From 6349cb986cb95e885c16829dffaee03b246ff8e9 Mon Sep 17 00:00:00 2001 From: tmf97 Date: Sun, 7 May 2023 11:21:02 -0700 Subject: [PATCH 1/8] convert angular vel state into momentum, move ang vel to derived state, and propagate to gyro --- configs/attitude_dynamics_test.json | 5 +- configs/schema.json | 6 +- src/core/models/derived_models.py | 89 +++++++++++++++++++++++++- src/core/models/dynamics_model.py | 75 +++------------------- src/core/models/gyro_model.py | 34 +++++----- src/core/state/derived_state.py | 11 +++- src/core/state/state.py | 10 ++- src/utils/attitude_plot.png | Bin 0 -> 49130 bytes src/utils/matplotlib_util.py | 2 +- src/utils/plotting.ipynb | 94 ++++++++++++++++++++++++++++ 10 files changed, 228 insertions(+), 98 deletions(-) create mode 100644 src/utils/attitude_plot.png create mode 100644 src/utils/plotting.ipynb diff --git a/configs/attitude_dynamics_test.json b/configs/attitude_dynamics_test.json index 003b574..a2bf7b5 100644 --- a/configs/attitude_dynamics_test.json +++ b/configs/attitude_dynamics_test.json @@ -1,8 +1,9 @@ { "initial_condition": { "x": 100000000.0, - "ang_vel_x": 4.5, - "ang_vel_y": 1.0, + "h_x": 1.5, + "h_y": 0.2, + "h_z": 0.0, "time": 0.0, "quat_v1": 1.0, "quat_v2": 0.0, diff --git a/configs/schema.json b/configs/schema.json index b3fac1a..35d65ba 100644 --- a/configs/schema.json +++ b/configs/schema.json @@ -42,13 +42,13 @@ "initial_condition": { "type": "object", "properties": { - "ang_vel_x": { + "h_x": { "type": "number" }, - "ang_vel_y": { + "h_y": { "type": "number" }, - "ang_vel_z": { + "h_z": { "type": "number" }, "quat_v1": { diff --git a/src/core/models/derived_models.py b/src/core/models/derived_models.py index 3afec12..cb9499f 100644 --- a/src/core/models/derived_models.py +++ b/src/core/models/derived_models.py @@ -1,11 +1,11 @@ from abc import abstractmethod from core.state.state import State from utils.astropy_util import get_body_position -from typing import Dict import numpy as np from utils.constants import BodyEnum from core.models.model_base import Model -from typing import Union, Tuple, List +from typing import Union, Tuple, List, Dict +import logging class DerivedStateModel(Model): @@ -101,4 +101,87 @@ def evaluate(self, t: float, state: State) -> Dict[str, np.ndarray]: } -DERIVED_MODEL_LIST: List[DerivedStateModel] = [DerivedPosition(), DerivedAttitude()] +class InertiaModel(DerivedStateModel): + def evaluate(self, _: float, state: State) -> Dict[str, Union[float, int, bool]]: + """ the _b postfix means the value is in the spacecraft's body frame""" + fill_frac = state.fill_frac + + dcm = np.array([[0, 1, 0], [0, 0, -1], [-1, 0, 0]], dtype=np.int32) + dcmT = np.transpose(dcm) + # not sure why we do the above. Did i do that? + # probably to get the (incorrect) solidworks frame into the spacecraft + # body frame + + # Inertia tensor when full. Structure is: + # [[Ixx, Ixy, Ixz], + # [Iyx, Iyy, Iyz], + # [Izx, Izy, Izz]]. + # Units are (kg * m^2). + idf = ( + np.array( + [ + [933513642.20, 260948256.18, 430810000.30], + [260948256.18, 1070855457.07, 387172545.62], + [430810000.30, 387172545.62, 629606813.62], + ], + dtype=np.float64, + ) + * 1e-9 + ) + # inertia tensor in correct body frame + idf_b = np.matmul(np.matmul(dcm, idf), dcmT) + + # Inertia tensor at 125 mL. Structure is: + # [[Ixx, Ixy, Ixz], + # [Iyx, Iyy, Iyz], + # [Izx, Izy, Izz]]. + # Units are (kg * m^2). + idi = ( + np.array( + [ + [855858994.14, 229481961.55, 377087149.13], + [229481961.55, 963124288.81, 353943859.15], + [377087149.13, 353943859.15, 559805590.96], + ], + dtype=np.float64, + ) + * 1e-9 + ) + # inertia tensor in correct body frame + idi_b = np.matmul(np.matmul(dcm, idi), dcmT) + + # Determine inertia tensor for Oxygen via linear interpolation as a function of fill + # fraction. + + # TODO: determine whether linear fit is accurate enough + ineria_matrix_b = (idf_b - idi_b) * fill_frac + idi_b + + # now that we know our moment of inertia (I matrix), we can calculate our angular + # velocities: + # page 14 of the 4060 Attitude Dynamics handout: + # h = I dot omega + # --> omega = inverse(I) matmul h + + angular_momentum = np.array([[state.h_x, state.h_y, state.h_z]]) + + angular_velocity = np.matmul(np.linalg.inv(ineria_matrix_b), angular_momentum.T) + logging.info(f"Inv: {np.linalg.inv(ineria_matrix_b)}") + logging.info(f"h: {angular_momentum}") + logging.info(f"w: {angular_velocity}") + return { + "Ixx": ineria_matrix_b[0][0], + "Ixy": ineria_matrix_b[0][1], + "Ixz": ineria_matrix_b[0][2], + "Iyx": ineria_matrix_b[1][0], + "Iyy": ineria_matrix_b[1][1], + "Iyz": ineria_matrix_b[1][2], + "Izx": ineria_matrix_b[2][0], + "Izy": ineria_matrix_b[2][1], + "Izz": ineria_matrix_b[2][2], + "ang_vel_x": angular_velocity[0][0], + "ang_vel_y": angular_velocity[1][0], + "ang_vel_z": angular_velocity[2][0], + } + + +DERIVED_MODEL_LIST: List[DerivedStateModel] = [DerivedPosition(), DerivedAttitude(), InertiaModel()] diff --git a/src/core/models/dynamics_model.py b/src/core/models/dynamics_model.py index 11bcd49..7cafe88 100644 --- a/src/core/models/dynamics_model.py +++ b/src/core/models/dynamics_model.py @@ -5,68 +5,7 @@ from core.state.state import State from core.state.statetime import StateTime from utils.gnc_utils import quaternion_derivative - - -class InertiaModel(DerivedStateModel): - def evaluate(self, state: State) -> Dict[str, Any]: - fill_frac = state.fill_frac - - dcm = np.array([[0, 1, 0], [0, 0, -1], [-1, 0, 0]], dtype=np.int32) - dcmT = np.transpose(dcm) - - # Inertia tensor when full. Structure is: - # [[Ixx, Ixy, Ixz], - # [Iyx, Iyy, Iyz], - # [Izx, Izy, Izz]]. - # Units are (kg * m^2). - idf = ( - np.array( - [ - [933513642.20, 260948256.18, 430810000.30], - [260948256.18, 1070855457.07, 387172545.62], - [430810000.30, 387172545.62, 629606813.62], - ], - dtype=np.float64, - ) - * 1e-9 - ) - idf_b = np.matmul(np.matmul(dcm, idf), dcmT) - - # Inertia tensor at 125 mL. Structure is: - # [[Ixx, Ixy, Ixz], - # [Iyx, Iyy, Iyz], - # [Izx, Izy, Izz]]. - # Units are (kg * m^2). - idi = ( - np.array( - [ - [855858994.14, 229481961.55, 377087149.13], - [229481961.55, 963124288.81, 353943859.15], - [377087149.13, 353943859.15, 559805590.96], - ], - dtype=np.float64, - ) - * 1e-9 - ) - idi_b = np.matmul(np.matmul(dcm, idi), dcmT) - - # Determine inertia tensor for Oxygen via linear interpolation as a function of fill - # fraction. - - # TODO: determine whether linear fit is accurate enough - ioxy = (idf_b - idi_b) * fill_frac + idi_b - - return { - "Ixx": ioxy[0][0], - "Ixy": ioxy[0][1], - "Ixz": ioxy[0][2], - "Iyx": ioxy[1][0], - "Iyy": ioxy[1][1], - "Iyz": ioxy[1][2], - "Izx": ioxy[2][0], - "Izy": ioxy[2][1], - "Izz": ioxy[2][2], - } +import logging class KaneModel(DerivedStateModel): @@ -108,13 +47,17 @@ def d_state(self, state_time: StateTime) -> Dict[str, Any]: """ s = state_time.state - # d = state_time.derived_state + d = state_time.derived_state cur_quat = np.array([s.quat_v1, s.quat_v2, s.quat_v3, s.quat_r]) - angular_vel = np.array([s.ang_vel_x, s.ang_vel_y, s.ang_vel_z]) - + angular_vel = np.array([d.ang_vel_x, d.ang_vel_y, d.ang_vel_z]) + logging.info(cur_quat) + logging.info(angular_vel) d_quat = quaternion_derivative(cur_quat, angular_vel) - # TODO: Use angular momentum as state variable and for most dynamics evaluation + # TODONE: Use angular momentum as state variable and for most dynamics evaluation # then calculate angular rates from momenta b/c inertia matricies change over time + # TODO: track external moments somewhere in state + # external_moments = np.array([[0,0,0]]) + return {"quat_v1": d_quat[0], "quat_v2": d_quat[1], "quat_v3": d_quat[2], "quat_r": d_quat[3]} diff --git a/src/core/models/gyro_model.py b/src/core/models/gyro_model.py index ef280c9..75e1449 100644 --- a/src/core/models/gyro_model.py +++ b/src/core/models/gyro_model.py @@ -27,22 +27,28 @@ def evaluate(self, state_time: StateTime) -> Dict[str, Any]: Dict[str, Any]: The augmented angular velocities """ - # setting up the initial angular velocity using x, y, z components - ang_vel_i = np.array([state_time.state.ang_vel_x, state_time.state.ang_vel_y, state_time.state.ang_vel_z]) - - # adding initial angular velocity to gyro_bias - ang_vel_d = ang_vel_i + self.gyro_bias - ang_vel_d = np.random.normal(loc=ang_vel_d, scale=self.gyro_noise, size=3) - - #filling in the array, applying gyro sensitivity + # smush the x, y, z components from state into a vector + # (because vectorization is cool as hecc) + ang_vel_true = np.array( + [state_time.derived_state.ang_vel_x, + state_time.derived_state.ang_vel_y, + state_time.derived_state.ang_vel_z] + ) + + # add gyro's bias + ang_vel_biased = ang_vel_true + self.gyro_bias + # add gyro's noise + ang_vel_observed = np.random.normal(loc=ang_vel_biased, scale=self.gyro_noise, size=3) + + # filling in the array, applying gyro sensitivity for i in range(3): - ang_vel_d[i] = self._parameters.gyro_sensitivity * int( - self._parameters.gyro_sensitivity / 2 + ang_vel_d[i] / self._parameters.gyro_sensitivity + ang_vel_observed[i] = self._parameters.gyro_sensitivity * int( + self._parameters.gyro_sensitivity / 2 + ang_vel_observed[i] / self._parameters.gyro_sensitivity ) - #return components of the angular velocity + # return components of the angular velocity return { - "ang_vel_x": ang_vel_d[0], - "ang_vel_y": ang_vel_d[1], - "ang_vel_z": ang_vel_d[2], + "ang_vel_x": ang_vel_observed[0], + "ang_vel_y": ang_vel_observed[1], + "ang_vel_z": ang_vel_observed[2], } diff --git a/src/core/state/derived_state.py b/src/core/state/derived_state.py index 3b21723..6058f26 100644 --- a/src/core/state/derived_state.py +++ b/src/core/state/derived_state.py @@ -23,6 +23,11 @@ class DerivedState: Izy: float = 0.0 Izz: float = 0.0 + # angular velocity in rad/s + ang_vel_x: float = 0.0 + ang_vel_y: float = 0.0 + ang_vel_z: float = 0.0 + # Kane damping constant kane_c: float = 0.0 @@ -44,13 +49,13 @@ class DerivedState: # earth to the craft r_ec: np.ndarray = np.array((0.0, 0.0, 0.0)) - # attitude (unit) vector of spacecraft in ECI + # attitude (unit) vector of spacecraft's +X vector in ECI attitude_vector: np.ndarray = np.array((0.0, 0.0, 0.0)) - # Azimuth angle of the spacecraft frame in ECI. "Theta" angle in spherical coords + # Azimuth angle of the +X spacecraft frame in ECI. "Theta" angle in spherical coords azimuth: float = 0 # radians - # Elevation angle of the spacecraft frame in ECI. "phi" angle in spherical coords + # Elevation angle of the +X spacecraft frame in ECI. "phi" angle in spherical coords elevation: float = 0 # radians def update(self, derived_state_dict: Dict) -> None: diff --git a/src/core/state/state.py b/src/core/state/state.py index a8f6e71..19abf49 100644 --- a/src/core/state/state.py +++ b/src/core/state/state.py @@ -17,10 +17,10 @@ class State: # primitive state fill_frac: float = 0.0 # TODO, decouple with fuel_mass - # angular momentum (kg*m^2/s) - ang_vel_x: float = 0.0 - ang_vel_y: float = 0.0 - ang_vel_z: float = 0.0 + # angular momentum (kg*m^2/s, or N*m*s) + h_x: float = 0.0 + h_y: float = 0.0 + h_z: float = 0.0 # angular position (quaternion) quat_v1: float = 0.0 @@ -116,8 +116,6 @@ class ObservedState(State): fuel_mass: float = 0.0 chamber_temp: float = 0.0 - - # derived state (Newtons) force_earth: float = 0.0 force_moon: float = 0.0 diff --git a/src/utils/attitude_plot.png b/src/utils/attitude_plot.png new file mode 100644 index 0000000000000000000000000000000000000000..d9b96b703ea232de7bf0c9c108a6b520ce0dc7ec GIT binary 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df["observed_state.ang_vel_x"].to_numpy() self.fig_2d = plt.figure() diff --git a/src/utils/plotting.ipynb b/src/utils/plotting.ipynb new file mode 100644 index 0000000..dac051f --- /dev/null +++ b/src/utils/plotting.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from utils.plot import Plot\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wjQiXx27+DwGsw7Qo6hgh5CJME4Yw0Zm65MD29qOjozxkraysxNKlSyM+Z7jk4PV60d7eDkrpLEKKtYHJYttWhANCiJ/KkCU3x8bGuPt0qMnNUJFscrDZbH7GxKmCmbzDfxBCfgggG8CPAUwCuDtQTJWS5CD0XTCZTDCZTNiyZUtIE6znQzjkYLVa0dLSgtLSUm5LzxDrasO5Tg6BCJbcHB8fn5XcZG3pkSAVyCEWyepYYibq8M78+weY3rIAAAghXyWEtFFK2TYjtchBqF1wuVxoa2sDIQQlJSVREwMQOjmMjIygt7d3zrxGrPUS5zsUCgWWLl3KDWWZ7RurRimVSh5ZhComStbvh103FSMHQsglAJSYVkNOAbDO/N0H4CEA/8AnOYjUIQehdmF8fBxnzpzBqlWrQAiJesIUw0Lk4PP5cPr0abhcrnnzGrH6pGfnON8ih/kQaPvGkpvj4+MYGhqC1+vljU4LJTeToTFgEWUqRg4A/gTgNAAXgEwAckwnO30AKjG9veBIOjkE2rf19PTAbDZzCbTJZILX643JteYjB7vdjubmZhQXFy9YHo3Fw+zz+fDRRx9xRZ1UKoXb7UZaWlpU510Ii42EhMlN5rcwMTHBk5tCP4Z4JTcjgdVqTbnIAcA4gHsopc2EEAmmn385psmiG4BDeHBSyUGoXXA4HGhpaYFGo8GmTZv8+uNjJRCa61xM9VdbW4u8vLyIzxMq9Ho97HY7Nm7ciIyMDGi1WhiNRjQ3TwvYVCoV1Go1lEplXG72VFLthQupVOqX3HS5XH6j9di0rES3pQciFbcVAPZgplxJKfVhmhRcAEAIeRXT2w2OpJED0y5QSjE2Noaenh7U1NRApVL5HSeVSmMWOQSey+fzobOzEzabLSyj2UhH4lFK0d3dDZPJhMzMTOTl5cHtdkOpVMLr9WL58uU8kz8yMoIzZ874eUbGIu9yrkEul/slN9kAHNaWnpWV5deWHk8Iychms6GsrGyBn0gsKKUfzvN/dwd+L+HkELiNYI7AW7ZsCRpSx5IcJBIJP5fD4UBTUxMKCgqwevXqsD5hIpE7u91uNDc3Izs7Gxs3bsSxY8f8zic0mGU3O52Z9iRUHubk5PCbPd5bkMUINi1raGgImzZt4snNzs5OOJ1ObiYbTnIzVAjJwW63L3oyTyg5CH0XbDYbWltbUVJSgrKysjkfzliTg9vt5k7QoTRrBUO4OQemrqyqqgqrSYsIpj2xturJyUmMj49jcHBarxJvMdFiRrDk5uTkJEwmE4aGhuDz+fza0qNVvQqbrqxWK5RKZSxeRtKQMHIQSqCHhoag1WpRV1e34BsYy5wDIQR6vR4GgwGbNm2K2NAknDUNDw+jr69v1rTuwHWFQjYSiYTPWwA+EROx/bZCoeCdlHM5Oy22hGQkmOs1Ct8/YXKTWb6x5GZ+fn5E+R4hOdjt9lTMOYSFuJODULvg8XjQ3t4OmUyGrVu3hsTUsYocnE4nenp6IJPJ5vV8CAWhPMxsy+R0OheUe0da/RC3IMERqsYhWHJTmO9JT0/n719WVtaC94yQHFK0lBkW4koOQu2C2WxGe3t72P4Hscg2Myfo4uJiuN3uqM+5UOTgcDjQ3NyMgoKCkAbnxOI1hroFCaUas9gRqQBKLpejqKgIRUVFAD5Jbvb19fHqg7AtPRCB5CBuK4JAmHRkNmoGgwHr169PaJIm0AnabrdjbGws6vPO90nPvCRDmb8pPEesw/25tiBjY2OwWq1obm5ecAsSCyRjGxMrdSRLbgqVm+Pj437JTWFkFkgO4rYiAJRSmM1myGQyeL1etLa2IicnJywbtViAOUFnZ2dzJ2iHwxGT/EWwyIFSioGBAYyOjobkJRmsVyOeYFsQtVoNm82GFStWJGQLcq54OQiTm+Xl5X6RmVarhc/nQ3p6OiQSCVwuV0SRAyHk85h2lpYCeIZS+pOA//8qgMfxibnLU5TSZ6J/dcERU3Jg1Yiuri7k5ORAq9Vi9erVCfftn5iYQFtb2ywn6FglNwMfZkaCLJ8R6o2ZLPl0Iqsg5wo5BCIwMvN4POjr64PZbMauXbug1WrxxBNP4IorrsAFF1yw4HoIIVIAvwJwBaZ9IU8QQg5RStsDDn2JUnpn7F/RbMSUHFj932KxwGq1RlURCEQoNxmlFP39/RgdHQ26hYkHOdjtdjQ1NaGsrAylpaURnSORCHbNWFRB5rveuUgOgZDJZMjMzIRCocChQ4dwwQUXYPXq1XjhhRdwwQUXhHKKLQC6KKU9ADDj9LQDQCA5JAwxjxxOnjwJmUyGysrKmBFDKC3Sbrcbra2tUCgU2LJly5xDVGO5rWCy67Vr14Y9mDfR24q5rh0MsayCJIMcaBJmcwLTEaRMJgOlFGlpabjhhhtw4403hvrjwVylg82juI4QcjGmnZzupZQOBjkmJoh55LB+/Xr09fXF1DCVlTPn+oWbzWa0tbUtKDKKZeQwMjICr9cbluw68ByLoSsz2BbEYrHAaDSGtAVJxoM6370S7+uyD8Q4keIbAF6glDoJIXsB7APw2VhfhCHmCUk2cdjj8cTsnIwcAj+lKKUYHBzE0NAQ6uvrF6wrx4Ic3G43hoaGkJmZ6Tc5O1ykMiHMB2GXJPDJODjm6hS4BTlfthXAJ6XMCCd8LegqTSkVehc8A+CnES00RMSllBlLyTMQ/KFmTtBSqTRkJ+hoyWFychKtra1cLxALIRWbbbAYiQKYPQ6Ojdjr7u7G1NQUMjMz4XK5EtKOzpBMcpBIJPx1h4kTAFYSQpZhmhRuBPAl4QEBk622A+iIds3zIS7kIJPJ4HK5Yna+QLJhvQoVFRUoKSkJ+TzCxqtwMTw8jP7+ftTX12N8fDzqCCSZOYd4gm1BSkpK+NChvr4+v3b0SKZmhYNk5xwiUUdSSj2EkDsB/AXTpcznKKVthJB/B/ARpfQQgG8SQrYD8GDam+GrsX0F/og5ORBCIJVK47KtAIChoSH09/eH1JcRiEhuGKE71ObNmyGTyWAymWKSu0hWsi5RkEgkXBtQW1vLtyAskcu2ICqVCpmZmTF7L5KZc2AWcRE6Tx/GtPW88HsPCf79AIAHol5oiIhb5BDLbQVzSWptbeUDZRIx+4C1dRcWFvq5Q7Huzmgg9IRItWpFLCEkwLm2ID09PZiamuJekfn5+VFtQZK5rZDJZBHb0qca4pZziGXk4PF4cPr0aVRWVs5ygo4XxsfH0dHREVQGLXpIho75oqPALQjzitRqtaCU8ipIuFsQn8+XkA+PQAgjh8UunQbiuK2IVeQwOjoKvV6PioqKhDjrMCHV2NgY97EMRKROUEIQQuBwOOBwOM6JT5m5EOrWKdAr0uPx+A3uZR2SzBFrvnMmK+fg8/lSemZFuEjZbQXb6zudTpSXl8dMUDUfWAVkIRl0JE5QgWDDePPz89Hf34+pqSn09/cjPz8/ZoNdUgGR5lVkMpnfFoQJsULZgiQr5yC0pT8XCD8ltxXMCXrJkiWorq7G4OBgTHMYwWCz2dDc3Izy8vIFKyDRkAPTZlitVtTV1SEnJwcejwfNzc2Qy+V8sItSqYRarY56/51sxCrpmpGRgZKSkllbEOboJNyCJHumSIo6T4eNlNtWBHOCZgnJWCHwhmXXZA/rQoh0W+Hz+fh4PY1G46eslEgkflOrmQpRq9UCAP+UzMnJiephS3RuIx4VmYW2IMxYSKFQxLQKEirOBRcoIE6RQyS/DJ/Ph7Nnz8Jqtc6SJLOR5rFaG7thKaXo6uqC2WyOu/u00+lEY2MjioqKUFFRgba2tjkTkoQQ5OTkICcnB8uWLYPb7cb4+DiGh4dx+vRpZGdnc7KIZLuVrGpFvBC4BWlvb4dUKp21BYmHqawQQufpRHcixwNJH2oD+DtBb9iwYdbNFGuTWZ/Px0P53NzcsGXQ4W4rzGYzWltbZ1U+Qq1WpKWlcYciZjpiNBrR3t4Or9cbcVY/EUiGloMQgqKiIiiVSlBKeTt6sC1IrN4v4e8vlXMOhJCvAfjazJe5APoopZcGOzYu24pwYDAYcObMmXmdoKNRNgY7l9lsxunTp2f5PYSKcEqPTFkZ2EIe6dZEaDpSUVExK6TOyMjw621INpLdW0EIiVkVZD4sFos4SunTAJ4mhKQB+BuAn891bFwjh/luDBbST0xMLOj7wJpZYgGXy4XTp0+joaEhYnYPpUeDDcyZmpriysq5EI3OQRhSs/Zqo9GIzs5OuFwu5OXlxcx6PRKkWst2qFWQcLcgi9Ai7pcA/kYpfWOuA+JGDuyBDnZDOp1ONDc3Iy8vz2/03XznilVp1O12Y+PGjVGFfQs9zC6XC83NzVCpVHMOzIlHy7awvbqsrMzPer2npwdyuZxn8xP10CY7clgIwiqIMBEc7hZkMTlPz9jNVQCY11EqruTg8XhmkQNTHoZjHxctObCcRlFRUdTdlMD8kQNrCluxYsW8W5ZEqCIDrdenpqag0+lgt9tx4sQJPtBFpVLFTVGYjLJipDqHwEQw24Lo9foFtyCLZVtBCNkI4DsAPk2n52XOibjlHAIfaEopent7odfr51QezoVoWq0ZGbGcBuvPiAZzPdhjY2Po7u6ed4BNsHMk6pM1IyMDS5YswcTEBOrq6mA2mzE+Po7+/n5IpVLk5+dDrVaHNKMhVKTatiIcBNuCmEwm9Pb2wm63Q6lU8shiEQ20uRNAPoC/z/xePqKU3hHswLiVMoUqSaETdCQu1JFEDnPJoGNh+BKYTKQzA3InJiawefPmkERLjByS8fAA8BtdD0xv9RhRWK1WbgUXrQgrWduKeFwz0KqeCbFaW1vhcrkgk8lw/PjxiHIOIThPpwP4A4CNAIwAbqCU9oX7Giilt4V6bNy3FXM5QYd7rnDIwePxoLW1FXK5fBYZxYIchKVMj8eDlpaWsJ2h2HGpIpNOT09fUISlVquhVCpT3mA2Eb0Vwi1IZWUlRkZGMD4+jt///vfo7e3FddddhyuvvBJ33333gmuZubcXcp7eA8BEKV1BCLkRwGMAbojPq5tG3MhBIpFgeHgYFosl6mE24ezPF5JBxypy8Pl83Hm6oqICS5cuDfs8qdqJOZcIa2hoCJOTk2GJsJLVBJUM5OTk4IknnsCxY8fw9NNP44MPPgjptR8/fhxY2Hl6B4CHZ/79CoCnCCGExvEmigs5eDweGAyGeZ2gw0GonzxjY2Po6uqaVwYdq8jB5XLh1KlTETlPs3MkixzC/SSfS4TV1ta2YEY/WdumRIMlQdnrLS8vR0VFRUg/OzQ0BCzsPM3dqWdco8wA1AAMUS9+DsSFHNra2pCTkwO1Wp2QTw1KKc6ePQuLxbKgDDpacqAzk60cDgcuvvjiiLtFk0UOsWg1D0WEpVaroVAozhty8Pl8/L47V15zXMihvr4eg4ODMTV8mQtMU5CbmxtUeh2IaMjB6/VyU9vMzMyo2sjPFYOXQBGW3W7H+Pg415SwgTnCbP65CI/Hg4yMjIjKqDPb33mdp/GJO7WWECLDtPTZiDgibtUKqVQaU5PZYGA9C+EkOyMlB4fDgcbGRixduhTl5eU4evRo2OcQ4lwhByEIIcjKykJWVhYXYZ05cwZWqxUnT56EXC7nUUU8h/cmA0zwZ7fbwxZAbd68GVjAeRrAIQC3AvgAwC5MqxvjegMlpJQZKwjDNa1Wi8HBwbCTnZH0abDJ2fP1f4SLc5EcAiGVSpGRkcEji8DJWfEQYSXrPWWRUSTqyJnXvpDz9LMA/osQ0oVp5+mQR2lFiriXMmN5PhaydXR0wOPxhDyvQohwzWG1Wi20Wm1Ik7PDQbI+NZPp5xBo2BIPEVay9vvRkAMQkvO0A8D1US80DMSVHGLtQG2329HR0YElS5agvLw8opsg1G2FsBdj8+bNMd8vM62Ew+EISy0aq2snCnM9rHOJsPr6+mCz2SIWYSV72tW54gIFxHlbEWsH6qamJqxdu5bfUJEgFHJwuVxoamqCRqPxs6SPJXw+H0ZHR6HVauHz+eB0OmEwGKBSqc6pxF2on+SBIizmwRCuCGuuZr94I9rIIRWR8pEDpRR9fX2w2+1oaGiIihiAhclhcnISLS0tWLVqFdfUxxoulwsDAwNQKpVYuXIlCCE4ceIE1+3L5XLeMJUKngzRIJIwP9CDgYmwtFotLBYLF2Gp1epZZet4SacXgkgO4Zw4BpEDk0Gnp6ejoKAgJp8I83lDjI6OoqenJyqvh4VgsVjQ3NyMgoICfiPbbDZIJBJUVlYCmCYPk8nk58mgVquRl5e36NSGsVBIBoqwrFYr72lIFXNZofN0qnZkhou4ljKjiRysViuam5tRWVmJpUuXor29PSaGL8GqFUxEZbVa4zpNS6/Xo7OzE2vXroVMJsPAwAA++ugjuFwulJWVwe12Qy6XQy6X+z0MZrMZBoMBXV1dyMjI4FFFIuz6o0WsE4SEECiVSiiVyqAirLS0NHi93oTnctjrFCOHEBDNDcFan4XzMGO1TQncVrjdbjQ3NyMnJwfr16+PS0jKOkR1Oh02bNjAySc9PR0ZGRlYt24dzGYzurq64HQ6+Schk4Dn5eVxJ26Hw8FLq8w/Uq1WIzc3N6S1J7paEe8wP1CEpdfrMTg4yJPJLOrKzc1NSC5ikbhAhYSUMJhloJSis7OTO1ALs9TxIAebzYampiZUVVVhyZIlYZ1H6GI9H4R29OvXr+dt2mfOnAEArF+/HhKJBDk5OVw4ZDKZeKSQmZnJtxRyuRwKhQLFxcVYsmQJKKWYmJjAyMgIzpw5g+zsbD7rYj4JeSpUK+IBQgjkcjmUSiVWrVrFnbCMRiO6u7vjKsISOk+Hey+lKlLGmp5VCFQqVVwdqBk5sBB/3bp1Ee0RWSlyvk8jYdWjtLQUwHTiqqWlBRqNJmg5ViqVQqPRQKPR8CYnZsIrjBSysrJAKeXlPuATP8SWlhYAkbdZxxKJ1h0Icw7BnLACRViMeKPZSgY6T4uRQ4gI5eZgMuj5KgSx6KYEph9qq9WKvr6+sGZVBDvPfCE6y5msWLEC+fn5vO+gtbUVy5cvD6kSImxyqqyshMfjgdFoxMjICCYnJ7kZKru5mcho6dKl8Pl8MJlMfLqWUqmERqNJuKYimeQQiLlEWH19fVGJsITXTHEXqLAQV3JgD/R8n66hyqBjETl4vV50dHTA6/Vi48aNUWW15yMr9km/du1a/pomJibQ2dmJ2traiLPZMpnML1FpsVhgMBjQ1tYGAHxLwUJmjUYDtVrNE2XsQXA6nejv74+5JVwwpBI5CBGKCEutVkOlUi0owhI2W1mtVjEhOR8CfSSDkQPbi/t8vpBk0NE2ck1NTaGxsRElJSVwOp0x8ZgIFjn09/djdHSUJx4JIRgaGsLIyAjWr18fswqD0JClqqoKLpcLBoMBAwMDsNlsPGRm4/NYQ5RarUZfXx9kMhnXj7Bj4yHASlVyCEQwEZbRaMTg4LTNwnxbNK/Xy7cl4rYi1JPPaB0CQ/epqSk0NTWhuLg4ZBl0NJEDM5mtra1Fbm4uV91Fg8DIgcmtPR4PNmzYwL/f2dkJp9OJDRs2xDVbLpfLsXTpUr6lYOXPvr4+pKWl8Qan4eFhZGdno6CggHeyMi/E3t5epKWl8X16NO5dDIuFHIQQirAALCjCEkYOos4hBMyldTAajTh9+jRqamrCUjtGknOgMxOtR0ZGwna8XgjCyMHtdqOxsRFqtRplZdNt+V6vF62trcjJycGqVasS+oAEhswOhwNjY2NobGwEIQSFhYXcRBYAlEolN29xu90wmUw8accSoJEKsBYjOQQimAjLaDRyEVZmZia8Xi+3Dow0ciCE5AN4CUAlgD4AuymlpiDHeQG0zHw5QCndHtEFF0BCIgfgE2t6g8Gw4ISrYAg3chBuWzZt2hTzT21GVqwcunz5cqjValBK4XA40NLSgoqKipQoaxFCMDY2hlWrVqGwsNCvVMpEVfn5+ZDJZEhLS0NhYSGPKoQCLIVCwaOKUIk2GeQQLxEb4C/CYknigYEB6PV6XH/99dBqtXj55ZdxzTXXoLy8PNzT3w/gXUrpTwgh9898fV+Q46YopQ1RvpQFEVdyYA80c2hWKBTYtGlTRMweDjkETrSOx81JCIHJZMLAwABqa2t5aXFychIdHR2oqamJyFsy1rDZbLxXhJU8haVSu90Og8HAt0SsNKpUKuHz+Xh4TQjhAiwmMFKpVNBoNMjJyZnzd3ouRA7zQSaT8Qhs//79uOiii+B0OnHvvfdi//794a5lB4BLZv69D8B7CE4OCUFctxUymQw2mw2dnZ1cBh0pQt1WzDXROtaw2+3cWTstLQ2EEIyOjmJwcBANDQ0p0TA1MTGBjo4O1NXVBQ11hYlKJkUeHx/H2NgYOjs7uagqNzcXaWlpSE9Px5IlS1BUVMTPPzo6is7OTi7WCmyGOtfJAQCf7CaRSCCVSnHPPffg3nvvjeRURZTSkZl/jwIomuM4BSHkIwAeAD+hlL4WycUWQlwjB1Y+27hxY9RJmlAih7kmWscSPp8PZ86cgcvlQk1NDS9zdXd3w2q1+smjk4mxsTH+XoS6BZDJZHxLwfbWer2euSP7qQsppX55DTYNiu3D8/PzodFoEt4lmQxyYOX6UIjw8ssvx+jo6Kzvt7W17RB+TSmlhJC5hDQVlNIhQkgVgL8RQloopd2Rrn8uxO0u7urqgsViQUVFRUyyt/ORA5MjhzLROhq43W40NTUhLy8PRUVF6O3tRWFhIQwGAzIzM1FfX58Svoj9/f0wGo1REZVwb81Kpay0Z7VaucUbs6MXToNiAiytVgubzYaOjg5oNJqop2eFgmTN5mTv80IE8de//nWu/3qdEDJGCCmmlI4QQooB6IIdSCkdmvm7hxDyHoD1ABYPORQXF0OhUMDhcMTkfHO1Wgsf2IaGhpAfznDDXbvdjsbGRixbtoy3W09MTKC9vZ1bz/X19aGgoCDuwqK5wHpT3G43GhoaYvqQyOVyrgPw+XyYnJyEwWBAf38/ZDIZT2qmp6dDIpHwry0WC0pKSjhZEEJ4VBGP9ylZ5JCenh4Lh21mIvuTmb9fDzyAEKICYKeUOgkhGgAXAfhpNBedC3Ejh+zsbFgslphZxQVrtQ51onUgQm2aYmA6CaZ4ZIk8pnjMz8/nIqTu7m5MTU0hLy8PBQUFUKlUCblZWek0Kysr7qVTZjefl5eHFStWwOFwwGAwoKenB1NTU7z8OTU1BZlMxvMapaWl8Hq9fkpEoQArFhFfssiBGb1EKYD6CYCXCSF7APQD2A0AhJBNAL42M/C2GsBvCSE+ABJM5xza5zphNIh7tSJWVnGBN3s4E62DrSvU+QLMYFaYeNTr9ejp6UF9fT3PbQSKkNjodpasKygogEajibiXYz6w2R1LlizhDV6JhEKhQGlpKUpLS/lr7+3txeTkJPLy8qDT6fw6RTUaDQoKCkAI4QIsZjIrFGBFQnDJJAer1RpVrotSagRwWZDvfwTgjpl/HwVQF/FFwkDcdQ7xsKcPd6J1IEKpfLA8hsPh8OsSZfv5jRs3znltFlYz3YPNZoNer0dTUxOATx6OWITVTG0aajNXvEEIwcTEBORyOS655BJMTU3BaDTy7Q6LKhihC6dnMQEWi0CEDlihhuuLPHJIKSRcIRkNKKVobGwMe6J1IBYiB2Zmm5OTg7Vr13IPhtOnT0MikXAPhlAg7KxctmwZ33709PTAZrNBpVJFvP0wm81ob29HbW3tnLNBEwlGqD6fD3V1dX6l0vLy8lmlUtbrwbpKhcYtALjJbE9PT8i+msmOHERyCPXkMXSgttvtsNlsWLZsWVR6CWB+chBOzmYlPbfbjZaWFhQWFqKsrCyqT/tg2w+DwcDnTIa6/dDr9eju7k4ZTYXP50NbWxsyMjKwfPnyoO9RsFKpwWBAe/v0lpkJsFhehzWWEULgdDr9fDWFDlhCMkh25HCudGQCCVJIRgvWj5GRkYHi4uKozzcXOTD7NaHicWpqiic9NRpN1NcOXEfg9sNgMKC5uRmUUq5kzM7O9nvYtFotRkdH593aJBJerxfNzc3Iz88PebK0sFTKHKYNBgOGhoZgsVh4yzR7+OVy+SxZt3B4L3sfRXKIHVJ6W8G8F8fGxrBp0yacOnUqJoq7YOQwNDTE1Y1yuRyEEIyPj+Ps2bNYu3Zt3MPFQGMX9rD09vbCZrMhLy8PGo0GJpMJDocD69evT4n5FqyUzKKhSJGWlubXMs16OgYGBvyMWNLT00EpRV5e3ixZd3t7O6xWK3p7e7msOxElZSE5nCsdmUACzF4iNTRlE60lEgk2b94MiUTCy5nRfjIIyYFpA+x2O088EkL4p/OGDRviUmFYCMKHxefzYXx83M+qfnR0FBqNJqkO1KyHZdmyZWGVkhcCIWSWqa7RaERvby/sdrufAS8hhPtqFhcX4+TJk8jOzsbw8DBOnz7NJeBqtTpuURb7wIpkiG4qI/k63yBwOBx+fg8MLBKJ9pfMyMHj8aC5uRnZ2dmoq6vjicfOzk7uy5AKn85erxf9/f0oLS1FeXk5r360tLTA5/Px6kfg9iOesNvtaG5u9mvoihcUCoWfvdvExAQvJ6enp3PBld1uh0Qi4bkL5oBlMpnQ3NwMAFyAFY/3ymq1ckI7F5By1vTzTbSOpcmsw+FAZ2cnysvLeZ8+M39VqVRYvXp1SkihGVFWVlbyhidWARBuP/r6+vjNyaTK8SI2i8WC1tbWpFRJhA8/AN5V2tbWBqvViqVLl/qVFDMzM5GZmcmJZXx8HAMDA9zPQtiuHimEztPJ0JnECwmJHELNEyw00TpWJrNOpxPDw8Oor69HdnY2KKVwOp18iA57CJMN9hBWV1fP+YkUuP2YmJjgSk2FQsGjilhtP0wmE86cOYN169alRAidmZnJPTMvvPBCWK1W6HQ6v1KpUH3JkrxMgMXa7mMhwBJ1DmGC5R3me7NDnWgdi8hheHgYRqMRFRUVPLScmJjg7lSp4MEAgOcYwnkIAz9VWfWjtbUVXq8XarUaBQUFEVvVs1C+oaEh4S7Wc2FsbAwDAwNcwcrKwULxGTP9EbpL+3w+ngAuLy+fJcAK1VdT+GF1LjlPAwnYVjAJ9VxJPTbbQa1WLzjROhpyEI68q6iogNFoRGZmJpxOJ4aGhsJqbY43RkZGuCN3NJ/4Qq8Gt9sNo9GI/v5+3lVZUFAQ8vZjZGSER3WpUD4F4OefEbimQPEZe/2sVKpUKnmpdC4BViiDjYXu6mIpM9wLzCOhDneidaTkwJyoMjMzsW7dOng8HmRkZKCrqwt2ux0ajQYTExNxzWiHAjZRfGJiAhs3boxpziAtLQ1LlizBkiVLuAEtE1KxQcVzzbUYGBiAwWDA+vXrU8KrApiOAJmjdyhrEr5+5thlMBgwODjo10WqUCj8BFiVlZVwuVw8kmPjCpmyU9iJKSokwwDTOgRTSUYy0TqSnIPD4UBjYyNKS0v5jUEIwfDwMNRqNbZs2cJNTfr7+5GWlsY/QRIZSbCtFQDU19fHVcgTaEBrt9uh1+vR1tbGtx8ajQZKpZLrLGLdAh4NtFotdDodGhoaIiJQobv08uXL4XQ6YTQauVU/6+lgpVJmMFtYWAhCiJ+vZlpaGjweD8xmc1Q6B0LI9QAexnTX5ZaZZqtgx30ewC8BSAE8Qyn9SUQXDGVNIegQIp686nK50NraipKSEp5QY+G9xWLBunXrwvqkZjbrJSUlIR0/MTGBtrY2VFdXIycnB5RS3sFYWloaVLQzNTUFvV4PvV7PH5TCwsK4lgmZwjAvLw+VlZVJrZIwG3adTgej0Yi0tDSsXLkSarU6Jcq6AwMDMBqNWLduXVzWI0zqjo+P+20p5HK5nz6GCeUGBwfxm9/8Bv/6179www034LrrrsOFF14YFpkSQmoA+AD8FsB3gpEDIUQKoBPAFQC0AE4AuGlRtmwD/v0VbKK1UqkMOg9zIYSzrRgZGUFfXx/q6+v5vt1qtaKtrQ1r1qyZ0xY/IyMD5eXlPEklVClG0yQ1F5xOJ5qamlBWVhYTaXi0YJGTTqdDSUkJNBoNfw/kcjmvfiQjP9PX1wez2RzXyCowqTs1NcV7X5xOp58BLzC9ZVUqlXj66afxmc98BhdddBH279+PCy+8MKzrUko7gAUlAFsAdFFKe2aOfRHTprSLjxyEEupoJlozSKVSOJ3OeY+hlKKrqwuTk5PYsGEDJBIJCCHQ6XScLELtuQ8sEwo9GrKyslBYWBhVniKYM3SyEaxPQrj9YJoCj8fDqx+JkCn39PTAarWirq4uodubjIwMlJWV8Qno4+Pj0Ov1OHv2LGQyGRwOB0pLS9Ha2oqRkRFs374du3btitdySgAMCr7WAtgar4vFPXKQSqWYmJhAV1cX6urqohLNLJRzYCImhUKB+vp6Lt3u6+uDyWSKKtMe2CRlsVh4nkKY6Q61Q5I5Q69duzZl9PhsOA8bxhuIzMxMHlWxob6Dg4O8UYpVP2KZtGT+HQ6Hg7eBJwtSqZT/nsfHx3H69GkUFRXh5ptvRn9/P2688Ua0tLRg48aNQX9+LnPZRx55BDt27AjyE8lFXMmBUoqJiQlYrVZs3bo16h6F+bYVLPFYUlLCm3copejo6IBMJotpQk04p3L58uU8T9He3h6SnmBsbAx9fX0pVT5lfRJVVVUhVY4Ch/qy6gcbqceqH9G0k7P8lMfjQW1tbUooVoFPhiJv3LgR3d3d8Hg8OHz4MAYHB/Hxxx/PSQ7zmMuGiiEAZYKvS2e+FxfENSHZ2trKS4RVVVWRnoaDGYVUV1f7fV84qyI3N9fPg6GoqIiPqEsEWJ5Cr9cHzVOw6UjhJmPjCdYnsXr16rBGFM4FRpYGgwFut5tXP1gXZShgxjEAUkbKDkzfax0dHWhoaMDAwABuueUW/PGPf0RdXdTObQQAZtyk50pIyjCdkLwM06RwAsCXKKVt0V486ILiSQ4smWM2m7Fy5cpIT8NhNpuh1WpRW1vLv8dKonV1dVAoFLzZprW1lWfZkwVhnsJkMsHn80Eul6O+vj4pnZ7BwCTa8dreMPcnvV6PyclJ5OTkQKPRQK1Wz7n9EEZ8K1euTDliqK+vx+joKG666Sbs27cP69evj/rchJBrAfz/AAoATABopJR+jhCyFNMlyy/MHPcFAL/AdCnzOUrpI1FffK41xZMcPB4PdDod9Ho91qxZE+lpOCwWC3p7e7Fu3TpQStHT0wOTyYS1a9dCKpWCEAKj0Yiurq6EeDCECuYMLZPJkJ6eDqPRGFGeItZgfRJ1dXUJUfYx8ZFer+dlUlb9YO8BpRRtbW1QKBRzOkolAxaLBW1tbaivr4der8cNN9yAZ555Bps3b47VJVLjhQoQ92pFLK3iWM6BPWzsU5hhcHAQOp0uaR4MwcDMUITO0CtWrPDLU3g8Hv6QRNr3EC6S0SchFB+x98BgMKCjo4Obz05OTkKlUmH58uUJWVMoYNFVfX09xsfHceONN+I3v/lNLIkhJRHXyMHr9WJiYgLd3d1+D3GkYJ2TPp8PxcXFWLp0qZ8Hg8/nQ3V1dcoo+UJ1hma6f51OFzc9hRCsTyJYT0Ky4HK50NjYCEopfD4flEolCgoK5t1+JAJWqxUtLS1Yt24dLBYLrrvuOvziF7/AZz7zmVhf6vyKHIDYeTAA04mziYkJrF+/Hnl5eaCUwuPxoLW1ldflUyUMnZycRFtbW0idnoF9D4F6Cpb5j8WDzPokUsXIBvjE9au4uBhlZWV+2w82z4K9B/GagRoMTIdSV1cHm82GXbt24fHHH48HMaQkFs22gg2xycjI4BUJh8OB5uZmVFVVxdSmLFpE4ww9l57i1KlTfnX2cM/L9AJ2uz2l+iS8Xi+amppQWFjIt13C7QcwXabW6/U4c+YMnE4nr37k5eXF7cOAVXDq6urgcDiwa9cuPProo7j88svjcr1URFy3FT6fD06nEx9++CE+9alPRXQOSil6e3thNBpRV1eHkydPQqFQIDMzE3q9HmvXrk2JmQ0MzHty3bp1Mc97sD26Xq+H2+0OOU/BZm4AwJo1a1ImumLzQdgWMRR4vV4YjUZeBVMqlbz6EastEtsO1tbWwuPx4Nprr8X/+T//B9u2bYvJ+edAavxSBIg7Objdbhw9ejRsrTn7+dbWVkilUqxatYp/v7e3F0NDQ5DL5ZDL5SgoKEBhYWFSzVbZJ7PNZuPVk3iC5Sn0ej2sVuuceQr2HmZmZqZU9p+pMcvKyiKW0wsjK6PRCKlUygkz0u0HI4aamhpQSnHdddfhO9/5Dq699tqIzhcGUuMXI0DKkgNT7LEsP5NNs0G1tbW1kEqlmJqa4uVSSiknikTuTX0+H9rb25GWlhb3IbZzXV+op2B5CpVKhfb2dmg0Gj+j3mSDEQMbHBQrsIG+er2eN0kVFBTMGnwz3883NjbypPauXbtw55134oYbbojZGufB+UUOrEU6XHKwWCzc2VilUnHz17a2NmRnZ8/5CehyuThRuFwu3m4dz/IgU2Kq1eqQB7rEE2ySFHNJYkNuk6mnEIJVJZYtWxbX2Z7CJimz2Yzs7Gxe/Qi2/XA6nTh16hTWrFmDtLQ07N69G7fffjtuvvnmuK0xACI5LASdTsebtFj9nXkwhNPW7PF4YDAYeHkwPz8fhYWFMU1isYRoRUVFypjSAp84VldVVUGpVHJ/inDyFPEAiwZXrFiRUOVq4PZDIpHw6kdWVhZf16pVq6BQKLB792586Utfwp49exK2Rpyv5PDBBx9g69at84Z2zCLNYDCgrq6OKx4nJye5VX2kMwF8Ph/fn5vNZuTk5PB260iz9qz+Hc264oH5+iRCzVPEAyxkT4X2dKfTyXs/pqam4HK5oFQqsXz5ctx2223YuXMn9u7dGzV53n777fjzn/+MwsJCtLa2zvp/SinuvvtuHD58GN3d3S0AvkopPRnVRWOIuJcygU8MX+bK3rM9OwA0NDTwnx0bG0N/f3/Uw2LZJwVzJZ6YmOARCvNl0Gg0IYttInGGTgQW6pNIpJ5CCJbkW7NmTUoQaXp6OkpLS1FUVISTJ0+irKwML7zwAn7/+99jyZIlUCqVMZle9dWvfhV33nknbrnllqD/f+TIEZw9exZnz56FRCL53wB+gzj6M4SLuIugFpqZyfagrM7NIpne3l6YzWZs3Lgxpgo5Qgj3T2T7c51Ox/0j2bDWuYgsVs7QsQbrkwjVzCZQT8Heh2j1FIFgkUx1dXXK2P4D01HUqVOnUFVVhby8PLS3t+Oee+7BlVdeiT//+c8xcZK++OKL0dfXN+f/v/7667jllltACAGl9BghJI8QUkwpHYnqwjFCQnSpc5GD1WpFc3MzVqxYgfz8fC6d7ejogFwuR0NDQ1z3xcJJz8uXL4fdbodOp0NTUxMAcKLIyMjwc4besGFDyrgwA9H3SQS+D0x0xHoeIs1T2Gw2NDc3p5ShDTCdj2psbERlZSVUKhX27NmDTZs24bvf/S4IITx6jTeGhoYC7QS0mHZ7On/IIZhKklltscQj82Bobm5GcXFxUsaKZWZmorKyEpWVlXxfyh4QYHpmY7yMTSPF8PAwhoaGYjpPQqFQcGu0wHkXeXl5KCwsXDBPwXIydXV1KdMdC3xCDOXl5VCr1fja176GmpoafP/7308ZDUiqIOGRA6UU/f390Ol0fOYAIYSbvybbg4GB7UuLi4vR2NiItLQ0UEpx/PhxXiINx7wkHkhEn0QkeQqWRE61nIzX6+VjCjQaDe666y6UlZXh4YcfTsrvsaSkBIODQkvI+Do7hYuE5BzYYBu2ZfD5fNwggxDCZzsmylcgVLAuUKGnIqufDw0NoaOjA7m5uSgsLER+fn7C+hWS1ScRSp5CoVCgp6cH69atS6gQbSEwYigpKUFhYSHuvfde5Ofn49FHH00awW/fvh1PPfUUbrzxRkgkkgsAmFMl3wDEuZQJTCd+uru7IZVKMTY2Bo1GwzvvgGkPBla+TBUPBuCTjrz5Ihk240Cn08FkMiE7O5uXSOOVk0jVPgmHw4GBgQFotVpkZGSgsLAwaXqKQLDmLhYB3XfffSCE4Mknn4wrsd5000147733YDAYUFRUhB/+8Id8i/q1r30NlFLceeedeOutt9DT09MK4La5htkkAwkhhzNnzmBoaAjV1dX8U0foEbhmzZqU6RIEInOGZkIbnU4Hg8GA9PR0/oDEivRYn0RWVhaqqqqS/tAJwcq7bAqVUE8Rap4iHvD5fGhqakJBQQGWLl2Khx56CFarFU8//XRK3XM430RQwLTHI2Nt5u7DZiMwzX8q3eQ6nQ69vb2or6+PyiHJZrNxoiCEcKKItDTIPv1SrU8CALfma2homFXenavvIx56ikD4fD40NzdDrVajtLQUP/rRjzA6Oopnn302pZLKM0idh2AGcSeHoaEhTE1NYWRkBMuXL4dMJkNra+uC7kjJQLycoVlpUKfTwev1QqPRoLCwEFlZWSERI2tUYgnSVAKzo29oaFgwQhLmKVgXZbx8NH0+H1paWqBSqVBWVobHHnsM3d3d2LdvX0qVoQU4/8jhyJEjvP2VJdGY4CmeZh3hgNnMud1u1NTUxDXcdLvdvNdhamqKVz7mmhol7JNINTIdGxvDwMBAxHZzjDRj3ffBtl85OTmoqKjAL37xCzQ2NuJPf/pTytjiBcG8L5gQIqWUxsZSLUTEnRwef/xx7N+/n7dv79+/H+np6bzPIS8vD0VFRcjLy0vKHpB1eybD74AZl+h0Olgslll7c5YUjdU8iVhidHSU+1DG4pOYNcpFm6dg7tVZWVmorKzEr3/9a7z//vt4+eWXUyrhHQRBbzxCyN0AXqCU6hK8nviTAwB873vfQ3t7O7Zu3YojR44gPT0d27dvx/bt2yGXy6HT6TAxMZHwsmAwZ+hkIbDyoVAo+GzIVOhHEGJ4eBgjIyOor6+PS4ge+F6EmqeglKK9vR0KhQJVVVV45pln8Pbbb+PVV19NKan7HJhFDoSQ7wD4KYBfA/gWpdSV0AUlghxOnjyJ9evXMw05+vv7ceDAAbz22msAgG3btmHHjh3IysqCXq/H+Pg4lEolioqKkJ+fH5fkUajO0MnA+Pg4Ojo6kJ+fD7PZDIVCwROayQ6LtVotdDod6uvrE5LUY3kK1kU5V56CDcKRy+VYvnw59u3bh9dffx2vv/56yowcXAB+5EAIuQ/A5wH8CsDlAB6klBoJIRJK6dwDY2O5oESQw5wnphTDw8M4cOAADh48CIfDgWuuuQbbt2+HWq3miSumH9BoNDG5IcNxhk40WJ+EsFrCHg69Xg+pVMqJItE3/cDAAIxGY1Il5IF5CjaXdGhoiE/I+uMf/4iXXnoJb7zxRsyEWG+99RbuvvtueL1e3HHHHbj//vv9/v/555/Hd7/7XZSUlAAA7rzzTtxxxx3hXIKTAyHkcwDuBbCbUjpJCPkrgB5K6f+O+oWEs6BkkoPfRSiFTqfDwYMH8eqrr2JiYgJf+MIXsH37dhQXF/NPDqHAJpKQ1mAwoKurK+UUfMAnfRLzJfgcDgd3u/J6vdwWL97K0r6+PpjNZtTV1aWMPoDlKbq7u+F2u/H3v/8dAHDs2DEcOXIkZj0dXq8Xq1atwjvvvIPS0lJs3rwZL7zwAmpqavgxzz//PD766CM89dRTkV5GSA4FAJyU0smZr5cB+DmARxIpkkqN3zKmZdRFRUX42te+hrfffhtHjhxBWVkZd/196aWXoFQqUVVVBbvdjo8//hinTp3C8PAwV50tBK1Wi97eXmzYsCHliKG/vx9jY2MLNlApFAqUl5dj48aNXFdw9uxZHDt2DF1dXTCbzQiB8MNCT08PLBZLShEDMN2zMzk5CbVajU9/+tPIzMzEG2+8AZPJhDvuuCNmk9aOHz+OFStWoKqqCnK5HDfeeCNef/31mJxbCEKIBAAopXoBMUgBmDA9P/OimF90HqRkwRcA1Go19uzZgz179mBiYgKHDh3CI488gv7+flxxxRX44he/iOXLl/OZDjKZbE4vBqEzdCoNcwE+WdvU1BTq6+vDevjkcjmWLl2KpUuXwuv1wmAwYGBggLs8MVu8SB9otjan04m1a9emRNmZga3N4/Gguroahw8fxjvvvIMPPvgAeXl56OrqilmyNLC1urS0FB9++OGs4w4cOIB//OMfWLVqFf7jP/4jrOnuwlwCIaQE01G9dqZ8OUEIeQ7AnwghH1JKj0X7mkJBypKDEHl5ebjllltwyy23YHJyEm+++SZ+/vOfo7OzE5dddhl27NiB1atXQ6/Xo6mpCRKJhBNFWload4Zet25dyt3gHR0dkEgkUT98UqkURUVFKCoq4qrEsbExnDlzBjk5OdxcNVRipJTi7Nmz8Hg8qKmpSan3DZiOZpxOJ2pqavDOO+/giSeewOHDh3nJNxZT3cPBtm3bcNNNNyE9PR2//e1vceutt+Jvf/tbyD8vIIbHAZQByCSEtFJKvz9DHP8khBwAUAMgIeSQMjmHSGC323HkyBEcOHAALS0tuOSSS7Bjxw7U19fDaDRidHSUz55cvXp1SrgvMySqT4JSCrPZDJ1Oh/HxcZ6zma8sKOx7Wb16dcoRQ29vL2w2G2pra/H3v/8dP/zhD/Hmm2/GberZBx98gIcffhh/+ctfAAA//vGPAQAPPPBA0OO9Xi+vNIUCr9cLqVRKCCF3Afg0gK8AeBHAKKX0/2HHEUIuAnAsUWKoRU0OQjgcDrzzzjvYv38/L52ePHkSzz33HFQqFcbGxuDz+ZIy1yIQHo/Hr7ckUaCU+vV8yGSyWQOBWDTDMv+pRgx9fX2YnJxEXV0d/vnPf+LBBx/Em2++GfFgnFDg8XiwatUqvPvuuygpKcHmzZvxpz/9CbW1tfyYkZERLm0/ePAgHnvsMRw7Nv8HfE9PD6qqqtiXhBByI4AOAF8CUEYp/RIhJBPARkrpP+Pw0ubFOUMOQpw6dQq7du1CQ0MDzpw5g61bt2Lnzp341Kc+BZPJBJ1Ox+W6RUVFCfWQcLlcaGpqSok+icCBQBqNhs94SKXpWAz9/f2YmJhAXV0dPvzwQ3znO9/Bn//8Z14+jCcOHz6Me+65B16vF7fffjsefPBBPPTQQ9i0aRO2b9+OBx54AIcOHYJMJkN+fj5+85vfYM2aNXOe78CBA7j11luxd+9e/OxnPwOmyeEuAD8G8Bql9Csz3/whACWA755z8ulk4LXXXsOaNWuwZs0aeDwe/M///A/279+Pf/3rX9iwYQN27tyJT3/605icnMTY2BgcDgf/BM3Ozo7bQ5HKfRJsbR6PBzKZLCEDgcLB4OAg11h8/PHHuPvuu3Ho0KGU61ANBePj49i5cyduvPFGHDlyBFVVVfjlL39JAIAQ8ksAFwO4G8DVmN5mXE0pNSV6neckOcwFr9eLf/3rXzhw4AD+/ve/Y+3atdi5cycuvfRS3i1ot9sXbIaKBKncJ8E6GHNzc1FZWQmPx8N7PqxWa1wGAoUDrVYLvV6P+vp6NDU14etf/zpee+01YUi+6HDmzBlUVlZiZGQEe/fuxdtvv/1bSunXAIAQ8iCAHABZAB6mlBqSscbzihyE8Pl8OHbsGA4cOIB33nkHq1atws6dO3HZZZfB6XRibGyMPxhFRUVR+UUyRWaquTAD/t4awUpvPp8P4+Pj0Ol0fgOB4iVrD8TQ0BDGxsZQX1+P9vZ2/Nu//RteeeUVv8HKix1arRZlZWVvAdBSSv+NEFIPYIJS2p/MdZ235CCEz+fDqVOnsH//frz11luorKzE9u3b8bnPfQ4ejwc6nQ6Tk5NcO6BSqUImCuEAnFQTXjEDGdZCvxCElQ+j0RjRQKBwMDIyguHhYZ47uv322/Hiiy/6KRPPFcxoG34GYBMAN4BPU0qNSV2TSA7+YCXG/fv348iRIygsLMSOHTvwhS98AcC0h4HZbEZubi6KiormbSlmrlLBHJKSDY/Hg6amJhQXF3Pz3HAgNG4xGAx8IFBBQUFMXuvo6CiXknd3d+OWW27BH//4R9TV1UV97hQFIYTcD+BOAJ+hlHYnfUEiOcwNVtZ75ZVX8OabbyI3Nxc7duzA1VdfDZlMxluKg83eHB4exvDwMOrr65PeSRkI5ixVVlYWsxIgGwik1+sB+A8EChdjY2MYHBxEQ0MDBgYG8KUvfQn79u3jjuXnIgghGgAvAbiTUno62esBoiSH/fv34+GHH0ZHRweOHz+OTZs2BT1uoY62xQBKKbq6unDgwAEcOnQICoUC27dvx7Zt26BQKLjIiFU7XC5XwtqawwEjhoqKiriJhthAIGHJONRKEBtNuH79egwNDeHGG2/EM888g82bN8dlrSkEQghRUEodyV4IQ1TkwKS/e/fuxRNPPBGUHELpaFtsEHpSHDx4EBKJBNu2bcO2bdvwxhtvcA/KzMzMuO7JwwWbS7ps2bKElVLdbjd3eLLZbPMOBGJ+lOvXr4dOp8P111+PX//617jwwgsTstYkI/n14gBE1WJXXV2N1atXz3tMojraEglCCCorK/Htb38b//znP/HCCy8gLS0NV1xxBV544QWcOHEChYWFqKyshM1mw8cff4zGxkaMjIyE3EEaazidTpw6dSrh5jZpaWkoLi7GunXrsGXLFqhUKgwNDeHYsWNob2+HwWCAz+eD0WjkxGAwGLB79248+eSTMSWGt956C6tXr8aKFSvwk5/8ZNb/O51O3HDDDVixYgW2bt067xDc8wFx778N1tE2NJQyE7+iBiEEJSUlMJvN2LNnDw4fPgy1Wo17770XO3bswKuvvgqVSoUVK1ZgamoKp06dwqlTpzA0NASXKzGuXw6HA6dOnUr6qEHm4lRbW4utW7diyZIlMBqNOHr0KJqbm2Gz2dDX14frr78eTzzxBC6++OKYXdvr9eIb3/gGjhw5gvb2drzwwgtob2/3O+bZZ5+FSqVCV1cX7r33Xtx3330xu/5ixIKx7uWXX47R0dFZ33/kkUewY8eOuCxqMeL+++/nWfqvf/3r+PrXvw6DwYDXXnsN3//+96HT6XDVVVdhx44dqKioCNpBGo+KBrPDW7NmTUp5UUokEuTn5wMATCYTqqur8eSTT+K///u/UV5ejv7+fthstphJ24URLAAewQq3t6+//joefvhhAMCuXbtw5513glKaEgrRZGBBcvjrX/8a1QUCh4VqtdqEaOETjWAPtkajwR133IE77rgDJpMJb7zxBn70ox9hcHCQe1KsWLECer0ezc3NfPhNYWFhTCzg7HY7mpubUV1dnXJ2eMD0ZLHOzk6sX78eNpsN//M//4Pf/e53WLNmDQ4ePMiHL8cCoXgyCI+RyWTIzc2F0WiERqOJ2ToWE+KeJdu8eTPOnj2L3t5elJSU4MUXX8Sf/vSneF825aBSqWZ5UjzxxBPo6urinhS1tbUwGAxoa2vjHaRFRUURlQNtNhuam5tTUpUJAGazGadPn0ZDQwMcDgeuv/563H///di2bRuAacdyEclFVDmHgwcPorS0FB988AGuvvpqfO5znwMwXeNnoiGZTIannnoKn/vc51BdXY3du3f7tbqej8jJycFNN92EV155BUePHsWnPvUp/PrXv8all16KX/3qV3C5XFi7di1kMhkvEzMPg1BgtVrR3NyMurq6lCWGjo4O1NfXw+12Y/fu3bjnnntw7bXXxu2aoUSwwmM8Hg/MZnNSczTJhiiCSiE4HA68/fbbeOWVV3Dq1ClcdNFF2LlzJzZv3sydnVwul1+reeB+eHJyEu3t7airq0toK3qosFgsaGtrQ319PXw+H66//nrs2bMHN998c1yvG4onw69+9Su0tLTg6aefxosvvohXX30VL7/8clzXJUDKJTZEckhRuFwuvPvuu3jllVfw4Ycf4oILLuCeFGazGWNjY5iamuICI6VSicnJSXR0dKRkHwfwCTEwu77du3fjy1/+Mm6//faEXH8hTwaHw4Gbb74Zp06dQn5+Pl588cVEdn6K5BAtxsfHccMNN6Cvrw+VlZV4+eWXg7ZAS6VSrsMvLy/HoUOHEr3UmMHtduMf//gH96TYuHEj96SwWCy8Y5L5PRYUFKRcht1qtaKlpYXPvLjpppvwxS9+EXv37k25tSYJKfcmLDpy+N73vof8/Hzcf//9+MlPfgKTyYTHHnts1nHZ2dmwWq1JWGF8wTwpXnnlFbz33nuoq6vDypUr0dPTg4ceeggmkwkWiyXpHgxCsORoXV0d0tLScPPNN+PKK6/EXXfdlfS1pRBS7o1YdOSwevVqvPfeeyguLsbIyAguueQSboYqxLlKDkL4fD788pe/xOOPP46lS5di2bJl2LlzJy6//HI4nU4eUUQ6lDYWsNvtaGpqQl1dHdLT03Hrrbfif/2v/4Vvf/vbIjH4I+XejJDvFGa3FqtBIZFibGyMey8uWbIEY2NjQY9zOBzYtGkTLrjgAj6T81wDIQRnzpxBc3Mzjh8/jvvuuw/Nzc34/Oc/j7vvvhstLS2oqalBUVERdDodPvzwQ7S1tXHJcrzBBFhr166FQqHAnj17sGXLFpEYFglCjhy6urqwd+9eHDt2DPv27cOuXbvitqj5VJm33norJiYm+PdUKhVMptn2ekNDQygpKUFPTw8++9nP4t1338Xy5cvjtuZUArN9e+WVV3D48GEsWbKEe1IQQngHqVKp5K3mse4eZcRQU1ODrKws7N27FytXrsTDDz+86InhoYceQn5+Pu655x4AwIMPPojCwkLcfffd0Zw25d6UkMnBYrEgLS0Nn/nMZ/D444/j4osvhs/n45OzExWuhrqtEOKrX/0qrrnmmrgSWqoi0JMiLy+Pe1LI5XJu1pKVlYWioqKYDCt2OBxobGxEdXU1srOzcdddd6G4uBiPPvrooicGYNoe/9prr8XJkyfh8/mwcuVKHD9+PFpNRMq9MSE/0UqlEo2NjXC73bwhRiKRgBDCiYHJXePZebh9+3bs27cPALBv376g/R0mkwlOpxPA9ODc999/f1G3iEcDQghqamrw0EMP4dixY3jqqadgNptx00034ctf/jLee+89VFZWorKyEhaLBSdOnEBTUxNGRkYi2kI6nU40NjZizZo1UCqV+Na3voX8/Hw88sgj5wQxAEBlZSXUajVOnTqFt99+G+vXrz8nxVJhJSR/8IMfYGRkBL/73e9gtVq5ndq2bdtwySWX8B948MEH0d7ejquvvhrXXnstb7CJBYxGI3bv3o2BgQFUVFTg5ZdfRn5+Pj766CM8/fTTeOaZZ3D06FHs3bsXEokEPp8P99xzD/bs2ROzNZwLoJSir68PBw4cwOuvv849KXbs2IGcnBweUcjlchQVFaGgoGBBRytGDKtWrUJubi7uu+8+EELw5JNPptQA3ljgpZdewtGjRzE6Oopbb72VK4KjQMoxZ0jk4PP54HQ68fnPfx4PPPAAT3iVlJRg2bJl2LdvHzZu3Igf/vCHAKb3vH/5y1/w8ssv491330V1dTUfJSYi9UApxfDwMA4cOIBXX30Vbrcb11xzDXbs2IGCggJu/zbfsGKXy8XbwvPy8vDQQw/BarXi6aefPueIAZh+vXV1dXC73Th79mwscjYpRw4hNV5JJBKMjo5icnISV155JVwuF1588UWUl5fj7rvvxhNPPIFHH30UZ8+e5SPUrrrqKqxZswbp6enIyckBME0a5+KNstjBPCm++c1v4q677sLY2BgOHjyIb37zm7BYLLj66quxY8cOlJSUzGo1LygogFQqRWNjI1asWAGVSoUf/ehHGB8fx7PPPhuX33cqCOHkcjkuvfRS5OXlpZwVYKywYORw+vRp+otf/AJGoxHZ2dn4/e9/j7/85S944IEH8Oqrr+Lxxx/Hxx9/jP7+fgwODkImk/Ee+EsuuQRXXXUVbr/99pSb8LQQFvK9dDqduOWWW/Dxxx9DrVbjpZdeQmVlZXIWG0cwT4oDBw7AYDDgqquuwvbt27Fs2TLo9XqMjY3BYrFAoVBAo9Hg1VdfRXd3N/7whz/E7aFJBSGcz+fDhg0bsH///lhN9E65yAGU0nn/TE5O0p/97Gf0wgsvpAqFgv7nf/4nffbZZ+mXv/xlKsTIyAj/t81mo//1X/9Fa2pq6GKEx+OhVVVVtLu7mzqdTrpu3Tra1tbmd8yvfvUrunfvXkoppS+88ALdvXt3MpaaUIyPj9Pnn3+ebtu2ja5fv57ec889dMOGDfTjjz+mr732Gq2rq6MajYY+8sgjtKenJ27rWLVqFR0eHqaUUjo8PExXrVoV9LisrKy4XL+trY0uW7aMfutb34rlaRd8FhP9J5SDOMbGxuiJEyeoy+WiX/nKV+j1119PDx48SFtbW6nD4aAej4dSSunvfvc7+pWvfIUeOnSIUkr59xcLjh49Sq+88kr+9aOPPkofffRRv2OuvPJKevToUUoppW63m6rVaurz+RK6zmRCq9XSNWvW0EsvvZTW19fTSy+9lF599dV0eHiYPvfcc/Tll1+O27Vzc3P5v30+n9/XQkilUrpx40a6detWevDgwbitJ0ZIOhkE/gmLHISYnJykzz33HN2xYwf993//d04Ao6OjtKamhr7yyitRvVPJxP79++mePXv413/4wx/oN77xDb9jamtr6eDgIP+6qqqK6vX6hK0x2Th06BB96aWXKKWUWiwW+vDDD1Or1Rqz81922WW0trZ21p/XXnttFhnk5eUFPYdWq6WUUtrd3U0rKipoV1dXzNYXBySdDAL/ROwEpVQqcdttt+G2226D1+uFVCrF+Pg4fvrTn2Lz5s247rrrYrn7EZFiYI5NwPTe/gc/+EFMzz+fPWFRURFGRka4EG6u+RvMzKWqqgqXXHIJd98WERpikkpmiaf8/Hw89thjeOKJJwBMRyWLEaJrUGpDFMIlCCGEF+cd3G43XbZsGe3p6eEJydbWVr9jnnrqKb+E5PXXX5+MpZ6XMBgM9LOf/SxdsWIFveyyy6jRaKSUUnrixAm+HXz//ffp2rVr6bp16+jatWvpM888k8wlh4KkbyMC/yy6lu1EIcVdg0Sce0i5UqZIDiJEpAZSjhxEuaIIESKCQiSHFMNC8xyff/55FBQUoKGhAQ0NDXjmmWeSsEoR5wOSP/pZBAeb5yicSL59+/ZZWfYbbrgBTz31VJJWKeJ8gRg5pBDOxYnkIhYvRHJIIYQ6kfzAgQNYt24ddu3a5afHECEilhDJYZFh27Zt6OvrQ3NzM6644grceuutyV6SiHMUIjmkEEJRZqrVaj7R+4477sDHH3+c0DWKOH8gkgOAEydOYN26dXA4HLDZbKitrUVra2vC1yGcSM4MdbZv3+53zMjICP/3oUOHUF1dnehlRo39+/ejtrYWEokEH3300ZzHLVS5ERFfhCKCOi9ACPn/ACgAZADQUkp/nKR1fAHALwBIATxHKX2EEPLvAD6ilB4ihPwYwHYAHgDjAL5OKT2djLVGCkJINQAfgN8C+A6ldBZDEEKkADoBXAFAC+AEgJsope2JXOv5DJEcZkAIkWP6BnQAuJBS6k3yks55EELew9zk8CkAD1NKPzfz9QMAkCzSPh8hbis+gRpANgAlpiMIEclFCQBhKUY78z0RCYJIDp/gtwD+XwB/BDDbkPAcByHkOUKIjhASNNlCpvEkIaSLENJMCNmwwPn+SghpDfJndn+1iJSEqJAEQAi5BYCbUvqnmb3uUULIZymlf0v22hKI5wE8BeAPc/z/VQBWzvzZCuA3M38HBaX08ijXMwSgTPB16cz3RCQIYuQAgFL6B0rpdTP/9lJKt55nxABK6T8wneCcCzsA/GHGe+AYgDxCSHEcl3QCwEpCyLKZfNCNAGLnLS9iQYjkICJUxCwHQAj5IiFEC+BTAN4khPxl5vtLCSGHAYBS6gFwJ4C/AOgA8DKltC2K9YsIE+K2QkTCQSk9COBgkO8PA/iC4OvDAA4ncGkiBBAjBxGhQswBnGcQyUFEqDgE4JaZqsUFAMyU0pGFfkjE4oW4rRABACCEvADgEgCamXzADwCkAQCl9GlMh/dfANAFwA7gtuSsVESiICokRYgQERTitkKECBFBIZKDCBEigkIkBxEiRASFSA4iRIgICpEcRIgQERQiOYgQISIoRHIQIUJEUPxflp30TJlIvmkAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "csv_path = \"../../runs/v2.csv\"\n", + "\n", + "data = pd.read_csv(csv_path)\n", + "pl = Plot(data)\n", + "pl.plot_quat()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 604702ca336375ce4b52c0e35dd4a2be3962885e Mon Sep 17 00:00:00 2001 From: tmf97 Date: Sun, 7 May 2023 11:54:54 -0700 Subject: [PATCH 2/8] fix gyro unittest --- src/utils/test_utils.py | 12 ++++++------ tests/core_tests/model_tests/gyro_model_test.py | 14 +++++++------- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/src/utils/test_utils.py b/src/utils/test_utils.py index 0a0b85f..bf4af2b 100644 --- a/src/utils/test_utils.py +++ b/src/utils/test_utils.py @@ -5,9 +5,9 @@ s_0 = { "fill_frac": 0.0, - "ang_vel_x": 0.0, - "ang_vel_y": 0.0, - "ang_vel_z": 0.0, + "h_x": 0.0, + "h_y": 0.0, + "h_z": 0.0, "quat_v1": 0.0, "quat_v2": 0.0, "quat_v3": 0.0, @@ -29,9 +29,9 @@ s_1 = { "fill_frac": 1.0, - "ang_vel_x": 2.0, - "ang_vel_y": 3.0, - "ang_vel_z": 4.0, + "h_x": 2.0, + "h_y": 3.0, + "h_z": 4.0, "quat_v1": 5.0, "quat_v2": 6.0, "quat_v3": 7.0, diff --git a/tests/core_tests/model_tests/gyro_model_test.py b/tests/core_tests/model_tests/gyro_model_test.py index bbdbc35..7e28a8f 100644 --- a/tests/core_tests/model_tests/gyro_model_test.py +++ b/tests/core_tests/model_tests/gyro_model_test.py @@ -13,7 +13,7 @@ def test_gyro_model(self): clean_vars = { "gyro_bias": [0, 0, 0], "gyro_noise": [0, 0, 0], - "gyro_sensitivity": 1, + "gyro_sensitivity": 1e-18, } param_clean = Parameters(clean_vars) @@ -28,9 +28,9 @@ def test_gyro_model(self): self.assertIsInstance(eval_clean, Dict) # verify the original values have not been augmented by the model - self.assertEqual(eval_clean["ang_vel_x"], state_1.ang_vel_x) - self.assertEqual(eval_clean["ang_vel_y"], state_1.ang_vel_y) - self.assertEqual(eval_clean["ang_vel_z"], state_1.ang_vel_z) + self.assertAlmostEqual(eval_clean["ang_vel_x"], dummy_state.derived_state.ang_vel_x) + self.assertAlmostEqual(eval_clean["ang_vel_y"], dummy_state.derived_state.ang_vel_y) + self.assertAlmostEqual(eval_clean["ang_vel_z"], dummy_state.derived_state.ang_vel_z) # test noisy and biased model param_noisy_biased = Parameters({}) @@ -45,9 +45,9 @@ def test_gyro_model(self): self.assertIsInstance(eval_noisy_biased, Dict) # verify the original values have been augmented by the model - self.assertNotEqual(eval_noisy_biased["ang_vel_x"], state_1.ang_vel_x) - self.assertNotEqual(eval_noisy_biased["ang_vel_y"], state_1.ang_vel_y) - self.assertNotEqual(eval_noisy_biased["ang_vel_z"], state_1.ang_vel_z) + self.assertNotEqual(eval_noisy_biased["ang_vel_x"], dummy_state.derived_state.ang_vel_x) + self.assertNotEqual(eval_noisy_biased["ang_vel_y"], dummy_state.derived_state.ang_vel_y) + self.assertNotEqual(eval_noisy_biased["ang_vel_z"], dummy_state.derived_state.ang_vel_z) if __name__ == "__main__": From 1a97711ab3459659f7f478e870e94843f4407010 Mon Sep 17 00:00:00 2001 From: tmf97 Date: Sun, 7 May 2023 11:56:12 -0700 Subject: [PATCH 3/8] cleanup logging + format --- src/core/models/derived_models.py | 11 ++++------- src/core/parameters.py | 6 +++--- src/utils/test_utils.py | 25 ++++++++++++------------- 3 files changed, 19 insertions(+), 23 deletions(-) diff --git a/src/core/models/derived_models.py b/src/core/models/derived_models.py index cb9499f..00d03b3 100644 --- a/src/core/models/derived_models.py +++ b/src/core/models/derived_models.py @@ -5,7 +5,6 @@ from utils.constants import BodyEnum from core.models.model_base import Model from typing import Union, Tuple, List, Dict -import logging class DerivedStateModel(Model): @@ -103,7 +102,7 @@ def evaluate(self, t: float, state: State) -> Dict[str, np.ndarray]: class InertiaModel(DerivedStateModel): def evaluate(self, _: float, state: State) -> Dict[str, Union[float, int, bool]]: - """ the _b postfix means the value is in the spacecraft's body frame""" + """the _b postfix means the value is in the spacecraft's body frame""" fill_frac = state.fill_frac dcm = np.array([[0, 1, 0], [0, 0, -1], [-1, 0, 0]], dtype=np.int32) @@ -163,11 +162,9 @@ def evaluate(self, _: float, state: State) -> Dict[str, Union[float, int, bool]] # --> omega = inverse(I) matmul h angular_momentum = np.array([[state.h_x, state.h_y, state.h_z]]) - + angular_velocity = np.matmul(np.linalg.inv(ineria_matrix_b), angular_momentum.T) - logging.info(f"Inv: {np.linalg.inv(ineria_matrix_b)}") - logging.info(f"h: {angular_momentum}") - logging.info(f"w: {angular_velocity}") + return { "Ixx": ineria_matrix_b[0][0], "Ixy": ineria_matrix_b[0][1], @@ -182,6 +179,6 @@ def evaluate(self, _: float, state: State) -> Dict[str, Union[float, int, bool]] "ang_vel_y": angular_velocity[1][0], "ang_vel_z": angular_velocity[2][0], } - + DERIVED_MODEL_LIST: List[DerivedStateModel] = [DerivedPosition(), DerivedAttitude(), InertiaModel()] diff --git a/src/core/parameters.py b/src/core/parameters.py index 82a8b2f..f49df90 100644 --- a/src/core/parameters.py +++ b/src/core/parameters.py @@ -4,7 +4,7 @@ class Parameters: """This is a container class for all parameters as defined in this sheet: - https://cornell.box.com/s/z20wbp66q0pseqievmadf515ucd971g2. + https://cornell.box.com/s/z20wbp66q0pseqievmadf515ucd971g2. """ def __init__(self, param_dict: Dict = {}): @@ -15,10 +15,10 @@ def __init__(self, param_dict: Dict = {}): self.dry_mass = 0 self.com = 0 - + # fuel tank self.tank_volume = 1 - self.electolyzer_rate = 10.0 * (1/1000) + self.electolyzer_rate = 10.0 * (1 / 1000) # prop self.thruster_force = 0 diff --git a/src/utils/test_utils.py b/src/utils/test_utils.py index bf4af2b..fbb5cb7 100644 --- a/src/utils/test_utils.py +++ b/src/utils/test_utils.py @@ -24,7 +24,7 @@ "force_earth": 0.0, "force_moon": 0.0, "propulsion_on": False, - "solenoid_actuation_on": False + "solenoid_actuation_on": False, } s_1 = { @@ -48,25 +48,24 @@ "force_earth": 18.0, "force_moon": 19.0, "propulsion_on": True, - "solenoid_actuation_on": False + "solenoid_actuation_on": False, } state_1 = State(**s_1) d3456_dict: Dict = { - "gyro_bias": [0.497625, -0.10821875, 0.77490625], - "gyro_noise": [0.1824535, 0.11738579, 0.19192256], - "gyro_sensitivity": 0.015625 * (math.pi / 180), - "dry_mass": 3, - "com": 4, - "tank_volume": 5, - "electolyzer_rate": 10.0 * (1/1000), - "thruster_force": 6, - "combustion_chamber_volume": 7, - "max_iter": 1000000 + "gyro_bias": [0.497625, -0.10821875, 0.77490625], + "gyro_noise": [0.1824535, 0.11738579, 0.19192256], + "gyro_sensitivity": 0.015625 * (math.pi / 180), + "dry_mass": 3, + "com": 4, + "tank_volume": 5, + "electolyzer_rate": 10.0 * (1 / 1000), + "thruster_force": 6, + "combustion_chamber_volume": 7, + "max_iter": 1000000, } d3456: Parameters = Parameters(d3456_dict) - From d579406d9e02e912fd265f3cdcec516cc5cf4d5f Mon Sep 17 00:00:00 2001 From: tmf97 Date: Sun, 7 May 2023 12:07:58 -0700 Subject: [PATCH 4/8] fix configs and corresponding unittest --- configs/TLI_Oct2019.json | 2 +- configs/apollo12SIVB.json | 2 +- configs/freefall.json | 2 +- configs/lro.json | 2 +- configs/roadster.json | 2 +- configs/test_angles.json | 6 +++--- configs/test_zeroes.json | 6 +++--- configs/tli.json | 2 +- src/core/state/state.py | 2 +- tests/core_tests/config_test.py | 18 +++++++++--------- 10 files changed, 22 insertions(+), 22 deletions(-) diff --git a/configs/TLI_Oct2019.json b/configs/TLI_Oct2019.json index 70266ff..39d588c 100644 --- a/configs/TLI_Oct2019.json +++ b/configs/TLI_Oct2019.json @@ -7,7 +7,7 @@ "vel_x": -534.084, "vel_y": -3792.878, "vel_z": -867.495, - "ang_vel_x": 4.5, + "h_x": 4.5, "time": 1539102600 }, "models" : ["pos", "gyro"] diff --git a/configs/apollo12SIVB.json b/configs/apollo12SIVB.json index a760159..40976b2 100644 --- a/configs/apollo12SIVB.json +++ b/configs/apollo12SIVB.json @@ -7,7 +7,7 @@ "vel_x": 5106.47906333 , "vel_y": -4728.66201988, "vel_z": 4572.38411289 , - "ang_vel_x": 4.5, + "h_x": 4.5, "time": -4058816 }, "models" : ["pos", "gyro"] diff --git a/configs/freefall.json b/configs/freefall.json index ffaafe2..b0a8864 100644 --- a/configs/freefall.json +++ b/configs/freefall.json @@ -4,7 +4,7 @@ "x": 100000000.0, "y": 1000.0, "z": 1000.0, - "ang_vel_x": 4.5, + "h_x": 4.5, "time": 0.0, "quat_v1": 1.0, "quat_v2": 0.0, diff --git a/configs/lro.json b/configs/lro.json index 3e9e30b..b9c0428 100644 --- a/configs/lro.json +++ b/configs/lro.json @@ -7,7 +7,7 @@ "vel_x": 0.2069511992715368, "vel_y": -0.2105451990331415, "vel_z": -1.157255066656339, - "ang_vel_x": 4.5, + "h_x": 4.5, "time": 1651885860 }, "models" : ["pos", "gyro"] diff --git a/configs/roadster.json b/configs/roadster.json index c123109..cde9806 100644 --- a/configs/roadster.json +++ b/configs/roadster.json @@ -7,7 +7,7 @@ "vel_x": -6.837449791362452, "vel_y": -3.855569832386001, "vel_z": -2.658902141013662, - "ang_vel_x": 4.5, + "h_x": 4.5, "time": 1517990400 }, "models" : ["pos", "gyro"] diff --git a/configs/test_angles.json b/configs/test_angles.json index 2c8d176..1ce56f5 100644 --- a/configs/test_angles.json +++ b/configs/test_angles.json @@ -1,9 +1,9 @@ { "initial_condition": { - "ang_vel_x" : 5.0, - "ang_vel_y" : 5.0, - "ang_vel_z" : 5.0, + "h_x" : 5.0, + "h_y" : 5.0, + "h_z" : 5.0, "quat_v1" : 10.0, "quat_v2" : 10.0, diff --git a/configs/test_zeroes.json b/configs/test_zeroes.json index 0d63bce..7e76106 100644 --- a/configs/test_zeroes.json +++ b/configs/test_zeroes.json @@ -10,9 +10,9 @@ }, "initial_condition": { - "ang_vel_x" : 0.0, - "ang_vel_y" : 0.0, - "ang_vel_z" : 0.0, + "h_x" : 0.0, + "h_y" : 0.0, + "h_z" : 0.0, "quat_v1" : 0.0, "quat_v2" : 0.0, diff --git a/configs/tli.json b/configs/tli.json index 70266ff..39d588c 100644 --- a/configs/tli.json +++ b/configs/tli.json @@ -7,7 +7,7 @@ "vel_x": -534.084, "vel_y": -3792.878, "vel_z": -867.495, - "ang_vel_x": 4.5, + "h_x": 4.5, "time": 1539102600 }, "models" : ["pos", "gyro"] diff --git a/src/core/state/state.py b/src/core/state/state.py index 19abf49..59ad063 100644 --- a/src/core/state/state.py +++ b/src/core/state/state.py @@ -89,7 +89,7 @@ def array_to_state(values: np.ndarray) -> State: @dataclass class ObservedState(State): # This is the true state with some noise applied - # TODO: Implement noise application + # TODONE: Implement noise application (in gyro model) # angular velocity (radians/second) ang_vel_x: float = 0.0 diff --git a/tests/core_tests/config_test.py b/tests/core_tests/config_test.py index 1cfe969..028671c 100644 --- a/tests/core_tests/config_test.py +++ b/tests/core_tests/config_test.py @@ -19,9 +19,9 @@ class ConfigTestCases(unittest.TestCase): } ic_pos = {"x": 3.0, "y": 3.0, "z": 3.0} ic_all = { - "ang_vel_x": 10.0, - "ang_vel_y": 10.0, - "ang_vel_z": 10.0, + "h_x": 10.0, + "h_y": 10.0, + "h_z": 10.0, "quat_v1": 10.0, "quat_v2": 10.0, "quat_v3": 10.0, @@ -52,9 +52,9 @@ class ConfigTestCases(unittest.TestCase): } zeroes_ic = { - "ang_vel_x": 0.0, - "ang_vel_y": 0.0, - "ang_vel_z": 0.0, + "h_x": 0.0, + "h_y": 0.0, + "h_z": 0.0, "quat_v1": 0.0, "quat_v2": 0.0, "quat_v3": 0.0, @@ -75,9 +75,9 @@ class ConfigTestCases(unittest.TestCase): default_model = ["att", "pos"] angles_ic = { - "ang_vel_x": 5.0, - "ang_vel_y": 5.0, - "ang_vel_z": 5.0, + "h_x": 5.0, + "h_y": 5.0, + "h_z": 5.0, "quat_v1": 10.0, "quat_v2": 10.0, "quat_v3": 10.0, From f50374f055ffbb0fe2da87d69a3572fbe480777d Mon Sep 17 00:00:00 2001 From: tmf97 Date: Sun, 7 May 2023 12:10:06 -0700 Subject: [PATCH 5/8] fix warning --- src/core/sim.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/core/sim.py b/src/core/sim.py index 806925c..8ddd4bc 100644 --- a/src/core/sim.py +++ b/src/core/sim.py @@ -39,8 +39,8 @@ def step(self) -> PropagatedOutput: try: shm = shared_memory.SharedMemory(name=SHRD_MEM_NAME) except (FileNotFoundError) as e: - log.warn(e) - log.warn("Shared Memory likely closed by external process. Recreating with current data.") + log.warning(e) + log.warning("Shared Memory likely closed by external process. Recreating with current data.") shm = shared_memory.SharedMemory(create=True, name=SHRD_MEM_NAME, size=getsizeof(observed_array)) state_array = np.ndarray(observed_array.shape, dtype=observed_array.dtype, buffer=shm.buf) state_array[:] = observed_array[:] From 9b5de5949a2aa33481d3f138d564209b98aa0c55 Mon Sep 17 00:00:00 2001 From: tmf97 Date: Sun, 7 May 2023 12:18:26 -0700 Subject: [PATCH 6/8] formatting --- src/core/sim.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/src/core/sim.py b/src/core/sim.py index 8ddd4bc..a2d8c15 100644 --- a/src/core/sim.py +++ b/src/core/sim.py @@ -11,20 +11,21 @@ from sys import getsizeof from utils.constants import SHRD_MEM_NAME + class CislunarSim: - """This class consolidates all parts of the sim (config, models, state). It is responsible for + """This class consolidates all parts of the sim (config, models, state). It is responsible for stepping the sim and checking stop conditions. """ def __init__(self, config: Config) -> None: self._config = config - self._models = ModelContainer(self._config) #wouldn't need for event-based + self._models = ModelContainer(self._config) # wouldn't need for event-based self.state_time: StateTime = self._config.init_cond self.observed_state = ObservedState() self.should_run = True self.num_iters = 0 - self.event_queue : "Queue[Event]" = Queue() + self.event_queue: "Queue[Event]" = Queue() def step(self) -> PropagatedOutput: """step() is the combined true and observed state after one step.""" @@ -37,7 +38,7 @@ def step(self) -> PropagatedOutput: # Feed the current observed state of the simulator into the shared memory. observed_array = self.observed_state.to_array() try: - shm = shared_memory.SharedMemory(name=SHRD_MEM_NAME) + shm = shared_memory.SharedMemory(name=SHRD_MEM_NAME) except (FileNotFoundError) as e: log.warning(e) log.warning("Shared Memory likely closed by external process. Recreating with current data.") @@ -74,7 +75,7 @@ def should_stop(self) -> bool: log.error("Stopping sim because two years have passed") log.debug(f"Elapsed time = {int(self.state_time.time - self._config.init_cond.time)}s > 6.312e7") return True - + r_e = (state.x**2 + state.y**2 + state.z**2) ** 0.5 if r_e < R_EARTH: log.error("Stopping sim because craft is inside the Earth") From 75c53027d2ffdc87c9f5f4c4b82dc24620db8dca Mon Sep 17 00:00:00 2001 From: tmf97 Date: Sun, 7 May 2023 12:54:37 -0700 Subject: [PATCH 7/8] remove debug logging --- src/core/models/dynamics_model.py | 3 --- src/core/state/statetime.py | 3 ++- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/src/core/models/dynamics_model.py b/src/core/models/dynamics_model.py index 7cafe88..b475236 100644 --- a/src/core/models/dynamics_model.py +++ b/src/core/models/dynamics_model.py @@ -5,7 +5,6 @@ from core.state.state import State from core.state.statetime import StateTime from utils.gnc_utils import quaternion_derivative -import logging class KaneModel(DerivedStateModel): @@ -51,8 +50,6 @@ def d_state(self, state_time: StateTime) -> Dict[str, Any]: cur_quat = np.array([s.quat_v1, s.quat_v2, s.quat_v3, s.quat_r]) angular_vel = np.array([d.ang_vel_x, d.ang_vel_y, d.ang_vel_z]) - logging.info(cur_quat) - logging.info(angular_vel) d_quat = quaternion_derivative(cur_quat, angular_vel) # TODONE: Use angular momentum as state variable and for most dynamics evaluation # then calculate angular rates from momenta b/c inertia matricies change over time diff --git a/src/core/state/statetime.py b/src/core/state/statetime.py index 642fecf..4c2bd50 100644 --- a/src/core/state/statetime.py +++ b/src/core/state/statetime.py @@ -43,7 +43,8 @@ def update(self, state_dict: Dict[str, Union[int, float, bool]]) -> None: self.state.update(state_dict) def update_derived(self, state_dict: Dict) -> None: - """update_derived() is a procedure that updates the fields of the derived state with specified key/value pairs in state_dict. + """update_derived() is a procedure that updates the fields of the derived state with specified key/value pairs + in state_dict. 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", 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", 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