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| author | CoprDistGit <infra@openeuler.org> | 2023-05-17 04:01:11 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-17 04:01:11 +0000 |
| commit | 6d3c4fe194963f88f4e556ebef9677d62c9905e4 (patch) | |
| tree | 5b178859808f030e44493922c5ef07fef536e944 /python-ahrs.spec | |
| parent | fdc08e1c5806974b576a50ea2fa14ae3e109a863 (diff) | |
automatic import of python-ahrs
Diffstat (limited to 'python-ahrs.spec')
| -rw-r--r-- | python-ahrs.spec | 838 |
1 files changed, 838 insertions, 0 deletions
diff --git a/python-ahrs.spec b/python-ahrs.spec new file mode 100644 index 0000000..afd27f6 --- /dev/null +++ b/python-ahrs.spec @@ -0,0 +1,838 @@ +%global _empty_manifest_terminate_build 0 +Name: python-AHRS +Version: 0.3.1 +Release: 1 +Summary: Attitude and Heading Reference Systems. +License: MIT License +URL: https://github.com/Mayitzin/ahrs/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/66/56/7fc17b6c330eff8986e01dfd3744154ffa45cf4ccd45fd779dc6e2713ec1/AHRS-0.3.1.tar.gz +BuildArch: noarch + +Requires: python3-numpy + +%description +# AHRS: Attitude and Heading Reference Systems + + + + + + + + + +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 + +- 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 °, 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 10° 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 = 50° N +9.810786421572386 +>>> ahrs.utils.welmec_gravity(50.0, 500.0) # Latitude = 50° 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]) +``` + +- [Type hints](https://www.python.org/dev/peps/pep-0484/) are added. +- NumPy is now the only third-party dependency. +- 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 with the corresponding articles describing them. They have in-code references to the 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 | YES | 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. + + + + +%package -n python3-AHRS +Summary: Attitude and Heading Reference Systems. +Provides: python-AHRS +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-AHRS +# AHRS: Attitude and Heading Reference Systems + + + + + + + + + +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 + +- 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 °, 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 10° 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 = 50° N +9.810786421572386 +>>> ahrs.utils.welmec_gravity(50.0, 500.0) # Latitude = 50° 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]) +``` + +- [Type hints](https://www.python.org/dev/peps/pep-0484/) are added. +- NumPy is now the only third-party dependency. +- 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 with the corresponding articles describing them. They have in-code references to the 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 | YES | 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. + + + + +%package help +Summary: Development documents and examples for AHRS +Provides: python3-AHRS-doc +%description help +# AHRS: Attitude and Heading Reference Systems + + + + + + + + + +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 + +- 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 °, 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 10° 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 = 50° N +9.810786421572386 +>>> ahrs.utils.welmec_gravity(50.0, 500.0) # Latitude = 50° 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]) +``` + +- [Type hints](https://www.python.org/dev/peps/pep-0484/) are added. +- NumPy is now the only third-party dependency. +- 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 with the corresponding articles describing them. They have in-code references to the 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 | YES | 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. + + + + +%prep +%autosetup -n AHRS-0.3.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-AHRS -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.1-1 +- Package Spec generated |