Metadata-Version: 2.1
Name: AHRS
Version: 0.3.0
Summary: Attitude and Heading Reference Systems.
Home-page: https://github.com/Mayitzin/ahrs/
Author: Mario Garcia
Author-email: mariogc@protonmail.com
License: UNKNOWN
Download-URL: https://github.com/Mayitzin/ahrs/archive/master/ahrs-master.zip
Project-URL: Source Code, https://github.com/Mayitzin/ahrs/
Project-URL: Bug Tracker, https://github.com/Mayitzin/ahrs/issues
Description: # AHRS: Attitude and Heading Reference Systems
        
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        AHRS is a collection of functions and algorithms in pure Python used to estimate the orientation of mobile systems.
        
        Orginally, an [AHRS](https://en.wikipedia.org/wiki/Attitude_and_heading_reference_system) is a set of orthogonal sensors providing attitude information about an aircraft. This field has now expanded to smaller devices, like wearables, automated transportation and all kinds of systems in motion.
        
        This package's focus is **fast prototyping**, **education**, **testing** and **modularity**. Performance is _NOT_ the main goal. For optimized implementations there are endless resources in C/C++ or Fortran.
        
        AHRS is compatible with **Python 3.6** and newer.
        
        ## Installation
        
        The most recommended method is to install AHRS directly from this repository to get the latest version:
        
        ```shell
        git clone https://github.com/Mayitzin/ahrs.git
        cd ahrs
        python setup.py install
        ```
        
        Or using [pip](https://pip.pypa.io) for the stable releases:
        
        ```shell
        pip install ahrs
        ```
        
        AHRS depends on common packages [NumPy](https://numpy.org/) and [SciPy](https://www.scipy.org/). More packages are avoided, to reduce its third-party dependency.
        
        ## New in 0.3 (release candidate)
        
        - [Type hints](https://www.python.org/dev/peps/pep-0484/) are added.
        - NumPy is now the only third-party dependency.
        - The **World Magnetic Model** ([WMM](https://www.ngdc.noaa.gov/geomag/WMM/DoDWMM.shtml)) is fully implemented. It can be used to estimate all magnetic field elements on any given place of Earth for dates between 2015 and 2025.
        
        ```python
        >>> from ahrs.utils import WMM
        >>> wmm = WMM(latitude=10.0, longitude=-20.0, height=10.5)
        >>> wmm.magnetic_elements
        {'X': 30499.640469609083, 'Y': -5230.267158472566, 'Z': -1716.633311360368,
        'H': 30944.850352270452, 'F': 30992.427998627096, 'I': -3.1751692563622993,
        'D': -9.73078560629778, 'GV': -9.73078560629778}
        ```
        
        - The _ellipsoid model_ of the **World Geodetic System** ([WGS84](https://earth-info.nga.mil/GandG/update/index.php?dir=wgs84&action=wgs84)) is included. A full implementation of the **Earth Gravitational Model** ([EGM2008](https://earth-info.nga.mil/GandG/wgs84/gravitymod/egm2008/egm08_wgs84.html)) is _NOT_ available here, but the estimation of the main and derived parameters of the WGS84 using the ellipsoid model are implemented:
        
        ```python
        >>> from ahrs.utils import WGS
        >>> wgs = WGS()      # Creates an ellipsoid model, using Earth's characteristics by default
        >>> wgs_properties = [x for x in dir(wgs) if not (hasattr(wgs.__getattribute__(x), '__call__') or x.startswith('__'))]
        >>> for p in wgs_properties:
        ...     print('{:<{w}}  {}'.format(p, wgs.__getattribute__(p), w=len(max(wgs_properties, key=len))))
        ...
        a                                          6378137.0
        arithmetic_mean_radius                     6371008.771415059
        aspect_ratio                               0.9966471893352525
        atmosphere_gravitational_constant          343591934.4
        authalic_sphere_radius                     6371007.1809182055
        b                                          6356752.314245179
        curvature_polar_radius                     6399593.625758493
        dynamic_inertial_moment_about_X            8.007921777277886e+37
        dynamic_inertial_moment_about_Y            8.008074799852911e+37
        dynamic_inertial_moment_about_Z            8.03430094201443e+37
        dynamical_form_factor                      0.0010826298213129219
        equatorial_normal_gravity                  9.78032533590406
        equivolumetric_sphere_radius               6371000.790009159
        f                                          0.0033528106647474805
        first_eccentricity_squared                 0.0066943799901413165
        geometric_dynamic_ellipticity              0.003258100628533992
        geometric_inertial_moment                  8.046726628049449e+37
        geometric_inertial_moment_about_Z          8.073029370114392e+37
        gm                                         398600441800000.0
        gravitational_constant_without_atmosphere  398600098208065.6
        is_geodetic                                True
        linear_eccentricity                        521854.00842338527
        mass                                       5.972186390142457e+24
        mean_normal_gravity                        9.797643222256516
        normal_gravity_constant                    0.0034497865068408447
        normal_gravity_potential                   62636851.71456948
        polar_normal_gravity                       9.832184937863065
        second_degree_zonal_harmonic               -0.00048416677498482876
        second_eccentricity_squared                0.006739496742276434
        w                                          7.292115e-05
        ```
        
        It can be used, for example, to estimate the normal gravity acceleration (in m/s^2) at any location on Earth.
        
        ```python
        >>> wgs.normal_gravity(50.0, 1000.0)    # Normal gravity at latitude = 50.0 Â°, 1000 m above surface
        9.807617683884756
        ```
        
        Setting the fundamental parameters (`a`, `f`, `GM`, `w`) yields a different ellipsoid. For the moon, for instance, we build a new model:
        
        ```python
        >>> moon_a = ahrs.MOON_EQUATOR_RADIUS
        >>> moon_f = (ahrs.MOON_EQUATOR_RADIUS-ahrs.MOON_POLAR_RADIUS)/ahrs.MOON_EQUATOR_RADIUS
        >>> moon_gm = ahrs.MOON_GM
        >>> moon_w = ahrs.MOON_ROTATION
        >>> moon = WGS(a=moon_a, f=moon_f, GM=moon_gm, w=moon_w)
        >>> moon.normal_gravity(10.0, h=500.0)  # Gravity on moon at 10Â° N and 500 m above surface
        1.6239259827292798
        >>> moon.is_geodetic     # Only the Earth is geodetic
        False
        ```
        
        - The [International Gravity Formula](http://earth.geology.yale.edu/~ajs/1945A/360.pdf) and the EU's [WELMEC](https://www.welmec.org/documents/guides/2/) normal gravity reference system are also implemented.
        
        ```python
        >>> ahrs.utils.international_gravity(50.0)       # Latitude = 50Â° N
        9.810786421572386
        >>> ahrs.utils.welmec_gravity(50.0, 500.0)       # Latitude = 50Â° N,   height above sea = 500 m
        9.809152687885897
        ```
        
        - New class `DCM` (derived from `numpy.ndarray`) for orientation/rotation representations as 3x3 Direction Cosine Matrices.
        
        ```python
        >>> from ahrs import DCM
        >>> R = DCM(x=10.0, y=20.0, z=30.0)
        >>> type(R)
        <class 'ahrs.common.dcm.DCM'>
        >>> R.view()
        DCM([[ 0.81379768 -0.46984631  0.34202014],
             [ 0.54383814  0.82317294 -0.16317591],
             [-0.20487413  0.31879578  0.92541658]])
        >>> R.inv     # or R.I
        array([[ 0.81379768  0.54383814 -0.20487413]
               [-0.46984631  0.82317294  0.31879578]
               [ 0.34202014 -0.16317591  0.92541658]])
        >>> R.log
        array([0.26026043, 0.29531805, 0.5473806 ])
        >>> R.to_axisangle()        # Axis in 3D NumPy array, and angle as radians
        (array([0.38601658, 0.43801381, 0.81187135]), 0.6742208510527136)
        >>> R.to_quaternion()
        array([0.94371436, 0.12767944, 0.14487813, 0.26853582])
        >>> R.to_quaternion(method='itzhack', version=2)
        array([ 0.94371436, -0.12767944, -0.14487813, -0.26853582])
        ```
        
        - New class `QuaternionArray` to simultaneously handle an array with more quaternions at once.
        
        ```python
        >>> Q = QuaternionArray(np.random.random((3, 4))-0.5)
        >>> Q.view()
        QuaternionArray([[ 0.31638467,  0.59313477, -0.62538687, -0.39621099],
                         [ 0.24973118, -0.37958194, -0.67851278, -0.57721079],
                         [-0.44643469,  0.17200957, -0.72678553,  0.49284031]])
        >>> Q.w
        array([ 0.31638467,  0.24973118, -0.44643469])
        >>> Q.to_DCM()
        array([[[-0.09618377, -0.49116723, -0.86573866],
                [-0.99258756, -0.017584  ,  0.1202528 ],
                [-0.07428738,  0.8708878 , -0.48583519]],
        
               [[-0.58710377,  0.80339746,  0.09930598],
                [ 0.22680733,  0.04549051,  0.97287669],
                [ 0.77708918,  0.5937029 , -0.20892408]],
        
               [[-0.54221755,  0.19001389,  0.81847104],
                [-0.69007015,  0.45504228, -0.56279633],
                [-0.47937805, -0.86996048, -0.115609  ]]])
        >>> Q.conjugate()
        array([[ 0.31638467, -0.59313477,  0.62538687,  0.39621099],
               [ 0.24973118,  0.37958194,  0.67851278,  0.57721079],
               [-0.44643469, -0.17200957,  0.72678553, -0.49284031]])
        >>> Q.average()
        array([ 0.19537239,  0.17826049, -0.87872408, -0.39736232])
        ```
        
        - New submodule `frames` to represent the position of an object in different reference frames.
        - [Metrics](https://ahrs.readthedocs.io/en/latest/metrics.html) for rotations in 3D spaces using quaternions and direction cosine matrices.
        - New operations, properties and methods for class `Quaternion` (now also derived from `numpy.ndarray`)
        - A whole bunch of [new constant values](https://ahrs.readthedocs.io/en/latest/constants.html) (mainly for Geodesy) accessed from the top level of the package.
        - Docstrings are improved with further explanations, references and equations whenever possible.
        
        ## More Attitude Estimators
        
        One of the biggest improvements in this version is the addition of many new attitude estimation algorithms.
        
        All estimators are refactored to be consistent to the original articles describing them. They have in-code references to the original equations, so that you can follow the original articles along with the code.
        
        Implemented attitude estimators are:
        
        | Algorithm     | Gyroscope | Accelerometer | Magnetometer |
        |---------------|:---------:|:-------------:|:------------:|
        | AQUA          | YES       | YES           | Optional     |
        | Complementary | YES       | YES           | Optional     |
        | Davenport's   | NO        | YES           | YES          |
        | EKF           | YES       | YES           | YES          |
        | FAMC          | NO        | YES           | YES          |
        | FLAE          | NO        | YES           | YES          |
        | Fourati       | YES       | YES           | YES          |
        | FQA           | NO        | YES           | Optional     |
        | Integration   | YES       | NO            | NO           |
        | Madgwick      | YES       | YES           | Optional     |
        | Mahony        | YES       | YES           | Optional     |
        | OLEQ          | NO        | YES           | YES          |
        | QUEST         | NO        | YES           | YES          |
        | ROLEQ         | NO        | YES           | YES          |
        | SAAM          | NO        | YES           | YES          |
        | Tilt          | NO        | YES           | Optional     |
        | TRIAD         | NO        | YES           | YES          |
        
        More Estimators are still a *Work In Progress*, or *planned* to be added in the future.
        
        | Algorithm | Gyroscope | Accelerometer | Magnetometer | Status  |
        |-----------|:---------:|:-------------:|:------------:|:-------:|
        | ESOQ      | NO        | YES           | YES          | WIP     |
        | ESOQ-2    | NO        | YES           | YES          | WIP     |
        | FKF       | NO        | YES           | YES          | WIP     |
        | FCF       | NO        | YES           | YES          | Planned |
        | FOAM      | NO        | YES           | YES          | Planned |
        | GDA-LKF   | YES       | YES           | YES          | Planned |
        | MAGYQ     | YES       | YES           | YES          | Planned |
        | QRAUKF    | YES       | YES           | YES          | Planned |
        | REQUEST   | NO        | YES           | YES          | Planned |
        | Sabatini  | YES       | YES           | YES          | Planned |
        | SOLEQ     | NO        | YES           | YES          | Planned |
        
        To use the sensor data to estimate the attitude simply pass the data to a desired estimator, and it will automatically estimate the quaternions with the given parameters.
        
        ```python
        >>> attitude = ahrs.filters.Madgwick(acc=acc_data, gyr=gyro_data)
        >>> attitude.Q.shape
        (6959, 4)
        ```
        
        Some algorithms allow a finer tuning of its estimation with different parameters. Check their documentation to see what can be tuned.
        
        ```python
        >>> attitude = ahrs.filters.Madgwick(acc=acc_data, gyr=gyro_data, mag=mag_data, gain=0.1, frequency=100.0)
        ```
        
        Speaking of documentation...
        
        ## Documentation
        
        A comprehensive documentation, with examples, is now available in
        [Read the Docs](https://ahrs.readthedocs.io).
        
        ## Note for future versions
        
        `ahrs` moves away from plotting and data handling submodules to better focus in the algorithmic parts. Submodules `io` and `plot` are not built in the package anymore and, eventually, will be entirely removed from the base code.
        
        This way you can also choose your favorite libraries for data loading and visualization. This also means, getting rid of its dependency on `matplotlib` too.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Embedded Systems
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
