RC v0.7.0

.github/pull_request_template.md

@@ -38,6 +38,7 @@ import ocbpy
 - [ ] Make sure you are merging into the ``develop`` (not ``main``) branch
 - [ ] My commits are formatted appropriately (following the SciPy/NumPy style) 
 - [ ] My code follows the style guidelines of this project
+- [ ] I assert that I have not used AI in the development of this pull request
 - [ ] I have performed a self-review of my own code
 - [ ] I have commented my code, particularly in hard-to-understand areas
 - [ ] I have made corresponding changes to the documentation

.github/workflows/docs.yml

@@ -15,13 +15,13 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.11"]
+        python-version: ["3.14"]
 
     name: Documentation tests
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: ${{ matrix.python-version }}
 

.github/workflows/main.yml

@@ -13,15 +13,20 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest", "macos-latest", "windows-latest"]
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
-        install-extras: ["base", "pysat_instruments", "dmsp_ssj"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+        install-extras: ["base", "dmsp_ssj"]
+        include:
+          # Pysat test for only pysat-passing Python version
+          - python-version: "3.10"
+            os: "ubuntu-latest"
+            install-extras: "pysat_instruments"
 
     name: Python ${{ matrix.python-version }} on ${{ matrix.os }} with ${{ matrix.install-extras }}
     runs-on: ${{ matrix.os }}
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: ${{ matrix.python-version }}
 
@@ -50,43 +55,21 @@ jobs:
         coverage report
         coverage xml --rcfile=pyproject.toml
 
-    - name: Install and run Coveralls Reporter(Linux)
-      if: startsWith(matrix.os, 'ubuntu')
-      env:
-        COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
-        COVERALLS_PARALLEL: true
-      run: |
-        curl -sL https://coveralls.io/coveralls-linux.tar.gz | tar -xz
-        ./coveralls report -f coverage.xml --parallel --repo-token=${{ secrets.COVERALLS_REPO_TOKEN }} --build-number ${{ github.run_number }}
-
-    - name: Install and run Coveralls Reporter (Windows)
-      if: startsWith(matrix.os, 'windows')
-      env:
-        COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
-        COVERALLS_PARALLEL: true
-      run: |
-        curl -L https://github.com/coverallsapp/coverage-reporter/releases/latest/download/coveralls-windows.exe -o coveralls.exe
-        ./coveralls.exe report -f coverage.xml --parallel --repo-token=${{ secrets.COVERALLS_REPO_TOKEN }} --build-number ${{ github.run_number }}
-
-    - name: Report  and run Coveralls (macOS)
-      if: startsWith(matrix.os, 'macos')
-      env:
-        COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
-        COVERALLS_PARALLEL: true
-      run: |
-        brew tap coverallsapp/coveralls --quiet
-        brew install coveralls --quiet
-        coveralls report -f coverage.xml --parallel --repo-token=${{ secrets.COVERALLS_REPO_TOKEN }} --build-number ${{ github.run_number }}
+    - name: Coveralls Parallel
+      uses: coverallsapp/github-action@v2
+      with:
+        flag-name: run=${{ join(matrix.*, '-') }}
+        parallel: true
+        format: cobertura
+        debug: true
 
   finish:
     name: Finish Coverage Analysis
     needs: build
     runs-on: "ubuntu-latest"
     steps:
       - name: Coveralls Finished
-        env:
-          COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
-          COVERALLS_PARALLEL: true
-        run: |
-          curl -sL https://coveralls.io/coveralls-linux.tar.gz | tar -xz
-          ./coveralls done --build-number ${{ github.run_number }}
+        uses: coverallsapp/github-action@v2
+        with:
+          parallel-finished: true
+          carryforward: "all"

.github/workflows/pip_rc_install.yml

@@ -13,21 +13,21 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest", "macos-latest", "windows-latest"]
-        python-version: ["3.11"]  # Keep this version at the highest supported Python version
+        python-version: ["3.14"]  # Keep this version at the highest supported Python version
 
     name: Python ${{ matrix.python-version }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Install standard dependencies
       run: pip install -r requirements.txt
 
-    - name: Install pysat RC
+    - name: Install ocbpy RC
       run: pip install --no-deps --pre -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ ocbpy
 
     - name: Check that installation imports correctly

CONTRIBUTING.rst

@@ -88,3 +88,17 @@ To run a subset of tests from the test directory for a specific environment::
 To run all the tests for a specific environment::
 
     python -m unittest discover
+
+Use of 'AI'
+-----------
+
+This project is human centered and developers do not use Artificial Inteligence
+(AI) tools such as large language models in its creation or maintenance.
+The purpose of this tool is to improve high-latitude science, and thus values
+interactions with scientists and scientific programmers. Code generated by a
+large language model or similar technology, such as Anthropic’s Claude,
+GitHub/Microsoft’s Copilot, OpenAI’s ChatGPT, Facebook/Meta’s Code Llama et al.,
+is not compliant with the covenants and representations of OCBpy’s Contributor’s
+Agreement, and is thus not acceptable as code for OCBpy.
+
+

Changelog.rst

@@ -3,6 +3,17 @@ Changelog
 
 Summary of all changes made since the first stable release
 
+0.7.0 (05-29-2026)
+------------------
+* ENH: Added the Gussenhoven (1983) model for the EAB
+* ENH: Added the CH-Aurora-2014 model for the OCB and EAB
+* ENH: Added an AI usage policy (no AI use allowed)
+* DEP: Removed support for older versions of `zenodo_get`, updated download
+  checks to account for breaking changes made in the newer versions
+* MAINT: Added support for Python 3.14
+* MAINT: Updated GitHub CI actions
+* MAINT: Updated numpy usage
+
 0.6.0 (07-07-2025)
 ------------------
 * ENH: Updated the AMPERE boundaries to include 2022-2024, inclusive

README.md

@@ -45,15 +45,15 @@ These routines may be used as a guide to write routines for other datasets.
 
 # Python versions
 
-This module currently supports Python version 3.10 - 3.13.
+This module currently supports Python version 3.10 - 3.14.
 
 # Dependencies
 
 The listed dependecies were tested with the following versions:
   * numpy
   * aacgmv2
   * pysat (3.2.1+)
-  * zenodo_get
+  * zenodo_get (2.0.0+)
 
 Testing is performed using the python module, unittest.  To limit dependency
 issues, the pysat and zenodo_get dependencies are optional.

docs/citing.rst

@@ -98,7 +98,7 @@ boundary method and data set are provided below.
 * **SSJ Auroral Boundaries (2010-2014)**: Kilcommons, L., et al. (2019).
   Defense Meteorology Satellite Program (DMSP) Electron Precipitation (SSJ)
   Auroral Boundaries, 2010-2014 (Version 1.0.0) [Data set]. Zenodo.
-  http://doi.org/10.5281/zenodo.3373812
+  doi:10.5281/zenodo.3373812
 
 
 .. _cite-starkov:
@@ -112,3 +112,37 @@ when publishing your work.
 
 * Starkov, G. V. (1994) Mathematical model of the auroral boundaries,
   Geomagnetism and Aeronomy, English Translation, 34(3), 331-336.
+
+
+.. _cite-guss:
+
+Gussenhoven Model
+-----------------
+
+The Gussenhoven mathematical diffuse auroral boundary model is described in the
+following article. If you use this model please cite both it and your Kp data
+source when publishing your work.
+
+* Gussenhoven, M. S. et al. (1983) Systematics of the Equatorward Diffuse
+  Auroral Boundary, J. Geophys. Res, 88(A7), 5692-5708.
+
+
+.. _cite-xiong:
+
+CH-Aurora-2014 Model
+--------------------
+
+The CH-Aurora-2014 auroral boundary model provides either a mathematical
+description of the poleward and equatorward auroral boundaries in each
+hemisphere or an assimilated location based on FAC observations of the auroral
+boundaries in that hemisphere.  The data that may be used to adjust the model
+are described in the first article below, while the model itself is described in
+the second article. If you use this model please cite both it and your Newell
+Couping Function data source when publishing your work.
+
+* Xiong, C., et al. (2014) Determining the boundaries of the auroral oval from
+  CHAMP field-aligned current signatures - Part 1, Ann. Geophys., 32,
+  pp 609-622, doi:10.5194/angeo-32-609-2014.
+* Xiong, C. and H. Luhr (2014) An empirical model of the auroral oval derived
+  from CHAMP field-aligned current signatures - Part 2, Ann. Geophys., 32,
+  pp 623-631, doi:10.5194/angeo-32-623-2014

docs/examples/ex_dmsp.rst

@@ -13,13 +13,12 @@ need the
 `ssj_auroral_boundary <https://github.com/lkilcommons/ssj_auroral_boundary>`__
 package, but now we preferentially support using the
 `zendodo_get <https://github.com/dvolgyes/zenodo_get>`__ package to obtain the
-boundary files from their
-`archive <https://zenodo.org/record/3373812>`__. Once installed,
-you can download DMSP SSJ data and obtain a boundary file for a specified time
-period (or all available times) using :py:mod:`ocbpy.boundaries.dmsp_ssj_files`.
-For this example, we'll use a single day. You can download the files into any
-directory, but this example will put them in the same directory as the other
-boundary files.
+boundary files from their archive at the website: zenodo.org/record/3373812.
+Once installed, you can download DMSP SSJ data and obtain a boundary file for a
+specified time period (or all available times) using
+:py:mod:`ocbpy.boundaries.dmsp_ssj_files`. For this example, we'll use a single
+day. You can download the files into any directory, but this example will put
+them in the same directory as the other boundary files.
 
 ::
 

docs/examples/ex_model_boundaries.rst

@@ -32,7 +32,7 @@ be calculated for a set of MLTs using the
 
 This provides the AACGMV2 magnetic latitude for the entire range of MLT at the
 specified AL values. We can plot these boundaries, using the formatting function
-previously defined in Example :ref:`format-polar-axes`.
+previously defined in :ref:`set_up_polar_plot <format-polar-axes>`.
 
 ::
 

docs/ocb_models.rst

@@ -27,6 +27,32 @@ in the code here using the boundary keyworkds: 'ocb', 'eab', and 'diffuse',
 respectively.
 
 
+.. _bound-model-guss:
+
+Gussenhoven
+-----------
+
+The Gussenhoven 1983 model (see :ref:`cite-guss`) uses a mathematical
+formulation based on DMSP data and the Kp index. They specify a single boundary:
+the equatorward edge of the diffuse aurora. As this model defines the boundary
+using separate linear fits for different MLT bins, the results may be returned
+at the binned times ('binned'), at the nearest binned time ('closest'), or at
+the requested times using a circle fit to all of binned times ('circle').
+
+
+.. _bound-model-champ:
+
+CH-Aurora-2014
+--------------
+
+The CH-Aurora-2014 model (see :ref:`cite-xiong`) uses a mathematical
+formulation based on CHAMP field-aligned current (FAC) data and a delayed
+time-history of the Newell Coupling Function. They specify both auroral
+boundaries in each hemisphere.  Although this model may be driven entirely by
+the Newell Coupling Function, the authors recommend adjusting the model output
+with CHAMP measurements of the FAC boundaries.
+
+
 .. _bound-model-module:
 
 Boundary Models Module

ocbpy/boundaries/dmsp_ssj_files.py

@@ -20,15 +20,14 @@
 
 .. [7] Kilcommons, L., Redmon, R., & Knipp, D. (2019). Defense Meteorology
    Satellite Program (DMSP) Electron Precipitation (SSJ) Auroral Boundaries,
-   2010-2014 (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3373812
+   2010-2014 (1.0.0) [Data set]. Zenodo. doi:10.5281/zenodo.3373812
 
 """
 
 import datetime as dt
-from io import StringIO
+import hashlib
 import numpy as np
 import os
-import sys
 import zipfile
 
 import aacgmv2
@@ -108,50 +107,42 @@ def fetch_ssj_boundary_files(stime=None, etime=None, out_dir=None,
     if not os.path.isdir(out_dir):
         raise ValueError("can't find the output directory")
 
-    # Download the zenodo archive, capturing the output
-    zenodo_io = StringIO()
-    sys.stdout = zenodo_io
-    sys.stderr = zenodo_io
+    # Download the zenodo archive
+    zenodo_get.download(doi=doi, output_dir=out_dir)
 
-    # TODO(#151): remove the old (second) way of calling zenodo_get
-    if hasattr(zenodo_get, "download"):
-        zenodo_get.download(doi=doi, output_dir=out_dir)
-        zenodo_checksum = None
-    else:
-        zenodo_get.zenodo_get([doi, '-o', out_dir])
-        zenodo_checksum = os.path.join(out_dir, 'md5sums.txt')
+    # Get the checksum file (does not download)
+    zenodo_get.download(doi=doi, output_dir=out_dir, md5=True)
+    zenodo_checksum = os.path.join(out_dir, "md5sums.txt")
 
-    # Parse the output and retrieve files from the zip archive
-    sys.stdout = sys.__stdout__
-    sys.stderr = sys.__stderr__
-    zen_msg = zenodo_io.getvalue()
-    zen_split = zen_msg.split()
+    # Verify the checksum and retrieve the zip archive name
+    if os.path.isfile(zenodo_checksum):
+        with open(zenodo_checksum, 'r') as zcheck:
+            csum, zip_name = zcheck.read().split()
 
-    if zen_msg.find('Checksum is correct') < 0 and zen_msg.find(
-            'already downloaded correctly') < 0:
-        raise IOError('Bad checksum: {:s}'.format(zen_msg))
+            # Set the archive name
+            archive_name = os.path.join(out_dir, zip_name)
 
-    # Remove the checksum file if the download problem wasn't found there
-    if zenodo_checksum is not None:
-        os.remove(zenodo_checksum)
+            if not os.path.isfile(archive_name):
+                raise IOError(
+                    'error downloading archive to output dir: {:}'.format(
+                        archive_name))
 
-    # Get the archive name from the output
-    try:
-        link_ind = zen_split.index('Link:') + 1
+            # Get the archive checkshum
+            with open(os.path.join(out_dir, archive_name), 'rb') as afile:
+                asum = hashlib.md5(afile.read()).hexdigest()
 
-        # If the archive is already available, message may differ
-        zip_name = os.path.split(zen_split[link_ind])
-        while zip_name[-1].find(".zip") <= 0:
-            zip_name = os.path.split(zip_name[0])
+        zen_msg = "Checksum is correct" if asum == csum else "Corrupted data"
+    else:
+        zen_msg = "Checksum file not created"
 
-        # Set the archive name
-        archive_name = os.path.join(out_dir, zip_name[-1])
-    except (ValueError, IndexError):
-        raise IOError('unable to identify zenodo archive: {:}'.format(zen_msg))
+    # Remove the checksum file
+    if zenodo_checksum is not None:
+        os.remove(zenodo_checksum)
 
-    if not os.path.isfile(archive_name):
-        raise IOError('error downloading archive to output dir: {:}'.format(
-            archive_name))
+    # Evaluate checksum output
+    if zen_msg.find('Checksum is correct') < 0 and zen_msg.find(
+            'already downloaded correctly') < 0:
+        raise IOError('Bad checksum: {:s}'.format(zen_msg))
 
     # Access the zip archive
     with zipfile.ZipFile(archive_name, 'r') as zref: