transforms84

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Python library for geographic system transformations with additional helper functions.

This package focuses on:

Performance
Support for different number of inputs:
- Ideal array shape of (3,1) and (nPoints,3,1) (as well as (3,) and (nPoints,3))
- Separate input for each axis in the coordinate system of size (nPoints,)
Support for different inputs types:
- NumPy ndarray
- Pandas Series
- List
- Float/int
Functions that adapt to differing input matrices shapes: one-to-one, many-to-many and one-to-many points. See below for an example.

Installation

pip install transforms84

transforms84 is supported on Windows, Linux and MacOS.

Operations

Coordinate Transformations

The following coordinate transformations have been implemented:

geodetic → ECEF 🔗
ECEF → geodetic 🔗
ECEF → ENU 🔗
ENU → ECEF 🔗
ENU → AER 🔗
AER → ENU 🔗
ECEF → NED 🔗 🔗
NED → ECEF 🔗 🔗
NED → AER 🔗 🔗
AER → NED 🔗 🔗
geodetic → UTM 🔗
UTM → geodetic 🔗

Velocity Transformations

The following velocity transformations have been implemented:

ECEF → NED
NED → ECEF
ENU → ECEF
ECEF → ENU

Distances

The following distance formulae have been implemented:

Haversine 🔗

Additional Functions

The following functions have been implemented:

Angular difference (smallest and largest)
[rad, rad, X] → [deg, deg, X]
[deg, deg, X] → [rad, rad, X]

Examples

See the Jupyter notebooks in examples to see how to use the transform84. Run pip install transforms84[examples] to run the examples locally.

Many-to-many & one-to-many

The transforms.ECEF2ENU transformation accepts same and differing matrix shape sizes. Below showcases the many-to-many method where three target points, rrm_target, in the geodetic coordinate system are transformed to the local ENU coordinate system about the point rrm_local, where both matrices are of shape (3, 3, 1):

>>> import numpy as np
>>> from transforms84.systems import WGS84
>>> from transforms84.helpers import DDM2RRM
>>> from transforms84.transforms import ECEF2ENU, geodetic2ECEF
>>
>>> rrm_local = DDM2RRM(
>>>     np.array(
>>>         [[[30], [31], [0]], [[30], [31], [0]], [[30], [31], [0]]], dtype=np.float64
>>>     )
>>> )  # convert each point from [deg, deg, X] to [rad, rad, X]
>>> rrm_target = DDM2RRM(
>>>     np.array(
>>>         [[[31], [32], [0]], [[31], [32], [0]], [[31], [32], [0]]], dtype=np.float64
>>>     )
>>> )
>>> ECEF2ENU(
>>>     rrm_local, geodetic2ECEF(rrm_target, WGS84.a, WGS84.b), WGS84.a, WGS84.b
>>> )  # geodetic2ECEF -> ECEF2ENU
array(
    [
        [[95499.41373564], [111272.00245298], [-1689.19916788]],
        [[95499.41373564], [111272.00245298], [-1689.19916788]],
        [[95499.41373564], [111272.00245298], [-1689.19916788]],
    ]
)

We can achieve the same result using the one-to-many method with a single local point of shape (3, 1):

>>> rrm_local_one_point = DDM2RRM(np.array([[30], [31], [0]], dtype=np.float64))
>>> ECEF2ENU(rrm_local_one_point, geodetic2ECEF(rrm_target, WGS84.a, WGS84.b), WGS84.a, WGS84.b)
array(
    [
        [[95499.41373564], [111272.00245298], [-1689.19916788]],
        [[95499.41373564], [111272.00245298], [-1689.19916788]],
        [[95499.41373564], [111272.00245298], [-1689.19916788]],
    ]
)

Again, we can achieve the same result by splitting the arrays over each coordiante system axis:

>>> import pandas as pd
>>> df = pd.DataFrame(
>>>    {
>>>        "radLatTarget": rrm_target[:, 0, 0],
>>>        "radLonTarget": rrm_target[:, 1, 0],
>>>        "mAltTarget": rrm_target[:, 2, 0],
>>>    }
>>> )
>>> df["e"], df["n"], df["u"] = ECEF2ENU(
>>>    np.deg2rad(30),
>>>    np.deg2rad(31),
>>>    0,
>>>    *geodetic2ECEF(
>>>        df["radLatTarget"],
>>>        df["radLonTarget"],
>>>        df["mAltTarget"],
>>>        WGS84.a,
>>>        WGS84.b,
>>>    ),
>>>    WGS84.a,
>>>    WGS84.b,
>>> )
>>> df[["e", "n", "u"]]
              e              n            u
0  95499.413736  111272.002453 -1689.199168
1  95499.413736  111272.002453 -1689.199168
2  95499.413736  111272.002453 -1689.199168

World Geodetic Systems Standards

transforms84.systems includes the WGS84 class, which is the WGS 84 version of the standard. Other standards can be created:

>>> from transforms84.systems import WGS, WGS72
>>> WGS72 == WGS(6378135.0, 6356750.520016094)
True

Helpful Resources

...in no particular order:

Contributing

PRs are always welcome and appreciated!

After forking the repo install the dev requirements: pip install -e .[dev].

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transforms84 1.3.1

transforms84

Installation

Operations

Coordinate Transformations

Velocity Transformations

Distances

Additional Functions

Examples

Many-to-many & one-to-many

World Geodetic Systems Standards

Helpful Resources

Contributing

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1 maintainer

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BLAKE2b-256	`667da89672dfe5a804f5d60defe5cbfa64c27d79791bcc039958ee798607f113`

Algorithm	Hash digest
SHA256	`6e6ac2773396e923fa3af5cc647c96874afdea436a6d49cb53ed43ff8648a72f`
MD5	`dbdfbbccc6cb66a84b5953701406ae76`
BLAKE2b-256	`f76079ef828d5ccb803e965ed07331327a5d5a66f23d38ebe53037b2ed3b87bc`

Algorithm	Hash digest
SHA256	`7fab2d6529a35252c3ac6162649e1bd1fda9c169939ef0157b0414fe9d9a3115`
MD5	`0168c7ab91a82e915e9ce23e23644622`
BLAKE2b-256	`e50c87a860cb6af1ec7d833b4ea3494d4d9e4c5cedf653772b106474bb470200`