+67777VFRU5D57atSoUUpNTXX3v++++3TkyBFNmDBBP/30k1atWqVnnnlGycnJZr0FAADQiJh+KvjIkSN16NAhTZkyRXl5eerevbtWr17tnmSck5Mjf///y2CRkZFas2aNJk6cqCuvvFLt27fXhAkT9PDDD5v1FgAAQCNieriRpPHjx2v8+PFen8vKyqrQ1qdPH/3rX/+q56oAAIAvMv2wFAAAQF0i3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEtpFNe5AYCGlp+f7/HY4XDI6XSaUwyAOkW4AdCkOBwO2Ww2ZWZmerTbbDYlJCQQcAALINwAaFKcTqcSEhLkcrncbfn5+crMzJTL5SLcABZAuAHQ5DidTkIMYGFMKAYAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJbCXcEB1LnCwkK5XC5JUn5+vrnFAGhyCDcA6lRhYaGWLVumkydPuttsNpscDoeJVQFoSgg3AOqUy+XSyZMnNXDgQIWGhkqSHA6HnE6nuYUBaDIINwDqRWhoqFq3bm12GQCaICYUAwAASyHcAAAAS+GwFAD8f2ee2cU8IcB3EW4ANHkOh0M2m02ZmZnuNpvNpoSEBAIO4IMINwCaPKfTqYSEBI9r82RmZsrlchFuAB9EuAEAnQo4BBnAGphQDAAALKVRhJs5c+YoKipKDodDvXv31saNG6u13pIlS+Tn56fhw4fXb4EAAMBnmB5uli5dqpSUFE2dOlVbtmxRdHS04uLidPDgwSrX27NnjyZNmqQ//OEPDVQpAADwBaaHmxkzZmjcuHFKSkrSpZdeqrlz5yooKEjz58+vdJ3S0lLdcccdmjZtmjp16tSA1QIAgMbO1HBTUlKizZs3KzY21t3m7++v2NhYZWdnV7rek08+qbZt22rMmDFnfY3i4mIVFBR4LAAAwLpMDTeHDx9WaWmpwsLCPNrDwsKUl5fndZ3PP/9c8+bN0+uvv16t10hLS1NISIh7iYyMPOe6AQBA42X6YamaOHbsmO688069/vrr1b4hX2pqqo4ePepecnNz67lKwHyFhYU6fPiwx1JYWGh2WQDQIEy9zk3r1q0VEBCgAwcOeLQfOHBA4eHhFfrv3LlTe/bsUXx8vLutrKxM0qmriW7fvl2dO3f2WMdut8tut9dD9UDjVFhYqGXLlunkyZMe7VxxF0BTYWq4CQwMVI8ePZSRkeE+nbusrEwZGRkaP358hf6XXHKJtm3b5tH2+OOP69ixY5o9ezaHnABJLpdLJ0+e1MCBAxUaGirJ+xV3CwsL3VfkPY37KQGwAtOvUJySkqLExET17NlTvXr10qxZs1RUVKSkpCRJ0qhRo9S+fXulpaXJ4XDo8ssv91j/9C/v8u1AUxcaGlrp4VtGdwBYmenhZuTIkTp06JCmTJmivLw8de/eXatXr3ZPMs7JyZG/v09NDQIaveqO7gCALzI93EjS+PHjvR6GkqSsrKwq1124cGHdFwQ0EVWN7gCAr2JIBAAAWArhBgAAWArhBgAAWArhBgAAWEqjmFAMAI1Rfn6+x2OuAwT4BsINAJTjcDhks9mUmZnp0c51gADfQLgBgHKcTqcSEhI8ruDMdYAA30G4AZqQ04dZyh9uQUVOp5MQA/gowg3QBHg7zGKz2eRwOEysCgDqB+EGaAK8HWZhciwAqyLcAE0Eh1kANBVc5wYAAFgKIzcAUANnTsbm0B7QOBFuAKAaKpuUzXVvgMaHcAMA1VB+UjbXvQEaL8INAFQTk7IB38CEYgAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCncWwqwgMLCQo8bOgJAU0a4AXxcYWGhli1bppMnT7rbbDabHA5HrbZ3ZjhyOBzcKBKAzyHcAD7O5XLp5MmTGjhwoEJDQyXVLpQ4HA7ZbDZlZma622w2mxISEgg4AHwK4QawiNDQULVu3brW6zudTiUkJHgc3srMzJTL5SLcAPAphBvAx5w5v0aq2zk2TqeTIAPA5xFuAB/ibX6NdG5zbHBuyodL5ikB5iPcAD7E2/waiS9UM3iboyQxTwloDAg3gA861/k1OHfl5yhJzFMCGotGcRG/OXPmKCoqSg6HQ71799bGjRsr7fv666/rD3/4g8477zydd955io2NrbI/ANQXp9Op1q1bu5czR9MAmMf0cLN06VKlpKRo6tSp2rJli6KjoxUXF6eDBw967Z+VlaXbbrtNn3zyibKzsxUZGalBgwZp3759DVw5AABojEwPNzNmzNC4ceOUlJSkSy+9VHPnzlVQUJDmz5/vtf9bb72l+++/X927d9cll1yiN954Q2VlZcrIyGjgygEAQGNkargpKSnR5s2bFRsb627z9/dXbGyssrOzq7WN3377TSdOnFCrVq3qq0wAAOBDTJ1QfPjwYZWWliosLMyjPSwsTD/++GO1tvHwww+rXbt2HgHpTMXFxSouLnY/LigoqH3BAACg0fPps6WeffZZLVmyRFlZWZVe4yMtLU3Tpk1r4MqAusNNMX0P9+cCzGVquGndurUCAgJ04MABj/YDBw4oPDy8ynVffPFFPfvss/r444915ZVXVtovNTVVKSkp7scFBQWKjIw8t8KBBlLXN8VE/eL+XEDjYGq4CQwMVI8ePZSRkaHhw4dLknty8Pjx4ytd7/nnn9fTTz+tNWvWqGfPnlW+ht1ul91ur8uygQZTVzfFRMPg/lxA42D6YamUlBQlJiaqZ8+e6tWrl2bNmqWioiIlJSVJkkaNGqX27dsrLS1NkvTcc89pypQpWrx4saKiopSXlyeJe+LA2rhon+/gdxFgPtPDzciRI3Xo0CFNmTJFeXl56t69u1avXu2eZJyTkyN///87qevVV19VSUmJbrnlFo/tTJ06VU888URDlg4AABoh08ONJI0fP77Sw1BZWVkej/fs2VP/BQEAAJ9l+kX8AAAA6hLhBgAAWEqjOCwFAFZW/vpEnPEG1C/CDQDUE2/XvZG49g1Q3wg3AFBPyl/3RuLaN0BDINwAQD2q7Lo33KIBqD+EG8BEZ943SuJLringFg1A/SPcACap7L5RfMlZG7doAOof4QYwSfn7RvEl13RwiwagfhFuAJNx3ygAqFuEG6CROXOiafnrowAAzo5wAzQSVV0TxeFwmFQVAPgewg3QSHi7JorEGVQAUFOEG6ARYaIpAJw7bpwJAAAshZEboIGUv2Afk4UBoH4QboAG4O2CfRKThQGgPhBugAZQ/oJ9pzFZGKeVH8njswHUHuEGaEBcsA/lVXUJgEGDBnmM7BF4gOoh3AC1wA0vUVe8XQLA5XJp7dq1+vDDDz36cu8xoHoIN0ANVfeGl2cGICYPoyreLgFQPvBw7zGg+gg3QA1VdsPLvLw893ya0395lw9ATB5GdVV2zaMzgzIjhoB3hBuglk7Pn6lqzsTQoUPdgYYvIpwLb58zDlMB3hFugHPEbRPQEMp/zryNGEp87gCJcAPUCSvfNoFTlBuPMz9nVY0YMpqDpo5wA8ArvjwbN28jhkw6Bk4h3ADlcJr3KXx5Nn5WHjEEzgXhBjhDZad5n3kxtaZ0Wjdfnr6vfFiXmm5gR9NBuAHOUP4076oupsZp3WisTgdwb5ckkCoGdsIOrIZwA3hx5m0SOBMKvqKy08XPvCSBt8DOPCpYDeEGTVr5IXtvh5w4NANf4W2elLcg7u2UcuZRwUoIN2iyvM2vkTjkBN9WnTBenT5MrIcvI9ygSSl/v6cz59ecxi9xNEVnjlpWdvsQDl3BVxBu0GRUdiZUeHg4v7DRZFXn9iGVXQ3Z27b4v4TGgHADy/B2yuuZvI3U8MsYTV11bh9SWQAqj9EdNBaEG/iEswWXyk55LY+RGqCis83BqSwAnYmJyWhMGkW4mTNnjl544QXl5eUpOjpaL7/8snr16lVp/3feeUeTJ0/Wnj171KVLFz333HMaOnRoA1aM6qrOpMS6DC5nnvLqDSM1da86Z5zB99X2rMGz/f+WKv6/rM06wJlMDzdLly5VSkqK5s6dq969e2vWrFmKi4vT9u3b1bZt2wr9N2zYoNtuu01paWm64YYbtHjxYg0fPlxbtmzR5ZdfbsI7QGUqm+Ny5rB1ZWcslUdwaZw44wzlnW1isjdnXlSwJuvU5hAYZ4E1DaaHmxkzZmjcuHFKSkqSJM2dO1erVq3S/Pnz9cgjj1ToP3v2bA0ePFgPPfSQJOmpp55Senq6/vd//1dz585t0Nq9Mfs/TnX+4imvNqMp1VF+jou3SYmVnbFUnRphvvJXdD6Nn1fTU52Jyd5UdlHBqtap7gTnyl6rqtur1KXqjEjVZtSqOq/VkBrjLT5MDTclJSXavHmzUlNT3W3+/v6KjY1Vdna213Wys7OVkpLi0RYXF6eVK1d67V9cXKzi4mL346NHj0qSCgoKzrH6igoLC7VixYoK/3EGDhzYIH/FulwuZWZmnvUvnvLK11jb7VS27aCgIAUGBiooKEgnTpzQqlWrKu1TmbKysnr5maFmjh07puPHjys3N1fHjh1Tfn6+jh8/roCAAI+fHz+vpmnw4MEev28lyW63V/klFxgYWGG9s61T2e+S6jjz993p33XvvfdejbdT29fyNsp5tj41fa2GVNX7uummm+o04Jz+nWIYxtk7Gybat2+fIcnYsGGDR/tDDz1k9OrVy+s6zZo1MxYvXuzRNmfOHKNt27Ze+0+dOtWQxMLCwsLCwmKBJTc396z5wvTDUvUtNTXVY6SnrKxMR44c0fnnn69jx44pMjJSubm5Cg4ONrHKxqWgoID9Ug77xDv2S0XsE+/YLxWxT7yrbL8YhqFjx46pXbt2Z92GqeGmdevWCggI0IEDBzzaDxw4oPDwcK/rhIeH16i/3W6X3W73aDt9jNbPz0+SFBwczAfLC/ZLRewT79gvFbFPvGO/VMQ+8c7bfgkJCanWuv71UVB1BQYGqkePHsrIyHC3lZWVKSMjQ3369PG6Tp8+fTz6S1J6enql/QEAQNNi+mGplJQUJSYmqmfPnurVq5dmzZqloqIi99lTo0aNUvv27ZWWliZJmjBhgq655hpNnz5d119/vZYsWaJNmzbpr3/9q5lvAwAANBKmh5uRI0fq0KFDmjJlivLy8tS9e3etXr1aYWFhkqScnBz5+//fAFPfvn21ePFiPf7443r00UfVpUsXrVy5slbXuLHb7Zo6dWqFw1ZNHfulIvaJd+yXitgn3rFfKmKfeFcX+8XPMKpzThUAAIBvMHXODQAAQF0j3AAAAEsh3AAAAEsh3AAAAEsh3JzhxhtvVIcOHeRwOBQREaE777xTv/76q9llmWbPnj0aM2aMOnbsqObNm6tz586aOnWqSkpKzC7NVE8//bT69u2roKCgGt20z2rmzJmjqKgoORwO9e7dWxs3bjS7JFOtW7dO8fHxateunfz8/Cq9311TkpaWpt/97ndq2bKl2rZtq+HDh2v79u1ml2W6V199VVdeeaX7InV9+vTRRx99ZHZZjcqzzz4rPz8/Pfjgg7Van3BzhgEDBmjZsmXavn273nvvPe3cuVO33HKL2WWZ5scff1RZWZlee+01fffdd5o5c6bmzp2rRx991OzSTFVSUqJbb71V9913n9mlmGbp0qVKSUnR1KlTtWXLFkVHRysuLk4HDx40uzTTFBUVKTo6WnPmzDG7lEbj008/VXJysv71r38pPT1dJ06c0KBBg1RUVGR2aaa64IIL9Oyzz2rz5s3atGmTBg4cqGHDhum7774zu7RG4csvv9Rrr72mK6+8svYbqc4NLpuq999/3/Dz8zNKSkrMLqXReP75542OHTuaXUajsGDBAiMkJMTsMkzRq1cvIzk52f24tLTUaNeunZGWlmZiVY2HJGPFihVml9HoHDx40JBkfPrpp2aX0uicd955xhtvvGF2GaY7duyY0aVLFyM9Pd245pprjAkTJtRqO4zcVOLIkSN666231LdvXzVr1szschqNo0ePqlWrVmaXAROVlJRo8+bNio2Ndbf5+/srNjZW2dnZJlaGxu7o0aOSxO+QM5SWlmrJkiUqKiriNkKSkpOTdf3113v8fqkNwk05Dz/8sFq0aKHzzz9fOTk5ev/9980uqdHYsWOHXn75Zd1zzz1mlwITHT58WKWlpe6riJ8WFhamvLw8k6pCY1dWVqYHH3xQ/fr1q9UV5a1m27Ztcjqdstvtuvfee7VixQpdeumlZpdlqiVLlmjLli3u2y2dC8uHm0ceeUR+fn5VLj/++KO7/0MPPaSvvvpKa9euVUBAgEaNGiXDYhdxruk+kaR9+/Zp8ODBuvXWWzVu3DiTKq8/tdknAKovOTlZ3377rZYsWWJ2KY3CxRdfrK1bt+qLL77Qfffdp8TERH3//fdml2Wa3NxcTZgwQW+99ZYcDsc5b8/yt184dOiQ/v3vf1fZp1OnTgoMDKzQ/ssvvygyMlIbNmyw1HBhTffJr7/+qv79++vqq6/WwoULPe71ZRW1+ZwsXLhQDz74oPLz8+u5usalpKREQUFBevfddzV8+HB3e2JiovLz8xntlOTn56cVK1Z47J+mbPz48Xr//fe1bt06dezY0exyGqXY2Fh17txZr732mtmlmGLlypW66aabFBAQ4G4rLS2Vn5+f/P39VVxc7PHc2Zh+48z61qZNG7Vp06ZW65aVlUmSiouL67Ik09Vkn+zbt08DBgxQjx49tGDBAksGG+ncPidNTWBgoHr06KGMjAz3l3dZWZkyMjI0fvx4c4tDo2IYhv785z9rxYoVysrKIthUoayszHLfNTVx7bXXatu2bR5tSUlJuuSSS/Twww/XKNhITSDcVNcXX3yhL7/8Ur///e913nnnaefOnZo8ebI6d+5sqVGbmti3b5/69++vCy+8UC+++KIOHTrkfi48PNzEysyVk5OjI0eOKCcnR6Wlpdq6dask6aKLLpLT6TS3uAaSkpKixMRE9ezZU7169dKsWbNUVFSkpKQks0szTWFhoXbs2OF+vHv3bm3dulWtWrVShw4dTKzMPMnJyVq8eLHef/99tWzZ0j0nKyQkRM2bNze5OvOkpqZqyJAh6tChg44dO6bFixcrKytLa9asMbs007Rs2bLCXKzT819rNUer7k7g8m3ffPONMWDAAKNVq1aG3W43oqKijHvvvdf45ZdfzC7NNAsWLDAkeV2assTERK/75JNPPjG7tAb18ssvGx06dDACAwONXr16Gf/617/MLslUn3zyidfPRWJiotmlmaay3x8LFiwwuzRT3XXXXcaFF15oBAYGGm3atDGuvfZaY+3atWaX1eicy6nglp9zAwAAmhZrTqAAAABNFuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGaASeeOIJde/e3ewyFBUVpVmzZpldhld//OMftXjxYrPLgEm+//57XXDBBSoqKjK7FPgAwg2ajNGjR7vv8N2sWTN17NhRf/nLX+RyuRq0Dj8/P61cudKjbdKkScrIyKi318zKyjrrXc+zsrL05Zdf6u677663Omrrgw8+0IEDB/SnP/3J7FK0cOFC9z7z9/dXRESERo4cqZycHO3Zs+es+3nhwoVmvwWfdOmll+rqq6/WjBkzzC4FPoB7S6FJGTx4sBYsWKATJ05o8+bNSkxMlJ+fn5577jlT63I6nfV6X6q+fftq//797scTJkxQQUGBFixY4G5r1aqVx13PG5OXXnpJSUlJjebGrcHBwdq+fbsMw9Du3bt1//3369Zbb9WGDRs89vOLL76o1atX6+OPP3a3hYSE1FtdJSUlDfYzrOy1Tpw4oWbNmtV4e9VZLykpSePGjVNqaqpsNr6+ULnG8ZsCaCB2u13h4eGKjIzU8OHDFRsbq/T0dPfzZWVlSktLU8eOHdW8eXNFR0fr3XffdT9fWlqqMWPGuJ+/+OKLNXv27AqvM3/+fF122WWy2+2KiIhw3y07KipKknTTTTfJz8/P/bj8YamysjI9+eSTuuCCC2S329W9e3etXr3a/fzpEYLly5drwIABCgoKUnR0tLKzs72+78DAQIWHh7uX5s2bu/fF6SUwMLDCYSk/Pz+99tpruuGGGxQUFKRu3bopOztbO3bsUP/+/dWiRQv17dtXO3fu9Hi9999/X1dddZUcDoc6deqkadOm6eTJk5JO3Sn6iSeeUIcOHWS329WuXTs98MADlf7MDh06pMzMTMXHx3u0+/n56Y033tBNN92koKAgdenSRR988IFHn2+//VZDhgyR0+lUWFiY7rzzTh0+fNj9/LFjx3THHXeoRYsWioiI0MyZM9W/f389+OCDldZz+rXDw8MVERGhvn37asyYMdq4caOKioo89qnT6ZTNZnM//vHHH3XNNdeoRYsWCg0NVb9+/bR3795KX+fhhx9W165dFRQUpE6dOmny5Mk6ceKE+/nTn5s33nhDHTt2lMPhkCTl5+dr7NixatOmjYKDgzVw4EB9/fXXVb6n3NxcJSQkKDQ0VK1atdKwYcO0Z88e9/OjR4/W8OHD9fTTT6tdu3a6+OKL3Z/DpUuX6pprrpHD4dBbb71V7c9v+fX27t2r+Ph4nXfeeWrRooUuu+wyffjhh+71rrvuOh05ckSffvpple8FINygyfr222+1YcMGj78+09LStGjRIs2dO1ffffedJk6cqP/6r/9y/zItKyvTBRdcoHfeeUfff/+9pkyZokcffVTLli1zb+PVV19VcnKy7r77bm3btk0ffPCBLrroIknSl19+KUlasGCB9u/f735c3uzZszV9+nS9+OKL+uabbxQXF6cbb7xRP//8s0e/xx57TJMmTdLWrVvVtWtX3Xbbbe4QUVeeeuopjRo1Slu3btUll1yi22+/Xffcc49SU1O1adMmGYbhDm+S9Nlnn2nUqFGaMGGCvv/+e7322mtauHChnn76aUnSe++9p5kzZ+q1117Tzz//rJUrV+qKK66o9PU///xzd7Aqb9q0aUpISNA333yjoUOH6o477tCRI0cknfqCHzhwoGJiYrRp0yatXr1aBw4cUEJCgnv9lJQUrV+/Xh988IHS09P12WefacuWLTXaPwcPHtSKFSsUEBCggICASvudPHlSw4cP1zXXXKNvvvlG2dnZuvvuu+Xn51fpOi1bttTChQv1/fffa/bs2Xr99dc1c+ZMjz47duzQe++9p+XLl7vvUH/rrbfq4MGD+uijj7R582ZdddVVuvbaa937prwTJ04oLi5OLVu21Geffab169fL6XRq8ODBKikpcffLyMjQ9u3blZ6ern/+85/u9kceeUQTJkzQDz/8oLi4uGp/fsuvl5ycrOLiYq1bt07btm3Tc8895zGiGRgYqO7du+uzzz6rdJ8Bkpr47Z3RpCQmJhoBAQFGixYtDLvdbkgy/P39jXfffdcwDMNwuVxGUFCQsWHDBo/1xowZY9x2222Vbjc5Odm4+eab3Y/btWtnPPbYY5X2l2SsWLHCo23q1KlGdHS0xzaefvppjz6/+93vjPvvv98wDMPYvXu3Icl444033M9/9913hiTjhx9+qPS1T0tMTDSGDRtWof3CCy80Zs6c6VHr448/7n6cnZ1tSDLmzZvnbnv77bcNh8PhfnzttdcazzzzjMd233zzTSMiIsIwDMOYPn260bVrV6OkpOSsdRqGYcycOdPo1KlThfbytRUWFhqSjI8++sgwDMN46qmnjEGDBnmsk5uba0gytm/fbhQUFBjNmjUz3nnnHffz+fn5RlBQUJV3Il6wYIEhyWjRooURFBTkvtP1Aw88UKHvmT/Xf//734YkIysrq1rv25sXXnjB6NGjh8f2mzVrZhw8eNDd9tlnnxnBwcGGy+XyWLdz587Ga6+95nW7b775pnHxxRcbZWVl7rbi4mKjefPmxpo1awzDOPWZCQsLM4qLi919Tn8OZ82a5bG96n5+y693xRVXGE888USV++Cmm24yRo8eXWUfgIOWaFIGDBigV199VUVFRZo5c6ZsNptuvvlmSaf+Av7tt9903XXXeaxTUlKimJgY9+M5c+Zo/vz5ysnJ0fHjx1VSUuI+pHTw4EH9+uuvuvbaa2tdY0FBgX799Vf169fPo71fv34VDi1ceeWV7n9HRES4a7jkkktq/frlnfkaYWFhkuQx0hIWFiaXy6WCggIFBwfr66+/1vr1690jNdKpw3kul0u//fabbr31Vs2aNUudOnXS4MGDNXToUMXHx1c6h+L48ePuwy1V1daiRQsFBwfr4MGDkqSvv/5an3zyide5TDt37tTx48d14sQJ9erVy90eEhKiiy+++Kz7pGXLltqyZYtOnDihjz76SG+99ZbH+/WmVatWGj16tOLi4nTdddcpNjZWCQkJ7p+bN0uXLtVLL72knTt3qrCwUCdPnlRwcLBHnwsvvFBt2rRxP/76669VWFio888/36Pf8ePHKxw+PHOdHTt2qGXLlh7tLpfLY50rrrjC6zybnj17uv9dk8/vmetJ0gMPPKD77rtPa9euVWxsrG6++WaPn7EkNW/eXL/99pvX9wGcRrhBk9KiRQv3IaL58+crOjpa8+bN05gxY1RYWChJWrVqldq3b++xnt1ulyQtWbJEkyZN0vTp09WnTx+1bNlSL7zwgr744gtJp37xNqQzJ2CePrxRVlZW769R1esWFhZq2rRpGjFiRIVtORwORUZGavv27fr444+Vnp6u+++/Xy+88II+/fRTrxNKW7durf/85z9nre10LWfWER8f73WyeEREhHbs2FHl+66Kv7+/+3PUrVs37dy5U/fdd5/efPPNKtdbsGCBHnjgAa1evVpLly7V448/rvT0dF199dUV+mZnZ+uOO+7QtGnTFBcXp5CQEC1ZskTTp0/36NeiRQuPx4WFhYqIiFBWVlaFbYaGhnqtq7CwUD169NBbb71V4bkzg1P51zpb+9mUX2/s2LGKi4vTqlWrtHbtWqWlpWn69On685//7O5z5MgRde7cuVavh6aDcIMmy9/fX48++qhSUlJ0++2369JLL5XdbldOTo6uueYar+usX79effv21f333+9uO/Mv25YtWyoqKkoZGRkaMGCA1200a9ZMpaWlldYVHBysdu3aaf369R51rF+/3mOUobG66qqrtH37dveXvzfNmzdXfHy84uPjlZycrEsuuUTbtm3TVVddVaFvTEyM8vLy9J///EfnnXdejep47733FBUV5XVUqFOnTmrWrJm+/PJLdejQQZJ09OhR/fTTT/rjH/9Y7deRTs0d6dy5syZOnOj1PZR/PzExMUpNTVWfPn20ePFir+Fmw4YNuvDCC/XYY4+526qafHzaVVddpby8PNlsNveE9eqss3TpUrVt27bCyFBNnevnNzIyUvfee6/uvfdepaam6vXXX/cIN99++61uueWWc6oR1seEYjRpt956qwICAjRnzhy1bNlSkyZN0sSJE/W3v/1NO3fu1JYtW/Tyyy/rb3/7mySpS5cu2rRpk9asWaOffvpJkydPrjAp+IknntD06dP10ksv6eeff3Zv47TT4ef0F7Y3Dz30kJ577jktXbpU27dv1yOPPKKtW7dqwoQJ9bcz6siUKVO0aNEiTZs2Td99951++OEHLVmyRI8//rikU9eJmTdvnr799lvt2rVLf//739W8eXNdeOGFXrcXExOj1q1ba/369TWqIzk5WUeOHNFtt92mL7/8Ujt37tSaNWuUlJSk0tJStWzZUomJiXrooYf0ySef6LvvvtOYMWPk7+9f5SRfbyIjI3XTTTdpypQplfbZvXu3UlNTlZ2drb1792rt2rX6+eefvU6Ulk591nJycrRkyRLt3LlTL730klasWHHWWmJjY9WnTx8NHz5ca9eu1Z49e7RhwwY99thj2rRpk9d17rjjDrVu3VrDhg3TZ599pt27dysrK0sPPPCAfvnll+rthDPU9vP74IMPas2aNdq9e7e2bNmiTz75xGP/7NmzR/v27VNsbGyNa0LTQrhBk2az2TR+/Hg9//zzKioq0lNPPaXJkycrLS1N3bp10+DBg7Vq1Sp17NhRknTPPfdoxIgRGjlypHr37q1///vfHqM4kpSYmKhZs2bplVde0WWXXaYbbrjB4yyR6dOnKz09XZGRkR5zec70wAMPKCUlRf/93/+tK664QqtXr9YHH3ygLl261N/OqCNxcXH65z//qbVr1+p3v/udrr76as2cOdMdXkJDQ/X666+rX79+uvLKK/Xxxx/rH//4R4U5IqcFBAQoKSnJ6yGTqpwePSgtLdWgQYN0xRVX6MEHH1RoaKj7ejkzZsxQnz59dMMNNyg2Nlb9+vVTt27dKp3jU5WJEydq1apV2rhxo9fng4KC9OOPP+rmm29W165ddffddys5OVn33HOP1/433nijJk6cqPHjx6t79+7asGGDJk+efNY6/Pz89OGHH+qPf/yjkpKS1LVrV/3pT3/S3r173XOmvNW2bt06dejQQSNGjFC3bt00ZswYuVyuWo3k1PbzW1paquTkZPf/va5du+qVV15xP//2229r0KBBlQZh4DQ/wzAMs4sAgKrk5eXpsssu05YtW+r1i62oqEjt27fX9OnTNWbMmHp7HdRcSUmJunTposWLF1eYrAyUx8gNgEYvPDxc8+bNU05OTp1u96uvvtLbb7/tPgR5xx13SJKGDRtWp6+Dc5eTk6NHH32UYINqYeQGQJP11VdfaezYsdq+fbsCAwPVo0cPzZgxo8qLCgJo/Ag3AADAUjgsBQAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALIVwAwAALOX/AQtv7bEsyicBAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " sim_out_2[\"rts\"][sim_out_2[\"rts\"] != -999] * sim_out_2[\"choices\"][sim_out_2[\"rts\"] != -999],\n", + " bins=100,\n", + " histtype=\"step\",\n", + " color=\"black\",\n", + " label=\"Weibull Deadline\",\n", + " density=True,\n", + " alpha=0.4,\n", + ")\n", + "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", + "plt.ylabel(\"Density\")" + ] } ], "metadata": { diff --git a/src/cssm.pyx b/src/cssm.pyx index 26d2820b..39df5298 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -4946,13 +4946,15 @@ def exgauss(np.ndarray[float, ndim = 1] mu, def shifted_wald(np.ndarray[float, ndim = 1] gamma, np.ndarray[float, ndim = 1] alpha, np.ndarray[float, ndim = 1] theta, + np.ndarray[float, ndim = 1] p, # choice probability np.ndarray[float, ndim = 1] s, # noise sigma float delta_t = 0.001, float max_t = 20, int n_samples = 20000, int n_trials = 1, random_state = None, - return_option = 'full', + return_option = 'full', + smooth_unif = False, **kwargs): """ Uhhhhhh thing for shifted wald """ @@ -4961,6 +4963,7 @@ def shifted_wald(np.ndarray[float, ndim = 1] gamma, cdef float[:] gamma_view = gamma cdef float[:] alpha_view = alpha cdef float[:] theta_view = theta + cdef float[:] p_view = p cdef float[:] s_view = s traj = np.zeros((int(max_t / delta_t) + 1, 1), dtype=DTYPE) @@ -4992,14 +4995,15 @@ def shifted_wald(np.ndarray[float, ndim = 1] gamma, if n == 0: if k == 0: traj_view[0, 0] = y - + + # Random choice depending on p random_val = rand() / float(RAND_MAX) - if random_val <= p: + if random_val <= p_view[k]: choices_view[n, k, 0] = 1 else: choices_view[n, k, 0] = -1 - + # Random walk while (y < alpha_view[k]) and (t_particle <= max_t): y += (gamma_view[k] * delta_t) + (sqrt_st * gaussian_values[m]) t_particle += delta_t @@ -5044,7 +5048,7 @@ def shifted_wald(np.ndarray[float, ndim = 1] gamma, return {'rts': rts, 'choices': choices, 'metadata': {'simulator': 'shifted_wald', 'n_samples': n_samples, - 'n_trials': n, + 'n_trials': n_trials, 'possible_choices': [-1, 1]}} else: raise ValueError("return_option must be 'full' or 'minimal'") diff --git a/ssms/config/_modelconfig/__init__.py b/ssms/config/_modelconfig/__init__.py index 27bd4faa..eb1a9086 100644 --- a/ssms/config/_modelconfig/__init__.py +++ b/ssms/config/_modelconfig/__init__.py @@ -127,6 +127,10 @@ get_exgauss_config ) +from .shifted_wald import ( + get_shifted_wald_config +) + def get_model_config(): """Accessor for model configurations. @@ -238,6 +242,7 @@ def get_model_config(): "full_ddm2": get_full_ddm_config(), "ddm_legacy": get_ddm_legacy_config(), "exgauss": get_exgauss_config(), + "shifted_wald": get_shifted_wald_config(), } From e3151c74dc1b4f61192841d8675230ae50a30dac Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Thu, 6 Nov 2025 16:49:05 -0500 Subject: [PATCH 05/14] implemented likelihood with 'p' for exgauss --- notebooks/test_exgauss_wald.ipynb | 91 +++++++++++++++++++++++++------ 1 file changed, 73 insertions(+), 18 deletions(-) diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index 1dad2b0e..d73db100 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -8,7 +8,9 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", - "import ssms" + "import ssms\n", + "import pytensor \n", + "import pytensor.tensor as pt\n" ] }, { @@ -134,36 +136,36 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from ssms.basic_simulators.simulator import simulator\n", "\n", "sim_out = simulator(\n", - " model=\"exgauss\", theta={\"mu\": 0.5, \"sigma\":0.05, \"tau\": 0.3, \"p\": 0}, n_samples=10000, random_state = 100,\n", + " model=\"exgauss\", theta={\"mu\": 2, \"sigma\":0.05, \"tau\": 1, \"p\": 0.2}, n_samples=10000, random_state = 100,\n", " )\n", "sim_out_2 = simulator(\n", - " model=\"shifted_wald\", theta={\"gamma\": 2.0, \"alpha\":1, \"theta\": 0.0, \"p\": 0.8}, n_samples=10000, random_state = 100,\n", + " model=\"shifted_wald\", theta={\"gamma\": 2.0, \"alpha\":1, \"theta\": 0.0, \"p\": 0.1}, n_samples=10000, random_state = 100,\n", " )\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.6775269 ]\n", - " [0.47768405]\n", - " [0.99526304]\n", + "[[2.685313 ]\n", + " [1.9990021]\n", + " [3.8226187]\n", " ...\n", - " [0.70154023]\n", - " [0.4867602 ]\n", - " [1.0765221 ]]\n" + " [2.819895 ]\n", + " [2.0777347]\n", + " [3.9112484]]\n" ] } ], @@ -174,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -201,13 +203,63 @@ "metadata": {}, "outputs": [], "source": [ - "# Define analytical likelihood for ex-Gaussian\n", + "# Define exgauss analytical likelihoods\n", + "import numpy as np \n", + "from scipy.stats import norm \n", + "\n", + "\n", + "def exgauss_likelihood(x, choice, mu, sigma, tau, p):\n", + " x = np.asarray(x, dtype=float)\n", + " tau = float(tau)\n", + " sigma= float(sigma)\n", + " mu = float(mu)\n", + " tau_inv = 1.0 / tau \n", + " sig2 = sigma ** 2 \n", + " z = (x - mu - sig2 * tau_inv) / sigma \n", + " cdf_gauss = norm.cdf(z)\n", + " pdf_rt = tau_inv * np.exp(mu*tau_inv + (sig2 * tau_inv**2) / 2 - x*tau_inv) * cdf_gauss \n", + " \n", + " # Bernoulli part \n", + " choice = np.asarray(choice, dtype=int)\n", + " p = float(p)\n", + " y = (choice > 0).astype(float)\n", + " pdf_choice = y * p + (1 - y) * (1 - p)\n", + "\n", + " return pdf_rt*pdf_choice \n", + "\n", + "# def exgauss_joint_logpdf(x, choice, mu, sigma, tau, p): \n", + "# pdf = exgauss_likelihood(x, mu, sigma, tau)\n", + "# choice = np.asarray(choice, dtype=int) \n", + "# p = float(p) \n", + "\n", + "# y = (choice > 0).astype(float)\n", + "# ll_rt = np.log(pdf).sum()\n", + "# ll_choice = (y * np.log(p) + (1 - y) * np.log(1 - p)).sum()\n", + " \n", + "# return ll_rt + ll_choice\n", + "\n", + "\n", + "\n", + "x = np.linspace(0, 9, 1100)\n", + "p = 0.8\n", + "choice = np.where(np.random.rand(len(x)) < p, 1, -1)\n", + "# pdf_vals_loglik = exgauss_joint_logpdf(x, choice, mu=2, sigma=0.05, tau=1, p=0.8)\n", + "pdf_vals = exgauss_likelihood(x, choice, mu=2, sigma=0.05, tau=1, p=0.2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define shifted wald analytical likelihoods\n", "\n" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -216,13 +268,13 @@ "Text(0, 0.5, 'Density')" ] }, - "execution_count": 24, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -242,6 +294,9 @@ " density=True,\n", " alpha=0.4,\n", ")\n", + "s = choice*x\n", + "idx = np.argsort(s) # sort the values so theyre not zig zaggy \n", + "plt.plot(s[idx], pdf_vals[idx], color=\"red\", label=\"Ex-Gaussian Likelihood\")\n", "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", "plt.ylabel(\"Density\")\n", "\n" @@ -249,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -258,7 +313,7 @@ "Text(0, 0.5, 'Density')" ] }, - "execution_count": 25, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, From e800ca3b1e5cb8b68739a028512233ff96168db9 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Fri, 7 Nov 2025 13:30:59 -0500 Subject: [PATCH 06/14] compared likelihood of shifted wald with simulator --- notebooks/test_exgauss_wald.ipynb | 43 ++++++++++++++++++++++++------- 1 file changed, 34 insertions(+), 9 deletions(-) diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index d73db100..21e96d73 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ " model=\"exgauss\", theta={\"mu\": 2, \"sigma\":0.05, \"tau\": 1, \"p\": 0.2}, n_samples=10000, random_state = 100,\n", " )\n", "sim_out_2 = simulator(\n", - " model=\"shifted_wald\", theta={\"gamma\": 2.0, \"alpha\":1, \"theta\": 0.0, \"p\": 0.1}, n_samples=10000, random_state = 100,\n", + " model=\"shifted_wald\", theta={\"gamma\": 2.0, \"alpha\":2.5, \"theta\": 0.0, \"p\": 0.8}, n_samples=10000, random_state = 100,\n", " )\n" ] }, @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -249,17 +249,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# define shifted wald analytical likelihoods\n", - "\n" + "\n", + "def shifted_wald_likelihood(x, choice, gamma, alpha, theta, p):\n", + " x = np.asarray(x, dtype=float)\n", + " gamma = float(gamma)\n", + " alpha = float(alpha)\n", + " theta = float(theta)\n", + "\n", + " term_1 = (alpha / np.sqrt(2*np.pi*(x - theta)**3))\n", + " term_2 = np.exp((-(alpha-gamma*(x - theta))**2) / (2*(x - theta)))\n", + " pdf_rt = term_1 * term_2\n", + "\n", + " # Bernoulli part\n", + " choice = np.asarray(choice, dtype=int)\n", + " p = float(p)\n", + " y = (choice > 0).astype(float)\n", + " pdf_choice = y * p + (1 - y) * (1 - p)\n", + "\n", + " return pdf_rt*pdf_choice\n", + "\n", + "x_sw = np.linspace(0.001, 9, 1100)\n", + "p_sw = 0.8\n", + "choice_sw = np.where(np.random.rand(len(x_sw)) < p_sw, 1, -1)\n", + "pdf_vals_sw = shifted_wald_likelihood(x_sw, choice_sw, gamma=2.0, alpha=2.5, theta=0.0, p=0.8)\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -268,13 +290,13 @@ "Text(0, 0.5, 'Density')" ] }, - "execution_count": 12, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAGwCAYAAABCV9SaAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAYBpJREFUeJzt3XlcVGXfP/DPLDADDIuIshgK7ksKLknoXZZSWGZuqZml8Zht8mSR/ZIWTbuLyr0e7/Q2l3axsuXOcgmzRbndcF/DVNwAEVkGHWBmrt8fs8jAgCyzHODzfr3m5cyZc858hwH8cH2vc45MCCFARERE1MTJ3V0AERERkSsw9BAREVGzwNBDREREzQJDDxERETULDD1ERETULDD0EBERUbPA0ENERETNgtLdBbia0WjExYsX4evrC5lM5u5yiIiIqBaEECguLkZYWBjk8vqN2TS70HPx4kWEh4e7uwwiIiKqh3PnzuGWW26p17bNLvT4+voCMH3R/Pz83FwNERER1UZRURHCw8Ot/4/XR7MLPZaWlp+fH0MPERFRI9OQqSmcyExERETNAkMPERERNQsMPURERNQsMPQQERFRs8DQQ0RERM0CQw8RERE1Cww9RERE1Cww9BAREVGzwNBDREREzQJDDxERETULDD1ERETULDD0EBERUbPA0ENERETNAkMPETnG338DZ864uwoiompJIvQsXboUERERUKvViImJwa5du6pd96677oJMJqtyGzZsmAsrJiIbOh3Qty/Qvz9gMLi7GiIiu9weelJTU5GUlITZs2cjIyMDUVFRiI+PR25urt31169fj0uXLllvhw8fhkKhwNixY11cORFZ5eQABQXA5ctAWZm7qyEissvtoWfhwoWYOnUqEhIS0L17dyxbtgze3t5YtWqV3fUDAwMREhJivW3ZsgXe3t4MPUTulJd3475e7746iIhq4NbQU1ZWhr179yIuLs66TC6XIy4uDunp6bXax8qVK/Hwww/Dx8fH7vOlpaUoKiqyuRGRg125cuM+21tEJFFuDT15eXkwGAwIDg62WR4cHIzs7Oybbr9r1y4cPnwYTzzxRLXrpKSkwN/f33oLDw9vcN1EVAlDDxE1Am5vbzXEypUr0bNnT/Tv37/adZKTk1FYWGi9nTt3zoUVEjUTFUMP21tEJFFKd754UFAQFAoFcnJybJbn5OQgJCSkxm1LSkqwdu1azJ07t8b1VCoVVCpVg2slIvu0Wi1k587B0mDOv3wZnj4+0Gg0bq2LiKgyt470eHp6om/fvkhLS7MuMxqNSEtLQ2xsbI3bfvXVVygtLcWjjz7q7DKJqBparRbr1q3D6Qqnmfj5xx+xbt06aLVaN1ZGRFSV29tbSUlJWLFiBT7++GMcO3YMzzzzDEpKSpCQkAAAmDRpEpKTk6tst3LlSowcORItW7Z0dclEZKbT6aDX69GuwqjOwJgY6PV66HQ6N1ZGRFSVW9tbADB+/HhcvnwZs2bNQnZ2NqKjo7Fx40br5OasrCzI5bbZ7MSJE/jzzz+xefNmd5RMRJWoKozq+FVzJCURkbu5PfQAQGJiIhITE+0+t23btirLunTpAiGEk6siotqSXb16477R6MZKiIiq5/b2FhE1fvL8/BsPePQWEUkUQw8RNZisYujheXqISKIYeoioQeR6PeQlJTcWMPQQkUQx9BBRg6gqBh4AMoYeIpIohh4iahB15fPxMPQQkUQx9BBRgzD0EFFjwdBDRA2irtTe4tFbRCRVDD1E1CCqSiM9PE8PEUkVQw8RNUiVkR62t4hIohh6iKhBqszpYXuLiCSKoYeIGqTyIetge4uIJIqhh4gapPJID8/TQ0RSxdBDRA1SZaSH7S0ikiiGHiJqEOtEZrn51wlHeohIohh6iKhBrO2tVq1M/3JODxFJFEMPEdWfwQDVtWum+8HBADinh4iki6GHiOpNVlgImRCmB5aRHs7pISKJYughonqTXb0KADD6+gJeXqaFHOkhIoli6CGiepNfuQIAEIGBgEJhWsg5PUQkUQw9RFRvcstIT4sW1tAjY3uLiCSKoYeI6k2Wnw8AEC1aAEqlaSHbW0QkUQw9RFRv1pGeli1vtLcYeohIohh6iKjebEZ6LO0thh4ikiiGHiKqN7k59BgDA9neIiLJY+ghonqzN9LD0ENEUsXQQ0T1Zp3TU/GQdYYeIpIohh4iqjfLyQlFhfYW5/QQkVQx9BBRvVlOTmhke4uIGgGGHiKqHyFsR3osoYcnJyQiiWLoIaL60WohKy8HYB7psRy9xctQEJFEMfQQUf2YW1t6pRLw9uZ5eohI8hh6iKh+zKFHp9EAMhnbW0QkeQw9RFQ/eXkAgFKNxvSYJyckIolj6CGi+rGM9Pj4mB5bRno4p4eIJIqhh4jqxxx6SiuFHhnbW0QkUQw9RFQ/Fef0AGxvEZHkMfQQUf2Y5/SwvUVEjQVDDxHVj6W9ZRnp4dFbRCRxDD1EVD+VJzLz2ltEJHFuDz1Lly5FREQE1Go1YmJisGvXrhrXLygowLRp0xAaGgqVSoXOnTvjp59+clG1RGRVeU4Pr71FRBKndOeLp6amIikpCcuWLUNMTAwWL16M+Ph4nDhxAq1bt66yfllZGe655x60bt0aX3/9Ndq0aYOzZ88iICDA9cUTNXfVHL3F0ENEUuXW0LNw4UJMnToVCQkJAIBly5Zhw4YNWLVqFWbOnFll/VWrViE/Px87duyAh4cHACAiIqLG1ygtLUVpaan1cVFRkePeAFFzZpnIXOnoLba3iEiq3NbeKisrw969exEXF3ejGLkccXFxSE9Pt7vNDz/8gNjYWEybNg3BwcG49dZb8fbbb8NQwy/ZlJQU+Pv7W2/h4eEOfy9Ezc7160BJiemur69pGUd6iEji3BZ68vLyYDAYEBwcbLM8ODgY2dnZdrf5+++/8fXXX8NgMOCnn37C66+/jgULFuCf//xnta+TnJyMwsJC6+3cuXMOfR9EzdLlywAA4eGBcrXatIyhh4gkzq3trboyGo1o3bo1/v3vf0OhUKBv3764cOEC5s2bh9mzZ9vdRqVSQaVSubhSoibOHHqMLVuaLjYKMPQQkeS5LfQEBQVBoVAgJyfHZnlOTg5CQkLsbhMaGgoPDw8oLL9cAXTr1g3Z2dkoKyuDp6enU2smIjPLSE/LljeWcU4PEUmc29pbnp6e6Nu3L9LS0qzLjEYj0tLSEBsba3ebgQMHIjMzE8YKZ3w9efIkQkNDGXiIXMky0hMUdGMZR3qISOLcep6epKQkrFixAh9//DGOHTuGZ555BiUlJdajuSZNmoTk5GTr+s888wzy8/Mxffp0nDx5Ehs2bMDbb7+NadOmuestEDVPFdtbFgw9RCRxbp3TM378eFy+fBmzZs1CdnY2oqOjsXHjRuvk5qysLMjlN3JZeHg4Nm3ahBdeeAG9evVCmzZtMH36dLz88svuegtEzVMN7S2GHiKSKrdPZE5MTERiYqLd57Zt21ZlWWxsLP773/86uSoiqlENIz2c00NEUuX2y1AQUSNkb6SH7S0ikjiGHiKqO3sjPWxvEZHEMfQQUZ1otVoYzCcQLap4DiyO9BCRxDH0EFGtabVarFu3Dnpz6Nnx119QKpVQq9U35vTo9e4skYioWm6fyExEjYdOp4NRp4Pq+nUAwJCHH4aqTRtoNJob7a0K59EiIpIShh4iqhOV+UKjkMvRslMnwHJaCba3iEji2N4iojrxKi423WnZ8kbgAW6EHra3iEiiGHqIqE7UltDTqpXtE7z2FhFJHEMPEdWJl1ZrulM59FhGejinh4gkiqGHiOqk2pEetreISOIYeoioTtTVjfTw5IREJHEMPURUJ143GemRsb1FRBLF0ENEdVLtSA/bW0QkcQw9RFQnNzt6i+0tIpIqhh4iqpObHb0lMxgAIVxcFRHRzTH0EFGd3PToLQAyhh4ikiCGHiKqPYMBastlKKprb4GTmYlImhh6iKjWZFev3hjFadnS9smKIz0MPUQkQQw9RFRr8itXAADGgADAw8P2SYYeIpI4hh4iqjVZXh4AwFh5lAewaW/JGXqISIIYeoio1uT5+QAAERhY9UmO9BCRxDH0EFGtWdtbQUF2nrzx64Shh4ikiKGHiGpNZg49wl57SyazBh+2t4hIihh6iKjW5DXN6QGs83o40kNEUsTQQ0S1VmN7C+BFR4lI0hh6iKjWamxvAdbQw/YWEUkRQw8R1Zp1pIftLSJqhBh6iKjWajxPD8D2FhFJGkMPEdWOEDfO03OTOT1sbxGRFDH0EFHtFBZCVl4OgO0tImqcGHqIqHYuXwYAlKlUgFptfx22t4hIwhh6iKh2zKFH5+tb/TpsbxGRhDH0EFHtWEKPRlP9OmxvEZGEMfQQUe2YQ8/1Woz0MPQQkRQx9BBR7dRmpIftLSKSMIYeIqqd2szpYXuLiCSMoYeIasd8YsLrtRjpYeghIili6CGi2uHRW0TUyDH0EFHt1GYiM9tbRCRhkgg9S5cuRUREBNRqNWJiYrBr165q112zZg1kMpnNTV3didKIyHHqMJGZoYeIpMjtoSc1NRVJSUmYPXs2MjIyEBUVhfj4eOTm5la7jZ+fHy5dumS9nT171oUVEzVDQvDoLSJq9NweehYuXIipU6ciISEB3bt3x7Jly+Dt7Y1Vq1ZVu41MJkNISIj1FhwcXO26paWlKCoqsrkRUR1ptcD16wCA635+1a/H9hYRSZhbQ09ZWRn27t2LuLg46zK5XI64uDikp6dXu51Wq0W7du0QHh6OESNG4MiRI9Wum5KSAn9/f+stPDzcoe+BqFm4dAkAYNRooFepql+P7S0ikjC3hp68vDwYDIYqIzXBwcHIzs62u02XLl2watUqfP/99/jss89gNBoxYMAAnD9/3u76ycnJKCwstN7OnTvn8PdB1OSZfx5F69Y1r8f2FhFJmNLdBdRVbGwsYmNjrY8HDBiAbt26Yfny5XjzzTerrK9SqaCq6S9TIro5c+gx3iz0sL1FRBLm1pGeoKAgKBQK5OTk2CzPyclBSEhIrfbh4eGB3r17IzMz0xklEhFwI/TUMH8OANtbRCRpbg09np6e6Nu3L9LS0qzLjEYj0tLSbEZzamIwGHDo0CGEhoY6q0wiqu1IjyX0COHsioiI6szt7a2kpCRMnjwZ/fr1Q//+/bF48WKUlJQgISEBADBp0iS0adMGKSkpAIC5c+fi9ttvR8eOHVFQUIB58+bh7NmzeOKJJ9z5NoiaNstE5lq2t+QGg7MrIiKqM7eHnvHjx+Py5cuYNWsWsrOzER0djY0bN1onN2dlZUEuvzEgdfXqVUydOhXZ2dlo0aIF+vbtix07dqB79+7uegtETV9dR3rY3iIiCXJ76AGAxMREJCYm2n1u27ZtNo8XLVqERYsWuaAqIrKqOKensLD69djeIiIJc/vJCYmoEajj0VtsbxGRFDH0EFHNDAbAfFkYtreIqDFj6CGiml2+DBiNgFwOERRU87psbxGRhDH0EFHNLGdHb9XKGmqqZTkjM9tbRCRBDD1EVDNL6KnNubB4RmYikjCGHiKqmSX01OYs6WxvEZGEMfQQUc3qEXrY3iIiKWLoIaKamc/GXKvQw/YWEUkYQw8R1YztLSJqIhh6iKhaWq0W5efOAQCKfHxQUFBQ8wZsbxGRhEniMhREJD1arRbr1q3D6L//RgCAbcePI9tohFKphFqttr+Rpb3FkR4ikiCGHiKyS6fTQa/Xw0+rBQD8Y+xYGDt0gFqthkajsb8Rz8hMRBLG0ENE1VKWlkJeUgIACOzWDfDzq3kDtreISMI4p4eIquVVVGS+4wX4+t58A7a3iEjCGHqIqFreltATGgrIZDffgO0tIpIwhh4iqpZXYaHpTm0OVwfY3iIiSWPoIaJq+VgOUa/NdbcAtreISNIYeoioWj5Xr5ruhIfXbgO2t4hIwhh6iKha1pGeW26p3QZsbxGRhDH0EFG1NPn5pju1DT1sbxGRhDH0EFG16jvSw/YWEUkRQw8R2Wc03gg9dZzTw/YWEUkRQw8R2SXLy4NCr4eQyXj0FhE1CQw9RGSX/NIlAICxdWvAw6N2G7G9RUQSxtBDRHYpLl4EABjbtKnDRgw9RCRdDD1EZJf8wgUAgLG2rS3A2t6SM/QQkQQx9BCRXXLLSE9YWO034kgPEUkYQw8R2WWd08P2FhE1EQw9RGSXwtzeMtRlpIftLSKSMIYeIrLLOtJTlzk9HOkhIglj6CGiqoS4MaeH7S0iaiIYeoioqsuXISsrg5DJYAwOrv12bG8RkYQx9BBRVefPAwCu+/oCnp61344jPUQkYQw9RFTVuXMAAG1gYN22Y+ghIgmrV+j5+++/HV0HEUmJeaSnJCCgbttZLjjK0ENEElSv0NOxY0fcfffd+Oyzz6DT6RxdExG5myX0tGhRt+0sFxxl6CEiCapX6MnIyECvXr2QlJSEkJAQPPXUU9i1a5ejayMid7G0t+oaetjeIiIJq1foiY6OxpIlS3Dx4kWsWrUKly5dwj/+8Q/ceuutWLhwIS5fvuzoOonIleo70sP2FhFJWIMmMiuVSowePRpfffUV3n33XWRmZmLGjBkIDw/HpEmTcMl8cjMiamTY3iKiJqhBoWfPnj149tlnERoaioULF2LGjBk4deoUtmzZgosXL2LEiBG12s/SpUsREREBtVqNmJiYWrfK1q5dC5lMhpEjRzbgXRCRDSGsoYftLSJqSuoVehYuXIiePXtiwIABuHjxIj755BOcPXsW//znPxEZGYk77rgDa9asQUZGxk33lZqaiqSkJMyePRsZGRmIiopCfHw8cnNza9zuzJkzmDFjBu644476vAUiqk5eHlBaCgC4xqO3iKgJqVfo+fDDD/HII4/g7Nmz+O677/DAAw9ALrfdVevWrbFy5cqb7mvhwoWYOnUqEhIS0L17dyxbtgze3t5YtWpVtdsYDAZMnDgRc+bMQfv27Wvcf2lpKYqKimxuRFQD8yiPsVUrGM3tqlrjGZmJSMLqFXq2bNmCl19+GaGVLkQohEBWVhYAwNPTE5MnT65xP2VlZdi7dy/i4uJuFCSXIy4uDunp6dVuN3fuXLRu3RpTpky5aa0pKSnw9/e33sLDw2+6DVGzZj5yy1CXa25ZmEd6AAAMPkQkMfUKPR06dEBeXl6V5fn5+YiMjKz1fvLy8mAwGBBc6do+wcHByM7OtrvNn3/+iZUrV2LFihW1eo3k5GQUFhZab+fMv9CJqBqWkZ6wsLpvWzH06PUOKoiIyDHqOHZtIoSwu1yr1UKtVjeooJoUFxfjsccew4oVKxAUFFSrbVQqFVQqldNqImpyLKGn0khurVRshxkMDiqIiMgx6hR6kpKSAAAymQyzZs2Ct7e39TmDwYCdO3ciOjq61vsLCgqCQqFATk6OzfKcnByEhIRUWf/UqVM4c+YMhg8fbl1mNA+hK5VKnDhxAh06dKjLWyKiCrRaLZSZmVADKKnrdbcA25Eehh4ikpg6hZ59+/YBMI30HDp0CJ4Vrr7s6emJqKgozJgxo9b78/T0RN++fZGWlmY97NxoNCItLQ2JiYlV1u/atSsOHTpks+y1115DcXExlixZwvk6RA2g1Wqxbt06DD1wAGEADuTnQ6lU1m30tkLokTH0EJHE1Cn0/PrrrwCAhIQELFmyBH5+fg0uICkpCZMnT0a/fv3Qv39/LF68GCUlJUhISAAATJo0CW3atEFKSgrUajVuvfVWm+0DzIfUVl5ORHWj0+mg1+sRZD5cve+DDyJm8GBoNJra74TtLSKSsHrN6Vm9erXDChg/fjwuX76MWbNmITs7G9HR0di4caN1cnNWVlaVw+GJyEmEgIe53RzQsydQl8ADABV/Vhl6iEhiah16Ro8ejTVr1sDPzw+jR4+ucd3169fXqYjExES77SwA2LZtW43brlmzpk6vRUTVU5WUQKbTmR7U5+gtmQxCLjedkZmhh4gkptahx9/fHzKZzHqfiJoezdWrpjutWwP1PepRqQTKyjinh4gkp9ahp2JLy5HtLSKSDp/8fNOdhhwUYJnMzNBDRBJTr8ky169fx7Vr16yPz549i8WLF2Pz5s0OK4yIXM+noMB055Zb6r0PYZnXw9BDRBJTr9AzYsQIfPLJJwCAgoIC9O/fHwsWLMCIESPw4YcfOrRAInIda3urAaHHcgQX21tEJDX1Cj0ZGRnWq5t//fXXCAkJwdmzZ/HJJ5/g/fffd2iBROQ6PpbQw/YWETVB9Qo9165dg6+vLwBg8+bNGD16NORyOW6//XacPXvWoQUSkev4OGKkh6GHiCSqXqGnY8eO+O6773Du3Dls2rQJ9957LwAgNzfXIScsJCL3cEToEZbQwwuOEpHE1Cv0zJo1CzNmzEBERARiYmIQGxsLwDTq07t3b4cWSEQuIsSNOT0OaG/JzNfFIyKSinqdkfmhhx7CP/7xD1y6dAlRUVHW5UOGDMGoUaMcVhwRuY7s6lUoy8tND+pzYkILtreISKLqFXoAICQkpMqV0Pv379/ggojIPeTnzwMAjEFBkNflIqOVsb1FRBJVr9BTUlKCd955B2lpacjNzYWx0jD233//7ZDiiMh1FOfOAQAMbdvWr+9tJjjSQ0QSVa/Q88QTT+C3337DY489htDQUOvlKYio8ZKbQ4+xIfN5AM7pISLJqlfo+fnnn7FhwwYMHDjQ0fUQkZsozKebMLRt27AdmU9OyPYWEUlNvUaxW7RogcDAQEfXQkRuZB3paWjo4WUoiEii6hV63nzzTcyaNcvm+ltE1LgpsrIAAIYGtrc4p4eIpKpe7a0FCxbg1KlTCA4ORkREBDw8PGyez8jIcEhxROQiQkBuDj3Gdu0ati/LnB6GHiKSmHqFnpEjRzq4DCJyq/x8yEtKAACGhlyCAuB5eohIsuoVembPnu3oOojInU6fBgCU+PsDDTlHD8DQQ0SSVe/TcRQUFOCjjz5CcnIy8vPzAZjaWhcuXHBYcUTkIuZzaxW3bNngXXFODxFJVb1Geg4ePIi4uDj4+/vjzJkzmDp1KgIDA7F+/XpkZWXhk08+cXSdRORMltDTqhVaNHRf5kPWOaeHiKSmXiM9SUlJePzxx/HXX39BXWEo/P7778fvv//usOKIyEVOnQIAFLVq1fB98ZB1IpKoeoWe3bt346mnnqqyvE2bNsjOzm5wUUTkYo4MPbz2FhFJVL1Cj0qlQlFRUZXlJ0+eRCtH/NIkItdyYOgRljMy8zIURCQx9Qo9Dz74IObOnYvy8nIAgEwmQ1ZWFl5++WWMGTPGoQUSkZOVlgLmszEXBQU1fH/m9hbn9BCR1NQr9CxYsABarRatWrXC9evXMWjQIHTs2BG+vr546623HF0jETmBVqtFXl4e8vfvB4SA0dsb1/38Gr5jtreISKLqdfSWv78/tmzZgu3bt+PAgQPQarXo06cP4uLiHF0fETmBVqvFunXroNfrEX74MO4DcLVFCyg9PGwOTqgPa3uLIz1EJDF1Dj1GoxFr1qzB+vXrcebMGchkMkRGRiIkJARCCMhkMmfUSUQOpNPpoNfrMXjwYISYz7OliYrCuHHjoNFoGrZzy2UoOKeHiCSmTu0tIQQefPBBPPHEE7hw4QJ69uyJHj164OzZs3j88ccxatQoZ9VJRE4QEBAATU4OAEDVrVvDAw/AQ9aJSLLqNNKzZs0a/P7770hLS8Pdd99t89zWrVsxcuRIfPLJJ5g0aZJDiyQiJzIfuYUOHRyzP0t7i3N6iEhi6jTS8+WXX+KVV16pEngAYPDgwZg5cyY+//xzhxVHRC5gPhsz2rd3yO6sl6Fge4uIJKZOoefgwYMYOnRotc/fd999OHDgQIOLIiIXEeJG6HHUSI9lTg/bW0QkMXUKPfn5+QgODq72+eDgYFy9erXBRRGRa8hzcoDr101BpV07x+yUh6wTkUTVKfQYDAYoldVPA1IoFNDzFx1RoyE/fdp0p21bwMPDMTvlVdaJSKLqNJFZCIHHH38cKpXK7vOlpaUOKYqIXENx5ozpjqNaW+CcHiKSrjqFnsmTJ990HR65RdR4WEOPgyYxm3ZqntPDUV8ikpg6hZ7Vq1c7qw4icgOF5XD1Ll0cuFO2t4hImup17S0iahqsoadzZwfulKGHiKSJoYeouRICCsvh6g4MPbz2FhFJlSRCz9KlSxEREQG1Wo2YmBjs2rWr2nXXr1+Pfv36ISAgAD4+PoiOjsann37qwmqJmgbvggLIrl0zjcxERjpux+bLUPA8PUQkNW4PPampqUhKSsLs2bORkZGBqKgoxMfHIzc31+76gYGBePXVV5Geno6DBw8iISEBCQkJ2LRpk4srJ2rcAszX3EL79o47XB1ge4uIJMvtoWfhwoWYOnUqEhIS0L17dyxbtgze3t5YtWqV3fXvuusujBo1Ct26dUOHDh0wffp09OrVC3/++aeLKydq3PwtoceR83mAG9feYughIolxa+gpKyvD3r17ERcXZ10ml8sRFxeH9PT0m24vhEBaWhpOnDiBO++80+46paWlKCoqsrkRkfNCj+BIDxFJlFtDT15eHgwGQ5VLWwQHByM7O7va7QoLC6HRaODp6Ylhw4bhgw8+wD333GN33ZSUFPj7+1tv4eHhDn0PRI2Vv6WF7MjD1QHO6SEiyXJ7e6s+fH19sX//fuzevRtvvfUWkpKSsG3bNrvrJicno7Cw0Ho7d+6ca4slkiint7d4ckIikpg6nZzQ0YKCgqBQKJBj+eVrlpOTg5CQkGq3k8vl6NixIwAgOjoax44dQ0pKCu66664q66pUqmovm0HUbJWXwy8vz3Tf0aGHl6EgIoly60iPp6cn+vbti7S0NOsyo9GItLQ0xMbG1no/RqOR1/0iqgN5VhbkRiOEtzcQFubQfQtze4tzeohIatw60gMASUlJmDx5Mvr164f+/ftj8eLFKCkpQUJCAgDTtbzatGmDlJQUAKY5Ov369UOHDh1QWlqKn376CZ9++ik+/PBDd74NokZFmZkJADBERkIpkzl257z2FhFJlNtDz/jx43H58mXMmjUL2dnZiI6OxsaNG62Tm7OysiCX3xiQKikpwbPPPovz58/Dy8sLXbt2xWeffYbx48e76y0QNTqKkycBAIbOnR3/S4CHrBORRLk99ABAYmIiEhMT7T5XeYLyP//5T/zzn/90QVVETZfixAkAgL5LFzh8xhvn9BCRRDXKo7eIqGGsIz2OPlwdN+b0sL1FRFLD0EPU3AgBpXmkxxmhh+0tIpIqhh6i5ubcOciuXYNBoYAhIsLx+2d7i4gkiqGHqLk5ehQAUBgc7NgLjVrwMhREJFEMPUTNzZEjAICroaFO2b3gIetEJFEMPUTNjXmkp8BJoYcjPUQkVQw9RM2NOfQ4a6SHc3qISKoYeoiaEyGcHnos7S1ecJSIpIahh6g5uXgRKCqCUChQ2Lq1c17DMqeH7S0ikhiGHqLmxDzKY4iMhNEZR24BbG8RkWQx9BA1J5bQ44yTElpYTk7I9hYRSQxDD1FzYgk9nTs77SUsl6Hg0VtEJDUMPUTNhFarRfmBAwCAoltucd4LcU4PEUkUQw9RM6DVarEuNRWGQ4cAANuvXoVSqYRarXb8i/HaW0QkUUp3F0BEzqfT6aDKy4P62jUIhQKDnn4aan9/aDQax78YT05IRBLF0EPUTASePw8AkHXtiqA2bZz2OpzTQ0RSxfYWUTPR0hx6EBXl3Bcyt7c4p4eIpIahh6iZCLxwwXSnVy/nvhDbW0QkUQw9RM2Ey0Z6GHqISKIYeoiaA50O/tnZpvtOHumxXHtLxpMTEpHEMPQQNQOKEycgFwLGli0BZ11d3fpiihv3eSkKIpIQhh6iZkB55AgAQN+9OyCTOffFKoYetriISEIYeoiaAaXl8hPdu7vgxSqcCYMtLiKSEIYeomZAcfgwAPNIj5NZz9MDcKSHiCSFoYeoqTMaoTx4EACgd/aRWwDbW0QkWQw9RE1dZibkxcXQe3jA0KWL81+P7S0ikiiGHqKmbu9eAEBeeLhtIHEWtreISKIYeoiauj17AAB57dq55vVkMhgtR4gx9BCRhDD0EDV15tBz2VWhBxUmM7O9RUQSwtBD1JQZDEBGBgAXjvSAV1onImli6CFqyk6eBLRaCG9vFISEuOxljQw9RCRBDD1ETZm5taXv2dP2/DlOxpEeIpIihh6ipsx85JY+OtqlL8s5PUQkRQw9RE2ZZaTHFSclrIDtLSKSIoYeoqZKrwf27TPddddID0MPEUkIQw9RU3X8OHDtGqDRwNChg0tfmu0tIpIihh6ipsrc2kLfvrZnSXYBtreISIoYeoiaKkvo6dfP5S/N9hYRSRFDD1ETpNVqUb5zJwCgqHNnFBQUuPT12d4iIimSROhZunQpIiIioFarERMTg127dlW77ooVK3DHHXegRYsWaNGiBeLi4mpcn6i50Wq1+ObzzyHfvx8A8FNuLrZu3QqlUgm1Wu2SGtjeIiIpcnvoSU1NRVJSEmbPno2MjAxERUUhPj4eubm5dtfftm0bJkyYgF9//RXp6ekIDw/HvffeiwsXLri4ciJp0ul0CDh1Cgq9HsZWrRD31FMYPXo0xo0bB41G45Ia2N4iIilye+hZuHAhpk6dioSEBHTv3h3Lli2Dt7c3Vq1aZXf9zz//HM8++yyio6PRtWtXfPTRRzAajUhLS3Nx5UTSFXLqFABA/o9/IKhVKwQFBbks8ABsbxGRNLk19JSVlWHv3r2Ii4uzLpPL5YiLi0N6enqt9nHt2jWUl5cjMDDQ7vOlpaUoKiqyuRE1dcHm0IOBA93y+mxvEZEUuTX05OXlwWAwIDg42GZ5cHAwsrOza7WPl19+GWFhYTbBqaKUlBT4+/tbb+Hh4Q2um0jShHB76GF7i4ikyO3trYZ45513sHbtWnz77bfVTtBMTk5GYWGh9Xbu3DkXV0nkWopTp+Cl1UKo1UCfPm6pge0tIpIipTtfPCgoCAqFAjk5OTbLc3JyEBISUuO28+fPxzvvvINffvkFvXr1qnY9lUoFlUrlkHqJGgOl+VB1fXQ0PDw93VID21tEJEVuHenx9PRE3759bSYhWyYlx8bGVrvde++9hzfffBMbN25EPzeceI1IyjzMp3Aoj4lxWw1sbxGRFLl1pAcAkpKSMHnyZPTr1w/9+/fH4sWLUVJSgoSEBADApEmT0KZNG6SkpAAA3n33XcyaNQtffPEFIiIirHN/NBqNS49OIZIqpSX09O/vthrY3iIiKXJ76Bk/fjwuX76MWbNmITs7G9HR0di4caN1cnNWVhbkFa4b9OGHH6KsrAwPPfSQzX5mz56NN954w5WlE0nP5ctQZmYCAPS33ea2MtjeIiIpcnvoAYDExEQkJibafW7btm02j8+cOeP8gogaqx07AAD5oaEQLVq4rQy2t4hIihr10VtEVMn27QCAnA4d3FoG21tEJEUMPURNyW+/AQCyO3Z0axkc6SEiKWLoIWoqioqAvXsBABe7dHFrKZzTQ0RSxNBD1FT8+SdgMMAQEYGSai7L4ipsb7mQEMCmTcCxY+6uhEjyGHqImopffwUAlP/jH24uhO0tl/r5Z2DoUKBfP+D0aXdXQyRpDD1ETYUl9LjpelsVsb3lQuvXm/69dg1Yvdq9tRBJHEMPUVNw9Sqwbx8AjvQ0OwcP3ri/dq376iBqBBh6iJqCTZsAoxHo3h3Gm1y3zhU4p8eFKl5E+a+/gBMn3FcLkcQx9BA1Bf/5j+nf4cPdW4cZ21suUlYGWC7YHBVl+vfHH91XD5HEMfQQNXZ6vWkyKwA88IB7azETMpnpDkOPc+Xmmo7eUioB8/UKrQGYiKpg6CFq7HbsMM3padkSiI11dzUAAKFQmO6wveVcBQWmfwMCgAcfNN3/80/T9wMRVcHQQ9TYmdsZuiFDkHf1Kgos/xG6kaW9dU2rRV5eHrRarZsraqIKC03/+vkBkZFAjx6m0bWNG91bF5FEMfQQNXLG778HAPzp74/169dj69atUCqVUKvVbqlHrVZDZh7pyTx+HOvXr8e6desYfJyhqMj0r7+/6V9Le5PzeojsksRV1omonjIzIT95Eka5HO2feQbR4eEATMFDo9G4pSSNRoOut94KbNyITpGR8B48GFu3boVOp3NbTU2WZaTHEnqGDwfefdc0x0uvN831ISIrjvQQNWbmv+gvdeoEv/BwBAUFISgoyO3hwtPLCwDg5emJgIAAt9bSpFUOPbffbprbdfWqaa4XEdlg6CFqzMxH6pzt1cvNhVRimcjMo7ecq+KcHsD0db//ftN9HsVFVAVDD1FjVVgI/P47ACBLaqHH0lbh0VvOVXlOD8B5PUQ1YOghaqw2bQL0eug7dUJR69bursYWR3pco3J7CwDi402h8/hxIDPTPXURSRRDD1FjZf5Lviw+3s2F2MHQ4xqVQo9Wq0VeeTnKzOdr0q5dy6PmiCrg1H6ixshgAH76CQBQds89QHa2mwuqhO0tl9AXFkIJQCsECs6fx+bNm6HX63FrSAgGACj87DNsCgvDuHHj3D65nUgKONJD1BilpwNXrgAtWkDfv7+7q6mKIz1Op9Vqcen0aQDAniNH8JM5BN9///3oOmMGACDs1CnIiouh0+ncVieRlHCkh6iR0Wq1kK9bB28AusGDUSDF9gVDj9PpdDrIy8oAAH0GDsSto0ffOD/TLbcAXbtCdvw4wo8ccXOlRNLBkR6iRkSr1WLdunXQff01AGB7ixZuPwOzXWxvuYSivBwA4Ne6ddXzM5mP4mp76JA7SiOSJI70EDUiOp0OXpcuIfDSJQiFAtEzZyLK39+tZ2C2iyM9LqE0hx7YC7zDhwPz56PtoUPQ8nMgAsCRHqJGx/KXu+yOO9CyQwdJnIG5CoYel7CM9MB8BmwbAwbAGBAAdUkJlHv2uLYwIoli6CFqZNodPGi6M3y4ewupCdtbLqGoaaRHqUTZkCEAAM9Nm1xYFZF0MfQQNSIyrRahJ0+aHljOvCtFHOlxiRrbW7hxDifVf/4DCOGqsogki6GHqBHx3LgRCoMB+o4dgc6d3V1O9Rh6XKLG9hZM53Aq9/SE4swZYNcu1xVGJFEMPUSNiOqbbwAAZSNHureQm2F7yyVqbG8BgEaDM9HRpvuff+6SmoikjKGHqLG4dAkev/4KACgdM8bNxdwER3qcz2iE0hIqqxnpAYDMmBjTndRUhlBq9hh6iBqLzz+HzGBAdvv2MHTs6O5qasbQ43ylpTfu13COpvPdusEYFATk5gK//OKCwoiki6GHqDEQAlizBgBw0nwxSUlje8vpZBUvLVFD6BEKBUpHjDA9YIuLmjmGHqLGICMDOHIEQqXC3/36ubuam+NIj9PJzCM9QqEAPDxqXLf0oYdMd9avB4qKnF0akWQx9BA1BuZRnrL770eZt7d7a6kNhh7ns4z0qFQ3XVXfty/QrRtw7Rqwdq2TCyOSLoYeIqkrLQW++AIAoHv4YTcXU0uW9hZDj9NY2luihknMN1aWAU88Ybq/YoUTqyKSNoYeIqnbsAHIzwfCwlA+aJC7q6kdy0gP5/Q4j6W9VYuRnoKCAly5/34IDw9gzx5g/34nF0ckTQw9RFK3fLnp30mTboQJqWN7y+lk16+b7tQQetRqNZRKJbZu3Ypvfv8df/fqBQAo+9e/XFEikeTwKutEUnbyJLB5s6k98eST7q6m9tjecjrrROYa2lsajQbjxo2DztwKK1WpgMcfhzI1FVi8GGgM88OIHIgjPURS9uGHpn+HDQMiI91bS12wveV8tWxvaTQaBAUFISgoCKr77kNRUBDkRUXAl1+6okoiSXF76Fm6dCkiIiKgVqsRExODXTVcH+bIkSMYM2YMIiIiIJPJsHjxYtcVSuRqJSXA6tWm+88+695a6ortLaeztrdqOEdPFXI5jt55JwBAv3Ah8i5fRl5eHrRarRMqJJIet4ae1NRUJCUlYfbs2cjIyEBUVBTi4+ORm5trd/1r166hffv2eOeddxASEuLiaolc7MsvgcJCoEMHwHy17EaDocfpZHWYyGyhVquRedddKFepoDx6FP9NScH69euxbt06Bh9qFtwaehYuXIipU6ciISEB3bt3x7Jly+Dt7Y1Vq1bZXf+2227DvHnz8PDDD0NVhx90osZCq9UiLy8Pebm50L/3nmnZ5MnIy89HXl4eCgoK3FtgbfGMzM5Xl0PWzTQaDUYmJEA/cSIA4N4jRzB48GDo9XrrvB+ipsxtE5nLysqwd+9eJCcnW5fJ5XLExcUhPT3dYa9TWlqK0grXqCni2UhJorRaLdatWwe9Xo92Bw4g/q+/UOrlha/8/FC+fr11PaVSCXVdWhruwJEep5PV4eSEFWk0GuDll4FVq+C5eTMCr1xxQnVE0uS20JOXlweDwYDg4GCb5cHBwTh+/LjDXiclJQVz5sxx2P6InEWn00Gv12Pw4MFot3IlAMAwdSqGm/8qt1Cr1ab/uKSMocfp6tPesurc2TQ5fsMGeC1fDtx+u4OrI5Imt09kdrbk5GQUFhZab+fOnXN3SUQ1CjpxAh67dgGenvCeOdN65I3lJvnAA7C95Qr1aG/ZSEoCAKi//BJehYWOqopI0twWeoKCgqBQKJCTk2OzPCcnx6GTlFUqFfz8/GxuRFLmtXSp6c6kSUBoqHuLqS+O9DhdfdtbVnffDdx+O2Q6HXpt2eK4wogkzG2hx9PTE3379kVaWpp1mdFoRFpaGmJjY91VFpFbtbhwAaqffzadjHDGDHeXU38MPU5nvfZWfed3yWTA668DALr//juK/v7bNImeh7BTE+bWMzInJSVh8uTJ6NevH/r374/FixejpKQECQkJAIBJkyahTZs2SElJAWCa/Hz06FHr/QsXLmD//v3QaDTo2LGj294HkaP0++EH052HHgK6dHFvMQ1haW8JARiN7q2lqbLM6WnIpPb77oOhd2947NuHwjfewC8jRwIwTZYfN25c42ilEtWBW0PP+PHjcfnyZcyaNQvZ2dmIjo7Gxo0brZObs7KyIJffGIy6ePEievfubX08f/58zJ8/H4MGDcK2bdtcXT6RQykOHEDk/v0QMhlkb7zh7nIapuI1wjja4xQNbm8BgEwGxaxZwKhRiPrjD7RdsgRXZTJs3boVOp2OoYeaHLdfeysxMRGJiYl2n6scZCIiIiCEcEFVRK7n8+67AIDSMWOg7t7dzdU0EEOP0zW4vWXx4INAVBTkBw6g5b//DfHyyw6ojkiamvzRW0SNwo4d8NyyBUa5HNca81weC2WFv6d4BJdzOKK9BQByOfDOO6b7H3wA+fnzDSyMSLoYeojczWgEpk8HAJyMjYWxQwc3F+QAFUZ6ZJzT4xTW9pYjTlQZH286mqu0FN6WAETUBDH0ELmRVqtF8QcfAHv2wKDRYLd5Immjx/aW01nbW464JI9MBpjbq6p16xDI85lRE8XQQ+QmWq0W365ZA4X5sOFdQ4eiPDBQ+peYqI2KoYftLedw1Jwei9tuA8aNg0wIDFy71nTkHVETw9BD5CY6nQ69fvgB3sXF0HfsiE5LljSdw4RlMtNcEYAjPU5iuQyFQ9pbFvPmQXh7IzQzE6qvvnLcfokkgqGHyE0UmZnoaT45p/L99xEUFtY0Ao+FebSHc3qcw6HtLYu2bXHNfHkKnzfeAHh5CmpiGHqI3EEI+CQnQ240ouyee4D77nN3RY7H6285l6PbW2bXn3kGBcHBkF++DMya5dB9E7kbQw+RO/zf/8Fz2zboPTygffNNd1fjHLwUhVM5pb0FAJ6e2P7ww6b7H3wApKc7dv9EbsTQQ+RqR44AL70EANg5ZkzTOETdHkvoYXvLKZzS3jK70L07ikaNAoSA/rHHkHfuHK/HRU0CQw+RK5WWAo88ApSWomzIEBy56y53V+Q85vaWjO0txxMCsmvXTHe9vR26a7VaDaVSifV33IGSgAAoT53ChSlTsG7dOgYfavQYeohc6dVXgYMHgaAgFC9ZYjrKqalie8t5LK0tAPDycuiuNRoNxo0bhwceewz6pUsBAL1++QWtDx2CznJCRKJGiqGHyFV++QVYsMB0f9UqCPOFdZsshh7nMY/yAI6fyAyYgk9QUBD8H3kEeOIJyITA4FWrIMvNdfhrEbkSQw+RK1y5AkyebLr/1FPA8OHurccVLEdvcU6P45lDj0GhADw8nPtaS5ZA37UrvIuK4DttGj9PatQYeoicTQhT0Ll4Eejc+cZoT1NnOU8P5/Q43vXrAACDswMPAHh7o/ijj6D38IDntm3AW285/zWJnIShh8jZ5s0DvvnGNPLxxReAj4+7K3INtrecxzzSo/f0dMnLGbp0wZ8TJpgezJoFfPutS16XyNGU7i6AqKnSarUwfvMNfGfOhAyAdu5c6Nq1A/LyAAAFBQVurc/pLO0thh7Hc3HoAYCTAwcixtMTXh99BDz6KK5t3oxrXbrYrKNWq5vWWcWpyWHoIXICrVaLtPfew7B33oFMCBy56y5sb9kSWL/eZj2lUtk0LjBqj6W9xdDjeOb2litDDwCUvPkmvLKygM2bYRw+HBtffhnXWrSwPq9UKpvO9eOoSWLoIXKCsiNHcM+SJVCWl6Ns8GAEf/45Riur/rg16b+MLSfNq3h4NTlGhZEel85RUCqB1FTob7sNmsxMjP3oI2g3bIAIDERBQQG2bt0KnU7XdL+nqdFj6CFytL//hv+oUVAUFUHfowc8v/0WQX5+7q7K9Xx9AQAyntDO8SqEHleO9RQUFAABAdCuWIGgUaOgycyE6tFHgbQ0ICDAhZUQ1Q8nMhM5UlYWMGQIFJcuIT80FIVffw00x8ADWN+3rLjYzYU0QZb2liuO3sKNszRv3boV69evx+aTJ7HpxRchAgOB3buBYcMYbqlR4EgPkaP8/Tdw773AmTMwtG+PDU8/jfuCgtxdlftYQo9WCzj4UgnNnuU8PS6a02M5S3PFMzKr1WrIhg4FhgwB/vgDfmPGmEZ9iCSMIz1EjrB3LxAbC5w6BURGonD9elz393d3Ve7FkR7nccPRW5azNFtuGo0G6NcP2LoVaNkSHhkZeGDhQp61mSSNoYeoobZsAe66C8jNBaKjge3bYWzTxt1VuZ95To+cocfxXNzeqlHfvsBvv8HYujVanj+PgKFDgUOH3F0VkV0MPUT1JQTwr38B998PaLWmYf7ffgNCQ91dmTRUbG+RY7lhpKdGPXqg4D//QWHr1lCcOwdjbCwKv/iCV2UnyWHoIaqPkhLgsceAadMAvR7lY8ci7+OPkVdWhry8vKZ/4sHaYHvLeaQWegB4du+OH199FRc7d4a8pAS+jz6Kk48+Cm1hobtLI7LiRGaiujp+HBg7Fjh8GFAoUDp3Lj4PDoZ+wwab1Zr0iQdrg6HHedx0csKaaDQajPif/4HuoYegmzkT6k8/RZ/vv0fZgw8CqalASIi7SyRi6CGqNYMBWLwYeO01QKcz/RJPTUVx9+7Qr1+PwYMHI6DCuUqa9IkHa4Ohx3kkONIDmIKPRqMBPvkExf37Q/3ii/D8/XegVy9g6VLgoYcAmczdZVIzxtBDVIlWq61yaK7mwgUgIQFITwcA6IcMQeHixRAhIdZWVkBAAIKa8yHqlVlOTlhS4uZCmiDLIetSmMhcjdKHH8ZP+fkYs24dlEeOAOPGAaNGoeS993CdfxyQmzD0EFWg1Wqxbt066PV6AIDH9evo9/PPuHXrVsjKywE/P+jefhtfqNXQ79hh3a7Zt7Ls4UiP8xQVAQDKJP49VxgSgoJNmxD0738Db78NfPstlJs24eSwYTh6110wKpW8Xhe5FEMPUQU6nQ56vR6DBw1C8IYN8HrrLSjz8wEAZUOGQLtgAfJ9fKDfutWmncW/Vu1g6HEe8+TgMi8vNxdSCyoVMGcOMGYMyidNgurAAQz46iv0z8hA7ksv4cfycl6vi1yGoYeoIr0eHXbtQsSCBVCePAkAKAgNRfpDD+HcrbcCO3cCMI3shISE8Bd1TSqGHiHcXEwT04hCj/VIxrAwFKSm4uI//4l/bNoE5alTCHv6aYyIjESZQoG8ESOs8334RwQ5C0MPEWCaI7FqFVrMm4chWVmmZS1aALNnQzlpEm4zGHBbhdX5S7kWLHN69HoozO1CcpBGEHoqXq+rIuXdd6PPu+9C88EHEEuWIPj0aWDqVFx+800ciI/H6d69oVCp2PIip2DooebtxAlgxQpgzRrgyhUoAFz39YVx2jT4zJwJ+PtDA4C/euuhwn9YHuZDrMlBGkHosXe9LqDCHwwpKZA9/zzKUlLgsWIFWmVlIW7FCuiDg7EvJgalMTHQ9OjhpuqpqWLooebnyhXg+++Bjz8Gfv/dutjQrh2uJCTgPy1bYsTDD8OnuV87q6HkctNoT3ExPCv9x0cNUFZmOmUCpB16gAqHsFcnOBieixcDr74KfPABsHw5lDk5uO2HHyA2bACGDgUefRR48EFetJYcgqGHmofLl4Fvv4U+NRWK336DzGAAABhlMmT16oXj//gHzvXoAaFQ8EgsR/LzA4qL4cHQ4zhXrgAAhFyOcomHnlpr1QqYOxd49VUUr1mDkvfeQ8jffwMbNgAbNsDo4wPDAw/AY/RoID4e4B8kVE8MPdQ0lZebJh1v2QL88gvw3/8CRqP1Gz4vPBx/9+mDv++4A/8YPx791Gr0Mz/H+ToOZJ7X48n2luPk5AAARMuWEPImdiUhlQqyiRPxk4cHfM6fR6edO9Fx5074XbkCeWqq6czOHh7AnXcCw4YBd99tOvFhU/s6kNMw9FDTUFAA7NplCjrp6cAff5guAlqB7tZbcaBTJ4RMmwafqCi0B9CdAce5zEdwcaTHgcyhx9i6tZsLcQ6buUDPPosyIXB+61ZcWbkS3TIz4Xn6NJCWZroBMPr7ozw2FuUDBkA+YAC8Y2PZCqNqMfRQ4yIEcO4cru/ZA+PBg1AcOwZlRgaUmZlVVr3u64sLXbuabt26QduyJZRKJXrExDDouIo59HBOT8NZzhSuysyEL4CyCmc1bmoqzwXSDhuGzSUl2KnXwz8nB20PHkSb48cR8tdf8CwshGrjRqg2bgQACIUCsh49gL59TbcePYBu3YDWrXkJDGLoIfeqfMkHAFB7eEBTVAScPg2cPo3SkychTp2C4u+/oTh+HPLiYtibyVDYqhVyIyORGxmJS506oahdO9w7dCh6qNWwHAPC1pWLcaTHISqeKbz35s24DcA5o7HZzD+r7kiwIr0eyoMH4bFjB/Dbb5Dt3Qvv4mLg4EHTbfXqGyu3aGEKP926AV26ABERplu7dqY5RQxEzYIkQs/SpUsxb948ZGdnIyoqCh988AH69+9f7fpfffUVXn/9dZw5cwadOnXCu+++i/vvv9+FFVO9lJaa2lCXLwPZ2dCdOYOjmzdDffUqvIqK4F1YCM3Vq/C6csV0cU8zVaXdGOVyFIaEQNWnD0SPHtD36gV9nz4QQUFoAaAFgC5gwJEEy0gP5/Q0iPVM4YMH45bNmwEAt8TFNatz2VR7JFhICHDvvdBqtUhNTYUqLw9BZ8+i1dmzCDp/HmEFBVCcOwfZ1avAjh2mW2VeXqbw064dEBKCssBAlAUGQrRqBWOrVvC45RZ4R0YCgYGAQuH8N0tO4/bQk5qaiqSkJCxbtgwxMTFYvHgx4uPjceLECbS207PesWMHJkyYgJSUFDzwwAP44osvMHLkSGRkZODWW291wztoooQwTQYuLTWduK+k5Ma/JSW4npeH8sJCyK5ds70VF0NWWAh5QQFkhYWmW0EB5IWFkFX6j08NoLpoK5RKGG+5BaVhYTgrl6P1bbfBo1s3GLp2haFDB6j9/ODdTH7ZN2qWicwc6XGIgIAAqI8eBQD49Otncy6k5k6j0WDc+PHW0SCdTofNmzdDr9dDUVaGgJwcBFy6hIDsbPjn5kJz5Qp88/PhbfnddPy46QbA03yzx6jRQAQEQPj7Q/j7w+jvD+HnB3lQEDyDggAfH9ubt7f9ZWo14OlpmpjNUSaXkQnh3vPDx8TE4LbbbsP//d//AQCMRiPCw8Pxv//7v5g5c2aV9cePH4+SkhL8+OOP1mW33347oqOjsWzZspu+XlFREfz9/VFYWAg/81+hjmSvXVMflUcparPfKiMbRiMwdqzpvB7l5UBZGQzXr8NYWgqZeVm1/5aXN/g92CNkMpR6e+Oavz+u+ftD5++Ptv37w7NtWyAkBNcDA/HDoUMo8vWFMP9FxQsSNmLz5wMvvYSili0hmzsXvi1bmo60qXy0TeVfQ+58LKVazI+LtVrsy8hAv7AweM+bZxptyMsDmvC8Hke42e9NnU6HXzZsgPryZWiuXIEmPx9excXQFBcj0tsbyvx8yHJzYbhwAeqSEqfVKTw9ITw8AJXK9K+nJ4Snp82/8PAw3ZfLAYXC9PtRoaj62HwTFZ6zPFZ6esLT29u6rLS8HHqDwRS65HJAJoPSwwMqLy+bZZX/1Xl7Qzt8eI3vyRkj7Y74/9utIz1lZWXYu3cvkpOTrcvkcjni4uKQnp5ud5v09HQkJSXZLIuPj8d3331nd/3S0lKUlpZaHxeaz2RaZL5KsSNptVp8++231it0N4RSqcTgwYOhVquh0+mwdevWm+634jYAACEQuH49HPE3RLmHB+QaDeDjA72nJ4r0evi0agWlnx+EWg3h7Q3h5QXh4wMREGD960f4+8No/hf+/hC+vjb/4XmrVNBpNKj4a+mOqCibz0ylUsFoNDrlMyMnGzYMxjfegPzKFYhp08BPsP6iAegB09dwwgTTzxF/Jm7K07O6MRvTc/cOH27z+wYw/c7RazSw/MbVarUo1WohKyoCiopMI9dFRaZR7eJiGK5cwblDh6C8dg3KsjLTrbQUyvJy+/crtO8BmP4wLSszjaQ7kR7Azf4kNwAovck6BcHB+PYm33tKpRKjRo1yaPCx/B/QkLEat4aevLw8GAwGBAcH2ywPDg7GcfMwY2XZ2dl218/Ozra7fkpKCubMmVNleXh4eD2rbqbKy4GrV003C8s1qojItT77zHQjcoecHOD552+6WmJiolNevri4GP71PEGl2+f0OFtycrLNyJDRaER+fj5atmwJmYT6qEVFRQgPD8e5c+ec0nZzN76/xq2pvz+g6b9Hvr/Gje/PNMJTXFyMsLCwer+OW0NPUFAQFAoFcswn27LIyclBSEiI3W1CQkLqtL5KpYJKZXv8T4CE++B+fn5N8hvagu+vcWvq7w9o+u+R769xa+7vr74jPBZuPXe3p6cn+vbtizTzmTUB00hMWloaYmNj7W4TGxtrsz4AbNmypdr1iYiIiAAJtLeSkpIwefJk9OvXD/3798fixYtRUlKChIQEAMCkSZPQpk0bpKSkAACmT5+OQYMGYcGCBRg2bBjWrl2LPXv24N///rc73wYRERFJnNtDz/jx43H58mXMmjUL2dnZiI6OxsaNG62TlbOysiCvcLTPgAED8MUXX+C1117DK6+8gk6dOuG7775r9OfoUalUmD17dpVWXFPB99e4NfX3BzT998j317jx/TmG28/TQ0REROQKbp3TQ0REROQqDD1ERETULDD0EBERUbPA0ENERETNAkOPi7z11lsYMGAAvL29qz05YlZWFoYNGwZvb2+0bt0aL7300k2vt5Wfn4+JEyfCz88PAQEBmDJlCrRarRPeQd1s27YNMpnM7m337t3VbnfXXXdVWf/pp592YeW1FxERUaXWd955p8ZtdDodpk2bhpYtW0Kj0WDMmDFVTrYpBWfOnMGUKVMQGRkJLy8vdOjQAbNnz0ZZWVmN20n581u6dCkiIiKgVqsRExODXbt21bj+V199ha5du0KtVqNnz5746aefXFRp3aWkpOC2226Dr68vWrdujZEjR+LEiRM1brNmzZoqn5X1un0S88Ybb1SptWvXrjVu05g+P3u/S2QyGaZNm2Z3/cbw2f3+++8YPnw4wsLCIJPJqlwfUwiBWbNmITQ0FF5eXoiLi8Nff/110/3W9ee4MoYeFykrK8PYsWPxzDPP2H3eYDBg2LBhKCsrw44dO/Dxxx9jzZo1mDVrVo37nThxIo4cOYItW7bgxx9/xO+//44nn3zSGW+hTgYMGIBLly7Z3J544glERkaiX79+NW47depUm+3ee+89F1Vdd3PnzrWp9X//939rXP+FF17Af/7zH3z11Vf47bffcPHiRYwePdpF1dbe8ePHYTQasXz5chw5cgSLFi3CsmXL8Morr9x0Wyl+fqmpqUhKSsLs2bORkZGBqKgoxMfHIzc31+76O3bswIQJEzBlyhTs27cPI0eOxMiRI3H48GEXV147v/32G6ZNm4b//ve/2LJlC8rLy3Hvvfei5CYXsPTz87P5rM6ePeuiiuuuR48eNrX++eef1a7b2D6/3bt327y3LVu2AADGjh1b7TZS/+xKSkoQFRWFpUuX2n3+vffew/vvv49ly5Zh586d8PHxQXx8PHS66i+JWtefY7sEudTq1auFv79/leU//fSTkMvlIjs727rsww8/FH5+fqK0tNTuvo4ePSoAiN27d1uX/fzzz0Imk4kLFy44vPaGKCsrE61atRJz586tcb1BgwaJ6dOnu6aoBmrXrp1YtGhRrdcvKCgQHh4e4quvvrIuO3bsmAAg0tPTnVChY7333nsiMjKyxnWk+vn1799fTJs2zfrYYDCIsLAwkZKSYnf9cePGiWHDhtksi4mJEU899ZRT63SU3NxcAUD89ttv1a5T3e8iKZo9e7aIioqq9fqN/fObPn266NChgzAajXafb0yfnRBCABDffvut9bHRaBQhISFi3rx51mUFBQVCpVKJL7/8str91PXn2B6O9EhEeno6evbsaXMF+fj4eBQVFeHIkSPVbhMQEGAzchIXFwe5XI6dO3c6vea6+OGHH3DlyhXrmbZr8vnnnyMoKAi33norkpOTce3aNRdUWD/vvPMOWrZsid69e2PevHk1tiP37t2L8vJyxMXFWZd17doVbdu2RXp6uivKbZDCwkIEBgbedD2pfX5lZWXYu3evzdddLpcjLi6u2q97enq6zfqA6eexMXxOgOmzAnDTz0ur1aJdu3YIDw/HiBEjqv1dIwV//fUXwsLC0L59e0ycOBFZWVnVrtuYP7+ysjJ89tln+J//+Z8aL4rdmD67yk6fPo3s7Gybz8jf3x8xMTHVfkb1+Tm2x+1nZCaT7Oxsm8ADwPo4Ozu72m1at25ts0ypVCIwMLDabdxl5cqViI+Pxy233FLjeo888gjatWuHsLAwHDx4EC+//DJOnDiB9evXu6jS2nvuuefQp08fBAYGYseOHUhOTsalS5ewcOFCu+tnZ2fD09Ozypyu4OBgyX1elWVmZuKDDz7A/Pnza1xPip9fXl4eDAaD3Z+v48eP292mup9HqX9OgOn6hc8//zwGDhxY45nqu3TpglWrVqFXr14oLCzE/PnzMWDAABw5cuSmP6euFhMTgzVr1qBLly64dOkS5syZgzvuuAOHDx+Gr69vlfUb8+f33XffoaCgAI8//ni16zSmz84ey+dQl8+oPj/H9jD0NMDMmTPx7rvv1rjOsWPHbjrhrjGpz3s+f/48Nm3ahHXr1t10/xXnI/Xs2ROhoaEYMmQITp06hQ4dOtS/8Fqqy/tLSkqyLuvVqxc8PT3x1FNPISUlRbKniq/P53fhwgUMHToUY8eOxdSpU2vc1t2fHwHTpk3D4cOHa5zzApgu3lzxQs0DBgxAt27dsHz5crz55pvOLrNO7rvvPuv9Xr16ISYmBu3atcO6deswZcoUN1bmeCtXrsR9992HsLCwatdpTJ+d1DD0NMCLL75YYxoHgPbt29dqXyEhIVVmoVuO6gkJCal2m8oTuPR6PfLz86vdpqHq855Xr16Nli1b4sEHH6zz68XExAAwjTS44j/NhnymMTEx0Ov1OHPmDLp06VLl+ZCQEJSVlaGgoMBmtCcnJ8dpn1dldX1/Fy9exN13340BAwbU66K+rv787AkKCoJCoahylFxNX/eQkJA6rS8ViYmJ1gMa6voXv4eHB3r37o3MzEwnVec4AQEB6Ny5c7W1NtbP7+zZs/jll1/qPDLamD474Mb/aTk5OQgNDbUuz8nJQXR0tN1t6vNzbFfdpiNRQ91sInNOTo512fLly4Wfn5/Q6XR292WZyLxnzx7rsk2bNklqIrPRaBSRkZHixRdfrNf2f/75pwAgDhw44ODKHO+zzz4Tcrlc5Ofn233eMpH566+/ti47fvy4ZCcynz9/XnTq1Ek8/PDDQq/X12sfUvn8+vfvLxITE62PDQaDaNOmTY0TmR944AGbZbGxsZKdCGs0GsW0adNEWFiYOHnyZL32odfrRZcuXcQLL7zg4Oocr7i4WLRo0UIsWbLE7vON7fOzmD17tggJCRHl5eV12k7qnx2qmcg8f/5867LCwsJaTWSuy8+x3VrqVjrV19mzZ8W+ffvEnDlzhEajEfv27RP79u0TxcXFQgjTN+2tt94q7r33XrF//36xceNG0apVK5GcnGzdx86dO0WXLl3E+fPnrcuGDh0qevfuLXbu3Cn+/PNP0alTJzFhwgSXv7/q/PLLLwKAOHbsWJXnzp8/L7p06SJ27twphBAiMzNTzJ07V+zZs0ecPn1afP/996J9+/bizjvvdHXZN7Vjxw6xaNEisX//fnHq1Cnx2WefiVatWolJkyZZ16n8/oQQ4umnnxZt27YVW7duFXv27BGxsbEiNjbWHW+hRufPnxcdO3YUQ4YMEefPnxeXLl2y3iqu01g+v7Vr1wqVSiXWrFkjjh49Kp588kkREBBgPVryscceEzNnzrSuv337dqFUKsX8+fPFsWPHxOzZs4WHh4c4dOiQu95CjZ555hnh7+8vtm3bZvNZXbt2zbpO5fc4Z84csWnTJnHq1Cmxd+9e8fDDDwu1Wi2OHDnijrdQoxdffFFs27ZNnD59Wmzfvl3ExcWJoKAgkZubK4Ro/J+fEKb/wNu2bStefvnlKs81xs+uuLjY+v8cALFw4UKxb98+cfbsWSGEEO+8844ICAgQ33//vTh48KAYMWKEiIyMFNevX7fuY/DgweKDDz6wPr7Zz3FtMPS4yOTJkwWAKrdff/3Vus6ZM2fEfffdJ7y8vERQUJB48cUXbRL/r7/+KgCI06dPW5dduXJFTJgwQWg0GuHn5ycSEhKsQUoKJkyYIAYMGGD3udOnT9t8DbKyssSdd94pAgMDhUqlEh07dhQvvfSSKCwsdGHFtbN3714RExMj/P39hVqtFt26dRNvv/22zahc5fcnhBDXr18Xzz77rGjRooXw9vYWo0aNsgkSUrF69Wq7368VB4cb2+f3wQcfiLZt2wpPT0/Rv39/8d///tf63KBBg8TkyZNt1l+3bp3o3Lmz8PT0FD169BAbNmxwccW1V91ntXr1aus6ld/j888/b/16BAcHi/vvv19kZGS4vvhaGD9+vAgNDRWenp6iTZs2Yvz48SIzM9P6fGP//IQwjdIDECdOnKjyXGP87Cz/X1W+Wd6H0WgUr7/+uggODhYqlUoMGTKkyntv166dmD17ts2ymn6Oa0MmhBC1b4YRERERNU48Tw8RERE1Cww9RERE1Cww9BAREVGzwNBDREREzQJDDxERETULDD1ERETULDD0EBERUbPA0ENERETNAkMPkQS88cYb1V5oz5UiIiKwePFid5dh15133okvvvjC3WWQmxw9ehS33HILSkpK3F0KNWIMPdRsPP7445DJZJDJZPDw8EBkZCT+3//7f9DpdC6tQyaT4bvvvrNZNmPGDKSlpTntNbdt22Z979Xdtm3bht27d+PJJ590Wh319cMPPyAnJwcPP/ywu0vBmjVrrF8zuVyO0NBQjB8/HllZWThz5sxNv85r1qxx91tolLp3747bb78dCxcudHcp1Igp3V0AkSsNHToUq1evRnl5Ofbu3YvJkydDJpPh3XffdWtdGo0GGo3GafsfMGAALl26ZH08ffp0FBUVYfXq1dZlgYGB8PT0dFoNDfH+++8jISEBcrk0/k7z8/PDiRMnIITA6dOn8eyzz2Ls2LHYsWOHzdd5/vz52LhxI3755RfrMn9/f6fVVVZW5rLPsLrXKi8vh4eHR533V5vtEhISMHXqVCQnJ0Op5H9fVHfS+A1C5CIqlQohISEIDw/HyJEjERcXhy1btlifNxqNSElJQWRkJLy8vBAVFYWvv/7a+rzBYMCUKVOsz3fp0gVLliyp8jqrVq1Cjx49oFKpEBoaisTERACm9hEAjBo1CjKZzPq4cnvLaDRi7ty5uOWWW6BSqRAdHY2NGzdan7eMKKxfvx533303vL29ERUVhfT0dLvv29PTEyEhIdabl5eX9WthuXl6elZpb8lkMixfvhwPPPAAvL290a1bN6SnpyMzMxN33XUXfHx8MGDAAJw6dcrm9b7//nv06dMHarUa7du3x5w5c6DX6wEAQgi88cYbaNu2LVQqFcLCwvDcc89V+5ldvnwZW7duxfDhw22Wy2QyfPTRRxg1ahS8vb3RqVMn/PDDDzbrHD58GPfddx80Gg2Cg4Px2GOPIS8vz/p8cXExJk6cCB8fH4SGhmLRokW466678Pzzz1dbj+W1Q0JCEBoaigEDBmDKlCnYtWsXSkpKbL6mGo0GSqXS+vj48eMYNGgQfHx8EBAQgIEDB+Ls2bPVvs7LL7+Mzp07w9vbG+3bt8frr7+O8vJy6/OW75uPPvoIkZGRUKvVAICCggI88cQTaNWqFfz8/DB48GAcOHCgxvd07tw5jBs3DgEBAQgMDMSIESNw5swZ6/OPP/44Ro4cibfeegthYWHo0qWL9fswNTUVgwYNglqtxueff17r79/K2509exbDhw9HixYt4OPjgx49euCnn36ybnfPPfcgPz8fv/32W43vhag6DD3UbB0+fBg7duyw+Ws1JSUFn3zyCZYtW4YjR47ghRdewKOPPmr9JWs0GnHLLbfgq6++wtGjRzFr1iy88sorWLdunXUfH374IaZNm4Ynn3wShw4dwg8//ICOHTsCAHbv3g0AWL16NS5dumR9XNmSJUuwYMECzJ8/HwcPHkR8fDwefPBB/PXXXzbrvfrqq5gxYwb279+Pzp07Y8KECdZw4ShvvvkmJk2ahP3796Nr16545JFH8NRTTyE5ORl79uyBEMIa6gDgjz/+wKRJkzB9+nQcPXoUy5cvx5o1a/DWW28BAL755hssWrQIy5cvx19//YXvvvsOPXv2rPb1//zzT2vgqmzOnDkYN24cDh48iPvvvx8TJ05Efn4+ANN//IMHD0bv3r2xZ88ebNy4ETk5ORg3bpx1+6SkJGzfvh0//PADtmzZgj/++AMZGRl1+vrk5ubi22+/hUKhgEKhqHY9vV6PkSNHYtCgQTh48CDS09Px5JNPQiaTVbuNr68v1qxZg6NHj2LJkiVYsWIFFi1aZLNOZmYmvvnmG6xfvx779+8HAIwdOxa5ubn4+eefsXfvXvTp0wdDhgyxfm0qKy8vR3x8PHx9ffHHH39g+/bt0Gg0GDp0KMrKyqzrpaWl4cSJE9iyZQt+/PFH6/KZM2di+vTpOHbsGOLj42v9/Vt5u2nTpqG0tBS///47Dh06hHfffddmBNTT0xPR0dH4448/qv2aEdWonleNJ2p0Jk+eLBQKhfDx8REqlUoAEHK5XHz99ddCCCF0Op3w9vYWO3bssNluypQpYsKECdXud9q0aWLMmDHWx2FhYeLVV1+tdn0A4ttvv7VZNnv2bBEVFWWzj7feestmndtuu008++yzQgghTp8+LQCIjz76yPr8kSNHBABx7Nixal/bYvLkyWLEiBFVlrdr104sWrTIptbXXnvN+jg9PV0AECtXrrQu+/LLL4VarbY+HjJkiHj77bdt9vvpp5+K0NBQIYQQCxYsEJ07dxZlZWU3rVMIIRYtWiTat29fZXnl2rRarQAgfv75ZyGEEG+++aa49957bbY5d+6cACBOnDghioqKhIeHh/jqq6+szxcUFAhvb28xffr0autZvXq1ACB8fHyEt7e3ACAAiOeee67KuhU/1ytXrggAYtu2bbV63/bMmzdP9O3b12b/Hh4eIjc317rsjz/+EH5+fkKn09ls26FDB7F8+XK7+/30009Fly5dhNFotC4rLS0VXl5eYtOmTUII0/dMcHCwKC0tta5j+T5cvHixzf5q+/1bebuePXuKN954o8avwahRo8Tjjz9e4zpE1WFTlJqVu+++Gx9++CFKSkqwaNEiKJVKjBkzBoDpL+Zr167hnnvusdmmrKwMvXv3tj5eunQpVq1ahaysLFy/fh1lZWXW1lRubi4uXryIIUOG1LvGoqIiXLx4EQMHDrRZPnDgwCotil69elnvh4aGWmvo2rVrvV+/soqvERwcDAA2IzPBwcHQ6XQoKiqCn58fDhw4gO3bt1tHdgBTW1Cn0+HatWsYO3YsFi9ejPbt22Po0KG4//77MXz48GrnaFy/ft3atqmpNh8fH/j5+SE3NxcAcODAAfz6669250qdOnUK169fR3l5Ofr3729d7u/vjy5dutz0a+Lr64uMjAyUl5fj559/xueff27zfu0JDAzE448/jvj4eNxzzz2Ii4vDuHHjrJ+bPampqXj//fdx6tQpaLVa6PV6+Pn52azTrl07tGrVyvr4wIED0Gq1aNmypc16169fr9KGrLhNZmYmfH19bZbrdDqbbXr27Gl3Hk+/fv2s9+vy/VtxOwB47rnn8Mwzz2Dz5s2Ii4vDmDFjbD5jAPDy8sK1a9fsvg+im2HooWbFx8fH2mpatWoVoqKisHLlSkyZMgVarRYAsGHDBrRp08ZmO5VKBQBYu3YtZsyYgQULFiA2Nha+vr6YN28edu7cCcD0C9mVKk78tLRJjEaj01+jptfVarWYM2cORo8eXWVfarUa4eHhOHHiBH755Rds2bIFzz77LObNm4fffvvN7kTWoKAgXL169aa1WWqpWMfw4cPtTlIPDQ1FZmZmje+7JnK53Pp91K1bN5w6dQrPPPMMPv300xq3W716NZ577jls3LgRqampeO2117BlyxbcfvvtVdZNT0/HxIkTMWfOHMTHx8Pf3x9r167FggULbNbz8fGxeazVahEaGopt27ZV2WdAQIDdurRaLfr27YvPP/+8ynMVA1Xl17rZ8pupvN0TTzyB+Ph4bNiwAZs3b0ZKSgoWLFiA//3f/7Wuk5+fjw4dOtTr9YgYeqjZksvleOWVV5CUlIRHHnkE3bt3h0qlQlZWFgYNGmR3m+3bt2PAgAF49tlnrcsq/iXs6+uLiIgIpKWl4e6777a7Dw8PDxgMhmrr8vPzQ1hYGLZv325Tx/bt221GJaSqT58+OHHihDUU2OPl5YXhw4dj+PDhmDZtGrp27YpDhw6hT58+Vdbt3bs3srOzcfXqVbRo0aJOdXzzzTeIiIiwO4rUvn17eHh4YPfu3Wjbti0AoLCwECdPnsSdd95Z69cBTHNTOnTogBdeeMHue6j8fnr37o3k5GTExsbiiy++sBt6duzYgXbt2uHVV1+1Lqtp0rNFnz59kJ2dDaVSaZ0oX5ttUlNT0bp16yojSXXV0O/f8PBwPP3003j66aeRnJyMFStW2ISew4cP46GHHmpQjdR8cSIzNWtjx46FQqHA0qVL4evrixkzZuCFF17Axx9/jFOnTiEjIwMffPABPv74YwBAp06dsGfPHmzatAknT57E66+/XmUy8htvvIEFCxbg/fffx19//WXdh4UlFFn+I7fnpZdewrvvvovU1FScOHECM2fOxP79+zF9+nTnfTEcZNasWfjkk08wZ84cHDlyBMeOHcPatWvx2muvATCd52blypU4fPgw/v77b3z22Wfw8vJCu3bt7O6vd+/eCAoKwvbt2+tUx7Rp05Cfn48JEyZg9+7dOHXqFDZt2oSEhAQYDAb4+vpi8uTJeOmll/Drr7/iyJEjmDJlCuRyeY2Ti+0JDw/HqFGjMGvWrGrXOX36NJKTk5Geno6zZ89i8+bN+Ouvv+xO0AZM32tZWVlYu3YtTp06hffffx/ffvvtTWuJi4tDbGwsRo4cic2bN+PMmTPYsWMHXn31VezZs8fuNhMnTkRQUBBGjBiBP/74A6dPn8a2bdvw3HPP4fz587X7IlRQ3+/f559/Hps2bcLp06eRkZGBX3/91ebrc+bMGVy4cAFxcXF1rokIYOihZk6pVCIxMRHvvfceSkpK8Oabb+L1119HSkoKunXrhqFDh2LDhg2IjIwEADz11FMYPXo0xo8fj5iYGFy5csVm1AcAJk+ejMWLF+Nf//oXevTogQceeMDmqJUFCxZgy5YtCA8Pt5krVNFzzz2HpKQkvPjii+jZsyc2btyIH374AZ06dXLeF8NB4uPj8eOPP2Lz5s247bbbcPvtt2PRokXWUBMQEIAVK1Zg4MCB6NWrF3755Rf85z//qTIHxUKhUCAhIcFu66UmltEGg8GAe++9Fz179sTzzz+PgIAA6/l+Fi5ciNjYWDzwwAOIi4vDwIED0a1bt2rnENXkhRdewIYNG7Br1y67z3t7e+P48eMYM2YMOnfujCeffBLTpk3DU089ZXf9Bx98EC+88AISExMRHR2NHTt24PXXX79pHTKZDD/99BPuvPNOJCQkoHPnznj44Ydx9uxZ65wse7X9/vvvaNu2LUaPHo1u3bphypQp0Ol09Rr5qe/3r8FgwLRp06w/e507d8a//vUv6/Nffvkl7r333moDMtHNyIQQwt1FEBHVJDs7Gz169EBGRoZT/8MrKSlBmzZtsGDBAkyZMsVpr0N1V1ZWhk6dOuGLL76oMkmaqLY40kNEkhcSEoKVK1ciKyvLofvdt28fvvzyS2src+LEiQCAESNGOPR1qOGysrLwyiuvMPBQg3Ckh4iarX379uGJJ57AiRMn4Onpib59+2LhwoU1niyRiBovhh4iIiJqFtjeIiIiomaBoYeIiIiaBYYeIiIiahYYeoiIiKhZYOghIiKiZoGhh4iIiJoFhh4iIiJqFhh6iIiIqFn4/xDcMXjr+DfYAAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
" ] @@ -319,7 +341,7 @@ }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGwCAYAAABVdURTAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAXtNJREFUeJzt3XlcVPX+P/DXLMwMMCwiCKgoLrmmoqiI3TKNosxy6YqZN5Wv2aY3i2uLLZr2K+qWS3W92bdcrqW5XbO+WS5RtIjlQu5KroALKCLLAMMwM+f3x3EGRgZkmZkzzLyej8c8Bs6c5TMMM7x4fz7nfGSCIAggIiIi8hByqRtARERE5EgMN0RERORRGG6IiIjIozDcEBERkUdhuCEiIiKPwnBDREREHoXhhoiIiDyKUuoGuJrZbMbFixcREBAAmUwmdXOIiIioAQRBQGlpKdq2bQu5vP7ajNeFm4sXLyIqKkrqZhAREVET5Obmon379vWu43XhJiAgAID4wwkMDJS4NURERNQQJSUliIqKsv4dr4/XhRtLV1RgYCDDDRERUQvTkCElHFBMREREHoXhhoiIiDwKww0RERF5FIYbIiIi8igMN0RERORRGG6IiIjIozDcEBERkUdhuCEiIiKPwnBDREREHoXhhoiIiDwKww0RERF5FIYbIiIi8igMN0RERORRGG6IiBqgqAj4/XdAEKRuCRHdDMMNEdFNZGcDPXsCQ4YA8+dL3RoiuhmGGyKim1i8GMjLE7/+5z+Bigpp20NE9WO4ISK6iW+/rf66ogL46Sfp2kJEN8dwQ0RUj4IC4ORJ8eu//lW8/+476dpDRDfnFuFm6dKliI6OhkajQVxcHPbs2VPnunfeeSdkMlmt2/333+/CFhORt/j9d/G+Rw9g4kTx6x9/lK49RHRzkoeb9evXIyUlBfPmzUNmZib69euHxMREXL582e76mzdvxqVLl6y3I0eOQKFQYPz48S5uORF5gxMnxPt+/YBBg8Svjx8HKiulaxMR1U/ycLNo0SJMnz4dycnJ6NWrF5YtWwY/Pz+sWLHC7vohISGIiIiw3nbu3Ak/Pz+GGyJyirNnxftOnYD27YFWrQCjETh2TNp2EVHdJA03BoMB+/fvR0JCgnWZXC5HQkICdu/e3aB9LF++HA8//DD8/f3tPl5ZWYmSkhKbGxFRQ507J9536gTIZGIFBwAOHpSsSUR0E5KGm4KCAphMJoSHh9ssDw8PR57lvMt67NmzB0eOHMFjjz1W5zqpqakICgqy3qKioprdbiLyHpbKTXS0eN+zp3hvGWRMRO5H8m6p5li+fDn69OmDwYMH17nOnDlzUFxcbL3l5ua6sIVE1JIJgm3lBgC6dhXvT52SpElE1ABKKQ8eGhoKhUKB/Px8m+X5+fmIiIiod9uysjKsW7cOCxYsqHc9tVoNtVrd7LYSkfe5cgUoLxe7ozp0EJdZwg0rN0TuS9LKjUqlQmxsLNLS0qzLzGYz0tLSEB8fX++2GzduRGVlJf72t785u5lE5KUsVZu2bYGqKh0KCgrQuvU1AGLlhvNMEbknSSs3AJCSkoIpU6Zg4MCBGDx4MJYsWYKysjIkJycDACZPnox27dohNTXVZrvly5djzJgxaN26tRTNJiIvYBlvExVlwoYNG2A0GlFVpYBM9j8oLZXhyhWgTRtp20hEtUkebiZMmIArV65g7ty5yMvLQ0xMDLZt22YdZJyTkwO53LbAlJWVhV9//RU7duyQoslE5CUuXhTvIyKMMBqNGDFiBAAgOLgM165pceoUww2RO5I83ADAzJkzMXPmTLuPpaen11rWvXt3CKwHE5GTXbki3oeGmgEAwcHBAIDWrUtx7ZoWPD+ByD216LOliIicyXKhdEu4sWjVqgwAcP68q1tERA3BcENEVIfqyo1tpdgSbli5IXJPDDdERHWwVG40mlKb5a1a6QCwckPkrhhuiIjqcPmy2B2Vnb0HSqUSGo0GABASIoYbVm6I3JNbDCgmInJHlm6phIQYDB3aGlqtFnq9HsHB7JYicmes3BAR2VFZCZSWih+RnTtrodVqrY+FhIjhJi8PqKqSpHlEVA+GGyIiOyxVG7ncjKAg2wHFWm0FVCoBglB9LRwich8MN0REdlgGE2u1eshkto/J5UDbtuJ4HHZNEbkfhhsiIjsKC8V7rVZv9/HwcDHc5OW5qkVE1FAMN0REdlwT58eEn1+l3cdbtRKXZ2fbf5yIpMNwQ0RkhyXc+PvbhheNRgOlUgm9/hwA4Mcfj0On07m4dURUH4YbIiI7LN1SN1ZutFotkpKSEB/fCQBQVKSBXm+/64qIpMHr3BAR2VFft5RWq0WXLuLXJSW+LmwVETUEKzdERHbU1S1lEREh3hcX+7moRUTUUAw3RER2FBeL976+BruPW8INKzdE7ofhhojIjtLrc2VqNPYvQVwdbvxgNruoUUTUIAw3RER2lJSI9xqN/cpNmzbivdksx7VrMrvrEJE0GG6IiOywhBtfX/uVGx8foHVrsWRz+TI/SoncCd+RRER2VHdL2a/cAEBYGMMNkTviO5KIyI7qbqm6p/1u04bhhsgd8R1JRHSD0lIdSkrEmcAbEm7y8/lRSuRO+I4kIqpBp9Nh7dr/wmgUBwlrtWZoNBq764aFiQHo6lUOKCZyJww3REQ16PV6lJZWh5VHHx0LrVZrd13LgOKCAn6UErkTviOJiG6g16sAAFotEBhoP9gAQEiIGG6uXuVHKZE74TuSiOgGer0PACAwsP71QkPZLUXkjhhuiIhuYKnc3CzcWLqlWLkhci98RxIR3aCiQqzcBATUv171mBtWbojcCcMNEdENKisb1y1VViaHXu/sVhFRQzHcEBHdoKJC7Ja6WeUmMFCAQmECAJw7V+bsZhFRAzHcEBHdoKFjbnx9NdBqKwEAX3yxEzqdztlNI6IGYLghIrpBQ8+W0mq1iIpSAwCKi32gZ98UkVtguCEiuoEl3NysWwoAwsMVAIDSUl9nNomIGoHhhojoBpYxNzer3ABAWJh4X1pqf4oGInI9hhsiohs09GwpoDrc6HSs3BC5C4YbIqIbNPRsKQAIDRXvWbkhch8MN0REN2jogGKgZuWG4YbIXTDcEBHdoDEDiqvH3LBbishdMNwQEd3Acp2bxoQbVm6I3Ifk4Wbp0qWIjo6GRqNBXFwc9uzZU+/6RUVFmDFjBiIjI6FWq9GtWzd8++23LmotEXkDg0EJAPD3v/m6DDdE7kcp5cHXr1+PlJQULFu2DHFxcViyZAkSExORlZWFNm3a1FrfYDDg7rvvRps2bbBp0ya0a9cO2dnZCA4Odn3jichjVVY2PtyUlWlgNPIKxUTuQNJws2jRIkyfPh3JyckAgGXLlmHr1q1YsWIFXnrppVrrr1ixAoWFhcjIyICPj9gnHh0d7comE5GHM5kAo7Hh4SYkBJDJBAiCDIWFMkREOLmBRHRTknVLGQwG7N+/HwkJCdWNkcuRkJCA3bt3293m66+/Rnx8PGbMmIHw8HDceuuteOutt2Aymeo8TmVlJUpKSmxuRER1KS+XWb9uSLhRKIBWrcTZwQsLJe/pJyJIGG4KCgpgMpkQHh5uszw8PBx5eXl2tzlz5gw2bdoEk8mEb7/9Fq+99hoWLlyI//f//l+dx0lNTUVQUJD1FhUV5dDnQUSepez65N4ymQBNA4fRtGplBgAUFspusiYRuUKL+jfDbDajTZs2+N///V/ExsZiwoQJeOWVV7Bs2bI6t5kzZw6Ki4utt9zcXBe2mIhaGkvlxs9PgKyBWSUkRKzcXLvWoj5SiTyWZGNuQkNDoVAokJ+fb7M8Pz8fEXV0WkdGRsLHxwcKhcK6rGfPnsjLy4PBYIBKpaq1jVqthlqtdmzjichjVYebhm9T3S3Fyg2RO5Ds3wyVSoXY2FikpaVZl5nNZqSlpSE+Pt7uNrfddhtOnToFs9lsXfbnn38iMjLSbrAhImosS7jx9xcavI2lW4qVGyL3IOk7MSUlBZ988gn+85//4Pjx43jqqadQVlZmPXtq8uTJmDNnjnX9p556CoWFhZg1axb+/PNPbN26FW+99RZmzJgh1VMgIg/TlHBT3S3Fyg2RO5D0VPAJEybgypUrmDt3LvLy8hATE4Nt27ZZBxnn5ORALq/OX1FRUdi+fTuee+459O3bF+3atcOsWbPw4osvSvUUiMjDlJVVj7lpqOoBxazcELkDScMNAMycORMzZ860+1h6enqtZfHx8fjtt9+c3Coi8lY1BxQ3FCs3RO6F/2YQEdXQlHDDyg2Re+E7kYiohvJy8Z6VG6KWi+GGiKgGS+XG17fh2/BsKSL3wnciEVENFRWWcNO0yo3Q8M2IyEkYboiIatDrGx9uLJUbo1EGTl9HJD2GGyKiGvR68b4x4UajAVSqKgDA1avOaBURNQbDDRFRDZZuqYZOmmnh718JgOGGyB0w3BAR1dCUMTcAoNWKJR+GGyLpMdwQEdVQXblpXLjx9xfDTUGBw5tERI3EcENEVINlzE1jrnMDsFuKyJ0w3BAR1WA5W6qxY27YLUXkPhhuiIhqaGq3FMMNkftguCEiqqGpA4rZLUXkPhhuiIhqaMp1boDqAcUMN0TSY7ghIqqhqde50WpZuSFyFww3REQ1NGX6BYCVGyJ3wnBDRHSdIHBAMZEnYLghIrrOMt4GAPz8GretZUBxWRlQWenARhFRozHcEBFdV1FR/XVjKze+vgYoFOI2rN4QSYvhhojouvJy8V4uN0OpbNy2MhnQqhXDDZE7YLghIrrOUrlRqYxN2j4wUNzu/PmKm6xJRM7EcENEdF1Tw41Go4FSqYRMVggA+L//y4BOp3N084iogRhuiIius3RL+fg0LtxotVokJSXhlltaAQBKSnygrzk6mYhcqpG9ykREnqs53VJarRaRkeLXZWWNvAIgETkUKzdERNdZwo2Pj6lJ27duLd6Xlakd1CIiagqGGyKi6yzdUk0dUGwJNzodKzdEUmK4ISK6rrlnS1VXbhhuiKTEcENEdF31gGJ2SxG1ZAw3RETXOapyw24pImkx3BARXee4bilWboikxHBDRHSdI7ulzGYHNYqIGo3hhojoOkdVbgRBjpISmYNaRUSNxXBDRHRd9XVumhZuVCrA318s2RQWMtwQSYXhhojouuZe5wYAQkLEmcGvXePHK5FU+O4jIrquud1SANCqlRhuWLkhkg7DDRHRdc0dUAwAISFitxQrN0TS4buPiOg6y0TeTR1zA7ByQ+QOGG6IiK6rrBTvm1O5adWKlRsiqfHdR0R0naVyo1Q2p1tKrNzk5VVBp9M5ollE1EhuEW6WLl2K6OhoaDQaxMXFYc+ePXWuu2rVKshkMpubRsNLnRNR8zmictOmjQIAkJVVgA0bNjDgEElA8nCzfv16pKSkYN68ecjMzES/fv2QmJiIy5cv17lNYGAgLl26ZL1lZ2e7sMVE5KkcUblp21acekGtjoTRaITeslMichnJw82iRYswffp0JCcno1evXli2bBn8/PywYsWKOreRyWSIiIiw3sLDw+tct7KyEiUlJTY3IiJ7HFG5sVyluLhY5YAWEVFTSBpuDAYD9u/fj4SEBOsyuVyOhIQE7N69u87tdDodOnbsiKioKIwePRpHjx6tc93U1FQEBQVZb1FRUQ59DkTkOSzhRqls+sRQlnBz7RrPliKSiqThpqCgACaTqVblJTw8HHl5eXa36d69O1asWIGvvvoKn3/+OcxmM4YOHYrz58/bXX/OnDkoLi623nJzcx3+PIjIMzjiVPDqcCN5YZzIaymlbkBjxcfHIz4+3vr90KFD0bNnT3z88cd44403aq2vVquhVqtd2UQiaqEcWbmpqJDBYFA4oFVE1FiS/msRGhoKhUKB/Px8m+X5+fmIiIho0D58fHzQv39/nDp1yhlNJCIv4ojKTWAgoLz+b2NZGc/kJJKCpOFGpVIhNjYWaWlp1mVmsxlpaWk21Zn6mEwmHD58GJGRkc5qJhF5AaMRMF8v2DSnciOTVVdvdDpWjYmkIHm3VEpKCqZMmYKBAwdi8ODBWLJkCcrKypCcnAwAmDx5Mtq1a4fU1FQAwIIFCzBkyBB07doVRUVFePfdd5GdnY3HHntMyqdBRC1czTO2m3MqOCCGm/x8Vm6IpCJ5uJkwYQKuXLmCuXPnIi8vDzExMdi2bZt1kHFOTg7k8uoC07Vr1zB9+nTk5eWhVatWiI2NRUZGBnr16iXVUyAiD2AZbwM4JtwAQFkZKzdEUpA83ADAzJkzMXPmTLuPpaen23y/ePFiLF682AWtIiJvYgk3CoUAhUJo1r6qu6VYuSGSAs9VJCJCdbeUI06urK7cMNwQSYHhhogI1ZUbtbp5VRuAA4qJpMZwQ0SEmqeBN/1MKQtWboikxXBDRATg2rVyAIDZXA6lUgmNpunBhAOKiaTFcENEBKC01AAACAzUICkpCVqttsn74oBiImkx3BARAaisFCe69POTNyvYAKzcEEmN4YaICIDBIIYbRw4o5pgbImkw3BARwTmngpeXq2Fq3vUAiagJGG6IiFBduVGpml+5CQkR7wVBhuJiWbP3R0SNw3BDRATHXufGxwcICBBPKS8s5McskavxXUdEhOoBxY7olgKAkBAxJBUWsnJD5GoMN0REcOyAYgBo1Uqs3Fy7xo9ZIlfju46ICI4dUAwArVqxckMkFYYbIiI4dkAxAISEsHJDJBW+64iIABjECxSzckPkARhuiIgA6PWOHXNTXblhuCFyNYYbIiLUrNw4akCxpXLDj1kiV+O7jogINSs3jtkfKzdE0mG4ISKC4wcUWyo3HFBM5Hp81xERwfEDii2VGw4oJnI9hhsiIjhjQHF15UZwzC6JqIEYboiI4IwBxWLlprJShvJyh+ySiBqI4YaICI4fUOzvDyiVJgDA1auO2ScRNQzDDRERqis3jhpQLJMB/v7inA4MN0SuxXBDRISaE2c6bp/+/pUAGG6IXI3hhogIjh9QDABaLSs3RFJguCEiguNPBQeqKzcFBY7bJxHdHMMNERHEs5oAx1ZuLGNucnPLoNPpHLZfIqofww0REYBKscjisAHFGo0GgYFiOWjv3jPYsGEDAw6RizDcEBGhZuXGMfvTarUYOrTH9a+jYTQaodfrHbNzIqoXww0ReT1BqHm2lOO6pdq2FZOSTufAgTxEdFNNCjdnzpxxdDuIiCRjGUwMOHZAcevW4j1nBidyrSaFm65du2L48OH4/PPPWWYlohav5seYIys31eGGRXIiV2rSOy4zMxN9+/ZFSkoKIiIi8MQTT2DPnj2ObhsRkUtYBhMDgErluP1awg1nBidyrSaFm5iYGLz//vu4ePEiVqxYgUuXLuEvf/kLbr31VixatAhXrlxxdDuJiJzGUrlRKk2QOTCHWMJNSYkcJhMDDpGrNKtWqlQqMW7cOGzcuBHvvPMOTp06hdmzZyMqKgqTJ0/GpUuXHNVOIiKnsVRuLBNdOkqrVtVfl5VxUDGRqzQr3Ozbtw9PP/00IiMjsWjRIsyePRunT5/Gzp07cfHiRYwePdpR7SQichpLuPHxcWy4USqB4GDx67IyjUP3TUR1UzZlo0WLFmHlypXIysrCyJEjsXr1aowcORJyuZiVOnXqhFWrViE6OtqRbSUicoqa3VKO1ro1UFTEyg2RKzWpcvPRRx/hkUceQXZ2NrZs2YJRo0ZZg41FmzZtsHz58gbtb+nSpYiOjoZGo0FcXFyDByevW7cOMpkMY8aMaexTICKyclblBqged6PTsXJD5CpNCjc7d+7Eiy++iMjISJvlgiAgJycHAKBSqTBlypSb7mv9+vVISUnBvHnzkJmZiX79+iExMRGXL1+ud7tz585h9uzZuP3225vyFIiIrJxZuQkNFe8Zbohcp0nhpkuXLiiwM81tYWEhOnXq1Kh9LVq0CNOnT0dycjJ69eqFZcuWwc/PDytWrKhzG5PJhEmTJmH+/Pno3LlzvfuvrKxESUmJzY2IqCZnDSgGgLAw8b601Nfh+yYi+5oUbgTB/kWudDodNJqG/3diMBiwf/9+JCQkVDdILkdCQgJ2795d53YLFixAmzZtMG3atJseIzU1FUFBQdZbVFRUg9tHRN7Bmd1SlnDDyg2R6zRqQHFKSgoAQCaTYe7cufDz87M+ZjKZ8PvvvyMmJqbB+ysoKIDJZEJ4eLjN8vDwcJw4ccLuNr/++iuWL1+OAwcONOgYc+bMsbYbAEpKShhwiMiGbbdUk86zqBPDDZHrNepd/McffwAQKzeHDx+GqsalPFUqFfr164fZs2c7toU1lJaW4tFHH8Unn3yCUEtH9k2o1WqoHTlZDBF5HFdUbkpLGW6IXKVR4ebHH38EACQnJ+P9999HYGBgsw4eGhoKhUKB/Px8m+X5+fmIiIiotf7p06dx7tw5PPDAA9ZlZrMZgHhBwaysLHTp0qVZbSIi7+PMAcUcc0Pkek0ac7Ny5cpmBxtArPbExsYiLS3NusxsNiMtLQ3x8fG11u/RowcOHz6MAwcOWG8PPvgghg8fjgMHDrC7iYiahGNuiDxLgys348aNw6pVqxAYGIhx48bVu+7mzZsb3ICUlBRMmTIFAwcOxODBg7FkyRKUlZUhOTkZADB58mS0a9cOqamp0Gg0uPXWW222D75++c8blxMRNZTrzpaqrHddInKMBoeboKAgyK7PKBcUFOSwBkyYMAFXrlzB3LlzkZeXh5iYGGzbts06yDgnJ6fWBQKJiBzJ0i3lzMpNVZUSZWXV170hIueRCXWd1+2hSkpKEBQUhOLiYod0rRFRy/fKK8BbbwEjRhzG+vWRDT5hoSEEAfD1FVBZKcP+/YUYMCDEYfsm8iaN+fvdpJJIRUUFysvLrd9nZ2djyZIl2LFjR1N2R0QkKWcOKJbJgNatxRMfrl5lFZrIFZr0Ths9ejRWr14NACgqKsLgwYOxcOFCjB49Gh999JFDG0hE5GzOHFAMAK1biwXyggKZU/ZPRLaaFG4yMzOtczpt2rQJERERyM7OxurVq/HBBx84tIFERM5WPaDY7JT9s3JD5FpNeqeVl5cjICAAALBjxw6MGzcOcrkcQ4YMQXZ2tkMbSETkbNUDio1O2X9oKMMNkSs16Z3WtWtXbNmyBbm5udi+fTvuueceAMDly5c5SJeIWhznV27EbqmrV9ktReQKTQo3c+fOxezZsxEdHY24uDjrBfd27NiB/v37O7SBRETO5uzKjaVbqqCAlRsiV2jSDHF//etf8Ze//AWXLl1Cv379rMvvuusujB071mGNIyJyBWdXbtgtReRaTZ7+NiIiotb8T4MHD252g4iIXM2ZVygGqrulCgvZLUXkCk0KN2VlZXj77beRlpaGy5cvWyevtDhz5oxDGkdE5ArOvEIxAISEsFuKyJWaFG4ee+wx/PTTT3j00UcRGRlpnZaBiKglcnblJjTUcp0bQKfTQavVOuU4RCRqUrj57rvvsHXrVtx2222Obg8Rkcs5u3LTtq0PAECnU2DNmk2YNOmvDDhETtSkGmmrVq0QEsL5UYjIMzi7ctO+vRYKhVi9KSpSQm9JU0TkFE0KN2+88Qbmzp1rM78UEVFL5exwI5cDoaFi971O5+uUYxBRtSZ1Sy1cuBCnT59GeHg4oqOj4ePjY/N4ZmamQxpHROQKzu6WAoCwMCA/Hygt1TjtGEQkalK4GTNmjIObQUQkHWdXbgAx3ACATsdwQ+RsTQo38+bNc3Q7iIgk46rKDQCUlrJbisjZmnzRhaKiInz66aeYM2cOCgsLAYjdURcuXHBY44iInM1oBEzXM40rKjfsliJyviZVbg4dOoSEhAQEBQXh3LlzmD59OkJCQrB582bk5ORg9erVjm4nEZFTWLqkAFd1S7FyQ+RsTarcpKSkYOrUqTh58iQ0mur/QkaOHImff/7ZYY0jInK2muHGmd1S4eHifUkJww2RszUp3OzduxdPPPFEreXt2rVDXl5esxtFROQqlnAjlwvWa9E4A8MNkes0Kdyo1WqUlJTUWv7nn38izFJ7JSJqAa5eLQMAqNXOCzYAYJlnmOGGyPmaFG4efPBBLFiwAFVVVQAAmUyGnJwcvPjii3jooYcc2kAiImfR6XT46qttAACZzAClUmnT1e5I1ZUbPwjOzVFEXq9J4WbhwoXQ6XQICwtDRUUFhg0bhq5duyIgIABvvvmmo9tIROQUer3eehq4VqtCUlKS0+Z8soSbqiolyso42TCRMzXpbKmgoCDs3LkTu3btwsGDB6HT6TBgwAAkJCQ4un1ERE5lNIr/4/n5yZ06maW/P+Dvb0ZZmRz5+TJERzvtUERer9Hhxmw2Y9WqVdi8eTPOnTsHmUyGTp06ISIiAoIgQCbjfyRE1HJUVSkAAGq1848VFiagrAy4cqXJlxgjogZo1DtMEAQ8+OCDeOyxx3DhwgX06dMHvXv3RnZ2NqZOnYqxY8c6q51ERE5hNIrhxklDbWyEhZkBMNwQOVujKjerVq3Czz//jLS0NAwfPtzmsR9++AFjxozB6tWrMXnyZIc2kojIWVxZuWnThuGGyBUa9Q774osv8PLLL9cKNgAwYsQIvPTSS1izZo3DGkdE5GyurNxYws3lyww3RM7UqHfYoUOHcO+999b5+H333YeDBw82u1FERK7i2jE3lsoNxyYSOVOjwk1hYSHCLecz2hEeHo5r1641u1FERK5iqdy4akAxwG4pImdr1DvMZDJBqax7mI5CoYDRaGx2o4iIXMVSueGAYiLP0agBxYIgYOrUqVDX8S9OZc0Z6IiIWgBXVm445obINRoVbqZMmXLTdXimFBG1JFJVbgQB4GXBiJyjUeFm5cqVzmoHEZEkXDvmRgw3FRUy6HRAQIDzj0nkjVgbJSKv5spw4+8PqNXihMN5ec4/HpG3YrghIq/mym4pAAgMLAcA5Oe75nhE3ojhhoi8misrNwAQGFgBgOGGyJkYbojIq7m6chMQwHBD5GxuEW6WLl2K6OhoaDQaxMXFYc+ePXWuu3nzZgwcOBDBwcHw9/dHTEwMPvvsMxe2log8iVSVG465IXIeycPN+vXrkZKSgnnz5iEzMxP9+vVDYmIiLl++bHf9kJAQvPLKK9i9ezcOHTqE5ORkJCcnY/v27S5uORF5AteHG465IXI2ycPNokWLMH36dCQnJ6NXr15YtmwZ/Pz8sGLFCrvr33nnnRg7dix69uyJLl26YNasWejbty9+/fVXF7eciDyB6wcUs1uKyNkkDTcGgwH79+9HQkKCdZlcLkdCQgJ279590+0FQUBaWhqysrJwxx132F2nsrISJSUlNjciIgujUfwYZLcUkeeQNNwUFBTAZDLVmowzPDwcefW884uLi6HVaqFSqXD//ffjww8/xN1332133dTUVAQFBVlvUVFRDn0ORNSyVVWJ1zJ1deXm0iUTdDqdaw5K5GUk75ZqioCAABw4cAB79+7Fm2++iZSUFKSnp9tdd86cOSguLrbecnNzXdtYInJrrqzcaDQahIQYAACXLglYv34DAw6REzRq+gVHCw0NhUKhQP4Nnc/5+fmIiIioczu5XI6uXbsCAGJiYnD8+HGkpqbizjvvrLWuWq2uc6JPIiJXDijWarWYOvU+vPSSWDHS6WTQ6/XQarXOPziRF5G0cqNSqRAbG4u0tDTrMrPZjLS0NMTHxzd4P2azmTOSE1GTuHpAcXi4FoGB4tfFxX6uOSiRl5G0cgMAKSkpmDJlCgYOHIjBgwdjyZIlKCsrQ3JyMgBxlvF27dohNTUVgDiGZuDAgejSpQsqKyvx7bff4rPPPsNHH30k5dMgohbK1aeCA0DbtkBJCVBU5O+6gxJ5EcnDzYQJE3DlyhXMnTsXeXl5iImJwbZt26yDjHNyciCXVxeYysrK8PTTT+P8+fPw9fVFjx498Pnnn2PChAlSPQUiasFcXbkBxHBz4gRQVMTKDZEzyARBEKRuhCuVlJQgKCgIxcXFCLTUhonIKxUUFKBDhwBUVKiRlQV06+aa406eDHz2GTBu3O/4+OMuCA0Ndc2BiVqwxvz9bpFnSxEROYpU3VIAKzdEzsJwQ0ReSxBcf50boGa44ZgbImdguCEir1VVVf21FJUbni1F5BwMN0TktSorZdavXVm5addOvGe3FJFzMNwQkdeqeXkslcp1x62u3PjDu07pIHINhhsi8lqWyo2PjwC5Cz8NIyPFe6NRgbNnSzgFA5GDMdwQkdeyVG7UateWT1QqIDTUDAD45ptMbNjAOaaIHInhhoi8lsEgVm6kmH6uXTvx4zc6eiiMRiP0er3rG0HkoRhuiMhrSVW5AarH3ZSWBrj82ESejuGGiLyWZcyNKwcTW1jCTV4eP4aJHI3vKiLyWpZwI0XlxnI6OMMNkePxXUVEXqu6W8r1x2blhsh5+K4iIq9VPaBYujE3DDdEjsd3FRF5LXcYUHzpEj+GiRyN7yoi8lpSDii2jLm5ckUOk0lW/8pE1CgMN0TktSzhRqNxfeUmLAxQKACzWYaSEl+XH5/IkzHcEJHXsnRLSVG5USiqqzfXrmld3wAiD8ZwQ0ReyzKgWKWSZvbKqCjx/to1f0mOT+SpGG6IyGtZKjdSdEsBQIcO4n1hISs3RI7EcENEXkvKAcVAdeWG4YbIsRhuiMhrsXJD5JkYbojIa0lduWG4IXIOhhsi8lruM6CY4YbIkRhuiMhrSTm3FFBduSkt9UVFhTRtIPJEDDdE5LX0euku4gcArVoBfn7isS9e5McxkaPw3UREXstgEO+lmFsKAGQyoF07EwAgK6sCBQUF0Ol0krSFyJMopW4AEZFUqis30rWhQwfg5Enghx9OQq//E0qlEklJSdBqOQ6HqKlYuSEir1V9tpQ0lRsAiI4W/8eMiBiEESNGwGg0Qq/XS9YeIk/AcENEXqv6OjfStcEyqLigwB/BwcHSNYTIgzDcEJHXslRupBpzA1SfDp6bK1kTiDwOww0ReS1L74+U4cZSucnJkawJRB6H4YaIvJblIn7u0C2VkwMI0mUsIo/CcENEXstytpSUlZv27cX78nKgqEgmWTuIPAnDDRF5reorFEsXbnx9gbAw8evcXH4kEzkC30lE5LWqBxRL245OncT7nByFtA0h8hAMN0TklQTBPc6WAoDOncX77GyGGyJHYLghIq9kmXoBkHZAMVBducnO5kcykSPwnUREXqnmRYBZuSHyLG4RbpYuXYro6GhoNBrExcVhz549da77ySef4Pbbb0erVq3QqlUrJCQk1Ls+EZE9NcONj4907QBqjrlxi49kohZP8nfS+vXrkZKSgnnz5iEzMxP9+vVDYmIiLl++bHf99PR0TJw4ET/++CN2796NqKgo3HPPPbhw4YKLW05ELZkl3Pj4GCGT+AxsS+UmJ0cBs1nathB5AsnDzaJFizB9+nQkJyejV69eWLZsGfz8/LBixQq7669ZswZPP/00YmJi0KNHD3z66acwm81IS0tzccuJqCWrDjcmaRsCcQoGhUK8qGBxsZ/UzSFq8SQNNwaDAfv370dCQoJ1mVwuR0JCAnbv3t2gfZSXl6OqqgohISF2H6+srERJSYnNjYjIEm6USunDjVJZcwLNQGkbQ+QBJA03BQUFMJlMCA8Pt1keHh6OvLy8Bu3jxRdfRNu2bW0CUk2pqakICgqy3qIss9QRkVezXMDPx8cobUOus3RNFRQESNsQIg8gebdUc7z99ttYt24dvvzyS2jqOJdzzpw5KC4utt5yOfUukSR0Oh0KCgpQUFAAnU4ndXPcqlsKqB5UzHBD1HxKKQ8eGhoKhUKB/Px8m+X5+fmIiIiod9v33nsPb7/9Nr7//nv07du3zvXUajXUUl9+lMjL6XQ6bNiwAUajWCVRKpVISkqCVquVrE3u1C0F1KzcsFuKqLkkrdyoVCrExsbaDAa2DA6Oj4+vc7t//vOfeOONN7Bt2zYMHDjQFU0lombQ6/UwGo0YMWIERowYAaPRCH3Nc7ElaZN4z8oNkeeRtHIDACkpKZgyZQoGDhyIwYMHY8mSJSgrK0NycjIAYPLkyWjXrh1SU1MBAO+88w7mzp2LtWvXIjo62jo2R6vVSvpfIBHdXHBwsNRNsLINN9JfPM92zI20wY+opZM83EyYMAFXrlzB3LlzkZeXh5iYGGzbts06yDgnJwdyeXWB6aOPPoLBYMBf//pXm/3MmzcPr7/+uiubTkQtmGVAsbt1SxUVaVFRwXBD1ByShxsAmDlzJmbOnGn3sfT0dJvvz5075/wGEZHHc7duqdatgaAgM4qL5Th3TgGe2EnUdC36bCkioqZytwHFMhnQpYvYltOnpe8mI2rJGG6IyCu5W+UGYLghchS36JYiIu9QWQmcOKFAeblK6qYw3BB5MIYbInKJAwc6YvbsEJSWyuHn9zDatKnEI49I156aE2e6i86dGW6IHIHdUkTkdPv3K/HJJwkoLZVDrRZQXq7B9OmByMmRrk3udrYUUB1uzpxhuCFqDoYbInIqsxl46SV/GI0K3HtvJU6evIquXS+hvFyGl16Srl3u2S1lBgAUFMhx7ZrEjSFqwRhuiMipNm4EDhzwgVptwMKFOvj6AhMmZFx/TMCVK9K0y93OlgIArVZAUFAZAODkSYkbQ9SCMdwQkdOYzcCrr4pfJyYeRJs2AjQaDTp3LkaHDldgNMqwenWlJG1zx8oNAISHFwMA/vxT4oYQtWAMN0TkNNu2AadOiRenS0g4DECcKiUpKQmPPiqus3mzNB9D7hpu2rRhuCFqLoYbInKaf/9bvB83rgRqdfVZSVqtFqNGiSdr7t+vtA7udSXLMd0t3LByQ9R8DDdE5BR5ecB33wkAgE6dtkOpVEKj0Vgf79LFhICAclRWyrBvn+vb546nggPV4SYrS+KGELVgDDdE5BQbNgBmswydOuVj4sRYJCUlQavVWh+XyYCuXfMBAL/84vr2VQ8oNrv+4PWIiCgCAGRlCTC7V9OIWgyGGyJyirVrxfvBg08hODjYJthYdO16CYC04cadKjcajQaRkeVQKo2oqJAhI+MSCgoKoNPppG4aUYvCcENEDnfmDPD774BcLiA29kyd691ySx4AYNcuwOTioS/ueSq4Fg8/PB7duoklmy++OITNmzdjw4YNDDhEjcBwQ0QOo9PpUFBQgP/8Rxyte8cdVQgKqqhz/fbtr8Lf34ziYuDIEVe1UuSuZ0tptVrExIhzb7Vq9ReMGDECRqMRekuDieimGG6IyCF0Oh02bNiAzZs347PPSgAADz5Y/2lQCoWAQYPEbqHffnN6E22469lSAHDrreL9uXP+CA4OlrQtRC0Rww0ROYRer4fRaESHDoNx9mwYAODuuw033a53bzHcHDvm1ObV4o7dUha9e4v3rq5mEXkKhhsicqj9+8MBADExVYiIEG66/i23iOFCqnDjjpUbS7g5ccL1Y5GIPAHDDRE5VHq6eFbUPffcvGoDAN27i3+9jx93WpPscudw06kT4Osrdp2dO8ePaaLG4ruGiBymqkqOjAx/AA0PN+HhhQCACxeA4mKnNc2G2QxUVYlfu9Op4BZyOdCrl/j18eNKaRtD1AIx3BCRw5w8GYnycjmCg8vQt2/9FRGNRgOlUom9e79HcLB4mvP+/eWuaKbNdA/uOOYGqO6ayspSSNsQohaI4YaIHObgwY4AgFtvzYFMVv+6lgk0x40bh169xI+iw4ddEzQqapyd7o7dUkB1uGHlhqjxGG6IyCEEATh8uAMAoG/fnAZto9VqERoait69xSR08qRrqhTl1wtEPj4CFIqbD3qWQt++4v3Ro6zcEDUWww0ROURWlgJXrwZCpTKjR48LjdrWMqg4K8s1VQpL5Uajcc9gAwAxMeL96dMKVFYqUVRUxKsUEzUQww0ROcSOHeJVdYcMKYda3bhBut26iev/+adrKze+vi45XJNERIg3QZDh0qUw/PDDD5yGgaiBGG6IyCG2bxfDzZ13Nv6Pb7duYuUmJ0dhMx7GWSzhxs/PfSs3ANC/v3gfFnY3p2EgagSGGyJqtoICYN8+sUtp2LDGh5uQEAFqtXjqeE7Dhus0S0volgKqw82xYxpOw0DUCAw3RNRs27YBZrMM7dtfRdu2jb9ujEwGtG4thqLsbEe3rrbqbin3DjcDBoj3f/whbTuIWhqGGyJqtm++Ee/79Gl62SUkRAw3rqjctIQxN0B15ebw4eqLDhLRzTHcEFGzVFWJlRsA6NOn6WUXS7g5ebJhVzZujpbSLdWpExAUBBgMrhtsTeQJGG6IqFl+/VWcNiE01IxOna40aR8ajQZhYWI55Zdfcpx+RpClcqNUOj9INYdMVn1K+MGDvJgfUUMx3BBRs2zeLAaE22/XQS5vWiVEq9Xi/vtvBQAUFPg5/YygoiJx/oXi4ktQKpXQaDROPV5zDB4s3mdmMtwQNRTDDRE1mU6nw8aNYh9PWNjvNkGhqKgIRUVFDd5X9+7idoWFAQ5v541KSsRBz9HRbZCUlAStVuv0YzbVkCHi/f79PtI2hKgF4b8CRNRkR48akJ8fAqVSwEsvDUBYWDwAQKlU4ocffrB+3ZDKSEdxWipcu+YPk6my/pWbqaJCnO4hKEgFrda9RxVbws2xY+KViono5vhOIaIm275dDQAYOrQKnTq1ti5PSkqydi1pNJoGVUYiIgClUoDRKEdenhzh4c5pMwDo9bLrbXPvAcUA0LYt0L49cP68DNnZYVI3h6hFYLcUETXZtm3iVYkTE20H5lomxAwNDW1wl49CAbRrZwYA5OY696PJcraUu1+h2MJSvTlzpo20DSFqIRhuiKhJCgqA338Xi7/33uuYs47atxenYTh/3rmnPVsqN+5+nRsLS7g5e7YNioqKUFBQwDmmiOrBbikiapJvv62+KnGHDo6pgLRv75rKTXl5y+mWAmqGm3CkpX0OmUwcy+Tug6GJpMLKDRE1yddfi/f9+p1z2D4t4ebCBWd3S1kqNy0j3AwYACiVQHGxHwYP/isn0SS6CcnDzdKlSxEdHQ2NRoO4uDjs2bOnznWPHj2Khx56CNHR0ZDJZFiyZInrGkpEVnp99VWJ+/Z13GRQlm6p3Fxnd0uJ9y0l3Pj6Vk/FcPRoCCfRJLoJScPN+vXrkZKSgnnz5iEzMxP9+vVDYmIiLl++bHf98vJydO7cGW+//TYiIiJc3FoisvjxR6CsDIiIMKFjxwKH7TcqSqzcnD/vqsqNUw/jUHfeKd6np0vZCqKWQdJws2jRIkyfPh3Jycno1asXli1bBj8/P6xYscLu+oMGDcK7776Lhx9+GGq1ukHHqKysRElJic2NiJrnq6/E+3vvNUAmc9x+aw4oFpxYVKkeUNwyKjcAMHy4eP/jj9K2g6glkCzcGAwG7N+/HwkJCdWNkcuRkJCA3bt3O+w4qampCAoKst6ioqIctm8ib2Q2A//3f+LXjjpLysJyKnh5uQwnT1512hlBlgHFLSnc3HabeLr8mTPOr2wRtXSSvUMKCgpgMpkQfsOVusLDw5GXl+ew48yZMwfFxcXWW25ursP2TeSN9u0DLl4EtFrgL3+pcui+g4M1CAwUZ7VcvTodGzZscErAaSmzgtcUGAjExopfZ2RwKgai+nh8/Fer1QgMDLS5EVHj6HQ667VVNm4UlyUmVqGiosihx9FqtejWTexybtt2qNPOCLJ0S7WUi/hZWMbd7NrFcENUH8nCTWhoKBQKBfLz822W5+fnc7AwkRvR6XTYsGEDNm/ejPXrN2DDBrHrKDz8Z/zwww8On1W7UyfxTClnTqBpGVDsxpOB22UZd8NwQ1Q/ycKNSqVCbGws0tLSrMvMZjPS0tIQHx8vVbOI6AZ6vR5GoxEDBgzAqVOtkJMjh5+fgJ49z2HEiBEOv5Bchw7ivbOuUmwyAQZDyxtzA4jjbpRKIDtbgStXnD97OlFLJWm3VEpKCj755BP85z//wfHjx/HUU0+hrKwMycnJAIDJkydjzpw51vUNBgMOHDiAAwcOwGAw4MKFCzhw4ABOnTol1VMg8hparRb793cGIM4lpVKZEBwc7PAr5FpmB3fWoFnLeBug5YWbgAAx4ADAkSM8OYKoLpJOvzBhwgRcuXIFc+fORV5eHmJiYrBt2zbrIOOcnBzI5dUfcBcvXkR/y5WsALz33nt47733MGzYMKTz4g9ETiUIQGamGG4SEgqddhxnh5vy8uqvW1q3FACMHAn89BNw5EgHqZtC5LYkn1tq5syZmDlzpt3Hbgws0dHREJx58QsiqtOJE0G4ejUAarUBZvO3Dh9rY1EdbpzTLWU5+Uqtduw1elxl5EjgxReBrKy2Dh/QTeQpJA83RNQyfP11MADggQfMePjh0dBoNE6ZtNESbq5elaOy0vEfUaWl4r1abXT4vl2hd2+gXTsTLlxQYtcuHzz8sDjoW6/XO+01IWppPP5UcCJqvqoqBTZvFk/RfvJJDUJDQ532RzQoSBxbAgDXrvk7fP+Wyo1G49gLELqKTAYkJIht//57lc3ZbM66LhBRS8NwQ0Q3deBANEpK5OjYsfp0ZGeRyarPmCosdHyAaumVGwBISBAvnrhzpwoVFdVns3GmcCIRww0R3VRGRjcAwJQpgNwFnxrODDfVY24ce3VlV7r9dgNUqirk5Chw6JA4NondUUTVGG6IqF4XL8px/Hh7AGK4cQXLFHDOrNxoNC033Pj7A3365AAAvvqqYZMIE3kThhsiqtf69WoIggxDh1ahc2fXHNNSubl2jZWbusTGngEAfP212qkzqBO1RAw3RFQnQQDWrRNP9374YdeN5ajulnL8gGJPqNwAYuXG19eM7GwFsrNDpW4OkVthuCGiOmVkAGfOKKBWV+GBBypddlxndkt5QuVGo9HAz0+GXr3OAgD++KOrU645RNRSMdwQUZ3+9S/xfuDA03DleNWa3VKO7nKpPluq5YYbrVaLpKQkzJgRBgA4dOhW+PpyQDGRBcMNEdl14QKwaZP49fDhR1167HbtAJlMQFWVElevOvYywtXXuWm54QYQA84jjwQiJEQc9P3DD5wpnMiC4YaI7Fq2DDAagSFDqhAVddWlx1argbAwsWRz4YJjP6Y8oXJjoVYDjz4qfr1mDbuliCwYboiolrIy4OOPxa+nT6+of2Unad/eBAC4cMGxc0x5SuXGYto08X77dhVKShhwiACGGyKyY+lS4MoVIDrahKFDL0vShnbtzAAcPzu4J1VuAKBPH2DQIMBolOG337pJ3Rwit8BwQ0Q2SkuBd94Ru4TuvPNn/PzzD06bAbw+lnDj6G4pT6vcAMD06eJ9enpvGFvurBJEDsNwQ0Q2PvgAKCyUITy8CC++GIVx48YhKSnJ5Zf3b9fOOd1Snla5AYC//Q1o3dqMq1cD8M03KqmbQyQ5hhsisrpwQYd33xUrJqNG7UdoaLBTZwCvT/v2zqnceMpF/Gry9QWSk8WLLH74oQpXrhRwdnDyagw3RAQA0Ol0ePrpkyguliMyshBDhuRIemE4Z4y5EQSgqEj82s/P4LD9uoOnnxagVBpx6JAG//xnBjZs2MCAQ16L4YbIS+l0OhQUFKCgQPwv/9KlSuzY0RsA8MYbSjz88HhJZ5q2dEvl58tR5aAiS1kZrGNSfH1dd8VlV+jUyR+PPiqOldq1KwFGoxF6veumzCByJ0qpG0BErqfT6bBhwwYYr/+lVyqV2L9/HPR6FXr3NiI5ORByif/1CQ0VKxFGoxIXLgDR0c3fp6Vqo1QKUKs9b+TtvHk+WLMG2L3bD3FxbaVuDpFkWLkh8kJ6vR5GoxEjRozAiBEjkJenwYoVgQCAF14olzzYAIBcDrRqVQYAyM11zD6vXRPvg4MFyBx74WO30LEj8OST4tdbtgzmbOHktdzgI4yIpBIcHIygoGCsW3cb9Ho5unW7iPvuc5+xKCEh4piRnBzH7M9SuQkM9Ny/+i+/DPj5CTh3rg2++kpVq/uRyBuwW4rIy23dqsLhw6Hw8REwadIvkMnukrpJVpZwc/x4GXQ6odljgKorN+bmNs1thYcDM2ZU4N13/fDaa36orFwLHx9x7I1SqZTktH4iV2PlhsiLXbkiwwsviH/opk27ioiIYolbVE2j0SA0tBwA8Msv2Q45+8cbKjcA8Pe/lyM0tAR5eUp89VU/a/cjBxmTt2C4IfJSggA895wWV67I0bZtIf72t2ypm2RDq9Xivvt6AgBMpmiH/GGuOebGk/n6AhMn7gIApKX1wdmzoQgODpa2UUQuxHBD5KV++qkXtm9XQ6US8PjjP+Ho0f2STLNQn1tvFdty4YLaIfu7fFk8/Vuj8fzqxa235uK++0pgNsvx5JMB4HAb8iYcc0PkhfbuVWLDhngAwNtvyzB9+t3Q6/XQaDRuNR6jSxfxPjdXDpOpeac36XQ6/PZbDoBeKC4+7XZBzhn+8Y/T+O23W3DmjBavvabF7bdL3SIi12DlhsjLnD4NTJkSCJNJgVGjKvHss2IXkFTTLNQnMlLsYjGZZCgoCGzWvvR6PYqLxQrQX/7SzaMH1mo0GiiVSpw+vRePPfYzZDIBn3+uwc8/95C6aUQuwcoNkRfJzwcSE4ErV+Ro374AH34og0zmmC4fZ5DJgAEDgF27gMOHo5q9v5ISXwBAly5aeGiuASCG1aSkJGs1LihIhldfBb744i8YP74Eo0dL3UIi52LlhshLFBcDI0eKlZuOHU145pnvoNW6/8Dahx8W7/fu7drsfZWU+AEQT5f2dDWrcS+/DIwdWwmzWY7JkwOQkVEudfOInIrhhsiDWS7gdvZsGRISgMxMICwMWL++GEFBFVI3r0HGjwcUCvGidGfONO8jq7RUHGPTpo0jWtZyyGTAxx9XoWvXfJSUKHDvvTIGHPJoDDdEHsoyf9S//52GwYPN2LcPaN0a2LKlHK1bF0rdvAYLDwduv12cOfPLL5vehWYwAOXlGus+vU1YmBbp6f649VY9Skt9kZioxpYtxbxqMXkkhhsiD6XX6/HHH5FYuPAhFBQEIDrahJ07y3DixDr88MMPLepsoYceEk/h/u9/1U2eL6mgQPy4UyoFtGrlqJa1LO3aafHdd0Z0734JOp0C48dr8cwzmTh//jxDDnkUhhuiFsjS3WTvD5JOp0NOTgFeeEGJf/3rPpSUKNG5cx7WrctGQMAl64SZLelsofvvN0CpNOLkSSUOHmzaPs6fFz/u2rY1u8XEoFJp316LX38NwKhRlTAaFVi58g4kJZXhs882M+CQx+DZUkQtjKW7yWg01porqLRUhxde2IP16wfj2jVx2WOPlWHgwG04eFCcEFOpVCIiIqLFBBsACAgQ0LdvDjIzO2PtWiAmpvH7yM5WABAHUwMKh7avpQkN1eKrr4DUVGDuXAG7d3fHn3+2RXCwCRMnSt06ouZjuCFqYfR6PYxGIwYMGIDMzEzk5eUhODgYp0754fnnVfj11xEAgKgoExYuNGD8eH/odH+1Tl3gbhfqa6hBg04hM7MzvvgCePttNLr6kp0tbtChg+dOmtkYcjnwyivAbbfJ8OijJpw/H4BHHgFWr67EvHkmDBniJ3UTiZrMi4uzRC1baGgo5HIlPvroOO66qwzx8X749VcVfHyMeP75cmRlKTB+vHhdF8tpwe54ob6G6tMnF1qtCefPA7/+2vjtz52rWbkhizvvBPbtq0Bi4mHI5WZs26ZGfLwfRo0qx86d5U0e40QkJYYbohYoPz8In3wShtTUqVi06AEcOtQRMpmAhIRCvP76BrzwQjl8faVupeNoNBr4+srQp88pAMDq1VWN3seZM2K4iY5muLlReLgWmzZ1wi+/FGPUKPEU8a1b/XDPPX7o1cuAt97SISurTOJWEjWcTBC8K5eXlJQgKCgIxcXFCAxs3uXciVylpATYswfYvh3YssWIU6eqe5QDAoCkpCp07vwVQkMLa43D8RQ6nQ6bNhUjObkdWrUyIy9PDpWqYdsajUBQkIDychkyMq4hPt5LT5dqoN9/L8fixQK+/FINg6H6d23gQBPGjlXgzjuBgQPR4J8/kSM05u+3W4SbpUuX4t1330VeXh769euHDz/8EIMHD65z/Y0bN+K1117DuXPncMstt+Cdd97ByJEjG3QshhtyNzqdzjoeBgAKCmQ4d84X5875ITMTyMgw4dgxOQSheuJIhcKEv/zFhKlTVRg/HvD3r95PSx1T0xB5eQXo3t0PJSV++OYb4P77G7bdH3+I0zio1Qbk5JSgTZtQ5zbUQ+Tm6vDZZ8DmzQpkZmpsfgd9fQXExpoRG6tA375Av35Az56AH4fqkJM05u+35AOK169fj5SUFCxbtgxxcXFYsmQJEhMTkZWVhTZ2LiOakZGBiRMnIjU1FaNGjcLatWsxZswYZGZm4tZbb5XgGRDdnNksTn+QnV2GS5eqoNOpUVbmi/PnK5Geno2CAn8UFmpRWKhFWdmN154Ru1NCQ0sxbJgcd9xRCkH4DpMmPYDQ0Oo/0lqt1mNDjYVSCQwceBo//NAHq1bpMWyYsUHPecMGAwAVevS4CLncs39GjhQVJU7d8PjjBVi5chNKS0fg228N+PPPCJSW+uLXXxW1xj+FhZnRsaMJHTqY0LGjHFFRKgQG6hEYWInQUDPatVMhMtIf/v61B4XXDPqeHNLJ+SSv3MTFxWHQoEH417/+BQAwm82IiorC3//+d7z00ku11p8wYQLKysrwzTffWJcNGTIEMTExWLZs2U2P56zKzdWrQHq67bIbf7LN/Z77sL+Pyko9qqqM8PFRQqXS1LtNQ9phNovdGFVV9d+Xl1ehstIEoxEwmxWoqvJBaakROp3Y/VF9A8rLZTb/9d5MaGgpIiKuoW3bQtxyy1VMmtQZx4//CKPRCAAe2/V0MzqdDqmpP+Kttx6AWl2FiRN/w5Ahg+Dnp4FcLv6xVCjEe7MZqKgA8vIqsWABUF6uxvTp6Vi0aKDX/dya68bLD9x99z04eVKJNWv+RHZ2MM6fb43z51vbCeZ1k8kE+PvLEBAA+Pub4etrhMFwFQqFEUqlGT4+ZnTp0h5+fj5Qq8UuMJnMALncCF9fJTQaVa3XvKHfy2Q122H/6/oea+zXTd2mJQsJEQerO1KL6ZYyGAzw8/PDpk2bMGbMGOvyKVOmoKioCF999VWtbTp06ICUlBQ8++yz1mXz5s3Dli1bcNDO1b0qKytRWVlp/b64uBgdOnRAbm6uQ8PN3r1AQoLDdkceSq02oHVrGZTKYvj5VcDf34CQkHIMH34LunTRoH17oFMnQBB01t9btVoNrVYLna72Mm9UWqpDfLwvcnMbd62aXr30+O47I4KDvfPn1lyW37+av3s1fycFASguliE3V4bz5xU4e9aMvXsvobRUBZ3OD0BrFBYqcPWqDILAc1k83aBBwPffO3afJSUliIqKQlFREYKCgupdV9JuqYKCAphMJoTfMNFLeHg4Tpw4YXebvLw8u+vn5eXZXT81NRXz58+vtTwqKqqJrSZquspK4OLF2st37nR9W7zNsWNAx45St4LIO+zdC9wkfzRZaWmpe4cbV5gzZw5SUlKs35vNZhQWFqJ169aQeVINsA6WpOvoSpW789bnDfC587l713P31ucNeN9zFwQBpaWlaNu27U3XlTTchIaGQqFQID8/32Z5fn4+IiIi7G4TERHRqPXVajXUatuZhIODg5ve6BYqMDDQK375b+Stzxvgc+dz9y7e+rwB73ruN6vYWEja8alSqRAbG4u0tDTrMrPZjLS0NMTHx9vdJj4+3mZ9ANi5c2ed6xMREZF3kbxbKiUlBVOmTMHAgQMxePBgLFmyBGVlZUhOTgYATJ48Ge3atUNqaioAYNasWRg2bBgWLlyI+++/H+vWrcO+ffvwv//7v1I+DSIiInITkoebCRMm4MqVK5g7dy7y8vIQExODbdu2WQcN5+TkQF7jYghDhw7F2rVr8eqrr+Lll1/GLbfcgi1btvAaN3VQq9WYN29era45T+etzxvgc+dz967n7q3PG/Du534zkl/nhoiIiMiReLEBIiIi8igMN0RERORRGG6IiIjIozDcEBERkUdhuPEg6enpkMlkdm979+6tc7s777yz1vpPPvmkC1vuGNHR0bWex9tvv13vNnq9HjNmzEDr1q2h1Wrx0EMP1bpIpLs7d+4cpk2bhk6dOsHX1xddunTBvHnzYDAY6t2upb7uS5cuRXR0NDQaDeLi4rBnz55619+4cSN69OgBjUaDPn364Ntvv3VRSx0nNTUVgwYNQkBAANq0aYMxY8YgKyur3m1WrVpV6/XVaBo+saU7eP3112s9hx49etS7jSe83oD9zzOZTIYZM2bYXd8TXm9HkvxUcHKcoUOH4tKlSzbLXnvtNaSlpWHgwIH1bjt9+nQsWLDA+r2fn59T2uhsCxYswPTp063fBwQE1Lv+c889h61bt2Ljxo0ICgrCzJkzMW7cOOzatcvZTXWYEydOwGw24+OPP0bXrl1x5MgRTJ8+HWVlZXjvvffq3balve7r169HSkoKli1bhri4OCxZsgSJiYnIyspCmzZtaq2fkZGBiRMnIjU1FaNGjcLatWsxZswYZGZmtqjLR/z000+YMWMGBg0aBKPRiJdffhn33HMPjh07Bn9//zq3CwwMtAlBLXHKmd69e+P7GjMwKpV1/9nylNcbAPbu3QuTyWT9/siRI7j77rsxfvz4OrfxhNfbYQTyWAaDQQgLCxMWLFhQ73rDhg0TZs2a5ZpGOVHHjh2FxYsXN3j9oqIiwcfHR9i4caN12fHjxwUAwu7du53QQtf55z//KXTq1KnedVri6z548GBhxowZ1u9NJpPQtm1bITU11e76SUlJwv3332+zLC4uTnjiiSec2k5nu3z5sgBA+Omnn+pcZ+XKlUJQUJDrGuUE8+bNE/r169fg9T319RYEQZg1a5bQpUsXwWw2233cE15vR2K3lAf7+uuvcfXqVevVnuuzZs0ahIaG4tZbb8WcOXNQXl7ughY63ttvv43WrVujf//+ePfdd2E0Gutcd//+/aiqqkJCQoJ1WY8ePdChQwfs3r3bFc11muLiYoSEhNx0vZb0uhsMBuzfv9/m9ZLL5UhISKjz9dq9e7fN+gCQmJjoEa8vgJu+xjqdDh07dkRUVBRGjx6No0ePuqJ5DnXy5Em0bdsWnTt3xqRJk5CTk1Pnup76ehsMBnz++ef4n//5n3qrMZ7wejsKu6U82PLly5GYmIj27dvXu94jjzyCjh07om3btjh06BBefPFFZGVlYfPmzS5qqWM888wzGDBgAEJCQpCRkYE5c+bg0qVLWLRokd318/LyoFKpak2kGh4ejry8PBe02DlOnTqFDz/88KZdUi3tdS8oKIDJZLJevdwiPDwcJ06csLtNXl6e3fVb8utrNpvx7LPP4rbbbqu3q6V79+5YsWIF+vbti+LiYrz33nsYOnQojh49etPPBHcRFxeHVatWoXv37rh06RLmz5+P22+/HUeOHLHb5eyJrzcAbNmyBUVFRZg6dWqd63jC6+1QUpeO6OZefPFFAUC9t+PHj9tsk5ubK8jlcmHTpk2NPl5aWpoAQDh16pSjnkKTNeW5WyxfvlxQKpWCXq+3+/iaNWsElUpVa/mgQYOEF154waHPoyma8tzPnz8vdOnSRZg2bVqjj+dOr7s9Fy5cEAAIGRkZNsuff/55YfDgwXa38fHxEdauXWuzbOnSpUKbNm2c1k5ne/LJJ4WOHTsKubm5jdrOYDAIXbp0EV599VUntcz5rl27JgQGBgqffvqp3cc98fUWBEG45557hFGjRjVqG094vZuDlZsW4B//+Ee9iR0AOnfubPP9ypUr0bp1azz44IONPl5cXBwAsQLQpUuXRm/vSE157hZxcXEwGo04d+4cunfvXuvxiIgIGAwGFBUV2VRv8vPzERER0ZxmO0Rjn/vFixcxfPhwDB06tEkTybrT625PaGgoFApFrbPZ6nu9IiIiGrW+u5s5cya++eYb/Pzzz43+b9zHxwf9+/fHqVOnnNQ65wsODka3bt3qfA6e9noDQHZ2Nr7//vtGV1Q94fVuDoabFiAsLAxhYWENXl8QBKxcuRKTJ0+Gj49Po4934MABAEBkZGSjt3W0xj73mg4cOAC5XG73LBoAiI2NhY+PD9LS0vDQQw8BALKyspCTk4P4+Pgmt9lRGvPcL1y4gOHDhyM2NhYrV660mWy2odzpdbdHpVIhNjYWaWlpGDNmDACxiyYtLQ0zZ860u018fDzS0tLw7LPPWpft3LnTLV7fxhAEAX//+9/x5ZdfIj09HZ06dWr0PkwmEw4fPoyRI0c6oYWuodPpcPr0aTz66KN2H/eU17umlStXok2bNrj//vsbtZ0nvN7NInXpiBzv+++/r7O75vz580L37t2F33//XRAEQTh16pSwYMECYd++fcLZs2eFr776SujcubNwxx13uLrZzZKRkSEsXrxYOHDggHD69Gnh888/F8LCwoTJkydb17nxuQuCWOLv0KGD8MMPPwj79u0T4uPjhfj4eCmeQpOdP39e6Nq1q3DXXXcJ58+fFy5dumS91VzHE173devWCWq1Wli1apVw7Ngx4fHHHxeCg4OFvLw8QRAE4dFHHxVeeukl6/q7du0SlEql8N577wnHjx8X5s2bJ/j4+AiHDx+W6ik0yVNPPSUEBQUJ6enpNq9veXm5dZ0bn/v8+fOF7du3C6dPnxb2798vPPzww4JGoxGOHj0qxVNokn/84x9Cenq6cPbsWWHXrl1CQkKCEBoaKly+fFkQBM99vS1MJpPQoUMH4cUXX6z1mCe+3o7EcOOBJk6cKAwdOtTuY2fPnhUACD/++KMgCIKQk5Mj3HHHHUJISIigVquFrl27Cs8//7xQXFzswhY33/79+4W4uDghKChI0Gg0Qs+ePYW33nrLZrzNjc9dEAShoqJCePrpp4VWrVoJfn5+wtixY21CQUuwcuXKOsfkWHjS6/7hhx8KHTp0EFQqlTB48GDht99+sz42bNgwYcqUKTbrb9iwQejWrZugUqmE3r17C1u3bnVxi5uvrtd35cqV1nVufO7PPvus9ecUHh4ujBw5UsjMzHR945thwoQJQmRkpKBSqYR27doJEyZMsBkT5qmvt8X27dsFAEJWVlatxzzx9XYkmSAIgsvLRUREREROwuvcEBERkUdhuCEiIiKPwnBDREREHoXhhoiIiDwKww0RERF5FIYbIiIi8igMN0RERORRGG6IiIjIozDcELmB119/HTExMVI3A9HR0ViyZInUzbDrjjvuwNq1a6VuBknk2LFjaN++PcrKyqRuCrUADDfkNaZOnQqZTAaZTAYfHx906tQJL7zwAvR6vUvbIZPJsGXLFptls2fPRlpamtOOmZ6ebn3udd3S09Oxd+9ePP74405rR1N9/fXXyM/Px8MPPyx1U7Bq1Srrz0wulyMyMhITJkxATk4Ozp07d9Of86pVq6R+Ci1Sr169MGTIECxatEjqplALwFnByavce++9WLlyJaqqqrB//35MmTIFMpkM77zzjqTt0mq10Gq1Ttv/0KFDcenSJev3s2bNQklJCVauXGldFhISApVK5bQ2NMcHH3yA5OTkJs127gyBgYHIysqCIAg4e/Ysnn76aYwfPx4ZGRk2P+f33nsP27Ztw/fff29dFhQU5LR2GQwGl72GdR2rqqoKPj4+jd5fQ7ZLTk7G9OnTMWfOHCiV/PNFdXOPTwoiF1Gr1YiIiEBUVBTGjBmDhIQE7Ny50/q42WxGamoqOnXqBF9fX/Tr1w+bNm2yPm4ymTBt2jTr4927d8f7779f6zgrVqxA7969oVarERkZiZkzZwIQu30AYOzYsZDJZNbvb+yWMpvNWLBgAdq3bw+1Wo2YmBhs27bN+rilQrB582YMHz4cfn5+6NevH3bv3m33eatUKkRERFhvvr6+1p+F5aZSqWp1S8lkMnz88ccYNWoU/Pz80LNnT+zevRunTp3CnXfeCX9/fwwdOhSnT5+2Od5XX32FAQMGQKPRoHPnzpg/fz6MRiMAQBAEvP766+jQoQPUajXatm2LZ555ps7X7MqVK/jhhx/wwAMP2CyXyWT49NNPMXbsWPj5+eGWW27B119/bbPOkSNHcN9990Gr1SI8PByPPvooCgoKrI+XlpZi0qRJ8Pf3R2RkJBYvXow777wTzz77bJ3tsRw7IiICkZGRGDp0KKZNm4Y9e/agrKzM5meq1WqhVCqt3584cQLDhg2Dv78/goODcdtttyE7O7vO47z44ovo1q0b/Pz80LlzZ7z22muoqqqyPm75vfn000/RqVMnaDQaAEBRUREee+wxhIWFITAwECNGjMDBgwfrfU65ublISkpCcHAwQkJCMHr0aJw7d876+NSpUzFmzBi8+eabaNu2Lbp37279PVy/fj2GDRsGjUaDNWvWNPj398btsrOz8cADD6BVq1bw9/dH79698e2331q3u/vuu1FYWIiffvqp3udCxHBDXuvIkSPIyMiw+e8zNTUVq1evxrJly3D06FE899xz+Nvf/mb9MDWbzWjfvj02btyIY8eOYe7cuXj55ZexYcMG6z4++ugjzJgxA48//jgOHz6Mr7/+Gl27dgUA7N27FwCwcuVKXLp0yfr9jd5//30sXLgQ7733Hg4dOoTExEQ8+OCDOHnypM16r7zyCmbPno0DBw6gW7dumDhxojVEOMobb7yByZMn48CBA+jRowceeeQRPPHEE5gzZw727dsHQRCs4Q0AfvnlF0yePBmzZs3CsWPH8PHHH2PVqlV48803AQD//e9/sXjxYnz88cc4efIktmzZgj59+tR5/F9//dUarG40f/58JCUl4dChQxg5ciQmTZqEwsJCAOIf+BEjRqB///7Yt28ftm3bhvz8fCQlJVm3T0lJwa5du/D1119j586d+OWXX5CZmdmon8/ly5fx5ZdfQqFQQKFQ1Lme0WjEmDFjMGzYMBw6dAi7d+/G448/DplMVuc2AQEBWLVqFY4dO4b3338fn3zyCRYvXmyzzqlTp/Df//4XmzdvxoEDBwAA48ePx+XLl/Hdd99h//79GDBgAO666y7rz+ZGVVVVSExMREBAAH755Rfs2rULWq0W9957LwwGg3W9tLQ0ZGVlYefOnfjmm2+sy1966SXMmjULx48fR2JiYoN/f2/cbsaMGaisrMTPP/+Mw4cP45133rGpaKpUKsTExOCXX36p82dGBACQdE5yIheaMmWKoFAoBH9/f0GtVgsABLlcLmzatEkQBEHQ6/WCn5+fkJGRYbPdtGnThIkTJ9a53xkzZggPPfSQ9fu2bdsKr7zySp3rAxC+/PJLm2Xz5s0T+vXrZ7OPN99802adQYMGCU8//bQgCIJw9uxZAYDw6aefWh8/evSoAEA4fvx4nce2mDJlijB69Ohayzt27CgsXrzYpq2vvvqq9fvdu3cLAITly5dbl33xxReCRqOxfn/XXXcJb731ls1+P/vsMyEyMlIQBEFYuHCh0K1bN8FgMNy0nYIgCIsXLxY6d+5ca/mNbdPpdAIA4bvvvhMEQRDeeOMN4Z577rHZJjc3VwAgZGVlCSUlJYKPj4+wceNG6+NFRUWCn5+fMGvWrDrbs3LlSgGA4O/vL/j5+QkABADCM888U2vdmq/r1atXBQBCenp6g563Pe+++64QGxtrs38fHx/h8uXL1mW//PKLEBgYKOj1epttu3TpInz88cd29/vZZ58J3bt3F8xms3VZZWWl4OvrK2zfvl0QBPF3Jjw8XKisrLSuY/k9XLJkic3+Gvr7e+N2ffr0EV5//fV6fwZjx44Vpk6dWu86ROy0JK8yfPhwfPTRRygrK8PixYuhVCrx0EMPARD/Ay4vL8fdd99ts43BYED//v2t3y9duhQrVqxATk4OKioqYDAYrF1Kly9fxsWLF3HXXXc1uY0lJSW4ePEibrvtNpvlt912W62uhb59+1q/joyMtLahR48eTT7+jWoeIzw8HABsKi3h4eHQ6/UoKSlBYGAgDh48iF27dlkrNYDYnafX61FeXo7x48djyZIl6Ny5M+69916MHDkSDzzwQJ1jKCoqKqzdLfW1zd/fH4GBgbh8+TIA4ODBg/jxxx/tjmU6ffo0KioqUFVVhcGDB1uXBwUFoXv37jf9mQQEBCAzMxNVVVX47rvvsGbNGpvna09ISAimTp2KxMRE3H333UhISEBSUpL1dbNn/fr1+OCDD3D69GnodDoYjUYEBgbarNOxY0eEhYVZvz948CB0Oh1at25ts15FRUWt7sOa25w6dQoBAQE2y/V6vc02ffr0sTvOZuDAgdavG/P7W3M7AHjmmWfw1FNPYceOHUhISMBDDz1k8xoDgK+vL8rLy+0+DyILhhvyKv7+/tYuohUrVqBfv35Yvnw5pk2bBp1OBwDYunUr2rVrZ7OdWq0GAKxbtw6zZ8/GwoULER8fj4CAALz77rv4/fffAYgfvK5UcwCmpXvDbDY7/Rj1HVen02H+/PkYN25crX1pNBpERUUhKysL33//PXbu3Imnn34a7777Ln766Se7A0pDQ0Nx7dq1m7bN0paa7XjggQfsDhaPjIzEqVOn6n3e9ZHL5dbfo549e+L06dN46qmn8Nlnn9W73cqVK/HMM89g27ZtWL9+PV599VXs3LkTQ4YMqbXu7t27MWnSJMyfPx+JiYkICgrCunXrsHDhQpv1/P39bb7X6XSIjIxEenp6rX0GBwfbbZdOp0NsbCzWrFlT67GawenGY91s+c3cuN1jjz2GxMREbN26FTt27EBqaioWLlyIv//979Z1CgsL0aVLlyYdj7wHww15LblcjpdffhkpKSl45JFH0KtXL6jVauTk5GDYsGF2t9m1axeGDh2Kp59+2rqs5n+2AQEBiI6ORlpaGoYPH253Hz4+PjCZTHW2KzAwEG3btsWuXbts2rFr1y6bKoO7GjBgALKysqx//O3x9fXFAw88gAceeAAzZsxAjx49cPjwYQwYMKDWuv3790deXh6uXbuGVq1aNaod//3vfxEdHW23KtS5c2f4+Phg79696NChAwCguLgYf/75J+64444GHwcQx4506dIFzz33nN3ncOPz6d+/P+bMmYP4+HisXbvWbrjJyMhAx44d8corr1iX1Tf42GLAgAHIy8uDUqm0DlhvyDbr169HmzZtalWGGqu5v79RUVF48skn8eSTT2LOnDn45JNPbMLNkSNH8Ne//rVZbSTPxwHF5NXGjx8PhUKBpUuXIiAgALNnz8Zzzz2H//znPzh9+jQyMzPx4Ycf4j//+Q8A4JZbbsG+ffuwfft2/Pnnn3jttddqDQp+/fXXsXDhQnzwwQc4efKkdR8WlvBj+YNtz/PPP4933nkH69evR1ZWFl566SUcOHAAs2bNct4Pw0Hmzp2L1atXY/78+Th69CiOHz+OdevW4dVXXwUgXidm+fLlOHLkCM6cOYPPP/8cvr6+6Nixo9399e/fH6Ghodi1a1ej2jFjxgwUFhZi4sSJ2Lt3L06fPo3t27cjOTkZJpMJAQEBmDJlCp5//nn8+OOPOHr0KKZNmwa5XF7vIF97oqKiMHbsWMydO7fOdc6ePYs5c+Zg9+7dyM7Oxo4dO3Dy5Em7A6UB8XctJycH69atw+nTp/HBBx/gyy+/vGlbEhISEB8fjzFjxmDHjh04d+4cMjIy8Morr2Dfvn12t5k0aRJCQ0MxevRo/PLLLzh79izS09PxzDPP4Pz58w37IdTQ1N/fZ599Ftu3b8fZs2eRmZmJH3/80ebnc+7cOVy4cAEJCQmNbhN5F4Yb8mpKpRIzZ87EP//5T5SVleGNN97Aa6+9htTUVPTs2RP33nsvtm7dik6dOgEAnnjiCYwbNw4TJkxAXFwcrl69alPFAYApU6ZgyZIl+Pe//43evXtj1KhRNmeJLFy4EDt37kRUVJTNWJ6annnmGaSkpOAf//gH+vTpg23btuHrr7/GLbfc4rwfhoMkJibim2++wY4dOzBo0CAMGTIEixcvtoaX4OBgfPLJJ7jtttvQt29ffP/99/i///u/WmNELBQKBZKTk+12mdTHUj0wmUy455570KdPHzz77LMIDg62Xi9n0aJFiI+Px6hRo5CQkIDbbrsNPXv2rHOMT32ee+45bN26FXv27LH7uJ+fH06cOIGHHnoI3bp1w+OPP44ZM2bgiSeesLv+gw8+iOeeew4zZ85ETEwMMjIy8Nprr920HTKZDN9++y3uuOMOJCcno1u3bnj44YeRnZ1tHTNlr20///wzOnTogHHjxqFnz56YNm0a9Hp9kyo5Tf39NZlMmDFjhvW9161bN/z73/+2Pv7FF1/gnnvuqTMIE1nIBEEQpG4EEVF98vLy0Lt3b2RmZjr1D1tZWRnatWuHhQsXYtq0aU47DjWewWDALbfcgrVr19YarEx0I1ZuiMjtRUREYPny5cjJyXHofv/44w988cUX1i7ISZMmAQBGjx7t0ONQ8+Xk5ODll19msKEGYeWGiLzWH3/8gcceewxZWVlQqVSIjY3FokWL6r2oIBG5P4YbIiIi8ijsliIiIiKPwnBDREREHoXhhoiIiDwKww0RERF5FIYbIiIi8igMN0RERORRGG6IiIjIozDcEBERkUf5/0fBu83sRYVmAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -338,6 +360,9 @@ " density=True,\n", " alpha=0.4,\n", ")\n", + "s_sw = choice_sw*x_sw\n", + "idx_sw = np.argsort(s_sw) # sort the values so theyre not zig zaggy\n", + "plt.plot(s_sw[idx_sw], pdf_vals_sw[idx_sw], color=\"blue\", label=\"Shifted Wald Likelihood\")\n", "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", "plt.ylabel(\"Density\")" ] From 0e3eb461f8404880169a654c8baeddbe85f4ec32 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Fri, 7 Nov 2025 13:40:18 -0500 Subject: [PATCH 07/14] additional updates preventing exgauss from being negative --- src/cssm.pyx | 23 +++++++++++++++++++++-- ssms/config/_modelconfig/exgauss.py | 2 +- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/src/cssm.pyx b/src/cssm.pyx index 39df5298..8ccc2064 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -4888,7 +4888,23 @@ def exgauss(np.ndarray[float, ndim = 1] mu, random_state = None, return_option = 'full', **kwargs): - """ Uhhhhhh thing """ + """ Fit reaction times and choices from an ex-Gaussian distribution + + Args: + mu (np.ndarray[float, ndim = 1]): mean of the gaussian component + sigma (np.ndarray[float, ndim = 1]): standard deviation of the gaussian component + tau (np.ndarray[float, ndim = 1]): mean of the exponential component + p (np.ndarray[float, ndim = 1]): probability of choice 1 + delta_t (float, optional): time step for simulation. Defaults to 0.001. + max_t (float, optional): maximum time for simulation. Defaults to 20. + n_samples (int, optional): number of samples per trial. Defaults to 20000. + n_trials (int, optional): number of trials to simulate. Defaults to 1. + random_state (int, optional): random seed. Defaults to None. + return_option (str, optional): 'full' or 'minimal' return data. Defaults to 'full'. + + Returns: + dict: simulated reaction times, choices, and metadata + """ set_seed(random_state) @@ -4916,7 +4932,10 @@ def exgauss(np.ndarray[float, ndim = 1] mu, exp_sample = tau_view[k] * random_exponential() - rt_val = norm_sample + exp_sample + rt_val = norm_sample + exp_sample + + if rt_val < 0.0: # ensure no negative rts + rt_val = 0.0 rts_view[n, k, 0] = rt_val if return_option == 'full': diff --git a/ssms/config/_modelconfig/exgauss.py b/ssms/config/_modelconfig/exgauss.py index bed0a5dd..1f359151 100644 --- a/ssms/config/_modelconfig/exgauss.py +++ b/ssms/config/_modelconfig/exgauss.py @@ -8,7 +8,7 @@ def get_exgauss_config(): return { "name": "exgauss", "params": ["mu", "sigma", "tau", "p"], - "param_bounds": [[-50.0, 0.0, 0.0, 0.0], [50.0, 50.0, 50.0, 1.0]], + "param_bounds": [[0.0, 0.0, 0.0, 0.0], [50.0, 50.0, 50.0, 1.0]], "boundary_name": 'constant', "boundary": bf.constant, "boundary_params": [], From 0c8f1480efec52b58da7634ac096286379a0c178 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Sun, 9 Nov 2025 19:33:10 -0500 Subject: [PATCH 08/14] small changes to names of parameters and other comments --- src/cssm.pyx | 58 +++++++++++++++--------- ssms/config/_modelconfig/shifted_wald.py | 2 +- 2 files changed, 37 insertions(+), 23 deletions(-) diff --git a/src/cssm.pyx b/src/cssm.pyx index 8ccc2064..684e931f 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -4882,7 +4882,6 @@ def exgauss(np.ndarray[float, ndim = 1] mu, np.ndarray[float, ndim = 1] tau, np.ndarray[float, ndim = 1] p, # choice probability float delta_t = 0.001, - float max_t = 20, int n_samples = 20000, int n_trials = 1, random_state = None, @@ -4896,7 +4895,6 @@ def exgauss(np.ndarray[float, ndim = 1] mu, tau (np.ndarray[float, ndim = 1]): mean of the exponential component p (np.ndarray[float, ndim = 1]): probability of choice 1 delta_t (float, optional): time step for simulation. Defaults to 0.001. - max_t (float, optional): maximum time for simulation. Defaults to 20. n_samples (int, optional): number of samples per trial. Defaults to 20000. n_trials (int, optional): number of trials to simulate. Defaults to 1. random_state (int, optional): random seed. Defaults to None. @@ -4906,16 +4904,15 @@ def exgauss(np.ndarray[float, ndim = 1] mu, dict: simulated reaction times, choices, and metadata """ - set_seed(random_state) + rng = np.random.default_rng(random_state) - cdef float[:] mu_view = mu - cdef float[:] sigma_view = sigma - cdef float[:] tau_view = tau + + mu = np.asarray(mu, dtype=float) + sigma = np.asarray(sigma, dtype=float) + tau = np.asarray(tau, dtype=float) rts = np.zeros((n_samples, n_trials, 1), dtype = DTYPE) choices = np.zeros((n_samples, n_trials, 1), dtype = np.intc) - cdef float[:, :, :] rts_view = rts - cdef int[:, :, :] choices_view = choices for k in range(n_trials): for n in range(n_samples): @@ -4923,9 +4920,9 @@ def exgauss(np.ndarray[float, ndim = 1] mu, random_val = rand() / float(RAND_MAX) if random_val <= p: - choices_view[n, k, 0] = 1 + choices[n, k, 0] = 1 else: - choices_view[n, k, 0] = -1 + choices[n, k, 0] = -1 norm_sample = random_gaussian() norm_sample = mu_view[k] + sigma_view[k] * norm_sample @@ -4949,7 +4946,6 @@ def exgauss(np.ndarray[float, ndim = 1] mu, 'simulator': 'exgauss', 'possible_choices': [-1, 1], 'delta_t': delta_t, - 'max_t': max_t, } } elif return_option == 'minimal': @@ -4962,9 +4958,9 @@ def exgauss(np.ndarray[float, ndim = 1] mu, # Simulate (rt, choice) tuples from: SHIFTED WALD ------------------------------------ # @cythonboundscheck(False) # @cythonwraparound(False) -def shifted_wald(np.ndarray[float, ndim = 1] gamma, - np.ndarray[float, ndim = 1] alpha, - np.ndarray[float, ndim = 1] theta, +def shifted_wald(np.ndarray[float, ndim = 1] v, # drift rate + np.ndarray[float, ndim = 1] a, # boundary separation + np.ndarray[float, ndim = 1] t, # nondecision time np.ndarray[float, ndim = 1] p, # choice probability np.ndarray[float, ndim = 1] s, # noise sigma float delta_t = 0.001, @@ -4975,13 +4971,31 @@ def shifted_wald(np.ndarray[float, ndim = 1] gamma, return_option = 'full', smooth_unif = False, **kwargs): - """ Uhhhhhh thing for shifted wald """ + """ Fit reaction times and choices from a shifted Wald distribution + + Args: + v (np.ndarray[float, ndim = 1]): drift rate + a (np.ndarray[float, ndim = 1]): boundary separation + t (np.ndarray[float, ndim = 1]): non-decision time + p (np.ndarray[float, ndim = 1]): probability of choice 1 + s (np.ndarray[float, ndim = 1]): noise standard deviation + delta_t (float, optional): time step for simulation. Defaults to 0.001. + max_t (float, optional): maximum time for simulation. Defaults to 20. + n_samples (int, optional): number of samples per trial. Defaults to 20000. + n_trials (int, optional): number of trials to simulate. Defaults to 1. + random_state (int, optional): random seed. Defaults to None. + return_option (str, optional): 'full' or 'minimal' return data. Defaults to 'full'. + smooth_unif (bool, optional): whether to use smooth uniform distribution for small time increments. Defaults to False. + + Returns: + dict: simulated reaction times, choices, and metadata + """ set_seed(random_state) - cdef float[:] gamma_view = gamma - cdef float[:] alpha_view = alpha - cdef float[:] theta_view = theta + cdef float[:] v_view = v + cdef float[:] a_view = a + cdef float[:] t_view = t cdef float[:] p_view = p cdef float[:] s_view = s @@ -5023,8 +5037,8 @@ def shifted_wald(np.ndarray[float, ndim = 1] gamma, choices_view[n, k, 0] = -1 # Random walk - while (y < alpha_view[k]) and (t_particle <= max_t): - y += (gamma_view[k] * delta_t) + (sqrt_st * gaussian_values[m]) + while (y < a_view[k]) and (t_particle <= max_t): + y += (b_view[k] * delta_t) + (sqrt_st * gaussian_values[m]) t_particle += delta_t ix += 1 m += 1 @@ -5046,14 +5060,14 @@ def shifted_wald(np.ndarray[float, ndim = 1] gamma, else: smooth_u = 0.0 - rts_view[n, k, 0] = t_particle + theta_view[k] + smooth_u + rts_view[n, k, 0] = t_particle + t_view[k] + smooth_u if return_option == 'full': return { 'rts': rts, 'choices': choices, 'metadata': { - 'gamma': gamma, 'alpha': alpha, 'theta': theta, 's': s, + 'v': v, 'a': a, 't': t, 's': s, 'n_samples': n_samples, 'n_trials': n_trials, 'simulator': 'shifted_wald', diff --git a/ssms/config/_modelconfig/shifted_wald.py b/ssms/config/_modelconfig/shifted_wald.py index 922c5906..e6e7612f 100644 --- a/ssms/config/_modelconfig/shifted_wald.py +++ b/ssms/config/_modelconfig/shifted_wald.py @@ -7,7 +7,7 @@ def get_shifted_wald_config(): """Get the configuration of the Shifted Wald model""" return { "name": "shifted_wald", - "params": ["gamma", "alpha", "theta", "p"], + "params": ["v", "a", "t", "p"], "param_bounds": [[0.0, 0.3, 0.0, 0.0], [5, 2.5, 2.0, 1.0]], "boundary_name": 'constant', "boundary": bf.constant, From eba0cda0398f763714f535d53025a760aee43e7f Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Sun, 9 Nov 2025 19:56:48 -0500 Subject: [PATCH 09/14] vectorized simulator for exgauss --- notebooks/test_exgauss_wald.ipynb | 4 ++-- src/cssm.pyx | 32 ++++++++++++++++++++----------- 2 files changed, 23 insertions(+), 13 deletions(-) diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index 21e96d73..64dec075 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ " model=\"exgauss\", theta={\"mu\": 2, \"sigma\":0.05, \"tau\": 1, \"p\": 0.2}, n_samples=10000, random_state = 100,\n", " )\n", "sim_out_2 = simulator(\n", - " model=\"shifted_wald\", theta={\"gamma\": 2.0, \"alpha\":2.5, \"theta\": 0.0, \"p\": 0.8}, n_samples=10000, random_state = 100,\n", + " model=\"shifted_wald\", theta={\"v\": 2.0, \"a\":2.5, \"t\": 0.0, \"p\": 0.8}, n_samples=10000, random_state = 100,\n", " )\n" ] }, diff --git a/src/cssm.pyx b/src/cssm.pyx index 684e931f..18ebf40c 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -4886,6 +4886,7 @@ def exgauss(np.ndarray[float, ndim = 1] mu, int n_trials = 1, random_state = None, return_option = 'full', + float max_t = -1, **kwargs): """ Fit reaction times and choices from an ex-Gaussian distribution @@ -4907,9 +4908,19 @@ def exgauss(np.ndarray[float, ndim = 1] mu, rng = np.random.default_rng(random_state) - mu = np.asarray(mu, dtype=float) - sigma = np.asarray(sigma, dtype=float) - tau = np.asarray(tau, dtype=float) + mu = np.asarray(mu, dtype=DTYPE).reshape(-1) + sigma = np.asarray(sigma, dtype=DTYPE).reshape(-1) + tau = np.asarray(tau, dtype=DTYPE).reshape(-1) + p = np.asarray(p, dtype=DTYPE).reshape(-1) + + if mu.size == 1: + mu = np.repeat(mu, n_trials) + if sigma.size == 1: + sigma = np.repeat(sigma, n_trials) + if tau.size == 1: + tau = np.repeat(tau, n_trials) + if p.size == 1: + p = np.repeat(p, n_trials) rts = np.zeros((n_samples, n_trials, 1), dtype = DTYPE) choices = np.zeros((n_samples, n_trials, 1), dtype = np.intc) @@ -4918,22 +4929,20 @@ def exgauss(np.ndarray[float, ndim = 1] mu, for n in range(n_samples): # Draw normal + exponential - random_val = rand() / float(RAND_MAX) - if random_val <= p: + random_val = rng.random() + if random_val <= p[k]: choices[n, k, 0] = 1 else: choices[n, k, 0] = -1 - norm_sample = random_gaussian() - norm_sample = mu_view[k] + sigma_view[k] * norm_sample - - exp_sample = tau_view[k] * random_exponential() + norm_sample = rng.normal(mu[k], sigma[k]) + exp_sample = rng.exponential(tau[k]) rt_val = norm_sample + exp_sample if rt_val < 0.0: # ensure no negative rts rt_val = 0.0 - rts_view[n, k, 0] = rt_val + rts[n, k, 0] = rt_val if return_option == 'full': return { @@ -4946,6 +4955,7 @@ def exgauss(np.ndarray[float, ndim = 1] mu, 'simulator': 'exgauss', 'possible_choices': [-1, 1], 'delta_t': delta_t, + 'max_t': max_t } } elif return_option == 'minimal': @@ -5038,7 +5048,7 @@ def shifted_wald(np.ndarray[float, ndim = 1] v, # drift rate # Random walk while (y < a_view[k]) and (t_particle <= max_t): - y += (b_view[k] * delta_t) + (sqrt_st * gaussian_values[m]) + y += (v_view[k] * delta_t) + (sqrt_st * gaussian_values[m]) t_particle += delta_t ix += 1 m += 1 From 3873294b2b711b6c095740ab230a487ba1135f98 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Tue, 11 Nov 2025 00:56:17 -0500 Subject: [PATCH 10/14] fixed simulator for exgauss --- notebooks/test_exgauss_wald.ipynb | 145 ++++++++++++++++------- src/cssm.pyx | 70 ++++++----- ssms/basic_simulators/theta_processor.py | 6 + ssms/config/_modelconfig/exgauss.py | 7 +- 4 files changed, 153 insertions(+), 75 deletions(-) diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index 64dec075..c14d58e9 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -136,60 +136,106 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "this is num trials 1\n" + ] + } + ], "source": [ "from ssms.basic_simulators.simulator import simulator\n", + "mu0 = 5.0\n", + "mu1 = 1.0\n", + "sigma0 = 0.5\n", + "sigma1 = 0.1\n", + "tau0 = 0.1\n", + "tau1 = 0.1\n", "\n", "sim_out = simulator(\n", - " model=\"exgauss\", theta={\"mu\": 2, \"sigma\":0.05, \"tau\": 1, \"p\": 0.2}, n_samples=10000, random_state = 100,\n", + " model=\"exgauss\", theta={\"mu0\": mu0, \"mu1\": mu1, \n", + " \"sigma0\": sigma0, \"sigma1\": sigma1, \n", + " \"tau0\": tau0,\n", + " \"tau1\":tau1, \"p\": np.array([0.8])}, n_samples=10000, random_state = 100,\n", " )\n", - "sim_out_2 = simulator(\n", - " model=\"shifted_wald\", theta={\"v\": 2.0, \"a\":2.5, \"t\": 0.0, \"p\": 0.8}, n_samples=10000, random_state = 100,\n", - " )\n" + "# sim_out_2 = simulator(\n", + "# model=\"shifted_wald\", theta={\"v\": [2.0, 1.5], \"a\":2.5, \"t\": 0.0, \"p\": 0.8}, n_samples=10000, random_state = 100,\n", + "# )\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[2.685313 ]\n", - " [1.9990021]\n", - " [3.8226187]\n", - " ...\n", - " [2.819895 ]\n", - " [2.0777347]\n", - " [3.9112484]]\n" + "[[1.0882273]\n", + " [4.5396304]\n", + " [5.479963 ]\n", + " [6.16694 ]\n", + " [6.02184 ]\n", + " [4.863364 ]\n", + " [4.09259 ]\n", + " [6.1096787]\n", + " [4.9302044]\n", + " [5.0182223]]\n" ] } ], "source": [ - "print(sim_out['rts'])\n", + "print(sim_out['rts'][:10])\n", "\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1],\n", + " [ 1],\n", + " [ 1],\n", + " ...,\n", + " [ 1],\n", + " [-1],\n", + " [ 1]], shape=(10000, 1), dtype=int32)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_out['choices']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[1.2415992 ]\n", - " [0.3367004 ]\n", - " [0.29839447]\n", + "[[1.7596234 ]\n", + " [0.5706974 ]\n", + " [1.0033857 ]\n", " ...\n", - " [0.25902444]\n", - " [0.37568384]\n", - " [0.4568465 ]]\n" + " [0.86808836]\n", + " [2.1055403 ]\n", + " [0.8975111 ]]\n" ] } ], @@ -199,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -249,20 +295,20 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# define shifted wald analytical likelihoods\n", "\n", - "def shifted_wald_likelihood(x, choice, gamma, alpha, theta, p):\n", + "def shifted_wald_likelihood(x, choice, v, a, t, p):\n", " x = np.asarray(x, dtype=float)\n", - " gamma = float(gamma)\n", - " alpha = float(alpha)\n", - " theta = float(theta)\n", + " v = float(v)\n", + " a = float(a)\n", + " t = float(t)\n", "\n", - " term_1 = (alpha / np.sqrt(2*np.pi*(x - theta)**3))\n", - " term_2 = np.exp((-(alpha-gamma*(x - theta))**2) / (2*(x - theta)))\n", + " term_1 = (a / np.sqrt(2*np.pi*(x - t)**3))\n", + " term_2 = np.exp((-(a-v*(x - t))**2) / (2*(x - t)))\n", " pdf_rt = term_1 * term_2\n", "\n", " # Bernoulli part\n", @@ -276,12 +322,12 @@ "x_sw = np.linspace(0.001, 9, 1100)\n", "p_sw = 0.8\n", "choice_sw = np.where(np.random.rand(len(x_sw)) < p_sw, 1, -1)\n", - "pdf_vals_sw = shifted_wald_likelihood(x_sw, choice_sw, gamma=2.0, alpha=2.5, theta=0.0, p=0.8)\n" + "pdf_vals_sw = shifted_wald_likelihood(x_sw, choice_sw, v=2.0, a=2.5, t=0.0, p=0.8)\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -290,13 +336,13 @@ "Text(0, 0.5, 'Density')" ] }, - "execution_count": 6, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -308,7 +354,7 @@ "source": [ "import matplotlib.pyplot as plt \n", "plt.hist(\n", - " sim_out[\"rts\"][sim_out[\"rts\"] != -999] * sim_out[\"choices\"][sim_out[\"rts\"] != -999],\n", + " sim_out[\"rts\"] * sim_out[\"choices\"],\n", " bins=100,\n", " histtype=\"step\",\n", " color=\"black\",\n", @@ -316,9 +362,9 @@ " density=True,\n", " alpha=0.4,\n", ")\n", - "s = choice*x\n", - "idx = np.argsort(s) # sort the values so theyre not zig zaggy \n", - "plt.plot(s[idx], pdf_vals[idx], color=\"red\", label=\"Ex-Gaussian Likelihood\")\n", + "# s = choice*x\n", + "# idx = np.argsort(s) # sort the values so theyre not zig zaggy \n", + "# plt.plot(s[idx], pdf_vals[idx], color=\"red\", label=\"Ex-Gaussian Likelihood\")\n", "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", "plt.ylabel(\"Density\")\n", "\n" @@ -326,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -335,13 +381,13 @@ "Text(0, 0.5, 'Density')" ] }, - "execution_count": 7, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -366,6 +412,23 @@ "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", "plt.ylabel(\"Density\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [] } ], "metadata": { diff --git a/src/cssm.pyx b/src/cssm.pyx index 18ebf40c..5123348f 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -4877,9 +4877,9 @@ def ddm_flexbound_tradeoff(np.ndarray[float, ndim = 1] vh, # Simulate (rt, choice) tuples from: EX GAUSSIAN ------------------------------------ # @cythonboundscheck(False) # @cythonwraparound(False) -def exgauss(np.ndarray[float, ndim = 1] mu, - np.ndarray[float, ndim = 1] sigma, - np.ndarray[float, ndim = 1] tau, +def exgauss(np.ndarray[float, ndim = 2] mu, + np.ndarray[float, ndim = 2] sigma, + np.ndarray[float, ndim = 2] tau, np.ndarray[float, ndim = 1] p, # choice probability float delta_t = 0.001, int n_samples = 20000, @@ -4907,11 +4907,11 @@ def exgauss(np.ndarray[float, ndim = 1] mu, rng = np.random.default_rng(random_state) + mu = np.asarray(mu, dtype=DTYPE) + sigma = np.asarray(sigma, dtype=DTYPE) + tau = np.asarray(tau, dtype=DTYPE) + p = np.asarray(p, dtype=DTYPE) - mu = np.asarray(mu, dtype=DTYPE).reshape(-1) - sigma = np.asarray(sigma, dtype=DTYPE).reshape(-1) - tau = np.asarray(tau, dtype=DTYPE).reshape(-1) - p = np.asarray(p, dtype=DTYPE).reshape(-1) if mu.size == 1: mu = np.repeat(mu, n_trials) @@ -4919,30 +4919,36 @@ def exgauss(np.ndarray[float, ndim = 1] mu, sigma = np.repeat(sigma, n_trials) if tau.size == 1: tau = np.repeat(tau, n_trials) - if p.size == 1: - p = np.repeat(p, n_trials) + rts = np.zeros((n_samples, n_trials, 1), dtype = DTYPE) choices = np.zeros((n_samples, n_trials, 1), dtype = np.intc) for k in range(n_trials): - for n in range(n_samples): - # Draw normal + exponential - + for n in range(n_samples): + # decide choice random_val = rng.random() - if random_val <= p[k]: + if random_val <= p[k]: + choice_idx = 0 choices[n, k, 0] = 1 + else: + choice_idx = 1 choices[n, k, 0] = -1 - - norm_sample = rng.normal(mu[k], sigma[k]) - exp_sample = rng.exponential(tau[k]) - - rt_val = norm_sample + exp_sample - - if rt_val < 0.0: # ensure no negative rts - rt_val = 0.0 - rts[n, k, 0] = rt_val + + + mu_val = mu[k, choice_idx] + sigma_val = sigma[k, choice_idx] + tau_val = tau[k, choice_idx] + + # draw components and compose RT + norm_sample = rng.normal(mu_val, sigma_val) + exp_sample = rng.exponential(tau_val) + rt_val = norm_sample + exp_sample + + if rt_val < 0.0: # ensure no negative rts + rt_val = 0.0 + rts[n, k, 0] = rt_val if return_option == 'full': return { @@ -4968,9 +4974,9 @@ def exgauss(np.ndarray[float, ndim = 1] mu, # Simulate (rt, choice) tuples from: SHIFTED WALD ------------------------------------ # @cythonboundscheck(False) # @cythonwraparound(False) -def shifted_wald(np.ndarray[float, ndim = 1] v, # drift rate - np.ndarray[float, ndim = 1] a, # boundary separation - np.ndarray[float, ndim = 1] t, # nondecision time +def shifted_wald(np.ndarray[float, ndim = 2] v, # drift rate + np.ndarray[float, ndim = 2] a, # boundary separation + np.ndarray[float, ndim = 2] t, # nondecision time np.ndarray[float, ndim = 1] p, # choice probability np.ndarray[float, ndim = 1] s, # noise sigma float delta_t = 0.001, @@ -5003,9 +5009,9 @@ def shifted_wald(np.ndarray[float, ndim = 1] v, # drift rate set_seed(random_state) - cdef float[:] v_view = v - cdef float[:] a_view = a - cdef float[:] t_view = t + cdef float[:, :] v_view = v + cdef float[:, :] a_view = a + cdef float[:, :] t_view = t cdef float[:] p_view = p cdef float[:] s_view = s @@ -5043,12 +5049,14 @@ def shifted_wald(np.ndarray[float, ndim = 1] v, # drift rate random_val = rand() / float(RAND_MAX) if random_val <= p_view[k]: choices_view[n, k, 0] = 1 + choice_idx = 0 else: choices_view[n, k, 0] = -1 + choice_idx = 1 # Random walk - while (y < a_view[k]) and (t_particle <= max_t): - y += (v_view[k] * delta_t) + (sqrt_st * gaussian_values[m]) + while (y < a_view[k, choice_idx]) and (t_particle <= max_t): + y += (v_view[k, choice_idx] * delta_t) + (sqrt_st * gaussian_values[m]) t_particle += delta_t ix += 1 m += 1 @@ -5070,7 +5078,7 @@ def shifted_wald(np.ndarray[float, ndim = 1] v, # drift rate else: smooth_u = 0.0 - rts_view[n, k, 0] = t_particle + t_view[k] + smooth_u + rts_view[n, k, 0] = t_particle + t_view[k, choice_idx] + smooth_u if return_option == 'full': return { diff --git a/ssms/basic_simulators/theta_processor.py b/ssms/basic_simulators/theta_processor.py index 36339463..6d4074ee 100644 --- a/ssms/basic_simulators/theta_processor.py +++ b/ssms/basic_simulators/theta_processor.py @@ -225,6 +225,12 @@ def process_theta( theta["v"] = np.column_stack([theta["v0"], theta["v1"]]) theta["t"] = np.expand_dims(theta["t"], axis=1) theta["a"] = np.expand_dims(theta["a"], axis=1) + + if model in ["exgauss"]: + theta["mu"] = np.column_stack([theta["mu0"], theta["mu1"]]) + theta["sigma"] = np.column_stack([theta["sigma0"], theta["sigma1"]]) + theta["tau"] = np.column_stack([theta["tau0"], theta["tau1"]]) + # 3 Choice models diff --git a/ssms/config/_modelconfig/exgauss.py b/ssms/config/_modelconfig/exgauss.py index 1f359151..f7ec807b 100644 --- a/ssms/config/_modelconfig/exgauss.py +++ b/ssms/config/_modelconfig/exgauss.py @@ -7,12 +7,13 @@ def get_exgauss_config(): """Get the configuration of the Ex-Gaussian model""" return { "name": "exgauss", - "params": ["mu", "sigma", "tau", "p"], - "param_bounds": [[0.0, 0.0, 0.0, 0.0], [50.0, 50.0, 50.0, 1.0]], + "params": ["mu0", "mu1", "sigma0", "sigma1", "tau0", "tau1", "p"], + "param_bounds": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 1.0]], "boundary_name": 'constant', "boundary": bf.constant, "boundary_params": [], - "n_params": 4, + "n_params": 7, "default_params": [0.5, 0.05, 0.3, 0.5], "nchoices": 2, "choices": [-1, 1], From 56f047ed6d704135d591adffdfbe12f704639e64 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Tue, 11 Nov 2025 11:39:59 -0500 Subject: [PATCH 11/14] initial attempt at race exgauss --- src/cssm.pyx | 125 +++++++++++++++-------- ssms/basic_simulators/simulator.py | 3 + ssms/basic_simulators/theta_processor.py | 7 ++ ssms/config/_modelconfig/__init__.py | 4 +- ssms/config/_modelconfig/exgauss.py | 22 +++- ssms/config/_modelconfig/shifted_wald.py | 9 +- 6 files changed, 123 insertions(+), 47 deletions(-) diff --git a/src/cssm.pyx b/src/cssm.pyx index 5123348f..cd10ea78 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -4887,6 +4887,7 @@ def exgauss(np.ndarray[float, ndim = 2] mu, random_state = None, return_option = 'full', float max_t = -1, + race = False, **kwargs): """ Fit reaction times and choices from an ex-Gaussian distribution @@ -4911,7 +4912,7 @@ def exgauss(np.ndarray[float, ndim = 2] mu, sigma = np.asarray(sigma, dtype=DTYPE) tau = np.asarray(tau, dtype=DTYPE) p = np.asarray(p, dtype=DTYPE) - + n_choices = mu.shape[1] if mu.size == 1: mu = np.repeat(mu, n_trials) @@ -4927,50 +4928,94 @@ def exgauss(np.ndarray[float, ndim = 2] mu, for k in range(n_trials): for n in range(n_samples): # decide choice - random_val = rng.random() - if random_val <= p[k]: - choice_idx = 0 - choices[n, k, 0] = 1 - - else: - choice_idx = 1 - choices[n, k, 0] = -1 - - - mu_val = mu[k, choice_idx] - sigma_val = sigma[k, choice_idx] - tau_val = tau[k, choice_idx] - - # draw components and compose RT - norm_sample = rng.normal(mu_val, sigma_val) - exp_sample = rng.exponential(tau_val) - rt_val = norm_sample + exp_sample - - if rt_val < 0.0: # ensure no negative rts - rt_val = 0.0 - rts[n, k, 0] = rt_val + if race: + # choose 'choice' from race + rt_candidates = np.empty(n_choices, dtype=DTYPE) + for c in range(n_choices): + mu_val = mu[k, c] + sigma_val = sigma[k, c] + tau_val = tau[k, c] + + # draw components and compose RT + norm_sample = rng.normal(mu_val, sigma_val) + exp_sample = rng.exponential(tau_val) + rt_val = norm_sample + exp_sample + + if rt_val < 0.0: # ensure no negative rts + rt_val = 0.0 + rt_candidates[c] = rt_val + choice_idx = np.argmin(rt_candidates) + choices[n, k, 0] = 1 if choice_idx == 0 else -1 + rts[n, k, 0] = rt_candidates[choice_idx] + + else: + # choose 'choice' from Bernoulli + random_val = rng.random() + if random_val <= p[k]: + choice_idx = 0 + choices[n, k, 0] = 1 + + else: + choice_idx = 1 + choices[n, k, 0] = -1 + + mu_val = mu[k, choice_idx] + sigma_val = sigma[k, choice_idx] + tau_val = tau[k, choice_idx] + + # draw components and compose RT + norm_sample = rng.normal(mu_val, sigma_val) + exp_sample = rng.exponential(tau_val) + rt_val = norm_sample + exp_sample + + if rt_val < 0.0: # ensure no negative rts + rt_val = 0.0 + rts[n, k, 0] = rt_val - if return_option == 'full': - return { - 'rts': rts, - 'choices': choices, - 'metadata': { - 'mu': mu, 'sigma': sigma, 'tau': tau, - 'n_samples': n_samples, - 'n_trials': n_trials, - 'simulator': 'exgauss', - 'possible_choices': [-1, 1], - 'delta_t': delta_t, - 'max_t': max_t + if race: + if return_option == 'full': + return { + 'rts': rts, + 'choices': choices, + 'metadata': { + 'mu': mu, 'sigma': sigma, 'tau': tau, + 'n_samples': n_samples, + 'n_trials': n_trials, + 'simulator': 'exgauss_race', + 'possible_choices': [-1, 1], + 'delta_t': delta_t, + 'max_t': max_t + } } - } - elif return_option == 'minimal': - return {'rts': rts, 'choices': choices, 'metadata': {'simulator': 'exgauss', 'n_samples': n_samples, 'n_trials': n_trials, 'possible_choices': [-1, 1]}} - else: - raise ValueError("return_option must be 'full' or 'minimal'") + elif return_option == 'minimal': + return {'rts': rts, 'choices': choices, 'metadata': {'simulator': 'exgauss_race', 'n_samples': n_samples, 'n_trials': n_trials, 'possible_choices': [-1, 1]}} + else: + raise ValueError("return_option must be 'full' or 'minimal'") + else: + if return_option == 'full': + return { + 'rts': rts, + 'choices': choices, + 'metadata': { + 'mu': mu, 'sigma': sigma, 'tau': tau, + 'n_samples': n_samples, + 'n_trials': n_trials, + 'simulator': 'exgauss', + 'possible_choices': [-1, 1], + 'delta_t': delta_t, + 'max_t': max_t + } + } + elif return_option == 'minimal': + return {'rts': rts, 'choices': choices, 'metadata': {'simulator': 'exgauss', 'n_samples': n_samples, 'n_trials': n_trials, 'possible_choices': [-1, 1]}} + else: + raise ValueError("return_option must be 'full' or 'minimal'") # ----------------------------------------------------------------------------------------------- + + + # Simulate (rt, choice) tuples from: SHIFTED WALD ------------------------------------ # @cythonboundscheck(False) # @cythonwraparound(False) diff --git a/ssms/basic_simulators/simulator.py b/ssms/basic_simulators/simulator.py index ea54458d..d991455d 100755 --- a/ssms/basic_simulators/simulator.py +++ b/ssms/basic_simulators/simulator.py @@ -502,6 +502,9 @@ def check_if_z_gt_a(z: np.ndarray, a: np.ndarray) -> None: "dev_rlwm_lba_race_v2", ]: check_if_z_gt_a(theta["z"], theta["a"]) + + if model in ["exgauss_race"]: + def make_noise_vec( diff --git a/ssms/basic_simulators/theta_processor.py b/ssms/basic_simulators/theta_processor.py index 6d4074ee..a2971017 100644 --- a/ssms/basic_simulators/theta_processor.py +++ b/ssms/basic_simulators/theta_processor.py @@ -230,6 +230,13 @@ def process_theta( theta["mu"] = np.column_stack([theta["mu0"], theta["mu1"]]) theta["sigma"] = np.column_stack([theta["sigma0"], theta["sigma1"]]) theta["tau"] = np.column_stack([theta["tau0"], theta["tau1"]]) + + if model in ["shifted_wald"]: + theta["v"] = np.column_stack([theta["v0"], theta["v1"]]) + theta["a"] = np.column_stack([theta["a0"], theta["a1"]]) + theta["t"] = np.expand_dims(theta["t"], axis=1) + + # 3 Choice models diff --git a/ssms/config/_modelconfig/__init__.py b/ssms/config/_modelconfig/__init__.py index eb1a9086..c04c007a 100644 --- a/ssms/config/_modelconfig/__init__.py +++ b/ssms/config/_modelconfig/__init__.py @@ -124,7 +124,8 @@ ) from .exgauss import ( - get_exgauss_config + get_exgauss_config, + get_exgauss_race_config ) from .shifted_wald import ( @@ -242,6 +243,7 @@ def get_model_config(): "full_ddm2": get_full_ddm_config(), "ddm_legacy": get_ddm_legacy_config(), "exgauss": get_exgauss_config(), + "exgauss_race": get_exgauss_race_config(), "shifted_wald": get_shifted_wald_config(), } diff --git a/ssms/config/_modelconfig/exgauss.py b/ssms/config/_modelconfig/exgauss.py index f7ec807b..f886868e 100644 --- a/ssms/config/_modelconfig/exgauss.py +++ b/ssms/config/_modelconfig/exgauss.py @@ -14,9 +14,27 @@ def get_exgauss_config(): "boundary": bf.constant, "boundary_params": [], "n_params": 7, - "default_params": [0.5, 0.05, 0.3, 0.5], + "default_params": [0.5, 0.5, 0.05, 0.05, 0.3, 0.3, 0.5], "nchoices": 2, "choices": [-1, 1], "n_particles": 1, "simulator": cssm.exgauss, - } \ No newline at end of file + } + +def get_exgauss_race_config(): + """Get the configuration of the race version of Ex-Gaussian model""" + return { + "name": "exgauss_race", + "params": ["mu0", "mu1", "sigma0", "sigma1", "tau0", "tau1", "p"], + "param_bounds": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 1.0]], + "boundary_name": 'constant', + "boundary": bf.constant, + "boundary_params": [], + "n_params": 7, + "default_params": [0.5, 0.5, 0.05, 0.05, 0.3, 0.3, 0.5], + "nchoices": 2, + "choices": [-1, 1], + "n_particles": 1, + "simulator": cssm.exgauss_race, + } \ No newline at end of file diff --git a/ssms/config/_modelconfig/shifted_wald.py b/ssms/config/_modelconfig/shifted_wald.py index e6e7612f..3d00ec98 100644 --- a/ssms/config/_modelconfig/shifted_wald.py +++ b/ssms/config/_modelconfig/shifted_wald.py @@ -7,13 +7,14 @@ def get_shifted_wald_config(): """Get the configuration of the Shifted Wald model""" return { "name": "shifted_wald", - "params": ["v", "a", "t", "p"], - "param_bounds": [[0.0, 0.3, 0.0, 0.0], [5, 2.5, 2.0, 1.0]], + "params": ["v0", "v1", "a0", "a1", "t", "p"], + "param_bounds": [[0.0, 0.0, 0.3, 0.3, 0.0, 0.0], + [5, 5, 2.5, 2.5, 2.0, 1.0]], "boundary_name": 'constant', "boundary": bf.constant, "boundary_params": [], - "n_params": 4, - "default_params": [2.0, 1, 0.0, 0.5], + "n_params": 6, + "default_params": [2.0, 2.0, 1, 1, 0.0, 0.5], "nchoices": 2, "choices": [-1, 1], "n_particles": 1, From 3acc6fa430a2477acaae546081ac1bde9db26722 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Tue, 11 Nov 2025 15:15:36 -0500 Subject: [PATCH 12/14] crude simulator additions for race in shifted_wald and exgauss --- notebooks/test_exgauss_wald.ipynb | 250 ++++++++++++++++++----- src/cssm.pyx | 178 ++++++++++------ ssms/basic_simulators/simulator.py | 5 +- ssms/basic_simulators/theta_processor.py | 12 ++ ssms/config/_modelconfig/__init__.py | 4 +- ssms/config/_modelconfig/exgauss.py | 2 +- ssms/config/_modelconfig/shifted_wald.py | 18 ++ 7 files changed, 360 insertions(+), 109 deletions(-) diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index c14d58e9..74a4af0d 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -9,8 +9,8 @@ "import numpy as np\n", "import pandas as pd\n", "import ssms\n", - "import pytensor \n", - "import pytensor.tensor as pt\n" + "# import pytensor \n", + "# import pytensor.tensor as pt\n" ] }, { @@ -122,7 +122,9 @@ " 'full_ddm2',\n", " 'ddm_legacy',\n", " 'exgauss',\n", - " 'shifted_wald']" + " 'exgauss_race',\n", + " 'shifted_wald',\n", + " 'shifted_wald_race']" ] }, "execution_count": 2, @@ -136,40 +138,52 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "this is num trials 1\n" - ] - } - ], + "outputs": [], "source": [ "from ssms.basic_simulators.simulator import simulator\n", - "mu0 = 5.0\n", - "mu1 = 1.0\n", + "mu0 = 5.0 # mu of ex-gaussian for choice 1\n", + "mu1 = 1.0 # mu of ex-gaussian for choice -1 \n", "sigma0 = 0.5\n", "sigma1 = 0.1\n", "tau0 = 0.1\n", "tau1 = 0.1\n", "\n", + "v0 = 2.0 \n", + "v1 = 2.4 \n", + "a0 = 2.5 \n", + "a1 = 2.5 \n", + "t = 0\n", + "\n", + "sim_out_race = simulator(\n", + " model=\"exgauss_race\", theta={\"mu0\": mu0, \"mu1\": mu1, \n", + " \"sigma0\": sigma0, \"sigma1\": sigma1, \n", + " \"tau0\": tau0,\n", + " \"tau1\":tau1, \"p\": np.array([0.8])}, n_samples=10000, random_state = 100,\n", + " )\n", "sim_out = simulator(\n", " model=\"exgauss\", theta={\"mu0\": mu0, \"mu1\": mu1, \n", " \"sigma0\": sigma0, \"sigma1\": sigma1, \n", " \"tau0\": tau0,\n", " \"tau1\":tau1, \"p\": np.array([0.8])}, n_samples=10000, random_state = 100,\n", " )\n", - "# sim_out_2 = simulator(\n", - "# model=\"shifted_wald\", theta={\"v\": [2.0, 1.5], \"a\":2.5, \"t\": 0.0, \"p\": 0.8}, n_samples=10000, random_state = 100,\n", - "# )\n" + "\n", + "sim_out_2 = simulator(\n", + " model=\"shifted_wald\", theta={\"v0\": v0, \"v1\": v1,\n", + " \"a0\":a0, \"a1\":a1,\n", + " \"t\": t, \"p\": 0.8}, n_samples=10000, random_state = 100,\n", + " )\n", + "sim_out_2_race = simulator(\n", + " model=\"shifted_wald_race\", theta={\"v0\": v0, \"v1\": v1,\n", + " \"a0\":a0, \"a1\":a1,\n", + " \"t\": t, \"p\": 0.8}, n_samples=10000, random_state = 100\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -196,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -211,7 +225,7 @@ " [ 1]], shape=(10000, 1), dtype=int32)" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -222,7 +236,33 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1],\n", + " [-1],\n", + " [-1],\n", + " ...,\n", + " [-1],\n", + " [-1],\n", + " [-1]], shape=(10000, 1), dtype=int32)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_out_race['choices']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -230,12 +270,12 @@ "output_type": "stream", "text": [ "[[1.7596234 ]\n", - " [0.5706974 ]\n", - " [1.0033857 ]\n", + " [0.1787014 ]\n", + " [0.67438966]\n", " ...\n", - " [0.86808836]\n", - " [2.1055403 ]\n", - " [0.8975111 ]]\n" + " [0.9025264 ]\n", + " [0.73628825]\n", + " [0.17353109]]\n" ] } ], @@ -245,7 +285,53 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1]\n", + " [-1]\n", + " [ 1]\n", + " ...\n", + " [ 1]\n", + " [ 1]\n", + " [-1]]\n" + ] + } + ], + "source": [ + "print(sim_out_2['choices'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-1]\n", + " [-1]\n", + " [-1]\n", + " ...\n", + " [-1]\n", + " [-1]\n", + " [-1]]\n" + ] + } + ], + "source": [ + "print(sim_out_2_race['choices'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -295,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -327,22 +413,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0, 0.5, 'Density')" + "Text(0.5, 1.0, 'Ex-Gaussian')" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -367,7 +453,51 @@ "# plt.plot(s[idx], pdf_vals[idx], color=\"red\", label=\"Ex-Gaussian Likelihood\")\n", "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", "plt.ylabel(\"Density\")\n", - "\n" + "plt.title(\"Ex-Gaussian\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Ex-Gaussian Race')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " sim_out_race[\"rts\"] * sim_out_race[\"choices\"],\n", + " bins=100,\n", + " histtype=\"step\",\n", + " color=\"black\",\n", + " label=\"Weibull Deadline\",\n", + " density=True,\n", + " alpha=0.4,\n", + ")\n", + "# s = choice*x\n", + "# idx = np.argsort(s) # sort the values so theyre not zig zaggy \n", + "# plt.plot(s[idx], pdf_vals[idx], color=\"red\", label=\"Ex-Gaussian Likelihood\")\n", + "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", + "plt.ylabel(\"Density\")\n", + "plt.title(\"Ex-Gaussian Race\")" ] }, { @@ -387,7 +517,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -406,29 +536,55 @@ " density=True,\n", " alpha=0.4,\n", ")\n", - "s_sw = choice_sw*x_sw\n", - "idx_sw = np.argsort(s_sw) # sort the values so theyre not zig zaggy\n", - "plt.plot(s_sw[idx_sw], pdf_vals_sw[idx_sw], color=\"blue\", label=\"Shifted Wald Likelihood\")\n", + "# s_sw = choice_sw*x_sw\n", + "# idx_sw = np.argsort(s_sw) # sort the values so theyre not zig zaggy\n", + "# plt.plot(s_sw[idx_sw], pdf_vals_sw[idx_sw], color=\"blue\", label=\"Shifted Wald Likelihood\")\n", "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", "plt.ylabel(\"Density\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mnotebook controller is DISPOSED. \n", - "\u001b[1;31mView Jupyter log for further details." - ] + "data": { + "text/plain": [ + "Text(0, 0.5, 'Density')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], - "source": [] + "source": [ + "plt.hist(\n", + " sim_out_2_race[\"rts\"][sim_out_2_race[\"rts\"] != -999] * sim_out_2_race[\"choices\"][sim_out_2_race[\"rts\"] != -999],\n", + " bins=100,\n", + " histtype=\"step\",\n", + " color=\"black\",\n", + " label=\"Weibull Deadline\",\n", + " density=True,\n", + " alpha=0.4,\n", + ")\n", + "# s_sw = choice_sw*x_sw\n", + "# idx_sw = np.argsort(s_sw) # sort the values so theyre not zig zaggy\n", + "# plt.plot(s_sw[idx_sw], pdf_vals_sw[idx_sw], color=\"blue\", label=\"Shifted Wald Likelihood\")\n", + "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", + "plt.ylabel(\"Density\")" + ] } ], "metadata": { diff --git a/src/cssm.pyx b/src/cssm.pyx index cd10ea78..6517d4de 100755 --- a/src/cssm.pyx +++ b/src/cssm.pyx @@ -5031,6 +5031,7 @@ def shifted_wald(np.ndarray[float, ndim = 2] v, # drift rate random_state = None, return_option = 'full', smooth_unif = False, + race = False, **kwargs): """ Fit reaction times and choices from a shifted Wald distribution @@ -5076,78 +5077,141 @@ def shifted_wald(np.ndarray[float, ndim = 2] v, # drift rate cdef int m = 0 cdef int num_draws = int(max_t / delta_t) + 1 cdef float[:] gaussian_values = draw_gaussian(num_draws) - + n_choices = v.shape[1] for k in range(n_trials): sqrt_st = delta_t_sqrt * s_view[k] for n in range(n_samples): - y = 0.0 - t_particle = 0.0 - ix = 0 - - if n == 0: - if k == 0: - traj_view[0, 0] = y - # Random choice depending on p - random_val = rand() / float(RAND_MAX) - if random_val <= p_view[k]: - choices_view[n, k, 0] = 1 - choice_idx = 0 - else: - choices_view[n, k, 0] = -1 - choice_idx = 1 + if race: + rt_candidates = np.empty(n_choices, dtype=DTYPE) + for c in range(n_choices): + y = 0.0 + t_particle = 0.0 + ix = 0 - # Random walk - while (y < a_view[k, choice_idx]) and (t_particle <= max_t): - y += (v_view[k, choice_idx] * delta_t) + (sqrt_st * gaussian_values[m]) - t_particle += delta_t - ix += 1 - m += 1 - if m == num_draws: - gaussian_values = draw_gaussian(num_draws) - m = 0 + if n == 0: + if k == 0: + traj_view[0, 0] = y + while (y < a_view[k, c]) and (t_particle <= max_t): + y += (v_view[k, c] * delta_t) + (sqrt_st * gaussian_values[m]) + t_particle += delta_t + ix += 1 + m += 1 + if m == num_draws: + gaussian_values = draw_gaussian(num_draws) + m = 0 + + if n == 0: + if k == 0: + traj_view[ix, 0] = y + + if smooth_unif: + if t_particle == 0.0: + smooth_u = random_uniform() * 0.5 * delta_t + elif t_particle < max_t: + smooth_u = (0.5 - random_uniform()) * delta_t + else: + smooth_u = 0.0 + else: + smooth_u = 0.0 + rt_candidates[c] = t_particle + t_view[k, c] + smooth_u + + choices_idx = np.argmin(rt_candidates) + choices_view[n, k, 0] = 1 if choices_idx == 0 else -1 + rts_view[n, k, 0] = rt_candidates[choices_idx] + + else: + y = 0.0 + t_particle = 0.0 + ix = 0 if n == 0: if k == 0: - traj_view[ix, 0] = y + traj_view[0, 0] = y + + random_val = rand() / float(RAND_MAX) + if random_val <= p_view[k]: + choices_view[n, k, 0] = 1 + choice_idx = 0 + else: + choices_view[n, k, 0] = -1 + choice_idx = 1 + + # Random walk + while (y < a_view[k, choice_idx]) and (t_particle <= max_t): + y += (v_view[k, choice_idx] * delta_t) + (sqrt_st * gaussian_values[m]) + t_particle += delta_t + ix += 1 + m += 1 + if m == num_draws: + gaussian_values = draw_gaussian(num_draws) + m = 0 - if smooth_unif: - if t_particle == 0.0: - smooth_u = random_uniform() * 0.5 * delta_t - elif t_particle < max_t: - smooth_u = (0.5 - random_uniform()) * delta_t + if n == 0: + if k == 0: + traj_view[ix, 0] = y + + if smooth_unif: + if t_particle == 0.0: + smooth_u = random_uniform() * 0.5 * delta_t + elif t_particle < max_t: + smooth_u = (0.5 - random_uniform()) * delta_t + else: + smooth_u = 0.0 else: smooth_u = 0.0 - else: - smooth_u = 0.0 - rts_view[n, k, 0] = t_particle + t_view[k, choice_idx] + smooth_u - - if return_option == 'full': - return { - 'rts': rts, - 'choices': choices, - 'metadata': { - 'v': v, 'a': a, 't': t, 's': s, - 'n_samples': n_samples, - 'n_trials': n_trials, - 'simulator': 'shifted_wald', - 'possible_choices': [-1, 1], - 'delta_t': delta_t, - 'max_t': max_t, - 'trajectory': traj, + rts_view[n, k, 0] = t_particle + t_view[k, choice_idx] + smooth_u + if race: + if return_option == 'full': + return { + 'rts': rts, + 'choices': choices, + 'metadata': { + 'v': v, 'a': a, 't': t, 's': s, + 'n_samples': n_samples, + 'n_trials': n_trials, + 'simulator': 'shifted_wald_race', + 'possible_choices': [-1, 1], + 'delta_t': delta_t, + 'max_t': max_t, + 'trajectory': traj, + } } - } - elif return_option == 'minimal': - return {'rts': rts, 'choices': choices, 'metadata': - {'simulator': 'shifted_wald', - 'n_samples': n_samples, - 'n_trials': n_trials, - 'possible_choices': [-1, 1]}} - else: - raise ValueError("return_option must be 'full' or 'minimal'") + elif return_option == 'minimal': + return {'rts': rts, 'choices': choices, 'metadata': + {'simulator': 'shifted_wald_race', + 'n_samples': n_samples, + 'n_trials': n_trials, + 'possible_choices': [-1, 1]}} + else: + raise ValueError("return_option must be 'full' or 'minimal'") + else: + if return_option == 'full': + return { + 'rts': rts, + 'choices': choices, + 'metadata': { + 'v': v, 'a': a, 't': t, 's': s, + 'n_samples': n_samples, + 'n_trials': n_trials, + 'simulator': 'shifted_wald', + 'possible_choices': [-1, 1], + 'delta_t': delta_t, + 'max_t': max_t, + 'trajectory': traj, + } + } + elif return_option == 'minimal': + return {'rts': rts, 'choices': choices, 'metadata': + {'simulator': 'shifted_wald', + 'n_samples': n_samples, + 'n_trials': n_trials, + 'possible_choices': [-1, 1]}} + else: + raise ValueError("return_option must be 'full' or 'minimal'") # ----------------------------------------------------------------------------------------------- diff --git a/ssms/basic_simulators/simulator.py b/ssms/basic_simulators/simulator.py index d991455d..df4a9ab8 100755 --- a/ssms/basic_simulators/simulator.py +++ b/ssms/basic_simulators/simulator.py @@ -502,9 +502,8 @@ def check_if_z_gt_a(z: np.ndarray, a: np.ndarray) -> None: "dev_rlwm_lba_race_v2", ]: check_if_z_gt_a(theta["z"], theta["a"]) - - if model in ["exgauss_race"]: - + + def make_noise_vec( diff --git a/ssms/basic_simulators/theta_processor.py b/ssms/basic_simulators/theta_processor.py index a2971017..6343024f 100644 --- a/ssms/basic_simulators/theta_processor.py +++ b/ssms/basic_simulators/theta_processor.py @@ -231,10 +231,22 @@ def process_theta( theta["sigma"] = np.column_stack([theta["sigma0"], theta["sigma1"]]) theta["tau"] = np.column_stack([theta["tau0"], theta["tau1"]]) + if model in ["exgauss_race"]: + theta["mu"] = np.column_stack([theta["mu0"], theta["mu1"]]) + theta["sigma"] = np.column_stack([theta["sigma0"], theta["sigma1"]]) + theta["tau"] = np.column_stack([theta["tau0"], theta["tau1"]]) + theta["race"] = True + if model in ["shifted_wald"]: theta["v"] = np.column_stack([theta["v0"], theta["v1"]]) theta["a"] = np.column_stack([theta["a0"], theta["a1"]]) theta["t"] = np.expand_dims(theta["t"], axis=1) + + if model in ["shifted_wald_race"]: + theta["v"] = np.column_stack([theta["v0"], theta["v1"]]) + theta["a"] = np.column_stack([theta["a0"], theta["a1"]]) + theta["t"] = np.expand_dims(theta["t"], axis=1) + theta["race"] = True diff --git a/ssms/config/_modelconfig/__init__.py b/ssms/config/_modelconfig/__init__.py index c04c007a..f2ca9299 100644 --- a/ssms/config/_modelconfig/__init__.py +++ b/ssms/config/_modelconfig/__init__.py @@ -129,7 +129,8 @@ ) from .shifted_wald import ( - get_shifted_wald_config + get_shifted_wald_config, + get_shifted_wald_race_config ) def get_model_config(): @@ -245,6 +246,7 @@ def get_model_config(): "exgauss": get_exgauss_config(), "exgauss_race": get_exgauss_race_config(), "shifted_wald": get_shifted_wald_config(), + "shifted_wald_race": get_shifted_wald_race_config(), } diff --git a/ssms/config/_modelconfig/exgauss.py b/ssms/config/_modelconfig/exgauss.py index f886868e..37d6ab2c 100644 --- a/ssms/config/_modelconfig/exgauss.py +++ b/ssms/config/_modelconfig/exgauss.py @@ -36,5 +36,5 @@ def get_exgauss_race_config(): "nchoices": 2, "choices": [-1, 1], "n_particles": 1, - "simulator": cssm.exgauss_race, + "simulator": cssm.exgauss, } \ No newline at end of file diff --git a/ssms/config/_modelconfig/shifted_wald.py b/ssms/config/_modelconfig/shifted_wald.py index 3d00ec98..5cc2dbe5 100644 --- a/ssms/config/_modelconfig/shifted_wald.py +++ b/ssms/config/_modelconfig/shifted_wald.py @@ -19,4 +19,22 @@ def get_shifted_wald_config(): "choices": [-1, 1], "n_particles": 1, "simulator": cssm.shifted_wald, + } + +def get_shifted_wald_race_config(): + """Get the configuration of the race version of Shifted Wald model""" + return { + "name": "shifted_wald_race", + "params": ["v0", "v1", "a0", "a1", "t", "p"], + "param_bounds": [[0.0, 0.0, 0.3, 0.3, 0.0, 0.0], + [5, 5, 2.5, 2.5, 2.0, 1.0]], + "boundary_name": 'constant', + "boundary": bf.constant, + "boundary_params": [], + "n_params": 6, + "default_params": [2.0, 2.0, 1, 1, 0.0, 0.5], + "nchoices": 2, + "choices": [-1, 1], + "n_particles": 1, + "simulator": cssm.shifted_wald, } \ No newline at end of file From fe7ed5cf5d0541cd31db78d8a2c3833c877d9356 Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Tue, 11 Nov 2025 22:44:47 -0500 Subject: [PATCH 13/14] likelihoods for bernoulli exgauss and shifted wald --- notebooks/test_exgauss_wald.ipynb | 115 +++++++++++++++--------------- 1 file changed, 56 insertions(+), 59 deletions(-) diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index 74a4af0d..0b2e3880 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -146,27 +146,28 @@ "mu0 = 5.0 # mu of ex-gaussian for choice 1\n", "mu1 = 1.0 # mu of ex-gaussian for choice -1 \n", "sigma0 = 0.5\n", - "sigma1 = 0.1\n", + "sigma1 = 0.5\n", "tau0 = 0.1\n", - "tau1 = 0.1\n", + "tau1 = 0.5\n", + "p_exgauss = 0.5\n", "\n", "v0 = 2.0 \n", - "v1 = 2.4 \n", + "v1 = 3.0\n", "a0 = 2.5 \n", - "a1 = 2.5 \n", + "a1 = 1\n", "t = 0\n", "\n", "sim_out_race = simulator(\n", " model=\"exgauss_race\", theta={\"mu0\": mu0, \"mu1\": mu1, \n", " \"sigma0\": sigma0, \"sigma1\": sigma1, \n", " \"tau0\": tau0,\n", - " \"tau1\":tau1, \"p\": np.array([0.8])}, n_samples=10000, random_state = 100,\n", + " \"tau1\":tau1, \"p\": p_exgauss}, n_samples=10000, random_state = 100,\n", " )\n", "sim_out = simulator(\n", " model=\"exgauss\", theta={\"mu0\": mu0, \"mu1\": mu1, \n", " \"sigma0\": sigma0, \"sigma1\": sigma1, \n", " \"tau0\": tau0,\n", - " \"tau1\":tau1, \"p\": np.array([0.8])}, n_samples=10000, random_state = 100,\n", + " \"tau1\":tau1, \"p\": p_exgauss}, n_samples=10000, random_state = 100,\n", " )\n", "\n", "sim_out_2 = simulator(\n", @@ -204,8 +205,7 @@ } ], "source": [ - "print(sim_out['rts'][:10])\n", - "\n" + "print(sim_out['rts'][:10])" ] }, { @@ -270,12 +270,12 @@ "output_type": "stream", "text": [ "[[1.7596234 ]\n", - " [0.1787014 ]\n", - " [0.67438966]\n", + " [0.56169754]\n", + " [0.69038945]\n", " ...\n", - " [0.9025264 ]\n", - " [0.73628825]\n", - " [0.17353109]]\n" + " [1.3998891 ]\n", + " [1.0962074 ]\n", + " [0.99489033]]\n" ] } ], @@ -297,7 +297,7 @@ " [ 1]\n", " ...\n", " [ 1]\n", - " [ 1]\n", + " [-1]\n", " [-1]]\n" ] } @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "[[-1]\n", - " [-1]\n", + " [ 1]\n", " [-1]\n", " ...\n", " [-1]\n", @@ -331,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -340,11 +340,13 @@ "from scipy.stats import norm \n", "\n", "\n", - "def exgauss_likelihood(x, choice, mu, sigma, tau, p):\n", + "def exgauss_likelihood_bernoulli(x, choice, mu0, mu1, sigma0, sigma1, tau0, tau1, p):\n", " x = np.asarray(x, dtype=float)\n", - " tau = float(tau)\n", - " sigma= float(sigma)\n", - " mu = float(mu)\n", + " y = (np.asarray(choice, dtype=int) > 0) # 1 choice \n", + " mu = np.where(y, (float(mu0)), float(mu1))\n", + " sigma = np.where(y, float(sigma0), float(sigma1))\n", + " tau = np.where(y, float(tau0), float(tau1))\n", + "\n", " tau_inv = 1.0 / tau \n", " sig2 = sigma ** 2 \n", " z = (x - mu - sig2 * tau_inv) / sigma \n", @@ -352,68 +354,63 @@ " pdf_rt = tau_inv * np.exp(mu*tau_inv + (sig2 * tau_inv**2) / 2 - x*tau_inv) * cdf_gauss \n", " \n", " # Bernoulli part \n", - " choice = np.asarray(choice, dtype=int)\n", " p = float(p)\n", - " y = (choice > 0).astype(float)\n", - " pdf_choice = y * p + (1 - y) * (1 - p)\n", + " pdf_choice = np.where(y, p, 1 - p)\n", "\n", " return pdf_rt*pdf_choice \n", "\n", - "# def exgauss_joint_logpdf(x, choice, mu, sigma, tau, p): \n", - "# pdf = exgauss_likelihood(x, mu, sigma, tau)\n", - "# choice = np.asarray(choice, dtype=int) \n", - "# p = float(p) \n", - "\n", - "# y = (choice > 0).astype(float)\n", - "# ll_rt = np.log(pdf).sum()\n", - "# ll_choice = (y * np.log(p) + (1 - y) * np.log(1 - p)).sum()\n", - " \n", - "# return ll_rt + ll_choice\n", - "\n", - "\n", "\n", "x = np.linspace(0, 9, 1100)\n", "p = 0.8\n", "choice = np.where(np.random.rand(len(x)) < p, 1, -1)\n", - "# pdf_vals_loglik = exgauss_joint_logpdf(x, choice, mu=2, sigma=0.05, tau=1, p=0.8)\n", - "pdf_vals = exgauss_likelihood(x, choice, mu=2, sigma=0.05, tau=1, p=0.2)\n" + "pdf_vals = exgauss_likelihood_bernoulli(x, choice, mu0=mu0, mu1=mu1, sigma0=sigma0,\n", + " sigma1=sigma1, tau0=tau0, tau1=tau1, p=p_exgauss)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define exgauss race likelihoods \n", + "\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# define shifted wald analytical likelihoods\n", "\n", - "def shifted_wald_likelihood(x, choice, v, a, t, p):\n", + "def shifted_wald_likelihood(x, choice, v0, v1, a0, a1, t, p):\n", " x = np.asarray(x, dtype=float)\n", - " v = float(v)\n", - " a = float(a)\n", + " y = (np.asarray(choice, dtype=int) > 0) # 1 choice\n", + " v = np.where(y, float(v0), float(v1))\n", + " a = np.where(y, float(a0), float(a1))\n", " t = float(t)\n", + " p = float(p)\n", "\n", " term_1 = (a / np.sqrt(2*np.pi*(x - t)**3))\n", " term_2 = np.exp((-(a-v*(x - t))**2) / (2*(x - t)))\n", " pdf_rt = term_1 * term_2\n", "\n", " # Bernoulli part\n", - " choice = np.asarray(choice, dtype=int)\n", - " p = float(p)\n", - " y = (choice > 0).astype(float)\n", - " pdf_choice = y * p + (1 - y) * (1 - p)\n", + " pdf_choice = np.where(y, p, 1 - p)\n", "\n", " return pdf_rt*pdf_choice\n", "\n", "x_sw = np.linspace(0.001, 9, 1100)\n", "p_sw = 0.8\n", "choice_sw = np.where(np.random.rand(len(x_sw)) < p_sw, 1, -1)\n", - "pdf_vals_sw = shifted_wald_likelihood(x_sw, choice_sw, v=2.0, a=2.5, t=0.0, p=0.8)\n" + "pdf_vals_sw = shifted_wald_likelihood(x_sw, choice_sw, v0=v0, v1=v1, a0=a0, a1=a1, t=0.0, p=0.8)\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -422,13 +419,13 @@ "Text(0.5, 1.0, 'Ex-Gaussian')" ] }, - "execution_count": 12, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAHHCAYAAABXx+fLAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAerlJREFUeJzt3XlclOX+//HXLMCwo6IshoK4m4q5cNTKMhJbNSu1+h6Vn1kn81SH03Js0dTOsU2zOpYdS21X65h1WlzCaFHS1Mw0NTUVN0BUwAGHgZn798csOrLIMnDP8nk+HvNwuOeee66bQebN57ru69IoiqIghBBCCOFHtGo3QAghhBCiuUkAEkIIIYTfkQAkhBBCCL8jAUgIIYQQfkcCkBBCCCH8jgQgIYQQQvgdCUBCCCGE8DsSgIQQQgjhdyQACSGEEMLvSAASQogmNGHCBBITE9VuhhDiAhKAhBB1tmTJEjQaTY23H3/80a2v9/333zN69Gjatm1LYGAgkZGRpKamMnPmTPLz8936WkII/6JXuwFCCO8zc+ZMkpKSqmzv2LGj215j2rRpzJo1iw4dOjBhwgQ6dOiAyWRiy5YtzJkzh7fffpv9+/e77fWaysKFC7FarWo3QwhxAQlAQoh6u+666+jXr1+THX/ZsmXMmjWL0aNH8+677xIYGOjy+EsvvcRLL73UZK/vTgEBAWo3QQhRDekCE0K41fTp09FqtWRlZblsv+eeewgMDOSXX3656DGmTZtGdHQ0b731VpXwAxAZGcnTTz/tsu3TTz/lhhtuID4+nqCgIJKTk5k1axYWi8Vlv8TERCZMmFDlmFdddRVXXXWVy7ZXX32VHj16EBISQosWLejXrx8ffPCB8/EzZ87w0EMPkZiYSFBQEG3atOHaa69l69atzn2qGwP04osvMmjQIFq1akVwcDB9+/bl448/rtImjUbDlClTWLlyJZdeeilBQUH06NGDVatW1fCdE0LUlVSAhBD1VlxcTGFhocs2jUZDq1atePLJJ/nf//7HxIkT+fXXXwkPD2f16tUsXLiQWbNm0bt371qP/fvvv/P7779z9913ExYWVuc2LVmyhLCwMDIzMwkLC2PdunVMmzaNkpISXnjhhXqf48KFC3nggQe47bbbePDBBzGZTGzfvp2NGzdy5513AvCXv/yFjz/+mClTptC9e3dOnjzJDz/8wK5du7jssstqPPbLL7/MzTffzF133YXZbGbp0qXcfvvtfP7559xwww0u+/7www+sWLGCyZMnEx4eziuvvMKtt95Kbm4urVq1qvd5CSHsFCGEqKPFixcrQLW3oKAg536//vqrEhgYqNx9993K6dOnlbZt2yr9+vVTKioqLvoan376qQIo8+bNc9lutVqVEydOuNzOP15ZWVmVY917771KSEiIYjKZnNvat2+vjB8/vsq+Q4YMUYYMGeL8esSIEUqPHj1qbWtkZKRy//3317rP+PHjlfbt27tsu7CtZrNZufTSS5WhQ4e6bAeUwMBAZd++fc5tv/zyiwIor776aq2vK4SonVSAhBD1Nn/+fDp37uyyTafTOe9feumlzJgxg6lTp7J9+3YKCwtZs2YNev3Ff+WUlJQAVKn+FBcX07p1a5dtP/30k3MsUnBwsHP7mTNnKC8v54orruCNN95g9+7dF608XSgqKoojR47w008/0b9//xr32bhxI8eOHSM+Pr7Oxz6/radPn8ZisXDFFVfw4YcfVtk3LS2N5ORk59e9evUiIiKCP/74ox5nI4S4kAQgIUS9DRgw4KKDoB955BGWLl3Kpk2b+Ne//kX37t1dHs/Ly3P5OjIykuDgYMLDwwEwGo0uj4eFhbF27VoA1qxZU6Vba+fOnTz55JOsW7fOGaIciouL635ydo899hhff/01AwYMoGPHjgwbNow777yTwYMHO/d5/vnnGT9+PAkJCfTt25frr7+ecePG0aFDh1qP/fnnn/PMM8+wbds2ysvLnds1Gk2Vfdu1a1dlW4sWLTh9+nS9z0kIcY4MghZCNIk//viDvXv3AvDrr79WeTwuLs7ltmzZMgC6du0KwI4dO1z21+v1pKWlkZaWViVMFRUVMWTIEH755RdmzpzJ//73P9auXctzzz0H4HIZenUhA6gyWLpbt27s2bOHpUuXcvnll/Pf//6Xyy+/nOnTpzv3GT16NH/88Qevvvoq8fHxvPDCC/To0YOvvvqqxu/L999/z80334zBYOC1117jyy+/ZO3atdx5550oilJl//Mra+erbl8hRN1JBUgI4XZWq5UJEyYQERHBQw89xL/+9S9uu+02Ro0a5dzHUc1x6NGjBwBdunShU6dOrFy5knnz5hEaGnrR18vOzubkyZOsWLGCK6+80rn9wIEDVfZt0aIFRUVFVbYfOnSoSuUmNDSUMWPGMGbMGMxmM6NGjeKf//wnU6dOxWAwALYgN3nyZCZPnkxBQQGXXXYZ//znP7nuuuuqbet///tfDAYDq1evJigoyLl98eLFFz1PIYT7SAVICOF2c+fOZcOGDfznP/9h1qxZDBo0iPvuu8/lyjFHNcdxi4uLcz729NNPU1hYyKRJk6ioqKhy/AurH44qyfnbzWYzr732WpXnJicn8+OPP2I2m53bPv/8cw4fPuyy38mTJ12+DgwMpHv37iiKQkVFBRaLpUrXWps2bYiPj3fp1rqQTqdDo9G4VJwOHjzIypUra3yOEML9pAIkhKi3r776it27d1fZPmjQIMrLy3nqqaeYMGECN910E2C7RD0lJYXJkyezfPnyix7/zjvvZMeOHcyePZtNmzYxduxYkpKSKC0tZceOHXz44YeEh4fTokUL5+u2aNGC8ePH88ADD6DRaHj33Xer7Sa6++67+fjjjxk+fDijR49m//79vPfeey4DjQGGDRtGbGwsgwcPJiYmhl27dvHvf/+bG264gfDwcIqKirjkkku47bbb6N27N2FhYXz99df89NNPzJkzp8Zzu+GGG5g7dy7Dhw/nzjvvpKCggPnz59OxY0e2b99+0e+NEMJN1LwETQjhXWq7DB5Q3nzzTaV///7KJZdcohQVFbk89+WXX1YAZdmyZXV+vezsbOW2225T4uLilICAACUiIkLp16+fMn36dOX48eMu+65fv17505/+pAQHByvx8fHKo48+qqxevVoBlG+++cZl3zlz5iht27ZVgoKClMGDByubN2+uchn8G2+8oVx55ZVKq1atlKCgICU5OVl55JFHlOLiYkVRFKW8vFx55JFHlN69eyvh4eFKaGio0rt3b+W1115zea3qLoN/6623lE6dOilBQUFK165dlcWLFyvTp09XLvyVDFR7mX1Nl/ILIepOoygykk4IIYQQ/kXGAAkhhBDC70gAEkIIIYTfkQAkhBBCCL8jAUgIIYQQfkcCkBBCCCH8jgQgIYQQQvgdmQixGlarlWPHjhEeHl7jukFCCCGE8CyKonDmzBni4+PRamuv8UgAqsaxY8dISEhQuxlCCCGEaIDDhw9zySWX1LqPBKBqhIeHA7ZvYEREhMqtEUIIIURdlJSUkJCQ4Pwcr40EoGo4ur0iIiIkAAkhhBBepi7DV2QQtBBCCCH8jgQgIYQQQvgdCUBCCCGE8DseEYDmz59PYmIiBoOB1NRUNm3aVKfnLV26FI1Gw8iRI122K4rCtGnTiIuLIzg4mLS0NPbu3dsELRdCCCGEN1I9AC1btozMzEymT5/O1q1b6d27N+np6RQUFNT6vIMHD/Lwww9zxRVXVHns+eef55VXXmHBggVs3LiR0NBQ0tPTMZlMTXUaQgghhPAiqgeguXPnMmnSJDIyMujevTsLFiwgJCSERYsW1fgci8XCXXfdxYwZM+jQoYPLY4qiMG/ePJ588klGjBhBr169eOeddzh27BgrV65s4rMRQgghhDdQNQCZzWa2bNlCWlqac5tWqyUtLY2cnJwanzdz5kzatGnDxIkTqzx24MAB8vLyXI4ZGRlJampqjccsLy+npKTE5SaEEEII36VqACosLMRisRATE+OyPSYmhry8vGqf88MPP/DWW2+xcOHCah93PK8+x5w9ezaRkZHOm8wCLYQQQvg21bvA6uPMmTP8+c9/ZuHChURHR7vtuFOnTqW4uNh5O3z4sNuOLYQQQgjPo+pM0NHR0eh0OvLz81225+fnExsbW2X//fv3c/DgQW666SbnNqvVCoBer2fPnj3O5+Xn5xMXF+dyzJSUlGrbERQURFBQUGNPRwghhBBeQtUKUGBgIH379iUrK8u5zWq1kpWVxcCBA6vs37VrV3799Ve2bdvmvN18881cffXVbNu2jYSEBJKSkoiNjXU5ZklJCRs3bqz2mEIIIYTwP6qvBZaZmcn48ePp168fAwYMYN68eZSWlpKRkQHAuHHjaNu2LbNnz8ZgMHDppZe6PD8qKgrAZftDDz3EM888Q6dOnUhKSuKpp54iPj6+ynxBQgghhPBPqgegMWPGcOLECaZNm0ZeXh4pKSmsWrXKOYg5NzcXrbZ+hapHH32U0tJS7rnnHoqKirj88stZtWoVBoOhKU5BCCGEEF5GoyiKonYjPE1JSQmRkZEUFxfLavBCCOErFAXMZpAxnz6rPp/fXnUVmBBCCNEQxvx8zEOGoISEYJ48We3mCA8gAUgIIYRPMxqN7LnvPgK//x6N1Urg669z9rPP1G6WUJkEICGEED7NVFpKN/uVwdbQUAC0b72lZpOEB5AAJIQQwqfpf/6ZkJISrBERlKxYAUDg6tVQWqpyy4SaJAAJIYTwaYH26k/F1VdT2acPZ1q2RFNeTvFXX1FYWIjRaFS5hUINql8GL4QQQjQl/ZYtAFQMHowhOJiCjh0J37SJ3999l59PnUKv1zN69GjCwsJUbqloTlIBEkII4bsUBf2vvwJQ2bs3YWFhtB01CoDeJhNDhw6lsrISk8mkZiuFCiQACSGE8F3HjqEtLMSq1VLZrRsAQVdeCUDgzz8TFRmpZuuEiiQACSGE8F1btwJwOi4OgoNt2/r0Aa0WTpxAc8Fi3MJ/SAASQgjhu+wB6GRCwrltBgMkJQGg379fjVYJDyABSAghhO/auROAk5dc4rq9SxcAdPv2NXeLhIeQACSEEMJ32Ss8xW3auG7v2hUA3d69zd0i4SEkAAkhhPBNigL2Ck9J69auj0kFyO9JABJCCOGbCguhpARFo+HMhQHIUQGSMUB+SwKQEEII32QPN9b4eCwBAa6P2StA2kOH0FVUNHfLhAeQACSEEMI32bu3LImJVR9r0wZCQ9EoCqGnTjVvu4RHkAAkhBDC5xiNRkrtM0Cfbdu26g4aDbRrB0CYBCC/JAFICCGETzEajSxfvpyj330HwO6KCvR6PQaDwXXH9u0BCJcA5JdkMVQhhBA+xWQyUVlZiWPmn+7p6fSobrFTqQD5NQlAQgghfFJQQQEA4V27QnUrvTsC0MmTzdks4SGkC0wIIYTvURS0x4/b7l84C7SDVID8mgQgIYQQPiewrAzN2bO2L6obBA3OMUBhp083U6uEJ5EAJIQQwueEFhXZ7rRqZVv8tDrnV4Cs1uZpmPAYEoCEEEL4nFBHVaem7i+Atm1RNBp0lZVoTpxonoYJjyEBSAghhM9xVoBq6v4CCAhAiY4GQGsfMC38hwQgIYQQPiesLhUgwGpfJV6bn9/UTRIeRgKQEEIInxNSlwoQYI2JASQA+SMJQEIIIXxOncYAcV4FSLrA/I4EICGEED7HOQboYgFIKkB+SwKQEEIInxNSXGy7ExdX637OACQVIL8jAUgIIYRvqazEUFpqu28PODWRQdD+SwKQEEIIn6I5eRKNoqBoNLaJEGshFSD/JQFICCGET9EWFgKgtGoFOl2t+7oEIEVp8rYJzyEBSAghhE9xBCCrfZLD2ji6wDRlZXDmTJO2S3gWjwhA8+fPJzExEYPBQGpqKps2bapx3xUrVtCvXz+ioqIIDQ0lJSWFd99912WfCRMmoNFoXG7Dhw9v6tMQQgjhAbT2ZS2srVtffOfQUMyOtcLy8pqwVcLTqB6Ali1bRmZmJtOnT2fr1q307t2b9PR0Cmroj23ZsiVPPPEEOTk5bN++nYyMDDIyMli9erXLfsOHD+f48ePO24cfftgcpyOEEEJlGkcXWB0qQABlkZG2O8ePN1WThAdSPQDNnTuXSZMmkZGRQffu3VmwYAEhISEsWrSo2v2vuuoqbrnlFrp160ZycjIPPvggvXr14ocffnDZLygoiNjYWOetRYsWzXE6QgghVOasANU1AEVE2O5IBcivqBqAzGYzW7ZsIS0tzblNq9WSlpZGTk7ORZ+vKApZWVns2bOHK6+80uWx7Oxs2rRpQ5cuXbjvvvs4efJkjccpLy+npKTE5SaEEMI7OccA1aULDDCFh9vuyIrwfkWv5osXFhZisViIuWCehpiYGHbv3l3j84qLi2nbti3l5eXodDpee+01rr32Wufjw4cPZ9SoUSQlJbF//34ef/xxrrvuOnJyctBVc0XA7NmzmTFjhvtOTAghhGrqWwEyhYXZ7kgA8iuqBqCGCg8PZ9u2bRiNRrKyssjMzKRDhw5cddVVAIwdO9a5b8+ePenVqxfJyclkZ2dzzTXXVDne1KlTyczMdH5dUlJCQkJCk5+HEEII93OOAapjBeisVID8kqoBKDo6Gp1OR/4FM3Dm5+cTGxtb4/O0Wi0dO3YEICUlhV27djF79mxnALpQhw4diI6OZt++fdUGoKCgIIKCghp+IkIIITxGfS6DB6kA+StVxwAFBgbSt29fsrKynNusVitZWVkMHDiwzsexWq2Ul5fX+PiRI0c4efIkcRdZE0YIIYSXU5T6XQaPjAHyV6p3gWVmZjJ+/Hj69evHgAEDmDdvHqWlpWRkZAAwbtw42rZty+zZswHbeJ1+/fqRnJxMeXk5X375Je+++y6vv/46AEajkRkzZnDrrbcSGxvL/v37efTRR+nYsSPp6emqnacQQohmYDSiMZmAuleAzjoqQPbKkfAPqgegMWPGcOLECaZNm0ZeXh4pKSmsWrXKOTA6NzcXrfZcoaq0tJTJkydz5MgRgoOD6dq1K++99x5jxowBQKfTsX37dt5++22KioqIj49n2LBhzJo1S7q5hBDC19nnkKsIDITQ0Do9xdEFZi0ooMxoJMwRiIRP0yiKLH5yoZKSEiIjIykuLibCMT+EEEIIz7dhAwweTEl0NOZdu4i+SBXIaDTyvzfe4I6HH8aq1bLkjTcYPXashCAvVZ/Pb9UnQhRCCCHcxl4BMtUxwISFhXGTfciF1mpFV1KCyd6FJnybBCAhhBC+wz7pbV0DEEBYy5ZgXw7DYDQ2SbOE55EAJIQQwnecOgXULwABYL9iLFhWhPcbEoCEEEL4DnsFqDwkpH7PswcggwQgvyEBSAghhO9oQBcYcK4CJF1gfkMCkBBCCN/hqADV8RJ4J6kA+R0JQEIIIXyHfQxQgwOQVID8hgQgIYQQPsFoNFLpuAy+vgGoZUsAgsrK3N0s4aEkAAkhhPB6RqOR5cuXU37sGACVkZEYDIa6H8ARgEpLm6J5wgOpvhSGEEII0Vgmk4nKigqCz54FIG3MGELrMxC6VStAusD8iVSAhBBC+AS92YzWbAYgNCGhfk+WLjC/IxUgIUSzMhqNzqUGDAaDrLkk3MbZfRUQAPX9uTqvC6zSze0SnkkCkBCi2TjGaVRW2j5i9Ho9o0ePlhAk3MLgCECtWoFGU78nnxeASq1WN7dMeCLpAhNCNBuTyURlZSVDhw5l6NChVFZWysKTwm2CHON37GGmXlq0AECrKGhkHJBfkAqQEKLZRUVFqd0E4YNcKkD1FRyMEhyM5uxZNKdPu7dhwiNJBUgIIYRPCGpMAAKs9mCuKSpyT4OER5MAJIQQwic0qgIEKPauM61UgPyCBCAhhBA+oVFjgDivAiQByC9IABJCCOETnHP4NLQCZA9AUgHyDxKAhBBC+ATnLM4NDUD2K8GkAuQfJAAJIYTwCc5B0A3tAnNcCi+DoP2CBCAhhBA+odGDoGUMkF+RACSEEMInNPoyeEcXmFSA/IIEICGEEN7Pam10AJJB0P5FApAQQgivpykpQasoti8aOAbIMQ+QVID8gyyFIYRoenl5sHUrms6d1W6J8FGOcTtKSAiaoKAGHcMqFSC/IhUgIUTT+vln6NwZbriBqCuvJFQ+XEQT0BYXA+dCTEO4XAbvqCYJnyUBSAjRdIxGGDECzpwBQHf8OFcvWiQfLsLtNPYApERGNvgYzpmgKyttP7vCp0kAEkI0nYUL4fBhLO3acfrrr7EGBhL/++/of/xR7ZYJH6MpKQFAiYho+EGCg6nU20eGnDrlhlYJTyYBSAjRZCzvvgtAzuDBfLR/P7/37w9A6Gefqdks4YPcUQFCo6E8NNR2XwKQz5MAJIRoGn/8ge7nn7FqNLR98EFGjRpF3OTJAASsWSPdYMKttPYKkLUxFSCQAORH5CowIUTT+PhjAI537kx4cjLR0dFwyy0QHAy5ueh27VK5gcKXOCtAjQ1AISG2OxKAfJ5UgIQQTePTTwH4o2/fc9uCg2HoUAAC165Vo1XCRznHADWmC4zzKkAnTza2ScLDSQASQrifyQSbNwNwpHt318duuAGAwDVrmrtVwoe5LQA5KkAyGaLP84gANH/+fBITEzEYDKSmprJp06Ya912xYgX9+vUjKiqK0NBQUlJSeNc+0NJBURSmTZtGXFwcwcHBpKWlsXfv3qY+DSGEw+bNYDZjbdOGM9HRro+lpwOg37oVndmsQuOEL3J0gTV2DJBZApDfUD0ALVu2jMzMTKZPn87WrVvp3bs36enpFBQUVLt/y5YteeKJJ8jJyWH79u1kZGSQkZHB6tWrnfs8//zzvPLKKyxYsICNGzcSGhpKeno6JpOpuU5LCP+2fj0AFf37g0bj+lhSEsTEoKmsJDo3V4XGCV+kdfcYIAlAPk/1ADR37lwmTZpERkYG3bt3Z8GCBYSEhLBo0aJq97/qqqu45ZZb6NatG8nJyTz44IP06tWLH374AbBVf+bNm8eTTz7JiBEj6NWrF++88w7Hjh1j5cqVzXhmQvgxRwBKTa36mEYD9nFBLY8ebc5WCR8mXWCivlQNQGazmS1btpCWlubcptVqSUtLIycn56LPVxSFrKws9uzZw5VXXgnAgQMHyMvLczlmZGQkqampNR6zvLyckpISl5sQou6MRiOFhYW224kTKPYAVDlgQPVP6NYNgKi8vOZqovBx7gpA5uBg2x1ZssXnqXoZfGFhIRaLhZiYGJftMTEx7N69u8bnFRcX07ZtW8rLy9HpdLz22mtce+21AOTZf6FWd8y8Gn7Zzp49mxkzZjTmVITwW0ajkeXLl1NZWQlA6KlT3HXqFIpeT2XPnnDoUNUn2QNQi+PHKTrvL22DwUBYWFhzNFv4GHeNAZIKkP/wynmAwsPD2bZtG0ajkaysLDIzM+nQoQNXXXVVg443depUMjMznV+XlJSQkJDgptYK4dtMJhOVlZUMHTqUqKgozJ98AoClUycIDKz+SedVgD5Yt865Wa/XM3r0aAlBon4sFrT29eYaOwZIBkH7D1UDUHR0NDqdjvz8fJft+fn5xMbG1vg8rVZLx44dAUhJSWHXrl3Mnj2bq666yvm8/Px84uLiXI6ZkpJS7fGCgoIICgpq5NkI4d+ioqKIjo6m1D6w2dK1a8072wNQ2OnT3DpsGEpYGEVFRaxbtw6TySQBSNTPecMW3DYIWrrAfJ6qY4ACAwPp27cvWVlZzm1Wq5WsrCwGDhxY5+NYrVbKy8sBSEpKIjY21uWYJSUlbNy4sV7HFEI0jM7efV1pDznVatEC7N3UrU6cIDo6mij7StxC1Ju9+6syIAAa+cescwxQUZEs1+LjVO8Cy8zMZPz48fTr148BAwYwb948SktLycjIAGDcuHG0bduW2bNnA7bxOv369SM5OZny8nK+/PJL3n33XV5//XUANBoNDz30EM888wydOnUiKSmJp556ivj4eEaOHKnWaQrhN/T2JS4sF06AeKFu3SA/H3btAvsiqUI0iL27ylm9aQTnMSoq4OxZcMMxhWdSPQCNGTOGEydOMG3aNPLy8khJSWHVqlXOQcy5ublotecKVaWlpUyePJkjR44QHBxM165dee+99xgzZoxzn0cffZTS0lLuueceioqKuPzyy1m1ahUGg6HZz08Iv1JRge7334GLVIAAuneH7Gz47bemb5fwbfYAZHZDWKkMCkLR6dBYLLbjSgDyWaoHIIApU6YwZcqUah/Lzs52+fqZZ57hmWeeqfV4Go2GmTNnMnPmTHc1UQhRF3v3oqmowBwUhPWSS2rf1xGQZFFU0ViOAGQwoGvssTQalMhINKdO2cYBxcc39ojCQ6k+EaIQwof8+isAp+PjQXuRXy8SgIS72McAuaMLDM4bSC1Xgvk0CUBCCPexB6BTbdtefF9HANq/H+wXMQjRIG7sAgOwOgbkSwDyaRKAhBDuYx/Pc7ou3QZxcRARAVYryGLFojEcAchxBVcjOWeTlgDk0yQACSHcZ98+AIovmIm9WhqNdIMJ93B3AHJ0gclcQD5NApAQwj0UxdadBZS0bl2350gAEu7gxsvgARTpAvMLHnEVmBDC+2ny86GsDEWr5UyrVi5rfNXIMVeQBCDRGPZB0O6qAFmlC8wvSAASQriF7uBBAJSEBLQGA+vsa3zp9fqa5+ByLJexZ08ztFD4LDcPgpYuMP8gAUgI4Ra6AwcA0HbsyOjRozGZTMBFVnhPTLT9W92K8ULUlZvHAJUGBhIKlOfnU2E0ytp0PkrGAAkh3MJRAaJjR8LCwoiOjiY6Orr2D4/27W3/njqFxmhs8jYKH+UYA9TIAGQwGNDr9fx6+DAAhfv2sXz5cozys+mTJAAJIdzCUQEiObnuT4qIsC2MCmjtHzpC1JtjDFAju8DCwsIYPXo0/dLSAGil11NZWemsZgrfIgFICOEWWkcFqD4BCJzdYBKARIMoiluvAgsLCyPSXpkMkMqPT5MAJIRwi/O7wOrF/mGjkwAkGsJotE2mifvGAGG/DF5jrywJ3yQBSAjRaIGlpWgdV8x06FC/J0sFSDSGvfqjBARgCQhwzzHt3bKakhJnuBK+RwKQEKLRIk6csN2JjYX6XjEjFSDRGPYqjRIZaZtd3B0cFSCrlQBZp85nSQASQjRapCMA1Xf8D0gFSDSOowLkmLvHHQwGCAoCIKiszH3HFR5FApAQotEiGhOAEhIA0B475sYWCb9hD0DO2Zvdxd4NFigByGdJABJCNFpEQYHtTn0HQANccgkA2oICNBaLG1sl/EJTVIDA2Q0WdPase48rPIYEICFEo4WfPGm7U98B0ACtW0NAABpFIUSuuhH11cQBSCpAvksCkBCi0cJOnbLdcczsXB9aLbRtazuOrL0k6uv8QdDu5KgASQDyWRKAhBCNY7EQ6ggu7do17Bj2brBQCUCivpp4DJAEIN8lAUgI0SjaggJ0FguKTgfx8Q07iAQg0VCOLrDwcPce19EFJmOAfJYEICFEo2iPHAHAGhcHen3DDiIBSDSUIwA1UReYjAHyXRKAhBCN4gxA9nE8DeIIQPYPMyHqrInHAEkFyHdJABJCNIru6FEALPYQ0yBSARIN1VRjgGQQtM+TACSEaBRnBcgNAUiuAhP1YTQasdinYDijdfPHmT1QSQXId0kAEkI0ijsDUEhxMchkiKIOjEYjy5cvx2yfhTxn1y70ej0Gg8E9LyBjgHxeA0csCiGEjc4egCyNGQMUE4Oi0aC1WtGcPAkxMW5qnfBVJpOJyooKDCYTANeMGkVgcjJh9V2MtybnjQEyu+eIwsNIBUgI0Sha+xigRlWA9HqUVq1sx3OsKybERegqKtBUVADQskMH94UfkKUw/IAEICFEw505g9YxCLUxAQiwtm4NSAASdeccn6PVgjvDD7heBWa1uvfYwiNIABJCNNzhwwCUh4Q0eiI6CUCivpxXaEVGgkbj3oPbB0FrFAWN0ejeYwuPIAFICNFwubkAGFu2bPShnAHIsbK8EBfhHKBsr9a4lcGAYh9QrZFFen2SBCAhRMMdOgSA0b5uUmMo9gCkkQqQqCNnF1hTBCDOrTAvAcg3SQASQjRcU1SAJACJOgpq4gDkmFxRApBvkgAkhGg4RwCyX8HVGNIFJuor8PwxQE3AsbyGtqSkSY4v1OURAWj+/PkkJiZiMBhITU1l06ZNNe67cOFCrrjiClq0aEGLFi1IS0ursv+ECRPQaDQut+HDhzf1aQjhfxwByA1dYNY2bQCpAIm6a/IuMPvAfqkA+SbVA9CyZcvIzMxk+vTpbN26ld69e5Oenk5BDX8FZmdnc8cdd/DNN9+Qk5NDQkICw4YN46h9LhKH4cOHc/z4ceftww8/bI7TEcK/2CdBlC4woYYmD0DSBebTVA9Ac+fOZdKkSWRkZNC9e3cWLFhASEgIixYtqnb/999/n8mTJ5OSkkLXrl158803sVqtZGVluewXFBREbGys89bCDX+hCiHOoyhg/8OjzA0fQIq9AqQ5eZLCggKMcumxuIigprwKjPPGAJ050yTHF+pSNQCZzWa2bNlCWlqac5tWqyUtLY2cnJw6HaOsrIyKigpaXvAXaHZ2Nm3atKFLly7cd999nLQvmFed8vJySkpKXG5CiIs4dQrKywEodcMYjED7Uhoai4Uv332X5cuXSwgStWquq8C0UgHySaoGoMLCQiwWCzEXrPsTExNDXl5enY7x2GOPER8f7xKihg8fzjvvvENWVhbPPfcc3377Lddddx2WGhZZnD17NpGRkc5bQkJCw09KCH/hWAKjZUusAQGNPlxYixYo9j9kruralcrKSkz2dZ6EqE5zDYKWLjDf5NWLoT777LMsXbqU7OxslxWAx44d67zfs2dPevXqRXJyMtnZ2VxzzTVVjjN16lQyMzOdX5eUlEgIEuJijh0DwBoX57ZDamJj4dQpIiX4iDpotjFA0ivgk1StAEVHR6PT6cjPz3fZnp+fT2xsbK3PffHFF3n22WdZs2YNvXr1qnXfDh06EB0dzb59+6p9PCgoiIiICJebEOIiHBWgi/xfrRd7NVgGQou6kHmARGOoGoACAwPp27evywBmx4DmgQMH1vi8559/nlmzZrFq1Sr69et30dc5cuQIJ0+eJM6Nf6kK4fccAcid/6/sYUpmgxZ10aRLYSBjgHyd6leBZWZmsnDhQt5++2127drFfffdR2lpKRkZGQCMGzeOqVOnOvd/7rnneOqpp1i0aBGJiYnk5eWRl5fnHCxpNBp55JFH+PHHHzl48CBZWVmMGDGCjh07kp6erso5CuGTmiIASQVI1ENQc40Bki4wn6T6GKAxY8Zw4sQJpk2bRl5eHikpKaxatco5MDo3Nxet9lxOe/311zGbzdx2220ux5k+fTpPP/00Op2O7du38/bbb1NUVER8fDzDhg1j1qxZBAUFNeu5CeHT7AHI0hQBSGaDFhdjNqOvqLDdl3mARAOoHoAApkyZwpQpU6p9LDs72+XrgwcP1nqs4OBgVq9e7aaWCSFqdP4YIHfNkyIVIFFHLlWZJhq3aXXMBF1SAlYraFXvNBFuJO+mEKJhmrILTCpA4iIcVRlreDjodE3yGs4KkNUKMieVz5EAJISov/JysFdpmiIAySBocTGOBUqVJhr/A4DBgEVv6yg5deCATMzpYyQACSHq7/hx27+Bgc7JC93CUQEqLLR1OQhRA0cFSGnCaUsMwcGYQ0IAyFqxQmYn9zESgIQQ9WefBJH4eNBo3Hdcx3pgFsu5K3yEqIZjDFBTBqCwsDCC7D+TA2V2cp8jAUgIUX/28T/Y1+9ym8BAsFeUQuTSY1EL5xigpuwCA7T2n8cIqUj6HAlAQoj6a6oABM5usGAJQKIWzTIGCJyX2Mul8L5HApAQov7sAehsy5YUFRW599gSgEQdOMcA2S9VbzISgHyWBCAhRL1VHDoEwLbCQtatW4der3dZkLhRJACJOtA0dwVIfh59jkdMhCiE8DJHjgCQNHgwnUaNwmAwEBYW5p5j2wOQjAEStWnuACTrgfkeCUBCiHrT2i+DNyQnExUd7d6DSwVI1IEjkFib8CowwLnOmHSB+Z4GdYH98ccf7m6HEMJbKAravDzAzZMgOjgCkLuW1xA+qTnmAQJkDJAPa1AA6tixI1dffTXvvfeezIkghL85fRqN/f+9NTbW/ce3H1MqQKI2zT4GSAK5z2lQANq6dSu9evUiMzOT2NhY7r33XjZt2uTutgkhPJH9CjBTaCi4a+Dz+aQLTNRBswcgqQD5nAYFoJSUFF5++WWOHTvGokWLOH78OJdffjmXXnopc+fO5YSs4yOE77LPAl1q/2Bwu/O7wBSlaV5DeD1Nc40BkkHQPqtRl8Hr9XpGjRrFRx99xHPPPce+fft4+OGHSUhIYNy4cRx3rBckhPAd9gpQkwUg+9IDOosFjbvnGBK+obISrX1NLrkMXjRUowLQ5s2bmTx5MnFxccydO5eHH36Y/fv3s3btWo4dO8aIESPc1U4hhKewB6CypgpAQUHO5Q20Uk0W1TkvjDTrIGipSPqUBl0GP3fuXBYvXsyePXu4/vrreeedd7j++uvRam15KikpiSVLlpCYmOjOtgohPMF5FSA3XwDvZG3dGm1xMdqCgiZ6BeHV7N1RFYGBEBDQtK/lCEAWC/ry8qZ9LdGsGhSAXn/9df7f//t/TJgwgbgaLoNt06YNb731VqMaJ4TwQE3dBQYorVvDvn1opAIkqmPvGjWHhDT9awUH20JWRQVBZ882/euJZtOgALR27VratWvnrPg4KIrC4cOHadeuHYGBgYwfP94tjRRCeBBHAGrRoslewmofByQVIFEtewAqDw5u+tfSaGxVoBMnCCwra/rXE82mQWOAkpOTKSwsrLL91KlTJCUlNbpRQggP1tRjgLB1gYGMARI1cFSAmiMAgXM26ECpAPmUBgUgpYaBYEaj0X0LIgohPI/ZDPaqTFN2gUkAErWyjwFqli4wcI4DCpIKkE+pVxdYZmYmABqNhmnTphFy3g+fxWJh48aNpKSkuLWBQggPYp/aQgkMxOSuxU+rIQFI1Oq8ClCzRCB7AJIKkG+pVwD6+eefAVsF6NdffyUwMND5WGBgIL179+bhhx92bwuFEJ7DPgmiNTbWNjaiiSj2MUAyCFpUyzEGqJkrQDIGyLfUKwB98803AGRkZPDyyy8T0dTzLwghPIbRaMS6axcRgNndK8BfQCpAolbNPQbI0QUmFSCf0qAxQIsXL5bwI4QfMRqNLF++nB2rVwNwTKNBr9c32Zg/lwAkk8+JC6kUgKQC5FvqXAEaNWoUS5YsISIiglGjRtW674oVKxrdMCGE5zCZTFRWVtLN/odPfL9+jB49mrAmGgfkCEAas9n2YdeEl9wLL6TSIGgZA+Rb6hyAIiMj0dj7/CObeu0VIYRHCj592vZvx47QhIOgMRgoDw62dTnk50sAEq6acx4gkKvAfFSdA9DixYurvS+E8B86xwLHbds2+WudjYg4F4C6dm3y1xNepDlnggapAPmoBo0BOnv2LGXnJeFDhw4xb9481qxZ47aGCSE8j7Y5A1B4uO1Ofn6Tv5bwMjIGSLhBgwLQiBEjeOeddwAoKipiwIABzJkzhxEjRvD666+7tYFCCA+hKGjz8mz3m6kCBEgAElXZxwA122Xw9mEfchWYb2lQANq6dStXXHEFAB9//DGxsbEcOnSId955h1deecWtDRRCeIbAsjI0jg+A+Pgmfz0JQKJaVuu5QdBSARKN0KAAVFZWRri9PL1mzRpGjRqFVqvlT3/6E4cOHXJrA4UQniHU3u1Ay5a2FbKbWJkjAMmCqOJ8RqMtBKHSGCCZlsFnNCgAdezYkZUrV3L48GFWr17NsGHDACgoKJD5gYTwUSH2v7qbo/sLOLfUhgQgcT57EFcCA7EEBDTPa9oDkM5iAekG8xkNCkDTpk3j4YcfJjExkdTUVAYOHAjYqkF9+vRxawOFEJ4h1H4JfHMFIOcgaJkNWpzPEYCaczqW0FAUnQ4AbUlJ872uaFINCkC33XYbubm5bN68mVWrVjm3X3PNNbz00kv1Pt78+fNJTEzEYDCQmprKpk2batx34cKFXHHFFbRo0YIWLVqQlpZWZX9FUZg2bRpxcXEEBweTlpbG3r17690uIcQ5zi6wZhj/A2ByBCCpAInz2X8Orc0ZgDQaZ+DSOCqhwus1KAABxMbG0qdPH7Tac4cYMGAAXes5X8eyZcvIzMxk+vTpbN26ld69e5Oenk5BDb/0srOzueOOO/jmm2/IyckhISGBYcOGcfToUec+zz//PK+88goLFixg48aNhIaGkp6ejslkatjJCiHOBSCpAAk1qVEBAhT78A4JQL6jQQGotLSUp556ikGDBtGxY0c6dOjgcquPuXPnMmnSJDIyMujevTsLFiwgJCSERYsWVbv/+++/z+TJk0lJSaFr1668+eabWK1WsrKyAFv1Z968eTz55JOMGDGCXr168c4773Ds2DFWrlzZkNMVQtD8AchZASouhvLyZnlN4QUcAaiZx5tapQLkc+q1GrzD3Xffzbfffsuf//xn4uLinEtk1JfZbGbLli1MnTrVuU2r1ZKWlkZOTk6djlFWVkZFRQUtW7YE4MCBA+Tl5ZGWlubcJzIyktTUVHJychg7dmyVY5SXl1N+3i/YEunjFaKKkGYOQOXBwSh6PZrKSigsbLbXFR5OrQqQ/fVkDJDvaFAA+uqrr/jiiy8YPHhwo168sLAQi8VCTEyMy/aYmBh2795dp2M89thjxMfHOwNPnn2ituqO6XjsQrNnz2bGjBn1bb4QfqW5K0BotSgtW6IpKLCNA5IAJECdMUBIF5gvalAXWIsWLZwVFzU9++yzLF26lE8++QSDwdDg40ydOpXi4mLn7fDhw25spRA+oKKC4DNnbPebMYg4VoWXcUDCSaUuMAlAvqdBAWjWrFlMmzbNZT2whoiOjkan05F/wUyv+fn5xMbG1vrcF198kWeffZY1a9bQq1cv53bH8+pzzKCgICIiIlxuQohztPn5aBQFJTAQoqOb7XXN9r/yz+zfj9FobLbXFR5M5S4wjXSB+YwGBaA5c+awevVqYmJi6NmzJ5dddpnLra4CAwPp27evcwAz4BzQ7JhbqDrPP/88s2bNYtWqVfTr18/lsaSkJGJjY12OWVJSwsaNG2s9phCiZtpjxwCwxsaCtsEXj9aZwWBAr9dzrKICgB3ffMPy5cslBAnVApBVApDPadAYoJEjR7qtAZmZmYwfP55+/foxYMAA5s2bR2lpKRkZGQCMGzeOtm3bMnv2bACee+45pk2bxgcffEBiYqJzXE9YWBhhYWFoNBoeeughnnnmGTp16kRSUhJPPfUU8fHxbm23EP7EsQq8NS4OXTO8XlhYGKNHj0b344/w0090bdmSXysrMZlMhDlmiBb+yT4hpzUyslmXpXB0gWmlC8xnNCgATZ8+3W0NGDNmDCdOnGDatGnk5eWRkpLCqlWrnIOYc3NzXeYaev311zGbzdx2221V2vT0008D8Oijj1JaWso999xDUVERl19+OatWrWrUOCEh/Nn5Aai5hIWFQfv2AARL5Uc4nD8GqBnDiEyE6HsaFIAAioqK+Pjjj9m/fz+PPPIILVu2ZOvWrcTExNC2noMkp0yZwpQpU6p9LDs72+XrgwcPXvR4Go2GmTNnMnPmzHq1QwhRPTUCEAD2QdCakyeb93WF5zq/C0yNACRdYD6jQQFo+/btpKWlERkZycGDB5k0aRItW7ZkxYoV5Obm8s4777i7nUIIFensAcjSTMtgONkDkLawsHlfV3guRwCKioLc3GZ7WatcBeZzGjSaMTMzkwkTJrB3716XbqXrr7+e7777zm2NE0J4BpdB0M2pTRvb60sAEgBWq7PqY23uy+BlIkSf06AA9NNPP3HvvfdW2d62bdsaJxsUQngvrf3/tWpdYBKABMCZM86Bz80+D9D5Y4CacfC1aDoNCkBBQUHVLhfx+++/09oxcZkQwjcoyrkxQM3dBeaoABmN6OyXxAs/5piN3GCw3ZqRMwCZzSALa/uEBgWgm2++mZkzZ1Jh/4Wk0WjIzc3lscce49Zbb3VrA4UQ6jEajZzcs8f2Sx+wXrDETJOLjISAAAAMjpmohf9yBKCoqGZ/aSU0FKtj3UtHO4RXa/BEiEajkdatW3P27FmGDBlCx44dCQ8P55///Ke72yiEUIHRaGT58uVkv/8+AGURERiae5Z0jcbZDWaQS+GFigEIjQZzcLBrO4RXa9BVYJGRkaxdu5b169fzyy+/YDQaueyyy1xWYBdCeDeTyURlZSUDExIACOrQAZ0akxC2bg3HjhEsg0+FmgEIMIeEYCgra9bL70XTqXcAslqtLFmyhBUrVnDw4EE0Go1z+QlFUdA4SoRCCJ8QYe960tmDULOzjwOSyRCF6gFIKkA+pV5dYIqicPPNN3P33Xdz9OhRevbsSY8ePTh06BATJkzglltuaap2CiFU4hgAzSWXqNMARxeYjAES9mUw1ApA5SEhtjsSgHxCvSpAS5Ys4bvvviMrK4urr77a5bF169YxcuRI3nnnHcaNG+fWRgoh1OOYA4h6zvDuNo4KkAQgIRUg4Ub1qgB9+OGHPP7441XCD8DQoUP5xz/+wfv2AZNCCN/grACpFYDsFSAJQMIZPFq0UOXlzVIB8in1CkDbt29n+PDhNT5+3XXX8csvvzS6UUIIz6F6F5i9AiRdYELtClC5VIB8Sr0C0KlTp5yrtFcnJiaG044+WiGET5AKkPAYaneBSQXIp9QrAFksFvT6mocN6XQ6KisrG90oIYRn0JtM59Y+UrsCJFeBCbUrQBKAfEq9BkErisKECRMICgqq9vHy8nK3NEoI4RlCHb/ow8NtNzWcVwEyq9MC4SnUrgBJF5hPqVcAGj9+/EX3kSvAhPAdzgCkVvcXOCtAAeXlUFamXjuE+iQACTeqVwBavHhxU7VDCOGBQh1j+tTq/gIID0cJDERjNqM9eRLatVOvLUJdagcgRxeYzATtExq0FpgQwj94RAVIo8EaHQ1A6cGDFBYWYpTxQP7HagXHeDS5Cky4QYPWAhNC+AePCEDgXA9sx7p1HD5xAr1ez+jRowlTY20yoY7iYlAU2/3ISFDhqkC5Csy3SAVICFEjj+gCA3T26Tcu79KFoUOHUllZiclkUrVNopk5QkdwMNRwIU5Tc14FZjLZbsKrSQASQtQoxFMqQPaB0OEmE1EqdX8Ilak8/gegIigIxbHgt4wD8noSgIQQNXJ2galcAXJcCk9BgbrtEOpReRkMALRalIgI233pBvN6EoCEENWrqCDEMejUQypAnDihbjuEejygAgSgREba7kgA8noSgIQQ1dIWFKBRFJSAgHMVGLVIBUh4SgCSCpDPkAAkhKiW9tgxAKyxsaBV+VeFVICEBCDhZnIZvBCiWo5FUK2xsehUbotUgIQjcJhDQykpLKRIpQBilS4wnyEBSAhRLWcAio9XuSVIBUg4A8euY8fYuGIFAHq9HoPB0KzNcFaA5CowrycBSAhRLWcAiotTuSWcqwCVlUFpqbptEeo4dQqAs0FBDB06lKioKAwGQ7NPhimDoH2HjAESQlTLowJQWBjY/9LXnjypcmOEKuwBqDwsjKioKKKjo1WZCfysfRLGs3l5siSLl5MAJISols4+CNriCQFIo3FWgbSFhSo3RqjCHoBMoaGqvLzBYECv17M7Lw+Aozt2sHz5cglBXkwCkBCiWlr7L3qPqACBcxyQRgKQf3JUgBzLUTSzsLAwRo8eTc8rrgAgLiRElmTxchKAhBBVKYpndYGBVID83XldYGoJCwsjPCEBgMCyMtXaIdxDApAQoqrCQjTl5YB9HiBPYK8AyRggP2S1qt4F5mSfh0gjg6C9ngQgIURVubkAlEZGqrbydhX2CpB0gfmhkhJbCALMKnWBOdnXItNKAPJ6qgeg+fPnk5iYiMFgIDU1lU2bNtW4786dO7n11ltJTExEo9Ewb968Kvs8/fTTaDQal1vXrl2b8AyE8EH2AGRs2VLlhpzHUQGSAOR/7NUfJSQES0CAum2x/5/QFBWBoqjbFtEoqgagZcuWkZmZyfTp09m6dSu9e/cmPT2dghpmey0rK6NDhw48++yzxNZSlu/RowfHjx933n744YemOgUhfJMnBiAZA+S/7AHIqvIyGAC0agWApqICvb2bWHgnVQPQ3LlzmTRpEhkZGXTv3p0FCxYQEhLCokWLqt2/f//+vPDCC4wdO5agWsryer2e2NhY5y06OrqpTkEI3+SJAchxFZiMAfI/jgqQvftJVcHBzm5hg0zK6dVUC0Bms5ktW7aQlpZ2rjFaLWlpaeTk5DTq2Hv37iU+Pp4OHTpw1113kWv/ZV6T8vJySkpKXG5C+DVPDEBSAfJf9tBr9YQApNE4u8GCJAB5NdUCUGFhIRaLhZiYGJftMTEx5NnnH2mI1NRUlixZwqpVq3j99dc5cOAAV1xxBWfOnKnxObNnzyYyMtJ5S7Bf5iiE3/LkAHTypIy98DeOCpAndIGBBCAfofogaHe77rrruP322+nVqxfp6el8+eWXFBUVsXz58hqfM3XqVIqLi523w4cPN2OLhfBAnhiAHF1gJpOMvfA3ntQFBucCkMwF5NVUWww1OjoanU5Hfn6+y/b8/PxaBzjXV1RUFJ07d2bfvn017hMUFFTrmCIh/IXRaMRUXEy0vQrrUQEoNNQ2/uLsWYJrqegKH+RJg6DBORDaIMtgeDXVKkCBgYH07duXrKws5zar1UpWVhYDBw502+sYjUb2799PnKfMZiuEhzIajSxfvpy1S5YAUBkQgCUyEoN9EVKPYK8CyQePn7GPAVI8JZBLF5hPULULLDMzk4ULF/L222+za9cu7rvvPkpLS8nIyABg3LhxTJ061bm/2Wxm27ZtbNu2DbPZzNGjR9m2bZtLdefhhx/m22+/5eDBg2zYsIFbbrkFnU7HHXfc0eznJ4Q3MZlMVFZWcmX79rYN7dszeswYVVbcrpF9HFCwXKjgXzytAiRdYD5BtS4wgDFjxnDixAmmTZtGXl4eKSkprFq1yjkwOjc3F632XEY7duwYffr0cX794osv8uKLLzJkyBCys7MBOHLkCHfccQcnT56kdevWXH755fz444+0tv/iFELULsI+w60+Kcmzwg84K0DBUgHyL+ePAfKE8V/2ACSXwXs3VQMQwJQpU5gyZUq1jzlCjUNiYiLKRa7+WLp0qbuaJoRf0h49arvTrp26DamO/Q8Zg4wB8i/nV4AuGDeqCvsYIOkC824+dxWYEKJxdEeO2O44usI8iaMCJAHIv3jqVWASgLyaBCAhhAutIwB5cAVIApAfUZRzAUgGQQs3kgAkhHDh0V1gjqvAJAD5j5ISsFgAsEZGqtwYOxkD5BMkAAkhzlEUdJ4cgKQC5H/s1R+Cg203T3D+GCCZldxrSQASQjgFlZaicVzae8kl6jamOjIPkP9xBCBP6f4CZ1t0FgtIFchrSQASQjiFOT5sYmOdK157lPMrQPKXt3/wxAAUEoISGAiA1j5thPA+EoCEEE7OAOSpCwLbA5CushKNVIH8g30WaEe3k0fQaJwLs2pOn1a3LaLBJAAJIZwiCgttd5KS1G1ITUJCUEJCANCcOKFyY0Sz8MQKEGC1t0crAchrSQASQjiFe3oAAqz2KpDWURkQvs1DA5BUgLyfBCAhhFO4I1QkJqrajtpY7V0hWkdYE77NQwOQ1T4po4wB8l4SgIQQTt5QAVLsAUgjAcg/eGgAkgqQ95MAJISwUZRzFSAPDkDW6GhAKkB+wxMHQXOuAiQByHtJABJCAKA5dYoAx0rbnjgJop0zAMkgaP/gqRUgRxeYBCCvJQFICAGALjcXAEtsLBgMKremZlb7ZIgSgPyD1V7pK9bpKPKg8TaKVIC8nl7tBgghPIP20CEArAkJ6FRuS22sMTEAaAsKVG6JaGpGoxFdXh7BwNotWziVn49er8fgAQHdeRm8o0IlvI4EICEEcF4FqH17AlRuS20cFSCNBCCfZzp7lpb2CS+H3nYb1vh4DAYDYWFhKrfsXADSyHQMXksCkBACAO3hwwBYPXj8D4AiFSC/oTEa0VqtALTs1MlzFkMFFMdYNAlAXkvGAAkhANDZu8AsnroMhp1zDFBJCZw9q3JrRFNyjK9RDAaPCj9w3nxURUVQWaluY0SDSAASQgCgtXeBWdu3V7kltVPCw6kMsHfS5eer2xjRpBwByGqfc8eTKC1aoGg0ti+kCuSVJAAJIcBqRXfkCOD5FSA0GsoiImz3jx9Xty2iSTlmWXZcceVRdDrK7evSIXNSeSUJQEIIOHwYTXk5Fp0O6yWXqN2aizrrCEB5eeo2RDQpjf0KK48MQIDJMRhbpmTwShKAhBCwbx8AZ6KjQe/510aURUba7kgA8mmOCpDVQwPQ2fBw2x0JQF5JApAQAvbuBaDYPsDY00kFyD84B0E7Aq+HcVaApAvMK0kAEkI4K0AlrVur3JC6kQqQf3BcYm71sHXAHKQLzLtJABJCOAOQt1SAyqQC5Bcckww65tzxNCZHF5hUgLySBCAhhLMLrMRLAtBZqQD5BakAiaYkAUgIf2e1wv79gFSAhGfR2isrnhqAzkoA8moSgITwd0eOQHk5il6P0b6+kadzGQOkKOo2RjQZZxeYhwYg6QLzbhKAhPB39vE/lvbtUXSevA78Oc7Lj81msF8qLXyMokgXmGhSEoCE8Hf28T/WpCSVG1J31oCAc8sjyGzQvsloRGM2A54bgM6eXwGSSqTXkQAkhL9zVIA6dFC5IfXjWBRVxgH5KHtVpTIgAEJDVW5M9ZwVILMZzpxRtzGi3iQACeHv7BUgrwtAMTG2OxKAfJM9ADmrLB7IEhiI4lgPTLrBvI4EICH8naMC5EVdYCAVIJ9nDxTOKouHcnbPyUBoryMBSAh/dt4l8N5WAXLMBVR24ABGo1Hl1gi385YA5LhyUipAXkf1ADR//nwSExMxGAykpqayadOmGvfduXMnt956K4mJiWg0GubNm9foYwrh144eBZMJ9HqvWAUewGAwoNfr2WO/+uvI5s0sX75cQpCvcQQgD+4Cg/Mu0ZcKkNdRNQAtW7aMzMxMpk+fztatW+nduzfp6ekUFBRUu39ZWRkdOnTg2WefJTY21i3HFMKv2cf/0KGDV6wCDxAWFsbo0aPpcc01AFyi01FZWYnJZFK5ZcKtHGOAPL0C5FimQypAXkfVADR37lwmTZpERkYG3bt3Z8GCBYSEhLBo0aJq9+/fvz8vvPACY8eOJSgoyC3HFMKv2cf/mNu1o8iL5tMJCwsjvHNnAALtc8UIH+MtFSDpAvNaqgUgs9nMli1bSEtLO9cYrZa0tDRycnKa9Zjl5eWUlJS43ITwB+bffgNgj9XKunXr0Ov1GAwGlVtVR/HxAGhlHiDf5AVXgQFYW7e23cnPV7chot5UC0CFhYVYLBZiHJey2sXExJDXwKs6GnrM2bNnExkZ6bwlJCQ06PWF8DbKnj0AxF5xBaNGjWL06NGEeXiXg1PbtgBoi4vR2SfMEz7EPmzB4wOQXI3otVQfBO0Jpk6dSnFxsfN2+PBhtZskRLPQ2wNQYEoK0dHR3hN+ACIinBPkhZ4+rXJjhNvZA8VZx8K3HsoZgKQC5HVUG/UYHR2NTqcj/4Ifmvz8/BoHODfVMYOCgmocUySEzzp7Fu2hQwBU2sfTeBWNBi65BPbsIdSLxi+JOlAUZ6BwLnzroYqDg4kErMePS0XBy6j2fgUGBtK3b1+ysrKc26xWK1lZWQwcONBjjimEz/r9dzRWK6bQUBTHOAZvY+8GkwDkY06fhooKwHMHQTumY/jWXkXlxAmMxcXqNkrUi6rXvWZmZjJ+/Hj69evHgAEDmDdvHqWlpWRkZAAwbtw42rZty+zZswHbIOff7IM2zWYzR48eZdu2bYSFhdGxY8c6HVMIYWf/v3Q6Lo4AjUblxjSQIwBJF5hvsVd/rJGRWAICVG5M9RzTMZhKS1Eeewyt1Ur5kSOEeXjFSpyjagAaM2YMJ06cYNq0aeTl5ZGSksKqVaucg5hzc3PRas8VqY4dO0afPn2cX7/44ou8+OKLDBkyhOzs7DodUwhhZw9ARXFxeGn9RypAvso+/sfq4ZXJsLAwwsLCsLZqhebECbQFBdCjh9rNEnWk+sxnU6ZMYcqUKdU+5gg1DomJiSiK0qhjCiHsHBWg2FgJQMKz2CtAimOAsYeztmmD1hGAhNeQMVtC+KtduwA4bZ9PxyvZA1CIBCDf4iUVIAfHlWASgLyLBCAh/JHZ7FwGoyguTuXGNIJUgHyTYwyQF1WAQAKQt5EAJIQ/2rcPKiuxhoVRGhWldmsazlEBKi4Gi0Xlxgi3cVSAJACJJiQBSAh/ZB//Y+nSxTafjreKiUHRatFarWhkLSbfIV1gohlIABLCHzkCkDdOgHg+vd754aOTpQh8h7d1gdmvMpYA5F0kAAnhj3wlAAFW+xgm7bFjKrdEuI2XdYE5rlaTKqR3kQAkhD/65RcAKrt1U7khjecMQLIqvG+wWp0LoXrNZfD2rjqpAHkXCUBC+JuyMvj9dwAqL71U5cY0ntW+zp8EIB9x8qRzQLs1OlrlxtSNcwxQcTGYTCq3RtSVBCAh/M2OHba/stu0QfGBGdKt9nmMJAD5CMdi1q1agYcug3EhJTISi94+r7CsCu81JAAJ4W/s3V/07q1uO9zEesklAOiOHFG5JcItHIPZvSmcazSURUTY7stgfK8hAUgIf+MIQCkpqjbDXSwJCQBoDx9WuSXCLRwVFG8KQMBZCUBeRwKQEP5m2zbbv75SAWrXDrBfBVZZqXJrRKMdPWr71z7Jpbcos68Cb9y7l8LCQoxGo8otEhej+mKoQohmZLXC9u22+74SgNq0waLXo6ustH14tm+vdpNEY3hhADIYDJxo0QKA37/5hs0REej1ekaPHk1YWJjKrRM1kQqQEP7k4EE4cwaCgqBLF7Vb4x5aLcaWLQEo2rZN/vL2dl4YgMLCwuhw5ZUAdI+IYOjQoVRWVmKSK8I8mgQgIfyJo/urRw+vucLmYgwGA0b75dI/r1zJ8uXLJQR5My8MQABByckAGAoLifLm9fX8iAQgIfyJPQCZunShsLCQIh9YRT0sLIw2/fsD0CsiQv7y9nZeGoCwX42IXI3oNWQMkBB+pPLHH9EDm4HfVqwAQK/XYzAYVG1XYwV06gRAqCxF4N2sVnDM5+StAejwYVAUddsi6kQCkBD+QlHQbt4MQPvbb6frFVcAti4krx+oaR/4LJfCe7mCAqisRNFqOanXe1eF0hHYSkvRnDmjbltEnUgAEsJf/PEH2tOnsej1BKemEu0lywzUSWIiADoJQN7N3v11NjycFZ99BnhRhTI0FFq0gNOnZVZyLyEBSAh/sWkTAIUJCegCA1VujJvZA5D26FE09nWkhBeyByBjVBRDhw4lKirKuyqUbdvaAtCxY2q3RNSBDIIWwl/YA9AJe1jwKXFxEBCAprKSkOJitVsjGsoegMqiooiKiiI6Otp7wg84xwFJAPIOEoCE8Bf2AFTgiwFIpwP7jNDhJ0+q3BjRYPYAVOqtl5E7ApB0gXkFCUBC+IOKCti6FYATSUkqN6aJ2AdCSwDyYvZLyL09AOmkAuQVJAAJ4Q927ACTCWtkJMWtW6vdmqZhr2yFSQDyXocOAWBs1UrlhjSQdIF5FQlAQvgB0/ff2/699FLQ+uh/e3sAkgqQF7MHoDMSgEQz8NHfhEIIB6PRSO7y5QDsti/S6BWXFdeXIwAVFqrbDtEwFottEkFwru3mdezj0LQyGaJXkMvghfBxJpOJmL17AUj+85/pfNNN3nVlTV3ZxwCFnTpFhcpNEQ1w/LhtEkS9njJvHQPkmI7BaCSotFTdtoiLkgqQED5OU1BAZEEBikZD5PDhvhl+4NwYoFOnbNUE4V3s3V/W+HgUb+2mDQ6G2FhAumK9gZf+lAkh6ipg40YALN262Waq9VVt26Lo9egsFrR5eWq3RtSXIwA51tTyVtIV6zUkAAnh4wJ++AGAitRUlVvSxHQ654en7uBBddsi6s8egCzeHoDs00xIBcjzSQASwscFfPstABVXXaVuQ5qBpUMHALT796vcElFvjgpQQoLKDWkkRwCSCpDHkwAkhC/LzUW/fz9WjYaKwYPVbk2Ts3TuDIDePuhbeBFfqQDJdAxeQwKQEL4sKwuwrf+lREaq3JimV9mpEwA6CUDex9cqQBKAPJ4EICF82dq1ABzt1k3lhjQPS8eOAOj27VO5JaJeFMX3KkCFhTIXkIfziAA0f/58EhMTMRgMpKamssm+aGNNPvroI7p27YrBYKBnz558+eWXLo9PmDABjUbjchs+fHhTnoIQnsdqha+/BvwoANkrQNrcXDh7VuXWiDo7cQLKykCjwdq2rdqtaZx27VA0GvQVFWhOnFC7NaIWqgegZcuWkZmZyfTp09m6dSu9e/cmPT2dgoKCavffsGEDd9xxBxMnTuTnn39m5MiRjBw5kh07drjsN3z4cI4fP+68ffjhh81xOkJ4jh074MQJlJAQ8u2Dg32dEh2NKSQEjaKAdIN5jbLt2wGwtG1LkcmkcmsaKTAQa3w8ALrcXJUbI2qjegCaO3cukyZNIiMjg+7du7NgwQJCQkJYtGhRtfu//PLLDB8+nEceeYRu3boxa9YsLrvsMv7973+77BcUFERsbKzz1sKX5z8Rojr27q+KgQOx6v1k0neNhqK4ONv93bvVbYuoE6PRyOalSwHICw1l3bp1Xr9ci9U+K7lMx+DZVA1AZrOZLVu2kJaW5tym1WpJS0sjJyen2ufk5OS47A+Qnp5eZf/s7GzatGlDly5duO+++zhZy4C08vJySkpKXG5CeDOj0YjZ3jVcMmCAyq1pXkX2mXhLt2yhsLAQo9GocotEbUwmE6H2iStbDRjAqFGjGD16tFfPWG5JTgZkLJqnUzUAFRYWYrFYiImJcdkeExNDXg0zuebl5V10/+HDh/POO++QlZXFc889x7fffst1112HpYbp8WfPnk1kZKTzluDtVyEIv2Y0Gvn4/ffR2CdA/CYgwOv/oq4rg8FAib374fg337BixQqWL18uIcjDRdjHyhguvZTo6GivDj8gg/G9hU/WxceOHeu837NnT3r16kVycjLZ2dlcc801VfafOnUqmZmZzq9LSkokBAmvZTKZiN6zhwCzGWvr1lw9ZQqG4GCv/1Cpi7CwMHredht89BGJZ88ydOhQ1q1bh8lk8ovz91aRjjGf9uDg7ZwVIJmQ06OpWgGKjo5Gp9ORn5/vsj0/P59Yexn7QrGxsfXaH6BDhw5ER0ezr4Y0HhQUREREhMtNCG+WtHUrANobbyS6dWu/+vAP7tMHAP3+/UTJ/2Wv4KgA+UoAqnRUgP74w3Y1pvBIqgagwMBA+vbtS5Z9sjYAq9VKVlYWAwcOrPY5AwcOdNkfYO3atTXuD3DkyBFOnjxJnGNwpBC+zGIh6eefbfdvv13dtqghKQkCA+HsWbRHj6rdGnERmtOnMZSW2r7wkasVre3aYdVq0ZSVwbFjajdH1ED1q8AyMzNZuHAhb7/9Nrt27eK+++6jtLSUjIwMAMaNG8fUqVOd+z/44IOsWrWKOXPmsHv3bp5++mk2b97MlClTANv4h0ceeYQff/yRgwcPkpWVxYgRI+jYsSPp6emqnKMQzcFoNFJYWEj5118TUlKCNSoKquny9Xl6PciM0F5Dt2cPYLsEHl+pVAYEUNK6te2+/fyE51F9DNCYMWM4ceIE06ZNIy8vj5SUFFatWuUc6Jybm4tWey6nDRo0iA8++IAnn3ySxx9/nE6dOrFy5UouvfRSAHQ6Hdu3b+ftt9+mqKiI+Ph4hg0bxqxZswgKClLlHIVoakajkeXLl1NZWcngDz6gLWC58Ua0gYFqN00dXbvCzp22AOT4IBIeSff77wBYunRBp3Jb3Kk4Joao/HxbAPLHP0S8gOoBCGDKlCnOCs6FsrOzq2y7/fbbub2G0n5wcDCrV692Z/OE8Hgmk4nKykqGDhlChyefBCDgrrtUbpWKunYFQL9njwQgD6d3VIC6dFG5Je5VFBNDe+DsL79gMRr9ahyet1C9C0wI4T6td+9Ge+IEtGjh33919uoFgO6331RuiLgYRxdYZefOKrfEfQwGA2fsY04Lf/hBpmLwUBKAhPAhgZ9+artzyy0QEKBuY9TUuzcA+t9+QyNX4Xg05xgge9XOF4SFhXHZuHEAxJ08SWVlJSZvX+LDB0kAEsJHaKxWgj7/3PaFP179db6OHSEkBM3Zs0TUsK6g8ABFRejsk9hafKgCBBBin4Fdn59PkFR/PJIEICF8RNzvv0v3l4NOBz17AtDq8GGVGyNqZO+iNLZogeJrczaFh4N9QsRWR46o3BhRHQlAQviILhs22O7ceqt/d3852CdEbC0rcnsuewA67atztNnHorWUAOSRJAAJ4QM0RUXO2Z+5+251G+Mp7F0QrWVFbs+1cyfg+wGolUzI6ZEkAAnhA4I++gh9RQWV3bs7P/j9Xv/+AEQfOgQ1LIQsVLZjBwCn7QvY+hz7YHypAHkmCUBCeDurFcNbbwFg+vOfQaNRuUEeols3lJAQAsvLZVVuT6QoYF+y5eQll6jcmCZirwC1OHYMKitVboy4kAQgIbzdV1+h378fs8FA+dixarfGc+h0VDouh3d0DwrPcfgwnDyJotdzqm1btVvTNJKSsIaGoq+slGVZPJAEICG83UsvAbDriitQZLZZFxWOleEdi8MKz2EPpZauXbH66qB9rRaLhHCPJQFICG+2bRtkZaFotey8+mq1W+NxKu3jgAIcV8gJz7FlCwCV9ukKfFVF374AWHNyKCwspLCwUGaF9hAesRaYEKKBZs4EwDxiBMZWrVRujOepGDwYRaOxrTeVl4cxLMxlRl6DwSBrNKklJweASntA8FWaP/0JXn2Vyh9+YMWKFQDo9XpGjx4tP3sqkwAkhLfatg0++QQ0Gsr+/nfnJcXiHKVFCwoTEmidm4vpq69YrihUnjcYVT6IVGKxwMaNAFT062dbMd1HBV91FQAtjx/n1mHDOF1Zybp16zCZTPJzpzLpAhPCWz39tO3fsWN9biVtdzrm+N5kZVFZWcnQoUMZNWoUQ4cOlTWa1LJjBxiNEB7uU2uAVSs+Htq1Q2O10urAAaKiotRukbCTACSEN/rmG/j0U9Bq4amn1G6NRztm/4DVffstAFFRUURHR8sHkZrs3V8MGGBbtsTXDRxo+/f779Vth3AhAUgIb1NZCQ88YLt/333QrZu67fFgBoOBE127YtVqCThyhKjTpzEYDGo3S3z3ne3fQYPUbUdzcVygkJWlbjuECwlAQnibBQtsXQgtWzoHQYvqhYWFMWrcOCz9+gFwc1iYjLtQm9UKX38NQFHfvhQVFanbnubgWJw4JwdKS9Vti3CSACSEN8nLg2nTADBNm0ah1UphYaF/fIg0UFhYGAHp6QAY5HJ49e3YASdOUBEYyMdHjrBu3Tr0er1vV+aSk6FdO6ioIMA++FuoT64CE8JbWK0wYQKcPo2ld28+DA2lwn5ZLeD7HyKNcfXVMGsW/PCD2i0R9urP8U6duCo9naioKN+fjkCjsVWBFi8m4LvvnEtkCHVJABLCW7zyCqxeDQYDJfPnU7FzJ0OHDnUO5vX5D5HGSE62/Xv8uG0NKlkvTT1r1wJwtFs3OtkHpPuF9HRYvJjA1aslAHkI6QITwhv88gs89pjt/ty5zsveHVc0RUdHS/ipjeNDtqLCdvm1UEdJCaxbB8CRHj1Ubkwzu+46CAhAv28fUcePq90agQQgITzf6dNwxx1gNlN5/fUU3nabjPmpr5AQcHQPFhaq2xZ/9sUXtp/j5GROx8Wp3ZrmFRHhHAydKGvTeQTpAhPCk5WVwU03wa5dWOPiWD5sGMZPPgFkzE+9tWoFR4/CyZOQlKR2a/yTfcya+cYb/bMb8pZbYNUqkiQAeQSpAAnhqSoqqLzlFli/HmtkJEf+8x+MwcHOmYxlCYd6cnSDnTypbjv81Zkz8OWXAJTfdJPKjVHJLbeg6PW0zs2lbONGWRhVZRKAhPBEVisV//d/6NesoTIggP/dcw+rjh1Dr9cTGxsrY34awrFYrASgZmM0Gp0roJuWLLFVNDt1wuKvg4Bbt8Zy3XUAFL/0EitWrGD58uUSglQiXWBCeJqKCrj3XgKWL8eq1VLw2msMHjkSkCu9GsURgGQMULMwGo0sX77cufjsLXPnYgC45x7/7P6y0//lL/C//9Hj558Jefllstavl4VRVSIBSAhPcvIklaNGof/uOxStlm8yMug9cqT/XCrclKQLrFmZTCbn4rNB27fT+uBBlIAANOPHq900daWnQ7t2aHNzifnqK9vgaKEK6QITwlNs2oS1Xz/0332HOSiI1X/5C4cGDZKBzu4iXWCqiIqKos3ChQCUjxwJrVur2yC16XTw8MMABL/6KhqLReUG+S+pAAmhAqPRiMlkAsCg1RI2dy48+yxai4Xi1q0pfvtt+vXvL11e7iRdYKrQbd9O0KpVKBoNZ//2NyTOA3ffDc88gy43l67r18Ptt6vdIr8kAUiIZnb+2Ii2v/3GoI8+gmPHADhz442sHDqUG/r3l24vd5MusOanKIQ99RQA+/v1sw0ClrXrIDgYnngCHnyQ/itXUjpt2rmfT9FsJAAJ0cxMJhOtdu/m2g0bCFm/HoCzYWH8cOedHOjbV+b3aSrSBdbsuqxfT8CGDSjBwWy57TaK7bNAg8xjxeTJVL7xBobffoMnn4Tly9Vukd+RACREcykqgg8/JPI//2HEtm22bXo95nvvpeyhh+gTFUUf5EqvJiNdYM1K99tvDF66FADN009zw+TJzm5fkJ9z9HqML7xAxI03YvjoI3jtNZg8We1W+RUJQEI0pfJy+PZbeOcd+O9/wWQiALDodFTceSeGGTMITEqildrt9AfSBdZ8/viDiDvvRFdRgfnqqwl8+GHCtFr/DjzVqBwwgM0338yATz9FmTKFM0FBaMeMke9TM/GIq8Dmz59PYmIiBoOB1NRUNm3aVOv+H330EV27dsVgMNCzZ0++tM8u6qAoCtOmTSMuLo7g4GDS0tLYu3dvU56CELZJ3/LyOP3NNxj/9S/M116L0rKl7bLX998Hkwl69MA4axbvP/ccxrlzZUmG5uSoAJWVwdmz6rbFl23eDEOGoDt6lKKYGM68/jpoPeKjxuMYDAZ23HQTu664Ao2iEDZpEjsnTsRYUqJ20/yC6j+Vy5YtIzMzk+nTp7N161Z69+5Neno6BQUF1e6/YcMG7rjjDiZOnMjPP//MyJEjGTlyJDt27HDu8/zzz/PKK6+wYMECNm7cSGhoKOnp6S7lVyEaxWq1DVz+7jt47TUq7r4b42WXEZmYSIuhQwl74gkCv/4aTVkZZRER7B4yhPzPPqPwm2/IGzsWU3i42mfgfyIiQG8veksVyP1KSjA/8gjK4MFw5AjmDh3439//jtJK6ps1CQsLY/SYMbRevhzT//0fWkUhdflygtLTYeNGtZvn8zSKoihqNiA1NZX+/fvz73//GwCr1UpCQgJ//etf+cc//lFl/zFjxlBaWsrnn3/u3PanP/2JlJQUFixYgKIoxMfH8/e//52H7XMtFBcXExMTw5IlSxg7duxF21RSUkJkZCTFxcVEyCRVfsF45gymkhI0ZWUYKisJNZlsY0UKC20flgUFkJtL5R9/oDl8GO3Ro2gqKqo9ljUsjMp+/ai48krMQ4dS2qEDa9audc6IC7YBoLKWlwpiYyE/H7Zto7BtW1asWMGoUaPkiruGOn3a9kfA//6HsmwZGvuSDgdSUvh23DiskZHyc15XioLxhRcImjaNgPJyACouuwzzTTdhHjIES9euGMLD5Xt5EfX5/FZ1DJDZbGbLli1MnTrVuU2r1ZKWlkZOTk61z8nJySEzM9NlW3p6OitXrgTgwIED5OXlkZaW5nw8MjKS1NRUcnJy6hSAmsyePbBzp+3++bnzYvfrs28zHNtkMlFRUeHcHhAQgCEoqN6vYyovp9JsBkAfEIAhMPDir2+1QmWl81ZeVobFZLItH2GxoHE8ZrGgVxQCNBrb1xUVVJaVYTUa0ZSWojWZ0J09C6WlKKWlhBiNhFmtXMz5/2GsWi2lLVuiT0nBlJzM1spKLhs/nhaDBhGo0xEIhAItgNGjR8sAUE/QqpUtAH36KYEJCSRt2UKgXm+rDjX2/4o7/9952rEqKmyD+E+fpqKwEOXoUXR//IHOPn0DgAY4HRfH2enTCR81ihs1Gvk5rw+NBiZPZgWQsnIlnTZuJGDrVgK2biUUqNTrOdOmDZXduqFv3x5atIDQUAgLs/2r19smWdRqq/6r1bp3+RF3HatLF7j0UvccqwFUDUCFhYVYLBZiYmJctsfExLB79+5qn5OXl1ft/nl5ec7HHdtq2udC5eXllNsTN9gqRmBLkm714YcwY4Z7j+kBzPab2se4UAXQkJEeFp0OpUULaNWKyshI8sxmTCEhGKOiONu6NV2vuQZdUhJnIyNZ9913zsqOXq+nW3IyutLSao8beF7As1qt7v/5EhfXooXt3+nTAUgF+M9/kHei4U63aUN+x44cuOwyCrt04ZabbiLQ/geR/JzX3xX/93+U3347ufn5BK5ZQ+Dateh/+QXtmTPojh2j7LzQ6fUyM53/F93F8fNWl84tuQoMmD17NjOqCSYJCQkqtEaozmI51/1VnbffrvGpU6ZMaaJGCeGhCgpstw0bAJjy17+q3CDhNebOtd2awJkzZ4iMjKx1H1UDUHR0NDqdjvz8fJft+fn5xMbGVvuc2NjYWvd3/Jufn09cXJzLPikpKdUec+rUqS7dalarlVOnTtGqVSs0Pr5qcUlJCQkJCRw+fNjvxjv567n763mDnLucu5y7r1MUhTNnzhAfH3/RfVUNQIGBgfTt25esrCxGjhwJ2MJHVlZWjX9JDxw4kKysLB566CHntrVr1zJw4EAAkpKSiI2NJSsryxl4SkpK2LhxI/fdd1+1xwwKCiLo/DEs2Bbw8ycRERF+8Z+jOv567v563iDnLufuf/zp3C9W+XFQvQssMzOT8ePH069fPwYMGMC8efMoLS0lIyMDgHHjxtG2bVtmz54NwIMPPsiQIUOYM2cON9xwA0uXLmXz5s385z//AUCj0fDQQw/xzDPP0KlTJ5KSknjqqaeIj493hiwhhBBC+DfVA9CYMWM4ceIE06ZNIy8vj5SUFFatWuUcxJybm4v2vEm0Bg0axAcffMCTTz7J448/TqdOnVi5ciWXnjeS/NFHH6W0tJR77rmHoqIiLr/8clatWuXf684IIYQQwkn1AAS2gaM1dXllZ2dX2Xb77bdz++2313g8jUbDzJkzmTlzprua6LOCgoKYPn16lS5Af+Cv5+6v5w1y7nLucu7iHNUnQhRCCCGEaG6qL4UhhBBCCNHcJAAJIYQQwu9IABJCCCGE35EAJIQQQgi/IwHIz2RnZ6PRaKq9/fTTTzU+76qrrqqy/1/+8pdmbHnjJSYmVjmHZ599ttbnmEwm7r//flq1akVYWBi33nprlZnIPd3BgweZOHEiSUlJBAcHk5yczPTp0zGba199zVvf8/nz55OYmIjBYCA1NZVNmzbVuv9HH31E165dMRgM9OzZky+//LKZWuo+s2fPpn///oSHh9OmTRtGjhzJnj17an3OkiVLqry/3jhVyNNPP13lPLp27Vrrc3zhPYfqf6dpNBruv//+avf3lffcXTziMnjRfAYNGsTx48ddtj311FNkZWXRr1+/Wp87adIkl6kFQkJCmqSNTWnmzJlMmjTJ+XV4eHit+//tb3/jiy++4KOPPiIyMpIpU6YwatQo1q9f39RNdZvdu3djtVp544036NixIzt27GDSpEmUlpby4osv1vpcb3vPly1bRmZmJgsWLCA1NZV58+aRnp7Onj17aNOmTZX9N2zYwB133MHs2bO58cYb+eCDDxg5ciRbt251mVvM03377bfcf//99O/fn8rKSh5//HGGDRvGb7/9RmhoaI3Pi4iIcAlK3rr0T48ePfj666+dX+v1NX+0+cp7DvDTTz9hsVicX+/YsYNrr7221mlifOU9dwtF+DWz2ay0bt1amTlzZq37DRkyRHnwwQebp1FNpH379spLL71U5/2LioqUgIAA5aOPPnJu27VrlwIoOTk5TdDC5vP8888rSUlJte7jje/5gAEDlPvvv9/5tcViUeLj45XZs2dXu//o0aOVG264wWVbamqqcu+99zZpO5taQUGBAijffvttjfssXrxYiYyMbL5GNZHp06crvXv3rvP+vvqeK4qiPPjgg0pycrJitVqrfdxX3nN3kS4wP/fZZ59x8uRJ59IjtXn//feJjo7m0ksvZerUqZSVlTVDC93r2WefpVWrVvTp04cXXniBysrKGvfdsmULFRUVpKWlObd17dqVdu3akZOT0xzNbTLFxcW0bNnyovt503tuNpvZsmWLy/ul1WpJS0ur8f3Kyclx2R8gPT3dJ95f4KLvsdFopH379iQkJDBixAh27tzZHM1zu7179xIfH0+HDh246667yM3NrXFfX33PzWYz7733Hv/v//2/Wqs6vvKeu4N0gfm5t956i/T0dC655JJa97vzzjtp37498fHxbN++nccee4w9e/awYsWKZmpp4z3wwANcdtlltGzZkg0bNjB16lSOHz/O3Llzq90/Ly+PwMDAKgvjxsTEkJeX1wwtbhr79u3j1VdfvWj3l7e954WFhVgsFucyOg4xMTHs3r272ufk5eVVu783v79Wq5WHHnqIwYMH19ql06VLFxYtWkSvXr0oLi7mxRdfZNCgQezcufOivw88SWpqKkuWLKFLly4cP36cGTNmcMUVV7Bjx45qu7h98T0HWLlyJUVFRUyYMKHGfXzlPXcbtUtQwj0ee+wxBaj1tmvXLpfnHD58WNFqtcrHH39c79fLyspSAGXfvn3uOoUGach5O7z11luKXq9XTCZTtY+///77SmBgYJXt/fv3Vx599FG3nkdDNOTcjxw5oiQnJysTJ06s9+t5yntek6NHjyqAsmHDBpftjzzyiDJgwIBqnxMQEKB88MEHLtvmz5+vtGnTpsna2dT+8pe/KO3bt1cOHz5cr+eZzWYlOTlZefLJJ5uoZc3j9OnTSkREhPLmm29W+7gvvueKoijDhg1Tbrzxxno9x1fe84aSCpCP+Pvf/15r8gfo0KGDy9eLFy+mVatW3HzzzfV+vdTUVMBWTUhOTq73892lIeftkJqaSmVlJQcPHqRLly5VHo+NjcVsNlNUVORSBcrPzyc2NrYxzXaL+p77sWPHuPrqqxk0aBD/+c9/6v16nvKe1yQ6OhqdTlflKr3a3q/Y2Nh67e/ppkyZwueff853331X77/oAwIC6NOnD/v27Wui1jWPqKgoOnfuXON5+Np7DnDo0CG+/vrreldnfeU9bygJQD6idevWtG7dus77K4rC4sWLGTduHAEBAfV+vW3btgEQFxdX7+e6U33P+3zbtm1Dq9VWe3UQQN++fQkICCArK4tbb70VgD179pCbm8vAgQMb3GZ3qc+5Hz16lKuvvpq+ffuyePFitNr6D//zlPe8JoGBgfTt25esrCxGjhwJ2LqDsrKyalxseeDAgWRlZfHQQw85t61du9Yj3t/6UBSFv/71r3zyySdkZ2eTlJRU72NYLBZ+/fVXrr/++iZoYfMxGo3s37+fP//5z9U+7ivv+fkWL15MmzZtuOGGG+r1PF95zxtM7RKUUMfXX39dY/fQkSNHlC5duigbN25UFEVR9u3bp8ycOVPZvHmzcuDAAeXTTz9VOnTooFx55ZXN3ewG27Bhg/LSSy8p27ZtU/bv36+89957SuvWrZVx48Y597nwvBXF1p3Qrl07Zd26dcrmzZuVgQMHKgMHDlTjFBrsyJEjSseOHZVrrrlGOXLkiHL8+HHn7fx9fOE9X7p0qRIUFKQsWbJE+e2335R77rlHiYqKUvLy8hRFUZQ///nPyj/+8Q/n/uvXr1f0er3y4osvKrt27VKmT5+uBAQEKL/++qtap9Ag9913nxIZGalkZ2e7vL9lZWXOfS489xkzZiirV69W9u/fr2zZskUZO3asYjAYlJ07d6pxCg3297//XcnOzlYOHDigrF+/XklLS1Oio6OVgoICRVF89z13sFgsSrt27ZTHHnusymO++p67iwQgP3XHHXcogwYNqvaxAwcOKIDyzTffKIqiKLm5ucqVV16ptGzZUgkKClI6duyoPPLII0pxcXEztrhxtmzZoqSmpiqRkZGKwWBQunXrpvzrX/9yGf9z4XkriqKcPXtWmTx5stKiRQslJCREueWWW1yCgzdYvHhxjWOEHHzpPX/11VeVdu3aKYGBgcqAAQOUH3/80fnYkCFDlPHjx7vsv3z5cqVz585KYGCg0qNHD+WLL75o5hY3Xk3v7+LFi537XHjuDz30kPP7FBMTo1x//fXK1q1bm7/xjTRmzBglLi5OCQwMVNq2bauMGTPGZZyar77nDqtXr1YAZc+ePVUe89X33F00iqIozV52EkIIIYRQkcwDJIQQQgi/IwFICCGEEH5HApAQQggh/I4EICGEEEL4HQlAQgghhPA7EoCEEEII4XckAAkhhBDC70gAEsJLPP3006SkpKjdDBITE5k3b57azajWlVdeyQcffKB2M4RKfvvtNy655BJKS0vVborwAhKAhDjPhAkT0Gg0aDQaAgICSEpK4tFHH8VkMjVrOzQaDStXrnTZ9vDDD5OVldVkr5mdne0895pu2dnZ/PTTT9xzzz1N1o6G+uyzz8jPz2fs2LFqN4UlS5Y4v2darZa4uDjGjBlDbm4uBw8evOj3ecmSJWqfglfq3r07f/rTn5g7d67aTRFeQBZDFeICw4cPZ/HixVRUVLBlyxbGjx+PRqPhueeeU7VdYWFhhIWFNdnxBw0axPHjx51fP/jgg5SUlLB48WLntpYtWxIYGNhkbWiMV155hYyMjAYt9NoUIiIi2LNnD4qicODAASZPnsztt9/Ohg0bXL7PL774IqtWreLrr792bouMjGyydpnN5mZ7D2t6rYqKigYtwlyX52VkZDBp0iSmTp2KXi8fcaJmnvGbQggPEhQURGxsLAkJCYwcOZK0tDTWrl3rfNxqtTJ79mySkpIIDg6md+/efPzxx87HLRYLEydOdD7epUsXXn755Sqvs2jRInr06EFQUBBxcXHOFcsTExMBuOWWW9BoNM6vL+wCs1qtzJw5k0suuYSgoCBSUlJYtWqV83FHpWHFihVcffXVhISE0Lt3b3Jycqo978DAQGJjY5234OBg5/fCcQsMDKzSBabRaHjjjTe48cYbCQkJoVu3buTk5LBv3z6uuuoqQkNDGTRoEPv373d5vU8//ZTLLrsMg8FAhw4dmDFjBpWVlYBtdfOnn36adu3aERQURHx8PA888ECN79mJEydYt24dN910k8t2jUbDm2++yS233EJISAidOnXis88+c9lnx44dXHfddYSFhRETE8Of//xnCgsLnY+fOXOGu+66i9DQUOLi4njppZe46qqrXFYTr45GoyE2Npa4uDgGDRrExIkT2bRpE6WlpS7f07CwMPR6vfPr3bt3M2TIEEJDQ4mKimLw4MEcOnSoxtd57LHH6Ny5MyEhIXTo0IGnnnqKiooK5+OOn5s333yTpKQkDAYDAEVFRdx99920bt2aiIgIhg4dyi+//FLrOR0+fJjRo0cTFRVFy5YtGTFiBAcPHnQ+PmHCBEaOHMk///lP4uPj6dKli/PncNmyZQwZMgSDwcD7779f55/fC5936NAhbrrpJlq0aEFoaCg9evTgyy+/dD7v2muv5dSpU3z77be1nosQEoCEqMWOHTvYsGGDy1+xs2fP5p133mHBggXs3LmTv/3tb/zf//2f8xeu1Wrlkksu4aOPPuK3335j2rRpPP744yxfvtx5jNdff53777+fe+65h19//ZXPPvuMjh07AvDTTz8BsHjxYo4fP+78+kIvv/wyc+bM4cUXX2T79u2kp6dz8803s3fvXpf9nnjiCR5++GG2bdtG586dueOOO5xBw11mzZrFuHHj2LZtG127duXOO+/k3nvvZerUqWzevBlFUZwBD+D7779n3LhxPPjgg/z222+88cYbLFmyhH/+858A/Pe//+Wll17ijTfeYO/evaxcuZKePXvW+Po//PCDM3xdaMaMGYwePZrt27dz/fXXc9ddd3Hq1CnAFgKGDh1Knz592Lx5M6tWrSI/P5/Ro0c7n5+Zmcn69ev57LPPWLt2Ld9//z1bt26t1/enoKCATz75BJ1Oh06nq3G/yspKRo4cyZAhQ9i+fTs5OTncc889aDSaGp8THh7OkiVL+O2333j55ZdZuHAhL730kss++/bt47///S8rVqxg27ZtANx+++0UFBTw1VdfsWXLFi677DKuueYa5/fmQhUVFaSnpxMeHs7333/P+vXrCQsLY/jw4ZjNZud+WVlZ7Nmzh7Vr1/L55587t//jH//gwQcfZNeuXaSnp9f55/fC591///2Ul5fz3Xff8euvv/Lcc8+5VEYDAwNJSUnh+++/r/F7JgSArAYvxHnGjx+v6HQ6JTQ0VAkKClIARavVKh9//LGiKIpiMpmUkJAQZcOGDS7PmzhxonLHHXfUeNz7779fufXWW51fx8fHK0888USN+wPKJ5984rJt+vTpSu/evV2O8c9//tNln/79+yuTJ09WFOXcCu9vvvmm8/GdO3cqgLJr164aX9th/PjxyogRI6psb9++vfLSSy+5tPXJJ590fp2Tk6MAyltvveXc9uGHHyoGg8H59TXXXKP861//cjnuu+++q8TFxSmKoihz5sxROnfurJjN5ou2U1EU5aWXXlI6dOhQZfuFbTMajQqgfPXVV4qiKMqsWbOUYcOGuTzn8OHDztW1S0pKlICAAOWjjz5yPl5UVKSEhIQoDz74YI3tWbx4sQIooaGhSkhIiHN19gceeKDKvue/rydPnlQAJTs7u07nXZ0XXnhB6du3r8vxAwIClIKCAue277//XomIiFBMJpPLc5OTk5U33nij2uO+++67SpcuXRSr1ercVl5ergQHByurV69WFMX2MxMTE6OUl5c793H8HM6bN8/leHX9+b3weT179lSefvrpWr8Ht9xyizJhwoRa9xFCOkiFuMDVV1/N66+/TmlpKS+99BJ6vZ5bb70VsP0lXVZWxrXXXuvyHLPZTJ8+fZxfz58/n0WLFpGbm8vZs2cxm83O7quCggKOHTvGNddc0+A2lpSUcOzYMQYPHuyyffDgwVW6MXr16uW8HxcX52xD165dG/z6Fzr/NWJiYgBcKjYxMTGYTCZKSkqIiIjgl19+Yf369c6KD9i6Dk0mE2VlZdx+++3MmzePDh06MHz4cK6//npuuummGsd0nD171tm1U1vbQkNDiYiIoKCgAIBffvmFb775ptqxVfv37+fs2bNUVFQwYMAA5/bIyEi6dOly0e9JeHg4W7dupaKigq+++or333/f5Xyr07JlSyZMmEB6ejrXXnstaWlpjB492vm+VWfZsmW88sor7N+/H6PRSGVlJRERES77tG/fntatWzu//uWXXzAajbRq1cplv7Nnz1bpqjz/Ofv27SM8PNxlu8lkcnlOz549qx33069fP+f9+vz8nv88gAceeID77ruPNWvWkJaWxq233uryHgMEBwdTVlZW7XkI4SABSIgLhIaGOrujFi1aRO/evXnrrbeYOHEiRqMRgC+++IK2bdu6PC8oKAiApUuX8vDDDzNnzhwGDhxIeHg4L7zwAhs3bgRsv5yb0/mDRh1dKVartclfo7bXNRqNzJgxg1GjRlU5lsFgICEhgT179vD111+zdu1aJk+ezAsvvMC3335b7SDY6OhoTp8+fdG2Odpyfjtuuummage4x8XFsW/fvlrPuzZardb5c9StWzf279/Pfffdx7vvvlvr8xYvXswDDzzAqlWrWLZsGU8++SRr167lT3/6U5V9c3JyuOuuu5gxYwbp6elERkaydOlS5syZ47JfaGioy9dGo5G4uDiys7OrHDMqKqradhmNRvr27cv7779f5bHzw9WFr3Wx7Rdz4fPuvvtu0tPT+eKLL1izZg2zZ89mzpw5/PWvf3Xuc+rUKZKTkxv0esJ/SAASohZarZbHH3+czMxM7rzzTrp3705QUBC5ubkMGTKk2uesX7+eQYMGMXnyZOe28/9CDg8PJzExkaysLK6++upqjxEQEIDFYqmxXREREcTHx7N+/XqXdqxfv96lWuGpLrvsMvbs2eMMCNUJDg7mpptu4qabbuL++++na9eu/Prrr1x22WVV9u3Tpw95eXmcPn2aFi1a1Ksd//3vf0lMTKy2utShQwcCAgL46aefaNeuHQDFxcX8/vvvXHnllXV+HbCNZUlOTuZvf/tbtedw4fn06dOHqVOnMnDgQD744INqA9CGDRto3749TzzxhHNbbQOmHS677DLy8vLQ6/XOQfZ1ec6yZcto06ZNlQpTfTX25zchIYG//OUv/OUvf2Hq1KksXLjQJQDt2LGD2267rVFtFL5PBkELcRG33347Op2O+fPnEx4ezsMPP8zf/vY33n77bfbv38/WrVt59dVXefvttwHo1KkTmzdvZvXq1fz+++889dRTVQYyP/3008yZM4dXXnmFvXv3Oo/h4AhIjg/16jzyyCM899xzLFu2jD179vCPf/yDbdu28eCDDzbdN8NNpk2bxjvvvMOMGTPYuXMnu3btYunSpTz55JOAbR6dt956ix07dvDHH3/w3nvvERwcTPv27as9Xp8+fYiOjmb9+vX1asf999/PqVOnuOOOO/jpp5/Yv38/q1evJiMjA4vFQnh4OOPHj+eRRx7hm2++YefOnUycOBGtVlvrwOTqJCQkcMsttzBt2rQa9zlw4ABTp04lJyeHQ4cOsWbNGvbu3Vvt4G6w/azl5uaydOlS9u/fzyuvvMInn3xy0bakpaUxcOBARo4cyZo1azh48CAbNmzgiSeeYPPmzdU+56677iI6OpoRI0bw/fffc+DAAbKzs3nggQc4cuRI3b4J52noz+9DDz3E6tWrOXDgAFu3buWbb75x+f4cPHiQo0ePkpaWVu82Cf8iAUiIi9Dr9UyZMoXnn3+e0tJSZs2axVNPPcXs2bPp1q0bw4cP54svviApKQmAe++9l1GjRjFmzBhSU1M5efKkSzUIYPz48cybN4/XXnuNHj16cOONN7pc/TJnzhzWrl1LQkKCy9ii8z3wwANkZmby97//nZ49e7Jq1So+++wzOnXq1HTfDDdJT0/n888/Z82aNfTv358//elPvPTSS86AExUVxcKFCxk8eDC9evXi66+/5n//+1+VMSsOOp2OjIyMartnauOoQlgsFoYNG0bPnj156KGHiIqKcs4nNHfuXAYOHMiNN95IWloagwcPplu3bjWOOarN3/72N7744gs2bdpU7eMhISHs3r2bW2+9lc6dO3PPPfdw//33c++991a7/80338zf/vY3pkyZQkpKChs2bOCpp566aDs0Gg1ffvklV155JRkZGXTu3JmxY8dy6NAh5xiu6tr23Xff0a5dO0aNGkW3bt2YOHEiJpOpQRWhhv78WiwW7r//fuf/vc6dO/Paa685H//www8ZNmxYjWFZCAeNoiiK2o0QQojGysvLo0ePHmzdurVJP/xKS0tp27Ytc+bMYeLEiU32OqL+zGYznTp14oMPPqgywFqIC0kFSAjhE2JjY3nrrbfIzc1163F//vlnPvzwQ2d351133QXAiBEj3Po6ovFyc3N5/PHHJfyIOpEKkBBC1OLnn3/m7rvvZs+ePQQGBtK3b1/mzp1b68SMQgjPJwFICCGEEH5HusCEEEII4XckAAkhhBDC70gAEkIIIYTfkQAkhBBCCL8jAUgIIYQQfkcCkBBCCCH8jgQgIYQQQvgdCUBCCCGE8DsSgIQQQgjhd/4/Z+QLiUFAeigAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -448,9 +445,9 @@ " density=True,\n", " alpha=0.4,\n", ")\n", - "# s = choice*x\n", - "# idx = np.argsort(s) # sort the values so theyre not zig zaggy \n", - "# plt.plot(s[idx], pdf_vals[idx], color=\"red\", label=\"Ex-Gaussian Likelihood\")\n", + "s = choice*x\n", + "idx = np.argsort(s) # sort the values so theyre not zig zaggy \n", + "plt.plot(s[idx], pdf_vals[idx], color=\"red\", label=\"Ex-Gaussian Likelihood\")\n", "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", "plt.ylabel(\"Density\")\n", "plt.title(\"Ex-Gaussian\")\n" @@ -502,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -511,13 +508,13 @@ "Text(0, 0.5, 'Density')" ] }, - "execution_count": 14, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -536,9 +533,9 @@ " density=True,\n", " alpha=0.4,\n", ")\n", - "# s_sw = choice_sw*x_sw\n", - "# idx_sw = np.argsort(s_sw) # sort the values so theyre not zig zaggy\n", - "# plt.plot(s_sw[idx_sw], pdf_vals_sw[idx_sw], color=\"blue\", label=\"Shifted Wald Likelihood\")\n", + "s_sw = choice_sw*x_sw\n", + "idx_sw = np.argsort(s_sw) # sort the values so theyre not zig zaggy\n", + "plt.plot(s_sw[idx_sw], pdf_vals_sw[idx_sw], color=\"blue\", label=\"Shifted Wald Likelihood\")\n", "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", "plt.ylabel(\"Density\")" ] From caf1f44832fd1c2b1e3e2276ee7ebdd8b7943bcd Mon Sep 17 00:00:00 2001 From: AndrewZhang599 Date: Tue, 10 Feb 2026 23:56:15 -0500 Subject: [PATCH 14/14] brief changes --- notebooks/test_exgauss_wald.ipynb | 150 +++++++++++++++++++++--------- 1 file changed, 104 insertions(+), 46 deletions(-) diff --git a/notebooks/test_exgauss_wald.ipynb b/notebooks/test_exgauss_wald.ipynb index 0b2e3880..219143e1 100644 --- a/notebooks/test_exgauss_wald.ipynb +++ b/notebooks/test_exgauss_wald.ipynb @@ -138,17 +138,17 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from ssms.basic_simulators.simulator import simulator\n", "mu0 = 5.0 # mu of ex-gaussian for choice 1\n", - "mu1 = 1.0 # mu of ex-gaussian for choice -1 \n", + "mu1 = 3 # mu of ex-gaussian for choice -1 \n", "sigma0 = 0.5\n", - "sigma1 = 0.5\n", + "sigma1 = 1\n", "tau0 = 0.1\n", - "tau1 = 0.5\n", + "tau1 = 0.2\n", "p_exgauss = 0.5\n", "\n", "v0 = 2.0 \n", @@ -191,16 +191,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[1.0882273]\n", - " [4.5396304]\n", - " [5.479963 ]\n", - " [6.16694 ]\n", - " [6.02184 ]\n", - " [4.863364 ]\n", - " [4.09259 ]\n", - " [6.1096787]\n", - " [4.9302044]\n", - " [5.0182223]]\n" + "[[1.4411364 ]\n", + " [4.5396304 ]\n", + " [1.9898669 ]\n", + " [6.16694 ]\n", + " [2.6007307 ]\n", + " [1.695057 ]\n", + " [0.32515216]\n", + " [6.1096787 ]\n", + " [1.4635196 ]\n", + " [5.0182223 ]]\n" ] } ], @@ -218,11 +218,11 @@ "text/plain": [ "array([[-1],\n", " [ 1],\n", - " [ 1],\n", + " [-1],\n", " ...,\n", " [ 1],\n", " [-1],\n", - " [ 1]], shape=(10000, 1), dtype=int32)" + " [-1]], shape=(10000, 1), dtype=int32)" ] }, "execution_count": 5, @@ -248,7 +248,7 @@ " ...,\n", " [-1],\n", " [-1],\n", - " [-1]], shape=(10000, 1), dtype=int32)" + " [-1]], shape=(1100, 1), dtype=int32)" ] }, "execution_count": 6, @@ -257,7 +257,7 @@ } ], "source": [ - "sim_out_race['choices']" + "sim_out_race['choices'][:1100]" ] }, { @@ -270,12 +270,12 @@ "output_type": "stream", "text": [ "[[1.7596234 ]\n", - " [0.56169754]\n", - " [0.69038945]\n", + " [0.1787014 ]\n", + " [0.67438966]\n", " ...\n", - " [1.3998891 ]\n", - " [1.0962074 ]\n", - " [0.99489033]]\n" + " [0.9025264 ]\n", + " [0.73628825]\n", + " [0.17353109]]\n" ] } ], @@ -297,7 +297,7 @@ " [ 1]\n", " ...\n", " [ 1]\n", - " [-1]\n", + " [ 1]\n", " [-1]]\n" ] } @@ -316,7 +316,7 @@ "output_type": "stream", "text": [ "[[-1]\n", - " [ 1]\n", + " [-1]\n", " [-1]\n", " ...\n", " [-1]\n", @@ -331,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -361,25 +361,55 @@ "\n", "\n", "x = np.linspace(0, 9, 1100)\n", - "p = 0.8\n", - "choice = np.where(np.random.rand(len(x)) < p, 1, -1)\n", + "choice = np.where(np.random.rand(len(x)) < p_exgauss, 1, -1)\n", "pdf_vals = exgauss_likelihood_bernoulli(x, choice, mu0=mu0, mu1=mu1, sigma0=sigma0,\n", " sigma1=sigma1, tau0=tau0, tau1=tau1, p=p_exgauss)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# Define exgauss race likelihoods \n", - "\n" + "\n", + "def exgauss_race_likelihoods(x, choice, mu0, mu1, sigma0, sigma1, tau0, tau1):\n", + " x = np.asarray(x, dtype=float)\n", + " y = (np.asarray(choice, dtype=int) > 0) # 1 choice \n", + " mu_winner = np.where(y, float(mu0), float(mu1))\n", + " sigma_winner = np.where(y, float(sigma0), float(sigma1))\n", + " tau_winner = np.where(y, float(tau0), float(tau1))\n", + "\n", + " mu_loser = np.where(y, float(mu1), float(mu0))\n", + " sigma_loser = np.where(y, float(sigma1), float(sigma0))\n", + " tau_loser = np.where(y, float(tau1), float(tau0))\n", + "\n", + " # Winner density \n", + " tau_inv_winner = 1.0 / tau_winner \n", + " sig2_winner = sigma_winner ** 2 \n", + " z_winner = (x - mu_winner - sig2_winner * tau_inv_winner) / sigma_winner \n", + " cdf_gauss_winner = norm.cdf(z_winner)\n", + " pdf_rt_winner = tau_inv_winner * np.exp(mu_winner*tau_inv_winner + (sig2_winner * tau_inv_winner**2) / 2 - x*tau_inv_winner) * cdf_gauss_winner \n", + "\n", + " # Loser survival \n", + " z0_loser = (x -mu_loser) / sigma_loser \n", + " z1_loser = z0_loser - (sigma_loser / tau_loser)\n", + " exp_factor = np.exp(- (x - mu_loser) / tau_loser + 0.5*(sigma_loser ** 2) / (tau_loser ** 2))\n", + " F_loser = norm.cdf(z0_loser) - exp_factor * norm.cdf(z1_loser)\n", + " surv_rt_loser = 1.0 - F_loser\n", + " return pdf_rt_winner * surv_rt_loser\n", + "\n", + "x_race = np.linspace(0, 9, 1000)\n", + "choice_race = (np.asarray(sim_out_race[\"choices\"][:len(x_race)])).reshape(-1)\n", + "# choice_race = np.where(np.random.rand(len(x_race)) < p_race, 1, -1)\n", + "pdf_race_vals = exgauss_race_likelihoods(x_race, choice_race, mu0=mu0, mu1=mu1, sigma0=sigma0,\n", + " sigma1=sigma1, tau0=tau0, tau1=tau1)\n" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -410,7 +440,35 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define shifted_wald race likelihoods \n", + "\n", + "def shifted_wald_race_likelihoods(x, choice, v0, v1, a0, a1, t):\n", + " x = np.asarray(x, dtype=float)\n", + " y = (np.asarray(choice, dtype=int) > 0) # 1 choice\n", + " v_winner = np.where(y, float(v0), float(v1))\n", + " a_winner = np.where(y, float(a0), float(a1))\n", + "\n", + " v_loser = np.where(y, float(v1), float(v0))\n", + " a_loser = np.where(y, float(a1), float(a0))\n", + "\n", + " # Winner density \n", + " term_1_winner = (a_winner / np.sqrt(2*np.pi*(x - t)**3))\n", + " term_2_winner = np.exp((-(a_winner-v_winner*(x - t))**2) / (2*(x - t)))\n", + " pdf_rt_winner = term_1_winner * term_2_winner \n", + "\n", + " # Loser survival \n", + " z_loser = (a_loser - v_loser * (x - t)) / np.sqrt(x - t)\n", + " F_loser = norm.cdf(z_loser)\n", + " surv_rt_loser = 1.0 - F_loser" + ] + }, + { + "cell_type": "code", + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -419,13 +477,13 @@ "Text(0.5, 1.0, 'Ex-Gaussian')" ] }, - "execution_count": 25, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -455,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -464,13 +522,13 @@ "Text(0.5, 1.0, 'Ex-Gaussian Race')" ] }, - "execution_count": 13, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -489,9 +547,9 @@ " density=True,\n", " alpha=0.4,\n", ")\n", - "# s = choice*x\n", - "# idx = np.argsort(s) # sort the values so theyre not zig zaggy \n", - "# plt.plot(s[idx], pdf_vals[idx], color=\"red\", label=\"Ex-Gaussian Likelihood\")\n", + "s_race = choice_race*x_race\n", + "idx_race = np.argsort(s_race) # sort the values so theyre not zig zaggy \n", + "plt.plot(s_race[idx_race], pdf_race_vals[idx_race], color=\"red\", label=\"Ex-Gaussian Likelihood\")\n", "plt.xlabel(\"Reaction Times (neg RTs are errors)\")\n", "plt.ylabel(\"Density\")\n", "plt.title(\"Ex-Gaussian Race\")" @@ -499,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -508,13 +566,13 @@ "Text(0, 0.5, 'Density')" ] }, - "execution_count": 31, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -542,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -551,13 +609,13 @@ "Text(0, 0.5, 'Density')" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGwCAYAAABVdURTAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAOCpJREFUeJzt3X1cVHXe//E3MMKoA04uCmgYWtmtoWkaultJJK5X5k0brnUlsmZ3mibr/hJLze0yak3EWjfb8mbbzdTK2q7VvAk0XWXXlOxeU0vx0kBZF3HQEQfm94cPZx0YkPsDZ17Px2MeD+c755z5zHGYec853+/3BLjdbrcAAABMItDoAgAAABoS4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJiKxegCmlp5ebmOHj2q0NBQBQQEGF0OAACoAbfbrVOnTqlTp04KDKz+2IzfhZujR48qOjra6DIAAEAdHD58WJdffnm1y/hduAkNDZV0fueEhYUZXA0AAKiJ4uJiRUdHe77Hq+N34ebCqaiwsDDCDQAALUxNupTQoRgAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJiKxegCAH/hcDjkdDq92qxWq2w2m0EVAYA5EW6AJuBwOLRq1Sq5XC6vdovFoqSkJAIOADQgwg3QBJxOp1wul+Lj42W32yVJRUVFys7OltPpJNwAQAMi3ABNyG63Kzw83OgyAMDU6FAMAABMhXADAABMhdNSgMGKioo8/2b0FADUH+EGMIjVapXFYlF2dranjdFTAFB/hBvAIDabTUlJSZ65bxg9BQANg3ADGMhmsxFkAKCB0aEYAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCpdfABqJw+Hwum4UAKBpEG6ARuBwOLRq1Sq5XC5Pm8VikdVqNbAqAPAPhBugETidTrlcLsXHx8tut0uSrFYrF8kEgCZAuAEakd1uV3h4uNFlAIBfoUMxAAAwFcINAAAwFU5LAc1MxZFV9NUBgNoh3ADNhNVqlcViUXZ2tle7xWJRUlISAQcAaohwAzQTNptNSUlJnrlxpPNHcbKzs+V0Ogk3AFBDhBugGbHZbIQYAKgnOhQDAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTYSg40AJcPGsxMxYDQPUIN0Az5mvWYmYsBoDqEW6AZqzirMXMWAwAl0a4AZo5Zi0GgNqhQzEAADAVjtwADcDhcFS64CUAwBiEG6CeHA6HVq1aJZfL5dVusVhktVoNqgoA/BfhBqgnp9Mpl8ul+Ph42e12TztDtgHAGIb3uVm4cKFiYmJktVrVr18/7dixo9rlMzMzdc0116h169aKjo7WlClTvE4HAEax2+0KDw/33Ag2AGAMQ8PNypUrlZqaqlmzZik3N1exsbFKTEzUsWPHfC6/fPlyTZs2TbNmzdK3336rxYsXa+XKlZo+fXoTVw4AAJorQ8NNRkaGxo8fr5SUFF1//fVatGiR2rRpoyVLlvhcfvv27RowYIDuv/9+xcTEaNCgQRo9enS1R3vOnj2r4uJirxsAADAvw8JNaWmpdu3apYSEhP8UExiohIQE5eTk+Fynf//+2rVrlyfMfP/991q7dq2GDBlS5fOkp6erXbt2nlt0dHTDvhAAANCsGNahuLCwUGVlZYqIiPBqj4iI0J49e3yuc//996uwsFA//elP5Xa75XK59Oijj1Z7WiotLU2pqame+8XFxQQcAABMzPAOxbWxefNmPf/88/rDH/6g3NxcrV69WmvWrNFzzz1X5TohISEKCwvzugEAAPMy7MhNeHi4goKCVFBQ4NVeUFCgyMhIn+vMmDFDDz74oB566CFJUo8ePVRSUqKHH35YTz/9tAIDW1RWAwAAjcCwNBAcHKzevXsrKyvL01ZeXq6srCzFxcX5XOf06dOVAkxQUJAkye12N16xAACgxTB0Er/U1FQlJyerT58+6tu3rzIzM1VSUqKUlBRJ0pgxY9S5c2elp6dLkoYOHaqMjAz16tVL/fr10/79+zVjxgwNHTrUE3IAAIB/MzTcjBo1SsePH9fMmTOVn5+vnj17at26dZ5Oxnl5eV5Hap555hkFBATomWee0ZEjR9ShQwcNHTpUc+bMMeolAACAZibA7Wfnc4qLi9WuXTudPHmSzsVoEIWFhVq9erVGjhyp8PBw0zwXADQntfn+pgcuAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFUPnuQFaKofDIafTKUkqKioythgAgBfCDVBLDodDq1atksvl8rRZLBZZrVYDqwIAXEC4AWrJ6XTK5XIpPj5edrtdkmS1WmWz2YwtDAAgiXAD1JndbmeWYABohuhQDAAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATMVidAEAaq+oqMjrvtVqlc1mM6YYAGhmCDdAC2K1WmWxWJSdne3VbrFYlJSURMABABFugBbFZrMpKSlJTqfT01ZUVKTs7Gw5nU7CDQCIcANcksPhqBQmjGSz2QgxAFANwg1QDYfDoVWrVsnlcnm1WywWWa1Wg6oCAFSHcANUw+l0yuVyKT4+Xna73dNOB14AaL4IN0AN2O12hYeHG10GAKAGmOcGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCkPBAcBgFWfBlphLCagPwg0AGKi6WbC5GCpQN4afllq4cKFiYmJktVrVr18/7dixo9rli4qKNGHCBEVFRSkkJETdu3fX2rVrm6ha+AOHw6HCwkIVFhYafh0pmN/Fs2CPHDlSI0eOVHx8vFwuV6WjOQBqxtAjNytXrlRqaqoWLVqkfv36KTMzU4mJidq7d686duxYafnS0lLddddd6tixo95991117txZhw4d8poWH6gPX7+iuY4UmgKzYAMNx9Bwk5GRofHjxyslJUWStGjRIq1Zs0ZLlizRtGnTKi2/ZMkSnThxQtu3b1erVq0kSTExMU1ZMkzO17Wk6PsAAC2LYaelSktLtWvXLiUkJPynmMBAJSQkKCcnx+c6H374oeLi4jRhwgRFREToxhtv1PPPP6+ysrIqn+fs2bMqLi72ugGXcuFXdHh4OMEGAFoYw8JNYWGhysrKFBER4dUeERGh/Px8n+t8//33evfdd1VWVqa1a9dqxowZmjdvnv7nf/6nyudJT09Xu3btPLfo6OgGfR0AAKB5MbxDcW2Ul5erY8eO+uMf/6jevXtr1KhRevrpp7Vo0aIq10lLS9PJkyc9t8OHDzdhxQAAoKkZ1ucmPDxcQUFBKigo8GovKChQZGSkz3WioqLUqlUrBQUFedquu+465efnq7S0VMHBwZXWCQkJUUhISMMWDwAAmi3DjtwEBwerd+/eysrK8rSVl5crKytLcXFxPtcZMGCA9u/fr/Lyck/bd999p6ioKJ/BBgCaI6YbABqXoaOlUlNTlZycrD59+qhv377KzMxUSUmJZ/TUmDFj1LlzZ6Wnp0uSHnvsMf3+97/X5MmT9cQTT2jfvn16/vnnNWnSJCNfBgDUGNMNAI3P0HAzatQoHT9+XDNnzlR+fr569uypdevWeToZ5+XlKTDwPweXoqOjtX79ek2ZMkU33XSTOnfurMmTJ+upp54y6iUAQK0w3QDQ+Ay//MLEiRM1ceJEn49t3ry5UltcXJz+8Y9/NHJVANC4mLQPaDwtarQUAADApRBuAACAqRh+WgoA4NvFI6nolwPUHOEGAJoZq9Uqi8Wi7OxsT5vFYlFSUhIBB6gBwg0ANCKHwyGn0+m5X5N5bWw2m5KSkjzrFRUVKTs7W06nk3AD1ADhBgAaia85baSazWtjs9kIMkAdEW4AoJH4mtNGov8M0NgINwDQyJjTBmhaDAUHAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmUqdw8/333zd0HQAAAA2iTuHmqquu0sCBA/WXv/zFa1pxAAAAo9Up3OTm5uqmm25SamqqIiMj9cgjj2jHjh0NXRsAAECt1Snc9OzZUwsWLNDRo0e1ZMkS/fjjj/rpT3+qG2+8URkZGTp+/HhD1wkAAFAj9epQbLFYNHLkSL3zzjt68cUXtX//fk2dOlXR0dEaM2aMfvzxx4aqEwAAoEbqFW527typxx9/XFFRUcrIyNDUqVN14MABbdy4UUePHtWwYcMaqk4A8HtFRUUqLCz03BwOh9ElAc1SnS6cmZGRoaVLl2rv3r0aMmSI3nzzTQ0ZMkSBgeezUteuXbVs2TLFxMQ0ZK0A4JesVqssFouys7O92i0Wi5KSkrjCOFBBncLNq6++ql/96lcaO3asoqKifC7TsWNHLV68uF7FAQAkm82mpKQkr9GpRUVFys7OltPpJNwAFdQp3GzcuFFdunTxHKm5wO126/Dhw+rSpYuCg4OVnJzcIEUCgL+z2WyEGKCG6tTn5sorr1RhYWGl9hMnTqhr1671LgoAAKCu6hRu3G63z3aHwyGr1VqvggAAAOqjVqelUlNTJUkBAQGaOXOm2rRp43msrKxM//znP9WzZ88GLRAAAKA2ahVuPvvsM0nnj9x8+eWXCg4O9jwWHBys2NhYTZ06tWErBAAAqIVahZtNmzZJklJSUrRgwQKFhYU1SlEAAAB1VafRUkuXLm3oOgAAABpEjcPNyJEjtWzZMoWFhWnkyJHVLrt69ep6FwYAAFAXNQ437dq1U0BAgOffAAAAzVGNw83Fp6I4LQUAAJqrOs1zc+bMGZ0+fdpz/9ChQ8rMzNSGDRsarDAAAIC6qFOH4mHDhmnkyJF69NFHVVRUpL59+yo4OFiFhYXKyMjQY4891tB1Ao3C4XBUul4PAKBlq1O4yc3N1fz58yVJ7777riIjI/XZZ5/pvffe08yZMwk3aBEcDodWrVoll8vl1W6xWFrkTNsXBzOr1cp1iAD4rTqFm9OnTys0NFSStGHDBo0cOVKBgYG69dZbdejQoQYtEGgsTqdTLpdL8fHxstvtnvaWFgysVqssFouys7M9bRaLRUlJSS3qdQBAQ6lTuLnqqqv0wQcfaMSIEVq/fr2mTJkiSTp27BgT+6HFsdvtCg8PN7qMOrPZbEpKSvKcXisqKlJ2dracTifhBoBfqlOH4pkzZ2rq1KmKiYlRv379FBcXJ+n8UZxevXo1aIEALs1msyk8PFzh4eFeR6EAwB/V6cjNL37xC/30pz/Vjz/+qNjYWE/7nXfeqREjRjRYcQAAALVVp3AjSZGRkYqMjPRq69u3b70LAgAAqI86hZuSkhK98MILysrK0rFjx1ReXu71+Pfff98gxQFAc1ZxKoGKmFoAMEadws1DDz2kTz75RA8++KCioqI8l2UAAH9R1VQCFbXUqQWAlqxO4eajjz7SmjVrNGDAgIauBwBahKqmEqiopU0tAJhBncLNZZddpvbt2zd0LQDQ4rT0qQQAM6rTUPDnnntOM2fO9Lq+FACg6RUVFamwsFCFhYVyOBxGlwM0C3U6cjNv3jwdOHBAERERiomJUatWrbwez83NbZDiAAC+MTM1ULU6hZvhw4c3cBkAgNpgZmqganUKN7NmzWroOgAAtWSz2QgygA916nMjnf+V8MYbbygtLU0nTpyQdP501JEjRxqsOAAAgNqq05GbL774QgkJCWrXrp0OHjyo8ePHq3379lq9erXy8vL05ptvNnSdAAAANVKnIzepqakaO3as9u3b5zU51ZAhQ7Rly5YGKw4AAKC26hRuPv30Uz3yyCOV2jt37qz8/Px6FwUAAFBXdQo3ISEhKi4urtT+3XffqUOHDvUuCgAAoK7qFG7uuece/fa3v9W5c+ckSQEBAcrLy9NTTz2le++9t0ELBAAAqI06hZt58+bJ4XCoQ4cOOnPmjG6//XZdddVVCg0N1Zw5cxq6RgAAgBqr02ipdu3aaePGjdq2bZs+//xzORwO3XzzzUpISGjo+gAAAGql1uGmvLxcy5Yt0+rVq3Xw4EEFBASoa9euioyMlNvtVkBAQGPUCQCogaKiIq/7XJUc/qhW4cbtduuee+7R2rVrFRsbqx49esjtduvbb7/V2LFjtXr1an3wwQeNVCoAoCq+rjUlcb0p+KdahZtly5Zpy5YtysrK0sCBA70ey87O1vDhw/Xmm29qzJgxDVokAKB6Fa81JXG9KfivWnUofvvttzV9+vRKwUaS4uPjNW3aNL311lsNVhwAoOZsNpvCw8M9N7vdbnRJgCFqFW6++OILDR48uMrHf/7zn+vzzz+vd1EAAAB1Vatwc+LECUVERFT5eEREhP7973/XuygAAIC6qlW4KSsrk8VSdTedoKAguVyuehcFAABQV7UeLTV27FiFhIT4fPzs2bMNUhQAAEBd1SrcJCcnX3IZRkoBAAAj1SrcLF26tFGKWLhwoebOnav8/HzFxsbqlVdeUd++fS+53ooVKzR69GgNGzaM+XUAAICkOl5bqiGtXLlSqampmjVrlnJzcxUbG6vExEQdO3as2vUOHjyoqVOn6mc/+1kTVQoAAFoCw8NNRkaGxo8fr5SUFF1//fVatGiR2rRpoyVLllS5TllZmR544AHNnj1b3bp1a8Jq0dI5HA4VFhaqsLCw0jT1AABzqNOFMxtKaWmpdu3apbS0NE9bYGCgEhISlJOTU+V6v/3tb9WxY0eNGzdOW7durfY5zp4969XRubi4uP6Fo0VyOBxatWqV14g+i8Uiq9VqYFUAgIZmaLgpLCxUWVlZpblzIiIitGfPHp/r/P3vf9fixYu1e/fuGj1Henq6Zs+eXd9SYQJOp1Mul0vx8fGemVu5qCAAmI/hp6Vq49SpU3rwwQf1+uuvKzw8vEbrpKWl6eTJk57b4cOHG7lKNHd2u90zPT3BBgDMx9AjN+Hh4QoKClJBQYFXe0FBgSIjIystf+DAAR08eFBDhw71tJWXl0s6f3ph7969uvLKK73WCQkJqXJeHgAAYD6GHrkJDg5W7969lZWV5WkrLy9XVlaW4uLiKi1/7bXX6ssvv9Tu3bs9t3vuuUcDBw7U7t27FR0d3ZTlAwCAZsjQIzeSlJqaquTkZPXp00d9+/ZVZmamSkpKlJKSIun8pICdO3dWenq6rFarbrzxRq/1L/SdqNgOAAD8k+HhZtSoUTp+/Lhmzpyp/Px89ezZU+vWrfN0Ms7Ly1NgYIvqGgQAAAxkeLiRpIkTJ2rixIk+H9u8eXO16y5btqzhCwIAAC0Wh0QAAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpNItrSwGNweFwyOl0eu4XFRUZVwwAoMkQbmBKDodDq1atksvl8mq3WCyyWq0GVQUAaAqEG5iS0+mUy+VSfHy87Ha7p91qtcpmsxlXWBOqeKTKn147vF38XuB9AH9AuIGp2e12hYeHG11Gk7JarbJYLMrOzvZqt1gsSkpK4ovNj/h6L/A+gD8g3AAmY7PZlJSUVKm/UXZ2tpxOJ19qfqTie4H3AfwF4QYwIZvNxpdXI7i4k3pL6aDOewH+iHADADXgq5M6HdSB5olwAwA14KuTOp1zgeaJcAMAteCPndSBloYZigEAgKlw5AYA/AxzIMHsCDcA4CeYAwn+gnADAH6COZDgLwg3AOBHmPcG/oAOxQAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFS4KjhMw+FwyOl0SpKKioqMLQYAYBjCDUzB4XBo1apVcrlcnjaLxSKr1WpgVQAAIxBuYApOp1Mul0vx8fGy2+2SJKvVKpvNZmxhAIAmR7iBqdjtdoWHhxtdBgDAQHQoBgAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApkK4AQAApsJVwQHAB4fDIafT6blfVFRkXDFN4OLXZ7VaZbPZjCsGqCfCDVokf/viQdNyOBxatWqVXC6XV7vFYpHVajWoqsZhtVplsViUnZ3tabNYLEpKSiLgoMUi3KDF8acvHhjD6XTK5XIpPj5edrvd027GIxo2m01JSUmeHwtFRUXKzs6W0+k03WuF/yDcoMXxpy8eGMtutys8PNzoMhqdzWbjbwemQrhBi+UvXzwAgNphtBQAADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADCVZhFuFi5cqJiYGFmtVvXr1087duyoctnXX39dP/vZz3TZZZfpsssuU0JCQrXLAwAA/2J4uFm5cqVSU1M1a9Ys5ebmKjY2VomJiTp27JjP5Tdv3qzRo0dr06ZNysnJUXR0tAYNGqQjR440ceVoKg6HQ4WFhZ4bl1oAAFTH8En8MjIyNH78eKWkpEiSFi1apDVr1mjJkiWaNm1apeXfeustr/tvvPGG3nvvPWVlZWnMmDGVlj979qzOnj3ruV9cXNzArwCNiUstAABqy9BwU1paql27diktLc3TFhgYqISEBOXk5NRoG6dPn9a5c+fUvn17n4+np6dr9uzZDVIvmh6XWgAA1Jahp6UKCwtVVlamiIgIr/aIiAjl5+fXaBtPPfWUOnXqpISEBJ+Pp6Wl6eTJk57b4cOH6103mt6FSy1cuBFsAABVMfy0VH288MILWrFihTZv3lzlKYqQkBCFhIQ0cWUAAMAohoab8PBwBQUFqaCgwKu9oKBAkZGR1a770ksv6YUXXtDHH3+sm266qTHLBOAHHA6HnE6nJNFpHWjhDA03wcHB6t27t7KysjR8+HBJUnl5ubKysjRx4sQq1/vd736nOXPmaP369erTp08TVQu0fBd/adNv6T98dVyn0zrQchl+Wio1NVXJycnq06eP+vbtq8zMTJWUlHhGT40ZM0adO3dWenq6JOnFF1/UzJkztXz5csXExHj65thsNj6ogSpYrVZZLBZlZ2d72iwWi5KSkvi7ke+O64Q/oOUyPNyMGjVKx48f18yZM5Wfn6+ePXtq3bp1nk7GeXl5Cgz8T7/nV199VaWlpfrFL37htZ1Zs2bp2WefbcrSgRbDZrMpKSnJ67RLdna2nE4nX+AXudBxHZVPzRH20JIYHm4kaeLEiVWehtq8ebPX/YMHDzZ+QYAJcXQTNeHrKJ/EkT60LM0i3AAAmoeKR/kkjvSh5SHcAAC8cJQPLR3hBs0OQ3IBAPVBuEGzwpBcAEB9EW7QrDAkFwBQX4QbNEsMyQUA1JWhF84EAABoaIQbAABgKoQbAABgKoQbAABgKoQbAABgKoyWAgDUyMWTajJFA5ozwg0AoFq+LqbJhTTRnBFuAADVqngxTS6kieaOcAMAuCQupomWhA7FAADAVAg3AADAVDgtBQCok4tHT0mMoELzQbgBANSKr9FTEiOo0HwQbmAoh8PhGYEhVf4lCKD5qTh6SmIEFZoXwg0M43A4tGrVKrlcLq92i8Uiq9VqUFUAaoLRU2jOCDcwjNPplMvlUnx8vOx2u6ed8/YAgPog3MBwdrtd4eHhRpcBADAJhoIDAABTIdwAAABTIdwAAABToc8NmtTFQ78Z9g0AaAyEGzQZX0O/GfYNAGhohBs0GV9Dvxn2DQBoaIQbNDmGfgMAGhMdigEAgKkQbgAAgKlwWgoA0GAuHgVJnzoYhXADAKg3q9Uqi8Wi7OxsT5vFYlFSUhIBB02OcAP4sYpzDfFLG3Vls9mUlJTkNY9Vdna2nE4n7yk0OcIN4Id8/cqW/OeX9sWTSUpMKNlQbDab6d87aBkIN4AfqvgrW/KfX9q+JpOUmFASMBPCDeCn/PVXtq/JJCVOyQFmQrgB4JeYTBIwL+a5AQAApsKRGzQaOm0CAIxAuEGjoNMmAInpBmAMwg0aBZ02Af/m79MNwFiEGzQqOm0C/smfpxuA8Qg3AIBG4a/TDcB4hBvUScXOwhXReRgAYBTCDWqtqs7CFdF5GIAvXDkcjY1wg1qrqrNwRXxoAbhYVVcOHzRokNcPIT47UF+EG9QZnYUB1EbFTsZOp1MbNmzQ2rVrvZbzNaKq4qlwAhCqQ7gBADSZip2MqxpRlZ+f7zkyfCEEXXwqnCHlqA7hBgBgmIphp7r5cYYMGSKr1cqQclwS4QYA0Gz4mh9H4jQUaodwg0p8DfPmgwVAU2F+HNQX4QZeqrsm1IURDcxhA6A54LpVqArhBl58DfP2NaKBOWwAGIXrVuFSCDfwqeIw74rnwPmFBMAoXLcKl0K4QY1wDhxAc8JnEqpDuAHghanxYWZMBugfCDcAJFU9NT59GGAWvgZM8B43J8INvH7JMBLKf1Xsx0AfBphNxQETvMfNi3BjInWZn6aqXzKMhPJP9GNAS1fdD7QLj3FdPPMj3JhETeankSqHHV9DvzkHDaClqWp4eEX8ePMPhBuTqM38NL7OL/NLBkBLVtVlGyqqyY83Oh23fIQbk6lufhrOL8Nf0a/MP9T1tOrF7wmuQG4OhJtmylf/mYpq8mvC1x/7xX/IfNDD7OhXhqpwBXLzahbhZuHChZo7d67y8/MVGxurV155RX379q1y+XfeeUczZszQwYMHdfXVV+vFF1/UkCFDmrDixlVV/5mKavtroro/ZD7oYVb0K0NVuAK5eRkeblauXKnU1FQtWrRI/fr1U2ZmphITE7V371517Nix0vLbt2/X6NGjlZ6errvvvlvLly/X8OHDlZubqxtvvNGAV+CtJudqL7WMrw/jii78msjPz/cMabwU/pDhDyr+fTFCBtWp6amsS33G1uSzvqbrXUpdRsb6G8PDTUZGhsaPH6+UlBRJ0qJFi7RmzRotWbJE06ZNq7T8ggULNHjwYP3mN7+RJD333HPauHGjfv/732vRokVNWntFNZkgqqplLh7RVJMP46omXLvUERiG+sLMqhs1yNFJ1EVtRmFd/Dnuq+9OVevV5gh8de/x2vYLaqiQ1BzDlqHhprS0VLt27VJaWpqnLTAwUAkJCcrJyfG5Tk5OjlJTU73aEhMT9cEHH/hc/uzZszp79qzn/smTJyVJxcXF9ay+sn/96186deqUbrvtNs/RlC1btmjfvn2eIzBFRUVeyzidTmVnZ+u9997z2pbFYlFpaWm1dQ4ePNjrtYWEhKi8vLxRXhv8z6lTp3TmzBkdPnxYp06dMrqcGqn493UBfxuoj4qftRVV9zkeHx9fZbD29R1xKb7e43XZzoWafYWk6mquzXZGjBjRoAHnwt+v2+2+5LKGhpvCwkKVlZUpIiLCqz0iIkJ79uzxuU5+fr7P5fPz830un56ertmzZ1dqj46OrmPVTWfixIlGlwAAQJ001nfYqVOn1K5du2qXMfy0VGNLS0vzOtJTXl6uEydO6Cc/+YkCAgIMrKzhFBcXKzo6WocPH1ZYWJjR5ZgK+7bxsG8bB/u18bBvG09N9q3b7dapU6fUqVOnS27P0HATHh6uoKAgFRQUeLUXFBQoMjLS5zqRkZG1Wj4kJEQhISFebTU9bNfShIWF8QfXSNi3jYd92zjYr42Hfdt4LrVvL3XE5oLAhiqoLoKDg9W7d29lZWV52srLy5WVlaW4uDif68TFxXktL0kbN26scnkAAOBfDD8tlZqaquTkZPXp00d9+/ZVZmamSkpKPKOnxowZo86dOys9PV2SNHnyZN1+++2aN2+e/uu//ksrVqzQzp079cc//tHIlwEAAJoJw8PNqFGjdPz4cc2cOVP5+fnq2bOn1q1b5+k0nJeXp8DA/xxg6t+/v5YvX65nnnlG06dP19VXX60PPvigWcxxY5SQkBDNmjWr0uk31B/7tvGwbxsH+7XxsG8bT0Pv2wB3TcZUAQAAtBCG9rkBAABoaIQbAABgKoQbAABgKoQbAABgKoQbkzl48KDGjRunrl27qnXr1rryyis1a9YslZaWGl1aizdnzhz1799fbdq0Me1EkE1l4cKFiomJkdVqVb9+/bRjxw6jS2rxtmzZoqFDh6pTp04KCAio8np7qJ309HTdcsstCg0NVceOHTV8+HDt3bvX6LJM4dVXX9VNN93kmbgvLi5OH330UYNsm3BjMnv27FF5eblee+01ff3115o/f74WLVqk6dOnG11ai1daWqr77rtPjz32mNGltGgrV65UamqqZs2apdzcXMXGxioxMVHHjh0zurQWraSkRLGxsVq4cKHRpZjKJ598ogkTJugf//iHNm7cqHPnzmnQoEEqKSkxurQW7/LLL9cLL7ygXbt2aefOnYqPj9ewYcP09ddf13vbDAX3A3PnztWrr76q77//3uhSTGHZsmV68sknVVRUZHQpLVK/fv10yy236Pe//72k87OSR0dH64knntC0adMMrs4cAgIC9P7772v48OFGl2I6x48fV8eOHfXJJ5/otttuM7oc02nfvr3mzp2rcePG1Ws7HLnxAydPnlT79u2NLgNQaWmpdu3apYSEBE9bYGCgEhISlJOTY2BlQM2cPHlSkvhMbWBlZWVasWKFSkpKGuRySobPUIzGtX//fr3yyit66aWXjC4FUGFhocrKyjwzkF8QERGhPXv2GFQVUDPl5eV68sknNWDAAL+eFb8hffnll4qLi5PT6ZTNZtP777+v66+/vt7b5chNCzFt2jQFBARUe6v45XDkyBENHjxY9913n8aPH29Q5c1bXfYrAP80YcIEffXVV1qxYoXRpZjGNddco927d+uf//ynHnvsMSUnJ+ubb76p93Y5ctNC/PrXv9bYsWOrXaZbt26efx89elQDBw5U//79uahoNWq7X1E/4eHhCgoKUkFBgVd7QUGBIiMjDaoKuLSJEyfqb3/7m7Zs2aLLL7/c6HJMIzg4WFdddZUkqXfv3vr000+1YMECvfbaa/XaLuGmhejQoYM6dOhQo2WPHDmigQMHqnfv3lq6dKnXhUfhrTb7FfUXHBys3r17Kysry9PZtby8XFlZWZo4caKxxQE+uN1uPfHEE3r//fe1efNmde3a1eiSTK28vFxnz56t93YINyZz5MgR3XHHHbriiiv00ksv6fjx457H+GVcP3l5eTpx4oTy8vJUVlam3bt3S5Kuuuoq2Ww2Y4trQVJTU5WcnKw+ffqob9++yszMVElJiVJSUowurUVzOBzav3+/5/4PP/yg3bt3q3379urSpYuBlbVsEyZM0PLly/XXv/5VoaGhys/PlyS1a9dOrVu3Nri6li0tLU0///nP1aVLF506dUrLly/X5s2btX79+vpv3A1TWbp0qVuSzxvqJzk52ed+3bRpk9GltTivvPKKu0uXLu7g4GB337593f/4xz+MLqnF27Rpk8/3Z3JystGltWhVfZ4uXbrU6NJavF/96lfuK664wh0cHOzu0KGD+84773Rv2LChQbbNPDcAAMBU6IwBAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADNAPPPvusevbsaXQZiomJUWZmptFl+HTbbbdp+fLlRpcBg3zzzTe6/PLLVVJSYnQpaAEIN/AbY8eOVUBAgAICAtSqVSt17dpV/+///T85nc4mrSMgIEAffPCBV9vUqVOVlZXVaM+5efNmz2uv6rZ582Z9+umnevjhhxutjrr68MMPVVBQoF/+8pdGl6Jly5Z59llgYKCioqI0atQo5eXl6eDBg5fcz8uWLTP6JbRI119/vW699VZlZGQYXQpaAC6cCb8yePBgLV26VOfOndOuXbuUnJysgIAAvfjii4bWZbPZGvXim/3799ePP/7ouT958mQVFxdr6dKlnrb27dsrODi40Wqoj5dfflkpKSnN5gr3YWFh2rt3r9xut3744Qc9/vjjuu+++7R9+3av/fzSSy9p3bp1+vjjjz1t7dq1a7S6SktLm+z/sKrnOnfunFq1alXr7dVkvZSUFI0fP15paWmyWPj6QtWaxycF0ERCQkIUGRmp6OhoDR8+XAkJCdq4caPn8fLycqWnp6tr165q3bq1YmNj9e6773oeLysr07hx4zyPX3PNNVqwYEGl51myZIluuOEGhYSEKCoqShMnTpR0/rSPJI0YMUIBAQGe+xVPS5WXl+u3v/2tLr/8coWEhKhnz55at26d5/ELRwhWr16tgQMHqk2bNoqNjVVOTo7P1x0cHKzIyEjPrXXr1p59ceEWHBxc6bRUQECAXnvtNd19991q06aNrrvuOuXk5Gj//v2644471LZtW/Xv318HDhzwer6//vWvuvnmm2W1WtWtWzfNnj1bLpdLkuR2u/Xss8+qS5cuCgkJUadOnTRp0qQq/8+OHz+u7OxsDR061Ks9ICBAb7zxhkaMGKE2bdro6quv1ocffui1zFdffaWf//znstlsioiI0IMPPqjCwkLP46dOndIDDzygtm3bKioqSvPnz9cdd9yhJ598ssp6Ljx3ZGSkoqKi1L9/f40bN047duxQSUmJ1z612WyyWCye+3v27NHtt9+utm3bym63a8CAATp06FCVz/PUU0+pe/fuatOmjbp166YZM2bo3LlznscvvG/eeOMNde3aVVarVZJUVFSkhx56SB06dFBYWJji4+P1+eefV/uaDh8+rKSkJNntdrVv317Dhg3TwYMHPY+PHTtWw4cP15w5c9SpUyddc801nvfhypUrdfvtt8tqteqtt96q8fu34nqHDh3S0KFDddlll6lt27a64YYbtHbtWs96d911l06cOKFPPvmk2tcCEG7gt7766itt377d69dnenq63nzzTS1atEhff/21pkyZov/+7//2fJiWl5fr8ssv1zvvvKNvvvlGM2fO1PTp07Vq1SrPNl599VVNmDBBDz/8sL788kt9+OGHuuqqqyRJn376qSRp6dKl+vHHHz33K1qwYIHmzZunl156SV988YUSExN1zz33aN++fV7LPf3005o6dap2796t7t27a/To0Z4Q0VCee+45jRkzRrt379a1116r+++/X4888ojS0tK0c+dOud1uT3iTpK1bt2rMmDGaPHmyvvnmG7322mtatmyZ5syZI0l67733NH/+fL322mvat2+fPvjgA/Xo0aPK5//73//uCVYVzZ49W0lJSfriiy80ZMgQPfDAAzpx4oSk81/w8fHx6tWrl3bu3Kl169apoKBASUlJnvVTU1O1bds2ffjhh9q4caO2bt2q3NzcWu2fY8eO6f3331dQUJCCgoKqXM7lcmn48OG6/fbb9cUXXygnJ0cPP/ywAgICqlwnNDRUy5Yt0zfffKMFCxbo9ddf1/z5872W2b9/v9577z2tXr1au3fvliTdd999OnbsmD766CPt2rVLN998s+68807Pvqno3LlzSkxMVGhoqLZu3apt27bJZrNp8ODBKi0t9SyXlZWlvXv3auPGjfrb3/7maZ82bZomT56sb7/9VomJiTV+/1Zcb8KECTp79qy2bNmiL7/8Ui+++KLXEc3g4GD17NlTW7durXKfAZKkBrm2ONACJCcnu4OCgtxt27Z1h4SEuCW5AwMD3e+++67b7Xa7nU6nu02bNu7t27d7rTdu3Dj36NGjq9zuhAkT3Pfee6/nfqdOndxPP/10lctLcr///vtebbNmzXLHxsZ6bWPOnDley9xyyy3uxx9/3O12u90//PCDW5L7jTfe8Dz+9ddfuyW5v/322yqf+4Lk5GT3sGHDKrVfccUV7vnz53vV+swzz3ju5+TkuCW5Fy9e7Gl7++233Var1XP/zjvvdD///PNe2/3zn//sjoqKcrvdbve8efPc3bt3d5eWll6yTrfb7Z4/f767W7duldor1uZwONyS3B999JHb7Xa7n3vuOfegQYO81jl8+LBbknvv3r3u4uJid6tWrdzvvPOO5/GioiJ3mzZt3JMnT66ynqVLl7oludu2betu06aNW5JbknvSpEmVlr34//Vf//qXW5J78+bNNXrdvsydO9fdu3dvr+23atXKfezYMU/b1q1b3WFhYW6n0+m17pVXXul+7bXXfG73z3/+s/uaa65xl5eXe9rOnj3rbt26tXv9+vVut/v8eyYiIsJ99uxZzzIX3oeZmZle26vp+7fiej169HA/++yz1e6DESNGuMeOHVvtMgAnLeFXBg4cqFdffVUlJSWaP3++LBaL7r33XknnfwGfPn1ad911l9c6paWl6tWrl+f+woULtWTJEuXl5enMmTMqLS31nFI6duyYjh49qjvvvLPONRYXF+vo0aMaMGCAV/uAAQMqnVq46aabPP+Oiory1HDttdfW+fkruvg5IiIiJMnrSEtERIScTqeKi4sVFhamzz//XNu2bfMcqZHOn85zOp06ffq07rvvPmVmZqpbt24aPHiwhgwZoqFDh1bZh+LMmTOe0y3V1da2bVuFhYXp2LFjkqTPP/9cmzZt8tmX6cCBAzpz5ozOnTunvn37etrbtWuna6655pL7JDQ0VLm5uTp37pw++ugjvfXWW16v15f27dtr7NixSkxM1F133aWEhAQlJSV5/t98WblypV5++WUdOHBADodDLpdLYWFhXstcccUV6tChg+f+559/LofDoZ/85Cdey505c6bS6cOL19m/f79CQ0O92p1Op9c6PXr08NnPpk+fPp5/1+b9e/F6kjRp0iQ99thj2rBhgxISEnTvvfd6/R9LUuvWrXX69GmfrwO4gHADv9K2bVvPKaIlS5YoNjZWixcv1rhx4+RwOCRJa9asUefOnb3WCwkJkSStWLFCU6dO1bx58xQXF6fQ0FDNnTtX//znPyWd/+BtShd3wLxweqO8vLzRn6O653U4HJo9e7ZGjhxZaVtWq1XR0dHau3evPv74Y23cuFGPP/645s6dq08++cRnh9Lw8HD9+9//vmRtF2q5uI6hQ4f67CweFRWl/fv3V/u6qxMYGOh5H1133XU6cOCAHnvsMf35z3+udr2lS5dq0qRJWrdunVauXKlnnnlGGzdu1K233lpp2ZycHD3wwAOaPXu2EhMT1a5dO61YsULz5s3zWq5t27Ze9x0Oh6KiorR58+ZK27Tb7T7rcjgc6t27t956661Kj10cnCo+16XaL6Xieg899JASExO1Zs0abdiwQenp6Zo3b56eeOIJzzInTpzQlVdeWafng/8g3MBvBQYGavr06UpNTdX999+v66+/XiEhIcrLy9Ptt9/uc51t27apf//+evzxxz1tF/+yDQ0NVUxMjLKysjRw4ECf22jVqpXKysqqrCssLEydOnXStm3bvOrYtm2b11GG5urmm2/W3r17PV/+vrRu3VpDhw7V0KFDNWHCBF177bX68ssvdfPNN1datlevXsrPz9e///1vXXbZZbWq47333lNMTIzPo0LdunVTq1at9Omnn6pLly6SpJMnT+q7777TbbfdVuPnkc73Hbnyyis1ZcoUn6+h4uvp1auX0tLSFBcXp+XLl/sMN9u3b9cVV1yhp59+2tNWXefjC26++Wbl5+fLYrF4OqzXZJ2VK1eqY8eOlY4M1VZ937/R0dF69NFH9eijjyotLU2vv/66V7j56quv9Itf/KJeNcL86FAMv3bfffcpKChICxcuVGhoqKZOnaopU6boT3/6kw4cOKDc3Fy98sor+tOf/iRJuvrqq7Vz506tX79e3333nWbMmFGpU/Czzz6refPm6eWXX9a+ffs827jgQvi58IXty29+8xu9+OKLWrlypfbu3atp06Zp9+7dmjx5cuPtjAYyc+ZMvfnmm5o9e7a+/vprffvtt1qxYoWeeeYZSefniVm8eLG++uorff/99/rLX/6i1q1b64orrvC5vV69eik8PFzbtm2rVR0TJkzQiRMnNHr0aH366ac6cOCA1q9fr5SUFJWVlSk0NFTJycn6zW9+o02bNunrr7/WuHHjFBgYWG0nX1+io6M1YsQIzZw5s8plfvjhB6WlpSknJ0eHDh3Shg0btG/fPp8dpaXz77W8vDytWLFCBw4c0Msvv6z333//krUkJCQoLi5Ow4cP14YNG3Tw4EFt375dTz/9tHbu3OlznQceeEDh4eEaNmyYtm7dqh9++EGbN2/WpEmT9H//93812wkXqev798knn9T69ev1ww8/KDc3V5s2bfLaPwcPHtSRI0eUkJBQ65rgXwg38GsWi0UTJ07U7373O5WUlOi5557TjBkzlJ6eruuuu06DBw/WmjVr1LVrV0nSI488opEjR2rUqFHq16+f/vWvf3kdxZGk5ORkZWZm6g9/+INuuOEG3X333V6jRObNm6eNGzcqOjraqy/PxSZNmqTU1FT9+te/Vo8ePbRu3Tp9+OGHuvrqqxtvZzSQxMRE/e1vf9OGDRt0yy236NZbb9X8+fM94cVut+v111/XgAEDdNNNN+njjz/W//7v/1bqI3JBUFCQUlJSfJ4yqc6FowdlZWUaNGiQevTooSeffFJ2u90zX05GRobi4uJ09913KyEhQQMGDNB1111XZR+f6kyZMkVr1qzRjh07fD7epk0b7dmzR/fee6+6d++uhx9+WBMmTNAjjzzic/l77rlHU6ZM0cSJE9WzZ09t375dM2bMuGQdAQEBWrt2rW677TalpKSoe/fu+uUvf6lDhw55+kz5qm3Lli3q0qWLRo4cqeuuu07jxo2T0+ms05Gcur5/y8rKNGHCBM/fXvfu3fWHP/zB8/jbb7+tQYMGVRmEgQsC3G632+giAKA6+fn5uuGGG5Sbm9uoX2wlJSXq3Lmz5s2bp3HjxjXa86D2SktLdfXVV2v58uWVOisDFXHkBkCzFxkZqcWLFysvL69Bt/vZZ5/p7bff9pyCfOCBByRJw4YNa9DnQf3l5eVp+vTpBBvUCEduAPitzz77TA899JD27t2r4OBg9e7dWxkZGdVOKgig+SPcAAAAU+G0FAAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMJX/Dxs+PfG5VASTAAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
" ]