Merge pull request #330 from simpeg/patches

Patches

.bumpversion.cfg

@@ -1,3 +1,3 @@
 [bumpversion]
-current_version = 0.3.13
+current_version = 0.3.14
 files = setup.py aurora/__init__.py

.github/workflows/tests.yml

@@ -22,7 +22,7 @@ jobs:
 #        python-version: ["3.10", ] 
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
 
     - name: Setup Miniconda
       uses: conda-incubator/setup-miniconda@v2.1.1
@@ -37,6 +37,7 @@ jobs:
         pip install -r requirements-dev.txt
         pip install git+https://github.com/kujaku11/mt_metadata.git@main
         pip install git+https://github.com/kujaku11/mth5.git@master
+        pip install git+https://github.com/MTgeophysics/mtpy-v2.git@main        
 
     - name: Install Our Package
       run: |
@@ -53,12 +54,12 @@ jobs:
     - name: Execute Jupyter Notebooks
       run: |
         jupyter nbconvert --to notebook --execute docs/examples/dataset_definition.ipynb
-        jupyter nbconvert --to notebook --execute docs/examples/make_cas04_single_station_h5.ipynb
         jupyter nbconvert --to notebook --execute docs/examples/operate_aurora.ipynb
-        jupyter nbconvert --to notebook --execute tests/test_run_on_commit.ipynb
         jupyter nbconvert --to notebook --execute docs/tutorials/pole_zero_fitting/lemi_pole_zero_fitting_example.ipynb
         jupyter nbconvert --to notebook --execute docs/tutorials/processing_configuration.ipynb
+        jupyter nbconvert --to notebook --execute docs/tutorials/process_cas04_single_station.ipynb
         jupyter nbconvert --to notebook --execute docs/tutorials/synthetic_data_processing.ipynb
+        jupyter nbconvert --to notebook --execute tests/test_run_on_commit.ipynb
         # Replace "notebook.ipynb" with your notebook's filename
 
 #    - name: Commit changes (if any)
@@ -72,13 +73,17 @@ jobs:
 
     - name: Run Tests
       run: |
-        # pytest -s -v tests/synthetic/test_fourier_coefficients.py --cov=./ --cov-report=xml --cov=aurora
+        # pytest -s -v tests/synthetic/test_fourier_coefficients.py
+        # pytest -s -v tests/test_general_helper_functions.py 
         pytest -s -v --cov=./ --cov-report=xml --cov=aurora
 
-    - name: "Upload coverage to Codecov"
-      uses: codecov/codecov-action@v1
+    - name: "Upload coverage reports to Codecov"
+      uses: codecov/codecov-action@v4
       with:
+        CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
         fail_ci_if_error: false
+        flags: tests
+        # token: ${{ secrets.CODECOV_TOKEN }}
 
     - name: Build Doc
       if: ${{ (github.ref == 'refs/heads/main') && (matrix.python-version == '3.8')}}

.gitignore

@@ -1,9 +1,17 @@
 .idea
 data/figures
+data/parkfield/*png
 data/parkfield/aurora_results/
 data/parkfield/*h5
 data/synthetic/aurora_results/
 data/synthetic/config/
+data/synthetic/mth5/
+docs/tutorials/*h5
+docs/tutorials/CAS04*edi
+docs/tutorials/CAS04*zrr
+docs/tutorials/CAS04*xml
+docs/tutorials/CAS04*png
+docs/tutorials/config.json
 tests/io/from_matlab.zss
 *ignore*
 *fix_issue*
@@ -27,6 +35,9 @@ tests/synthetic/data/*h5
 tests/synthetic/test2*csv
 tests/synthetic/out.png
 
+*.h5
+*.xml
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[codx]

CITATION.cff

@@ -0,0 +1,92 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: aurora
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Karl
+    name-particle: N.
+    family-names: Kappler
+    orcid: 'https://orcid.org/0000-0002-1877-1255'
+  - given-names: Jared
+    name-particle: R.
+    family-names: Peacock
+    orcid: 'https://orcid.org/0000-0002-0439-0224'
+  - given-names: Gary
+    name-particle: D.
+    family-names: Egbert
+    orcid: 'https://orcid.org/0000-0003-1276-8538'
+  - orcid: 'https://orcid.org/0000-0002-8818-3731'
+    given-names: Frassetto
+    family-names: Andrew
+  - given-names: Heagy
+    family-names: Lindsey
+    orcid: 'https://orcid.org/0000-0002-1551-5926'
+  - given-names: Kelbert
+    family-names: Anna
+    orcid: 'https://orcid.org/0000-0003-4395-398X'
+  - given-names: Laura
+    family-names: Keyson
+  - given-names: Douglas
+    family-names: Oldenburg
+    orcid: 'https://orcid.org/0000-0002-4327-2124'
+  - given-names: Timothy
+    family-names: Ronan
+    orcid: 'https://orcid.org/0000-0001-8450-9573'
+  - given-names: Justin
+    family-names: Sweet
+    orcid: 'https://orcid.org/0000-0001-7323-9758'
+identifiers:
+  - type: doi
+    value: 10.5281/zenodo.13334589
+    description: >-
+      Contains the software at time of manuscript
+      acceptance.
+  - type: doi
+    value: 10.21105/joss.06832
+    description: The JOSS manuscript
+repository-code: 'https://github.com/simpeg/aurora'
+url: 'https://simpeg.xyz/aurora/'
+abstract: >-
+  The Aurora software package robustly estimates single
+  station and remote reference electro-
+
+  magnetic 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,
+
+  which in turn leverages the general mt_metadata framework
+  to manage metadata. These
+
+  pre-existing packages simplify the processing workflow 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.
+keywords:
+  - open-source
+  - python
+  - 'magnetotelluric '
+  - processing
+  - transfer function
+license: MIT
+commit: abcdefgh
+version: 0.3.14
+date-released: '2024-08-28'

CONTRIBUTING.rst

@@ -0,0 +1,14 @@
+Contributing to Aurora
+----------------------
+
+
+If you'd like to contribute, start by searching through the `issues <https://github.com/simpeg/aurora/issues>`_ to see whether someone else has raised a similar idea or question.  If you don't see your idea listed, open an issue, and if there are code changes, also a pull request.
+
+
+There are many ways to contribute to the aurora, such as:
+
+* Report/fix bugs or issues encountered when using the software
+* Suggest additional features or functionalities
+* Fix editorial inconsistencies or inaccuracies
+
+Aurora is hosted by simpeg, please refer to their  `contributing guidelines page <https://docs.simpeg.xyz/latest/content/getting_started/contributing/index.html>`_. for more details.

README.rst

@@ -1,11 +1,9 @@
-.. image:: ../docs/figures/aurora_logo.png
+.. 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
 
@@ -15,6 +13,36 @@
 .. image:: https://img.shields.io/pypi/l/aurora.svg
     :target: https://pypi.python.org/pypi/aurora
 
-Aurora is a Python library for processing natural source electromagnetic data. It uses MTH5 formatted Magnetotelluric data and a configuration file as inputs and generates transfer functions that can be formatted as EMTF XML or other output formats. 
+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. 
+
+

aurora/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.3.13"
+__version__ = "0.3.14"
 
 import sys
 from loguru import logger