diff --git a/.vscode/settings.json b/.vscode/settings.json index e19f22298..ddd0efc1f 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -132,11 +132,14 @@ "github", "Glauert", "gmaps", + "Gnss", "Gomes", "Gonçalvez", "grav", "Guilherme", "Haim", + "headlength", + "headwidth", "hemis", "hgtprs", "hgtsfc", @@ -245,6 +248,7 @@ "reversesort", "reynolds", "rightarrow", + "rmul", "ROABs", "rocketpy", "rocketusage", diff --git a/CHANGELOG.md b/CHANGELOG.md index f54edd029..30dee4f73 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -32,7 +32,8 @@ Attention: The newest changes should be on top --> ### Added -- DOC : Cavour Flight Example [#682](https://github.com/RocketPy-Team/RocketPy/pull/682) +- ENH: Adds Sensors classes [683](https://github.com/RocketPy-Team/RocketPy/pull/683) +- DOC: Cavour Flight Example [#682](https://github.com/RocketPy-Team/RocketPy/pull/682) - DOC: Halcyon Flight Example [#681](https://github.com/RocketPy-Team/RocketPy/pull/681) - ENH: Adds GenericMotor.load_from_eng_file() method [#676](https://github.com/RocketPy-Team/RocketPy/pull/676) - ENH: Introducing local sensitivity analysis [#575](https://github.com/RocketPy-Team/RocketPy/pull/575) diff --git a/docs/notebooks/sensors.ipynb b/docs/notebooks/sensors.ipynb new file mode 100644 index 000000000..15af3ac0b --- /dev/null +++ b/docs/notebooks/sensors.ipynb @@ -0,0 +1,899 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nvAT8wcRNVEk" + }, + "source": [ + "# Sensor Class usage\n", + "\n", + "This code aims to briefly exemplify the usage of the Sensor classes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "XGK9M8ecNVEp" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from rocketpy import Environment, SolidMotor, Rocket, Flight" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "uRa566HoNVE9" + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "5kl-Je8dNVFI" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Gravity Details\n", + "\n", + "Acceleration of gravity at surface level: 9.7913 m/s²\n", + "Acceleration of gravity at 10.000 km (ASL): 9.7649 m/s²\n", + "\n", + "\n", + "Launch Site Details\n", + "\n", + "Launch Site Latitude: 32.99025°\n", + "Launch Site Longitude: -106.97500°\n", + "Reference Datum: SIRGAS2000\n", + "Launch Site UTM coordinates: 315468.64 W 3651938.65 N\n", + "Launch Site UTM zone: 13S\n", + "Launch Site Surface Elevation: 1400.0 m\n", + "\n", + "\n", + "Atmospheric Model Details\n", + "\n", + "Atmospheric Model Type: custom_atmosphere\n", + "custom_atmosphere Maximum Height: 10.000 km\n", + "\n", + "Surface Atmospheric Conditions\n", + "\n", + "Surface Wind Speed: 4.69 m/s\n", + "Surface Wind Direction: 219.81°\n", + "Surface Wind Heading: 39.81°\n", + "Surface Pressure: 856.02 hPa\n", + "Surface Temperature: 279.07 K\n", + "Surface Air Density: 1.069 kg/m³\n", + "Surface Speed of Sound: 334.55 m/s\n", + "\n", + "\n", + "Earth Model Details\n", + "\n", + "Earth Radius at Launch site: 6371.83 km\n", + "Semi-major Axis: 6378.14 km\n", + "Semi-minor Axis: 6356.75 km\n", + "Flattening: 0.0034\n", + "\n", + "\n", + "Atmospheric Model Plots\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "env = Environment(latitude=32.990254, longitude=-106.974998, elevation=1400)\n", + "env.set_atmospheric_model(\n", + " type=\"custom_atmosphere\", wind_u=[(0, 3), (10000, 3)], wind_v=[(0, 5), (10000, -5)]\n", + ")\n", + "env.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Motor" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Vx1dZObwNVFX" + }, + "outputs": [], + "source": [ + "Pro75M1670 = SolidMotor(\n", + " thrust_source=\"../../data/motors/Cesaroni_M1670.eng\",\n", + " dry_mass=1.815,\n", + " dry_inertia=(0.125, 0.125, 0.002),\n", + " nozzle_radius=33 / 1000,\n", + " grain_number=5,\n", + " grain_density=1815,\n", + " grain_outer_radius=33 / 1000,\n", + " grain_initial_inner_radius=15 / 1000,\n", + " grain_initial_height=120 / 1000,\n", + " grain_separation=5 / 1000,\n", + " grains_center_of_mass_position=0.397,\n", + " center_of_dry_mass_position=0.317,\n", + " nozzle_position=0,\n", + " burn_time=3.9,\n", + " throat_radius=11 / 1000,\n", + " coordinate_system_orientation=\"nozzle_to_combustion_chamber\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Rocket" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "D1fyK8u_NVFh" + }, + "outputs": [], + "source": [ + "calisto = Rocket(\n", + " radius=127 / 2000,\n", + " mass=14.426,\n", + " inertia=(6.321, 6.321, 0.034),\n", + " power_off_drag=\"../../data/calisto/powerOffDragCurve.csv\",\n", + " power_on_drag=\"../../data/calisto/powerOnDragCurve.csv\",\n", + " center_of_mass_without_motor=0,\n", + " coordinate_system_orientation=\"tail_to_nose\",\n", + ")\n", + "\n", + "rail_buttons = calisto.set_rail_buttons(\n", + " upper_button_position=0.0818,\n", + " lower_button_position=-0.618,\n", + " angular_position=45,\n", + ")\n", + "\n", + "calisto.add_motor(Pro75M1670, position=-1.255)\n", + "\n", + "nose_cone = calisto.add_nose(length=0.55829, kind=\"vonKarman\", position=1.278)\n", + "\n", + "fin_set = calisto.add_trapezoidal_fins(\n", + " n=4,\n", + " root_chord=0.120,\n", + " tip_chord=0.060,\n", + " span=0.110,\n", + " position=-1.04956,\n", + " cant_angle=0.5,\n", + " airfoil=(\"../../data/calisto/NACA0012-radians.csv\", \"radians\"),\n", + ")\n", + "\n", + "tail = calisto.add_tail(\n", + " top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194656\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adds Sensors to the Rocket" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from rocketpy import Accelerometer, Gyroscope, Barometer, GnssReceiver\n", + "\n", + "accel_noisy_nosecone = Accelerometer(\n", + " sampling_rate=100,\n", + " consider_gravity=False,\n", + " orientation=(60, 60, 60),\n", + " measurement_range=70,\n", + " resolution=0.4882,\n", + " noise_density=0.05,\n", + " random_walk_density=0.02,\n", + " constant_bias=1,\n", + " operating_temperature=25,\n", + " temperature_bias=0.02,\n", + " temperature_scale_factor=0.02,\n", + " cross_axis_sensitivity=0.02,\n", + " name='Accelerometer in Nosecone',\n", + ")\n", + "accel_clean_cdm = Accelerometer(\n", + " sampling_rate=100,\n", + " consider_gravity=False,\n", + " orientation=[\n", + " [0.25, -0.0581, 0.9665],\n", + " [0.433, 0.8995, -0.0581],\n", + " [-0.8661, 0.433, 0.25],\n", + " ],\n", + " name='Accelerometer in CDM',\n", + ")\n", + "calisto.add_sensor(accel_noisy_nosecone, 1.278)\n", + "calisto.add_sensor(accel_clean_cdm, -0.10482544178314143) # , 127/2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Identification:\n", + "\n", + "Name: Accelerometer in Nosecone\n", + "Type: Accelerometer\n", + "\n", + "Orientation of the Sensor:\n", + "\n", + "Orientation: (60, 60, 60)\n", + "Normal Vector: (0.9665063509461147, -0.058012701892006885, 0.2500000000000297)\n", + "Rotation Matrix:\n", + " [0.25, -0.06, 0.97]\n", + " [0.43, 0.9, -0.06]\n", + " [-0.87, 0.43, 0.25]\n", + "\n", + "Quantization:\n", + "\n", + "Measurement Range: -70 to 70 (m/s^2)\n", + "Resolution: 0.4882 m/s^2/LSB\n", + "\n", + "Noise:\n", + "\n", + "Noise Density: (0.05, 0.05, 0.05) m/s^2/√Hz\n", + "Noise Variance: (1, 1, 1) (m/s^2)^2\n", + "Random Walk Density: (0.02, 0.02, 0.02) m/s^2/√Hz\n", + "Random Walk Variance: (1, 1, 1) (m/s^2)^2\n", + "Constant Bias: (1, 1, 1) m/s^2\n", + "Operating Temperature: 25 K\n", + "Temperature Bias: (0.02, 0.02, 0.02) m/s^2/K\n", + "Temperature Scale Factor: (0.02, 0.02, 0.02) %/K\n", + "Cross Axis Sensitivity: 0.02 %\n", + "Identification:\n", + "\n", + "Name: Accelerometer in CDM\n", + "Type: Accelerometer\n", + "\n", + "Orientation of the Sensor:\n", + "\n", + "Orientation: [[0.25, -0.0581, 0.9665], [0.433, 0.8995, -0.0581], [-0.8661, 0.433, 0.25]]\n", + "Normal Vector: (0.9665010341566599, -0.05810006216709978, 0.25000026750042936)\n", + "Rotation Matrix:\n", + " [0.25, -0.06, 0.97]\n", + " [0.43, 0.9, -0.06]\n", + " [-0.87, 0.43, 0.25]\n", + "\n", + "Quantization:\n", + "\n", + "Measurement Range: -inf to inf (m/s^2)\n", + "Resolution: 0 m/s^2/LSB\n", + "\n", + "Noise:\n", + "\n", + "Noise Density: (0, 0, 0) m/s^2/√Hz\n", + "Noise Variance: (1, 1, 1) (m/s^2)^2\n", + "Random Walk Density: (0, 0, 0) m/s^2/√Hz\n", + "Random Walk Variance: (1, 1, 1) (m/s^2)^2\n", + "Constant Bias: (0, 0, 0) m/s^2\n", + "Operating Temperature: 25 K\n", + "Temperature Bias: (0, 0, 0) m/s^2/K\n", + "Temperature Scale Factor: (0, 0, 0) %/K\n", + "Cross Axis Sensitivity: 0 %\n" + ] + } + ], + "source": [ + "accel_noisy_nosecone.prints.all()\n", + "accel_clean_cdm.prints.all() # should have the same rotation matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "gyro_clean = Gyroscope(sampling_rate=100)\n", + "gyro_noisy = Gyroscope(\n", + " sampling_rate=100,\n", + " resolution=0.001064225153655079,\n", + " orientation=(-60, -60, -60),\n", + " noise_density=[0, 0.03, 0.05],\n", + " noise_variance=1.01,\n", + " random_walk_density=[0, 0.01, 0.02],\n", + " random_walk_variance=[1, 1, 1.05],\n", + " constant_bias=[0, 0.3, 0.5],\n", + " operating_temperature=25,\n", + " temperature_bias=[0, 0.01, 0.02],\n", + " temperature_scale_factor=[0, 0.01, 0.02],\n", + " cross_axis_sensitivity=0.5,\n", + " acceleration_sensitivity=[0, 0.0008, 0.0017],\n", + " name=\"Gyroscope\",\n", + ")\n", + "calisto.add_sensor(gyro_clean, -0.10482544178314143) # +0.5, 127/2000)\n", + "calisto.add_sensor(gyro_noisy, 1.278 - 0.4, 127 / 2000 - 127 / 4000)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "barometer_clean = Barometer(\n", + " sampling_rate=50,\n", + " measurement_range=100000,\n", + " resolution=0.16,\n", + " noise_density=19,\n", + " noise_variance=19,\n", + " random_walk_density=0.01,\n", + " constant_bias=1,\n", + " operating_temperature=25,\n", + " temperature_bias=0.02,\n", + " temperature_scale_factor=0.02,\n", + ")\n", + "calisto.add_sensor(barometer_clean, -0.10482544178314143 + 0.5, -127 / 2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "gnss_clean = GnssReceiver(\n", + " sampling_rate=1,\n", + " position_accuracy=1,\n", + " altitude_accuracy=1,\n", + ")\n", + "calisto.add_sensor(gnss_clean, -0.10482544178314143 + 0.5, +127 / 2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "calisto.draw(plane=\"yz\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Air Brakes to the Rocket" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def controller_function(\n", + " time, sampling_rate, state, state_history, observed_variables, air_brakes, sensors\n", + "):\n", + " # state = [x, y, z, vx, vy, vz, e0, e1, e2, e3, wx, wy, wz]\n", + " altitude_ASL = state[2]\n", + " altitude_AGL = altitude_ASL - env.elevation\n", + " vx, vy, vz = state[3], state[4], state[5]\n", + "\n", + " # Get winds in x and y directions\n", + " wind_x, wind_y = env.wind_velocity_x(altitude_ASL), env.wind_velocity_y(\n", + " altitude_ASL\n", + " )\n", + "\n", + " # Calculate Mach number\n", + " free_stream_speed = ((wind_x - vx) ** 2 + (wind_y - vy) ** 2 + (vz) ** 2) ** 0.5\n", + " mach_number = free_stream_speed / env.speed_of_sound(altitude_ASL)\n", + "\n", + " # Get previous state from state_history\n", + " previous_state = state_history[-1]\n", + " previous_vz = previous_state[5]\n", + "\n", + " # If we wanted to we could get the returned values from observed_variables:\n", + " # returned_time, deployment_level, drag_coefficient = observed_variables[-1]\n", + "\n", + " # Check if the rocket has reached burnout\n", + " accelerometer = sensors[0]\n", + " if accelerometer.measurement[2] > 0:\n", + " return None\n", + "\n", + " # If below 1500 meters above ground level, air_brakes are not deployed\n", + " if altitude_AGL < 1500:\n", + " air_brakes.deployment_level = 0\n", + "\n", + " # Else calculate the deployment level\n", + " else:\n", + " # Controller logic\n", + " new_deployment_level = (\n", + " air_brakes.deployment_level + 0.1 * vz + 0.01 * previous_vz**2\n", + " )\n", + "\n", + " # Limiting the speed of the air_brakes to 0.2 per second\n", + " # Since this function is called every 1/sampling_rate seconds\n", + " # the max change in deployment level per call is 0.2/sampling_rate\n", + " max_change = 0.2 / sampling_rate\n", + " lower_bound = air_brakes.deployment_level - max_change\n", + " upper_bound = air_brakes.deployment_level + max_change\n", + " new_deployment_level = min(max(new_deployment_level, lower_bound), upper_bound)\n", + "\n", + " air_brakes.deployment_level = new_deployment_level\n", + "\n", + " # Return variables of interest to be saved in the observed_variables list\n", + " return (\n", + " time,\n", + " air_brakes.deployment_level,\n", + " air_brakes.drag_coefficient(air_brakes.deployment_level, mach_number),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "air_brakes = calisto.add_air_brakes(\n", + " drag_coefficient_curve=\"../../data/calisto/air_brakes_cd.csv\",\n", + " controller_function=controller_function,\n", + " sampling_rate=10,\n", + " reference_area=None,\n", + " clamp=True,\n", + " initial_observed_variables=[0, 0, 0],\n", + " override_rocket_drag=False,\n", + " name=\"AirBrakes\",\n", + " controller_name=\"AirBrakes Controller\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# air_brakes.all_info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate Flight" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "v__Ud2p2NVFx" + }, + "outputs": [], + "source": [ + "test_flight = Flight(\n", + " rocket=calisto,\n", + " environment=env,\n", + " rail_length=5.2,\n", + " inclination=85,\n", + " heading=0,\n", + " time_overshoot=False,\n", + " terminate_on_apogee=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the results" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data saved to aaaa.csv\n" + ] + } + ], + "source": [ + "# To export sensor data to a csv file:\n", + "barometer_clean.export_measured_data(\"exported_barometer_data.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# To visualize the altitude.\n", + "# Notice that the air brakes makes the rocket achieve a perfect apogee\n", + "test_flight.altitude()\n", + "test_flight.pressure()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accelerometer" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get first column of every row as time from [(time,(ax,ay,az)),...] = a.measured_data\n", + "time1, ax, ay, az = zip(*accel_noisy_nosecone.measured_data)\n", + "time2, bx, by, bz = zip(*accel_clean_cdm.measured_data)\n", + "\n", + "\n", + "plt.plot(time1, ax, label=\"Noisy Accelerometer\")\n", + "plt.plot(time2, bx, label=\"Clean Accelerometer\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Acceleration ax (m/s^2)\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.title(\"Acceleration comparison - ax\")\n", + "plt.show()\n", + "\n", + "plt.plot(time1, ay, label=\"Noisy Accelerometer\")\n", + "plt.plot(time2, by, label=\"Clean Accelerometer\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Acceleration ay (m/s^2)\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.title(\"Acceleration comparison - ay\")\n", + "plt.show()\n", + "\n", + "plt.plot(time1, az, label=\"Noisy Accelerometer\")\n", + "plt.plot(time2, bz, label=\"Clean Accelerometer\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Acceleration az (m/s^2)\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.title(\"Acceleration comparison - az\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Total Acceleration" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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33nkHCoUCs2bNwo0bNxAUFIQXX3yx1HLBwcE4cOAApk+fjn79+kGj0SA0NBQDBgyATCaDIAhYt24dpkyZgtatW6N58+b48ssv0bNnz+pvVBViUCIiIqrFZDIZ3n77bbz99tul5pW81UDTpk2xadMmi9uKjIzE+fPmt3opvo0GDRqU2qaXl5fVtzSoTjz1RkRERGQBgxIRERGRBQxKRERERBYwKBERERFZwKBEREREZAGDEhEREZEFDEpEREREFjAoEREREVnAoERERERkAYMSERERVcqcOXPQvn17e5dhUwxKREREVCmvv/46du7cae8ybIqf9UZERESV4ubmBjc3N3uXYVPsUSIiIqqlevbsiSlTpuDNN9+Ej48PAgMDMWfOHNP8xMREDBs2DG5ubvDw8MCoUaOQnJxsml/y1NuePXvQuXNnuLq6wsvLC926dUNCQgKuXr0KmUyGY8eOme3/888/R2hoKIxGo62bWmkMSkRERFVNFAFtbvV+6fKk76JoVamrVq2Cq6srDh8+jPnz5+Pdd99FTEwMjEYjhg0bhvT0dOzduxcxMTG4cuUKHn/88TK3o9frMXz4cPTo0QOnT59GbGwsnn/+eQiCgAYNGiAyMhIrVqwwW2fFihV4+umnIZM5bhzhqTciIqKqpssD5gVX2+5kALyKnvzfDUDlWuF127Zti9mzZwMAmjZtikWLFpnGHZ05cwbx8fGoX78+AODbb79Fq1atcPToUTzwwANm28nKykJmZiYeeeQRNG7cGADQsmVL0/yJEyfixRdfxIIFC6BWq3HixAmcOXMGP//8c6XaXF0cN8IRERGRzbVt29bseVBQEFJSUnDhwgXUr1/fFJIAICwsDF5eXrhw4UKp7fj4+ODpp59G//79MWTIEHzxxRe4efOmaf7w4cMhl8vx008/AQBWrlyJXr16oUGDBrZpWBVxmB6ljz76CDNmzMArr7yCzz//HABQUFCA1157DevWrYNGo0H//v2xZMkSBAQEmNZLTEzEpEmTsHv3bri5uWH8+PH48MMPoVA4TNOIiKi2UbpIPTvVxGg0Iis7Gx7u7pApXaxaV6lUmj0XBKHSY4ZWrFiBKVOmYNu2bVi/fj1mzpyJmJgYdO3aFSqVCuPGjcOKFSswYsQIrF27Fl988UWl9lOdHCJNHD16FP/9739LpdqpU6fit99+w4YNG+Dp6YnJkydjxIgROHDgAADAYDBg8ODBCAwMxMGDB3Hz5k2MGzcOSqUS8+bNs0dTiIiIAEGw6vTXPTMaAaVB2qcgVMkmW7ZsiWvXruHatWumXqXz588jIyMDYWFhFtfr0KEDOnTogBkzZiAiIgJr165F165dAUin31q3bo0lS5ZAr9djxIgRVVKrLdn91FtOTg6efPJJfPPNN/D29jZNz8zMxLJly7BgwQL07t0b4eHhWLFiBQ4ePIhDhw4BAP744w+cP38eq1evRvv27TFw4EC89957WLx4MbRarb2aREREdN+LjIxEmzZt8OSTT+LEiRM4cuQIxo0bhx49eqBTp06llo+Pj8eMGTMQGxuLhIQE/PHHH7h06ZLZOKWWLVuia9eumD59OsaMGQNnZ+fqbFKl2D0oRUdHY/DgwYiMjDSbfvz4ceh0OrPpLVq0QEhICGJjYwEAsbGxaNOmjdmpuP79+yMrKwvnzp2rngYQERHVQIIg4Oeff4a3tze6d++OyMhINGrUCOvXry9zeRcXF/z999+IiopCs2bN8PzzzyM6OhovvPCC2XLPPvsstFotJkyYUB3NuGd2PfW2bt06nDhxAkePHi01LykpCSqVCl5eXmbTAwICkJSUZFqmeEgqml80zxKNRgONRmN6npWVBQDQ6XTQ6XSVasv9qKitjtJmURSrpRZHa3d1YbvZ7trAHu3W6XQQRRFGo9Fu9wMSC28JUFRHRe3atQsAzNbZtGmTaVq9evVMg6+LK1p+1qxZmDVrFoxGI+rUqYMff/yxzP0U3/7169fRpk0bhIeHV6hWo9Foen+Qy+Vm86rjONstKF27dg2vvPIKYmJi4OTkVK37/vDDDzF37txS03fv3g0XF+sGwdUEMTExdq5A+jHMzs7G1q1bq22v9m+3fbDdtQvbbXsKhQKBgYHIycmx+7CP7Oxsu+7/bnJycpCYmIhFixbh7bffNnVSlEer1SI/Px/79u2DXq83m5eXl2eLUs3YLSgdP34cKSkp6Nixo2mawWDAvn37sGjRImzfvh1arRYZGRlmvUrJyckIDAwEAAQGBuLIkSNm2y26Y2jRMmWZMWMGpk2bZnqelZWF+vXro1evXvD19a2K5t0XdDodYmJi0Ldv31JXPVSnV2L/AAC4u7tj0KAHbb4/R2l3dWO72e7awB7tLigowLVr1+Dm5lbt//gXEUUR2dnZcHd3h1BFg7mr2iuvvIJ169Zh2LBheOmll0r1DllSUFAAZ2dndO/evdTrm5aWZotSzdgtKPXp0wdnzpwxm/bMM8+gRYsWmD59OurXrw+lUomdO3ciKioKAHDx4kUkJiYiIiICABAREYEPPvgAKSkp8Pf3ByD9F+Hh4XHXEflqtRpqtbrUdKVSWav+oBRxlHYLglCtdThKu6sb2127sN22ZzAYIAgCZDKZ3e4wXXQKq6gOR7Rq1SqsWrXK6vVkMpnp/aHkMa2OY2y3oOTu7o7WrVubTXN1dYWvr69p+rPPPotp06bBx8cHHh4eePnllxEREWG6zLBfv34ICwvD2LFjMX/+fCQlJWHmzJmIjo4uMwgRERERWcMh7qNkyWeffQaZTIaoqCizG04Wkcvl2LJlCyZNmoSIiAi4urpi/PjxePfdd+1YNRER1UailZ+xRhVj79fVoYLSnj17zJ47OTlh8eLFWLx4scV1QkNDq3UAMBERUXFFp3/y8vLui/sC3W+KBmzb6xSyQwUlIiKi+41cLoeXlxdSUlIASPcTqu4B1UajEVqtFgUFBQ47RslaoigiLy8PKSkp8PLyqvDg76rGoERERHSPiq60LgpL1U0UReTn58PZ2dlhr3qrLC8vr7teyW5rDEpERET3SBAEBAUFwd/f3y43+dTpdNi3bx+6d+9eo65yVCqVdutJKsKgREREVEXkcrld3tjlcjn0ej2cnJxqVFByBDXjRCYRERGRDTAoEREREVnAoERERERkAYMSERERkQUMSkREREQWMCgRERERWcCgRERERGQBgxIRERGRBQxKRERERBYwKBERERFZwKBEREREZAGDEhEREZEFDEpEREREFjAoEREREVnAoERERERkAYMSERERkQUMSkREREQWMCgRERERWcCgRERERGQBgxIRERGRBQxKRERERBYwKBERERFZwKBEREREZAGDEhEREZEFDEpEREREFjAoEREREVnAoERERERkAYMSERERkQUMSkREREQWMCgRERERWcCgRERERGQBgxIRERGRBQxKRERERBYwKBERERFZwKBEREREZAGDEhEREZEFDEpEREREFjAoEREREVnAoERERERkAYMSERERkQUMSkREREQWMCgRERERWcCgRERERGQBgxIRERGRBQxKRERERBYwKBERERFZwKBEREREZAGDEhEREZEFDEpEREREFjAoEREREVnAoERERERkAYMSOQwRor1LICIiMsOgRERERGQBgxI5DAGCvUsgIiIyw6BEREREZAGDEhEREZEFDErkMDiYm4iIHM09BSWNRlNVdRARERE5HKuC0u+//47x48ejUaNGUCqVcHFxgYeHB3r06IEPPvgAN27csFWdVAtwMDcRETmaCgWln376Cc2aNcOECROgUCgwffp0bNq0Cdu3b8f//vc/9OjRAzt27ECjRo3w4osv4tatW7aum4iIiMjmFBVZaP78+fjss88wcOBAyGSls9WoUaMAAP/++y8WLlyI1atXY+rUqVVbKREREVE1q1BQio2NrdDG6tati48++uieCqLai4O5iYjI0dj1qrevvvoKbdu2hYeHBzw8PBAREYHff//dNL+goADR0dHw9fWFm5sboqKikJycbLaNxMREDB48GC4uLvD398cbb7wBvV5f3U0hIiKiGsiqoHTz5k2sXr0aW7duhVarNZuXm5uLd99916qd16tXDx999BGOHz+OY8eOoXfv3hg2bBjOnTsHAJg6dSp+/fVXbNiwAXv37sWNGzcwYsQI0/oGgwGDBw+GVqvFwYMHsWrVKqxcuRKzZs2yqg5yDBzMTUREjqbCQeno0aMICwtDdHQ0Ro4ciVatWpkCDQDk5ORg7ty5Vu18yJAhGDRoEJo2bYpmzZrhgw8+gJubGw4dOoTMzEwsW7YMCxYsQO/evREeHo4VK1bg4MGDOHToEADgjz/+wPnz57F69Wq0b98eAwcOxHvvvYfFixeXCnJERERE1qpwUPq///s/PProo7h9+zaSk5PRt29f9OjRAydPnqySQgwGA9atW4fc3FxERETg+PHj0Ol0iIyMNC3TokULhISEmMZMxcbGok2bNggICDAt079/f2RlZZmFOCIiIqLKqNBgbgA4fvw4Fi9eDJlMBnd3dyxZsgQhISHo06cPtm/fjpCQkEoVcObMGURERKCgoABubm746aefEBYWhlOnTkGlUsHLy8ts+YCAACQlJQEAkpKSzEJS0fyieZZoNBqzm2VmZWUBAHQ6HXQ6XaXacT8qaqujtNkoGqulFkdrd3Vhu9nu2oDtrp3ttqUKByVAGlxd3FtvvQWFQoF+/fph+fLllSqgefPmOHXqFDIzM7Fx40aMHz8ee/furdS2KurDDz8s8zTh7t274eLiYtN9O6KYmBg7VyD9GGZn52Dr1q3Vtlf7t9s+2O7ahe2uXWpbu/Py8my+jwoHpdatW+PgwYNo27at2fTXX38dRqMRY8aMqVQBKpUKTZo0AQCEh4fj6NGj+OKLL/D4449Dq9UiIyPDrFcpOTkZgYGBAIDAwEAcOXLEbHtFV8UVLVOWGTNmYNq0aabnWVlZqF+/Pnr16gVfX99KteN+pNPpEBMTg759+0KpVNqtjldi/wAAeLi7Y9CgB22+P0dpd3Vju9nu2oDtrl3tTktLs/k+KhyUxo0bh7179+LFF18sNe/NN9+EKIpYunTpPRdkNBqh0WgQHh4OpVKJnTt3IioqCgBw8eJFJCYmIiIiAgAQERGBDz74ACkpKfD39wcgpWkPDw+EhYVZ3IdarYZarS41XalU1qofsCKO0m5BEKq1Dkdpd3Vju2sXtrt2qW3tro62VjgoTZw4ERMnTrQ4f/r06Zg+fbpVO58xYwYGDhyIkJAQZGdnY+3atdizZw+2b98OT09PPPvss5g2bRp8fHzg4eGBl19+GREREejatSsAoF+/fggLC8PYsWMxf/58JCUlYebMmYiOji4zCBERERFZw6oxSlUtJSUF48aNw82bN+Hp6Ym2bdti+/bt6Nu3LwDgs88+g0wmQ1RUFDQaDfr3748lS5aY1pfL5diyZQsmTZqEiIgIuLq6Yvz48Vbfz4kcA+/MTUREjsbqoHTw4EE8+GDVjCNZtmzZXec7OTlh8eLFWLx4scVlQkNDq3UAMBEREdUeVt2Ze+vWrXj00UdtVQvVcrwzNxEROZoKB6XVq1dj9OjRWLNmjS3rISIiInIYFQpKn3/+OSZOnIjVq1eb3SmbiIiIqCar0BiladOm4csvv8TQoUNtXQ/VYhzMTUREjqZCPUrdunXDkiVLquXGTkRERESOokJBKSYmBg0bNkTfvn1Nn4tGVNU4mJuIiBxNhYKSk5MTfvnlF4SFhWHAgAG2romIiIjIIVT4qje5XI7Vq1ejc+fOtqyHiIiIyGFYdR8lQLoCjsgWOJibiIgcjdVBiYiIiKi2sDoobdu2Dfv37zc9X7x4Mdq3b48nnngCt2/frtLiqHbhYG4iInI0VgelN954w3Tl25kzZ/Daa69h0KBBiI+Px7Rp06q8QCIiIiJ7sfpDcePj4xEWFgYA+PHHH/HII49g3rx5OHHiBAYNGlTlBRIRERHZi9U9SiqVCnl5eQCAHTt2oF+/fgAAHx8f3mOJ7gkHcxMRkaOxukfpoYcewrRp09CtWzccOXIE69evBwD8888/qFevXpUXSERERGQvVvcoLVq0CAqFAhs3bsRXX32FunXrAgB+//133oyS7gkHcxMRkaOpcI/Srl270KNHD4SEhGDLli2l5n/22WdVWhgRERGRvVW4R2nixImoU6cOnnjiCfzwww/Izs62ZV1EREREdlfhoHTlyhXs2bMHYWFh+OSTT+Dv74++ffti4cKFSExMtGWNRERERHZh1Riltm3bYubMmThy5Aji4uIQFRWF33//Hc2bN0f79u0xa9YsHDt2zFa1EhEREVWrSn+ESXBwMF588UVs3boVqampmDlzJq5evYoBAwZg3rx5VVkjERERkV1YfXuAsri6umLkyJEYOXIkDAYD0tPTq2KzRERERHZVqaB09OhR7N69GykpKTAajabpgiDg008/RZ06daqsQCIiIiJ7sToozZs3DzNnzkTz5s0REBAAQbhz75vij4mIiIjud1YHpS+++ALLly/H008/bYNyiIiIiByH1YO5ZTIZunXrZotaiIiIiByK1UFp6tSpWLx4sS1qISIiInIoVp96e/311zF48GA0btwYYWFhUCqVZvM3bdpUZcURERER2ZPVQWnKlCnYvXs3evXqBV9fXw7gJiIiohrL6qC0atUq/Pjjjxg8eLAt6iEiIiJyGFaPUfLx8UHjxo1tUQsRERGRQ7E6KM2ZMwezZ89GXl6eLeohIiIichhWn3r78ssvERcXh4CAADRo0KDUYO4TJ05UWXFERERE9mR1UBo+fLgNyiAiIiJyPFYHpdmzZ9uiDiIiIiKHU6ExSqIo2roOIiIiIodToaDUqlUrrFu3Dlqt9q7LXbp0CZMmTcJHH31UJcURERER2VOFTr0tXLgQ06dPx0svvYS+ffuiU6dOCA4OhpOTE27fvo3z589j//79OHfuHCZPnoxJkybZum4iIiIim6tQUOrTpw+OHTuG/fv3Y/369VizZg0SEhKQn58PPz8/dOjQAePGjcOTTz4Jb29vW9dMREREVC2sGsz90EMP4aGHHrJVLUREREQOxeobThIRERHVFgxKRERERBYwKBERERFZwKBEREREZAGDEhEREZEFVn+ECQAYjUZcvnwZKSkpMBqNZvO6d+9eJYURERER2ZvVQenQoUN44oknkJCQUOqjTQRBgMFgqLLiiIiIiOzJ6qD04osvolOnTvjtt98QFBQEQRBsURcRERGR3VkdlC5duoSNGzeiSZMmtqiHiIiIyGFYPZi7S5cuuHz5si1qISIiInIoVvcovfzyy3jttdeQlJSENm3aQKlUms1v27ZtlRVHREREZE9WB6WoqCgAwIQJE0zTBEGAKIoczE1EREQ1itVBKT4+3hZ1EBERETkcq4NSaGioLeogIiIicjiVuuFkXFwcPv/8c1y4cAEAEBYWhldeeQWNGzeu0uKIiIiI7Mnqq962b9+OsLAwHDlyBG3btkXbtm1x+PBhtGrVCjExMbaokYiIiMgurO5ReuuttzB16lR89NFHpaZPnz4dffv2rbLiiIiIiOzJ6h6lCxcu4Nlnny01fcKECTh//nyVFEVERETkCKwOSnXq1MGpU6dKTT916hT8/f2roiYiIiIih2D1qbfnnnsOzz//PK5cuYIHH3wQAHDgwAF8/PHHmDZtWpUXSLWHCLH8hYiIiKqR1UHpnXfegbu7Oz799FPMmDEDABAcHIw5c+ZgypQpVV4gERERkb1YHZQEQcDUqVMxdepUZGdnAwDc3d2rvDCqfQQI9i6BiIjITKXuo1SEAYmIiIhqsgoFpY4dO2Lnzp3w9vZGhw4dIAiW//M/ceJElRVHREREZE8VCkrDhg2DWq02Pb5bUCKqLA7mJiIiR1OhoDR79mzT4zlz5tiqFiIiIiKHYvV9lBo1aoS0tLRS0zMyMtCoUSOrtvXhhx/igQcegLu7O/z9/TF8+HBcvHjRbJmCggJER0fD19cXbm5uiIqKQnJystkyiYmJGDx4MFxcXODv74833ngDer3e2qaRnXEwNxERORqrg9LVq1dhMBhKTddoNLh+/bpV29q7dy+io6Nx6NAhxMTEQKfToV+/fsjNzTUtM3XqVPz666/YsGED9u7dixs3bmDEiBGm+QaDAYMHD4ZWq8XBgwexatUqrFy5ErNmzbK2aURERERmKnzV2y+//GJ6vH37dnh6epqeGwwG7Ny5Ew0bNrRq59u2bTN7vnLlSvj7++P48ePo3r07MjMzsWzZMqxduxa9e/cGAKxYsQItW7bEoUOH0LVrV/zxxx84f/48duzYgYCAALRv3x7vvfcepk+fjjlz5kClUllVExEREVGRCgel4cOHA5DuozR+/HizeUqlEg0aNMCnn356T8VkZmYCAHx8fAAAx48fh06nQ2RkpGmZFi1aICQkBLGxsejatStiY2PRpk0bBAQEmJbp378/Jk2ahHPnzqFDhw6l9qPRaKDRaEzPs7KyAAA6nQ46ne6e2nA/KWqro7TZKBqrpRZHa3d1YbvZ7tqA7a6d7balCgclo9EIAGjYsCGOHj0KPz+/Ki3EaDTi1VdfRbdu3dC6dWsAQFJSElQqFby8vMyWDQgIQFJSkmmZ4iGpaH7RvLJ8+OGHmDt3bqnpu3fvhouLy7025b4TExNj5wqkH8Ps7Bxs3bq12vZq/3bbB9tdu7DdtUtta3deXp7N92H1DSfj4+NtUQeio6Nx9uxZ7N+/3ybbL27GjBlmn0uXlZWF+vXro1evXvD19bX5/h2FTqdDTEwM+vbtC6VSabc6Xon9AwDg4e6OQYMetPn+HKXd1Y3tZrtrA7a7drW7rIvLqlql7sydm5uLvXv3IjExEVqt1mxeZT7vbfLkydiyZQv27duHevXqmaYHBgZCq9UiIyPDrFcpOTkZgYGBpmWOHDlitr2iq+KKlilJrVab7gtVnFKprFU/YEUcpd2CIFRrHY7S7urGdtcubHftUtvaXR1ttToonTx5EoMGDUJeXh5yc3Ph4+OD1NRU06X51gQlURTx8ssv46effsKePXtKDQYPDw+HUqnEzp07ERUVBQC4ePEiEhMTERERAQCIiIjABx98gJSUFPj7+wOQuh49PDwQFhZmbfOIiIiITKy+PcDUqVMxZMgQ3L59G87Ozjh06BASEhIQHh6OTz75xKptRUdHY/Xq1Vi7di3c3d2RlJSEpKQk5OfnAwA8PT3x7LPPYtq0adi9ezeOHz+OZ555BhEREejatSsAoF+/fggLC8PYsWPx119/Yfv27Zg5cyaio6PL7DUix8U7cxMRkaOxOiidOnUKr732GmQyGeRyOTQaDerXr4/58+fj//7v/6za1ldffYXMzEz07NkTQUFBpq/169eblvnss8/wyCOPICoqCt27d0dgYCA2bdpkmi+Xy7FlyxbI5XJERETgqaeewrhx4/Duu+9a2zQiIiIiM1afelMqlZDJpHzl7++PxMREtGzZEp6enrh27ZpV2xLF8nsQnJycsHjxYixevNjiMqGhodV6tRTZBu/MTUREjsbqoNShQwccPXoUTZs2RY8ePTBr1iykpqbiu+++M13WT0RERFQTWH3qbd68eQgKCgIAfPDBB/D29sakSZNw69YtfP3111VeIBEREZG9WNWjJIoi/P39TT1H/v7+pT6GhKiyOJibiIgcjVU9SqIookmTJlaPRSIiIiK6H1kVlGQyGZo2bVotd8Kk2oeDuYmIyNFYPUbpo48+whtvvIGzZ8/aoh4iIiIih2H1VW/jxo1DXl4e2rVrB5VKBWdnZ7P56enpVVYcERERkT1ZHZQ+//xzG5RBxMHcRETkeKwOSuPHj7dFHUREREQOx+oxSgAQFxeHmTNnYsyYMUhJSQEA/P777zh37lyVFke1CwdzExGRo7E6KO3duxdt2rTB4cOHsWnTJuTk5AAA/vrrL8yePbvKCyQiIiKyF6uD0ltvvYX3338fMTExUKlUpum9e/fGoUOHqrQ4IiIiInuyOiidOXMGjz76aKnp/v7+SE1NrZKiqHbiYG4iInI0VgclLy8v3Lx5s9T0kydPom7dulVSFBEREZEjsDoojR49GtOnT0dSUhIEQYDRaMSBAwfw+uuvY9y4cbaokWoJDuYmIiJHY3VQmjdvHlq0aIH69esjJycHYWFh6N69Ox588EHMnDnTFjUSERER2YXV91FSqVT45ptv8M477+Ds2bPIyclBhw4d0LRpU1vUR0RERGQ3VgelIiEhIQgJCanKWqiW42BuIiJyNBUKStOmTavwBhcsWFDpYoiIiIgcSYWC0smTJyu0MUHgYFyqPA7mJiIiR1OhoLR7925b10FERETkcCr1WW8AcPnyZWzfvh35+fkAAFHk+BIiIiKqWawOSmlpaejTpw+aNWuGQYMGmW4++eyzz+K1116r8gKp9uBgbiIicjRWB6WpU6dCqVQiMTERLi4upumPP/44tm3bVqXFEREREdmT1bcH+OOPP7B9+3bUq1fPbHrTpk2RkJBQZYVR7cPB3ERE5Gis7lHKzc0160kqkp6eDrVaXSVFERERETkCq4PSww8/jG+//db0vOjz3ubPn49evXpVaXFERERE9mT1qbf58+ejT58+OHbsGLRaLd58802cO3cO6enpOHDggC1qpFqCg7mJiMjRWN2j1Lp1a/zzzz946KGHMGzYMOTm5mLEiBE4efIkGjdubIsaiYiIiOyiUp/15unpibfffruqa6FajoO5iYjI0Vjdo7RixQps2LCh1PQNGzZg1apVVVIUERERkSOwOih9+OGH8PPzKzXd398f8+bNq5KiiIiIiByB1UEpMTERDRs2LDU9NDQUiYmJVVIU1U4czE1ERI7G6qDk7++P06dPl5r+119/wdfXt0qKIiIiInIEVgelMWPGYMqUKdi9ezcMBgMMBgN27dqFV155BaNHj7ZFjVRLcDA3ERE5Gquvenvvvfdw9epV9OnTBwqFtLrRaMS4ceM4RomIiIhqFKuDkkqlwvr16/H+++/j1KlTcHZ2Rps2bRAaGmqL+oiIiIjsplL3UQKkD8Ft2rRpVdZCtRwHcxMRkaOxeoxSVFQUPv7441LT58+fj8cee6xKiiIiIiJyBFYHpX379mHQoEGlpg8cOBD79u2rkqKoduJgbiIicjRWB6WcnByoVKpS05VKJbKysqqkKCIiIiJHYHVQatOmDdavX19q+rp16xAWFlYlRRERERE5AqsHc7/zzjsYMWIE4uLi0Lt3bwDAzp078f3335f5GXBEFcXB3ERE5GisDkpDhgzB5s2bMW/ePGzcuBHOzs5o27YtduzYgR49etiiRiIiIiK7qNTtAQYPHozBgweXmn727Fm0bt36noui2omDuYmIyNFYPUappOzsbHz99dfo3Lkz2rVrVxU1ERERETmESgelffv2Ydy4cQgKCsInn3yC3r1749ChQ1VZGxEREZFdWXXqLSkpCStXrsSyZcuQlZWFUaNGQaPRYPPmzbzije4ZB3MTEZGjqXCP0pAhQ9C8eXOcPn0an3/+OW7cuIGFCxfasjYiIiIiu6pwj9Lvv/+OKVOmYNKkSfyMN7IJDuYmIiJHU+Eepf379yM7Oxvh4eHo0qULFi1ahNTUVFvWRkRERGRXFQ5KXbt2xTfffIObN2/ihRdewLp16xAcHAyj0YiYmBhkZ2fbsk4iIiKiamf1VW+urq6YMGEC9u/fjzNnzuC1117DRx99BH9/fwwdOtQWNVItwcHcRETkaO7pPkrNmzfH/Pnzcf36dXz//fdVVRMRERGRQ7jnG04CgFwux/Dhw/HLL79UxeaoluJgbiIicjRVEpSIiIiIaiIGJSIiIiILGJTIYXAwNxERORoGJSIiIiILGJTIYXAwNxERORoGJSIiIiILGJSIiIiILGBQIruTw4AGwk17l0FERFQKgxLZ3WLll9ijfg2R2h32LoWIiMgMgxLZ3QD5UQDASM1m+xZCRERUAoNSCRuPX0f3+btxMSnb3qUQERGRndk1KO3btw9DhgxBcHAwBEHA5s2bzeaLoohZs2YhKCgIzs7OiIyMxKVLl8yWSU9Px5NPPgkPDw94eXnh2WefRU5OTqVren3DX0hMz8O8rRcqvQ0iIiKqGewalHJzc9GuXTssXry4zPnz58/Hl19+iaVLl+Lw4cNwdXVF//79UVBQYFrmySefxLlz5xATE4MtW7Zg3759eP755++5Np3BeM/bICIiovubwp47HzhwIAYOHFjmPFEU8fnnn2PmzJkYNmwYAODbb79FQEAANm/ejNGjR+PChQvYtm0bjh49ik6dOgEAFi5ciEGDBuGTTz5BcHCwVfXkaPSmx14uykq2quy2pOdqIQJQyATIZQKUchnkMgEKmQBB4I0WiYiIHJFdg9LdxMfHIykpCZGRkaZpnp6e6NKlC2JjYzF69GjExsbCy8vLFJIAIDIyEjKZDIcPH8ajjz5a5rY1Gg00Go3peVZWFgDgSvKdcUk6vRE6ne6e2pCj0ePX0zex7uh1nL9pecyTTAAUchlcVXK4qRVwd1LATa1AXS8n9GhWB92b+sLdqeqCW5Gi9t1rO+9V8ZZVRy2O0u7qxnaz3bUB2107221LDhuUkpKSAAABAQFm0wMCAkzzkpKS4O/vbzZfoVDAx8fHtExZPvzwQ8ydO7fU9G0HjgFwBwDEXU/G1q1bK1X7tRzgQLIMx1MFaI3l9xYZRUCrN0KrN+J2nvlB/+nUTcgEEY3dRbTyFtHaW0Qd50qVZVFMTEzVbtBKwwq/G42GSr/mlWHvdtsL2127sN21S21rd15ens334bBByZZmzJiBadOmmZ5nZWWhfv36cA1sCFxPlSaq3TBoULcKbzNXo8eWM0lYd/Q6zt7IMk1v5OeC0Q/Ux/D2QfByVsJgFGEwitAVftcbRegNRugMIvK0euRoDMjR6JFdoMfZG1nYffEW4m7l4lKWgEtZwOYEoJGfKyJb1sHAVoFoFexe6VN3Op0OMTEx6Nu3L5TKqu+xqrCT0jeZTI5BgwbZfHcO0+5qxnaz3bUB21272p2WlmbzfThsUAoMDAQAJCcnIygoyDQ9OTkZ7du3Ny2TkpJitp5er0d6erpp/bKo1Wqo1epS06/fvjNIPCNfV6EftnM3MrH2cCJ+PnXDNMZJJZdhQOtAPNElBF0a+lQ6yAzvCMx8BLiamoudf6dg54VkHIlPx5XUXHz9Zy6+/vMqQnxcMLhtEAa3CUKrYI9K7UupVDrML1Z11uFI7a5ObHftwnbXLrWt3dXRVocNSg0bNkRgYCB27txpCkZZWVk4fPgwJk2aBACIiIhARkYGjh8/jvDwcADArl27YDQa0aVLF6v3efX2nS68jDwtDEYRclnp4FGgM+CXUzew5kgi/rqWYZreyM8VYzqHICq8HnxcVVbv35IGfq549qGGePahhsgq0GHvxVvYdjYJO/9ORmJ6Hr7aE4ev9sShkZ8rJvVsjEc71IVCzltkERER3Su7BqWcnBxcvnzZ9Dw+Ph6nTp2Cj48PQkJC8Oqrr+L9999H06ZN0bBhQ7zzzjsIDg7G8OHDAQAtW7bEgAED8Nxzz2Hp0qXQ6XSYPHkyRo8ebfUVbwCQkqlB0dBiowhk5evgXSLwpOZoMH75EZwrPL2mlAvo30rqPYpo5GvzK9g8nJQY0i4YQ9oFI0+rx+6/b+G3Mzew6+8UXEnNxRsbT+OrPXF4tW8zPNImCLIygh4RERFVjF2D0rFjx9CrVy/T86JxQ+PHj8fKlSvx5ptvIjc3F88//zwyMjLw0EMPYdu2bXBycjKts2bNGkyePBl9+vSBTCZDVFQUvvzyy0rVYzCKZs/T87RmQenfjHyM/d9hXEnNhY+rCs93b4SR4fXg51b6NF51cFEppNNubYOQq9Fj9aEELN0bhyupuZjy/Uks2X0ZU/s2Q7+wAN6CgIiIqBLsGpR69uwJURQtzhcEAe+++y7effddi8v4+Phg7dq1VVKPAea13M7VAnWkx5dTcjB22WHczCxAXS9nfPdsZzSq41Yl+60KrmoFXujRGE90CcGKA1fxzb4r+DspGy98dxxt63lixsCWiGjsa+8yiYiI7iscyFKMsbBHqWh8UXquFgBw5nomRv03FjczC9C4jis2TopwqJBUnLuTElP6NMX+6b0xuVcTuKjkOH09E2O+OYSJq44h7lblP96FiIiotmFQKqbozJtvYVC6nadFbFwaxnxzCOm5WrSt54kNLz6IIM8qvpGRDXi6KPF6/+bY92YvjO0aCrlMwI4Lyej/2T7M/vmsKQQSERGRZQxKxRgKg1LRmKMtp29i/IojyNHoEdHIF2uf61qlV7NVBz83Nd4b3hrbX30YfVr4Q28UsSo2AT3m78bXf8ZDY7B3hURERI7LYW8PYA+iUQTkgJ+7FJT+vCTdfLJvWAAWjukAJ6XcnuXdkyb+7lj29AM4eDkV7/92AedvZuE/f1yCSibHrty/MLBNEHq18IeHDT4qhYiI6H7FoFSMsXBguZ/bnV6jER3rYn5U2xpzX6IHm/hhy8sPYdPJf7Fo1yVcTcvDtnPJ2HYuGUq5gAcb+2FA60D0DQuw29V8REREjoJBqRijKJ2L7N6sDn796yaiOtbF9AEtaty9iGQyASPD62FoG38s3fA78r2bIubvW7ickoO9/9zC3n9u4f9+OoMHGvhgQKtA9G8diLpejj8ui4iIqKoxKJWhfT0vHH27T42/95AgCAh1Awb1bYrpg8JwOSUH288lYfu5JJy+nokj8ek4Ep+Od7ecx5TeTTCtX3N7l0xERFStGJTKIJMJNT4klaWJvxua+DdBdK8muH47D3+cS8a2s0k4cjUdX+66DBe1Ai/2aGzvMomIiKpNzRh4U8XK+ny32qaetwsmPNQQP7wYgbcGtgAAfPT73/gu9qp9CyMiIqpGDEplqPKcpNcCunzg8k7g+nEgOwnISwcM+irekW282KMxXuop9STN/fW8dMdyIiKiWoCn3sogu5fTbnnpgNIFOPEt8Psb5S8f2AZoPgjo/Dzg6mf9/g59BWx7S3osVwNdXgD6zALkd7nM36ADbv4FCCrIDQUV2s0b/Ztj/dFrSMvV4kpqDsJdfayvlYiI6D7DoFSMEjrMVy6BeuEbwLBFQOPe5gvoCgCIwL8nAIMWcK0D3DgJZN8Ebl8FTq2pwF4EaRtFks5IX3s/Bhr1BAYvAHwaAeWFteTzwPqngPS4O9MMGuDgl9LX9KuAs3fp+mMXArveL2wvMFBQQGzuDLQaeveqBQHNAtwReyUNV1PzEB7KoERERDUfg1Ix7ypXIEp+BMgC8N2jgG9TIO2SFFy8QoAre6zfqMIZGPG1FKyaDwIUTkDqP0DcTkCQ3ekNAqTtL+woPQ5qD3SaAHiHAv5hgEvhB9pm3wROrQV2f2C+n/ZPAuc2A7pc6fmqocAT6wGPYOl58jngh/FSe4qRi3qIG58G9F8B7R6/U0fcLiB+HxDcAWg7GgjpggZ+Loi9koaE9DzrXwciIqL7EINSMd1kZ80nFIWK9CvSV0lylRSgXHwBfQGQnSydSgt/GmjcSzoFV1bPkH8L6QsAOr8gBaczPwB/LoCpt+nmKeDXKeUX3fUloP88aT/DlwC/vgIcXwkknQYWtATqPQCkXQbybxeuIEj1RURD5xqIlP+OQN2MI8BPzwOHlgD+LYG/vr+z/RsngWPLAf9WaNp0IQAgIS23/LqIiIhqAAalYhQwSg9Gfw/EvCMFDABo2F06DadyA7wbSKfIZIryT49VhEwmhaY+s6Sv5HPAn58CZ3+8+3oPvgz0mA6o3c2nD/kCaPs4sGKg9Pz60Tvz6nWW5geESc91Ohxr8BKCxKaQ/bVGCmc3T5W9v5RzmJDSGz8L7yIhzasSDSUiIrr/MCgVI0fhJ8T6twRePm6fIgJaASOXS18AIIpARgKQcU0KZqIRCHkQkN/l0IU+CLwZLwWu+L3S4O0+s4EWg0ovK8hgGPgpZBlXgYQDQEAb4OGpgCCX9lWQIZ2Gu/ArAOC/qs8wMO3rKm82ERGRI2JQKsbUoyRzoJdFEKReLO8G1q3n4gP0/6D85QCpvc9stTy/0wTg6gFg5SAECrch5KUhM18HT+eq/QBdofggdyIiIgfA+ygVoyjqUbrbpfW1VYNugFcoAKCZ7DoS0zigm4iIaj4GpWLkQmGPhiP1KDkS3yYAgPpCCq5yQDcREdUCDEplkcntXYFjcg8CAPgjA4m8RQAREdUCDEplYY9S2TykoBQmu4qrqexRIiKimo9BqSwyjlEqU4OHAQDhsktI4BglIiKqBRiUysIepbL5S/dfChRu40Zahn1rISIiqgYMSmXhGKWyufpBVDgDAOQ5N5CvNdi5ICIiIttiUCpBD3nV3HG7JhIE6SNbANQTbnFANxER1XgMSiUYwN6kuxFMQSmVtwggIqIaj0GpBAalcnjVBwDUFW7xppNERFTjMSiVYBD4ktwVe5SIiKgWYSoowcCPv7u7YmOUeIsAIiKq6RiUSjAIDEp3Vfh5b3WFVCSks0eJiIhqNgalEjhGqRye0hilIKQj9XYmtHqjnQsiIiKyHQalEtijVA43f4iu/pAJIiKEs/g3I9/eFREREdkMg1IJRgaluxMECPU7AwCChTQO6CYiohqNQakEo8BTb+VyCwAA+Au3kcAPxyUiohqMQakE9ihVgGddAEB94RYSeHduIiKqwRiUSuAYpQqo0wIA0Ey4zlsEEBFRjcagVIJRxqBULv+WAIAmwg1cS82yczFERES2w6BUAk+9VYBXA4hyNdSCDvrb12EwivauiIiIyCYYlEpgUKoAmcz0mW+BYjKSsgrsXBAREZFtMCiVIPLUW4UIxT/KhFe+ERFRDcWgVAJ7lCqo8KNM6gm3cJUDuomIqIZiUCpBlCntXcL9obBHSbpFAHuUiIioZmJQKkHkDScrxlvqUQoVkpGQyh4lIiKqmRiUSuAYpQoKbAsAaCNcQVJaup2LISIisg0GpRJ46q2CfJtA7+wHlWCAOv0iRJG3CCAiopqHQakE9ihVkCBAFhAGAAjWX8etHI2dCyIiIqp6DEolsEep4mSF45TqC7eQyCvfiIioBmJQKok9ShVXxbcICDImAWlxQHYyoMm55+0RERHdK6aCEtijZIViPUqxafd+iwA1tMDCjuYTFU5Ao15Akz5SiG03GlA6S/PyMwBnr3veLxERkSUMSiXJ+ZJUmE9jAEBT2XV8X9m7c+/75O7z9QXAP79LXwCw5dXyt+niB3R6Brj1N+BZH+g4HtDlSVfqJZ8FIEezpJ8h3/A9oHYH+s8D3OoAoggIAmDQA3lpgHtA5dpEREQ1BlNBSexRqjj/lhAhg5+QhazU6wA6lruKSW4a8J9GpqdaUQ6joICTSgVocwDcw1V0eanAvv/ceX5oidlsJYCWAHCzcMKZH8rfZmAbwKcRIFcBPWcA348BjHqg/RgAAuDkCYQNA5y9AV2+1BN28xTg5CV9Lp4gk9Y1aAGFGtDmAnoN4OJzZx96DWA0ACqXYtO00n6KT7sfFb0mglB12zQagCPfAA27A4UXFhARVTUGpZLYo1RxKhdovRpDnXEJLrcvABhasfVEEVgWaXr6u+EBTNZNgVyuxH+Gt4W/uxMCPNQI1F2Hy/n1QNN+QP5t4NIfUpiI3wvUaQ5k3ZB6jcwIuKeQZUnSGekLAM5suDN91/t3Hm993frtthoBXN0P5Kbcmdb+KSCwtRQYi7b/9FYpLHk3BDa/BFzZA7R/Ahg4X/qQ4iIFWVIv2d0CiUEHxP0hLZd8DojbDRg0QOcXgBaD7ixz9U+p1/DmKWm/olEKbbcuAm0fl3rdbl+VQqBMDtQtFpTz0oG9H0s9eQ0eBjY9B7R6FBjwkTTPqz4gyAG5UlpGrpK2o1AD8fuA60eBgNZAvQeAlAtSoJSrpCAqGoGTq6X6Lu+Q9jfif0DLIYDSSfr5OrVWmt5iEJCZBI+8BMjXPwEoVIDaA2jUE2gzUqq7pMx/pZ+ruuHAwYVSjQ9NBY6vBEK6AkoX6XXQ5gGedQFnH+m5XCnt+/ZVqeey5RDp9LBbnTvbvrofcPUH6jSrwA/HXYiiFLrzb0u9rqJRquPGCUDlBiSdhuBXLDxqcqQ21et0b/slqoUEkTfAQVZWFjw9PZH5ljsut52GjmPm2LukaqHT6bB161YMGjQISmXletL0ax6H4tI2vK2bgDffng9Plwps5+wmYOMzpqcNC1ZDtHBdgZtaAX8PNQIKw1OAp1PhYyf4uang46qCt9oIn6OfQdasPxAaIYWFlAuAq5+0kZTzgFwtBS3fJjBe3oGChONQPzAecv/mwOYXK9V2hxH6EJCw33yaR10g61+zScag9kjOFRCUdbIaiyMAgIuvFCyLeDeQAlV52j8lhZ+U89Lz0IeAf48D+vzK1zL2J+Dwf4F/tkmhz7uh1Bt6/mdpDODV/YCbP9BnFpCdBBh1gH/YnbGBRW5fBVTugNpNCrhFCrKAv7dI21Q4mwf5alAVf9fuR7W13WlpafDz80NmZiY8PDxssg92n5QgyFX2LuG+ovCWPvMtREhGQnou2rp43X2F+H1mIemZwB8hXpXuwTS4bRDScjRIydIgKasAeVoDcjR65NzS48qt8sZAPQCPA1nwcd0Nb1cVPJyUcHPKhJtKAVd1Y7g5KeDmPgmuggLOLQfhgv4UejToAi9XJ7hNvgFXtQJuagWclDIIggAYjdJm9flATrIUtE58Kw0qz7wuBbGEg0DjXlLvhzYXyE2V/sPPSZJO4Qa3l+YB0hWCGQnWv8AVUTIkAaVCEgDIbp5CkG0qoPIUD0lAxUISAJxabf68rGNtre8evfNYlweknJO+ACDmnTvzTn5X8W32ex84uEjqoSv62ds8qfRyMiXw+HdA3C6pR7PdGClEylXS75VRJ/1Do8mRXiMnD6lnLqiD1Oup9pACW/JZacyhIABXD0inXp29pX2IIgIzjgNJ9QHfBuant4kqgUGpJN4ewDpB7QAA7WVxuJqWh7b1vO6+/KohpofLWvwPu09p4KqS4+fJ3dDE391s0RyNHslZBUjOKkBKlqbwsQYp2dK0tBwt0vO0yMzXQRSBrAI9sgr0FbxVgRzf/H2s9FSZAFeVHG5qBdycFKYA5aZWwFU9CG7ZCriqw+CiaguXZk/AVaWAc6sn4KqWw1mpgKtaDheVHC4qBVxUcjiLeVApFBDUbtIORFF609RrgIQD0umQB54DPAojTP5t6Sv9CqDJBv7ZLp2CS/kbSLsk9RRFREtvOJpM6dScq7/5qTsLRM8QCJmJpWeEPCiNtcpJNj+VWJxboPRGlX8bmLgLOL9ZeiN18QVCH5TewI7+TzoNBEinzUavBba9JfWA1O0EuAcCx5aVsf8Iad75XwDRIE0T5Hceu/hJbfYKAU6sAq4dkQbg/zatcF9tpFOVBi1w9scyy78QNALN2naFvG574OxG4MjXll8ozxDpFF1Aa6lnBbj78mUJ7ij1BNl6Hd8m0s9JTvKdaZ4h0um+f49bt6178cfMii1n1AHfj77z/M9Py1/n+EqrSlEC6AIAy76QJjh5AW1HAb5NgaTT0s+tyg1o/6TUi9b/A6BZf+mUq5On9PXvcakXz7+l9J5w/ZgU5rTZQMTL0ileXb70j19IBLBzrtT71nGc9PMZtxN4fDXgWe9OYaIo/YNVp7kUKIsuHinJ0nSyG556g/mptytd5qL98Kn2LqlaVElX7b/HgW96I0n0xsYeMZjcu2nZy+VnAB+Hmp7GNXoKfc5L42GWPtURA1pXvq/DYBSRma9Deq4Wt/O0SM/VIitfh1yNXuqR0hiKPdYjp0CH68lpUDq7IldrQE6BHrlaQ6X3Xx6FTICzSg7XwvDkopbDRamQvhcLVXe+S4+l8CWHq1oB58LpriqFaVum3i9RBDRZ0mmOzGvAzb+kP9pdJ5n9wTUd714REJUuUBi10EKBbKMaKoUM6sIvodg6oihCEARk5uuQmaeDQi4gI08HlUIm1aSUw1klN61X9OdEFIF/M/KhVsqQpzHAIIpwVkrtcRY0UBvyIGiyIfo2hsEoQiGXIT1XC73RCFXhY1f1nddFLitdk9EoIl9ngJNSbjbfjCYbOkGNrb//jkGDBkFjFKAoXDZPa4BLsdrLXF1vgFZvhAs0uK2VwUmfCWddBuQqFwCidAqtiDZPelNVlNErnRYnhWOlkxQCD34pDW43aIHIOYDKtfQ6ei2QehGo0wK4eRrwaQj8e0IaC+biA6NOg0tpWincyzRwdXWDq1plei3Mfr+NBdL25Aog55Y0vss9EMi4JvV6hnQFmg0Arh+RenOy/pVOzQlyIPsm0GKwFWHRRuMEazKlq3RsCjLvTOsw1rxXz6eR9I9S/m0p5J3ZAKT+I/0j1WkCjHnpyLlyDK4dRkAe+6XUC95m5J0hCB3GST2HnvWB2EVSD5xeI/XMyRVARqI0Lq9JJHD6ByAnRbry1ysUgCjd3+7PT6Ve9baPA6mXpH+SPIKlMXqZ16Tw6OQljd0DpJ55fb40ntTJ884p6PwM6XdHlydNL8iQTuPK5IVXHuuAGycBv6bAmY2Aax3pVG4Zv6fVceqNQQklglLEPLQfOtneJVWLKglK2cnAp81gFAXMCNuBjx+3MFh047PSf/KFWhrWIl8HvNSzMd4c0KJy+66kstptNIrI00mBKrtAj1yN9JWtKeuxAXlaKVzla6V18gvXzdcaTNO1BqNN2yEIKAwqd0KVi0oKLs6F0zU6A25mFkAmAFn5OqRn5UKuUuF2ntQLVxaVXAaVQgZBkHr15IIAvfHufyYEAXBWSgOjNXojDOUsD0jLixBRoCv/dVIpZDAaRTir5MjTGqCUC9AZRNN+lHIBTgo51Eo5nFUyUyhTK+VIzdYgPSsHRpkSWQX6UtuWCTALqTKZgKx8HWSCgJTssj+aR6WQmQVXhUyAURShkMmQq9VDJZfBSSmFMCelHIIgva5OhcfGSSmDVm8sfA0Ard4oBdXCdVRyGZSKO+0wiCJuZuTDIIpYczgRAoDbeboya3NSyuCmVsBZKYdRk4egOt5wVSvhVhg8i3pJXdTywmkKuKnlhcFUYVpO6kVVQKUoNsZIFKWLKfxbSW/AgiBd5HD2R+ChadKpsuJuX5Xe9I6vlN6Umw2Q3nz1Gqn3ceVgqXd0wnbplHxGAtBpgnRFY/JZKXjWDQfSLgMth0pvxv8elwbxuwdJp+72Lyj354eqmYufdPVxNcjSiPD8KJtjlKqTIK89g+CqhJs/NCofqLXpUCSfAlBGUDq6zCwkdXf+Efm3NejerA5e69e82kq9G5lMMJ1iC6ii3zWdwYg8rRSq8rQG5BUGrDxdscdag/kyhd9zNQbk66RQJoUvvel7UbAQRSC3MJilVvhG5gKgK/sNtojWYDQLefpiiaoonCnkMrMwKIpSD02pvRV2eLmrFdDozbebr7PciyeXSb1TRXlLq5fWyy4MOiWDmM4gQmeQwmzZBABlzzOKMPU2VpRWb4RWb0SGhbBSnYp6kIpekwKdEQU6beFcAf8mZNzT9pVyAa5qBVwLA7mrWglX1RW4qhMKpyngqh4N1wPJcFWnFZ5+VhQGP1fpe8vXoVZIAbEoDKoVMqhn3oZSLkg9eq+erlyBkbOl77cTAH0BdAYDth66iEG9u0EpaqVbheSlSz0xnZ6Relq8Q6W/S94NpJ6KW39L82+eBi78CmRdl07VOXtJvWrXDt3ZX/0uwLXD0mO3AOm0c9qle3mJa55qCknVhUGpBBmDknUEAdo6raH+dx/UmVdKzz+7yTSWRBTkmBK0GolXNAjxccGXo9tbPmVSAyjlMng6y+DpXLU/U0WnnEzhqXio0hlQoJPCV77WYDo1FeLjAo1Wh3/OHEfXrhEI8HKBl7MSBlFEHTc1dAbRdJpJUxgCNHqjqYdFJgBBns6mU2tFp6r0BiMK9EbkafUo0BqhNxrholIgT6tHPW+XOz0tcpnZ8vlaqc4CnQGezkrojNKpOS9nJfJ10ikxQOqdyivstVPIBeQUBiUnpdSzFOjpBINRNG2rQGdEvs4Ajc6AAr0B+VojNDodrpw7hS5duqBBHXd4u6qg1Rvh4aRAvs5g6gUsHlydlXKoFDLIBQFNA9yQXaCHp7MSRlE0Bdr8wmVztXroDCKUMgG5WgNcVXJAADQ6IzR6qSajKEJnMBYeE6lGlVwwzXNRyaEpfM01OimASo+NphqVChnqejlBozeiQ30vtK3nBQ9nJRr6uUIURbPXKkejR1aeBrv3x6JVu44o0ItSb2jh/KILJYpPkx5LP0e5Gj00heFUZxCRkaezWSgUBBQGp8IgpSgMUsUfK8x76Ip/Vxf77qRwgUIQce62HO7XDXB1coa6/Vyz7ZrWH/zZnb8/Pg2l7+2fAAZ+VMYvnUE6TVp05d+tf6TTS2WdMi0pN0065Vq0rDZXOk1edHsHtwDpysCcFOmUaPNB0n50+VJvmyADMq5KvXDXj0n7zU6WTkdlXZeuSDy7Cfr6Ebi6dw0aduoLeadnpFNyWf9KVzG2elQ63aZwlk65KQu/n98snSrr8JR0Wu3vLUBQe2l8YNIZKVQW7ac47wbAkC+AYyukbZTUcihwZS8AUaq/IKP816mIezCQfaPiy1cDBqWSFAxK1lL7hgD/Al6af5Gn1cNFpZC6Ea7uv3OFW/PB+Mzjdfz65004KWVY+lQ4vFx4hWFlyGSF/+Grrfv11el0MCQADzTwLnWqVaUQzE+xWFByLI9CLoObXDrVY7FeCFYtX7xdTko5nJRy+LgW/qx4lltiKTqdDluvn0TXRj532l14Nbu7XAZ3p/J/552UcrPHpnochCAIpV4rnU6HJC8RA1oFVOrUelGPaG45gcpsnimI6U1BT6M3mIXAosdFRLGoF8yIzHu464E5Of53sfyB8QqZUCp4qe4Syu6EMxkUsn+hlAtQyGVQyKSxbwq5TJomk0FR7LtSrodClmdaRiHXQimTQSF3hSI/t3AbblDWHwyFRoBSp4NcroQCApRyAXKfwpvz+rcsuyH1O0PU6XAuDgjtOAhyuULqDXP2AgJaScv0fbf0emHF7n03eo1VrzAA6X5kBr0U+sq7Ka6uQLqNRNFVxaKxcvctTL0sbeN2PKDXwLhnEYBt1m/HCgxKJch4w0mrqUIfAE6vRnfZaSSm56FFoAew/zPpSpBCextMwZc/S7fC/jiqLcKCbXMumYiqhq16RAGpV9TUa6Y3FAaqYqFKZ0SBabrBLGQVlPhefNkCvQEFWj2SbqXD2c0DGoPxzrYLlzc7rWwUC0+7VnkTq5QgAEqZDHKZUBi8pHCmlN+ZphAE5OXKsezaISjl8sJQJkAmSCFOLpPWkcuLnt/5Lj2WmU0rWkcuw511C7dZ9vzMEuvLTNu+M10DmSBAJoP0XSj6LpSYXjhPVsZjjwbSd8+GkAmAzrcLMKlO+S/iPWAqKIFjlCohtBsAoJnwL/bdykELT6NZSLo+9Ae8tDkDADDxoYYY1r6uPaokIgchkwlwkskLe+qq9m/unYs1IsrsSTMYRWjLClyFp0nv9r14KNMbjdAbxMKLCozQGUXoDYXTjIXTDIXTjKLpscEoQldsXb3RCIPhzrSyLpwQRWnsIAwA7noGVMD13Kwqey3vB0ZNRW4Hc28YlEqQ8YaT1vNuCL2ghAs0SP/3EpB+2DQrb+RajN2mRq42FxGNfPHWwOq9wo2IqDh54e06nFVlfHyNAxBFKSwZjNK4Nn2xEGWaVmyevjCgFWh1iD10BO3DwwFBBoMRUggzSssYjXe2qy8McnqjCEPhNozF9qs3FJtf5vrGYtspXF4sse3CeosvZyjcj1GU2mkQpe2KIqTHxecZ71zMYW8MSiUIHKNkPbkCGS4N4Jd7CV4XvgcyvwcAiN3fxMvH/BGfmoJgTycseqKDaVAvERGVJgjSuCSl3HxsXHl0Oh0yLoro08K/Rn2ESfHQZBTNQ5VoBG6lpqLp57atgUGpBDmDUqXI63YA/rmEQYUhCc7eWKgfjp1/X4VKIcPSseHwdVPffSNERETFCII0LsoSfTVcWMF/70uQGwrsXcJ9yStymtnz2J5rsWDXVQDAB8Nbl//RJkRERA6oxgSlxYsXo0GDBnByckKXLl1w5MiRSm3HqQKXSFNpgn9LrG3wAQDgO88X8PzWbADA2K6heKxTfXuWRkREVGk1IhWsX78e06ZNw+zZs3HixAm0a9cO/fv3R0pK+R8UWtwmfTeomvS0TZG1QFDXUWhQsBbvJPdAtkaPTqHeeOeRMHuXRUREVGk1IigtWLAAzz33HJ555hmEhYVh6dKlcHFxwfLly63aziz9BLi7ONmoypovorGv2fMlT3as0E0MiYiIHNV9P5hbq9Xi+PHjmDFjhmmaTCZDZGQkYmNjy1xHo9FAo7lzh7GsLOm+EyqFDIJogO4un0FVk+gKP/NLV85nf1WUHMDwdkHY/NdNPNWlPryd5VW27apU1e2+X7DdbHdtwHbXznbbkiCKooPcqaBybty4gbp16+LgwYOIiIgwTX/zzTexd+9eHD58uNQ6c+bMwdy5c0tNHzlvHZ4KY4/SvdAYgHO3BbTxEaFkZxIREdlQXl4ennjiCWRmZsLDwzaf+HDf9yhVxowZMzBt2p2rtLKyslC/fn0sfKYHfH1977JmzaLT6RATE4O+fftW6X03Hq2yLdmGrdrt6Nhutrs2YLtrV7vT0tJsvo/7Pij5+flBLpcjOTnZbHpycjICAwPLXEetVkOtLn1PH6VSWat+wIqw3bUL2127sN21S21rd3W09b4/OaJSqRAeHo6dO3eaphmNRuzcudPsVBwRERGRte77HiUAmDZtGsaPH49OnTqhc+fO+Pzzz5Gbm4tnnnnG3qURERHRfaxGBKXHH38ct27dwqxZs5CUlIT27dtj27ZtCAgIsHdpREREdB+rEUEJACZPnozJkyfbuwwiIiKqQe77MUpEREREtsKgRERERGQBgxIRERGRBQxKRERERBYwKBERERFZwKBEREREZAGDEhEREZEFDEpEREREFjAoEREREVlQY+7MfS9EUQQAZGdn16pPXdbpdMjLy0NWVhbbXQuw3Wx3bcB21652Z2dnA7jzPm4LDEoA0tLSAAANGza0cyVERERkrbS0NHh6etpk2wxKAHx8fAAAiYmJNnuhHVFWVhbq16+Pa9euwcPDw97lVBu2m+2uDdhutrs2yMzMREhIiOl93BYYlADIZNJQLU9Pz1r1A1bEw8OD7a5F2O7ahe2uXWpru4vex22ybZttmYiIiOg+x6BEREREZAGDEgC1Wo3Zs2dDrVbbu5RqxXaz3bUB28121wZst+3aLYi2vKaOiIiI6D7GHiUiIiIiCxiUiIiIiCxgUCIiIiKygEGJiIiIyIJaE5QWL16MBg0awMnJCV26dMGRI0fuuvyGDRvQokULODk5oU2bNti6dWs1VVo1PvzwQzzwwANwd3eHv78/hg8fjosXL951nZUrV0IQBLMvJyenaqq4asyZM6dUG1q0aHHXde73Yw0ADRo0KNVuQRAQHR1d5vL367Het28fhgwZguDgYAiCgM2bN5vNF0URs2bNQlBQEJydnREZGYlLly6Vu11r/z5Ut7u1W6fTYfr06WjTpg1cXV0RHByMcePG4caNG3fdZmV+V6pbecf76aefLtWGAQMGlLvd+/l4Ayjzd10QBPznP/+xuE1HP94Vec8qKChAdHQ0fH194ebmhqioKCQnJ991u5X9m1BcrQhK69evx7Rp0zB79mycOHEC7dq1Q//+/ZGSklLm8gcPHsSYMWPw7LPP4uTJkxg+fDiGDx+Os2fPVnPllbd3715ER0fj0KFDiImJgU6nQ79+/ZCbm3vX9Tw8PHDz5k3TV0JCQjVVXHVatWpl1ob9+/dbXLYmHGsAOHr0qFmbY2JiAACPPfaYxXXux2Odm5uLdu3aYfHixWXOnz9/Pr788kssXboUhw8fhqurK/r374+CggKL27T274M93K3deXl5OHHiBN555x2cOHECmzZtwsWLFzF06NByt2vN74o9lHe8AWDAgAFmbfj+++/vus37/XgDMGvvzZs3sXz5cgiCgKioqLtu15GPd0Xes6ZOnYpff/0VGzZswN69e3Hjxg2MGDHirtutzN+EUsRaoHPnzmJ0dLTpucFgEIODg8UPP/ywzOVHjRolDh482Gxaly5dxBdeeMGmddpSSkqKCEDcu3evxWVWrFghenp6Vl9RNjB79myxXbt2FV6+Jh5rURTFV155RWzcuLFoNBrLnF8TjjUA8aeffjI9NxqNYmBgoPif//zHNC0jI0NUq9Xi999/b3E71v59sLeS7S7LkSNHRABiQkKCxWWs/V2xt7LaPX78eHHYsGFWbacmHu9hw4aJvXv3vusy99vxLvmelZGRISqVSnHDhg2mZS5cuCACEGNjY8vcRmX/JpRU43uUtFotjh8/jsjISNM0mUyGyMhIxMbGlrlObGys2fIA0L9/f4vL3w8yMzMBoNwPDszJyUFoaCjq16+PYcOG4dy5c9VRXpW6dOkSgoOD0ahRIzz55JNITEy0uGxNPNZarRarV6/GhAkTIAiCxeVqwrEuLj4+HklJSWbH09PTE126dLF4PCvz9+F+kJmZCUEQ4OXlddflrPldcVR79uyBv78/mjdvjkmTJiEtLc3isjXxeCcnJ+O3337Ds88+W+6y99PxLvmedfz4ceh0OrNj16JFC4SEhFg8dpX5m1CWGh+UUlNTYTAYEBAQYDY9ICAASUlJZa6TlJRk1fKOzmg04tVXX0W3bt3QunVri8s1b94cy5cvx88//4zVq1fDaDTiwQcfxPXr16ux2nvTpUsXrFy5Etu2bcNXX32F+Ph4PPzww8jOzi5z+Zp2rAFg8+bNyMjIwNNPP21xmZpwrEsqOmbWHM/K/H1wdAUFBZg+fTrGjBlz1w9HtfZ3xRENGDAA3377LXbu3ImPP/4Ye/fuxcCBA2EwGMpcviYe71WrVsHd3b3cU1D30/Eu6z0rKSkJKpWqVPgv7728aJmKrlMWhRW1030qOjoaZ8+eLfd8dEREBCIiIkzPH3zwQbRs2RL//e9/8d5779m6zCoxcOBA0+O2bduiS5cuCA0NxQ8//FCh/7hqgmXLlmHgwIEIDg62uExNONZUmk6nw6hRoyCKIr766qu7LlsTfldGjx5tetymTRu0bdsWjRs3xp49e9CnTx87VlZ9li9fjieffLLcizHup+Nd0fes6lLje5T8/Pwgl8tLjYxPTk5GYGBgmesEBgZatbwjmzx5MrZs2YLdu3ejXr16Vq2rVCrRoUMHXL582UbV2Z6XlxeaNWtmsQ016VgDQEJCAnbs2IGJEydatV5NONZFx8ya41mZvw+OqigkJSQkICYm5q69SWUp73flftCoUSP4+flZbENNOt4A8Oeff+LixYtW/74Djnu8Lb1nBQYGQqvVIiMjw2z58t7Li5ap6DplqfFBSaVSITw8HDt37jRNMxqN2Llzp9l/1MVFRESYLQ8AMTExFpd3RKIoYvLkyfjpp5+wa9cuNGzY0OptGAwGnDlzBkFBQTaosHrk5OQgLi7OYhtqwrEubsWKFfD398fgwYOtWq8mHOuGDRsiMDDQ7HhmZWXh8OHDFo9nZf4+OKKikHTp0iXs2LEDvr6+Vm+jvN+V+8H169eRlpZmsQ015XgXWbZsGcLDw9GuXTur13W0413ee1Z4eDiUSqXZsbt48SISExMtHrvK/E2wVFyNt27dOlGtVosrV64Uz58/Lz7//POil5eXmJSUJIqiKI4dO1Z86623TMsfOHBAVCgU4ieffCJeuHBBnD17tqhUKsUzZ87YqwlWmzRpkujp6Snu2bNHvHnzpukrLy/PtEzJds+dO1fcvn27GBcXJx4/flwcPXq06OTkJJ47d84eTaiU1157TdyzZ48YHx8vHjhwQIyMjBT9/PzElJQUURRr5rEuYjAYxJCQEHH69Oml5tWUY52dnS2ePHlSPHnypAhAXLBggXjy5EnT1V0fffSR6OXlJf7888/i6dOnxWHDhokNGzYU8/PzTdvo3bu3uHDhQtPz8v4+OIK7tVur1YpDhw4V69WrJ546dcrs912j0Zi2UbLd5f2uOIK7tTs7O1t8/fXXxdjYWDE+Pl7csWOH2LFjR7Fp06ZiQUGBaRs17XgXyczMFF1cXMSvvvqqzG3cb8e7Iu9ZL774ohgSEiLu2rVLPHbsmBgRESFGRESYbad58+bipk2bTM8r8jehPLUiKImiKC5cuFAMCQkRVSqV2LlzZ/HQoUOmeT169BDHjx9vtvwPP/wgNmvWTFSpVGKrVq3E3377rZorvjcAyvxasWKFaZmS7X711VdNr1FAQIA4aNAg8cSJE9Vf/D14/PHHxaCgIFGlUol169YVH3/8cfHy5cum+TXxWBfZvn27CEC8ePFiqXk15Vjv3r27zJ/rorYZjUbxnXfeEQMCAkS1Wi326dOn1OsRGhoqzp4922za3f4+OIK7tTs+Pt7i7/vu3btN2yjZ7vJ+VxzB3dqdl5cn9uvXT6xTp46oVCrF0NBQ8bnnnisVeGra8S7y3//+V3R2dhYzMjLK3Mb9drwr8p6Vn58vvvTSS6K3t7fo4uIiPvroo+LNmzdLbaf4OhX5m1AeoXDDRERERFRCjR+jRERERFRZDEpEREREFjAoEREREVnAoERERERkAYMSERERkQUMSkREREQWMCgRERERWcCgRERERGQBgxIROZynn34aw4cPt9v+x44di3nz5lVo2dGjR+PTTz+1cUVEZC+8MzcRVStBEO46f/bs2Zg6dSpEUYSXl1f1FFXMX3/9hd69eyMhIQFubm7lLn/27Fl0794d8fHx8PT0rIYKiag6MSgRUbVKSkoyPV6/fj1mzZqFixcvmqa5ublVKKDYysSJE6FQKLB06dIKr/PAAw/g6aefRnR0tA0rIyJ74Kk3IqpWgYGBpi9PT08IgmA2zc3NrdSpt549e+Lll1/Gq6++Cm9vbwQEBOCbb75Bbm4unnnmGbi7u6NJkyb4/fffzfZ19uxZDBw4EG5ubggICMDYsWORmppqsTaDwYCNGzdiyJAhZtOXLFmCpk2bwsnJCQEBARg5cqTZ/CFDhmDdunX3/uIQkcNhUCKi+8KqVavg5+eHI0eO4OWXX8akSZPw2GOP4cEHH8SJEyfQr18/jB07Fnl5eQCAjIwM9O7dGx06dMCxY8ewbds2JCcnY9SoURb3cfr0aWRmZqJTp06maceOHcOUKVPw7rvv4uLFi9i2bRu6d+9utl7nzp1x5MgRaDQa2zSeiOyGQYmI7gvt2rXDzJkz0bRpU8yYMQNOTk7w8/PDc889h6ZNm2LWrFlIS0vD6dOnAQCLFi1Chw4dMG/ePLRo0QIdOnTA8uXLsXv3bvzzzz9l7iMhIQFyuRz+/v6maYmJiXB1dcUjjzyC0NBQdOjQAVOmTDFbLzg4GFqt1uy0IhHVDAxKRHRfaNu2remxXC6Hr68v2rRpY5oWEBAAAEhJSQEgDcrevXu3acyTm5sbWrRoAQCIi4srcx/5+flQq9VmA8779u2L0NBQNGrUCGPHjsWaNWtMvVZFnJ2dAaDUdCK6/zEoEdF9QalUmj0XBMFsWlG4MRqNAICcnBwMGTIEp06dMvu6dOlSqVNnRfz8/JCXlwetVmua5u7ujhMnTuD7779HUFAQZs2ahXbt2iEjI8O0THp6OgCgTp06VdJWInIcDEpEVCN17NgR586dQ4MGDdCkSROzL1dX1zLXad++PQDg/PnzZtMVCgUiIyMxf/58nD59GlevXsWuXbtM88+ePYt69erBz8/PZu0hIvtgUCKiGik6Ohrp6ekYM2YMjh49iri4OGzfvh3PPPMMDAZDmevUqVMHHTt2xP79+03TtmzZgi+//BKnTp1CQkICvv32WxiNRjRv3ty0zJ9//ol+/frZvE1EVP0YlIioRgoODsaBAwdgMBjQr18/tGnTBq+++iq8vLwgk1n+0zdx4kSsWbPG9NzLywubNm1C79690bJlSyxduhTff/89WrVqBQAoKCjA5s2b8dxzz9m8TURU/XjDSSKiYvLz89G8eXOsX78eERER5S7/1Vdf4aeffsIff/xRDdURUXVjjxIRUTHOzs749ttv73pjyuKUSiUWLlxo46qIyF7Yo0RERERkAXuUiIiIiCxgUCIiIiKygEGJiIiIyAIGJSIiIiILGJSIiIiILGBQIiIiIrKAQYmIiIjIAgYlIiIiIgsYlIiIiIgs+H9PpDnOjeintwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "abs_a = (np.array(ax) ** 2 + np.array(ay) ** 2 + np.array(az) ** 2) ** 0.5\n", + "abs_b = (np.array(bx) ** 2 + np.array(by) ** 2 + np.array(bz) ** 2) ** 0.5\n", + "\n", + "plt.plot(time2, abs_b, label=\"clean\")\n", + "plt.plot(time1, abs_a, label=\"noisy\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Acceleration (m/s^2)\")\n", + "plt.legend()\n", + "plt.xlim(0, 20)\n", + "plt.grid()\n", + "plt.title(\"Acceleration\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gyroscope" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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SUBT0/6w8mZcLF4LIN3a7Hf379ycBTRAFQihCTkshUdCixWazoW/fvujVqxfC4XC+l0MQcLvdsNu71RxSguh2iLlnEYVESyFR0KKF43A4KIeAIAiCsERUCAlFohQeKiToV0KCIAiiRyHmsYRJtBQUJFoIgiCIbs+OQ+148N212N8SRFioGKLwUGHRLcJDBEEQBJGMv3y2BX/+ZDO8bgcuOra/+jiFhwoLcloIgiCIbk9rMAIAaAtGEBbclSBVDxUUJFoIgiCIbg/PXYkqWnclEI7ma0lEBpBoIQiCILo9vPNtVFE0osVPoqWgINFCEARBdHv4jKGoAk14yB/qmaJFURg+3dCIJn9h9Tgj0UIQBEF0e6JqeEih8BCA+ev24eKnF+N//rMq30tJCxItBEEQRLeHlzZHGdOUPPfU8NDupgAAYE9zMM8rSQ8SLQRBEES3J5GIyzQdcXuqaAnHq6YKbco1iRaCIAii28OdlkiUaRrK9dScFi7iCq1PDYkWgiAIotujOi2MaVr3ByMKFKWwNu5sEIo7LIXWEZhEC0EQBJFTHvtwA57+ZFNe15AoeWYGdyEQ6XluS4iHhwpMsFEbf4IgCCJntATCuP+dtbDbgEsnD4TbmZ/flaNKIqclrHMX/KEofO6etR3yZORCGxhJTgtBEASRM3ibfIVBkwDb2YiJuHqnpasm467c2YT5a/fl5NxctFAiLkEQBEHEEcuL9Q5HZ6KWPCvMsFF31V4tVz+3BD+Z+wX2tQSyfu5CDQ+RaCEIgiAsEQhHsXJnExizvtGJrkY0j6GISFQMD+mcllDXdBv2twbBGHCwLZT1c4d49RAl4hIEQRDdkXv+vQrffmQBPl7faPk1IcHVyPVv9ftaAtjbLHcluMsTkTgtXTE8xBhT3ZBAOPvCIhEeIqeFIAiC6IbsOOQHAGza32r5NWJ4KJc5LYrC8O0/LsD0hz9WN3sRvjkrrDByWsI5HjVAibgEQRBEt4aLjmZ/xPJrRIGQy1BEIBLFvpYgDreHpUMAucsTicqrh7oaQaEMOxeiJZHTQuEhgiAIohvCfztvCVifDBzqJKdFdFf4Jt/YGlSvqfZpkTgtXTERV3s/XS88FI4quPP1lZi3am82l5USEi0EQRCEJVSnJQ3RonVaOk+0bNjXgon/Mw+/eOlrzTqiCjOsoyuGh0SxF8xB87uOJuIu2XoIf1m4FQ++t059LJ0E7Uwh0UIQBEFYgm/2LQHr4aHOymkJCqLFH45i3d5WKAxYtbs5to4kJc/tWQoPtQUjWB2/XkcJCu5KMKxkXRAkBiZmdt72UOzvABd8H67Zhwm/nYcP1uTWeSHRQhAEQVgi2kHREs5hIzPRmQiEFTVPha81mdOSrfDQJc8sxll/+ASfrN/f4XOJ9/PJhkYcfde7eHnJjg6fV3/+iMIyEkTc2eJ/zl+7DwfaQvh4nfXKskwg0UIQBEFYgouOdMJDYnWK3mnZcagdCzceyOragNhv/+1hLlrCYCwhVKIKM4inoKTaKBOWbjsMAJj76ZYOn0sMd/3r611oDkTwX/FQVzYIWyxFb2wN4p5/r8KGfS2ax/l7FlLznGLikDswuYJEC0EQBGGJTJyWSJLN8YT7PsRFf16ElTubOrw2cZP3h6IIxJ2W1mBEc11ZG/9s54zsPOzv8DlykcciIr5fyUJEry7diWcWbMZTH2sHXnIxys/DhWxbjiuxSLQQBEEQlkiIlo5XD4liZr3ut/hMEDfhYCSq5qkoDGgWSqAjimIoeQ6mqM55fdlOfPuRT7D9YLultewxaXCXDtlyf8zQOi3m12psDQIADrRqu/Ly9zvhvsWdliA5LQRBEEQXgG/26fRpEcND4m/0u5sSG3ulz93htemdFrEi6FB7QrQozOgshFLk2tz4wjKs3NmMO15faWkth9utizrOwo0H8LfFWxNryrFo0XQqTuK0cAflsK73TSjuBIV14aFcOy09axY3QRAEkTF8dlAoqiAQjsLrcqR8TcTEadlyoE36eKYENYm4UfiF3IpD7QmXIKIo6prcTjtCESWl08IRhVYqGGOw2WyWj7/oz4sAAEf2KcX4AVVZdVpkawlHEu95skGWvFGfvmFfSOioqyhMdd8op4UgCILoEoi5IVbzWszCEFsOJEIt2XAVNE5LWNE6LcLAQUWBOjCx2B0TXVbzR1Kts9ST8AH2tcTCKnM+3YwLn1poOXl51+GApWtZ5c0VuzH27vcMFU1hi06LqWgR1hdWFDUE1x6knBaCIAiiCyCKFqubsFn10DbBaUkVnrGCvrmc2HvFzGnxuWMiw6qrkeo4cevf0hi7v7v+tQqLNh3Ew++tt7R2t9NueKwjfLxuP5r8YXy6QVulZTk8FA8FNrWHNaXR2hwiBa3xXJZWymkhCIIgugJiqCcTp0UUMKLTko1QiF60BExyWqJKYpMu8aQrWpK7COIatuqSdj/fYl7aLW70boc9rTWlgjtOepGpqR6yEB6KhQQTx4WEz7KpPQyuR7PVqM8MEi0EQRCEJaKa8JA1p0VfbszZKjgt2Wg6Z+jTYuK0RBVFXVOxJx4eStJcTjxvstwXxpjGvdjc2KZ5/ptd5p1yxfeSry2UpZJn/j4068I7Vvu0iGGhw/7E+yiKngNC+K0tFMlpO38SLQRBEIQlNOEhixVEst/oFYVha7ZzWvSJuIIQOdymFQV8HcUWnBbRUQpEoqYbsn7jX7LlkOZnxsw774rX4O+FFaclqjA8+sF6LN5k7uLoOwPz14nLNRONisI0Do0oYELRxL0ciJdFA/w+c1f5lDPR8j//8z+YMmUKfD4fKioqcnUZgiAIopOIZOS0GKuHDrSFNJtyThJxBaflYLuYiMvUMBUPDyW7vnif4SgzLenVb/xfbT8EfyiKUm8iOdesiZ4oKHgIysp78tnGRjzw7jr8+jXzUmxZeEi/VrOcltZQBKJGaxLCbBqnRdfDpS2HFUQ5Ey2hUAgXXHABfvazn+XqEgRBEEQnwRjThHcyScTlokdfFpsN0RKMJHFaNIm4iYGJCafFPBSjz91pbAlKjxPvobbUg3CUYcnWQxqBsGz7YelrxZwWfXv8ZKzf2woA2LS/1dTFkYWH9C6OWU5Lk67fjNirRbzfxjbte5LLCqKciZa77roLN998M0aPHp2rSxAEQRBpMn/tPpzy+/lYsvVQ6oMF9L1UWgIRfLqhEW+t2J30dbIpz37dBpuNnBZ9Iq7GaRFLnoU5RFYScfXirLHVRLTE78FmA04cUgMAWLipUbOuvSadckU3J53w0KbGmGhRGLBpf5v0GN6vplkQX1adFn2Zs/izKEa7hdOSCcFgEM3NzZovgiAIInvMmPMFNu1vw9XPfZnW6/Q5G83+MC5+ejF+9rel2N1kPmtHlvCpz3kIZkO0GJrLiU6LNqclrJY88z4t1nJaAGC/idPCN3G3w47jGqoBAJ9vPqjJHTHrlJtOeEgRTigm+5qNQvALgyMTa9U7LXLRohdsZm6NKAqB3DaY61KiZfbs2SgvL1e/6uvr870kgiCIbkm6pan6jW2fsHkna1svdl6NxjdLfSgj+zkt+jb+iU2VsYTAUMNDktAKYwxPzN+IN5bt0jxu6rTEr+922NG73AvA6EDonQuOJjwUTu60iOJMdFd4qEgP/5wDYUUVROJnApg7XfqKI/FzFtdxQCda2rpKeGjmzJmw2WxJv9asWZPxYmbNmoWmpib1a/v27RmfiyAIgjCnyEILfpGoLoSwS2hpb0/Srl5sER8xCQ9lQ7SEdT1kRJGlNxK4aEoWHlq67TDue3sN/qMLf+3XCRH99V1OOzzxBnHNOpdGP7+HIzoaXAyY5dnw59tDEc1YgXV7TZwWQZxyR0es/AEyCw+JJdkHdEKuLRjBQRNx11HSmj106623YsaMGUmPaWhoyHgxHo8HHo8n49cTBEEQwDe7mrBhXyvG9KvAoJpi6TFW5gaJ6JM1dx5KhISSiQ5ZIq7e2ch2Tos+XKGHixSx5Fk/n8csxLG/RZ6Xwq/vcthU0dIa1G36lsJDiuZ8ZtfR94HZsM/otISjimH0Qk2JByGd02KaiKt3WkwScY05LVFM/8Nn0nN2lLRES21tLWpra3OyEIIgCKLjhCIKLnxqkboRvnzNZDz18SZ8++g6nHt0nXpckTtd0aLd6MQwSSBJ9Y1sYKI+pyXb4aFU3Xq501IsvAfhKIPbmRAtNmjdo7pyL3Y1BfDVtsPSc3Lh5Xba4XE64tfR3pfYnE2kVRQt4eQ5LVzU8HBQfVURth/0Y8uBNgQjUfXagDEEyMM9lnNa4r14SjxOtAYjuj4tYnhIVz0UisAfyk2vlpzltGzbtg3Lli3Dtm3bEI1GsWzZMixbtgytrfK4G0EQBGGdcFTBLf9Yhn98qQ2j72sJaDbtvy7aindX7cUzn2zSHJdueChZ19RknWJlg/kM4aEsOC3pJPPqnZbYY9o16fNuzhjVBwCwZk8L9kncloTTYofHJd9azXJaWqThIXOnZc6nm3HrS18DACY3VMPtsENhQKPO8fDrRUvARLSkCA/VV/kM69dMiY6/3m7j91OAibh33HEHxo0bhzvvvBOtra0YN24cxo0bhy+/TC9jnSAIgjCyfEcT/rl0Jx79YIPmcX1Z7f64I9IajGgqTzqa0yKSrM+JdmBi5yTicvgcHz3czfAJToteJOiF1YBqH0bVlQEAPt3QaDinWD3Ew0PqOuI/B8KKtJ+KJjwUTh0eemvFHkQVhlF1ZbjihAZUFrsAaKdZA8YQF3dO9Oc2C89xkdK/qij2s5DQLBOataWx9I5k1WQdJWeiZe7cuWCMGb6mTp2aq0sSBEH0GPiGpN+Y9jRprfrGlthG4w9FNf0zvGmGh8JJhuola9suK3nmgsAZ/9U8lEQQWUW2ydeUuKXHBgVXhAuKvy7aio/X7VeP0YuWUq8LJwyN9V9ZsN7YNl8WHuJU+dxwxO9V5rak01wuFFHUz/EX04djeJ9SVPpi96mv4tHfA3d09OeWuWiMMazdE0vuHVgdy4sSuwHL3u/eZbGqKXFEQ7bpUiXPBEEQhDW49a8PAezROS0836AtFNVsmOb1PnJ4PorMoUnmtEQ0Tos2p6WsKOYQmA0HjKQR8pFtomVFLrgcxjvlwsApJM0+PG89Ln32c/UYvSNS6nXihHjTuC+3HjQ9pyw85HbaUR6/V5lokSXimlcPJXrQ+Nyx8FZ1XJzpnRbz8JAuEVfyPi/f0YS1e1vgcdpx9pi+AIB2ibgSqS2JOS3bD5JoIQiCIAQC8U3DH9YO8dOHh3gljT8U1Qw5TLdih4uPEq/TIASSOS0hidPCwzNlXvPZP0u3HcLo37yLZxdstrQ+mTNR5HZo8lb0uBxGV4Sj3/BLvU4Mri0BAOw67Dd0CFZLngUhxBFFi6ynjZjTkqq5XFBwWnh4y8xpMSbiRjRrTaxdey8HWoN46uNYDtRZR/VBn3jfmfZwVA0xyoQmDw9tJdFCEARBiATiG5LCtL/17on37uBJkXxvDUUVTSmwuHG9vXIPTnlgPlbskA/0AxJlsS67DaVel+a5pE6L2KclmhBagOC0SATHV9sOwx+OYvFm8wnGIjIRVuRyoNhtLlqcdqPA4GvUCzGXw45epR447DaEo8zQGTchWuyGXBq3QxQtWmHBGJOHh5KIlvaQNienutjEaTELD+nOLQqwA61BnPS7D9X+ND84tl59DxlLVIrphQ4A1MSdFpNB2FmBRAtBEEQBIm5IYiiDh4ca4q6AiBg6Ejeua/66BJsa23Dt80tNr8ddEofDpplcDCRvgy9WmSTa+HOnxWU4Rr++UESBojD15z1NAY2zJLsfjs/tUBvIyZCFckQHS6RfZRGcDjv6xPM2dh7WJpty0eJx2mGzacWQy2lHhU8eHmoLRTXN71LNHgpFFEN4qDIuWpbvbMJv3vhG7eFiDA9F8NzCLbjpxWXatQvCcvmOJrSFoijzOvGL6cMxuaFaExLk3W6TJeLmEhItBEEQBYgoVMQNdl9cmMiayu0Rqjpkya/JWvvz38addrsqNmRr0SMbmJjIaYmHhyQboLh5X/rs55hy7/v4v4VbcNzs9zH7LWPndZlo8bocKPaYJxzHclq0z/N74X+ePbovXvnZFPQtj1XQHFER+1MvWsSSZyBRMQQAHod5TkurrjxY77ToBWJbMNHtl/faqYqLlo/X7cfcz7ZgxpzP0eQPS/u03P76N4b3Qcw72rg/1pbkxKG1uHbaENhsNtjtNtXV8YeiiCrMEB4DYj1jcg2JFoIgiAJEFCr8N2rGWMJpkYgWse27LCeBV/PI4Bubw56m05KkjT8XPzLBwVvNhyIKFmxoRGNrCHfEN1yeb5FqDb4UOS1Ou7E8mYsV/p6OrCvD+AGV6vNHVMY25l160RJ/f7hoEcWQ22lHhUlOi34ooT6nRS8Qxdf7dKKFs/VAO279xzK1soyHq8z6xIiJuFy0DK7V/v3hrk5bKCL9vGw24NiBVUjyVygrkGghCIIoQPwSp6XZH1FdjIES0SIm6cpyEhzJREtcfDgloiWp0yIOTNSFh7j7IBMcqcIkhuOjifVxilypwkM2jSMCABv3t2HOp5vVpFb9uIO6inh46JA8PMTPpwkPOWwojyfL6oWDfiih/r65Y8YnDPBW+m6HXRVIVT5jafe81fvw4HvrAAC9y2NhG7PxBmHBNdm4LxZaGtxLG17kjlW7iWjxuRwo9bowvE+Z9BrZgkQLQRBEASJ2oeUigLssFT4XKn0uw2tEp0WWuOqUlAdzuEvidMgScc2FRUTitPC1c/EjWwvfGNtMZgCZHf+dcUeojw3pXZrUaXFIEnEve/Zz3PWvVZi3ei8AY4n3ERWx7rAGp0UXHhJzZTTVQ3rREndaeEXWjkN+XPrs56oIu/u8UfjblZNwZrwjL0/kFccwVOn60cw860gAiXAfD23tOCxv+iaGehJOi1a0qE5LMCoN5/ni7/Mx/Suk18gWJFoIgiAKEDHJks954RUtvUo9KJJUzexpkificpI5LVE1PGTMaeEiZG9zQD3vmj3NuPIvX2pay/OOuIbqIWl4KPaY1Zbw/BzXnzIUX99xBv59/Qm46Nj61Im4JiXPHK8uUVd1WkwScd1x8aENDzmE8JDW7eDOC+9xAkDT5K6m1IPjh9Sojs+heHhI7OYrOi0VPheuOXmwWlEEQE0eNqtIOtgWwvaD7TjcHlIdJn1OFJ/T1B6KSEULf14MpeWCtAYmEgRBEF0DWXiITxUu9bo0mxpH7OMh23iS5rRoSp514aFIFFsPtGHaA/Nx2ojeeOrSCZjx7BeGRnc8L0YfHpIn4saObQnI8zD0iOGZcp8L5b5yAEieiGu3mc4J4uidln6VJom4UZ3TIjg4YslzcyCCA61BvL5sF3Ye9qsCsKbUg11NxplGPB+F/8lFj/j5VgoChZcdD+tdioWbYuXifeN9Vsx4eckOvLxkB/540TgAseGQeoeKOzttwahJpVbs+DH9KpJeq6OQ00IQBJEHGGP4bGOjobeGVWTVQ9yVKPE4U84WkjstsS1h6bZDeGXJDs2sIrXkWZaIG1awYV8rFAas2xtr/a4XLOI59Im4smqUkEm/FBmKwtRz6xvfpRse0qMfd1AXrx5qCUQ0SbQ8d0eW0+J2Jt6zFn8YP31uCe7+9yo8s2Aznl4QSyoWnRYRfh5+Xu7M+AQnzSX0heFhweF9StXHauP9ZVLxUnz4pqxcnvdqMc1pib9PQ3qV4LppQ1JeK1NItBAEQeSBhZsO4Ed/XozbX1+Z0es1fVrioaK2YEK0yJwWEWlOS3xju/nFZbj1pa9xyz+WqT1RokJOCw/rcIKRqNogTd/fRCRiUvIMGEWUWWt/AIYKFdGp0SfWmoWH3Go/leTvk178+dxO9RotgQgOt4dwxkMf4dlPY517EzktQnjIYVffs+ZABFvifVT4OQCgV5lctNjiGbj8mockOS0iFfFQ0bDeCdHiczvV7sPJ4GuR9VvxqYm4Jk6L8D7/1/ThuOKEQSmvlwkkWgiCIPLA7sMxJ2K3JCRgBZnT0iqIlmQOAxDrlBsVmrYBiZwWPvDutWW7MG/1PgCJ0E6sT4vRaeFNx5L3eolXxuicFsAYIko2+VkvJMREYL1oMeuI65U4IlauBcRCZECsVHjptkNYt7fVcH2t0yKIFn/YkIwLJMI6ZvDz8pLnYp1o4a8/5+g6AMDwPgm3xOd2qGImGVzIygRRsVryHFXL0bXPa1/jylHtM+W0EARB5AG+SSdrgZ8MMWySEC2xP4s9TtPfxDVriCgaseBy2AwOzPp9LTh9ZG9dybPRaeEuD08QdtpthunBXPjw9ZZ4kzgtSWYj6e9NfK2+hb6ZeOOJrUqKnvOy99HpsAOIIhxVDCXMbklOi8uREHpm95VKtHgEdwfQhocA4I3rjseKnU04Y2RvAMBQwWkJRRWDOwbEkozFv0dq6Eki1FSnJRhR8400z+vW43TkxhMh0UIQBJEFgpEodh7yS/MBZPCN1krOhgxZczk1POR1SjcewxqiiqYKSWHGDq18kxRzWob3KUWpxwm73YYmfxiBsKK6PBGFIRxV4HbaEdG5LtzZUTu6uhxwO+0G8QQkd1r0IZ2QWrljV8MpHLPwEBct+lb3huMk4SMeAgpHGZp0zeJc0uohO4rdTths5nN5KnwuuB12zftw6eQBmnOI6MVUXUWRmm8D6JrSsUTSMwCcP7YOJw+vRWNLCP/z5mr18QOtxiRfjtZpMc9p4ThS//XLCAoPEQRBZIFfv7oSp/z+IyzZejDpcVGF4VBbSGgilpnTIm62AUN4yAGnZHAfANQIPT1CEUXtmsp/Fof3AYnqHTGnpabEg89/fRqevmyCeg/iedpDUU1yKOfLrYcw7La31J+9Lgc88eOMOS3pOy36jR0wrx7ipcyBJNcBAK/beE4uTCJRhia/9v1y8fCQrk+L3W5DaZKQXZnXpVn/J7+chrvPOypxDr2DZMFJ+/0FR+Ps0X1x7tg6teQaAAZUF+M74/ppnC4gIYRl5fI+seRZmtOiXU+ySrSOQKKFIAgiC2yKN+VaL+Q3yLjhha9w7P/Mw6bG2HHBDJ0WUewYc1piG5QstFFT4lE3wHBU0eSghCKKoa18c3xTDgs5LfzcPN8j5rRonR8r1Soep13dqPVhqWQN6/Qbor4brUhHnRZZTgtvwheyGB7ij4khmgpd87+yIpemgqpS15pfn3sjExZ6vje+Hx67+Bh4XQ6N08LXYSYspE6LR2guJ/ls9LlD/O9JtiHRQhAEkQX45p+qGdrq3c2IKAyrdsdKg5O1wAdim+rTn2zC9oPthsf13/PQDncXZJtPbalHIxTEMFMoqkjCQ9xpMbbJ5xupmNMCxERUyrCLKxbKcZk5LUlyWgzl0Wo3WuMmbJrTEg/fpHK69G38Y9eJrTkSNYq8RCKuw/CYGLIZWK1t3lZe5NR8FnonRS/IUlWH6RFFC/9e5oYBcrGrcVriibiioNOvR/ZZZAMSLQRBEFkgIVqSN0Pjzgo/LtVsnXv+swq//c9qXPLMYvUxxpi0uRxvec97gsg2n9oSj7qhxMJDWqdFL7qaJTktHI+68Ssa0dIWjCQtfQYSYsCtCh/r4aGowvDAO2sx85XlYIypr5WHh+SihYdvRh9RnnSdso3dFXcRIgozOC1mzeUA7cTm6mK3ZtSCvsuwPjdHn8eTrmip0Fwrtg4zN0x2bp+Q08J70ojvrT4R15Ejp4UScQmCILIA3/ybUzgtfIPljkYwooAxptmk1u1tQanXib7lRXhlyQ4AwJYDCaclHGUQzQY1p4U7LfENRLb51JR6Eu5GVIFfzGmJJnJaeNIoF1dqybOwiat5IeGoJhdmf2sw6XsAJBwpLjTSyWkJKwoe/XADgNisIf5eyHJ4zHI/uGi67pQhKHI5sGF/K15ftivluoHk4SHZ7CGXJDxU7nOhusSjtuWXVfeIGJ2W9LbvMqnTkkZ4yM3DaREE4y5YiceBeJTTkDtEOS0EQRBdGL75pwoP8XCEuMmLLsOuw36c8dDHmDz7A8Nz6rV0LoYhpyX+m7TPZdzYaksS4aH5a/dj4cYD6nMxpyW2idbFh+zxnBZ1YKLEaVGYdnoxn4GUDB7iEfNrRJKFh0RBs2ZPC7YdjDVrkzVFMyu95aLF53bi+lOHpnRcRBLhIWaY0mw1PFRe5FKTot1OuzQMpTmvI3vhoUROi0l4SPL3xifJaUnmtCQbvtkRyGkhCILoIIwxtIcthofi/+GLYZlgRFE3rWXbD6e8nj4PxlDy7NGGh0q9TlVM1ZS61Q3w/nfWas4TiihoiZ/jiIoi7DzsN+S0aMJDgptwUBhH0GjBaeFk4rSIZeIb97eqgmdsvfVhfV6dc5FKNIgkqodkTgsvedZWDwHaDsAVRW5Ux3uz6ENDMlKVPKeiQuK0mAmLZE6LWD1UohEteqeFEnEJgiByxpKth/Dcoq1q2/p0CIQVtf9GMqeFMSbdjIOCCBEdGFGcVAnVJHrR0h6KQlEY2kKJ5nKxP2Mbidi4rEZwWvSIibh8mnFbKIpIVJHO9hE3ZnEYY2OL9XlKbiFUpVlLUtGSuP8N+1rx1bbDAICx9RWWr6sXKemJlsSa06keKtU4LU7UxD9TUcwActFgtdOvGeW+NJwWWU6LJ5HTYkm0UCIuQRBE7pj1z+W4/bWVWB2v6kkHsUdJS9DcaTFLuhUfFxNaNwszasRKDX14KBCOqkm4gOC0xG1+sTdLrZDToieqMLXFfF+hUVlrMKLmtIgJljabTd1MxYoeKzktPBlU5rSIAxBliO/Xih1N6pDGcf0rpMf3r/IBAAbVJCp2vC6902J9O+Qhp0A4ahhboIaHXLLwkOC0+MydFmkuUom2BDpdp6WmxAObLXZu7pqYCQtZmTd/TSiiYMXOJgCxfi9m6yGnhSAIIoccbItt1ofb05+6LG5cyZwWc9GSeL0oWtbvS/R88YejuOffq3DJM4s1x/DnuEPjFCYX882vvCix4VUXe5KWox6Md0Wt8rnVzavZH0k0l9MlWOrDLADQmCKn5SfHD8Rr1x4PQBAtgtOSLJ9FT0swAoUBdeVe9C7zSo9556aT8NEvpmJMv0TeisFpSTE4UYTP1TkgmdAtrR5Sw0PaEE3v+JDEKl1Plr7lRdAzuLZEs/50c1pqSjx44PtH448XjlOTvtNJxBVzVj5YsxcAcPLwWvUxvRAmp4UgCCKH8ETaQAYdakXnI7lokZ9bzNEQm7St25NwfdpDETyzYDM+Wd+Ij9Y1Akjkl/hDUU0Lf74p9SmPbeJj+pWj0udCVbEbVcVu0/AQkMhNKfE61RLd5kBYnT2kL5P1SH4rT+W0XH3SYHXcgVvSpyVVGbiMsSYuCxBzAQZUF8MhVGhlIzzE297LntPPHgK0jkpZkQtnjuqLiyb2x8+nDgYAPHHxMRjZtwwP/XCs4bw2mw2XTh6o/pyuaAFizeZOi88mAszdEFllkttpVx08hcWcl/EDEjlEenfGRSXPBEEQuUFMpM1kFpDofLQEwoYSZo5Z91tzpyUhWsR18ZyOSp8Lja0hBMKJ/ipirsOMKQPRUFOMk4bV4soTB8Fus8Fht5mGhwDgQFtMcJR6nSgrcmFfSzAmWqLGnBZAHlbhibjThtdiUE0Jhvcpwa9eWaE+L4YSXJLwkL6SKBW9yzy45LiBKY9zSBrjcdILD8WdFok44yLMLclp0YaHXCj3uTD7u6PVx84a3Rdnje5ret1vj+mLu//1DZwOO3qVyl2ldJD1abHZzN+LHx5bj2cWbAYATBhYhRKPE1edOAj+cFQz98js3NmARAtBED2SqMKwYV8rhvUuifdKiT2eqkOtDLH7azjKNNVAImZOiyhmxOojs9JhvqlX+NxobA0hFFXU/jBiA7Nij1O6Ceo3bBHVafEknJaWQERoLqd9rb7pGQAcjvceqSx2445zRmJ3k1/zvOgSJLrqCuEhi06Lx2nHWzeeiIHVxbBb2CTFkEUyp+W7446Ax2XHhcf2l56Hi77GuNPicdoNDe6kJc+SCp508Loc+OSXp0BhLO2cFhkyR6vI5ZAKbgD4+dTBqmjhSc+/Pnuk9Ng0om1pQeEhgiB6JH/+ZBOmP/wx/vHldo3oyCQ0oU/G1Ld255i5OGJI6pAwNVh/Xg53Y6p8iVwInkdi1gFWJJnTwq9f6nWp4Yxmf9g0pyWZAOLhBPG3bpdD6/Twa4hVODLRMuusIzHnJ8cazt9QW2JJsACAPWl4KLGmflU+zP7uGBxtUo3E3SbuKNXHE31j14j9KZ095O2YaAFiVUD6uUSZoh+HACQPO1WXePDwD8fixKE1mgnUMnKViEtOC0EQPZKtB2KVOZsb29XQEJCZ09Kue01LIIJepcbjTBNxBTFz2J9atHBhUe5zqZ1rb33pawDmAwJFkokWTqlX7rToEyyT5YLw3AhxA9MnvPJW9mICtCwRt9LnxgQhh0K2llSIgksfAhGdkVT5Irx6iCfiDqjyYUM8aZo39vPqpjwDsTLyo44oQ58yr6XPINeMH1CJE4fWoNLnxhtfx7oBp3Jwzh93BM4fd0TKc1NzOYIgiCzChUIgHNW0ss8kp0V8PWCejGuaiCs8Lm7e+iohTlNctBS5HPjuuH54ZekO9Tl9O3UZyRJxOTHREndaAmFEosaBiYA2T0NPSXwtGqdFd+2KuGtwSBQtEnHncdkNv72n+9u86MjoxZM3yfA/PWr1UNxpqS5x472bT0JEYapQk4WHnA47/nXdCabhl87GYbfhuSsmoTUYUUWLrItyJuRKlOVf6hEEQaTJ+r0tWLzpQOoDk8BdD32vjUyclrag3mmRh4esOC2HhDLatpCJaPEnRMvvf3A0nr9qkvqcmTsjYmVDKfE41aZnyXJajmuoNj0HD1WJQkffjp47LWJYTPY+uR12Q3KnFfElonVazMNDsj4lIvz949GVYo8TQ3uXYkTfMvUYbfVQ4rpdRbCIiPebjVwZAJZDdmmfNydnJQiCyCGX/+UL/OjpxZZm3JjBN0a/XrR0sOQZSOK0mFYPxR6PKkwzcNHM9TnsjwkbvsFMGVyDBy44GqVeJ753TL+U602WhwLEKkiK3U55TovO9j/lyF6m5ymW5LTohUZlPC/nsCWnRXvtdIfy2ZOEh0QRkyovSD/PSJaM7PM4YbPF7l32fFfCYU80CcyklFoGlTwTBEHE2dsURFRh2NsckA7JswIP1fhDUY3oMBMWyWg3hIfMnBazPi2xx/Ut4c3g1Tni7J/vj++H7447wtJvuOJv/vd/fwz6lhfhf99cjVW7mwEAJW4n7HabJqclbBIeGtKrxPQ6JTKnRR8ekjgtspwWj9MBu90Guy3hcJgNQzQjmdPiijs5USV1ZY6+7FsmAks8TvzmnFFwOexpO0L5wOd2IBRRsiZacjXlmUQLQRAFhaIwdVPTT9jV88A7axEIR3Hbt41lmSHBadFWD2WQiBuy6LSkaONv1o3Xabdp2trz4/VhDKuWvBgeGllXhlF15ZqNlVen8J4v7eGo6rToQzQ2mw0NtcXYtL9NIyiARHM7R9LwUOxaB1qDuPIvX6BXmRdTh9VCDxcGTrtd/fyTdfaVoW0uZxQSXqcdbaEofBbDQ+raTPqaXDZlYFrryydFLgcOI4yiNGcamUGJuARBENBu/GalxUDM/Xj0ww0AgGunDTGUifLzBMOKLqclk0RcfclzuqIl9nrRbRApL3JJW8anyr0wQxQoPAQkFS3xRNq2YEQVHrLk179dOQmPf7gRxw6qwg1//0o936i6WI6HmMehT8TlokVhwLzV+wDEcpb08BCLw24D4m93usmeYj6OLGRT5HagLRS1EB7SOy1dO/xjBe4upRJsVqHZQwRBENAmyjb7zVvmiwKgVVKFE9Q4LfLJylbh05X5b++m4SGTc/tDCvyhqKnTIk7oFck0aVIcZM0bnokOSFX8erwSpi0YSVQPSX6D7ltehHvOP0oVKQAw5ohy6Wbu1r2+yO0whFe+2HLI8DruZojXTzunRThcVqp9xQkNOGNkbxzZR1KvLqDP10iVI1QIcAGcrURccloIgiCgTZQVnZa1e1rQForgmP6xXh5iFY5MtIR4TktYm9OSWUfc2Pl7lXqx7WA7WtN0Wp79dDP+8eV2XH/KEOnzZo3I0hnyJyK+h6Ue7bRlQHRa4uGhUFQIz5hvRmI+xDG6niocWX5Hpc+NPc2BpGuWXT9dp0UMscnCQz+LzwBKhZWclkKDf3bZy2kpMKdly5YtuOKKKzBo0CAUFRVh8ODBuPPOOxEKpT9BlSAIgiMmyophmOkPf4zvPv4Z9rXENr9DKfqdmJU8d6Qjbq94UrBZqXKyc7cGI/h0o7yMW+ykKuLNcIMR30OeB6N1WrThofaQWPKcRLQIPT5GH1EuPUaf0wIkknGTkQgPJV6f7m/zfOgjkN6ARD2G6qEshVTyCX8/siZaCm3K85o1a6AoCp588kl88803eOihh/CnP/0J//3f/52rSxIEkWN2HGrHZc9+jk/W78/bGjROSzwRVxF+g97S2A5AGx5qkTotYnO5DnbEjb+eVzK1BaP4ZlcTlu84rDkuVZLvBkkuB2C+qWea0yK7x2SJuK3BiJqIm8zd8Hkyd1o4oosh6qOsOC3RxN+TjjQ/685OS9YScQuteujMM8/EmWeeqf7c0NCAtWvX4oknnsADDzyQq8sSBJFDfvnycny28QA+WrcfW+49Oy9rCISNibhh4TdoviGn6iyr5rSE9M3lMk/E5aKlyR/G2X9cAAD4+s4z1PBOqnLqXU3yEIlpeCiNycQiKUVLXETwjSwQVtT3K5nT4nLY8edLJyCqKDhCN/U3cR2j0KosTtzfcQ3V+GR9I4BY2TB302Q5LelWD8lKqTPBUD3UDUTLd8YdgZ2H/Zg63Fi5lQm5aqLXqTktTU1NqKqqMn0+GAwiGEw0i2pubu6MZREEYZEtjW35XoImmZUn4oq/QfMN+VBbwmmRi5bYcYGIYqm5nKIw05JiHg6qLYmJlj2C+Nh+sB3l8VBJJqEnAKgwES2ZOi1HVBoFhShaquIiQqyi4cnFqX6DPn1k76TPy4RGsfDb/aRBVapoKfW6VNHCw0ri9dPNmxD/nnQEK83lCo0zj+qLM48yTgTvanSaPNywYQMeeeQRXH311abHzJ49G+Xl5epXfX19Zy2PIAgLZLrpZpOApOQ5LPwGzZ8Xc1padW32GWNqeCjWhVZoIS9xQ5bvOIyxd7+L5xZtla6JOy29ymKihefVAMBuQcBY7QGj39jLTJ2WzDbLn00dgosn9de0/xdzTbjT4nEmWudzgZjMabGCzJUQReOYfhXq92JzOi4UxOunmzcRzpbTop90naHjRaRP2u/0zJkzYbPZkn6tWbNG85qdO3fizDPPxAUXXICrrrrK9NyzZs1CU1OT+rV9+/b074ggiJzRJUSL4LTwJm6i7c8reWThIcYYfvXycvz3qys1TdDESiOZsPjly8vRHIjg9tdWStekz2kRz739YDvaQxHc9MJX+Fd8KF0qepV6NT9nW7SUeJz4n++MxpTBNepjWqclJlpsNpsaIko0dOvYBi17vSga64SwEu/IK65NdFdkSb3JCGfJaemO4aFCIe3w0K233ooZM2YkPaahoUH9fteuXZg2bRqmTJmCp556KunrPB4PPJ7MWnITBJF7MukWm/01CE6Lnzstic2ICxkxEZeLlh2H/HjxS+MvQwcF0ZJuTsuepgD84SjsNqB/lc/w/I5Dfny8rhGvLbMmWACgd5kHOw/7AcSchRKTZmfZ6qkBaMMuYiO+YrdT0+G3o06LTGice3QdPlnfiFF1ZairSAg2/qmKokDTpyVPTkt3bC5XKKQtWmpra1Fbay1RZ+fOnZg2bRrGjx+POXPmwJ6jum2CIDqHbP2m2hE0zeV4eEgQMh+u3Ye/LNyC7Qf96mO8T8shk+ZtBzSixSjMkrkLX249CAAY0bcMtSVew/M7DrVjYI1RzCSjd1niPG6H3VScZJrTIkMM0Yg5NGJFENDxqpBJkqnQ3zumH/qUezH6iHK1oR0QnzTtdWK0EDLqUE6LQom4hU7OEnF37tyJqVOnYsCAAXjggQewf3+iRLJPnz65uixBEN0cMRG3NRiBojDNb9CfbjD2OuFOi9lUaHFQYURhiEQVTbJlst/ov4x3b50woFLtayKy/ZA/rWnUNhs0QyBdDptpa/VMq4dkiMnK4r3rXZ50hxRy5v/XVKzZ04zTRhinQtvtNpw41PjLcDiqYOGsUzVhMIc98+qhc8bU4c0VezC4tjit1+mxOnuIyD45Ey3vvfceNmzYgA0bNqBfP+2odMby/9saQRCFiRi+YSzWgyWVA9TYGsK8VXux7WC7tWtEFJQIG5O+bbvIkq0x0TJ+YBWcDjs8TrsmhLXjULupaCmOz7oRqShyaX5zdzsdGvdBJNOOuDLMGuLpm41V6WY4WWVgTTEG1qQnFsq8LsMcINFdSTe/5syj+uC1a4/vsGih8FD+yJk8nDFjBhhj0i+CIIhM0efVNPvDKXMVFmxoxJX/9yVmv7Xa2jV0ISIzp6U9FMGq3bHWDBPizdT0zkRLIIIN+1qlry/xGsVIVbFbsxm7HTYM71OKSYOqcOLQROKsx2m3PNXZCvoKK45Yjuxx2lEmWXO2ee6KiTh5WC1u+/YIw3PanJb0tjCbzYax9RUoNekwbBW9iM2m40Ukh95pgughfL39MB77cEPWkhHzhT5RtiUQsXxPVnNyAroqKbPNce2eFkQVhl6lHrXqRTYh+Kvth6WvFwUBnwGkFy0upx1upx0vXj0Zd3x7pPp4R9rQyzgpLoiqdU6KT7ifXmWenDUNEzlxaC3+cvlE9Ks05gJpwkM56rqaCpdTe910q5iIzKGBiQTRQzjvsU8BxJyAy6YMTPv1mbS3zwX6dTQHwlCy7ODqr2G2OfIEXzFkIpvdElW067PZYqGthtpinHN0HapL3Jj72Ra07I/ERUvieuKGKAqVbCbhAsBlUwaittSDSYO0ibLFwv3oS7HzgSYRN09iQQxReZz2ThFyRAyShwTRw1i/Tz7fJhUtJpOLOxt9r5hYeCg7ooWHdvSixazMl1fciNU9ZuXJIuJk5ZtPH4ZLJw9UHQ6D0+LQbpCcbJY78+ucN/YI9CnXChPROepdlv+WFA5NTkuenBbhulQ51LnQu00QPYxMR8aLDcAAIJKnMJPRaYloSp45w3uX4ocT0uuqrc4I0p1PFA6ia8I74YruirjJj+knn3TMcypkXWgNOS1OUbQkrpPt8JAZXc1pcWlmD+VnC9MIyW4w4bmQINFCED0M2ZRdK+idlmx3x73/nTV4ZsHmlMfp802a/WFD/41SrxPv3HwSfnHmcMvXryp2CwMCzRNxxedUp8Wl7S3CmTBAPmuNd7gVPwsucI6qKzcND4mltZ2V/CnmtIil2PmiI238s4WTnJa8Qe82QfQwMm0O1qJzWkJZFC37WgJ47MONuPet1SkrDHllD7+NlkAEIV14qMIXEwVWQjWcQTXFqnvB5w8t2nQAzy/eptko/YJo4d9rnZbE98cOrJReq0zSnv7aaUPw2cxTcNbovqZOiyhgsp3TYobWacm/aHFqEnHzs4W5TcJ3RO6hRFyC6GFk+p9sLp0WHmYJRxkiCkuaq8CdluoSD/a3BNEcCBvCQzyMks5vwQOri7HtYGyKNXdTLnxqEQBo+nqITgufc2QWHhpk0g9EdVocidfZbDa1Akmb05J4L+x2G9wOO0JRpfNEi6Z6KP/hIU1OizNfTktiDZSD27mQRCSIHoAi5GFYTV480BrEdc8vxYL1jQBy67SIAoh/rygMN7+4DL9/d63mWC4a+G/9sj4tXKykU9UxqManOi2BSBRNwuwisWOuNDxkkogr6wdisyVyZ8xCdc4keRs8RNRZOS1iY7uu4LSIf38zzc/qKKLb4yDV0qmQaCGIHoAoCqyWiT7w7lr8e/lu/PiZxQCMTksomr0S6KDQe4WLoQ37W/HqVzvxyAcbsF3oZMvvRRUtAXPRInLUEWWYMrgaw3uXStcwsKZYdWiCYQWbD7Spz4niR+wT054iEbfM61Sbzk0bXoult5+Or24/HdUlsaTbSp+8yZnbJDwUu7fYtTpLtLgFN6MriJaOtPHPFqKQ7OgASSI9SLQQRA9AdAes5rTsaQpofm7WiZZ0pyEnQ+xyy79vFWbh/Gt5YkIyz2mpVZ0WY06LzMHoX+XD81cdh58cP1C6hoHVxaqLEQhHsaUxIVoCQqt9bXiIi5aEUOGixWaLNY97/MfH4L/OGIb7vj8GVcVuVPjcuPKEBtx17ihcOLG/dC3ajrjy4XxF7s7571scpMgrnPJJRwYmZgtRqFCPls6FRAtB9AACkfRdkRJdaMMQHspiybMmPBQXQ62CSHpjWUK0BHSipSUoc1oSLgR3M846qi8AbTWMyKCaYjVPpD0cxSZBtMiSb/lxgDYptiSeiFviccJut6FXqRfXnTJUUy5cW+rBZVMGqmEiPS5nEqeFh4c6ad7NmCMq1O+zOTYgU7Q5LfnfwigPt3OhRFyC6AGIroi+O6sZYm6GojA0+3XhoazmtCSEABdDotOyZk8Lth1oR/9qn3ovXAQ0+yOGnjE/Pm6A+v1/bjgRK3c24fSRvQFoq2FEij1OVMRFRFN7GLsEpykivGfieylNxI27LmUdmG/jsifJaYmLlWw3lzOjf7UP79x0UsaDErONpk9LFxBRdnJaOhUSLQTRA/ALFn/EomgRN+JD7SG0BXNXPSTmtPDv9c7O/tYA+lf7VIEj5rTw8ND3x/fDT44fiJF9y9TX1VUUqVU5gDaUU+p1anJ1KuMb88G2EDY3yoccpkrE5dc6QrhmuogOglG0dG4iLgAM7yPPA8oHji7Qxl+EREvnQqKFIHoAYnjIqtMiuhcH2kKGEFPuqodi19En/vKcGu501ArVQ3wtZV4XRtXJu9ByxD4qlxw3AH3LvZgyJDYssEoULfvbpK/3S0SLKIRG1ZXhmcsmYJhJwq8VzPq0AImmcp0pWroSzi7QXE6EEnE7FxItBNEDEN0Bq06LGAZpbAkausRaFS1Lth5CVGGYOEjeHRbQhYcixvAQABxoDeGlL7erooGHhxQGNPlDAKz17dAnzV4yeaD6M080XbevBW0heR5QUJqIq+23cuqI3inXkQxxY3brNuZE9VD+XYZ8IOa0dIXpyqRZOhcSLQTRAxDDL4pV0SIIica2kKFaKGghuXfd3hZ874nPAACr7p6uEQx8LdsOtmvOzV0XvdPy5EcbsX5fImRT7nPB5bAhHGU40BoTLVY2MdFp0ZdGc6dl+0G/6es1Jc/h2Bqz7Xq4kyTifntMX+w87Mfxg2uyes1CQXRXuoLTQuGhzoVEC0H0APwZOS2CaBGcFo/TjmBESem0MMbw2/+sVn8+3B42iJb73l6DJz/ehL7CZGG15FknWkTBAsSchjKvCwfaQjjQFndaLIgWcQ36ctWq4tTJs/4UTks2MJvyDAAXTKjHBWkOguxOdIWSZxEKD3Uu+f/ECYLIOaIAiSrWwjp+MTzUGlQdkDKTSch6Vu1uxsfr9qs/i4m8bcEI2kMRPPnxJgDAbqFSJ2gSHtLjdtjVtRxMS7QkBIb+vbDSh0SWiJt90ZL/ScZdla7QXE6EnJbOhZwWgugBiCGNTJyWA60h9ecyrxP7W4IpnZZ9LUHNz1yEBCNRHDf7fdOSYC5amuPVQ33KvNjTHDAcZ7PZUBofPNjYGruWlU1MFAFhXVO68iIXbDYg2cxG7rQwxtTvs11+nCwRt6fjTFIOng/Iaelc8v+JEwSRc/zh9KuHxITTxtZEeIg3RJM1l1u5swmLNh2IPa8TNW3B2Os37mtDSyCCnYfleSN6p6VPufmQPi58eP5LupuY/r1wOuymDd84XAAGwooqbvRhr46SrCNuT8chvB9dIaeFREvnQv8aCKIH0OHqodag+rMaHgobE3Eve/ZzXPLMYjT5w4bw0ZKth3D1c1/igzV7k16Xn5cLkboKo2gZ178ivhatWEhXtOib0gFAlRAikoV9+PoOxyuWAGR94rLoGJHTokXTeK8L5LSQZulcKDxEED0AUWBEo9ZEi1/jtITUBFnuRAR1G34kqqgJsYfbQwan5aF56wAA73yTXLSoHXHjoqVvubZJ2wMXHI2zjuoDwNh1Nt0chyMqjQ3gKovdQLyFf32lD2v3tmie94ejeHjeOjw8bz2AWGJytn/bTpaI29NxdJE+Lf2rfNh2sB3fHlOXtzX0REi0EEQnE4xENbNxOoNApGM5LftaAuAv40JBL0rEa/jD0Yybz6mzh4JctGidluG9S9WhhDynhWPVlfi/yyfi0w2N+N4x/QzPicm49VVFBtESCEcxf20iwTjbSbiAXrTQr/Iizi6SpPzGdcdjzZ4WTErSf4jIPiThCaITue21FTjy9rexuVHebTVXiG38rVYPiaJFTFjlIRl9+Ec8PhBWLPVxkRGMKIgqTBAtWjdEzDkxOi3W/ks7aVgtZn1rhLQNvFj23K/SZ3g+EFaw41C7+nO281mAmJvADQUKD2nRDEzMo2ip8LlxXEM1TXnuZOhfA0F0In9dtA2MAU/M39Cp180op0XilNhsQIkntqk3+8M41JbI6wjo+pdk6rSEIgraQolyZ30irka0FGUmWpLB5w9V+lyaoZGcQ+0hNLYK+Sw5GlzIBRUl4mrhOS02GyXB9kToXwNB5AFJ/mdOEQWIkqyeN05UYVLR4XU61N/8/718N0783Ydoao+VJouJu4FwNOOBisFIVM1ncTvsqClJhGtsNm1ISJ+Im40cB56I26e8SHq+Dbomd/quutmCixVyWrRwodIVknCJzoc+dYLIA1ZDNNlC47RYSMQVQztiXwyvy67ZpFuDETXnQ+O0dCSnJaKolUOlXqfG7Sj1OGEX1qMPD2XDleBTmgfV+KTOjd6p2q/rR5MteC4LJeJqcarvC7ksPRH610AQOYYxhr9/vg2rdjWrj1ks4MkagTT7tIiuCZ/HA8Rm7Oh/899+MJbfIQodfyiq/pxuomoooqA1GHNvSrxOlAjOSrlPK1JKM8xpScb0UX0w+7uj8d/fGmFpY9Q30csW/H0X338ikdMiy0ciuj9UPUQQOebrHU2Y9c8VOCbeWwRIDC38aN1+uOw2TBnS8eF3rcGINAcDSD+nhR/vdthR6nWqG7PX5TCEQ7bFRYsodESnparYjfZQopHc9FG9Uex24p9f7ZReOxiJqk5LiccJj9MBt8OOUFRBRZF2Azf2aen4b99upx0XTewPIL+zbR656BjsONSO+ipjMnBPhue0kNPSMyGpShA5ptkfcw2ahQGAUYWhORDGZc9+jh89vVgjKsxobA1ina78ljN/7T6M+c07eGbBZunzoqCw4rTwHi0el10jhDxOu0G0bD/ERYtYPZTIaRGdApsNeOLi8fj+BGOpMUcfHhL/1HerzbR6yCou3b1Obqg2HDNlsPGxbDCyrgxnjOqTk3MXMjynpSsMSyQ6H3JaCCLHcJEgioUoYwgIZchtwQi8KbqqTvjtPADA/P+aioE1xZrnlu9ogsKAr7cflr5WO+U5da4JFyBel0NT0isLD+046I+/RpuIG5KIlvIiF+x2W9LBhLHwEHdaYqKkxOvEgbaQUbTofs520qpLV51y7tg6LIyPKXj0R+PQHozilBG9snpNIjlcSFKCcs+EPnWCyDERiWhRFAbR7+Bzeazw9Y7Dhsf4tGEzxybTnBavy642cuM/6xvjyZwWfziqdszVixb9Y3qCEUWtHuIOC3d79CKl2O3QtFHPutOiOx/vxAsAQ3uV4gfH1qOmxJPVaxLJOaquHGeP7oufntSQ76UQeYCcFoLIMbxSSO+0hIW655Z44qkVZL0p/PG+Jn5T0ZJeR1ze9r/I5UCJJyFSZE7LnuYAgpEoAppEXEXtbCvO8uGipcJnPpQwltMSez/0okXvtMQmPbvQFA/BObPct0MseXbYbajwufGHC8diT1MAw/uUZvVahDXcTjseu/iYfC+DyBMkWggix8iclqjCNKXHrUK+iwwm9FaRbczcaQmG5aGfYJpOi18MD4lOi9Ohhm6AWI5LMKJg1+GAMRGXOy0lRtHicTpQ7HagLWQUWaGIgpZgIhEXSFQJySYwlxU5VdGS7ZCBWELNxeJ5Y4/I6jUIgrAOhYcIIseoOS1MJ1qE3BJRCMgICa6MQ5KAyEVGwKR1vt9CnxZFYbjyL1/i1n98nQgPOR2aRFyvy47hvRMOw4DqWGXLlgNtmvBQMBxVhZLotIjhnQqTvBZtIm7s+EmDquB22DFhYKXheDEZN9vhIbGsVp/fQhBE50NOC0HkGC4S9E6LOM8nlWgRXQyZ08JnC/klzkUkqmhCQmYdcXce9mPe6tgE5uq4O+Jx2TV9VrwuB+oqijDvlpNQ4XPjrn+twrq9rfj1P1fgWGFwnOi0iOKkQhAtY+srsK8lgPIil6YtfjCcyGnhPVquOqkBl04ZIB00qRUt2Z62rA0PEQSRX8hpIYgcI6seUpg2PNSSIjykGT4o2TvVRFyJ09Kuy3Mxy2kR5/28Fu+hEstp0VYPAcCQXqWoKfHgl9OHo6GmGLuaAnh92S71OH84qoaqitwOVfiI4Z0/XjQOn//3aRjWW5sbEoomqodKNeXW8uoqsa1/LhNxqTMtQeSfnP4rPPfcc9G/f394vV707dsXl1xyCXbt2pX6hQRRYHyx5SB+9fJyHG4PGZ7jIkHROy1phIfEXJWoJLzDhUlAktPSrqtMMstpEfNqxGZyYvWQx6X9L6O+yocrTzRWcfhDCafF7bBLE2kddhsqi92GjrnBsDERNxliyCmXooWcFoLIPzkVLdOmTcM//vEPrF27Fq+88go2btyI73//+7m8JEHkhac+3oQXv9yO91fvMzzHq4cimuohICzM5kmViCs6LTKnhFcPBSThofaQ9txmfVpkwsmrDw/JwjNFRmEh9mlxO+WihVPk1r4+KEnETQYPDznstqwLC7F6iJwWgsg/Oc1pufnmm9XvBwwYgJkzZ+L8889HOByGy2Ve8kgQhQZPQpWVHEckibiKwjTiI52cFpnoSBoe0gkZmVNjtgavSXhIRCZEAmFFFVoep111TGSlzj7dOSMKU7sIl1hyWmLHZLvcGdBOEianhSDyT6cl4h48eBB/+9vfMGXKFFPBEgwGEQwmho81NzdLjyOIzoAxhmBESdmpFkiEXCJRo6CQ5bREFF2fljScFll4hyfghqMMkaiiqXppC+qdFpOclvhxTrtNPcbYEdfoNuhb6QPa2UMepx1XntiAf329C8dLZiwVxZ2cEo9TFU48MVd2bj28wigbE571uJwJoeKkWTcEkXdy7nf+6le/QnFxMaqrq7Ft2za8/vrrpsfOnj0b5eXl6ld9fX2ul0cQGg61hXD53C/w9srd+MncL3Dk7W9jX0sg5ev4Ji8TBNJEXH2flhTN5USnJSxxSkSHJxDRCiee78I3ddOclnjuywlDa9SqGStOi75LLV9PUBUtDpxzdB2eunSCYSozkJgCLctfsRYeih2jnxOUDcT5NrlwcgiCSI+0/5XPnDkTNpst6deaNWvU43/xi1/gq6++wrvvvguHw4FLL71U0yhLZNasWWhqalK/tm/fnvmdEUQGfLx+Pz5Ysw9/+Wwr5q/dDwD419e7U76OOywhidMiFTIsvT4tWqdFew3GmFa06EJUPBGXh1HMnBaeV1NXUYQTh9YCiJUo+zQdcY3/ZUjDQyFtTksyuGjxuR2GEIy18FDs+rmY+iu6NzSgjyDyT9rhoVtvvRUzZsxIekxDQ6KaoKamBjU1NRg2bBhGjBiB+vp6LFq0CJMnTza8zuPxwOOhOR5E/vBLckOs7IWJ8JC50yKi6Pu0pAgPJXNaAmEF4u8B+l4tvJS5zBvrh2LmtPDjSjxO3HP+URj1+TZ8b3w/jQiSJeLKHJLWUERdk34qtB6eiOtxOuB22OFXeDdeu6XkVx5CyoWoEENCFB4iiPyTtmipra1FbW1tRhdT4r8hinkrBNGV4I5FSAix2C2EBSJJclqsOC0taTkt2vPpq4OCEb3Toh0+mKp6qNjtxBEVRbj1jOEAtAmosvCQy2E3tOQXRZRVp8XjssPjsqufAZ/wnIrR/coxom8Zpg7P7P+lZLgcFB4iiK5EzhJxFy9ejC+++AInnHACKisrsXHjRtx+++0YPHiw1GUhiK4AdzQ0osVmQbTE3Y+wNKdFnpybqdOiF0H66iB9rxae08LDKCaaxdCFliNW9+j7tHDKilzSOUJAatHCxVSx26lxZaz0aAFiztBbN55o6dh0EUNOFB4iiPyTs3+FPp8P//znP3Hqqadi+PDhuOKKKzBmzBh89NFHFAIiss7fP9+G2W+uNs2XEtnd5Me8VXulx/Lf8oOCaLFS6srdC6tOizERN1VzOXF2kPYa+jJr/c9qTks8jCI6LSt2NOH/Fm4BY0ytHhKnOgMxp4m7IWaVVGZVPnZbaodi6vBe+NGk/vj5tMEoEs5vVbTkEo3TQuEhgsg7OftfYfTo0fjggw9ydXqC0HDvW2vQ5A/jR5P6Y0B1cdJjT/rdhwhHGZ64+BicNbqv5jkuDkT3wmHBaeEhG9E9aWwNYuXOJoQjRtES0Q1MbA9FEVWYqUASK4JSOy3ynBYuAhQWE02hqIJzHl0AABjeuzQRHpJU7FSXuNF+0I9KkyGHsmRcIOay2FK8fyUeJ/73O6MBxMYDbDnQrj6eb5w0e4gguhT5/1+BILKAXyI2zODC4rONBzSiRazCEUuQLWgW9Zxi75UzH/4Eja1BaUO1iC48BMTCM+WSYwFtG399sq8+p0WfiMt/Fp2LKGN4fdlO9efG1pAqWmRi4YHvH43NjW0YVCMXhLKuuID5vCAzxvWvUIc2dgmnRQgJUUdcgsg/+f9fgSA6CGOJRm2hiEnChgQxf+K+t9fgjWW7MKJvbHifmBdi5TdsWfVQY2ss4fxwu7EHSyiiGMI8LcGwqWgJJCl51jsr+j4tPNdEDOFEogzPLNicWE80KoSHjP8tTGqoxqSGaunaAGj6r3hddvX9S5XPoufofhXq91YTcXOJPT4aIJkLRhBE50GihSh4ogpTq1VkfVJExDwWcUN9Yv5GAMDOw37Ja1KvgYdswmZZrjpCUcUQ5kmW1yI6Lfpk31ThIV49JDaBO9gewrq9rcK1o0nDQ6kQ+7dU+tzY3RRryJeq3FnPmPpy9ftUn2Vn4XLEREsu+sAQBJEe5HcSBY8YZknltIgJtlZDF2Z9TbTH8ERcCwoHsXXq15qsgiiQtOQ5vZwWwNjavz0YSRoeSoX4XlYIeS/pOi2iG7RxX2uSIzsPHiJyUPUQQeQd+ldIFDzib+T6HiV6mvyJUI3VFAWzDrKaY3Q5LVaqmPTiQhZG4micFn31UArRkshpSQgCvWhp8ofVkE4mokUUJ6XC6zOZBzS2vgIAcNZRfdJ+bS7gybguCg8RRN6h8BBR8IiORSqnpVkQLUGL+S9mzdi0x2irh2TTnvXoHRKeAyMjHafFH5LntJR4nLDbYtVDbUHta/a1JK6dSXhIFCc1pQmnxWNh2KSeuT85Fp+sb8TpI3un/dpcwBNwKaeFIPIPOS1EwRPWOC3JBYbotOgdCTOshHzURNy4wGn2J++7AiTCNpz9LeaiJZikuZxfd56ASUdcn9uhNkjTX5uLFrfTnnZIB9DmrgzpVZp4PAOnpcLnxjlH11mart0ZcNHipOohgsg79K+QKHhE0ZLKaWmSOC2pQjlWclrCupyW5kDyqc2AMayTzGkRw176qiNZTsunGxox85Xl8IeiakfcYk9iIKH+2vuaY4mzmfZGqShOuCtDe5Wo35t10C0keAIutfEniPxD4SGiYGjyh1HmdRqalaXjtIhigjstqapUUuW0KEL1El+LGIYyg4uNqmI3DraFsD+ZaEnmtMTvw2m3IaIwBMJRXPz0YgDAwJpitSOuz+1UN16907I3LlqKPZm5GxeM74d3Vu7BiUNrMLR3QrRkktPS1XCqTguJFoLIN4X/PwrRI1iy9RCOuec93P3vVQBi7edve20FDrQGEYpYrx5qahdFS3yAZ4rXyFrza54XRAT/viXFLCEg0RSub7kXANDYEjI9NqBxWvThodhzvGpn9e4W9bmDbSFVlPncDnX4Y7sup+VQ/H3JtDeK1+XAX6+chKtPHqxpQHfYgnjr6vDwEDWXI4j8Q/8KiS7Bih1NWLWr2fT5vy7aiqjCMOfTLQCAcx5dgL8u2obf/me1NjyUQmA0C2KCOy2pcltSOS1ioq7qtFgID3GnRRUtFp0Ws0Tc6niIZtn2w+pzYp6M6LSY9YTRzx3KBLH8eWu8JX8hw8NDlIhLEPmHRAuRdwLhKH741EJc+NRCU1ejvrJI/X5PvHEZAGza35pxTgvvHBsMJ39NqpwWUdTw6qF0wkN94qJlU2MbLnpqEV77aqfhWNFp0Zc881BPVbFxLtDmxjYAsY3X7bSrGy93efRJtxUms4UyJZkQKxRUp4VEC0HkHRItRN451B5CeyiK5kDEvFRYyGP5cutB9ftijzPjPi0ByURnGam63EaFcE1EdVrSCQ8lBNnCTQdw04vL1J9DEmGlF1H8nnqXGaenbzkQEy0+dyx9LZHTEs+n0YmUmpLsiJYTh9YAACYnaf1fKPD3jJrLEUT+oX+FRN4RG50FTFyPduGYL7ccUr8v9jgz79OiipbkQkcUJSt3NuGdb/aoP7/zzR4s39mk/sxdF0vhoWAirKN3PBhjeG7RVhx15ztYsL5Rm9NiJlrijo0Ib1jnc8dCNg4Hz2mRuzPVxUbhkwmPXDQOs846Eg9fODYr58sn/LOhRFyCyD9UPUTknVYhKdQsv6RNKNH9YkvCaSnxONNq4691Wiwm4goi4duPLAAAvPjT4+B1OXD1c0s0xyaqhyw4Lbzqx2FHbYlHM/focHsYt7+2EgAw69Xluuoh7XpV0VKaEC3XThuMxz7cqP5cFBctiT4ticolkeosOS0VPjeuPnlwVs6Vb7jTQiXPBJF/yGkh8o7otJi5Hu1Cie7aPYnqmGKPI/NE3Pi1UuW0yDrivv3NHny947Dh8XQScXmYx+WwGda99WAigbXI5TCtHlIUprpHNaUJl+Ty4wdpzjewOlbRo89pMYqW7Dgt3QlqLkcQXQdyWoi80xJIHR4S286LzofXqRUtqQRIszSnJUV4SJKIu2pXM2okG3wkjURcjtNuN3TD3RrPRQGAIrdTM2lavP/WUAT8x9NH9MaVJwzC0fUVqC7xoNLnUkuZ+TwfNacl/n5W+rQlzjWSZN6eDp+OLQ6cJAgiP9C/QiLvaHNaUjstIhGFaUJCwVROSybhobgQETvnrtrdjLH9KwzHcgFlpU8Lx+mwoaG2GJv2J4TKSiFPxqPLdxFFC+8743baUeR24LZvj1Sf61XqNYgWvdPidTvgczvUSibRrSFi3HDKUAzvXYqzR/fN91IIosdDfifR6fzmjW8w65/L1Z/F7qymTktILmYiimI5pyWqMLRIQlGp+rREdcMQgZgo2XHIbzg2nURcjsthw6MXHYNzj67DBeP7AYhVEXH06xPLwvl1youMTeHE5N6j+1UAMDotHqdDI4qqyWkx0L/ah6tOashokCRBENmlx4mWqMLUluU9nZZAGN/sakp9YAdZsaMJD767FoFwFP5QFHM/24K/f74dB9tiHWBbrTgt8WP0uZCRKLPcp0V/7kBYAWPMQslzTIjow0ifbmg0HJsID6XhtNjtGFlXhj9eNA4nDqsFAKzcmWi0d6BV2ylXDFfxJFyZaNkt9LMpj4eB9E6Lx2nXvGfZ7tNCEASRTXqcaLnj9ZWY9L/vY7kkibKn8YuXluPsPy7Aih25FS6/f28t/vjBBsxfu1/jqrTGQyia8JBpIm7s8YFCi3gg5n5YFS2y54IRJaVoiSryMNLhdqObEooqmuRYK4iltAOqfIbn97VoRbZ4v/w6ZZJ8Cx4SEnuvOHR9WjxOu6Y3DnV9JQiiK9PjRMv6fa0AgI37W/O8kvyz43CsQkUstc0FPL+jORDWTBfmDkubpuTZLDwUO3ZQtVa0RBSt6JAl1W7a34prnluCJVsPGZ4LhhW1X4sZ3D1JVU7N2XnYn7KKSUScadNQW6y2jeeEdbOGrDot95w/CpdNHoAXr56sPsZFCb8Xr8sBC0OsCYIgugQ9Lkgry0/oqfCNS1bSm024MxAMRzVOC/++RTIPSA9vxKZ3WgzhIYlYeG3ZLrz9zR61L4rP7UAgHIXCYs6O1T4tqY7jrN5tPkNJhtj/o9TrwvRRffDv5bulx0UUpk3ETSJa+pYX4a7zjtKdQ/t7ij7JlyAIoivT4/7H4kmM+vktPREu3FLN1ukoXBwFworGVUk4LclFSyiiqGLEGB5SUoaHeBjqcHssN8TrcsDrcqjXSyVGth9sxwV/+gyvLt2R9DjOmngfmSKXteGD+unBP5rUX3ocb/wm9mlJJlpk6MM/HotrJAiC6Ar0OKeF/5YaIadF3eBz7TpxwRGMRDXhIS5WRPdFJiDE1xjDQyxl9ZA/HA9PxTd47i60h6IIhJWUfVp4SHHDPmshRd78bnifUs3EZTP07eEnN1Sjb7lXk0gLADUlHuxtDlp2WqTX0osWcloIgiggetz/WInwUNd3WhhjWLzpgNqLI9twMRHtpPBQIKxow0Nx0ZKqeoi/xu2wo65CO18nHFW0fVokooUn8fJuuB6nHd74Zh0IR1M2pOMcjguEAdXGZFkRHh4a0bfU0nn1IRubzYY3bzgRb990ouZx3q1WDOfxKqWyTJ0Wpx3XTRsCAPj1t0ZYOgdBEES+6HFOSzjaOe5CNnjj61248YVlaKgpxgf/NTXr5+80p0UND0U1TeL4zKFU4SH+Gp/HgV5lRtGSKjzERUuT6rQ4YI9PjY6Fh7TXtNsgTU7lveVShX02NcaaxI3oW5b0OI4+8RYAKovdqCx2w+O0q0KMVwFFOxAe0rs6HqcDt54xDN8f3y+lGCMIgsg3PdZpiRSA0/LGsl0AEptgtgmrTktuRQsXRYFIVBUQgBAeSlE9xJ8vdjtR4nGqE4sBa31aeHiJ36fbaVdzOQIRxeC09C4zTksW8bgchqnMMob1tui0JJlpUyI0NONjA8KC08JFi3WnRZeI67LDZrNhYE0xbDYqdyYIomvT40QL30ALITwU6aQE2c66TjCsqFVAQEK0tASM84BEeHiIi5VzxtSpz4V1OS2yNv76EQAepx1eV+yv/sqdTTgUT9C9dtpg/PpbI3Dp5IFJ78fjtGvEhIw+ZV6UeeVCQi94XEl6o4hdWHm3WlFkNncwp8XrpERcgiAKhx4nWtScli7cnOK5hVvw46cXp9UKPl0UoXQ2164Tz50JRLQ5La3BCBhjmhb9fll4KC50fPEN/L7vj8HcnxwLILb2kM5pEWcEAdC4O0DMXeCb9f3vrMWHa/cDAOorY+3ae6WYv+Nx2jGkV0nSYyqL3dKwDwBU6brO6quHRKROS5Sp99jx6qEe918AQRAFTMH8j6UoDJc++zl++fLXHToP36jDFntu5IPbX/8GCzY04qtth3N2DXGjz6XTwhjT5LToq4eCEUXjHEjDQ3GhUyyEhfhGH4kyQ0hI36tFL4Q8TofqtGgejz+WKvTjcTpw//fH4NiBlabHlHqcpmGfKt18H32eiYgoWqqFzrYKizlIB+KjEFKFtNRrUfUQQRAFTMH8j7ViZxM+Xrcf//hyh+E3aU5zIIyH561L2u2WV17kOiTS1RHDY7ks/xbf56DBaYlqKodix8gScXlTuMQGzjffsKIYQn16EWNwWpx2tU+L9vHYYylFi8uOAdXFeOmaKZg4sEp6TInXaRAIHL1oSea0FHsS6+ROCxD7e7w5nutU6XMZzmmG3SBaKDxEEEThUDCipTWYvJcHANz31ho8PG89TnvwI9Pz8MqLdNqsd0fEjT2XJc+ioAiEo4acljadaJHmtMSPETdwp+C0pBItfolokQkFr1WnRXit2bHFHqfpc5V6pyVJTkuJkBejES1Rhk37Y6KloTZ5qCrZtSg8RBBEIVEw/2OJG4+ZaFkeH/xnYsQASFReFEL1UC4RRVsu83s0PVTCuuqhUMTgtMjCQzKnheeLRKIKwhHt+sW/H4wxQyKu22nHtoPthutw18GTxPkAtBu9Wd5KicfcaanQ5Z8kG1JYEhdqNhtQWZx4XUQRRIuuS3AyZH1aCIIgCoWC+R9L0zXVZD6NWAprRlebPfToB+tx0u8+xN7mQNLjzEJimSJu9LkseRbFkTE8FFFb7HOSVQ+JOS28IVtYYQbXTN9sTn97HqcDq3YZ5wPxDTyV+yCGVMxCO6Ve85wWvQOTrNSY57SUF7ngFs4XiSpqGDRTp8Vmg+acBEEQXZ2C+R/rsNAV1sxpsSJaIl2sI+4D767DtoPt+NvibUmPy3YOTiiaEAe5fC9EARHQOS3tQe0ARSDWy0WPvnoISDgc+uZyQEIovb1yD+Z8usVwPo/Tjv/+1pGSx+M5LY7kf49Ed8IsBFTicZq6MMmcFT285LmiyAWbzaa+NqowbGrkosW603JERZH6vcdpp94sBEEUFAXTEVcULWaTgH0pemdEFaaGjrqCaBGTTlOVrAYjStKEzXQJdZLTIjpagbBiaC7H29BXFbtxsC2UNDwkdqJ1pchpYYzh5heXSUuoPS47LpsyECcMrcFfPtuK5xZtVR8HrFQPCaLF5DMp9jgN7fk5DrsNXpddeq96VKclXibtsNsQjbtLm+PhocFpOC2j+5Wr31MSLkEQhUanOC3BYBBjx46FzWbDsmXLMjrHYX8ocT4Tp0UMH8g2YnFmS1cYmLj1QCKvIqVoMRFqmdIZJc8rdzZhd5Nf/TkY0bbxbwtF1MZuvGRXJki5uBPLlJ0ap0Wf0xJFSzAiFSxAbLO22WwY0qsUR1RqnQfASvVQ4u+Z2bGlyZwWmw3Fbmu/L1TExUpNPHmXh3d++OQitIWicNht6F9lvf2+OFqA93ghCIIoFDrFafnlL3+Juro6fP115j1WLDktwkbQEgir/+FzRKHSFaqHNgml2bL28yJmQi1TtCXP2X8vVu5swrcfWaB5LBBW4LAnPjuFAXviuTy9yzxYvRvS4YX83mW5JBHF2KclGFFwuM18Qxadkr7lXuFxiyXPTjER1yQ85HWq4Ry9gLbbbfB5HDhgYTrDGaN64/Jdg3De2FgXYC5adh6OicHjGqosjRTg+CyKJYIgiK5Izp2Wt956C++++y4eeOCBDp3ncHtqp0VE9luk6Ch0Badl4/7ErsXdBLOE2y2NbVi86UDWri1u9LlwWj5Z32h4LKBzWgBg1+GYaOkTd1pCUcWwyfO1ipuzU8jt4H8feKpIKKJonDk94nlqhTJiLkZ6l3owrHcJhpvMDnJbzGkR1ymSjtNS5nXhjnNG4uj6itj5BJF0dL9yzP3JREvnERGFGkEQRCGRU9Gyd+9eXHXVVXjuuefg86W2sIPBIJqbmzVfnMP+1E6L6B6IzgxHdBS6Qk7LJkG0hCIKth5o07S0F/nR04vxw6cW4evth7Ny7VCOm8vJ3l/GjI3edh6KhcjE6c36BnP8Z9HhEDdvP68uiguFUESRfv4c0bGpErrM8pwWp8OOt288CX+7apLJ61M7LXwtspwXp8OGIgtJ4zJEETTtyF4Z5TmlGkFAEATRVcmZaGGMYcaMGbjmmmswYcIES6+ZPXs2ysvL1a/6+nr1OSvVQ+JGKXNaxN/gu8LsIbFz7/KdTTj5/vm48e9fJX3N0m2HsnJtbXO57L8XqUJOpd7Yps6dlt5lCcfjp/+3BFsaRReKh4fk/VHa4yKWuxuhqKLmysgQzzO4tgQDq30Y0bdMk+hrt9vgMkmkFUWP2yRvhd+frEW/PQ2nRY8oWqx2wdXzm3NHweO049LJAzJ6PUEQRL5IW7TMnDkTNpst6deaNWvwyCOPoKWlBbNmzbJ87lmzZqGpqUn92r59u/qcGB4yc1pExyBVeIjPHtrS2KbJLeksGGOa667ZHXOV1u9Lvhaz3h/pEs6R63SgNYh5q/YikCKEx8MyPKelWhgwuGBDI2a/tVo9lue5iAmwYmUOj6hxdyMYVpImmWqbw9kx75aT8cZ1xxvKf81mAlkteY6dw/i8w47MnRbhfJW+zETL4NoSfH3nGbjr3FEZvZ4gCCJfpP3r3q233ooZM2YkPaahoQEffPABFi5cCI9HOzF3woQJuPjii/GXv/zF8DqPx2M4HogNSxQ3ITOnJZTCaRFFTURR0BqMYOoD81HkcuDzX5+KUq+1SbnZoNkfQbPQWK0l/r0+fKInWcv3dLDqtPzjy+2o9Llx+sjels574VOLsH5fq+o0yPC5HYbny4pccNrtCMf7x/BSaCDxuYqhFlllTnWxGxsAHGwLmVYOAcZSXzMhaNZPRS96ZJTE788ly2mx2zWVbumQDacFgHT2EkEQRFcnbdFSW1uL2tralMf98Y9/xG9/+1v15127dmH69Ol48cUXMWmSPFfAjJZARNPV1Kz8N7XTIroLDB+s2QcgNgV4xc4mTBlck9a6OsIuoRQYSIgWvy5RVU86jcmSIYoWs1DZvuYAfvnycgDAut+eZalKhTtFLQHz+/C5nfEclib1sfIil0ZoDO+TSIJVc1oEsSCrzDmyTykWbz6ITY1t0inOHKut680EoiY8ZDZ7yJ3EabEBPz5uAF5btgsTBphPipauyZEd0UIQBFGI5Kz+sX///pqfS0piyX+DBw9Gv3790jpXU0Cbn2AWehBFScrwUFTB2yt3qz8v39G5omW3TrTwDbs9RT8We5Y6mIohIbOBiaKI2HqgDUNNqmk4ZmE7PT63Q9OZFYChPF0UI2p4SCcQnDrRMrxPrAfJpv2thvOLWC0RNnVaLCTi8tfKQkwOuw0TBlbhk19OU/vTWEX8/Em0EATR0yiINv76ShBZLw8ACIlOi7R6KPF8sz+MD9fsV39esaPJcHw2ueffq/BfL30NJb7J7m6SzxpKNWIoVT8Xq4ghNrM5TKKwMcu1aQmE8eB767BhX6smsTgZJR4n+lVqRYW+uV5YN7MIMIZ19JU53J3Z1NimqTbTY9VpMWtxb8Vp4ciSee1xQVNf5UurxwqgDR9W+DovnEkQBNEV6DTRMnDgQDDGMHbs2LRfq3dNZPNpAG3FSqrqoeaAtmPq1zsOp70uq3y17RCeWbAZLy/ZgbV7WwAAuw8nH5Bohr4c2Ix9LQHc9toKrN3TIn1eFCpmOS2isFm/Vy5I7vrXKvzx/fX4zmOfmh6jp67Cq3FCnHYbit0O/O57Y9THxPykkKR6CNC6GDYbMLR3zM3b3xKUTnHmdLR9vdtCG3+Oy2kUPh3JS2oJJP5eUxt+giB6GgXhtOjzI8ycllQlz2FJGGRwfNjcjkN+HGgNdmSZpsz9bIv6PRcRPKclWRhDhtXOuDe/uAx/XbQNP/rzIunzVprLaUTLPrn4mb82lhfUEoyYHqOnrqII/SoTfXvK48MAf3BsPe48ZySAhKhijEn7tADafBGXw44yrwu1pbFE7g1JqrBSTXFOhZXqIXWNMqelAyG+5iS5QgRBEN2dghAt+lwJM6clnCIRV+Yo9Cr1oqEmJly+2dVseL6j7GkK4D/LE7kzq/c0q48DwMAa63NjAHPBpufTDbHuuQfa5P1KrLTxF4WNmQgQN9F1lp2WIs3MH69kEGIo/hlHFKYmYeudBbEyhzse/LNMhtXwkOnrLVQPJZ6X57RkSi6HWxIEQXR1CkK0hPQdUk027lSJuLJ+JEVuB3rFG5sly4PIlD99tFHjZKzZHQ8PxUVL/6rUm6xIKJqdwYkhTSJuaqdlU2ObVNxYETZ6+pZ7USnkY4hhOi4+uAAV16B3SJySEugGCxOPOxpW0c5ASi5AZE5LtirACIIgehoFIVr0IRFTpyWS2HzbJKXDss25yOVQy1Pbg9m13vc0BfD859sAALeePgwAsGZPMxhj2BUfeDewOjdOSyo0Jc8WnJZQREmaJwIk3KNUHFFRpElybRXcGp4DwtckrkGfPyLmtHAhMbKvtsLpptOGoqbEg7NH902cp6NOSzrhoSw7LQRBED2ZghQtpjktgtPilzRpk+VuFLkd8MW7l6Zq7JYub67YjVBEwfgBlfjJCYMAAHubg9jU2KbeU/+qNEWLhZwWMVnXrMLEitOir1Ta15I85ydZQzeROl0ej7gWt8OhuTa/F5fDplbdcMTKHN7M7fxxR2iOuem0Yfji16dq+r50ODxkkoh7x7dH4sfH9cfbN52YWKO0T0vHRUumzekIgiAKmcIQLXGRwruomlXQ6Mtk9ZuxbDBgzGmJbQD6CcQdpS3u3AzrXYISjxMD4q7Kh/GmdjUlbnWz1VNe5MJ104bg4knafjdWqod2Hkr0gDGrbglbKHnWX6tN50SZ5cKk2lB7xZNluQYpE94DHm7hn2WiR4vxnKKLwf9ulHpd+O4xWuFis9k0QqMjosVht2nCUqLTUlfhxW/PH40j4/1iAHmlkF58ZUJZEZU7EwTR8ygM0RLfwHgvD9OcFt3mq0/glTVRK3I74IuHh8wmLGcK7zTL8xoGx/MtFm2KJcn2LS8yFRUDq334r+nDNQmrgNxpaQ6E8Yd567EsPgFaDOO0BSNo8oexX+eSZOK0tOpEi1mSb98UFVF803/x6skYVVeGZ2Ycqz7niosAvj7ZsET9eQBoRjDcde4ofGt0H9x9XmK2Dk/2dTvtpv1XrKBfh+ikyPJX+POiuOlIyfMNpwwBAPz2/KMyPgdBEEShkrOOuNkkFBcf5UUu7Djkt1Q9BMTCFXyInux5ILaZMWfs8WzntHAngucw9C2PdT9duu0wAKBPudc0J4JvyHqHQdZc7j/Ld+Oheevw0Lx1uOf8ozQd6tpCUZz+4EfY1xLE13eeoQo/bclz6llOANAW1L7veiEExJwrPgMoFccOrMJ/bjhR85iaiBvPT+LrlL1PbonTEvvehccvHq85louNjrgs00f1xpBe2kRfcV0OSf4Kd448Drt6Lx1xWm45YziuPKkBZZ04J4sgCKKrUBCiJRC25rToE0r1eS1mibicbDstPIeGb1xctByMOxR1yURLfGPTb7Iyp0XsGPy7t9bgh8fWa57nuShLtx7CtCN7AdCVPJtVD4X1oiUm6pZsPYjyIhf2tRgTb31uB6pLMm8v71Krh7Q5LVKnRXA2ypIMaAQSTktHKoeevGSC4TG3I7mD4hSdlrjG62hOCwkWgiB6KoURHopvXPw/aysdcQFjYqg0Eddlhy9HOS184+UbV99ybdikb0WR6SbqcsidAVlOi3ifLcEIPt9yUHrOHYcSYSNR/MhyfQCj09IajGDd3hZ874mFOO3Bj+VOi9shnYkzfVRvlBe58MhF46TX4rhNw0OpclqSb+R8gGJHk3D1iOEhWVUQF6waR4aqhwiCIDKiIJyWkMWcFh7+sdliERK90yJLHPW5neC/+OrDHx2FiwGXLjzE6VvuNd1E+YbscWk3a9m963N3lpvMUeIjBAC90yJ/P/WuTnsognmr96o/y0qgfW4HqnxG0TJteC/86cfjU+aTGBJx4yJN5kiJgqHUk/yvMhc92RYtGjEiuTfuBpFoIQiC6DgF4bTwTbk8Xr4bjESxaX8rTvrdh/h7vA8KkCh55huYFafF63aoeS+yMumOwMWA6rToElT7lhclCQ/JnQG9+wGYr1sfMlm3J9H8TcxpMW8upz1vazCK5dsTgmjlTmMH4SK3EycMrUWZ14nxAyrVx10OawmwbrUjrrZPizw8lDifWRUWp3+8cqs+zRLzVIjN5WRvoyo+SbQQBEF0mIIQLaF4UiZ3WgJhBYs2HcS2g+14c0WsRX5UYWr+KS8HNYgWWUdcl0MND8ka0nUE7rTwjatPmdFpMaseckk2O0DutPD7FM/VUFNsCEfxxnaANik5HGXq4yJcMPDQSksgjIXxyicAWLHT6Oj4XA5MHFSFr+88Az8+LlGu7bLocLhMOuLK5gW5TKqHZAyuLcG8W07CYxcfY2kdVhFFp+w9lFUPdWT2EEEQRE+mIEQLd1q4GAlGoupGzTdxMdzBN7CAPjxk1hE3R83l1ETcuGtS5Haozd5sNmvVQ159eChJTsvofuXqY6P7lRvch+ZABHubY3ko+iokhcXOLZ6fH8PDPV9sOagZj3BQUvLMBaDNZtMkOaeahqwep89pSbNPSzKG9CpFSYowUrqIDe6kTouaUO0wPEYQBEGkR0GIFn2fFoXFfusHEhu2KFp4WEQvQuQdcROJuPrmaR0lkYib2KS4+1Fb4oHLYTcVLa40qoe4OBt9hCBajijXlHtz1sQHNuorrVoDEZzx0Mf41h8+EbrRxv6sjCfWcsGTjCKhsZwouNxOaxu1WD3EGFM/e5noEauHrIiWjmCmM8TyZUXitPBGer3j860ACg8RBEFkSkGIFr6JijkavMyXV/yI4Q6z8JC85NmZmD2UbadFDQ8l3maejMv/dNptkEULErkQeqfFPDw0sq5M3VxHH1EuTU7dHu+Wqz/PB2v3YuuBdmzc34YP4h17VadFUg0k0lCbGProE0SL1mmxVmrMxQljMZEZjN+bPDyUeONyXQZsJaQjywy6cGJ//OnHx+CakwendS6CIAjCSEGIFh4eEvMWeJiC93Dh+Sp2W6KNfCAcxeebD+L8xz7FR+v2y9v4ux3weRI5LbK8hEzhibguu+i0cNESc1xsNpvcRYg/pm+yKmsux0VLRZELPz1pMKaP6o1x/StR7DEKhd3xQY16p+VfX+9Wv39l6Q7NtfSiRXSHepV6UClUC/HuwoDWdUk1DVk9TnBkwlElaUdcbU5Lbp2WZO7IeWPrMLx3KSY3VBue87ocOPOovqpLmOpcBEEQhDmFUfIcUQDYUeRywOO0IxhRcLg9lk+hhofURm52dbNcubMJv/3PagDAsws2Y4yQ88GJJeLG3gbGYiKoKMXsnO0H23H1c0tw1UmD8J1x/UyPC0uclpF1sbk0o+oS82n4PYlwoaMXWtKclrhDVOR2YOZZR6qPi+GhI/uUYs2eFnUSs74KibsrQGw20oHWoHqtSl0J88i+ZerIgP5VPo0LIjot4vfpJuICsa64yfq0iHt/qkTcjpJMaPzhwnFgjCWtjhJfT6KFIAgiMwrCaVG7orrsap7E4bjTwjdsPgDQ5Ugc89qyXeo5FMZME3G1XXFT57X8+rWVWLW7GTe/+HXS41SnRXAZLjy2P16/9nhcMzURLnBLk0xjH82gmmLN4+EoM4S5uBNVpEvaFcNDI/vGRNK2g+24/bWVmi66nF6lHjTUFCOiMKzc1awKm2qd0zJSEFz1VT7NdX1mOS0WE3HFcFkoqiRt4x8QKqmynWCrJ1VIJ1U5N4kWgiCIjlMQoiUQL3n2xp0WAGhqTyTiMsY0AsEncUpCEUVa8ux12+GwJypdrPRq2XagTfPzxv2teHjeOjQHtEJAdVqEGI/DbsPR9RUaR0E+DDC2sRV7nPjq9tPx+X+fqrkXEe426R0i0WnhQuPLrYfw3KKt0vtqqC1WW/C3ByNq5U6lXrT01YoWUZwUieEh4XGnxfCQzWbTJOMma+Mv5iyZJTRni8rijjk5YuO5jrbxJwiC6KkUhGgJC3kNfIM81J4otw1GFLWXi9NhNzgOQMxBkTkt3AEoFvJaUtES0B5z1h8+wcPz1uPBd9dpHo9IqodkSLu9CkKnstitySvRixaeQKy/b7HkWXRHzCjxOFWh0xqMqE6LPqdlhChaKotMnRZRRJk03ZUiNphLFh7SJ1rngmdnTMCIvmV48sfGuUPpILorkmHQBEEQhAUKIqeF43EmypMPC/1C/KGoJulV39sEiJX0yqqHuK0fy2sJWWrlrxctXER8Fc/z4OgHJpohT8S16X6OOUJRhcXdh8Rv/gETp6VEEh4SKfM6EY4ydfP3uZ2IxhOR24IRoWpL6zIMqPahxONEazCC/lU+zdgATXhIEBqycmAzxFb+ap8WSfWQfnxBLjjlyN445cjeHT6PKFqcpFoIgiAyoqD+9/S6HOpGLO6B/nBUDcW4nHZNBQunNRjRlEXrSWdoopjEKlYbiRUigDw8JCPVXB0OD5GISbvhqKJex+C0xN8rt8OO8iIXakoSvULu/e5ovHbt8RqRUexxqpVXbaGoeh39+ip9blx8XH9MHFiFo+srNGJJXIPdbsNpI3phVF0ZjuxTmuwt0CA2mAsl6dOS7bELuYScFoIgiI5TME6L3RZL0vRJEi5joiUeirHbUOQ27gotgQiiSWIUPCySzGnxh6JobNU2WBM7xOpn/XQkPCTrmup22tEe0natFd0GvcPERUuFzwWbzYYyr1Nd/5lH9UGFz63ZTIvdDkRZbC2i06LPJ3HYbZh11gjpdfWC8enLjk1ZWaNHbOWfrE9LZ4SHsoUmEZdyWgiCIDKiYESLx+mAzWZDiaT3iD8UTUxU1uW0uBy22OYXUTTVJnq443DPv1eh0ufCJEnPjav+70ss2NCoeWzdXvkQQiARHkrptCTp0yLCxYN4H3zjttmM4mJ0v3KM61+Bk4bWAtAKrIp4GbPo6BR7nGoIrT2J06KnyCXPY+GkI1iAxPuh7dNiPO/IvmX4ZpdxaGNXhKqHCIIgOk7BiBY+tK9YEvoJCE6LWPIMxKpbNu2PVfuIeTB6uGjZediPi59ejA3/+y3DMXrBAgCrdyc2TWP1kDWnJVW3V/W4+MYthqcCodj3RS6HQRz43E68+vPj1Z9lwTFxAy3xOFWh1RqMJK3cESky6dOSKS5NIq75Gm47eySqit34zjFHdPiaucbtsGPq8Fr4Q1FDGJEgCIKwRsGIFr5hy+bptIdE0WLThCh6lXqw67AfgbCCpnbjgD+O+JqIwiyHNPgsHwBo9mvzYVT3JxOnRfIaNacl7rQs3HgAb6+MdbKVVUzpuf/7Y/Czvy7Fb79zVOI6gjjyeRyq0yKGh1I5LV6T6qFM4V1xU/VpKfe5MOtbIwyPd0VsNhvm/mRi2qEygiAIIkHhiBbutMjCQ+GoutnqS56rit0o8bgQCAdxSNJQjfP1jsOanw+1h1PO3AGAVbtb1O/1TguvaMoop0XmtLh4Im6sN81Ff16kPiermNJz6oje+Obu6ZqQkFPvtEQTTouaBCusTyawikzKnDNFDQ9FkrfxL0RIsBAEQWROwewE3iROizY8pE3ErSp2q3NpDidxWs4fqw0xbDvYrvlZkZRLA9rwUFN7GHf96xv8/fNtANIoeZZWD0kScR2J6qH9LdqEYKtiQV+VJDo6PneiT0uzP6xWaMXyiWLfj5aMQkiWiJsJmkTcJDktBEEQRM+i4JwWWbt2fyiqJq7qc1qqij2qO9McMC9nvuKEQRhcW4KnPtmEr7cfxp/mb0R5kQt3nTcKXpcDAcnMH0CbfNsSjGDOp1sAANNH9UlMeU4RHpJ2xJWGhxzqNdfubdE8ZyU8JEN0dIqF8NBBQeB5nHa89vPj8edPNmlmG8munek6RBIlz1G1rNkryfshCIIgehYFsxOoTovkN3l/OJooL7Zr+7RUFLlM59KIk4GLPU6cPaYvhtSWAADe/mYPXvxyO95fHRskKFbsfDbzFBw/xFhdJPLv5bssJ+LKQi7SRFxXwmlZu0cnWjIMy+gTcbnTcqgtEepyO+w4ur4Cj/7oGPSr9BnOwUWix2nPSmWM6rREGA62xcSTlVAdQRAE0b0pOKdFFh7yh6NqbobbaTO0lS/xaKs1Zp11JDY3tuGKEwYZzjWgWrsp8w6xvLTY7bSjrqLIIJ7sNkCMIL2ydKcQHkquDfmE4kqfS827SZqIG4lijV60ZOhwuHThoahQPRRbuw32FEKEXzsbSbj8mkCsRJu/7yRaCIIgiMIRLU7zRNxAKKo6MU67XRNuKXI7NI4KAAyoLsbVJw+GjP5VWtHCwz9+3Xwfca5Ppc8Fl8OOfUKeyddCS39ZoziRH03qjyhj8DodeGhebH6RzJ3h06CDYYnTkqFoMZY8a3vNWJnOPKxPCSYMqMQxAyozWoMefp97mgPxn+05n+JMEARBdH0KZifwuMwTcf3hKIqFUIzdEPLQbujJRES9TrS0xR0H3nnWK8mt6V3mRURhGtGiuV6Kjb+uogi/OvNIvPbVTvUxmTvji78HTf4w1ulzWjJ0OaLCGAKfx4FoVHtdK9OTPU4HXv7ZlIyuL4M7LVy0VBe7qeqGIAiCKJycFu6eSBNxw1E1FMOdge8ecwSOOqIMJwytMYSHHElyTAbqwkN86rM6lJA7LcI6+pR7kzYMS1U9xBEFgkxYDaiJre2DNfs084cAayXPMsTzFAvVQ5x8VO3wz3BPU0y0UGiIIAiCAApKtCRxWkKKIen1wR+Mxb+vPxEepzE8lKzZW3WJB/d9b7Q6R4g7LX7VaTGuo0+Z1zB3SMTqVF9N/xSJ0zKsV2zo4IqdsanKw3snhhAmm6uUjKAwv8dht8HttGtCQlaclmzj0omWamHQI0EQBNFzKRjRooZlLLTx16N3Z1JVuPzw2P64dPJAAIkBirx6iIsWUQj1LvNqNve6cq/mfFadFvE42WuG9dZOSp44qEr9vinJiIJk6OclAbEwEScfooVfUwwPEQRBEEROd6SBAwfCZrNpvu69996MzsWdFp9JR1xxYKIevTtjRUTw6+idFh4eEquH+pR70R5KOBa9BdHisNss52O4NZ1qjffRr7JIk3B71BFl6veHk3T7TUZAMilZvLd8dKLlnyGvZCLRQhAEQQCdkIh7991346qrrlJ/Li0tTXK0OdxpcTnscDvtGoegPZRoOS/LBUnXaRFfo+a08Ooht7F6qE+ZVy0RBhI9ZczWY4ZLEAgyYWW32zCkV4kaHhpVl+hOGzXp2JsKfW4MoK3QyovTorv3qhISLQRBEEQnhIdKS0vRp08f9au4uDij84i//XNBwcWHP6wkdVr0OS1Wckx4g7rWeHhI77SIQqhXmQcOwU0Rpzan6tEikiqnBQCG9o41v3M77BjWuxT/852jUFXsxh3njLR8HRG5aOkaTguHnBaCIAgC6ATRcu+996K6uhrjxo3D/fffj0jEvJV+MsSwEHcCKn2xzSwQiqr9RWQORW2pNpEzVYdaACiJX6NdV/LMBYm4mfcp8+I3545Cr1IP7v3uaK3TYjGfRb92M4eG57UM71MKt9OOiycNwJLbTsOYfhWWryMSlIwnEAXi0RmetyO4nHrRQom4BEEQRI7DQzfccAOOOeYYVFVV4bPPPsOsWbOwe/duPPjgg9Ljg8EggsFEr5Pm5sQwQrHbKt9UK30uNLYG4Q9HEYqYOy1DaktQVexWW8JbCdlwt6HVJKdFDDFVFbtRXeLB578+DQCwaNMB9bl0wkNiTouZQ3Pu0XV4a8Vu/OT4RDffjvQwCUeNYSWxz823RvfN+NyZom9oR+EhgiAIAshAtMycORP33Xdf0mNWr16NI488Erfccov62JgxY+B2u3H11Vdj9uzZ8HiMvz3Pnj0bd911l/Sc4jwhLii40xLr08JLno2bvd1uw+TB1fjP8t0ArOW08OvxBFu9aBlbX4EfTqhHQ22xQTSIPVOsljsDuj4tJg5NXUURXr/uBMvnzIQvtxxUvx8jmeqca4xOC4kWgiAIIgPRcuutt2LGjBlJj2loaJA+PmnSJEQiEWzZsgXDhw83PD9r1iyN0GlubkZ9fT0AbciCi5bq+G/gLYGwWj1TbNIZ9oQhNaposZJnoibixp2WoK7k2Waz4b7vj5G+ViNa0goPCU5LGmIn25w3tg5//3w7pg6vzUsnWn0iLvVpIQiCIIAMREttbS1qa2szutiyZctgt9vRq1cv6fMej0fqwADanBaeb9JQWwyH3YZAWFHdgUE18kTf4wfXpLVWHo5Sw0O66qFkZCcRt3PEwvNXTsKsV1fgf78zWn3sV2ceiWP6V+LcsXWdsgY94vtwREWRqRAlCIIgehY5y2lZuHAhFi9ejGnTpqG0tBQLFy7EzTffjB//+MeorEx/sJ7otPQtLwIQC5UMqPZh0/42tMVFRUNtifT1/at9OHNUH+xtCaCuoijl9bjTEowoiEQVQ0fcZHgyLHnmZd02W3pipyNMGVKDj34xTfNYhc+NCybUd8r1ZfDP1+Ww4U8/Hk9zhwiCIAgAORQtHo8HL7zwAn7zm98gGAxi0KBBuPnmmzXhn3QQE3GvP2UIjq6vwBkje+OjtfuxaX8bAKDM60RNkqTNP10y3vL1xLLftlBUEC2pxYR4TKphiSKlXhduPHVorJV+HkqNuwrHNVThlZ9NxpDaUpT7zGc6EQRBED2LnImWY445BosWLcra+UTRUuFz49yjY6GLIb1K8O6qvQBiLku2fit3O+1wOWwIRxnaQxHDwMRkiCXPVlv4c24+fVh6C+2G2Gw2jB9QlfpAgiAIokdRML/OywYlAsBgIRzUUJtZ47pU12wLpilaXJmFhwiCIAiCMKcgRIvdZt6ZdUivhGgZbJLPkinFQldcNTxkISk00/AQQRAEQRDmFMSOWuR2mIZ9BmtES7adlkRXXHXKszO9RNx0w0MEQRAEQcgpCNHiSxKSKfE4MbRXCew27QDBbCB2xU2n5FnjtOSx3wpBEARBdCdyPuU5G/hM8lk4f7l8Ihpbg6iv8mX1ujw81JZuIq6LnBaCIAiCyDYFIVpSuRt1FUWWeq+kCw8PtQWjqmhJu+SZnBaCIAiCyAoFsaMWu/KjrVSnJRgxzB5KhifDKc8EQRAEQZhTEKLF687PMiviAxm/3nEYCuNrSS+npbM62xIEQRBEd6cgdtR8zZ7hs3feXLFHfSxtp4X6tBAEQRBEVigI0VLkzk94aGx9BY4fUq3+7HM7LDknYiKug0QLQRAEQWSFghAt+Zzye8vpw+F22tFQU4w/XjjO0mvE8JDCWK6WRhAEQRA9ioKoHvLmyWkBgPEDKvH1HWfA47TDbtE1EZ2WqJKrlREEQRBEz6IgREuy5nKdgZWGciJiHktUIdVCEARBENmgIMJDPk9+RUu6iCMHIgqFhwiCIAgiGxSGaMmz09IRoiRaCIIgCCIrFIZoSdHGvytDooUgCIIgskNBiBYrvVG6KiRaCIIgCCI7FIRo8eWx5LmjUE4LQRAEQWSHghAtQ3uV5HsJGUNOC0EQBEFkh4IQLVUlnnwvIWMiVPJMEARBEFmhIERLIVNXUZTvJRAEQRBEt4BES47425WTcO7Rdfj1t0bkeykEQRAE0S0o3FriLs7xQ2pw/JCafC+DIAiCILoN5LQQBEEQBFEQkGghCIIgCKIgINFCEARBEERBQKKFIAiCIIiCgEQLQRAEQRAFAYkWgiAIgiAKAhItBEEQBEEUBCRaCIIgCIIoCEi0EARBEARREJBoIQiCIAiiICDRQhAEQRBEQUCihSAIgiCIgoBEC0EQBEEQBQGJFoIgCIIgCgISLQRBEARBFAQ5FS3/+c9/MGnSJBQVFaGyshLnn39+Li9HEARBEEQ3xpmrE7/yyiu46qqr8L//+7845ZRTEIlEsHLlylxdjiAIgiCIbk5OREskEsGNN96I+++/H1dccYX6+MiRI3NxOYIgCIIgegA5CQ8tXboUO3fuhN1ux7hx49C3b1+cddZZKZ2WYDCI5uZmzRdBEARBEASQI9GyadMmAMBvfvMb3Hbbbfj3v/+NyspKTJ06FQcPHjR93ezZs1FeXq5+1dfX52J5BEEQBEEUIGmJlpkzZ8JmsyX9WrNmDRRFAQD8+te/xve+9z2MHz8ec+bMgc1mw0svvWR6/lmzZqGpqUn92r59e8fujiAIgiCIbkNaOS233norZsyYkfSYhoYG7N69G4A2h8Xj8aChoQHbtm0zfa3H44HH40lnSQRBEARB9BDSEi21tbWora1Nedz48ePh8Xiwdu1anHDCCQCAcDiMLVu2YMCAAZmtlCAIgiCIHk1OqofKyspwzTXX4M4770R9fT0GDBiA+++/HwBwwQUX5OKSBEEQBEF0c3LWp+X++++H0+nEJZdcAr/fj0mTJuGDDz5AZWVlri5JEARBEEQ3xsYYY/lehBnNzc0oLy9HU1MTysrK8r0cgiAIgiAskKv9m2YPEQRBEARREJBoIQiCIAiiICDRQhAEQRBEQUCihSAIgiCIgoBEC0EQBEEQBQGJFoIgCIIgCgISLQRBEARBFAQkWgiCIAiCKAhItBAEQRAEURCQaCEIgiAIoiAg0UIQBEEQREFAooUgCIIgiIKARAtBEARBEAUBiRaCIAiCIAoCEi0EQRAEQRQEJFoIgiAIgigISLQQBEEQBFEQkGghCIIgCKIgINFCEARBEERBQKKFIAiCIIiCgEQLQRAEQRAFAYkWgiAIgiAKAhItBEEQBEEUBCRaCIIgCIIoCEi0EARBEARREJBoIQiCIAiiICDRQhAEQRBEQUCihSAIgiCIgoBEC0EQBEEQBQGJFoIgCIIgCgISLQRBEARBFAQkWgiCIAiCKAhItBAEQRAEURCQaCEIgiAIoiAg0UIQBEEQREFAooUgCIIgiIKARAtBEARBEAVBzkTL/PnzYbPZpF9ffPFFri5LEARBEEQ3xZmrE0+ZMgW7d+/WPHb77bfj/fffx4QJE3J1WYIgCIIguik5Ey1utxt9+vRRfw6Hw3j99ddx/fXXw2az5eqyBEEQBEF0U3ImWvS88cYbOHDgAH7yk5+YHhMMBhEMBtWfm5ubO2NpBEEQBEEUAJ2WiPvMM89g+vTp6Nevn+kxs2fPRnl5ufpVX1/fWcsjCIIgCKKLk7ZomTlzpmmCLf9as2aN5jU7duzAO++8gyuuuCLpuWfNmoWmpib1a/v27ekujyAIgiCIbkra4aFbb70VM2bMSHpMQ0OD5uc5c+aguroa5557btLXeTweeDyedJdEEARBEEQPIG3RUltbi9raWsvHM8YwZ84cXHrppXC5XOlejiAIgiAIAkAn5LR88MEH2Lx5M6688spcX4ogCIIgiG5MzquHnnnmGUyZMgVHHnlk2q9ljAGgKiKCIAiCKCT4vs338WxhY9k+YxbZtGkTBg8enO9lEARBEASRARs3bjTkuXaETuvTkglVVVUAgG3btqG8vDzPq+k8mpubUV9fj+3bt6OsrCzfy+k06L7pvnsCdN903z2BpqYm9O/fX93Hs0WXFi12eyzlpry8vEd92JyysjK67x4E3XfPgu67Z9FT75vv41k7X1bPRhAEQRAEkSNItBAEQRAEURB0adHi8Xhw55139riGc3TfdN89Abpvuu+eAN13du+7S1cPEQRBEARBcLq000IQBEEQBMEh0UIQBEEQREFAooUgCIIgiIKARAtBEARBEAVB3kXLY489hoEDB8Lr9WLSpEn4/PPPkx7/0ksv4cgjj4TX68Xo0aPx5ptvdtJKs0s69z137lzYbDbNl9fr7cTVZoePP/4Y55xzDurq6mCz2fDaa6+lfM38+fNxzDHHwOPxYMiQIZg7d27O15lt0r3v+fPnGz5vm82GPXv2dM6Cs8Ds2bNx7LHHorS0FL169cL555+PtWvXpnxdof/7zuS+u8O/7yeeeAJjxoxRG6hNnjwZb731VtLXFPpnDaR/393hs5Zx7733wmaz4aabbkp6XDY+87yKlhdffBG33HIL7rzzTixduhRHH300pk+fjn379kmP/+yzz3DRRRfhiiuuwFdffYXzzz8f559/PlauXNnJK+8Y6d43EOumuHv3bvVr69atnbji7NDW1oajjz4ajz32mKXjN2/ejLPPPhvTpk3DsmXLcNNNN+HKK6/EO++8k+OVZpd075uzdu1azWfeq1evHK0w+3z00Ue49tprsWjRIrz33nsIh8M444wz0NbWZvqa7vDvO5P7Bgr/33e/fv1w7733YsmSJfjyyy9xyimn4LzzzsM333wjPb47fNZA+vcNFP5nreeLL77Ak08+iTFjxiQ9LmufOcsjEydOZNdee636czQaZXV1dWz27NnS43/wgx+ws88+W/PYpEmT2NVXX53TdWabdO97zpw5rLy8vJNW1zkAYK+++mrSY375y1+yUaNGaR774Q9/yKZPn57DleUWK/f94YcfMgDs0KFDnbKmzmDfvn0MAPvoo49Mj+ku/75FrNx3d/z3zRhjlZWV7Omnn5Y+1x0/a06y++5un3VLSwsbOnQoe++999jJJ5/MbrzxRtNjs/WZ581pCYVCWLJkCU477TT1MbvdjtNOOw0LFy6UvmbhwoWa4wFg+vTppsd3RTK5bwBobW3FgAEDUF9fn1LJdxe6w+fdEcaOHYu+ffvi9NNPx6effprv5XSIpqYmAEg6PK07ft5W7hvoXv++o9EoXnjhBbS1tWHy5MnSY7rjZ23lvoHu9Vlfe+21OPvssw2fpYxsfeZ5Ey2NjY2IRqPo3bu35vHevXubxu737NmT1vFdkUzue/jw4Xj22Wfx+uuv469//SsURcGUKVOwY8eOzlhy3jD7vJubm+H3+/O0qtzTt29f/OlPf8Irr7yCV155BfX19Zg6dSqWLl2a76VlhKIouOmmm3D88cfjqKOOMj2uO/z7FrF6393l3/eKFStQUlICj8eDa665Bq+++ipGjhwpPbY7fdbp3Hd3+awB4IUXXsDSpUsxe/ZsS8dn6zPv0lOeiRiTJ0/WKPcpU6ZgxIgRePLJJ3HPPffkcWVELhg+fDiGDx+u/jxlyhRs3LgRDz30EJ577rk8riwzrr32WqxcuRILFizI91I6Fav33V3+fQ8fPhzLli1DU1MTXn75ZVx22WX46KOPTDfw7kI6991dPuvt27fjxhtvxHvvvdfpicR5Ey01NTVwOBzYu3ev5vG9e/eiT58+0tf06dMnreO7Ipnctx6Xy4Vx48Zhw4YNuVhil8Hs8y4rK0NRUVGeVpUfJk6cWJCb/nXXXYd///vf+Pjjj9GvX7+kx3aHf9+cdO5bT6H++3a73RgyZAgAYPz48fjiiy/whz/8AU8++aTh2O70Wadz33oK9bNesmQJ9u3bh2OOOUZ9LBqN4uOPP8ajjz6KYDAIh8OheU22PvO8hYfcbjfGjx+P999/X31MURS8//77pvHAyZMna44HgPfeey9p/LCrkcl964lGo1ixYgX69u2bq2V2CbrD550tli1bVlCfN2MM1113HV599VV88MEHGDRoUMrXdIfPO5P71tNd/n0rioJgMCh9rjt81mYku289hfpZn3rqqVixYgWWLVumfk2YMAEXX3wxli1bZhAsQBY/8/TzhbPHCy+8wDweD5s7dy5btWoV++lPf8oqKirYnj17GGOMXXLJJWzmzJnq8Z9++ilzOp3sgQceYKtXr2Z33nknc7lcbMWKFfm6hYxI977vuusu9s4777CNGzeyJUuWsAsvvJB5vV72zTff5OsWMqKlpYV99dVX7KuvvmIA2IMPPsi++uortnXrVsYYYzNnzmSXXHKJevymTZuYz+djv/jFL9jq1avZY489xhwOB3v77bfzdQsZke59P/TQQ+y1115j69evZytWrGA33ngjs9vtbN68efm6hbT52c9+xsrLy9n8+fPZ7t271a/29nb1mO747zuT++4O/75nzpzJPvroI7Z582a2fPlyNnPmTGaz2di7777LGOuenzVj6d93d/iszdBXD+XqM8+raGGMsUceeYT179+fud1uNnHiRLZo0SL1uZNPPplddtllmuP/8Y9/sGHDhjG3281GjRrF/vOf/3TyirNDOvd90003qcf27t2bfetb32JLly7Nw6o7Bi/l1X/xe73sssvYySefbHjN2LFjmdvtZg0NDWzOnDmdvu6Oku5933fffWzw4MHM6/WyqqoqNnXqVPbBBx/kZ/EZIrtfAJrPrzv++87kvrvDv+/LL7+cDRgwgLndblZbW8tOPfVUdeNmrHt+1oylf9/d4bM2Qy9acvWZ2xhjLD1vhiAIgiAIovPJext/giAIgiAIK5BoIQiCIAiiICDRQhAEQRBEQUCihSAIgiCIgoBEC0EQBEEQBQGJFoIgCIIgCgISLQRBEARBFAQkWgiCIAiCKAhItBAEQRAEURCQaCEIgiAIoiAg0UIQBEEQREFAooUgCIIgiILg/wH5e+3JyRr/BAAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time1, wx, wy, wz = zip(*gyro_noisy.measured_data)\n", + "time2, zx, zy, zz = zip(*gyro_clean.measured_data)\n", + "\n", + "plt.plot(time1, wx, label='Noisy Gyroscope')\n", + "# plt.plot(time2, zx, label='Clean Gyroscope')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "plt.plot(time1, wy, label='Noisy Gyroscope')\n", + "# plt.plot(time2, zy, label='Clean Gyroscope')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "plt.plot(time1, wz, label='Noisy Gyroscope')\n", + "plt.xlim(0, 4)\n", + "# plt.plot(time2, zz, label='Clean Gyroscope')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# now plot the total angular velocity\n", + "\n", + "abs_w = (np.array(wx) ** 2 + np.array(wy) ** 2 + np.array(wz) ** 2) ** 0.5\n", + "abs_z = (np.array(zx) ** 2 + np.array(zy) ** 2 + np.array(zz) ** 2) ** 0.5\n", + "plt.plot(time1, abs_w, label=\"noisy\")\n", + "plt.plot(time2, abs_z, label=\"clean\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Angular Velocity (rad/s)\")\n", + "plt.legend()\n", + "plt.xlim(0, 10)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barometer" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_barometer, pressure_barometer = zip(*barometer_clean.measured_data)\n", + "\n", + "rocket_pressure = test_flight.pressure.y_array\n", + "rocket_time = test_flight.pressure.x_array\n", + "\n", + "plt.plot(rocket_time, rocket_pressure, label=\"Rocket\")\n", + "plt.plot(time_barometer, pressure_barometer, label=\"Barometer\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Pressure (Pa)\")\n", + "plt.title(\"Pressure comparison\")\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "colab": { + "name": "getting_started.ipynb", + "provenance": [], + "toc_visible": true + }, + "hide_input": false, + "kernelspec": { + "display_name": "Python 3.10.0 ('rocketpy_dev')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + }, + "vscode": { + "interpreter": { + "hash": "18e93d5347af13ace37d47ea4e2a2ad720f0331bd9cb28f9983f5585f4dfaa5c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/reference/classes/sensors/abstract/index.rst b/docs/reference/classes/sensors/abstract/index.rst new file mode 100644 index 000000000..e016c93de --- /dev/null +++ b/docs/reference/classes/sensors/abstract/index.rst @@ -0,0 +1,11 @@ +Sensors Abstract Classes +======================== + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + Sensor Class + Inertial Sensor Class + Scalar Sensor Class + diff --git a/docs/reference/classes/sensors/abstract/inertial_sensor.rst b/docs/reference/classes/sensors/abstract/inertial_sensor.rst new file mode 100644 index 000000000..2e85205d6 --- /dev/null +++ b/docs/reference/classes/sensors/abstract/inertial_sensor.rst @@ -0,0 +1,5 @@ +Inertial Class +-------------- + +.. autoclass:: rocketpy.sensors.InertialSensor + :members: diff --git a/docs/reference/classes/sensors/abstract/scalar_sensor.rst b/docs/reference/classes/sensors/abstract/scalar_sensor.rst new file mode 100644 index 000000000..42e8aae95 --- /dev/null +++ b/docs/reference/classes/sensors/abstract/scalar_sensor.rst @@ -0,0 +1,5 @@ +Scalar Sensor Class +------------------- + +.. autoclass:: rocketpy.sensors.ScalarSensor + :members: diff --git a/docs/reference/classes/sensors/abstract/sensor.rst b/docs/reference/classes/sensors/abstract/sensor.rst new file mode 100644 index 000000000..188744f98 --- /dev/null +++ b/docs/reference/classes/sensors/abstract/sensor.rst @@ -0,0 +1,5 @@ +Sensor Class +------------ + +.. autoclass:: rocketpy.sensors.Barometer + :members: diff --git a/docs/reference/classes/sensors/accelerometer.rst b/docs/reference/classes/sensors/accelerometer.rst new file mode 100644 index 000000000..0eac1e4c0 --- /dev/null +++ b/docs/reference/classes/sensors/accelerometer.rst @@ -0,0 +1,5 @@ +Accelerometer Class +--------------- + +.. autoclass:: rocketpy.sensors.Accelerometer + :members: diff --git a/docs/reference/classes/sensors/barometer.rst b/docs/reference/classes/sensors/barometer.rst new file mode 100644 index 000000000..4fba284ca --- /dev/null +++ b/docs/reference/classes/sensors/barometer.rst @@ -0,0 +1,5 @@ +Barometer Class +--------------- + +.. autoclass:: rocketpy.sensors.Barometer + :members: diff --git a/docs/reference/classes/sensors/gnss_receiver.rst b/docs/reference/classes/sensors/gnss_receiver.rst new file mode 100644 index 000000000..a5efa2332 --- /dev/null +++ b/docs/reference/classes/sensors/gnss_receiver.rst @@ -0,0 +1,5 @@ +GNSS Receiver Class +------------------- + +.. autoclass:: rocketpy.sensors.GnssReceiver + :members: diff --git a/docs/reference/classes/sensors/gyroscope.rst b/docs/reference/classes/sensors/gyroscope.rst new file mode 100644 index 000000000..948f93e4c --- /dev/null +++ b/docs/reference/classes/sensors/gyroscope.rst @@ -0,0 +1,5 @@ +Gyroscope Class +--------------- + +.. autoclass:: rocketpy.sensors.Gyroscope + :members: diff --git a/docs/reference/classes/sensors/index.rst b/docs/reference/classes/sensors/index.rst new file mode 100644 index 000000000..98c79aafd --- /dev/null +++ b/docs/reference/classes/sensors/index.rst @@ -0,0 +1,12 @@ +Sensor Classes +============== + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + Sensors Abstract Classes + Accelerometer Class + Barometer Class + Gyroscope Class + GNSS Receiver Class diff --git a/docs/user/index.rst b/docs/user/index.rst index 6f28d2b4d..e734abe5d 100644 --- a/docs/user/index.rst +++ b/docs/user/index.rst @@ -24,6 +24,7 @@ RocketPy's User Guide Compare Flights Class Deployable Payload Air Brakes Example + ../notebooks/sensors.ipynb ../matlab/matlab.rst .. toctree:: diff --git a/rocketpy/__init__.py b/rocketpy/__init__.py index cc6dfa644..90ffcf72a 100644 --- a/rocketpy/__init__.py +++ b/rocketpy/__init__.py @@ -38,6 +38,7 @@ TrapezoidalFins, ) from .sensitivity import SensitivityModel +from .sensors import Accelerometer, Barometer, GnssReceiver, Gyroscope from .simulation import Flight, MonteCarlo from .stochastic import ( StochasticEllipticalFins, diff --git a/rocketpy/control/controller.py b/rocketpy/control/controller.py index 81fc8b332..93a13ecfd 100644 --- a/rocketpy/control/controller.py +++ b/rocketpy/control/controller.py @@ -1,3 +1,5 @@ +from inspect import signature + from ..prints.controller_prints import _ControllerPrints @@ -52,6 +54,10 @@ def __init__( the controller function can interact with. The objects are listed in the same order as they are provided in the `interactive_objects`. + 7. `sensors` (list): A list of sensors that are attached to the + rocket. The most recent measurements of the sensors are provided + with the ``sensor.measurement`` attribute. The sensors are + listed in the same order as they are added to the rocket This function will be called during the simulation at the specified sampling rate. The function should evaluate and change the interactive @@ -78,7 +84,7 @@ def __init__( None """ self.interactive_objects = interactive_objects - self.controller_function = controller_function + self.controller_function = self.__init_controller_function(controller_function) self.sampling_rate = sampling_rate self.name = name self.prints = _ControllerPrints(self) @@ -88,7 +94,45 @@ def __init__( else: self.observed_variables = [] - def __call__(self, time, state_vector, state_history): + def __init_controller_function(self, controller_function): + """Checks number of arguments of the controller function and initializes + it with the correct number of arguments. This is a workaround to allow + the controller function to receive sensors without breaking changes""" + sig = signature(controller_function) + if len(sig.parameters) == 6: + + # pylint: disable=unused-argument + def new_controller_function( + time, + sampling_rate, + state_vector, + state_history, + observed_variables, + interactive_objects, + sensors, + ): + return controller_function( + time, + sampling_rate, + state_vector, + state_history, + observed_variables, + interactive_objects, + ) + + elif len(sig.parameters) == 7: + new_controller_function = controller_function + else: + raise ValueError( + "The controller function must have 6 or 7 arguments. " + "The arguments must be in the following order: " + "(time, sampling_rate, state_vector, state_history, " + "observed_variables, interactive_objects, sensors)." + "Sensors argument is optional." + ) + return new_controller_function + + def __call__(self, time, state_vector, state_history, sensors): """Call the controller function. This is used by the simulation class. Parameters @@ -104,6 +148,11 @@ def __call__(self, time, state_vector, state_history): history is a list of every state vector of every step of the simulation. The state history is a list of lists, where each sublist is a state vector and is ordered from oldest to newest. + sensors : list + A list of sensors that are attached to the rocket. The most recent + measurements of the sensors are provided with the + ``sensor.measurement`` attribute. The sensors are listed in the same + order as they are added to the rocket. Returns ------- @@ -116,6 +165,7 @@ def __call__(self, time, state_vector, state_history): state_history, self.observed_variables, self.interactive_objects, + sensors, ) if observed_variables is not None: self.observed_variables.append(observed_variables) diff --git a/rocketpy/environment/tools.py b/rocketpy/environment/tools.py index dfa2698a1..4fc3ca7c7 100644 --- a/rocketpy/environment/tools.py +++ b/rocketpy/environment/tools.py @@ -532,7 +532,7 @@ def utm_to_geodesic( # pylint: disable=too-many-locals,too-many-statements x, y, utm_zone, hemis, semi_major_axis=6378137.0, flattening=1 / 298.257223563 ): # NOTE: already documented in the Environment class. - # TODO: deprecated the static method from the environment class, use only this one. + # TODO: deprecate the static method from the environment class, use only this one. if hemis == "N": y = y + 10000000 diff --git a/rocketpy/mathutils/vector_matrix.py b/rocketpy/mathutils/vector_matrix.py index 6060d387f..f0ccc36ee 100644 --- a/rocketpy/mathutils/vector_matrix.py +++ b/rocketpy/mathutils/vector_matrix.py @@ -2,6 +2,8 @@ from functools import cached_property from itertools import product +from rocketpy.tools import euler321_to_quaternions, normalize_quaternions + class Vector: """Pure python basic R3 vector class designed for simple operations. @@ -152,7 +154,10 @@ def __len__(self): @cached_property def unit_vector(self): """R3 vector with the same direction of self, but normalized.""" - return self / abs(self) + try: + return self / abs(self) + except ZeroDivisionError: + return self @cached_property def cross_matrix(self): @@ -238,6 +243,30 @@ def __xor__(self, other): ] ) + def __and__(self, other): + """Element wise multiplication between two R3 vectors. Also known as + Hadamard product. + + Parameters + ---------- + other : Vector + R3 vector to be multiplied with self. + + Returns + ------- + Vector + R3 vector resulting from the element wise multiplication between + self and other. + + Examples + -------- + >>> v = Vector([1, 7, 3]) + >>> u = Vector([2, 5, 6]) + >>> (v & u) + Vector(2, 35, 18) + """ + return Vector([self.x * other[0], self.y * other[1], self.z * other[2]]) + def __matmul__(self, other): """Dot product between two R3 vectors.""" return self.x * other.x + self.y * other.y + self.z * other.z @@ -914,6 +943,47 @@ def dot(self, other): """ return self @ (other) + def round(self, decimals=0): + """Round all the values matrix to a given number of decimals. + + Parameters + ---------- + decimals : int, optional + Number of decimal places to round to. Defaults to 0. + + Returns + ------- + Matrix + The rounded matrix. + + Examples + -------- + >>> M = Matrix([[1.1234, 2.3456, 3.4567], [4.5678, 5.6789, 6.7890], [7.8901, 8.9012, 9.0123]]) + >>> M.round(2) + Matrix([1.12, 2.35, 3.46], + [4.57, 5.68, 6.79], + [7.89, 8.9, 9.01]) + """ + return Matrix( + [ + [ + round(self.xx, decimals), + round(self.xy, decimals), + round(self.xz, decimals), + ], + [ + round(self.yx, decimals), + round(self.yy, decimals), + round(self.yz, decimals), + ], + [ + round(self.zx, decimals), + round(self.zy, decimals), + round(self.zz, decimals), + ], + ] + ) + def __str__(self): return ( f"[{self.xx}, {self.xy}, {self.xz}]\n" @@ -949,7 +1019,6 @@ def transformation(quaternion): The quaternion representing the rotation from frame A to frame B. Example: (cos(phi/2), 0, 0, sin(phi/2)) represents a rotation of phi around the z-axis. - Note: the quaternion must be normalized. Returns ------- @@ -960,27 +1029,60 @@ def transformation(quaternion): --------- https://en.wikipedia.org/wiki/Conversion_between_quaternions_and_Euler_angles """ - q_w, q_x, q_y, q_z = quaternion + # normalize quaternion + q_w, q_x, q_y, q_z = normalize_quaternions(quaternion) + + # pre-compute common terms + q_x2 = q_x**2 + q_y2 = q_y**2 + q_z2 = q_z**2 + q_wx = q_w * q_x + q_wy = q_w * q_y + q_wz = q_w * q_z + q_xy = q_x * q_y + q_xz = q_x * q_z + q_yz = q_y * q_z return Matrix( [ [ - 1 - 2 * (q_y**2 + q_z**2), - 2 * (q_x * q_y - q_w * q_z), - 2 * (q_x * q_z + q_w * q_y), + 1 - 2 * (q_y2 + q_z2), + 2 * (q_xy - q_wz), + 2 * (q_xz + q_wy), ], [ - 2 * (q_x * q_y + q_w * q_z), - 1 - 2 * (q_x**2 + q_z**2), - 2 * (q_y * q_z - q_w * q_x), + 2 * (q_xy + q_wz), + 1 - 2 * (q_x2 + q_z2), + 2 * (q_yz - q_wx), ], [ - 2 * (q_x * q_z - q_w * q_y), - 2 * (q_y * q_z + q_w * q_x), - 1 - 2 * (q_x**2 + q_y**2), + 2 * (q_xz - q_wy), + 2 * (q_yz + q_wx), + 1 - 2 * (q_x2 + q_y2), ], ] ) + @staticmethod + def transformation_euler_angles(roll, pitch, yaw): + """Returns the transformation Matrix from frame B to frame A, where B + is rotated by the Euler angles roll, pitch and yaw with respect to A. + + Parameters + ---------- + roll : float + The roll angle in degrees. + pitch : float + The pitch angle in degrees. + yaw : float + The yaw angle in degrees. + + Returns + ------- + Matrix + The transformation matrix from frame B to frame A. + """ + return Matrix.transformation(euler321_to_quaternions(roll, pitch, yaw)) + if __name__ == "__main__": import doctest diff --git a/rocketpy/motors/tank.py b/rocketpy/motors/tank.py index 6fabaa341..a3b21f434 100644 --- a/rocketpy/motors/tank.py +++ b/rocketpy/motors/tank.py @@ -1158,7 +1158,7 @@ def gas_volume(self): Function Volume of the gas as a function of time. """ - # TODO: there's a bug on the gas_center_of_mass is I don't discretize here + # TODO: there's a bug on the gas_center_of_mass if I don't discretize here func = Function(self.geometry.total_volume).set_discrete_based_on_model( self.liquid_volume ) diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index bdeb89628..2add1e78f 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -160,7 +160,7 @@ def thrust_to_weight(self): lower=0, upper=self.rocket.motor.burn_out_time ) - def draw(self, vis_args=None): + def draw(self, vis_args=None, plane="xz"): """Draws the rocket in a matplotlib figure. Parameters @@ -184,6 +184,9 @@ def draw(self, vis_args=None): A full list of color names can be found at: \ https://matplotlib.org/stable/gallery/color/named_colors + plane : str, optional + Plane in which the rocket will be drawn. Default is 'xz'. Other + options is 'yz'. Used only for sensors representation. """ if vis_args is None: vis_args = { @@ -197,10 +200,8 @@ def draw(self, vis_args=None): "line_width": 1.0, } - # Create the figure and axis - _, ax = plt.subplots(figsize=(8, 6), facecolor="#EEEEEE") + _, ax = plt.subplots(figsize=(8, 6), facecolor=vis_args["background"]) ax.set_aspect("equal") - ax.set_facecolor(vis_args["background"]) ax.grid(True, linestyle="--", linewidth=0.5) csys = self.rocket._csys @@ -212,6 +213,7 @@ def draw(self, vis_args=None): self._draw_motor(last_radius, last_x, ax, vis_args) self._draw_rail_buttons(ax, vis_args) self._draw_center_of_mass_and_pressure(ax) + self._draw_sensors(ax, self.rocket.sensors, plane) plt.title("Rocket Representation") plt.xlim() @@ -380,7 +382,7 @@ def _draw_motor(self, last_radius, last_x, ax, vis_args): ) # Get motor patches translated to the correct position - motor_patches = self._generate_motor_patches(total_csys, ax, vis_args) + motor_patches = self._generate_motor_patches(total_csys, ax) # Draw patches if not isinstance(self.rocket.motor, EmptyMotor): @@ -401,7 +403,7 @@ def _draw_motor(self, last_radius, last_x, ax, vis_args): self._draw_nozzle_tube(last_radius, last_x, nozzle_position, ax, vis_args) def _generate_motor_patches( - self, total_csys, ax, vis_args + self, total_csys, ax ): # pylint: disable=unused-argument """Generates motor patches for drawing""" motor_patches = [] @@ -548,6 +550,57 @@ def _draw_center_of_mass_and_pressure(self, ax): cp, 0, label="Static Center of Pressure", color="red", s=10, zorder=10 ) + def _draw_sensors(self, ax, sensors, plane): + """Draw the sensor as a small thick line at the position of the sensor, + with a vector pointing in the direction normal of the sensor. Get the + normal vector from the sensor orientation matrix.""" + colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] + for i, sensor_pos in enumerate(sensors): + sensor = sensor_pos[0] + pos = sensor_pos[1] + if plane == "xz": + # z position of the sensor is the x position in the plot + x_pos = pos[2] + normal_x = sensor.normal_vector.z + # x position of the sensor is the y position in the plot + y_pos = pos[0] + normal_y = sensor.normal_vector.x + elif plane == "yz": + # z position of the sensor is the x position in the plot + x_pos = pos[2] + normal_x = sensor.normal_vector.z + # y position of the sensor is the y position in the plot + y_pos = pos[1] + normal_y = sensor.normal_vector.y + else: + raise ValueError("Plane must be 'xz' or 'yz'.") + + # line length is 2/5 of the rocket radius + line_length = self.rocket.radius / 2.5 + + ax.plot( + [x_pos, x_pos], + [y_pos + line_length, y_pos - line_length], + linewidth=2, + color=colors[(i + 1) % len(colors)], + zorder=10, + label=sensor.name, + ) + if abs(sensor.normal_vector) != 0: + ax.quiver( + x_pos, + y_pos, + normal_x, + normal_y, + color=colors[(i + 1) % len(colors)], + scale_units="xy", + angles="xy", + minshaft=2, + headwidth=2, + headlength=4, + zorder=10, + ) + def all(self): """Prints out all graphs available about the Rocket. It simply calls all the other plotter methods in this class. diff --git a/rocketpy/prints/sensors_prints.py b/rocketpy/prints/sensors_prints.py new file mode 100644 index 000000000..73ab062f8 --- /dev/null +++ b/rocketpy/prints/sensors_prints.py @@ -0,0 +1,121 @@ +from abc import ABC + + +class _SensorPrints(ABC): + def __init__(self, sensor): + self.sensor = sensor + self.units = sensor.units + + def _print_aligned(self, label, value): + """Prints a label and a value aligned vertically.""" + print(f"{label:<26} {value}") + + def identity(self): + """Prints the identity of the sensor.""" + print("Identification:\n") + self._print_aligned("Name:", self.sensor.name) + self._print_aligned("Type:", self.sensor.__class__.__name__) + + def quantization(self): + """Prints the quantization of the sensor.""" + print("\nQuantization:\n") + self._print_aligned( + "Measurement Range:", + f"{self.sensor.measurement_range[0]} " + + f"to {self.sensor.measurement_range[1]} ({self.units})", + ) + self._print_aligned("Resolution:", f"{self.sensor.resolution} {self.units}/LSB") + + def noise(self): + """Prints the noise of the sensor.""" + self._general_noise() + + def _general_noise(self): + """Prints the noise of the sensor.""" + print("\nNoise:\n") + self._print_aligned( + "Noise Density:", f"{self.sensor.noise_density} {self.units}/√Hz" + ) + self._print_aligned( + "Noise Variance:", f"{self.sensor.noise_variance} ({self.units})^2" + ) + self._print_aligned( + "Random Walk Density:", + f"{self.sensor.random_walk_density} {self.units}/√Hz", + ) + self._print_aligned( + "Random Walk Variance:", + f"{self.sensor.random_walk_variance} ({self.units})^2", + ) + self._print_aligned( + "Constant Bias:", f"{self.sensor.constant_bias} {self.units}" + ) + self._print_aligned( + "Operating Temperature:", f"{self.sensor.operating_temperature} K" + ) + self._print_aligned( + "Temperature Bias:", f"{self.sensor.temperature_bias} {self.units}/K" + ) + self._print_aligned( + "Temperature Scale Factor:", f"{self.sensor.temperature_scale_factor} %/K" + ) + + def all(self): + """Prints all information of the sensor.""" + self.identity() + self.quantization() + self.noise() + + +class _InertialSensorPrints(_SensorPrints): + + def orientation(self): + """Prints the orientation of the sensor.""" + print("\nOrientation of the Sensor:\n") + self._print_aligned("Orientation:", self.sensor.orientation) + self._print_aligned("Normal Vector:", self.sensor.normal_vector) + print("Rotation Matrix:") + for row in self.sensor.rotation_matrix: + value = " ".join(f"{val:.2f}" for val in row) + value = [float(val) for val in value.split()] + self._print_aligned("", value) + + def _general_noise(self): + super()._general_noise() + self._print_aligned( + "Cross Axis Sensitivity:", f"{self.sensor.cross_axis_sensitivity} %" + ) + + def all(self): + """Prints all information of the sensor.""" + self.identity() + self.orientation() + self.quantization() + self.noise() + + +class _GyroscopePrints(_InertialSensorPrints): + """Class that contains all gyroscope prints.""" + + def noise(self): + """Prints the noise of the sensor.""" + self._general_noise() + self._print_aligned( + "Acceleration Sensitivity:", + f"{self.sensor.acceleration_sensitivity} rad/s/g", + ) + + +class _GnssReceiverPrints(_SensorPrints): + """Class that contains all GnssReceiver prints.""" + + def accuracy(self): + """Prints the accuracy of the sensor.""" + print("\nAccuracy:\n") + self._print_aligned("Position Accuracy:", f"{self.sensor.position_accuracy} m") + self._print_aligned("Altitude Accuracy:", f"{self.sensor.altitude_accuracy} m") + + def all(self): + """Prints all information of the sensor.""" + self.identity() + self.accuracy() diff --git a/rocketpy/rocket/components.py b/rocketpy/rocket/components.py index 5132a315e..2d580de7e 100644 --- a/rocketpy/rocket/components.py +++ b/rocketpy/rocket/components.py @@ -23,6 +23,11 @@ def __init__(self): self.component_tuple = namedtuple("component_tuple", "component position") self._components = [] + # List of components and their positions to avoid extra for loops in + # simulation time + self.__component_list = [] + self.__position_list = [] + def __repr__(self): """Return a string representation of the Components instance.""" components_str = "\n".join( @@ -61,6 +66,8 @@ def add(self, component, position): ------- None """ + self.__component_list.append(component) + self.__position_list.append(position) self._components.append(self.component_tuple(component, position)) def get_by_type(self, component_type): @@ -108,10 +115,10 @@ def get_components(self): Returns ------- - list + list[Component] A list of all the components in the list of components. """ - return [c.component for c in self._components] + return self.__component_list def get_positions(self): """Return a list of all the positions of the components in the list of @@ -123,7 +130,7 @@ def get_positions(self): A list of all the positions of the components in the list of components. """ - return [c.position for c in self._components] + return self.__position_list def remove(self, component): """Remove a component from the list of components. If more than one diff --git a/rocketpy/rocket/parachute.py b/rocketpy/rocket/parachute.py index 742c82d00..c465c4367 100644 --- a/rocketpy/rocket/parachute.py +++ b/rocketpy/rocket/parachute.py @@ -1,3 +1,5 @@ +from inspect import signature + import numpy as np from ..mathutils.function import Function @@ -17,7 +19,33 @@ class Parachute: Drag coefficient times reference area for parachute. It has units of area and must be given in squared meters. Parachute.trigger : callable, float, str - Defines the trigger condition for the parachute ejection system. + This parameter defines the trigger condition for the parachute ejection + system. It can be one of the following: + + - A callable function that takes three arguments: + 1. Freestream pressure in pascals. + 2. Height in meters above ground level. + 3. The state vector of the simulation, which is defined as: + + `[x, y, z, vx, vy, vz, e0, e1, e2, e3, wx, wy, wz]`. + + 4. A list of sensors that are attached to the rocket. The most recent + measurements of the sensors are provided with the + ``sensor.measurement`` attribute. The sensors are listed in the same + order as they are added to the rocket. + + The function should return ``True`` if the parachute ejection system + should be triggered and False otherwise. + + - A float value, representing an absolute height in meters. In this + case, the parachute will be ejected when the rocket reaches this height + above ground level. + + - The string "apogee" which triggers the parachute at apogee, i.e., + when the rocket reaches its highest point and starts descending. + + Note: The function will be called according to the sampling rate + specified. Parachute.triggerfunc : function Trigger function created from the trigger used to evaluate the trigger condition for the parachute ejection system. It is a callable function @@ -154,12 +182,23 @@ def __evaluate_trigger_function(self, trigger): """This is used to set the triggerfunc attribute that will be used to interact with the Flight class. """ + # pylint: disable=unused-argument, function-redefined + # The parachute is deployed by a custom function if callable(trigger): - self.triggerfunc = trigger + # work around for having added sensors to parachute triggers + # to avoid breaking changes + triggerfunc = trigger + sig = signature(triggerfunc) + if len(sig.parameters) == 3: + + def triggerfunc(p, h, y, sensors): + return trigger(p, h, y) + + self.triggerfunc = triggerfunc elif isinstance(trigger, (int, float)): # The parachute is deployed at a given height - def triggerfunc(p, h, y): # pylint: disable=unused-argument + def triggerfunc(p, h, y, sensors): # pylint: disable=unused-argument # p = pressure considering parachute noise signal # h = height above ground level considering parachute noise signal # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3] @@ -169,7 +208,7 @@ def triggerfunc(p, h, y): # pylint: disable=unused-argument elif trigger.lower() == "apogee": # The parachute is deployed at apogee - def triggerfunc(p, h, y): # pylint: disable=unused-argument + def triggerfunc(p, h, y, sensors): # pylint: disable=unused-argument # p = pressure considering parachute noise signal # h = height above ground level considering parachute noise signal # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3] diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index 0e3024ecf..a5964808f 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -4,7 +4,7 @@ from rocketpy.control.controller import _Controller from rocketpy.mathutils.function import Function -from rocketpy.mathutils.vector_matrix import Matrix +from rocketpy.mathutils.vector_matrix import Matrix, Vector from rocketpy.motors.motor import EmptyMotor from rocketpy.plots.rocket_plots import _RocketPlots from rocketpy.prints.rocket_prints import _RocketPrints @@ -295,16 +295,11 @@ def __init__( # pylint: disable=too-many-statements self.thrust_eccentricity_y = 0 self.thrust_eccentricity_x = 0 - # Parachute, Aerodynamic and Rail buttons data initialization + # Parachute, Aerodynamic, Buttons, Controllers, Sensor data initialization self.parachutes = [] - - # Controllers data initialization self._controllers = [] - - # AirBrakes data initialization self.air_brakes = [] - - # Aerodynamic data initialization + self.sensors = Components() self.aerodynamic_surfaces = Components() self.rail_buttons = Components() @@ -1352,6 +1347,34 @@ def add_parachute( self.parachutes.append(parachute) return self.parachutes[-1] + def add_sensor(self, sensor, position): + """Adds a sensor to the rocket. + + Parameters + ---------- + sensor : Sensor + Sensor to be added to the rocket. + position : int, float, tuple, list, Vector + Position of the sensor. If a Vector, tuple or list is passed, it + must be in the format (x, y, z) where x, y, and z are defined in the + rocket's user defined coordinate system. If a single value is + passed, it is assumed to be along the z-axis (centerline) of the + rocket's user defined coordinate system and angular_position and + radius must be given. + + Returns + ------- + None + """ + if isinstance(position, (float, int)): + position = (0, 0, position) + position = Vector(position) + self.sensors.add(sensor, position) + try: + sensor._attached_rockets[self] += 1 + except KeyError: + sensor._attached_rockets[self] = 1 + def add_air_brakes( self, drag_coefficient_curve, @@ -1419,6 +1442,11 @@ def add_air_brakes( 6. `interactive_objects` (list): A list containing the objects that the controller function can interact with. The objects are listed in the same order as they are provided in the + `interactive_objects` + 7. `sensors` (list): A list of sensors that are attached to the + rocket. The most recent measurements of the sensors are provided + with the ``sensor.measurement`` attribute. The sensors are + listed in the same order as they are added to the rocket ``interactive_objects`` This function will be called during the simulation at the specified @@ -1623,7 +1651,7 @@ def add_thrust_eccentricity(self, x, y): self.thrust_eccentricity_x = y return self - def draw(self, vis_args=None): + def draw(self, vis_args=None, plane="xz"): """Draws the rocket in a matplotlib figure. Parameters @@ -1647,8 +1675,11 @@ def draw(self, vis_args=None): A full list of color names can be found at: https://matplotlib.org/stable/gallery/color/named_colors + plane : str, optional + Plane in which the rocket will be drawn. Default is 'xz'. Other + options is 'yz'. Used only for sensors representation. """ - self.plots.draw(vis_args) + self.plots.draw(vis_args, plane) def info(self): """Prints out a summary of the data and graphs available about diff --git a/rocketpy/sensors/__init__.py b/rocketpy/sensors/__init__.py new file mode 100644 index 000000000..eb2e20730 --- /dev/null +++ b/rocketpy/sensors/__init__.py @@ -0,0 +1,5 @@ +from .accelerometer import Accelerometer +from .barometer import Barometer +from .gnss_receiver import GnssReceiver +from .gyroscope import Gyroscope +from .sensor import InertialSensor, ScalarSensor, Sensor diff --git a/rocketpy/sensors/accelerometer.py b/rocketpy/sensors/accelerometer.py new file mode 100644 index 000000000..607b1632e --- /dev/null +++ b/rocketpy/sensors/accelerometer.py @@ -0,0 +1,273 @@ +import numpy as np + +from ..mathutils.vector_matrix import Matrix, Vector +from ..prints.sensors_prints import _InertialSensorPrints +from ..sensors.sensor import InertialSensor + +# pylint: disable=too-many-arguments + + +class Accelerometer(InertialSensor): + """Class for the accelerometer sensor + + Attributes + ---------- + consider_gravity : bool + Whether the sensor considers the effect of gravity on the acceleration. + prints : _InertialSensorPrints + Object that contains the print functions for the sensor. + sampling_rate : float + Sample rate of the sensor in Hz. + orientation : tuple, list + Orientation of the sensor in the rocket. + measurement_range : float, tuple + The measurement range of the sensor in m/s^2. + resolution : float + The resolution of the sensor in m/s^2/LSB. + noise_density : float, list + The noise density of the sensor in m/s^2/√Hz. + noise_variance : float, list + The variance of the noise of the sensor in (m/s^2)^2. + random_walk_density : float, list + The random walk density of the sensor in m/s^2/√Hz. + random_walk_variance : float, list + The variance of the random walk of the sensor in (m/s^2)^2. + constant_bias : float, list + The constant bias of the sensor in m/s^2. + operating_temperature : float + The operating temperature of the sensor in Kelvin. + temperature_bias : float, list + The temperature bias of the sensor in m/s^2/K. + temperature_scale_factor : float, list + The temperature scale factor of the sensor in %/K. + cross_axis_sensitivity : float + The cross axis sensitivity of the sensor in percentage. + name : str + The name of the sensor. + rotation_matrix : Matrix + The rotation matrix of the sensor from the sensor frame to the rocket + frame of reference. + normal_vector : Vector + The normal vector of the sensor in the rocket frame of reference. + _random_walk_drift : Vector + The random walk drift of the sensor in m/s^2. + measurement : float + The measurement of the sensor after quantization, noise and temperature + drift. + measured_data : list + The stored measured data of the sensor after quantization, noise and + temperature drift. + """ + + units = "m/s^2" + + def __init__( + self, + sampling_rate, + orientation=(0, 0, 0), + measurement_range=np.inf, + resolution=0, + noise_density=0, + noise_variance=1, + random_walk_density=0, + random_walk_variance=1, + constant_bias=0, + operating_temperature=25, + temperature_bias=0, + temperature_scale_factor=0, + cross_axis_sensitivity=0, + consider_gravity=False, + name="Accelerometer", + ): + """ + Initialize the accelerometer sensor + + Parameters + ---------- + sampling_rate : float + Sample rate of the sensor in Hz. + orientation : tuple, list, optional + Orientation of the sensor in the rocket. The orientation can be + given as: + - A list of length 3, where the elements are the Euler angles for + the rotation yaw (ψ), pitch (θ) and roll (φ) in radians. The + standard rotation sequence is z-y-x (3-2-1) is used, meaning the + sensor is first rotated by ψ around the x axis, then by θ around + the new y axis and finally by φ around the new z axis. + - A list of lists (matrix) of shape 3x3, representing the rotation + matrix from the sensor frame to the rocket frame. The sensor frame + of reference is defined as to have z axis along the sensor's normal + vector pointing upwards, x and y axes perpendicular to the z axis + and each other. + The rocket frame of reference is defined as to have z axis + along the rocket's axis of symmetry pointing upwards, x and y axes + perpendicular to the z axis and each other. A rotation around the x + axis configures a pitch, around the y axis a yaw and around z axis a + roll. Default is (0, 0, 0), meaning the sensor is aligned with all + of the rocket's axis. + measurement_range : float, tuple, optional + The measurement range of the sensor in the m/s^2. If a float, the + same range is applied both for positive and negative values. If a + tuple, the first value is the positive range and the second value is + the negative range. Default is np.inf. + resolution : float, optional + The resolution of the sensor in m/s^2/LSB. Default is 0, meaning no + quantization is applied. + noise_density : float, list, optional + The noise density of the sensor for a Gaussian white noise in m/s^2/√Hz. + Sometimes called "white noise drift", "angular random walk" for + gyroscopes, "velocity random walk" for accelerometers or + "(rate) noise density". Default is 0, meaning no noise is applied. + If a float or int is given, the same noise density is applied to all + axes. The values of each axis can be set individually by passing a + list of length 3. + noise_variance : float, list, optional + The noise variance of the sensor for a Gaussian white noise in (m/s^2)^2. + Default is 1, meaning the noise is normally distributed with a + standard deviation of 1 m/s^2. If a float or int is given, the same + variance is applied to all axes. The values of each axis can be set + individually by passing a list of length 3. + random_walk_density : float, list, optional + The random walk of the sensor for a Gaussian random walk in m/s^2/√Hz. + Sometimes called "bias (in)stability" or "bias drift"". Default is 0, + meaning no random walk is applied. If a float or int is given, the + same random walk is applied to all axes. The values of each axis can + be set individually by passing a list of length 3. + random_walk_variance : float, list, optional + The random walk variance of the sensor for a Gaussian random walk in + (m/s^2)^2. Default is 1, meaning the noise is normally distributed + with a standard deviation of 1 m/s^2. If a float or int is given, + the same variance is applied to all axes. The values of each axis + can be set individually by passing a list of length 3. + constant_bias : float, list, optional + The constant bias of the sensor in m/s^2. Default is 0, meaning no + constant bias is applied. If a float or int is given, the same bias + is applied to all axes. The values of each axis can be set + individually by passing a list of length 3. + operating_temperature : float, optional + The operating temperature of the sensor in Kelvin. + At 298.15 K (25 °C), the sensor is assumed to operate ideally, no + temperature related noise is applied. Default is 298.15. + temperature_bias : float, list, optional + The temperature bias of the sensor in m/s^2/K. Default is 0, + meaning no temperature bias is applied. If a float or int is given, + the same temperature bias is applied to all axes. The values of each + axis can be set individually by passing a list of length 3. + temperature_scale_factor : float, list, optional + The temperature scale factor of the sensor in %/K. Default is 0, + meaning no temperature scale factor is applied. If a float or int is + given, the same temperature scale factor is applied to all axes. The + values of each axis can be set individually by passing a list of + length 3. + cross_axis_sensitivity : float, optional + Skewness of the sensor's axes in percentage. Default is 0, meaning + no cross-axis sensitivity is applied. + consider_gravity : bool, optional + If True, the sensor will consider the effect of gravity on the + acceleration. Default is False. + name : str, optional + The name of the sensor. Default is "Accelerometer". + + Returns + ------- + None + + See Also + -------- + TODO link to documentation on noise model + """ + super().__init__( + sampling_rate, + orientation, + measurement_range=measurement_range, + resolution=resolution, + noise_density=noise_density, + noise_variance=noise_variance, + random_walk_density=random_walk_density, + random_walk_variance=random_walk_variance, + constant_bias=constant_bias, + operating_temperature=operating_temperature, + temperature_bias=temperature_bias, + temperature_scale_factor=temperature_scale_factor, + cross_axis_sensitivity=cross_axis_sensitivity, + name=name, + ) + self.consider_gravity = consider_gravity + self.prints = _InertialSensorPrints(self) + + def measure(self, time, **kwargs): + """Measure the acceleration of the rocket + + Parameters + ---------- + time : float + Current time in seconds. + kwargs : dict + Keyword arguments dictionary containing the following keys: + - u : np.array + State vector of the rocket. + - u_dot : np.array + Derivative of the state vector of the rocket. + - relative_position : np.array + Position of the sensor relative to the rocket center of mass. + - environment : Environment + Environment object containing the atmospheric conditions. + """ + u = kwargs["u"] + u_dot = kwargs["u_dot"] + relative_position = kwargs["relative_position"] + gravity = kwargs["environment"].gravity.get_value_opt(u[3]) + + # Linear acceleration of rocket cdm in inertial frame + gravity = ( + Vector([0, 0, -gravity]) if self.consider_gravity else Vector([0, 0, 0]) + ) + inertial_acceleration = Vector(u_dot[3:6]) + gravity + + # Vector from rocket cdm to sensor in rocket frame + r = relative_position + + # Angular velocity and accel of rocket + omega = Vector(u[10:13]) + omega_dot = Vector(u_dot[10:13]) + + # Measured acceleration at sensor position in inertial frame + A = ( + inertial_acceleration + + Vector.cross(omega_dot, r) + + Vector.cross(omega, Vector.cross(omega, r)) + ) + # Transform to sensor frame + inertial_to_sensor = self._total_rotation_matrix @ Matrix.transformation( + u[6:10] + ) + A = inertial_to_sensor @ A + + # Apply noise + bias and quantize + A = self.apply_noise(A) + A = self.apply_temperature_drift(A) + A = self.quantize(A) + + self.measurement = tuple([*A]) + self._save_data((time, *A)) + + def export_measured_data(self, filename, file_format="csv"): + """Export the measured values to a file + + Parameters + ---------- + filename : str + Name of the file to export the values to + file_format : str + Format of the file to export the values to. Options are "csv" and + "json". Default is "csv". + + Returns + ------- + None + """ + self._generic_export_measured_data( + filename=filename, + file_format=file_format, + data_labels=("t", "ax", "ay", "az"), + ) diff --git a/rocketpy/sensors/barometer.py b/rocketpy/sensors/barometer.py new file mode 100644 index 000000000..9e6fceb16 --- /dev/null +++ b/rocketpy/sensors/barometer.py @@ -0,0 +1,191 @@ +import numpy as np + +from ..mathutils.vector_matrix import Matrix +from ..prints.sensors_prints import _SensorPrints +from ..sensors.sensor import ScalarSensor + + +class Barometer(ScalarSensor): + """Class for the barometer sensor + + Attributes + ---------- + prints : _SensorPrints + Object that contains the print functions for the sensor. + sampling_rate : float + Sample rate of the sensor in Hz. + orientation : tuple, list + Orientation of the sensor in the rocket. + measurement_range : float, tuple + The measurement range of the sensor in Pa. + resolution : float + The resolution of the sensor in Pa/LSB. + noise_density : float + The noise density of the sensor in Pa/√Hz. + noise_variance : float + The variance of the noise of the sensor in Pa^2. + random_walk_density : float + The random walk density of the sensor in Pa/√Hz. + random_walk_variance : float + The variance of the random walk of the sensor in Pa^2. + constant_bias : float + The constant bias of the sensor in Pa. + operating_temperature : float + The operating temperature of the sensor in Kelvin. + temperature_bias : float + The temperature bias of the sensor in Pa/K. + temperature_scale_factor : float + The temperature scale factor of the sensor in %/K. + name : str + The name of the sensor. + measurement : float + The measurement of the sensor after quantization, noise and temperature + drift. + measured_data : list + The stored measured data of the sensor after quantization, noise and + temperature drift. + """ + + units = "Pa" + + def __init__( + self, + sampling_rate, + measurement_range=np.inf, + resolution=0, + noise_density=0, + noise_variance=1, + random_walk_density=0, + random_walk_variance=1, + constant_bias=0, + operating_temperature=25, + temperature_bias=0, + temperature_scale_factor=0, + name="Barometer", + ): + """ + Initialize the barometer sensor + + Parameters + ---------- + sampling_rate : float + Sample rate of the sensor in Hz. + measurement_range : float, tuple, optional + The measurement range of the sensor in Pa. If a float, the same + range is applied both for positive and negative values. If a tuple, + the first value is the positive range and the second value is the + negative range. Default is np.inf. + resolution : float, optional + The resolution of the sensor in Pa/LSB. Default is 0, meaning no + quantization is applied. + noise_density : float, optional + The noise density of the sensor for a Gaussian white noise in Pa/√Hz. + Sometimes called "white noise drift", "angular random walk" for + gyroscopes, "velocity random walk" for accelerometers or + "(rate) noise density". Default is 0, meaning no noise is applied. + noise_variance : float, optional + The noise variance of the sensor for a Gaussian white noise in Pa^2. + Default is 1, meaning the noise is normally distributed with a + standard deviation of 1 Pa. + random_walk_density : float, optional + The random walk of the sensor for a Gaussian random walk in Pa/√Hz. + Sometimes called "bias (in)stability" or "bias drift"". Default is 0, + meaning no random walk is applied. + random_walk_variance : float, optional + The random walk variance of the sensor for a Gaussian random walk in + Pa^2. Default is 1, meaning the noise is normally distributed with a + standard deviation of 1 Pa. + constant_bias : float, optional + The constant bias of the sensor in Pa. Default is 0, meaning no + constant bias is applied. + operating_temperature : float, optional + The operating temperature of the sensor in Kelvin. + At 298.15 K (25 °C), the sensor is assumed to operate ideally, no + temperature related noise is applied. Default is 298.15. + temperature_bias : float, optional + The temperature bias of the sensor in Pa/K. Default is 0, meaning no + temperature bias is applied. + temperature_scale_factor : float, optional + The temperature scale factor of the sensor in %/K. Default is 0, + meaning no temperature scale factor is applied. + name : str, optional + The name of the sensor. Default is "Barometer". + + Returns + ------- + None + + See Also + -------- + TODO link to documentation on noise model + """ + super().__init__( + sampling_rate=sampling_rate, + measurement_range=measurement_range, + resolution=resolution, + noise_density=noise_density, + noise_variance=noise_variance, + random_walk_density=random_walk_density, + random_walk_variance=random_walk_variance, + constant_bias=constant_bias, + operating_temperature=operating_temperature, + temperature_bias=temperature_bias, + temperature_scale_factor=temperature_scale_factor, + name=name, + ) + self.prints = _SensorPrints(self) + + def measure(self, time, **kwargs): + """Measures the pressure at barometer location + + Parameters + ---------- + time : float + Current time in seconds. + kwargs : dict + Keyword arguments dictionary containing the following keys: + - u : np.array + State vector of the rocket. + - u_dot : np.array + Derivative of the state vector of the rocket. + - relative_position : np.array + Position of the sensor relative to the rocket center of mass. + - environment : Environment + Environment object containing the atmospheric conditions. + """ + u = kwargs["u"] + relative_position = kwargs["relative_position"] + pressure = kwargs["environment"].pressure + + # Calculate the altitude of the sensor + relative_altitude = (Matrix.transformation(u[6:10]) @ relative_position).z + + # Calculate the pressure at the sensor location and add noise + P = pressure(relative_altitude + u[2]) + P = self.apply_noise(P) + P = self.apply_temperature_drift(P) + P = self.quantize(P) + + self.measurement = P + self._save_data((time, P)) + + def export_measured_data(self, filename, file_format="csv"): + """Export the measured values to a file + + Parameters + ---------- + filename : str + Name of the file to export the values to + file_format : str + file_format of the file to export the values to. Options are "csv" and + "json". Default is "csv". + + Returns + ------- + None + """ + self._generic_export_measured_data( + filename=filename, + file_format=file_format, + data_labels=("t", "pressure"), + ) diff --git a/rocketpy/sensors/gnss_receiver.py b/rocketpy/sensors/gnss_receiver.py new file mode 100644 index 000000000..9502bd918 --- /dev/null +++ b/rocketpy/sensors/gnss_receiver.py @@ -0,0 +1,125 @@ +import math + +import numpy as np + +from rocketpy.tools import inverted_haversine + +from ..mathutils.vector_matrix import Matrix, Vector +from ..prints.sensors_prints import _GnssReceiverPrints +from .sensor import ScalarSensor + + +class GnssReceiver(ScalarSensor): + """Class for the GNSS Receiver sensor. + + Attributes + ---------- + prints : _GnssReceiverPrints + Object that contains the print functions for the sensor. + sampling_rate : float + Sample rate of the sensor in Hz. + position_accuracy : float + Accuracy of the sensor interpreted as the standard deviation of the + position in meters. + altitude_accuracy : float + Accuracy of the sensor interpreted as the standard deviation of the + position in meters. + name : str + The name of the sensor. + measurement : tuple + The measurement of the sensor. + measured_data : list + The stored measured data of the sensor. + """ + + units = "°, m" + + def __init__( + self, + sampling_rate, + position_accuracy=0, + altitude_accuracy=0, + name="GnssReceiver", + ): + """Initialize the Gnss Receiver sensor. + + Parameters + ---------- + sampling_rate : float + Sample rate of the sensor in Hz. + position_accuracy : float + Accuracy of the sensor interpreted as the standard deviation of the + position in meters. Default is 0. + altitude_accuracy : float + Accuracy of the sensor interpreted as the standard deviation of the + position in meters. Default is 0. + name : str + The name of the sensor. Default is "GnssReceiver". + """ + super().__init__(sampling_rate=sampling_rate, name=name) + self.position_accuracy = position_accuracy + self.altitude_accuracy = altitude_accuracy + + self.prints = _GnssReceiverPrints(self) + + def measure(self, time, **kwargs): + """Measure the position of the rocket in latitude, longitude and + altitude. + + Parameters + ---------- + time : float + Current time in seconds. + kwargs : dict + Keyword arguments dictionary containing the following keys: + - u : np.array + State vector of the rocket. + - u_dot : np.array + Derivative of the state vector of the rocket. + - relative_position : np.array + Position of the sensor relative to the rocket center of mass. + - environment : Environment + Environment object containing the atmospheric conditions. + """ + u = kwargs["u"] + relative_position = kwargs["relative_position"] + lat, lon = kwargs["environment"].latitude, kwargs["environment"].longitude + earth_radius = kwargs["environment"].earth_radius + + # Get from state u and add relative position + x, y, z = (Matrix.transformation(u[6:10]) @ relative_position) + Vector(u[0:3]) + # Apply accuracy to the position + x = np.random.normal(x, self.position_accuracy) + y = np.random.normal(y, self.position_accuracy) + altitude = np.random.normal(z, self.altitude_accuracy) + + # Convert x and y to latitude and longitude + drift = (x**2 + y**2) ** 0.5 + bearing = (2 * math.pi - math.atan2(-x, y)) * (180 / math.pi) + + # Applies the haversine equation to find final lat/lon coordinates + latitude, longitude = inverted_haversine(lat, lon, drift, bearing, earth_radius) + + self.measurement = (latitude, longitude, altitude) + self._save_data((time, *self.measurement)) + + def export_measured_data(self, filename, file_format="csv"): + """Export the measured values to a file + + Parameters + ---------- + filename : str + Name of the file to export the values to + file_format : str + Format of the file to export the values to. Options are "csv" and + "json". Default is "csv". + + Returns + ------- + None + """ + self._generic_export_measured_data( + filename=filename, + file_format=file_format, + data_labels=("t", "latitude", "longitude", "altitude"), + ) diff --git a/rocketpy/sensors/gyroscope.py b/rocketpy/sensors/gyroscope.py new file mode 100644 index 000000000..eb1337a73 --- /dev/null +++ b/rocketpy/sensors/gyroscope.py @@ -0,0 +1,304 @@ +import numpy as np + +from ..mathutils.vector_matrix import Matrix, Vector +from ..prints.sensors_prints import _GyroscopePrints +from ..sensors.sensor import InertialSensor + +# pylint: disable=too-many-arguments + + +class Gyroscope(InertialSensor): + """Class for the gyroscope sensor + + Attributes + ---------- + acceleration_sensitivity : float, list + Sensitivity of the sensor to linear acceleration in rad/s/g. + prints : _GyroscopePrints + Object that contains the print functions for the sensor. + sampling_rate : float + Sample rate of the sensor in Hz. + orientation : tuple, list + Orientation of the sensor in the rocket. + measurement_range : float, tuple + The measurement range of the sensor in the rad/s. + resolution : float + The resolution of the sensor in rad/s/LSB. + noise_density : float, list + The noise density of the sensor in rad/s/√Hz. + noise_variance : float, list + The variance of the noise of the sensor in (rad/s)^2. + random_walk_density : float, list + The random walk density of the sensor in rad/s/√Hz. + random_walk_variance : float, list + The random walk variance of the sensor in (rad/s)^2. + constant_bias : float, list + The constant bias of the sensor in rad/s. + operating_temperature : float + The operating temperature of the sensor in Kelvin. + temperature_bias : float, list + The temperature bias of the sensor in rad/s/K. + temperature_scale_factor : float, list + The temperature scale factor of the sensor in %/K. + cross_axis_sensitivity : float + The cross axis sensitivity of the sensor in percentage. + name : str + The name of the sensor. + rotation_matrix : Matrix + The rotation matrix of the sensor from the sensor frame to the rocket + frame of reference. + normal_vector : Vector + The normal vector of the sensor in the rocket frame of reference. + _random_walk_drift : Vector + The random walk drift of the sensor in rad/s. + measurement : float + The measurement of the sensor after quantization, noise and temperature + drift. + measured_data : list + The stored measured data of the sensor after quantization, noise and + temperature drift. + """ + + units = "rad/s" + + def __init__( + self, + sampling_rate, + orientation=(0, 0, 0), + measurement_range=np.inf, + resolution=0, + noise_density=0, + noise_variance=1, + random_walk_density=0, + random_walk_variance=1, + constant_bias=0, + operating_temperature=25, + temperature_bias=0, + temperature_scale_factor=0, + cross_axis_sensitivity=0, + acceleration_sensitivity=0, + name="Gyroscope", + ): + """ + Initialize the gyroscope sensor + + Parameters + ---------- + sampling_rate : float + Sample rate of the sensor in Hz. + orientation : tuple, list, optional + Orientation of the sensor in the rocket. The orientation can be + given as: + - A list of length 3, where the elements are the Euler angles for + the rotation yaw (ψ), pitch (θ) and roll (φ) in radians. The + standard rotation sequence is z-y-x (3-2-1) is used, meaning the + sensor is first rotated by ψ around the x axis, then by θ around + the new y axis and finally by φ around the new z axis. + - A list of lists (matrix) of shape 3x3, representing the rotation + matrix from the sensor frame to the rocket frame. The sensor frame + of reference is defined as to have z axis along the sensor's normal + vector pointing upwards, x and y axes perpendicular to the z axis + and each other. + The rocket frame of reference is defined as to have z axis + along the rocket's axis of symmetry pointing upwards, x and y axes + perpendicular to the z axis and each other. Default is (0, 0, 0), + meaning the sensor is aligned with the rocket's axis of symmetry. + measurement_range : float, tuple, optional + The measurement range of the sensor in the rad/s. If a float, the + same range is applied both for positive and negative values. If a + tuple, the first value is the positive range and the second value is + the negative range. Default is np.inf. + resolution : float, optional + The resolution of the sensor in rad/s/LSB. Default is 0, meaning no + quantization is applied. + noise_density : float, list, optional + The noise density of the sensor for a Gaussian white noise in rad/s/√Hz. + Sometimes called "white noise drift", "angular random walk" for + gyroscopes, "velocity random walk" for the accelerometers or + "(rate) noise density". Default is 0, meaning no noise is applied. + If a float or int is given, the same noise density is applied to all + axes. The values of each axis can be set individually by passing a + list of length 3. + noise_variance : float, list, optional + The noise variance of the sensor for a Gaussian white noise in (rad/s)^2. + Default is 1, meaning the noise is normally distributed with a + standard deviation of 1 rad/s. If a float or int is given, the same + variance is applied to all axes. The values of each axis can be set + individually by passing a list of length 3. + random_walk_density : float, list, optional + The random walk density of the sensor for a Gaussian random walk in + rad/s/√Hz. Sometimes called "bias (in)stability" or "bias drift"". + Default is 0, meaning no random walk is applied. If a float or int + is given, the same random walk is applied to all axes. The values of + each axis can be set individually by passing a list of length 3. + random_walk_variance : float, list, optional + The random walk variance of the sensor for a Gaussian random walk in + (rad/s)^2. Default is 1, meaning the random walk is normally + distributed with a standard deviation of 1 rad/s. If a float or int + is given, the same variance is applied to all axes. The values of + each axis can be set individually by passing a list of length 3. + constant_bias : float, list, optional + The constant bias of the sensor in rad/s. Default is 0, meaning no + constant bias is applied. If a float or int is given, the same bias + is applied to all axes. The values of each axis can be set + individually by passing a list of length 3. + operating_temperature : float, optional + The operating temperature of the sensor in Kelvin. + At 298.15 K (25 °C), the sensor is assumed to operate ideally, no + temperature related noise is applied. Default is 298.15. + temperature_sensitivity : float, list, optional + The temperature bias of the sensor in rad/s/K. Default is 0, + meaning no temperature bias is applied. If a float or int is given, + the same temperature bias is applied to all axes. The values of each + axis can be set individually by passing a list of length 3. + temperature_scale_factor : float, list, optional + The temperature scale factor of the sensor in %/K. Default is 0, + meaning no temperature scale factor is applied. If a float or int is + given, the same temperature scale factor is applied to all axes. The + values of each axis can be set individually by passing a list of + length 3. + cross_axis_sensitivity : float, optional + Skewness of the sensor's axes in percentage. Default is 0, meaning + no cross-axis sensitivity is applied. + of each axis can be set individually by passing a list of length 3. + acceleration_sensitivity : float, list, optional + Sensitivity of the sensor to linear acceleration in rad/s/g. Default + is 0, meaning no sensitivity to linear acceleration is applied. If a + float or int is given, the same sensitivity is applied to all axes. + The values of each axis can be set individually by passing a list of + length 3. + + Returns + ------- + None + + See Also + -------- + TODO link to documentation on noise model + """ + super().__init__( + sampling_rate, + orientation, + measurement_range=measurement_range, + resolution=resolution, + noise_density=noise_density, + noise_variance=noise_variance, + random_walk_density=random_walk_density, + random_walk_variance=random_walk_variance, + constant_bias=constant_bias, + operating_temperature=operating_temperature, + temperature_bias=temperature_bias, + temperature_scale_factor=temperature_scale_factor, + cross_axis_sensitivity=cross_axis_sensitivity, + name=name, + ) + self.acceleration_sensitivity = self._vectorize_input( + acceleration_sensitivity, "acceleration_sensitivity" + ) + self.prints = _GyroscopePrints(self) + + def measure(self, time, **kwargs): + """Measure the angular velocity of the rocket + + Parameters + ---------- + time : float + Current time in seconds. + kwargs : dict + Keyword arguments dictionary containing the following keys: + - u : np.array + State vector of the rocket. + - u_dot : np.array + Derivative of the state vector of the rocket. + - relative_position : np.array + Position of the sensor relative to the rocket center of mass. + - environment : Environment + Environment object containing the atmospheric conditions. + """ + u = kwargs["u"] + u_dot = kwargs["u_dot"] + relative_position = kwargs["relative_position"] + + # Angular velocity of the rocket in the rocket frame + omega = Vector(u[10:13]) + + # Transform to sensor frame + inertial_to_sensor = self._total_rotation_matrix @ Matrix.transformation( + u[6:10] + ) + W = inertial_to_sensor @ omega + + # Apply noise + bias and quantize + W = self.apply_noise(W) + W = self.apply_temperature_drift(W) + + # Apply acceleration sensitivity + if self.acceleration_sensitivity != Vector.zeros(): + W += self.apply_acceleration_sensitivity( + omega, u_dot, relative_position, inertial_to_sensor + ) + + W = self.quantize(W) + + self.measurement = tuple([*W]) + self._save_data((time, *W)) + + def apply_acceleration_sensitivity( + self, omega, u_dot, relative_position, rotation_matrix + ): + """ + Apply acceleration sensitivity to the sensor measurement + + Parameters + ---------- + omega : Vector + The angular velocity to apply acceleration sensitivity to + u_dot : list + The time derivative of the state vector + relative_position : Vector + The vector from the rocket's center of mass to the sensor + rotation_matrix : Matrix + The rotation matrix from the rocket frame to the sensor frame + + Returns + ------- + Vector + The angular velocity with the acceleration sensitivity applied + """ + # Linear acceleration of rocket cdm in inertial frame + inertial_acceleration = Vector(u_dot[3:6]) + + # Angular velocity and accel of rocket + omega_dot = Vector(u_dot[10:13]) + + # Acceleration felt in sensor + A = ( + inertial_acceleration + + Vector.cross(omega_dot, relative_position) + + Vector.cross(omega, Vector.cross(omega, relative_position)) + ) + # Transform to sensor frame + A = rotation_matrix @ A + + return self.acceleration_sensitivity & A + + def export_measured_data(self, filename, file_format="csv"): + """Export the measured values to a file + + Parameters + ---------- + filename : str + Name of the file to export the values to + file_format : str + file_Format of the file to export the values to. Options are "csv" and + "json". Default is "csv". + + Returns + ------- + None + """ + self._generic_export_measured_data( + filename=filename, + file_format=file_format, + data_labels=("t", "wx", "wy", "wz"), + ) diff --git a/rocketpy/sensors/sensor.py b/rocketpy/sensors/sensor.py new file mode 100644 index 000000000..fe15384ba --- /dev/null +++ b/rocketpy/sensors/sensor.py @@ -0,0 +1,780 @@ +import json +import warnings +from abc import ABC, abstractmethod + +import numpy as np + +from rocketpy.mathutils.vector_matrix import Matrix, Vector + + +# pylint: disable=too-many-statements +class Sensor(ABC): + """Abstract class for sensors + + Attributes + ---------- + sampling_rate : float + Sample rate of the sensor in Hz. + measurement_range : float, tuple + The measurement range of the sensor in the sensor units. + resolution : float + The resolution of the sensor in sensor units/LSB. + noise_density : float, list + The noise density of the sensor in sensor units/√Hz. + noise_variance : float, list + The variance of the noise of the sensor in sensor units^2. + random_walk_density : float, list + The random walk density of the sensor in sensor units/√Hz. + random_walk_variance : float, list + The variance of the random walk of the sensor in sensor units^2. + constant_bias : float, list + The constant bias of the sensor in sensor units. + operating_temperature : float + The operating temperature of the sensor in Kelvin. + temperature_bias : float, list + The temperature bias of the sensor in sensor units/K. + temperature_scale_factor : float, list + The temperature scale factor of the sensor in %/K. + name : str + The name of the sensor. + measurement : float + The measurement of the sensor after quantization, noise and temperature + drift. + measured_data : list + The stored measured data of the sensor after quantization, noise and + temperature drift. + """ + + def __init__( + self, + sampling_rate, + measurement_range=np.inf, + resolution=0, + noise_density=0, + noise_variance=1, + random_walk_density=0, + random_walk_variance=1, + constant_bias=0, + operating_temperature=25, + temperature_bias=0, + temperature_scale_factor=0, + name="Sensor", + ): + """ + Initialize the accelerometer sensor + + Parameters + ---------- + sampling_rate : float + Sample rate of the sensor + measurement_range : float, tuple, optional + The measurement range of the sensor in the sensor units. If a float, + the same range is applied both for positive and negative values. If + a tuple, the first value is the positive range and the second value + is the negative range. Default is np.inf. + resolution : float, optional + The resolution of the sensor in sensor units/LSB. Default is 0, + meaning no quantization is applied. + noise_density : float, list, optional + The noise density of the sensor for a Gaussian white noise in sensor + units/√Hz. Sometimes called "white noise drift", + "angular random walk" for gyroscopes, "velocity random walk" for + accelerometers or "(rate) noise density". Default is 0, meaning no + noise is applied. + noise_variance : float, list, optional + The noise variance of the sensor for a Gaussian white noise in + sensor units^2. Default is 1, meaning the noise is normally + distributed with a standard deviation of 1 unit. + random_walk_density : float, list, optional + The random walk density of the sensor for a Gaussian random walk in + sensor units/√Hz. Sometimes called "bias (in)stability" or + "bias drift". Default is 0, meaning no random walk is applied. + random_walk_variance : float, list, optional + The random walk variance of the sensor for a Gaussian random walk in + sensor units^2. Default is 1, meaning the noise is normally + distributed with a standard deviation of 1 unit. + constant_bias : float, list, optional + The constant bias of the sensor in sensor units. Default is 0, + meaning no constant bias is applied. + operating_temperature : float, optional + The operating temperature of the sensor in Kelvin. + At 298.15 K (25 °C), the sensor is assumed to operate ideally, no + temperature related noise is applied. Default is 298.15. + temperature_bias : float, list, optional + The temperature bias of the sensor in sensor units/K. Default is 0, + meaning no temperature bias is applied. + temperature_scale_factor : float, list, optional + The temperature scale factor of the sensor in %/K. Default is 0, + meaning no temperature scale factor is applied. + name : str, optional + The name of the sensor. Default is "Sensor". + + Returns + ------- + None + + See Also + -------- + TODO link to documentation on noise model + """ + warnings.warn( + "The Sensor class (and all its subclasses) is still under " + "experimental development. Some features may be changed in future " + "versions, although we will try to keep the changes to a minimum.", + UserWarning, + ) + + self.sampling_rate = sampling_rate + self.resolution = resolution + self.operating_temperature = operating_temperature + self.noise_density = noise_density + self.noise_variance = noise_variance + self.random_walk_density = random_walk_density + self.random_walk_variance = random_walk_variance + self.constant_bias = constant_bias + self.temperature_bias = temperature_bias + self.temperature_scale_factor = temperature_scale_factor + self.name = name + self.measurement = None + self.measured_data = [] + self._counter = 0 + self._save_data = self._save_data_single + self._random_walk_drift = 0 + self.normal_vector = Vector([0, 0, 0]) + + # handle measurement range + if isinstance(measurement_range, (tuple, list)): + if len(measurement_range) != 2: + raise ValueError("Invalid measurement range format") + self.measurement_range = measurement_range + elif isinstance(measurement_range, (int, float)): + self.measurement_range = (-measurement_range, measurement_range) + else: + raise ValueError("Invalid measurement range format") + + # map which rocket(s) the sensor is attached to and how many times + self._attached_rockets = {} + + def __repr__(self): + return f"{self.name}" + + def __call__(self, *args, **kwargs): + return self.measure(*args, **kwargs) + + def _reset(self, simulated_rocket): + """Reset the sensor data for a new simulation.""" + self._random_walk_drift = ( + Vector([0, 0, 0]) if isinstance(self._random_walk_drift, Vector) else 0 + ) + self.measured_data = [] + if self._attached_rockets[simulated_rocket] > 1: + self.measured_data = [ + [] for _ in range(self._attached_rockets[simulated_rocket]) + ] + self._save_data = self._save_data_multiple + else: + self._save_data = self._save_data_single + + def _save_data_single(self, data): + """Save the measured data to the sensor data list for a sensor that is + added only once to the simulated rocket.""" + self.measured_data.append(data) + + def _save_data_multiple(self, data): + """Save the measured data to the sensor data list for a sensor that is + added multiple times to the simulated rocket.""" + self.measured_data[self._counter].append(data) + # counter for cases where the sensor is added multiple times in a rocket + self._counter += 1 + if self._counter == len(self.measured_data): + self._counter = 0 + + @abstractmethod + def measure(self, time, **kwargs): + """Measure the sensor data at a given time""" + + @abstractmethod + def quantize(self, value): + """Quantize the sensor measurement""" + + @abstractmethod + def apply_noise(self, value): + """Add noise to the sensor measurement""" + + @abstractmethod + def apply_temperature_drift(self, value): + """Apply temperature drift to the sensor measurement""" + + @abstractmethod + def export_measured_data(self, filename, file_format="csv"): + """Export the measured values to a file""" + + def _generic_export_measured_data(self, filename, file_format, data_labels): + """Export the measured values to a file given the data labels of each + sensor. + + Parameters + ---------- + sensor : Sensor + Sensor object to export the measured values from. + filename : str + Name of the file to export the values to + file_format : str + file_format of the file to export the values to. Options are "csv" + and "json". Default is "csv". + data_labels : tuple + Tuple of strings representing the labels for the data columns + + Returns + ------- + None + """ + if file_format.lower() not in ["json", "csv"]: + raise ValueError("Invalid file_format") + + if file_format.lower() == "csv": + # if sensor has been added multiple times to the simulated rocket + if isinstance(self.measured_data[0], list): + print("Data saved to", end=" ") + for i, data in enumerate(self.measured_data): + with open(filename + f"_{i+1}", "w") as f: + f.write(",".join(data_labels) + "\n") + for entry in data: + f.write(",".join(map(str, entry)) + "\n") + print(filename + f"_{i+1},", end=" ") + else: + with open(filename, "w") as f: + f.write(",".join(data_labels) + "\n") + for entry in self.measured_data: + f.write(",".join(map(str, entry)) + "\n") + print(f"Data saved to {filename}") + return + + if file_format.lower() == "json": + if isinstance(self.measured_data[0], list): + print("Data saved to", end=" ") + for i, data in enumerate(self.measured_data): + data_dict = {label: [] for label in data_labels} + for entry in data: + for label, value in zip(data_labels, entry): + data_dict[label].append(value) + with open(filename + f"_{i+1}", "w") as f: + json.dump(data_dict, f) + print(filename + f"_{i+1},", end=" ") + else: + data_dict = {label: [] for label in data_labels} + for entry in self.measured_data: + for label, value in zip(data_labels, entry): + data_dict[label].append(value) + with open(filename, "w") as f: + json.dump(data_dict, f) + print(f"Data saved to {filename}") + return + + +class InertialSensor(Sensor): + """Model of an inertial sensor (accelerometer, gyroscope, magnetometer). + Inertial sensors measurements are handled as vectors. The measurements are + affected by the sensor's orientation in the rocket. + + Attributes + ---------- + sampling_rate : float + Sample rate of the sensor in Hz. + orientation : tuple, list + Orientation of the sensor in the rocket. + measurement_range : float, tuple + The measurement range of the sensor in the sensor units. + resolution : float + The resolution of the sensor in sensor units/LSB. + noise_density : float, list + The noise density of the sensor in sensor units/√Hz. + noise_variance : float, list + The variance of the noise of the sensor in sensor units^2. + random_walk_density : float, list + The random walk density of the sensor in sensor units/√Hz. + random_walk_variance : float, list + The variance of the random walk of the sensor in sensor units^2. + constant_bias : float, list + The constant bias of the sensor in sensor units. + operating_temperature : float + The operating temperature of the sensor in Kelvin. + temperature_bias : float, list + The temperature bias of the sensor in sensor units/K. + temperature_scale_factor : float, list + The temperature scale factor of the sensor in %/K. + cross_axis_sensitivity : float + The cross axis sensitivity of the sensor in percentage. + name : str + The name of the sensor. + rotation_matrix : Matrix + The rotation matrix of the sensor from the sensor frame to the rocket + frame of reference. + normal_vector : Vector + The normal vector of the sensor in the rocket frame of reference. + measurement : float + The measurement of the sensor after quantization, noise and temperature + drift. + measured_data : list + The stored measured data of the sensor after quantization, noise and + temperature drift. + """ + + def __init__( + self, + sampling_rate, + orientation=(0, 0, 0), + measurement_range=np.inf, + resolution=0, + noise_density=0, + noise_variance=1, + random_walk_density=0, + random_walk_variance=1, + constant_bias=0, + operating_temperature=298.15, + temperature_bias=0, + temperature_scale_factor=0, + cross_axis_sensitivity=0, + name="Sensor", + ): + """ + Initialize the accelerometer sensor + + Parameters + ---------- + sampling_rate : float + Sample rate of the sensor + orientation : tuple, list, optional + Orientation of the sensor in the rocket. The orientation can be + given as: + - A list of length 3, where the elements are the Euler angles for + the rotation yaw (ψ), pitch (θ) and roll (φ) in radians. The + standard rotation sequence is z-y-x (3-2-1) is used, meaning the + sensor is first rotated by ψ around the x axis, then by θ around + the new y axis and finally by φ around the new z axis. + - A list of lists (matrix) of shape 3x3, representing the rotation + matrix from the sensor frame to the rocket frame. The sensor frame + of reference is defined as to have z axis along the sensor's normal + vector pointing upwards, x and y axes perpendicular to the z axis + and each other. + The rocket frame of reference is defined as to have z axis + along the rocket's axis of symmetry pointing upwards, x and y axes + perpendicular to the z axis and each other. Default is (0, 0, 0), + meaning the sensor is aligned with the rocket's axis of symmetry. + measurement_range : float, tuple, optional + The measurement range of the sensor in the sensor units. If a float, + the same range is applied both for positive and negative values. If + a tuple, the first value is the positive range and the second value + is the negative range. Default is np.inf. + resolution : float, optional + The resolution of the sensor in sensor units/LSB. Default is 0, + meaning no quantization is applied. + noise_density : float, list, optional + The noise density of the sensor for a Gaussian white noise in sensor + units/√Hz. Sometimes called "white noise drift", + "angular random walk" for gyroscopes, "velocity random walk" for + accelerometers or "(rate) noise density". Default is 0, meaning no + noise is applied. If a float or int is given, the same noise density + is applied to all axes. The values of each axis can be set + individually by passing a list of length 3. + noise_variance : float, list, optional + The noise variance of the sensor for a Gaussian white noise in + sensor units^2. Default is 1, meaning the noise is normally + distributed with a standard deviation of 1 unit. If a float or int + is given, the same noise variance is applied to all axes. The values + of each axis can be set individually by passing a list of length 3. + random_walk_density : float, list, optional + The random walk density of the sensor for a Gaussian random walk in + sensor units/√Hz. Sometimes called "bias (in)stability" or + "bias drift". Default is 0, meaning no random walk is applied. + If a float or int is given, the same random walk is applied to all + axes. The values of each axis can be set individually by passing a + list of length 3. + random_walk_variance : float, list, optional + The random walk variance of the sensor for a Gaussian random walk in + sensor units^2. Default is 1, meaning the noise is normally + distributed with a standard deviation of 1 unit. If a float or int + is given, the same random walk variance is applied to all axes. The + values of each axis can be set individually by passing a list of + length 3. + constant_bias : float, list, optional + The constant bias of the sensor in sensor units. Default is 0, + meaning no constant bias is applied. If a float or int is given, the + same constant bias is applied to all axes. The values of each axis + can be set individually by passing a list of length 3. + operating_temperature : float, optional + The operating temperature of the sensor in Kelvin. + At 298.15 K (25 °C), the sensor is assumed to operate ideally, no + temperature related noise is applied. Default is 298.15. + temperature_bias : float, list, optional + The temperature bias of the sensor in sensor units/K. Default is 0, + meaning no temperature bias is applied. If a float or int is given, + the same temperature bias is applied to all axes. The values of each + axis can be set individually by passing a list of length 3. + temperature_scale_factor : float, list, optional + The temperature scale factor of the sensor in %/K. Default is 0, + meaning no temperature scale factor is applied. If a float or int is + given, the same temperature scale factor is applied to all axes. The + values of each axis can be set individually by passing a list of + length 3. + cross_axis_sensitivity : float, optional + Skewness of the sensor's axes in percentage. Default is 0, meaning + no cross-axis sensitivity is applied. + name : str, optional + The name of the sensor. Default is "Sensor". + + Returns + ------- + None + + See Also + -------- + TODO link to documentation on noise model + """ + super().__init__( + sampling_rate=sampling_rate, + measurement_range=measurement_range, + resolution=resolution, + noise_density=self._vectorize_input(noise_density, "noise_density"), + noise_variance=self._vectorize_input(noise_variance, "noise_variance"), + random_walk_density=self._vectorize_input( + random_walk_density, "random_walk_density" + ), + random_walk_variance=self._vectorize_input( + random_walk_variance, "random_walk_variance" + ), + constant_bias=self._vectorize_input(constant_bias, "constant_bias"), + operating_temperature=operating_temperature, + temperature_bias=self._vectorize_input( + temperature_bias, "temperature_bias" + ), + temperature_scale_factor=self._vectorize_input( + temperature_scale_factor, "temperature_scale_factor" + ), + name=name, + ) + + self.orientation = orientation + self.cross_axis_sensitivity = cross_axis_sensitivity + self._random_walk_drift = Vector([0, 0, 0]) + + # rotation matrix and normal vector + if any(isinstance(row, (tuple, list)) for row in orientation): # matrix + self.rotation_matrix = Matrix(orientation) + elif len(orientation) == 3: # euler angles + self.rotation_matrix = Matrix.transformation_euler_angles( + *orientation + ).round(12) + else: + raise ValueError("Invalid orientation format") + self.normal_vector = Vector( + [ + self.rotation_matrix[0][2], + self.rotation_matrix[1][2], + self.rotation_matrix[2][2], + ] + ).unit_vector + + # cross axis sensitivity matrix + _cross_axis_matrix = 0.01 * Matrix( + [ + [100, self.cross_axis_sensitivity, self.cross_axis_sensitivity], + [self.cross_axis_sensitivity, 100, self.cross_axis_sensitivity], + [self.cross_axis_sensitivity, self.cross_axis_sensitivity, 100], + ] + ) + + # compute total rotation matrix given cross axis sensitivity + self._total_rotation_matrix = self.rotation_matrix @ _cross_axis_matrix + + def _vectorize_input(self, value, name): + if isinstance(value, (int, float)): + return Vector([value, value, value]) + elif isinstance(value, (tuple, list)): + return Vector(value) + else: + raise ValueError(f"Invalid {name} format") + + def quantize(self, value): + """ + Quantize the sensor measurement + + Parameters + ---------- + value : float + The value to quantize + + Returns + ------- + float + The quantized value + """ + x = min(max(value.x, self.measurement_range[0]), self.measurement_range[1]) + y = min(max(value.y, self.measurement_range[0]), self.measurement_range[1]) + z = min(max(value.z, self.measurement_range[0]), self.measurement_range[1]) + if self.resolution != 0: + x = round(x / self.resolution) * self.resolution + y = round(y / self.resolution) * self.resolution + z = round(z / self.resolution) * self.resolution + return Vector([x, y, z]) + + def apply_noise(self, value): + """ + Add noise to the sensor measurement + + Parameters + ---------- + value : float + The value to add noise to + + Returns + ------- + float + The value with added noise + """ + # white noise + white_noise = Vector( + [np.random.normal(0, self.noise_variance[i] ** 0.5) for i in range(3)] + ) & (self.noise_density * self.sampling_rate**0.5) + + # random walk + self._random_walk_drift = self._random_walk_drift + Vector( + [np.random.normal(0, self.random_walk_variance[i] ** 0.5) for i in range(3)] + ) & (self.random_walk_density / self.sampling_rate**0.5) + + # add noise + value += white_noise + self._random_walk_drift + self.constant_bias + + return value + + def apply_temperature_drift(self, value): + """ + Apply temperature drift to the sensor measurement + + Parameters + ---------- + value : float + The value to apply temperature drift to + + Returns + ------- + float + The value with applied temperature drift + """ + # temperature drift + value += (self.operating_temperature - 298.15) * self.temperature_bias + # temperature scale factor + scale_factor = ( + Vector([1, 1, 1]) + + (self.operating_temperature - 298.15) + / 100 + * self.temperature_scale_factor + ) + return value & scale_factor + + +class ScalarSensor(Sensor): + """Model of a scalar sensor (e.g. Barometer). Scalar sensors are used + to measure a single scalar value. The measurements are not affected by the + sensor's orientation in the rocket. + + Attributes + ---------- + sampling_rate : float + Sample rate of the sensor in Hz. + measurement_range : float, tuple + The measurement range of the sensor in the sensor units. + resolution : float + The resolution of the sensor in sensor units/LSB. + noise_density : float + The noise density of the sensor in sensor units/√Hz. + noise_variance : float + The variance of the noise of the sensor in sensor units^2. + random_walk_density : float + The random walk density of the sensor in sensor units/√Hz. + random_walk_variance : float + The variance of the random walk of the sensor in sensor units^2. + constant_bias : float + The constant bias of the sensor in sensor units. + operating_temperature : float + The operating temperature of the sensor in Kelvin. + temperature_bias : float + The temperature bias of the sensor in sensor units/K. + temperature_scale_factor : float + The temperature scale factor of the sensor in %/K. + name : str + The name of the sensor. + measurement : float + The measurement of the sensor after quantization, noise and temperature + drift. + measured_data : list + The stored measured data of the sensor after quantization, noise and + temperature drift. + """ + + def __init__( + self, + sampling_rate, + measurement_range=np.inf, + resolution=0, + noise_density=0, + noise_variance=1, + random_walk_density=0, + random_walk_variance=1, + constant_bias=0, + operating_temperature=25, + temperature_bias=0, + temperature_scale_factor=0, + name="Sensor", + ): + """ + Initialize the accelerometer sensor + + Parameters + ---------- + sampling_rate : float + Sample rate of the sensor + measurement_range : float, tuple, optional + The measurement range of the sensor in the sensor units. If a float, + the same range is applied both for positive and negative values. If + a tuple, the first value is the positive range and the second value + is the negative range. Default is np.inf. + resolution : float, optional + The resolution of the sensor in sensor units/LSB. Default is 0, + meaning no quantization is applied. + noise_density : float, list, optional + The noise density of the sensor for a Gaussian white noise in sensor + units/√Hz. Sometimes called "white noise drift", + "angular random walk" for gyroscopes, "velocity random walk" for + accelerometers or "(rate) noise density". Default is 0, meaning no + noise is applied. + noise_variance : float, list, optional + The noise variance of the sensor for a Gaussian white noise in + sensor units^2. Default is 1, meaning the noise is normally + distributed with a standard deviation of 1 unit. + random_walk_density : float, list, optional + The random walk density of the sensor for a Gaussian random walk in + sensor units/√Hz. Sometimes called "bias (in)stability" or + "bias drift". Default is 0, meaning no random walk is applied. + random_walk_variance : float, list, optional + The random walk variance of the sensor for a Gaussian random walk in + sensor units^2. Default is 1, meaning the noise is normally + distributed with a standard deviation of 1 unit. + constant_bias : float, list, optional + The constant bias of the sensor in sensor units. Default is 0, + meaning no constant bias is applied. + operating_temperature : float, optional + The operating temperature of the sensor in Kelvin. + At 298.15 K (25 °C), the sensor is assumed to operate ideally, no + temperature related noise is applied. Default is 298.15. + temperature_bias : float, list, optional + The temperature bias of the sensor in sensor units/K. Default is 0, + meaning no temperature bias is applied. + temperature_scale_factor : float, list, optional + The temperature scale factor of the sensor in %/K. Default is 0, + meaning no temperature scale factor is applied. + name : str, optional + The name of the sensor. Default is "Sensor". + + Returns + ------- + None + + See Also + -------- + TODO link to documentation on noise model + """ + super().__init__( + sampling_rate=sampling_rate, + measurement_range=measurement_range, + resolution=resolution, + noise_density=noise_density, + noise_variance=noise_variance, + random_walk_density=random_walk_density, + random_walk_variance=random_walk_variance, + constant_bias=constant_bias, + operating_temperature=operating_temperature, + temperature_bias=temperature_bias, + temperature_scale_factor=temperature_scale_factor, + name=name, + ) + + def quantize(self, value): + """ + Quantize the sensor measurement + + Parameters + ---------- + value : float + The value to quantize + + Returns + ------- + float + The quantized value + """ + value = min(max(value, self.measurement_range[0]), self.measurement_range[1]) + if self.resolution != 0: + value = round(value / self.resolution) * self.resolution + return value + + def apply_noise(self, value): + """ + Add noise to the sensor measurement + + Parameters + ---------- + value : float + The value to add noise to + + Returns + ------- + float + The value with added noise + """ + # white noise + white_noise = ( + np.random.normal(0, self.noise_variance**0.5) + * self.noise_density + * self.sampling_rate**0.5 + ) + + # random walk + self._random_walk_drift = ( + self._random_walk_drift + + np.random.normal(0, self.random_walk_variance**0.5) + * self.random_walk_density + / self.sampling_rate**0.5 + ) + + # add noise + value += white_noise + self._random_walk_drift + self.constant_bias + + return value + + def apply_temperature_drift(self, value): + """ + Apply temperature drift to the sensor measurement + + Parameters + ---------- + value : float + The value to apply temperature drift to + + Returns + ------- + float + The value with applied temperature drift + """ + # temperature drift + value += (self.operating_temperature - 298.15) * self.temperature_bias + # temperature scale factor + scale_factor = ( + 1 + + (self.operating_temperature - 298.15) + / 100 + * self.temperature_scale_factor + ) + value = value * scale_factor + + return value diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index d7e51a768..e7b53754a 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -1,4 +1,5 @@ # pylint: disable=too-many-lines +import json import math import warnings from copy import deepcopy @@ -14,7 +15,7 @@ from ..prints.flight_prints import _FlightPrints from ..tools import ( calculate_cubic_hermite_coefficients, - euler_angles_to_euler_parameters, + euler313_to_quaternions, find_closest, find_root_linear_interpolation, find_roots_cubic_function, @@ -664,18 +665,20 @@ def __simulate(self, verbose): # Initialize phase time nodes phase.time_nodes = self.TimeNodes() # Add first time node to the time_nodes list - phase.time_nodes.add_node(phase.t, [], []) + phase.time_nodes.add_node(phase.t, [], [], []) # Add non-overshootable parachute time nodes if self.time_overshoot is False: - # TODO: move parachutes to controllers phase.time_nodes.add_parachutes( self.parachutes, phase.t, phase.time_bound ) + phase.time_nodes.add_sensors( + self.rocket.sensors, phase.t, phase.time_bound + ) phase.time_nodes.add_controllers( self._controllers, phase.t, phase.time_bound ) # Add last time node to the time_nodes list - phase.time_nodes.add_node(phase.time_bound, [], []) + phase.time_nodes.add_node(phase.time_bound, [], [], []) # Organize time nodes with sort() and merge() phase.time_nodes.sort() phase.time_nodes.merge() @@ -702,8 +705,34 @@ def __simulate(self, verbose): for callback in node.callbacks: callback(self) + if self.sensors: + # u_dot for all sensors + u_dot = phase.derivative(self.t, self.y_sol) + for sensor, position in node._component_sensors: + relative_position = position - self.rocket._csys * Vector( + [0, 0, self.rocket.center_of_dry_mass_position] + ) + sensor.measure( + self.t, + u=self.y_sol, + u_dot=u_dot, + relative_position=relative_position, + environment=self.env, + gravity=self.env.gravity.get_value_opt( + self.solution[-1][3] + ), + pressure=self.env.pressure, + earth_radius=self.env.earth_radius, + initial_coordinates=(self.env.latitude, self.env.longitude), + ) + for controller in node._controllers: - controller(self.t, self.y_sol, self.solution) + controller( + self.t, + self.y_sol, + self.solution, + self.sensors, + ) for parachute in node.parachutes: # Calculate and save pressure signal @@ -713,7 +742,10 @@ def __simulate(self, verbose): ) ) if parachute.triggerfunc( - noisy_pressure, height_above_ground_level, self.y_sol + noisy_pressure, + height_above_ground_level, + self.y_sol, + self.sensors, ): # Remove parachute from flight parachutes self.parachutes.remove(parachute) @@ -743,7 +775,7 @@ def __simulate(self, verbose): ) # Prepare to leave loops and start new flight phase phase.time_nodes.flush_after(node_index) - phase.time_nodes.add_node(self.t, [], []) + phase.time_nodes.add_node(self.t, [], [], []) phase.solver.status = "finished" # Save parachute event self.parachute_events.append([self.t, parachute]) @@ -835,7 +867,7 @@ def __simulate(self, verbose): ) # Prepare to leave loops and start new flight phase phase.time_nodes.flush_after(node_index) - phase.time_nodes.add_node(self.t, [], []) + phase.time_nodes.add_node(self.t, [], [], []) phase.solver.status = "finished" # Check for apogee event @@ -863,7 +895,7 @@ def __simulate(self, verbose): self.flight_phases.add_phase(self.t) # Prepare to leave loops and start new flight phase phase.time_nodes.flush_after(node_index) - phase.time_nodes.add_node(self.t, [], []) + phase.time_nodes.add_node(self.t, [], [], []) phase.solver.status = "finished" elif len(self.solution) > 2: # adding the apogee state to solution increases accuracy @@ -910,7 +942,7 @@ def __simulate(self, verbose): self.flight_phases.add_phase(self.t) # Prepare to leave loops and start new flight phase phase.time_nodes.flush_after(node_index) - phase.time_nodes.add_node(self.t, [], []) + phase.time_nodes.add_node(self.t, [], [], []) phase.solver.status = "finished" # List and feed overshootable time nodes @@ -922,7 +954,7 @@ def __simulate(self, verbose): self.parachutes, self.solution[-2][0], self.t ) # Add last time node (always skipped) - overshootable_nodes.add_node(self.t, [], []) + overshootable_nodes.add_node(self.t, [], [], []) if len(overshootable_nodes) > 1: # Sort and merge equal overshootable time nodes overshootable_nodes.sort() @@ -956,6 +988,7 @@ def __simulate(self, verbose): noisy_pressure, height_above_ground_level, overshootable_node.y_sol, + self.sensors, ): # Remove parachute from flight parachutes self.parachutes.remove(parachute) @@ -996,7 +1029,7 @@ def __simulate(self, verbose): overshootable_index ) phase.time_nodes.flush_after(node_index) - phase.time_nodes.add_node(self.t, [], []) + phase.time_nodes.add_node(self.t, [], [], []) phase.solver.status = "finished" # Save parachute event self.parachute_events.append( @@ -1012,6 +1045,8 @@ def __simulate(self, verbose): if self._controllers: # cache post process variables self.__evaluate_post_process = np.array(self.__post_processed_variables) + if self.sensors: + self.__cache_sensor_data() if verbose: print(f"\n>>> Simulation Completed at Time: {self.t:3.4f} s") @@ -1091,7 +1126,7 @@ def __init_flight_state(self): pass # 3-1-3 Euler Angles to Euler Parameters - e0_init, e1_init, e2_init, e3_init = euler_angles_to_euler_parameters( + e0_init, e1_init, e2_init, e3_init = euler313_to_quaternions( self.phi_init, self.theta_init, self.psi_init ) # Store initial conditions @@ -1130,7 +1165,7 @@ def __init_flight_state(self): self.out_of_rail_time = self.initial_solution[0] self.out_of_rail_time_index = 0 self.initial_derivative = self.u_dot_generalized - if self._controllers: + if self._controllers or self.sensors: # Handle post process during simulation, get initial accel/forces self.initial_derivative( self.t_initial, self.initial_solution[1:], post_processing=True @@ -1155,19 +1190,36 @@ def __init_equations_of_motion(self): self.u_dot_generalized = self.u_dot def __init_controllers(self): - """Initialize controllers""" + """Initialize controllers and sensors""" self._controllers = self.rocket._controllers[:] - if self._controllers: + self.sensors = self.rocket.sensors.get_components() + if self._controllers or self.sensors: if self.time_overshoot: self.time_overshoot = False warnings.warn( - "time_overshoot has been set to False due to the " - "presence of controllers. " + "time_overshoot has been set to False due to the presence " + "of controllers or sensors. " ) # reset controllable object to initial state (only airbrakes for now) for air_brakes in self.rocket.air_brakes: air_brakes._reset() + self.sensor_data = {} + for sensor in self.sensors: + sensor._reset(self.rocket) # resets noise and measurement list + self.sensor_data[sensor] = [] + + def __cache_sensor_data(self): + """Cache sensor data for simulations with sensors.""" + sensor_data = {} + sensors = [] + for sensor in self.sensors: + # skip sensors that are used more then once in the rocket + if sensor not in sensors: + sensors.append(sensor) + sensor_data[sensor] = sensor.measured_data[:] + self.sensor_data = sensor_data + @cached_property def effective_1rl(self): """Original rail length minus the distance measured from nozzle exit @@ -3138,6 +3190,47 @@ class attributes which are instances of the Function class. Usage encoding="utf-8", ) + def export_sensor_data(self, file_name, sensor=None): + """Exports sensors data to a file. The file format can be either .csv or + .json. + + Parameters + ---------- + file_name : str + The file name or path of the exported file. Example: flight_data.csv + Do not use forbidden characters, such as / in Linux/Unix and + `<, >, :, ", /, \\, | ?, *` in Windows. + sensor : Sensor, string, optional + The sensor to export data from. Can be given as a Sensor object or + as a string with the sensor name. If None, all sensors data will be + exported. Default is None. + """ + if sensor is None: + data_dict = {} + for used_sensor, measured_data in self.sensor_data.items(): + data_dict[used_sensor.name] = measured_data + else: + # export data of only that sensor + data_dict = {} + + if not isinstance(sensor, str): + data_dict[sensor.name] = self.sensor_data[sensor] + else: # sensor is a string + matching_sensors = [s for s in self.sensor_data if s.name == sensor] + + if len(matching_sensors) > 1: + data_dict[sensor] = [] + for s in matching_sensors: + data_dict[s.name].append(self.sensor_data[s]) + elif len(matching_sensors) == 1: + data_dict[sensor] = self.sensor_data[matching_sensors[0]] + else: + raise ValueError("Sensor not found in the Flight.sensor_data.") + + with open(file_name, "w") as file: + json.dump(data_dict, file) + print("Sensor data exported to: ", file_name) + def export_kml( # TODO: should be moved out of this class. self, file_name="trajectory.kml", @@ -3461,15 +3554,15 @@ def __repr__(self): def add(self, time_node): self.list.append(time_node) - def add_node(self, t, parachutes, callbacks): - self.list.append(self.TimeNode(t, parachutes, callbacks)) + def add_node(self, t, parachutes, controllers, sensors): + self.list.append(self.TimeNode(t, parachutes, controllers, sensors)) def add_parachutes(self, parachutes, t_init, t_end): for parachute in parachutes: # Calculate start of sampling time nodes sampling_interval = 1 / parachute.sampling_rate parachute_node_list = [ - self.TimeNode(i * sampling_interval, [parachute], []) + self.TimeNode(i * sampling_interval, [parachute], [], []) for i in range( math.ceil(t_init / sampling_interval), math.floor(t_end / sampling_interval) + 1, @@ -3482,7 +3575,7 @@ def add_controllers(self, controllers, t_init, t_end): # Calculate start of sampling time nodes controller_time_step = 1 / controller.sampling_rate controller_node_list = [ - self.TimeNode(i * controller_time_step, [], [controller]) + self.TimeNode(i * controller_time_step, [], [controller], []) for i in range( math.ceil(t_init / controller_time_step), math.floor(t_end / controller_time_step) + 1, @@ -3490,6 +3583,22 @@ def add_controllers(self, controllers, t_init, t_end): ] self.list += controller_node_list + def add_sensors(self, sensors, t_init, t_end): + # Iterate over sensors + for sensor_component_tuple in sensors: + # Calculate start of sampling time nodes + sensor_time_step = 1 / sensor_component_tuple.component.sampling_rate + sensor_node_list = [ + self.TimeNode( + i * sensor_time_step, [], [], [sensor_component_tuple] + ) + for i in range( + math.ceil(t_init / sensor_time_step), + math.floor(t_end / sensor_time_step) + 1, + ) + ] + self.list += sensor_node_list + def sort(self): self.list.sort() @@ -3507,6 +3616,8 @@ def merge(self): tmp_dict[time].parachutes += node.parachutes tmp_dict[time]._controllers += node._controllers tmp_dict[time].callbacks += node.callbacks + tmp_dict[time]._component_sensors += node._component_sensors + tmp_dict[time]._controllers += node._controllers except KeyError: # If the node does not exist, add it to the dictionary tmp_dict[time] = node @@ -3522,7 +3633,7 @@ class TimeNode: exclusively within the TimeNodes class. """ - def __init__(self, t, parachutes, controllers): + def __init__(self, t, parachutes, controllers, sensors): """Create a TimeNode object. Parameters @@ -3535,18 +3646,23 @@ def __init__(self, t, parachutes, controllers): controllers : list[_Controller] List containing all the controllers that should be evaluated at this time node. + sensors : list[ComponentSensor] + List containing all the sensors that should be evaluated + at this time node. """ self.t = t self.parachutes = parachutes self.callbacks = [] self._controllers = controllers + self._component_sensors = sensors def __repr__(self): return ( f"" + f"controllers: {len(self._controllers)}, " + f"sensors: {len(self._component_sensors)})>" ) def __lt__(self, other): diff --git a/rocketpy/tools.py b/rocketpy/tools.py index 497c74fba..27b3dcda8 100644 --- a/rocketpy/tools.py +++ b/rocketpy/tools.py @@ -27,7 +27,7 @@ def tuple_handler(value): """Transforms the input value into a tuple that - represents a range. If the input is an input or float, + represents a range. If the input is an int or float, the output is a tuple from zero to the input value. If the input is a tuple or list, the output is a tuple with the same range. @@ -354,11 +354,15 @@ def inverted_haversine(lat0, lon0, distance, bearing, earth_radius=6.3781e6): # Apply inverted Haversine formula lat1_rad = math.asin( math.sin(lat0_rad) * math.cos(distance / earth_radius) - + math.cos(lat0_rad) * math.sin(distance / earth_radius) * math.cos(bearing) + + math.cos(lat0_rad) + * math.sin(distance / earth_radius) + * math.cos(math.radians(bearing)) ) lon1_rad = lon0_rad + math.atan2( - math.sin(bearing) * math.sin(distance / earth_radius) * math.cos(lat0_rad), + math.sin(math.radians(bearing)) + * math.sin(distance / earth_radius) + * math.cos(lat0_rad), math.cos(distance / earth_radius) - math.sin(lat0_rad) * math.sin(lat1_rad), ) @@ -1083,18 +1087,74 @@ def quaternions_to_nutation(e1, e2): return (180 / np.pi) * 2 * np.arcsin(-((e1**2 + e2**2) ** 0.5)) -def euler_angles_to_euler_parameters(phi, theta, psi): - """Convert 3-1-3 Euler Angles to Euler Parameters (quaternions). +def normalize_quaternions(quaternions): + """Normalizes the quaternions (Euler parameters) to have unit magnitude. Parameters ---------- + quaternions : tuple + Tuple containing the Euler parameters e0, e1, e2, e3 + + Returns + ------- + tuple + Tuple containing the Euler parameters e0, e1, e2, e3 + """ + q_w, q_x, q_y, q_z = quaternions + q_norm = (q_w**2 + q_x**2 + q_y**2 + q_z**2) ** 0.5 + if q_norm == 0: + return 1, 0, 0, 0 + return q_w / q_norm, q_x / q_norm, q_y / q_norm, q_z / q_norm + + +def euler321_to_quaternions(psi, theta, phi): + """Calculates the quaternions (Euler parameters) from the Euler angles in + yaw, pitch, and roll sequence (3-2-1). + + Parameters + ---------- + psi : float + Euler angle due to roll in degrees, also known as the spin angle. + theta : float + Euler angle due to pitch in degrees, also known as the nutation angle. phi : float - Rotation angle around the z-axis (in radians). Represents the precession angle. + Euler angle due to yaw in degrees, also known as the precession angle. + + Returns + ------- + tuple[float, float, float, float] + Tuple containing the Euler parameters e0, e1, e2, e3 + """ + phi = math.radians(phi) + theta = math.radians(theta) + psi = math.radians(psi) + cr = math.cos(phi / 2) + sr = math.sin(phi / 2) + cp = math.cos(theta / 2) + sp = math.sin(theta / 2) + cy = math.cos(psi / 2) + sy = math.sin(psi / 2) + e0 = cr * cp * cy + sr * sp * sy + e1 = sr * cp * cy - cr * sp * sy + e2 = cr * sp * cy + sr * cp * sy + e3 = cr * cp * sy - sr * sp * cy + return e0, e1, e2, e3 + + +def euler313_to_quaternions(phi, theta, psi): + """Convert 3-1-3 Euler angles to Euler parameters (quaternions). + + Parameters + ---------- + phi : float + Rotation angle around the z-axis (in radians). Represents the precession + angle or the yaw angle. theta : float - Rotation angle around the x-axis (in radians). Represents the nutation angle. + Rotation angle around the x-axis (in radians). Represents the nutation + angle or the pitch angle. psi : float - Rotation angle around the z-axis (in radians). Represents the spin angle. - + Rotation angle around the z-axis (in radians). Represents the spin angle + or the roll angle. Returns ------- diff --git a/tests/conftest.py b/tests/conftest.py index a1e4b7f99..6c4171b66 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -17,6 +17,7 @@ "tests.fixtures.monte_carlo.monte_carlo_fixtures", "tests.fixtures.monte_carlo.stochastic_fixtures", "tests.fixtures.monte_carlo.stochastic_motors_fixtures", + "tests.fixtures.sensors.sensors_fixtures", ] diff --git a/tests/fixtures/flight/flight_fixtures.py b/tests/fixtures/flight/flight_fixtures.py index c8f73d52c..c8fe437ca 100644 --- a/tests/fixtures/flight/flight_fixtures.py +++ b/tests/fixtures/flight/flight_fixtures.py @@ -158,3 +158,33 @@ def flight_calisto_air_brakes(calisto_air_brakes_clamp_on, example_plain_env): time_overshoot=False, terminate_on_apogee=True, ) + + +@pytest.fixture +def flight_calisto_with_sensors(calisto_with_sensors, example_plain_env): + """A rocketpy.Flight object of the Calisto rocket. This uses the calisto + with a set of ideal sensors. The environment is the simplest possible, with + no parameters set. + + Parameters + ---------- + calisto_with_sensors : rocketpy.Rocket + An object of the Rocket class. + example_plain_env : rocketpy.Environment + An object of the Environment class. + + Returns + ------- + rocketpy.Flight + A rocketpy.Flight object of the Calisto rocket in a more complex + condition. + """ + return Flight( + rocket=calisto_with_sensors, + environment=example_plain_env, + rail_length=5.2, + inclination=85, + heading=0, + time_overshoot=False, + terminate_on_apogee=True, + ) diff --git a/tests/fixtures/rockets/rocket_fixtures.py b/tests/fixtures/rockets/rocket_fixtures.py index ceab9d15a..9158452b9 100644 --- a/tests/fixtures/rockets/rocket_fixtures.py +++ b/tests/fixtures/rockets/rocket_fixtures.py @@ -244,6 +244,39 @@ def calisto_air_brakes_clamp_off(calisto_robust, controller_function): return calisto +@pytest.fixture +def calisto_with_sensors( + calisto, + calisto_nose_cone, + calisto_tail, + calisto_trapezoidal_fins, + ideal_accelerometer, + ideal_gyroscope, + ideal_barometer, + ideal_gnss, +): + """Create an object class of the Rocket class to be used in the tests. This + is the same Calisto rocket that was defined in the calisto fixture, but with + a set of ideal sensors added at the center of dry mass, meaning the readings + will be the same as the values saved on a Flight object. + + Returns + ------- + rocketpy.Rocket + An object of the Rocket class + """ + calisto.add_surfaces(calisto_nose_cone, 1.160) + calisto.add_surfaces(calisto_tail, -1.313) + calisto.add_surfaces(calisto_trapezoidal_fins, -1.168) + # double sensors to test using same instance twice + calisto.add_sensor(ideal_accelerometer, -0.1180124376577797) + calisto.add_sensor(ideal_accelerometer, -0.1180124376577797) + calisto.add_sensor(ideal_gyroscope, -0.1180124376577797) + calisto.add_sensor(ideal_barometer, -0.1180124376577797) + calisto.add_sensor(ideal_gnss, -0.1180124376577797) + return calisto + + @pytest.fixture # old name: dimensionless_rocket def dimensionless_calisto(kg, m, dimensionless_cesaroni_m1670): """The dimensionless version of the Calisto rocket. This is the same rocket diff --git a/tests/fixtures/sensors/__init__.py b/tests/fixtures/sensors/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/fixtures/sensors/sensors_fixtures.py b/tests/fixtures/sensors/sensors_fixtures.py new file mode 100644 index 000000000..1d01a59c3 --- /dev/null +++ b/tests/fixtures/sensors/sensors_fixtures.py @@ -0,0 +1,135 @@ +import pytest + +from rocketpy import Accelerometer, Gyroscope +from rocketpy.sensors.barometer import Barometer +from rocketpy.sensors.gnss_receiver import GnssReceiver + + +@pytest.fixture +def noisy_rotated_accelerometer(): + """Returns an accelerometer with all parameters set to non-default values, + i.e. with noise and rotation.""" + # mpu6050 approx values, variances are made up + return Accelerometer( + sampling_rate=100, + orientation=(60, 60, 60), + noise_density=[0, 0.03, 0.05], + noise_variance=1.01, + random_walk_density=[0, 0.01, 0.02], + random_walk_variance=[1, 1, 1.05], + constant_bias=[0, 0.3, 0.5], + operating_temperature=25 + 273.15, + temperature_bias=[0, 0.01, 0.02], + temperature_scale_factor=[0, 0.01, 0.02], + cross_axis_sensitivity=0.5, + consider_gravity=True, + name="Accelerometer", + ) + + +@pytest.fixture +def noisy_rotated_gyroscope(): + """Returns a gyroscope with all parameters set to non-default values, + i.e. with noise and rotation.""" + # mpu6050 approx values, variances are made up + return Gyroscope( + sampling_rate=100, + orientation=(-60, -60, -60), + noise_density=[0, 0.03, 0.05], + noise_variance=1.01, + random_walk_density=[0, 0.01, 0.02], + random_walk_variance=[1, 1, 1.05], + constant_bias=[0, 0.3, 0.5], + operating_temperature=25 + 273.15, + temperature_bias=[0, 0.01, 0.02], + temperature_scale_factor=[0, 0.01, 0.02], + cross_axis_sensitivity=0.5, + acceleration_sensitivity=[0, 0.0008, 0.0017], + name="Gyroscope", + ) + + +@pytest.fixture +def noisy_barometer(): + """Returns a barometer with all parameters set to non-default values, + i.e. with noise and temperature drift.""" + return Barometer( + sampling_rate=50, + noise_density=19, + noise_variance=19, + random_walk_density=0.01, + constant_bias=1000, + operating_temperature=25 + 273.15, + temperature_bias=0.02, + temperature_scale_factor=0.02, + ) + + +@pytest.fixture +def noisy_gnss(): + return GnssReceiver( + sampling_rate=1, + position_accuracy=1, + altitude_accuracy=1, + ) + + +@pytest.fixture +def quantized_accelerometer(): + """Returns an accelerometer with all parameters set to non-default values, + i.e. with noise and rotation.""" + return Accelerometer( + sampling_rate=100, + measurement_range=2, + resolution=0.4882, + ) + + +@pytest.fixture +def quantized_gyroscope(): + """Returns a gyroscope with all parameters set to non-default values, + i.e. with noise and rotation.""" + return Gyroscope( + sampling_rate=100, + measurement_range=15, + resolution=0.4882, + ) + + +@pytest.fixture +def quantized_barometer(): + """Returns a barometer with all parameters set to non-default values, + i.e. with noise and temperature drift.""" + return Barometer( + sampling_rate=50, + measurement_range=7e4, + resolution=0.16, + ) + + +@pytest.fixture +def ideal_accelerometer(): + return Accelerometer( + sampling_rate=10, + ) + + +@pytest.fixture +def ideal_gyroscope(): + return Gyroscope( + sampling_rate=10, + ) + + +@pytest.fixture +def ideal_barometer(): + return Barometer( + sampling_rate=10, + ) + + +@pytest.fixture +def ideal_gnss(): + return GnssReceiver( + sampling_rate=1, + ) diff --git a/tests/integration/test_sensor.py b/tests/integration/test_sensor.py new file mode 100644 index 000000000..744a4178b --- /dev/null +++ b/tests/integration/test_sensor.py @@ -0,0 +1,175 @@ +import json +import os +from unittest.mock import patch + +import numpy as np +import pytest + +from rocketpy.mathutils.vector_matrix import Vector +from rocketpy.rocket.components import Components +from rocketpy.sensors.accelerometer import Accelerometer +from rocketpy.sensors.barometer import Barometer +from rocketpy.sensors.gnss_receiver import GnssReceiver +from rocketpy.sensors.gyroscope import Gyroscope + + +def test_sensor_on_rocket(calisto_with_sensors): + """Test the sensor on the rocket. + + Parameters + ---------- + calisto_with_sensors : Rocket + Pytest fixture for the calisto rocket with a set of ideal sensors. + """ + sensors = calisto_with_sensors.sensors + assert isinstance(sensors, Components) + assert isinstance(sensors[0].component, Accelerometer) + assert isinstance(sensors[1].position, Vector) + assert isinstance(sensors[2].component, Gyroscope) + assert isinstance(sensors[2].position, Vector) + assert isinstance(sensors[3].component, Barometer) + assert isinstance(sensors[3].position, Vector) + assert isinstance(sensors[4].component, GnssReceiver) + assert isinstance(sensors[4].position, Vector) + + +class TestIdealSensors: + """Test a flight with ideal sensors on the rocket.""" + + @pytest.fixture(autouse=True) + def setup(self, flight_calisto_with_sensors): + """Setup an flight fixture for all tests.""" + self.flight = flight_calisto_with_sensors + + def test_accelerometer(self): + """Test an ideal accelerometer.""" + accelerometer = self.flight.rocket.sensors[0].component + time, ax, ay, az = zip(*accelerometer.measured_data[0]) + a = np.sqrt(np.array(ax) ** 2 + np.array(ay) ** 2 + np.array(az) ** 2) + sim_accel = self.flight.acceleration(time) + + # tolerance is bounded to numerical errors in the transformation matrixes + assert np.allclose(a, sim_accel, atol=1e-12) + # check if both added accelerometer instances saved the same data + assert ( + self.flight.sensors[0].measured_data[0] + == self.flight.sensors[0].measured_data[1] + ) + + def test_gyroscope(self): + """Test an ideal gyroscope.""" + gyroscope = self.flight.rocket.sensors[2].component + time, wx, wy, wz = zip(*gyroscope.measured_data) + w = np.sqrt(np.array(wx) ** 2 + np.array(wy) ** 2 + np.array(wz) ** 2) + flight_wx = np.array(self.flight.w1(time)) + flight_wy = np.array(self.flight.w2(time)) + flight_wz = np.array(self.flight.w3(time)) + sim_w = np.sqrt(flight_wx**2 + flight_wy**2 + flight_wz**2) + assert np.allclose(w, sim_w, atol=1e-12) + + def test_barometer(self): + """Test an ideal barometer.""" + barometer = self.flight.rocket.sensors[3].component + time, pressure = zip(*barometer.measured_data) + pressure = np.array(pressure) + sim_data = self.flight.pressure(time) + assert np.allclose(pressure, sim_data, atol=1e-12) + + def test_gnss_receiver(self): + """Test an ideal GnssReceiver.""" + gnss = self.flight.rocket.sensors[4].component + time, latitude, longitude, altitude = zip(*gnss.measured_data) + sim_latitude = self.flight.latitude(time) + sim_longitude = self.flight.longitude(time) + sim_altitude = self.flight.altitude(time) + assert np.allclose(np.array(latitude), sim_latitude, atol=1e-12) + assert np.allclose(np.array(longitude), sim_longitude, atol=1e-12) + assert np.allclose(np.array(altitude), sim_altitude, atol=1e-12) + + +@pytest.mark.parametrize("plane", ["xz", "yz"]) +@patch("matplotlib.pyplot.show") +def test_draw( + mock_show, calisto_with_sensors, plane +): # pylint: disable=unused-argument + """Test the drawing of the sensors.""" + calisto_with_sensors.draw(plane=plane) + + +def test_export_all_sensors_data(flight_calisto_with_sensors): + """Test the export of sensor data. + + Parameters + ---------- + flight_calisto_with_sensors : Flight + Pytest fixture for the flight of the calisto rocket with a set of ideal + sensors. + """ + flight_calisto_with_sensors.export_sensor_data("test_sensor_data.json") + # read the json and parse as dict + filename = "test_sensor_data.json" + with open(filename, "r") as f: + data = f.read() + sensor_data = json.loads(data) + # convert list of tuples into list of lists to compare with the json + flight_calisto_with_sensors.sensors[0].measured_data[0] = [ + list(measurement) + for measurement in flight_calisto_with_sensors.sensors[0].measured_data[0] + ] + flight_calisto_with_sensors.sensors[1].measured_data[1] = [ + list(measurement) + for measurement in flight_calisto_with_sensors.sensors[1].measured_data[1] + ] + flight_calisto_with_sensors.sensors[2].measured_data = [ + list(measurement) + for measurement in flight_calisto_with_sensors.sensors[2].measured_data + ] + flight_calisto_with_sensors.sensors[3].measured_data = [ + list(measurement) + for measurement in flight_calisto_with_sensors.sensors[3].measured_data + ] + flight_calisto_with_sensors.sensors[4].measured_data = [ + list(measurement) + for measurement in flight_calisto_with_sensors.sensors[4].measured_data + ] + assert ( + sensor_data["Accelerometer"] + == flight_calisto_with_sensors.sensors[0].measured_data + ) + assert ( + sensor_data["Gyroscope"] == flight_calisto_with_sensors.sensors[2].measured_data + ) + assert ( + sensor_data["Barometer"] == flight_calisto_with_sensors.sensors[3].measured_data + ) + assert ( + sensor_data["GnssReceiver"] + == flight_calisto_with_sensors.sensors[4].measured_data + ) + os.remove(filename) + + +def test_export_single_sensor_data(flight_calisto_with_sensors): + """Test the export of a single sensor data. + + Parameters + ---------- + flight_calisto_with_sensors : Flight + Pytest fixture for the flight of the calisto rocket with a set of ideal + sensors. + """ + flight_calisto_with_sensors.export_sensor_data("test_sensor_data.json", "Gyroscope") + # read the json and parse as dict + filename = "test_sensor_data.json" + with open(filename, "r") as f: + data = f.read() + sensor_data = json.loads(data) + # convert list of tuples into list of lists to compare with the json + flight_calisto_with_sensors.sensors[2].measured_data = [ + list(measurement) + for measurement in flight_calisto_with_sensors.sensors[2].measured_data + ] + assert ( + sensor_data["Gyroscope"] == flight_calisto_with_sensors.sensors[2].measured_data + ) + os.remove(filename) diff --git a/tests/unit/test_flight.py b/tests/unit/test_flight.py index 0d808c148..fdb60b69b 100644 --- a/tests/unit/test_flight.py +++ b/tests/unit/test_flight.py @@ -1,3 +1,5 @@ +import json +import os from unittest.mock import patch import matplotlib as plt @@ -164,6 +166,47 @@ def test_out_of_rail_stability_margin(flight_calisto_custom_wind): assert np.isclose(res, 2.14, atol=0.1) +def test_export_sensor_data(flight_calisto_with_sensors): + """Test the export of sensor data. + + Parameters + ---------- + flight_calisto_with_sensors : Flight + Pytest fixture for the flight of the calisto rocket with an ideal accelerometer and a gyroscope. + """ + flight_calisto_with_sensors.export_sensor_data("test_sensor_data.json") + # read the json and parse as dict + filename = "test_sensor_data.json" + with open(filename, "r") as f: + data = f.read() + sensor_data = json.loads(data) + # convert list of tuples into list of lists to compare with the json + flight_calisto_with_sensors.sensors[0].measured_data[0] = [ + list(measurement) + for measurement in flight_calisto_with_sensors.sensors[0].measured_data[0] + ] + flight_calisto_with_sensors.sensors[1].measured_data[1] = [ + list(measurement) + for measurement in flight_calisto_with_sensors.sensors[1].measured_data[1] + ] + flight_calisto_with_sensors.sensors[2].measured_data = [ + list(measurement) + for measurement in flight_calisto_with_sensors.sensors[2].measured_data + ] + assert ( + sensor_data["Accelerometer"][0] + == flight_calisto_with_sensors.sensors[0].measured_data[0] + ) + assert ( + sensor_data["Accelerometer"][1] + == flight_calisto_with_sensors.sensors[1].measured_data[1] + ) + assert ( + sensor_data["Gyroscope"] == flight_calisto_with_sensors.sensors[2].measured_data + ) + os.remove(filename) + + @pytest.mark.parametrize( "flight_time, expected_values", [ diff --git a/tests/unit/test_flight_time_nodes.py b/tests/unit/test_flight_time_nodes.py index 1e2661210..dcdc11eff 100644 --- a/tests/unit/test_flight_time_nodes.py +++ b/tests/unit/test_flight_time_nodes.py @@ -12,7 +12,7 @@ def test_time_nodes_init(flight_calisto): def test_time_nodes_getitem(flight_calisto): time_nodes = flight_calisto.TimeNodes() - time_nodes.add_node(1.0, [], []) + time_nodes.add_node(1.0, [], [], []) assert isinstance(time_nodes[0], flight_calisto.TimeNodes.TimeNode) assert time_nodes[0].t == 1.0 @@ -24,7 +24,7 @@ def test_time_nodes_len(flight_calisto): def test_time_nodes_add(flight_calisto): time_nodes = flight_calisto.TimeNodes() - example_node = flight_calisto.TimeNodes.TimeNode(1.0, [], []) + example_node = flight_calisto.TimeNodes.TimeNode(1.0, [], [], []) time_nodes.add(example_node) assert len(time_nodes) == 1 assert isinstance(time_nodes[0], flight_calisto.TimeNodes.TimeNode) @@ -33,7 +33,7 @@ def test_time_nodes_add(flight_calisto): def test_time_nodes_add_node(flight_calisto): time_nodes = flight_calisto.TimeNodes() - time_nodes.add_node(2.0, [], []) + time_nodes.add_node(2.0, [], [], []) assert len(time_nodes) == 1 assert time_nodes[0].t == 2.0 assert len(time_nodes[0].parachutes) == 0 @@ -51,9 +51,9 @@ def test_time_nodes_add_node(flight_calisto): def test_time_nodes_sort(flight_calisto): time_nodes = flight_calisto.TimeNodes() - time_nodes.add_node(3.0, [], []) - time_nodes.add_node(1.0, [], []) - time_nodes.add_node(2.0, [], []) + time_nodes.add_node(3.0, [], [], []) + time_nodes.add_node(1.0, [], [], []) + time_nodes.add_node(2.0, [], [], []) time_nodes.sort() assert len(time_nodes) == 3 assert time_nodes[0].t == 1.0 @@ -63,9 +63,9 @@ def test_time_nodes_sort(flight_calisto): def test_time_nodes_merge(flight_calisto): time_nodes = flight_calisto.TimeNodes() - time_nodes.add_node(1.0, [], []) - time_nodes.add_node(1.0, [], []) - time_nodes.add_node(2.0, [], []) + time_nodes.add_node(1.0, [], [], []) + time_nodes.add_node(1.0, [], [], []) + time_nodes.add_node(2.0, [], [], []) time_nodes.merge() assert len(time_nodes) == 2 assert time_nodes[0].t == 1.0 @@ -78,9 +78,9 @@ def test_time_nodes_merge(flight_calisto): def test_time_nodes_flush_after(flight_calisto): time_nodes = flight_calisto.TimeNodes() - time_nodes.add_node(1.0, [], []) - time_nodes.add_node(2.0, [], []) - time_nodes.add_node(3.0, [], []) + time_nodes.add_node(1.0, [], [], []) + time_nodes.add_node(2.0, [], [], []) + time_nodes.add_node(3.0, [], [], []) time_nodes.flush_after(1) assert len(time_nodes) == 2 assert time_nodes[0].t == 1.0 @@ -88,14 +88,14 @@ def test_time_nodes_flush_after(flight_calisto): def test_time_node_init(flight_calisto): - node = flight_calisto.TimeNodes.TimeNode(1.0, [], []) + node = flight_calisto.TimeNodes.TimeNode(1.0, [], [], []) assert node.t == 1.0 assert len(node.parachutes) == 0 assert len(node.callbacks) == 0 def test_time_node_lt(flight_calisto): - node1 = flight_calisto.TimeNodes.TimeNode(1.0, [], []) - node2 = flight_calisto.TimeNodes.TimeNode(2.0, [], []) + node1 = flight_calisto.TimeNodes.TimeNode(1.0, [], [], []) + node2 = flight_calisto.TimeNodes.TimeNode(2.0, [], [], []) assert node1 < node2 assert not node2 < node1 diff --git a/tests/unit/test_sensor.py b/tests/unit/test_sensor.py new file mode 100644 index 000000000..33c62bb87 --- /dev/null +++ b/tests/unit/test_sensor.py @@ -0,0 +1,615 @@ +import json +import os + +import numpy as np +import pytest +from pytest import approx + +from rocketpy.mathutils.vector_matrix import Matrix, Vector +from rocketpy.tools import euler321_to_quaternions + +# calisto standard simulation no wind solution index 200 +TIME = 3.338513236767685 +U = [ + 0.02856482783411794, + 50.919436628139216, + 1898.9056294848442, + 0.021620542063162787, + 30.468683793837055, + 284.19140267225384, + -0.0076008223256743114, + 0.0004430927976100488, + 0.05330950836930627, + 0.9985245671704497, + 0.0026388673982115224, + 0.00010697759229808481, + 19.72526891699468, +] +U_DOT = [ + 0.021620542063162787, + 30.468683793837055, + 284.19140267225384, + 0.0009380154986373648, + 1.4853035773069556, + 4.377014845613867, + -9.848086239924413, + 0.5257087555505318, + -0.0030529818895471124, + -0.07503444684343626, + 0.028008532884449017, + -0.052789015849051935, + 2.276425320359305, +] +GRAVITY = 9.81 + + +@pytest.mark.parametrize( + "sensor", + [ + "noisy_rotated_accelerometer", + "quantized_accelerometer", + "noisy_rotated_gyroscope", + "quantized_gyroscope", + "noisy_barometer", + "quantized_barometer", + "noisy_gnss", + ], +) +def test_sensors_prints(sensor, request): + """Test the print methods of the Sensor class. Checks if all attributes are + printed correctly. + """ + sensor = request.getfixturevalue(sensor) + sensor.prints.all() + assert True + + +def test_rotation_matrix(noisy_rotated_accelerometer): + """Test the rotation_matrix property of the InertialSensor class. Checks if + the rotation matrix is correctly calculated. + """ + # values from external source + expected_matrix = np.array( + [ + [0.2500000, -0.0580127, 0.9665064], + [0.4330127, 0.8995190, -0.0580127], + [-0.8660254, 0.4330127, 0.2500000], + ] + ) + rotation_matrix = np.array(noisy_rotated_accelerometer.rotation_matrix.components) + assert np.allclose(expected_matrix, rotation_matrix, atol=1e-8) + + +def test_inertial_quantization(quantized_accelerometer): + """Test the quantize method of the InertialSensor class. Checks if returned values + are as expected. + """ + # expected values calculated by hand + assert quantized_accelerometer.quantize(Vector([3, 3, 3])) == Vector( + [1.9528, 1.9528, 1.9528] + ) + assert quantized_accelerometer.quantize(Vector([-3, -3, -3])) == Vector( + [-1.9528, -1.9528, -1.9528] + ) + assert quantized_accelerometer.quantize(Vector([1, 1, 1])) == Vector( + [0.9764, 0.9764, 0.9764] + ) + + +def test_scalar_quantization(quantized_barometer): + """Test the quantize method of the ScalarSensor class. Checks if returned values + are as expected. + """ + # expected values calculated by hand + assert quantized_barometer.quantize(7e5) == 7e4 + assert quantized_barometer.quantize(-7e5) == -7e4 + assert quantized_barometer.quantize(1001) == 1000.96 + + +@pytest.mark.parametrize( + "sensor, input_value, expected_output", + [ + ( + "quantized_accelerometer", + Vector([3, 3, 3]), + Vector([1.9528, 1.9528, 1.9528]), + ), + ( + "quantized_accelerometer", + Vector([-3, -3, -3]), + Vector([-1.9528, -1.9528, -1.9528]), + ), + ( + "quantized_accelerometer", + Vector([1, 1, 1]), + Vector([0.9764, 0.9764, 0.9764]), + ), + ("quantized_barometer", 7e5, 7e4), + ("quantized_barometer", -7e5, -7e4), + ("quantized_barometer", 1001, 1000.96), + ], +) +def test_quantization(sensor, input_value, expected_output, request): + """Test the quantize method of various sensor classes. Checks if returned values + are as expected. + + Parameters + ---------- + sensor : str + Fixture name of the sensor to be tested. + input_value : any + Input value to be quantized by the sensor. + expected_output : any + Expected output value after quantization. + """ + sensor = request.getfixturevalue(sensor) + result = sensor.quantize(input_value) + assert result == expected_output + + +@pytest.mark.parametrize( + "sensor", + [ + "ideal_accelerometer", + "ideal_gyroscope", + ], +) +def test_inertial_measured_data(sensor, request, example_plain_env): + """Test the measured_data property of the Sensor class. Checks if + the measured data is treated properly when the sensor is added once or more + than once to the rocket. + """ + sensor = request.getfixturevalue(sensor) + + sensor.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + assert len(sensor.measured_data) == 1 + sensor.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + assert len(sensor.measured_data) == 2 + assert all(isinstance(i, tuple) for i in sensor.measured_data) + + # check case when sensor is added more than once to the rocket + sensor.measured_data = [ + sensor.measured_data[:], + sensor.measured_data[:], + ] + sensor._save_data = sensor._save_data_multiple + sensor.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + assert len(sensor.measured_data) == 2 + assert len(sensor.measured_data[0]) == 3 + assert len(sensor.measured_data[1]) == 2 + sensor.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + assert len(sensor.measured_data[0]) == 3 + assert len(sensor.measured_data[1]) == 3 + + +@pytest.mark.parametrize( + "sensor", + [ + "ideal_barometer", + "ideal_gnss", + ], +) +def test_scalar_measured_data(sensor, request, example_plain_env): + """Test the measure method of ScalarSensor. Checks if saved + measurement is (P) and if measured_data is [(t, P), ...] + """ + sensor = request.getfixturevalue(sensor) + + t = TIME + u = U + + sensor.measure( + t, + u=u, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + assert len(sensor.measured_data) == 1 + sensor.measure( + t, + u=u, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + assert len(sensor.measured_data) == 2 + assert all(isinstance(i, tuple) for i in sensor.measured_data) + + # check case when sensor is added more than once to the rocket + sensor.measured_data = [ + sensor.measured_data[:], + sensor.measured_data[:], + ] + sensor._save_data = sensor._save_data_multiple + sensor.measure( + t, + u=u, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + assert len(sensor.measured_data) == 2 + assert len(sensor.measured_data[0]) == 3 + assert len(sensor.measured_data[1]) == 2 + sensor.measure( + t, + u=u, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + assert len(sensor.measured_data[0]) == 3 + assert len(sensor.measured_data[1]) == 3 + + +def test_noisy_rotated_accelerometer(noisy_rotated_accelerometer, example_plain_env): + """Test the measure method of the Accelerometer class. Checks if saved + measurement is (ax,ay,az) and if measured_data is [(t, (ax,ay,az)), ...] + """ + + # calculate acceleration at sensor position in inertial frame + relative_position = Vector([0.4, 0.4, 1]) + inertial_acceleration = Vector(U_DOT[3:6]) + Vector([0, 0, -GRAVITY]) + omega = Vector(U[10:13]) + omega_dot = Vector(U_DOT[10:13]) + acceleration = ( + inertial_acceleration + + Vector.cross(omega_dot, relative_position) + + Vector.cross(omega, Vector.cross(omega, relative_position)) + ) + + # calculate total rotation matrix + cross_axis_sensitivity = Matrix( + [ + [1, 0.005, 0.005], + [0.005, 1, 0.005], + [0.005, 0.005, 1], + ] + ) + sensor_rotation = Matrix.transformation(euler321_to_quaternions(60, 60, 60)) + total_rotation = sensor_rotation @ cross_axis_sensitivity + rocket_rotation = Matrix.transformation(U[6:10]) + # expected measurement without noise + ax, ay, az = total_rotation @ (rocket_rotation @ acceleration) + # expected measurement with constant bias + ax += 0.5 + ay += 0.5 + az += 0.5 + + # check last measurement considering noise error bounds + noisy_rotated_accelerometer.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=relative_position, + environment=example_plain_env, + ) + assert noisy_rotated_accelerometer.measurement == approx([ax, ay, az], rel=0.1) + assert len(noisy_rotated_accelerometer.measurement) == 3 + assert noisy_rotated_accelerometer.measured_data[0][1:] == approx( + [ax, ay, az], rel=0.1 + ) + assert noisy_rotated_accelerometer.measured_data[0][0] == TIME + + +def test_noisy_rotated_gyroscope(noisy_rotated_gyroscope, example_plain_env): + """Test the measure method of the Gyroscope class. Checks if saved + measurement is (wx,wy,wz) and if measured_data is [(t, (wx,wy,wz)), ...] + """ + # calculate acceleration at sensor position in inertial frame + relative_position = Vector([0.4, 0.4, 1]) + omega = Vector(U[10:13]) + # calculate total rotation matrix + cross_axis_sensitivity = Matrix( + [ + [1, 0.005, 0.005], + [0.005, 1, 0.005], + [0.005, 0.005, 1], + ] + ) + sensor_rotation = Matrix.transformation(euler321_to_quaternions(-60, -60, -60)) + total_rotation = sensor_rotation @ cross_axis_sensitivity + rocket_rotation = Matrix.transformation(U[6:10]) + # expected measurement without noise + wx, wy, wz = total_rotation @ (rocket_rotation @ omega) + # expected measurement with constant bias + wx += 0.5 + wy += 0.5 + wz += 0.5 + + # check last measurement considering noise error bounds + noisy_rotated_gyroscope.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=relative_position, + environment=example_plain_env, + ) + assert noisy_rotated_gyroscope.measurement == approx([wx, wy, wz], rel=0.3) + assert len(noisy_rotated_gyroscope.measurement) == 3 + assert noisy_rotated_gyroscope.measured_data[0][1:] == approx([wx, wy, wz], rel=0.3) + assert noisy_rotated_gyroscope.measured_data[0][0] == TIME + + +def test_noisy_barometer(noisy_barometer, example_plain_env): + """Test the measure method of the Barometer class. Checks if saved + measurement is (P) and if measured_data is [(t, P), ...] + """ + # expected measurement without noise + relative_position = Vector([0.4, 0.4, 1]) + relative_altitude = (Matrix.transformation(U[6:10]) @ relative_position).z + P = example_plain_env.pressure(relative_altitude + U[2]) + # expected measurement with constant bias + P += 0.5 + + noisy_barometer.measure( + time=TIME, + u=U, + relative_position=relative_position, + environment=example_plain_env, + ) + assert noisy_barometer.measurement == approx(P, rel=0.03) + assert noisy_barometer.measured_data[0][1] == approx(P, rel=0.03) + assert noisy_barometer.measured_data[0][0] == TIME + + +def test_noisy_gnss(noisy_gnss, example_plain_env): + """Test the measure method of the GnssReceiver class. Checks if saved + measurement is (latitude, longitude, altitude) and if measured_data is [(t, (latitude, longitude, altitude)), ...] + """ + # expected measurement without noise + relative_position = Vector([0.4, 0.4, 1]) + lat, lon = example_plain_env.latitude, example_plain_env.longitude + earth_radius = example_plain_env.earth_radius + x, y, z = (Matrix.transformation(U[6:10]) @ relative_position) + Vector(U[0:3]) + drift = (x**2 + y**2) ** 0.5 + bearing = (2 * np.pi - np.arctan2(-x, y)) * (180 / np.pi) + latitude = np.degrees( + np.arcsin( + np.sin(np.radians(lat)) * np.cos(drift / earth_radius) + + np.cos(np.radians(lat)) + * np.sin(drift / earth_radius) + * np.cos(np.radians(bearing)) + ) + ) + longitude = np.degrees( + np.radians(lon) + + np.arctan2( + np.sin(np.radians(bearing)) + * np.sin(drift / earth_radius) + * np.cos(np.radians(lat)), + np.cos(drift / earth_radius) + - np.sin(np.radians(lat)) * np.sin(np.radians(latitude)), + ) + ) + altitude = z + + noisy_gnss.measure( + time=TIME, + u=U, + relative_position=relative_position, + environment=example_plain_env, + ) + assert noisy_gnss.measurement == approx([latitude, longitude, altitude], abs=3.2) + assert len(noisy_gnss.measurement) == 3 + assert noisy_gnss.measured_data[0][1:] == approx( + [latitude, longitude, altitude], abs=3.2 + ) + assert noisy_gnss.measured_data[0][0] == TIME + + # check last measurement considering noise error bounds + noisy_gnss.measure( + time=TIME, + u=U, + relative_position=relative_position, + environment=example_plain_env, + ) + assert noisy_gnss.measurement == approx([latitude, longitude, altitude], abs=3.2) + assert len(noisy_gnss.measurement) == 3 + assert noisy_gnss.measured_data[1][1:] == approx( + [latitude, longitude, altitude], abs=3.2 + ) + assert noisy_gnss.measured_data[1][0] == TIME + + +@pytest.mark.parametrize( + "sensor, file_format, expected_header", + [ + ("ideal_accelerometer", "csv", "t,ax,ay,az\n"), + ("ideal_gyroscope", "csv", "t,wx,wy,wz\n"), + ("ideal_barometer", "csv", "t,pressure\n"), + ("ideal_gnss", "csv", "t,latitude,longitude,altitude\n"), + ], +) +def test_export_data_csv( + sensor, file_format, expected_header, request, example_plain_env +): + """Test the export_data method for CSV format.""" + sensor = request.getfixturevalue(sensor) + + # Perform measurement + sensor.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + file_name = f"sensors.{file_format}" + + # Export data + sensor.export_measured_data(file_name, file_format=file_format) + + # Check CSV contents + with open(file_name, "r") as file: + contents = file.read() + + expected_data = expected_header + for data in sensor.measured_data: + expected_data += ",".join(map(str, data)) + "\n" + + assert contents == expected_data + + os.remove(file_name) + + +@pytest.mark.parametrize( + "sensor, file_format, expected_keys", + [ + ("ideal_accelerometer", "json", ("ax", "ay", "az")), + ("ideal_gyroscope", "json", ("wx", "wy", "wz")), + ("ideal_barometer", "json", ("pressure",)), + ("ideal_gnss", "json", ("latitude", "longitude", "altitude")), + ], +) +def test_export_data_json( + sensor, file_format, expected_keys, request, example_plain_env +): + """Test the export_data method for JSON format.""" + sensor = request.getfixturevalue(sensor) + + # Perform measurement + sensor.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + file_name = f"sensors.{file_format}" + + # Export data + sensor.export_measured_data(file_name, file_format=file_format) + + # Check JSON contents + with open(file_name, "r") as file: + contents = json.load(file) + + expected_data = {"t": []} + for key in expected_keys: + expected_data[key] = [] + + for data in sensor.measured_data: + expected_data["t"].append(data[0]) + for i, key in enumerate(expected_keys): + expected_data[key].append(data[i + 1]) + + assert contents == expected_data + + os.remove(file_name) + + +@pytest.mark.parametrize( + "sensor, file_format, expected_header", + [ + ("ideal_accelerometer", "csv", "t,ax,ay,az\n"), + ("ideal_gyroscope", "csv", "t,wx,wy,wz\n"), + ], +) +def test_export_multiple_sensors_csv( + sensor, file_format, expected_header, request, example_plain_env +): + """Test exporting data for multiple instances in CSV format.""" + sensor = request.getfixturevalue(sensor) + + # Perform measurement + sensor.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + sensor.measured_data = [sensor.measured_data[:], sensor.measured_data[:]] + file_name = f"sensors.{file_format}" + + # Export multiple data + sensor.export_measured_data(file_name, file_format=file_format) + + # Check CSV for both instances + with open(f"{file_name}_1", "r") as file: + contents_1 = file.read() + + with open(f"{file_name}_2", "r") as file: + contents_2 = file.read() + + expected_data = expected_header + for data in sensor.measured_data[0]: + expected_data += ",".join(map(str, data)) + "\n" + + assert contents_1 == expected_data + assert contents_2 == expected_data + + os.remove(f"{file_name}_1") + os.remove(f"{file_name}_2") + + +@pytest.mark.parametrize( + "sensor, file_format, expected_keys", + [ + ("ideal_accelerometer", "json", ("ax", "ay", "az")), + ("ideal_gyroscope", "json", ("wx", "wy", "wz")), + ], +) +def test_export_multiple_sensors_json( + sensor, file_format, expected_keys, request, example_plain_env +): + """Test exporting data for multiple instances in JSON format.""" + sensor = request.getfixturevalue(sensor) + + # Perform measurement + sensor.measure( + time=TIME, + u=U, + u_dot=U_DOT, + relative_position=Vector([0, 0, 0]), + environment=example_plain_env, + ) + sensor.measured_data = [sensor.measured_data[:], sensor.measured_data[:]] + file_name = f"sensors.{file_format}" + + # Export multiple data + sensor.export_measured_data(file_name, file_format=file_format) + + # Check JSON for both instances + with open(f"{file_name}_1", "r") as file: + contents_1 = json.load(file) + + with open(f"{file_name}_2", "r") as file: + contents_2 = json.load(file) + + expected_data = {"t": []} + for key in expected_keys: + expected_data[key] = [] + + for data in sensor.measured_data[0]: + expected_data["t"].append(data[0]) + for i, key in enumerate(expected_keys): + expected_data[key].append(data[i + 1]) + + assert contents_1 == expected_data + assert contents_2 == expected_data + + os.remove(f"{file_name}_1") + os.remove(f"{file_name}_2") diff --git a/tests/unit/test_tools.py b/tests/unit/test_tools.py index 75a526aac..d399d5fc5 100644 --- a/tests/unit/test_tools.py +++ b/tests/unit/test_tools.py @@ -1,11 +1,25 @@ import numpy as np +import pytest from rocketpy.tools import ( calculate_cubic_hermite_coefficients, + euler321_to_quaternions, find_roots_cubic_function, ) +@pytest.mark.parametrize( + "angles, expected_quaternions", + [((0, 0, 0), (1, 0, 0, 0)), ((90, 90, 90), (0.7071068, 0, 0.7071068, 0))], +) +def test_euler_to_quaternions(angles, expected_quaternions): + q0, q1, q2, q3 = euler321_to_quaternions(*angles) + assert round(q0, 7) == expected_quaternions[0] + assert round(q1, 7) == expected_quaternions[1] + assert round(q2, 7) == expected_quaternions[2] + assert round(q3, 7) == expected_quaternions[3] + + def test_calculate_cubic_hermite_coefficients(): """Test the calculate_cubic_hermite_coefficients method of the Function class.""" # Function: f(x) = x**3 + 2x**2 -1 ; derivative: f'(x) = 3x**2 + 4x diff --git a/tests/unit/test_tools_matrix.py b/tests/unit/test_tools_matrix.py index 89e75de0f..dfad1a360 100644 --- a/tests/unit/test_tools_matrix.py +++ b/tests/unit/test_tools_matrix.py @@ -244,6 +244,24 @@ def test_matrix_transformation(): assert matrix @ Vector([0, 0, 1]) == Vector([0, -1, 0]) +def test_matrix_transformation_euler_angles(): + phi = 0 + theta = 0 + psi = 90 + matrix = Matrix.transformation_euler_angles(phi, theta, psi) + matrix = matrix.round(12) + # Check that the matrix is orthogonal + assert matrix @ matrix.transpose == Matrix.identity() + # Check that the matrix rotates the vector correctly + assert matrix @ Vector([0, 0, 1]) == Vector([0, -1, 0]) + + +def test_matrix_round(): + matrix = [[2e-10, -2e-10, 0], [5.1234, -5.1234, 0], [0, 0, 9]] + matrix = Matrix(matrix).round(3) + assert matrix == Matrix([[0, 0, 0], [5.123, -5.123, 0], [0, 0, 9]]) + + @pytest.mark.parametrize("components", test_matrices) def test_matrix_x_y_z(components): matrix = Matrix(components)