import numpy as np
from ..mathutils.vector_matrix import Matrix
from ..prints.sensors_prints import _SensorPrints
from ..sensors.sensor import ScalarSensor
[docs]
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"
[docs]
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)
[docs]
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))
[docs]
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"),
)
@classmethod
def from_dict(cls, data):
return cls(
sampling_rate=data["sampling_rate"],
measurement_range=data["measurement_range"],
resolution=data["resolution"],
noise_density=data["noise_density"],
noise_variance=data["noise_variance"],
random_walk_density=data["random_walk_density"],
random_walk_variance=data["random_walk_variance"],
constant_bias=data["constant_bias"],
operating_temperature=data["operating_temperature"],
temperature_bias=data["temperature_bias"],
temperature_scale_factor=data["temperature_scale_factor"],
name=data["name"],
)
