diff --git a/docs/notebooks/sensors_testing.ipynb b/docs/notebooks/sensors_testing.ipynb index 83a777daa..e2b987c6b 100644 --- a/docs/notebooks/sensors_testing.ipynb +++ b/docs/notebooks/sensors_testing.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", @@ -44,13 +44,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", "id": "5kl-Je8dNVFI" }, - "outputs": [], + "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", + "\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", @@ -61,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", @@ -91,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": {}, "colab_type": "code", @@ -136,47 +197,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "from rocketpy import Accelerometer, Gyroscope, Barometer\n", - "accel_noisy_nosecone = Accelerometer(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(sampling_rate=100,\n", - " consider_gravity=False,\n", - " orientation=[[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", + "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": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Identification of the Sensor:\n", + "Identification:\n", "\n", "Name: Accelerometer in Nosecone\n", "Type: Accelerometer\n", @@ -184,29 +249,29 @@ "Orientation of the Sensor:\n", "\n", "Orientation: (60, 60, 60)\n", - "Normal Vector: (0.9665063509461097, -0.05801270189221941, 0.2500000000000002)\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 of the Sensor:\n", + "Quantization:\n", "\n", "Measurement Range: -70 to 70 (m/s^2)\n", "Resolution: 0.4882 m/s^2/LSB\n", "\n", - "Noise of the Sensor:\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 °C\n", - "Temperature Bias: (0.02, 0.02, 0.02) m/s^2/°C\n", - "Temperature Scale Factor: (0.02, 0.02, 0.02) %/°C\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 of the Sensor:\n", + "Identification:\n", "\n", "Name: Accelerometer in CDM\n", "Type: Accelerometer\n", @@ -220,21 +285,21 @@ " [0.43, 0.9, -0.06]\n", " [-0.87, 0.43, 0.25]\n", "\n", - "Quantization of the Sensor:\n", + "Quantization:\n", "\n", "Measurement Range: -inf to inf (m/s^2)\n", "Resolution: 0 m/s^2/LSB\n", "\n", - "Noise of the Sensor:\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 °C\n", - "Temperature Bias: (0, 0, 0) m/s^2/°C\n", - "Temperature Scale Factor: (0, 0, 0) %/°C\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" ] } @@ -246,80 +311,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.001064225153655079" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "import numpy as np\n", - "np.radians(0.06097560975609756097560975609756)" + "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": null, + "execution_count": 9, "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)" + "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, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "barometer_clean = Barometer(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)" + "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, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -334,12 +393,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -563,7 +622,7 @@ "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Acceleration az (m/s^2)\")\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -657,7 +716,7 @@ "plt.show()\n", "\n", "plt.plot(time1, wz, label='Noisy Gyroscope')\n", - "plt.xlim(0,4)\n", + "plt.xlim(0, 4)\n", "# plt.plot(time2, zz, label='Clean Gyroscope')\n", "plt.legend()\n", "plt.show()\n", @@ -692,8 +751,8 @@ } ], "source": [ - "t,p = zip(*barometer_clean.measured_data)\n", - "plt.plot(t,p)\n", + "t, p = zip(*barometer_clean.measured_data)\n", + "plt.plot(t, p)\n", "plt.show()" ] }, diff --git a/rocketpy/__init__.py b/rocketpy/__init__.py index d7d600802..276d60ce7 100644 --- a/rocketpy/__init__.py +++ b/rocketpy/__init__.py @@ -37,7 +37,7 @@ Tail, TrapezoidalFins, ) -from .sensors import Accelerometer, Barometer, Gyroscope +from .sensors import Accelerometer, Barometer, GnssReceiver, Gyroscope from .simulation import Flight, MonteCarlo from .stochastic import ( StochasticEllipticalFins, diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 465770afd..85751bc34 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -3,7 +3,6 @@ import matplotlib.pyplot as plt import numpy as np -from rocketpy.mathutils.vector_matrix import Vector from rocketpy.motors import EmptyMotor, HybridMotor, LiquidMotor, SolidMotor from rocketpy.rocket.aero_surface import Fins, NoseCone, Tail @@ -166,8 +165,6 @@ def thrust_to_weight(self): lower=0, upper=self.rocket.motor.burn_out_time ) - return None - def draw(self, vis_args=None, plane="xz"): """Draws the rocket in a matplotlib figure. @@ -204,7 +201,7 @@ def draw(self, vis_args=None, plane="xz"): "line_width": 1.0, } - fig, ax = plt.subplots(figsize=(8, 6), facecolor=vis_args["background"]) + _, ax = plt.subplots(figsize=(8, 6), facecolor=vis_args["background"]) ax.set_aspect("equal") ax.grid(True, linestyle="--", linewidth=0.5) @@ -217,7 +214,7 @@ def draw(self, vis_args=None, plane="xz"): 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, vis_args) + self._draw_sensors(ax, self.rocket.sensors, plane) plt.title("Rocket Representation") plt.xlim() @@ -386,7 +383,7 @@ 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): @@ -407,7 +404,7 @@ 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 = [] @@ -554,7 +551,7 @@ 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, vis_args): + 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.""" diff --git a/rocketpy/prints/sensors_prints.py b/rocketpy/prints/sensors_prints.py index a454aa0fa..73ab062f8 100644 --- a/rocketpy/prints/sensors_prints.py +++ b/rocketpy/prints/sensors_prints.py @@ -16,17 +16,6 @@ def identity(self): self._print_aligned("Name:", self.sensor.name) self._print_aligned("Type:", self.sensor.__class__.__name__) - def orientation(self): - """Prints the orientation of the sensor.""" - print("\nOrientation:\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 quantization(self): """Prints the quantization of the sensor.""" print("\nQuantization:\n") @@ -115,3 +104,18 @@ def noise(self): "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/parachute.py b/rocketpy/rocket/parachute.py index 7849ad950..4db84dc68 100644 --- a/rocketpy/rocket/parachute.py +++ b/rocketpy/rocket/parachute.py @@ -190,6 +190,7 @@ 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): # work around for having added sensors to parachute triggers diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index e01d017fa..4e3c9e04d 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -4,8 +4,7 @@ from rocketpy.control.controller import _Controller from rocketpy.mathutils.function import Function -from rocketpy.mathutils.vector_matrix import Vector, Matrix -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 diff --git a/rocketpy/sensors/__init__.py b/rocketpy/sensors/__init__.py index 40bac14cc..eb2e20730 100644 --- a/rocketpy/sensors/__init__.py +++ b/rocketpy/sensors/__init__.py @@ -1,4 +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 index bf67c88c1..607b1632e 100644 --- a/rocketpy/sensors/accelerometer.py +++ b/rocketpy/sensors/accelerometer.py @@ -4,6 +4,8 @@ from ..prints.sensors_prints import _InertialSensorPrints from ..sensors.sensor import InertialSensor +# pylint: disable=too-many-arguments + class Accelerometer(InertialSensor): """Class for the accelerometer sensor @@ -208,15 +210,13 @@ def measure(self, time, **kwargs): Derivative of the state vector of the rocket. - relative_position : np.array Position of the sensor relative to the rocket center of mass. - - gravity : float - Gravitational acceleration in m/s^2. - - pressure : Function - Atmospheric pressure profile as a function of altitude in Pa. + - environment : Environment + Environment object containing the atmospheric conditions. """ u = kwargs["u"] u_dot = kwargs["u_dot"] relative_position = kwargs["relative_position"] - gravity = kwargs["gravity"] + gravity = kwargs["environment"].gravity.get_value_opt(u[3]) # Linear acceleration of rocket cdm in inertial frame gravity = ( diff --git a/rocketpy/sensors/barometer.py b/rocketpy/sensors/barometer.py index fbed17f56..4a324faf5 100644 --- a/rocketpy/sensors/barometer.py +++ b/rocketpy/sensors/barometer.py @@ -150,16 +150,12 @@ def measure(self, time, **kwargs): Derivative of the state vector of the rocket. - relative_position : np.array Position of the sensor relative to the rocket center of mass. - - gravity : float - Gravitational acceleration in m/s^2. - - pressure : Function - Atmospheric pressure profile as a function of altitude in Pa. - - elevation : float - Elevation of the launch site in meters. + - environment : Environment + Environment object containing the atmospheric conditions. """ u = kwargs["u"] relative_position = kwargs["relative_position"] - pressure = kwargs["pressure"] + pressure = kwargs["environment"].pressure # Calculate the altitude of the sensor relative_altitude = (Matrix.transformation(u[6:10]) @ relative_position).z 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 index 049cde52d..455dcb449 100644 --- a/rocketpy/sensors/gyroscope.py +++ b/rocketpy/sensors/gyroscope.py @@ -4,6 +4,8 @@ from ..prints.sensors_prints import _GyroscopePrints from ..sensors.sensor import InertialSensor +# pylint: disable=too-many-arguments + class Gyroscope(InertialSensor): """Class for the gyroscope sensor @@ -210,10 +212,8 @@ def measure(self, time, **kwargs): Derivative of the state vector of the rocket. - relative_position : np.array Position of the sensor relative to the rocket center of mass. - - gravity : float - Gravitational acceleration in m/s^2. - - pressure : Function - Atmospheric pressure profile as a function of altitude in Pa. + - environment : Environment + Environment object containing the atmospheric conditions. """ u = kwargs["u"] u_dot = kwargs["u_dot"] diff --git a/rocketpy/sensors/sensor.py b/rocketpy/sensors/sensor.py index 8b0de3b6e..0a1e20bab 100644 --- a/rocketpy/sensors/sensor.py +++ b/rocketpy/sensors/sensor.py @@ -6,6 +6,7 @@ from rocketpy.mathutils.vector_matrix import Matrix, Vector +# pylint: disable=too-many-statements class Sensor(ABC): """Abstract class for sensors @@ -183,27 +184,22 @@ def _save_data_multiple(self, data): @abstractmethod def measure(self, time, **kwargs): """Measure the sensor data at a given time""" - pass @abstractmethod def quantize(self, value): """Quantize the sensor measurement""" - pass @abstractmethod def apply_noise(self, value): """Add noise to the sensor measurement""" - pass @abstractmethod def apply_temperature_drift(self, value): """Apply temperature drift to the sensor measurement""" - pass @abstractmethod def export_measured_data(self, filename, file_format="csv"): """Export the measured values to a file""" - pass def _generic_export_measured_data(self, filename, file_format, data_labels): """Export the measured values to a file given the data labels of each @@ -572,7 +568,7 @@ def apply_temperature_drift(self, value): class ScalarSensor(Sensor): - """Model of a scalar sensor (barometer, GPS, etc.). Scalar sensors are used + """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. diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index 94985aef9..9357bac98 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -664,10 +664,9 @@ 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 ) @@ -717,10 +716,13 @@ def __simulate(self, verbose): 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: @@ -891,7 +893,7 @@ 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" # Check for impact event if self.y_sol[2] < self.env.elevation: @@ -1143,7 +1145,7 @@ 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 @@ -3173,6 +3175,47 @@ 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", @@ -3469,8 +3512,6 @@ def __init__(self, t, derivative=None, callbacks=None, clear=True): self.derivative = derivative self.callbacks = callbacks[:] if callbacks is not None else [] self.clear = clear - self.time_bound = None - self.TimeNodes = None def __repr__(self): name = "None" if self.derivative is None else self.derivative.__name__ @@ -3564,8 +3605,8 @@ def merge(self): # Try to access the node and merge if it exists tmp_dict[time].parachutes += node.parachutes tmp_dict[time].callbacks += node.callbacks - tmp_dict[-1]._component_sensors += node._component_sensors - tmp_dict[-1]._controllers += node._controllers + 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 diff --git a/rocketpy/tools.py b/rocketpy/tools.py index c96dd2364..84f0d910a 100644 --- a/rocketpy/tools.py +++ b/rocketpy/tools.py @@ -353,11 +353,15 @@ 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), ) @@ -928,18 +932,18 @@ def quaternions_to_nutation(e1, e2): return (180 / np.pi) * 2 * np.arcsin(-((e1**2 + e2**2) ** 0.5)) -def euler_to_quaternions(yaw, pitch, roll): +def euler_to_quaternions(roll, pitch, yaw): """Calculates the quaternions (Euler parameters) from the Euler angles in yaw, pitch, and roll sequence (3-2-1). Parameters ---------- - yaw : float - Euler angle due to yaw (phi) in degrees - pitch : float - Euler angle due to pitch (theta) in degrees roll : float Euler angle due to roll (psi) in degrees + pitch : float + Euler angle due to pitch (theta) in degrees + yaw : float + Euler angle due to yaw (phi) in degrees Returns ------- diff --git a/tests/fixtures/rockets/rocket_fixtures.py b/tests/fixtures/rockets/rocket_fixtures.py index 1a3a6194b..dd524645f 100644 --- a/tests/fixtures/rockets/rocket_fixtures.py +++ b/tests/fixtures/rockets/rocket_fixtures.py @@ -253,6 +253,7 @@ def calisto_with_sensors( 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 @@ -272,6 +273,7 @@ def calisto_with_sensors( 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 diff --git a/tests/fixtures/sensors/sensors_fixtures.py b/tests/fixtures/sensors/sensors_fixtures.py index 5f148d00b..1d01a59c3 100644 --- a/tests/fixtures/sensors/sensors_fixtures.py +++ b/tests/fixtures/sensors/sensors_fixtures.py @@ -1,8 +1,8 @@ -import numpy as np import pytest from rocketpy import Accelerometer, Gyroscope from rocketpy.sensors.barometer import Barometer +from rocketpy.sensors.gnss_receiver import GnssReceiver @pytest.fixture @@ -65,6 +65,15 @@ def noisy_barometer(): ) +@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, @@ -117,3 +126,10 @@ def ideal_barometer(): return Barometer( sampling_rate=10, ) + + +@pytest.fixture +def ideal_gnss(): + return GnssReceiver( + sampling_rate=1, + ) diff --git a/tests/test_sensor.py b/tests/integration/test_sensor.py similarity index 66% rename from tests/test_sensor.py rename to tests/integration/test_sensor.py index ba9a32b75..fe099127e 100644 --- a/tests/test_sensor.py +++ b/tests/integration/test_sensor.py @@ -2,11 +2,12 @@ import os 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 @@ -24,6 +25,10 @@ def test_sensor_on_rocket(calisto_with_sensors): 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) def test_ideal_sensors(flight_calisto_with_sensors): @@ -69,6 +74,18 @@ def test_ideal_sensors(flight_calisto_with_sensors): sim_data = flight_calisto_with_sensors.pressure(time) assert np.allclose(pressure, sim_data, atol=1e-12) + gnss = flight_calisto_with_sensors.rocket.sensors[4].component + time, latitude, longitude, altitude = zip(*gnss.measured_data) + latitude = np.array(latitude) + longitude = np.array(longitude) + altitude = np.array(altitude) + sim_latitude = flight_calisto_with_sensors.latitude(time) + sim_longitude = flight_calisto_with_sensors.longitude(time) + sim_altitude = flight_calisto_with_sensors.altitude(time) + assert np.allclose(latitude, sim_latitude, atol=1e-12) + assert np.allclose(longitude, sim_longitude, atol=1e-12) + assert np.allclose(altitude, sim_altitude, atol=1e-12) + def test_export_all_sensors_data(flight_calisto_with_sensors): """Test the export of sensor data. @@ -102,6 +119,10 @@ def test_export_all_sensors_data(flight_calisto_with_sensors): 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 @@ -112,4 +133,34 @@ def test_export_all_sensors_data(flight_calisto_with_sensors): 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 b5516eea3..115a96413 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 diff --git a/tests/unit/test_flight_time_nodes.py b/tests/unit/test_flight_time_nodes.py index 10f6b6c30..446b4523f 100644 --- a/tests/unit/test_flight_time_nodes.py +++ b/tests/unit/test_flight_time_nodes.py @@ -14,7 +14,7 @@ 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 @@ -26,7 +26,7 @@ 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) @@ -35,7 +35,7 @@ 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 @@ -53,9 +53,9 @@ 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 @@ -65,9 +65,9 @@ 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 @@ -80,9 +80,9 @@ 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 @@ -90,14 +90,14 @@ 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 index 186466ccb..54fde52f9 100644 --- a/tests/unit/test_sensor.py +++ b/tests/unit/test_sensor.py @@ -52,6 +52,7 @@ "quantized_gyroscope", "noisy_barometer", "quantized_barometer", + "noisy_gnss", ], ) def test_sensors_prints(sensor, request): @@ -105,9 +106,6 @@ def test_scalar_quantization(quantized_barometer): assert quantized_barometer.quantize(1001) == 1000.96 -import pytest - - @pytest.mark.parametrize( "sensor, input_value, expected_output", [ @@ -156,7 +154,7 @@ def test_quantization(sensor, input_value, expected_output, request): "ideal_gyroscope", ], ) -def test_inertial_measured_data(sensor, request): +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. @@ -168,7 +166,7 @@ def test_inertial_measured_data(sensor, request): u=U, u_dot=U_DOT, relative_position=Vector([0, 0, 0]), - gravity=GRAVITY, + environment=example_plain_env, ) assert len(sensor.measured_data) == 1 sensor.measure( @@ -176,7 +174,7 @@ def test_inertial_measured_data(sensor, request): u=U, u_dot=U_DOT, relative_position=Vector([0, 0, 0]), - gravity=GRAVITY, + environment=example_plain_env, ) assert len(sensor.measured_data) == 2 assert all(isinstance(i, tuple) for i in sensor.measured_data) @@ -192,7 +190,7 @@ def test_inertial_measured_data(sensor, request): u=U, u_dot=U_DOT, relative_position=Vector([0, 0, 0]), - gravity=GRAVITY, + environment=example_plain_env, ) assert len(sensor.measured_data) == 2 assert len(sensor.measured_data[0]) == 3 @@ -202,61 +200,70 @@ def test_inertial_measured_data(sensor, request): u=U, u_dot=U_DOT, relative_position=Vector([0, 0, 0]), - gravity=GRAVITY, + environment=example_plain_env, ) assert len(sensor.measured_data[0]) == 3 assert len(sensor.measured_data[1]) == 3 -def test_scalar_measured_data(ideal_barometer, example_plain_env): +@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 - ideal_barometer.measure( + sensor.measure( t, u=u, relative_position=Vector([0, 0, 0]), - pressure=example_plain_env.pressure, + environment=example_plain_env, ) - assert len(ideal_barometer.measured_data) == 1 - ideal_barometer.measure( + assert len(sensor.measured_data) == 1 + sensor.measure( t, u=u, relative_position=Vector([0, 0, 0]), - pressure=example_plain_env.pressure, + environment=example_plain_env, ) - assert len(ideal_barometer.measured_data) == 2 - assert all(isinstance(i, tuple) for i in ideal_barometer.measured_data) + 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 - ideal_barometer.measured_data = [ - ideal_barometer.measured_data[:], - ideal_barometer.measured_data[:], + sensor.measured_data = [ + sensor.measured_data[:], + sensor.measured_data[:], ] - ideal_barometer._save_data = ideal_barometer._save_data_multiple - ideal_barometer.measure( + sensor._save_data = sensor._save_data_multiple + sensor.measure( t, u=u, relative_position=Vector([0, 0, 0]), - pressure=example_plain_env.pressure, + environment=example_plain_env, ) - assert len(ideal_barometer.measured_data) == 2 - assert len(ideal_barometer.measured_data[0]) == 3 - assert len(ideal_barometer.measured_data[1]) == 2 - ideal_barometer.measure( + 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]), - pressure=example_plain_env.pressure, + environment=example_plain_env, ) - assert len(ideal_barometer.measured_data[0]) == 3 - assert len(ideal_barometer.measured_data[1]) == 3 + assert len(sensor.measured_data[0]) == 3 + assert len(sensor.measured_data[1]) == 3 -def test_noisy_rotated_accelerometer(noisy_rotated_accelerometer): +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)), ...] """ @@ -296,7 +303,7 @@ def test_noisy_rotated_accelerometer(noisy_rotated_accelerometer): u=U, u_dot=U_DOT, relative_position=relative_position, - gravity=GRAVITY, + environment=example_plain_env, ) assert noisy_rotated_accelerometer.measurement == approx([ax, ay, az], rel=0.1) assert len(noisy_rotated_accelerometer.measurement) == 3 @@ -306,7 +313,7 @@ def test_noisy_rotated_accelerometer(noisy_rotated_accelerometer): assert noisy_rotated_accelerometer.measured_data[0][0] == TIME -def test_noisy_rotated_gyroscope(noisy_rotated_gyroscope): +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)), ...] """ @@ -337,7 +344,7 @@ def test_noisy_rotated_gyroscope(noisy_rotated_gyroscope): u=U, u_dot=U_DOT, relative_position=relative_position, - gravity=GRAVITY, + environment=example_plain_env, ) assert noisy_rotated_gyroscope.measurement == approx([wx, wy, wz], rel=0.3) assert len(noisy_rotated_gyroscope.measurement) == 3 @@ -360,22 +367,88 @@ def test_noisy_barometer(noisy_barometer, example_plain_env): time=TIME, u=U, relative_position=relative_position, - pressure=example_plain_env.pressure, + 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, expected_keys", [ ("ideal_accelerometer", "csv", "t,ax,ay,az\n", ("ax", "ay", "az")), - ("ideal_gyroscope", "csv", "t,wx,wy,wz\n", ("wx", "wy", "wz")), ("ideal_accelerometer", "json", None, ("ax", "ay", "az")), + ("ideal_gyroscope", "csv", "t,wx,wy,wz\n", ("wx", "wy", "wz")), ("ideal_gyroscope", "json", None, ("wx", "wy", "wz")), ("ideal_barometer", "csv", "t,pressure\n", ("pressure",)), ("ideal_barometer", "json", None, ("pressure",)), + ( + "ideal_gnss", + "csv", + "t,latitude,longitude,altitude\n", + ("latitude", "longitude", "altitude"), + ), + ("ideal_gnss", "json", None, ("latitude", "longitude", "altitude")), ], ) def test_export_data( @@ -391,16 +464,14 @@ def test_export_data( u=U, u_dot=U_DOT, relative_position=Vector([0, 0, 0]), - gravity=GRAVITY, - pressure=example_plain_env.pressure, + environment=example_plain_env, ) sensor.measure( time=TIME, u=U, u_dot=U_DOT, relative_position=Vector([0, 0, 0]), - gravity=GRAVITY, - pressure=example_plain_env.pressure, + environment=example_plain_env, ) file_name = f"sensors.{file_format}" diff --git a/tests/unit/test_tools.py b/tests/unit/test_tools.py index 3079c5286..4b2f8b14f 100644 --- a/tests/unit/test_tools.py +++ b/tests/unit/test_tools.py @@ -2,8 +2,8 @@ import pytest from rocketpy.tools import ( - euler_to_quaternions, calculate_cubic_hermite_coefficients, + euler_to_quaternions, find_roots_cubic_function, ) @@ -20,54 +20,6 @@ def test_euler_to_quaternions(angles, expected_quaternions): 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 - x = np.array([-3, -2, -1, 0, 1]) - y = np.array([-10, -1, 0, -1, 2]) - - # Selects two points as x0 and x1 - x0, x1 = 0, 1 - y0, y1 = -1, 2 - yp0, yp1 = 0, 7 - - a, b, c, d = calculate_cubic_hermite_coefficients(x0, x1, y0, yp0, y1, yp1) - - assert np.isclose(a, 1) - assert np.isclose(b, 2) - assert np.isclose(c, 0) - assert np.isclose(d, -1) - assert np.allclose( - a * x**3 + b * x**2 + c * x + d, - y, - ) - - -def test_cardanos_root_finding(): - """Tests the find_roots_cubic_function method of the Function class.""" - # Function: f(x) = x**3 + 2x**2 -1 - # roots: (-1 - 5**0.5) / 2; -1; (-1 + 5**0.5) / 2 - - roots = list(find_roots_cubic_function(a=1, b=2, c=0, d=-1)) - roots.sort(key=lambda x: x.real) - - assert np.isclose(roots[0].real, (-1 - 5**0.5) / 2) - assert np.isclose(roots[1].real, -1) - assert np.isclose(roots[2].real, (-1 + 5**0.5) / 2) - - assert np.isclose(roots[0].imag, 0) - assert np.isclose(roots[1].imag, 0) - assert np.isclose(roots[2].imag, 0) - - -import numpy as np - -from rocketpy.tools import ( - calculate_cubic_hermite_coefficients, - find_roots_cubic_function, -) - - 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 5d9e4b3f6..c2b5f0148 100644 --- a/tests/unit/test_tools_matrix.py +++ b/tests/unit/test_tools_matrix.py @@ -245,9 +245,9 @@ def test_matrix_transformation(): def test_matrix_transformation_euler_angles(): - phi = 90 + phi = 0 theta = 0 - psi = 0 + psi = 90 matrix = Matrix.transformation_euler_angles(phi, theta, psi) matrix = matrix.round(12) # Check that the matrix is orthogonal