| Name | aurora JSON |
| Version |
0.5.2
JSON |
| download |
| home_page | None |
| Summary | Processing Codes for Magnetotelluric Data |
| upload_time | 2025-08-03 00:41:30 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT license |
| keywords |
aurora
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
.. image:: docs/figures/aurora_logo.png
:width: 900
:alt: AURORA
|
.. image:: https://img.shields.io/pypi/v/aurora.svg
:target: https://pypi.python.org/pypi/aurora
.. image:: https://img.shields.io/conda/v/conda-forge/aurora.svg
:target: https://anaconda.org/conda-forge/aurora
.. image:: https://img.shields.io/pypi/l/aurora.svg
:target: https://pypi.python.org/pypi/aurora
Aurora is an open-source package that robustly estimates single station and remote reference electromagnetic transfer functions (TFs) from magnetotelluric (MT) time series. Aurora is part of an open-source processing workflow that leverages the self-describing data container `MTH5 <https://github.com/kujaku11/mth5>`_, which in turn leverages the general `mt-metadata <https://github.com/kujaku11/mth5>`_ framework to manage metadata. These pre-existing packages simplify the processing by providing managed data structures, transfer functions to be generated with only a few lines of code. The processing depends on two inputs -- a table defining the data to use for TF estimation, and a JSON file specifying the processing parameters, both of which are generated automatically, and can be modified if desired. Output TFs are returned as mt-metadata objects, and can be exported to a variety of common formats for plotting, modeling and inversion.
Key Features
-------------
- Tabular data indexing and management (Pandas dataframes),
- Dictionary-like processing parameters configuration
- Programmatic or manual editing of inputs
- Largely automated workflow
Documentation for the Aurora project can be found at http://simpeg.xyz/aurora/
Installation
---------------
Suggest using PyPi as the default repository to install from
``pip install aurora``
Can use Conda but that is not updated as often
``conda -c conda-forge install aurora``
General Work Flow
-------------------
1. Convert raw time series data to MTH5 format, see `MTH5 Documentation and Examples <https://mth5.readthedocs.io/en/latest/index.html>`_.
2. Understand the time series data and which runs to process for local station `RunSummary`.
3. Choose remote reference station ``KernelDataset``.
4. Create a recipe for how the data will be processed ``Config``.
5. Estimate transfer function `process_mth5` and out put as a ``mt_metadata.transfer_function.core.TF`` object which can output [ EMTFXML | EDI | ZMM | ZSS | ZRR ] files.
Raw data
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"description": ".. image:: docs/figures/aurora_logo.png\n :width: 900\n :alt: AURORA\n\n|\n\n.. image:: https://img.shields.io/pypi/v/aurora.svg\n :target: https://pypi.python.org/pypi/aurora\n\n.. image:: https://img.shields.io/conda/v/conda-forge/aurora.svg\n :target: https://anaconda.org/conda-forge/aurora\n\n.. image:: https://img.shields.io/pypi/l/aurora.svg\n :target: https://pypi.python.org/pypi/aurora\n\nAurora is an open-source package that robustly estimates single station and remote reference electromagnetic transfer functions (TFs) from magnetotelluric (MT) time series. Aurora is part of an open-source processing workflow that leverages the self-describing data container `MTH5 <https://github.com/kujaku11/mth5>`_, which in turn leverages the general `mt-metadata <https://github.com/kujaku11/mth5>`_ framework to manage metadata. These pre-existing packages simplify the processing by providing managed data structures, transfer functions to be generated with only a few lines of code. The processing depends on two inputs -- a table defining the data to use for TF estimation, and a JSON file specifying the processing parameters, both of which are generated automatically, and can be modified if desired. Output TFs are returned as mt-metadata objects, and can be exported to a variety of common formats for plotting, modeling and inversion. \n\nKey Features\n-------------\n\n- Tabular data indexing and management (Pandas dataframes), \n- Dictionary-like processing parameters configuration\n- Programmatic or manual editing of inputs\n- Largely automated workflow \n\nDocumentation for the Aurora project can be found at http://simpeg.xyz/aurora/\n\nInstallation\n---------------\n\nSuggest using PyPi as the default repository to install from\n\n``pip install aurora``\n\nCan use Conda but that is not updated as often\n\n``conda -c conda-forge install aurora``\n\nGeneral Work Flow\n-------------------\n\n1. Convert raw time series data to MTH5 format, see `MTH5 Documentation and Examples <https://mth5.readthedocs.io/en/latest/index.html>`_.\n2. Understand the time series data and which runs to process for local station `RunSummary`.\n3. Choose remote reference station ``KernelDataset``.\n4. Create a recipe for how the data will be processed ``Config``.\n5. Estimate transfer function `process_mth5` and out put as a ``mt_metadata.transfer_function.core.TF`` object which can output [ EMTFXML | EDI | ZMM | ZSS | ZRR ] files. \n\n\n",
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