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Merge branch 'main' into coast-add-q-alias
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.github/workflows/ci_tests.yaml

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@@ -97,7 +97,7 @@ jobs:
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mamba install gmt=6.3.0 numpy=${{ matrix.numpy-version }} \
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pandas xarray netCDF4 packaging \
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${{ matrix.optional-packages }} \
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dvc=2.8.2 make pytest>=6.0 \
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dvc make pytest>=6.0 \
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pytest-cov pytest-mpl sphinx-gallery tomli
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# Show installed pkg information for postmortem diagnostic

.github/workflows/ci_tests_dev.yaml

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dvc ipython 'pytest>=6.0' pytest-cov \
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pytest-mpl sphinx-gallery tomli
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# Pull baseline image data from dvc remote (DAGsHub)
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- name: Pull baseline image data from dvc remote
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run: |
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dvc pull
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ls -lhR pygmt/tests/baseline/
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# Build and install latest GMT from GitHub
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- name: Install GMT ${{ matrix.gmt_git_ref }} branch (Linux/macOS)
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run: curl https://raw.githubusercontent.com/GenericMappingTools/gmt/master/ci/build-gmt.sh | bash
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touch ~/.gmt/server/gmt_data_server.txt ~/.gmt/server/gmt_hash_server.txt
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ls -lhR ~/.gmt
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# Pull baseline image data from dvc remote (DAGsHub)
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- name: Pull baseline image data from dvc remote
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run: |
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dvc pull
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ls -lhR pygmt/tests/baseline/
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# Install the package that we want to test
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- name: Install the package

.github/workflows/release-drafter.yml

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runs-on: ubuntu-latest
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steps:
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# Drafts your next Release notes as Pull Requests are merged into "main"
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- uses: release-drafter/release-drafter@v5.17.5
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- uses: release-drafter/release-drafter@v5.17.6
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with:
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# (Optional) specify config name to use, relative to .github/. Default: release-drafter.yml
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config-name: release-drafter.yml

README.rst

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``xarray.DataArray`` for grids.
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Quickstart
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----------
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Installation
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++++++++++++
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Simple installation using `conda <https://docs.conda.io/projects/conda/en/latest/user-guide/index.html>`__::
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conda install --channel conda-forge pygmt
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For other ways to install ``pygmt``, see `full installation instructions <https://www.pygmt.org/latest/install.html>`__.
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Contacting Us
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-------------
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- Documentation
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- GMT
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- Python
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- Numpy
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- NumPy
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* - `Dev <https://github.com/GenericMappingTools/pygmt/milestone/9>`_ (upcoming release)
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- `Dev Documentation <https://www.pygmt.org/dev>`_ (reflects `main branch <https://github.com/GenericMappingTools/pygmt>`_)
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- >=6.3.0

doc/api/index.rst

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surface
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xyz2grd
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Operations on grids
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~~~~~~~~~~~~~~~~~~~
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Operations on raster data
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~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autosummary::
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:toctree: generated

doc/contributing.md

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* Have a good coding style
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- Use readable code, as it is better than clever code (even with comments).
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- Follow the [PEP8](http://pep8.org) style guide for code and the
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[numpy style guide](https://numpydoc.readthedocs.io/en/latest/format.html)
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[NumPy style guide](https://numpydoc.readthedocs.io/en/latest/format.html)
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for docstrings. Please refer to [Code style](#code-style).
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Pull requests will automatically have tests run by GitHub Actions.
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The API documentation is built from the docstrings in the Python `*.py` files under
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the `pygmt/src/` and `/pygmt/datasets/` folders. **All docstrings** should follow the
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[numpy style guide](https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard).
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[NumPy style guide](https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard).
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All functions/classes/methods should have docstrings with a full description of all
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arguments and return values.
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examples/gallery/3d_plots/grdview_surface.py

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# https://en.wikipedia.org/wiki/Ackley_function
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def ackley(x, y):
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return (
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-20 * np.exp(-0.2 * np.sqrt(0.5 * (x ** 2 + y ** 2)))
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-20 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2)))
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- np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
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+ np.exp(1)
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+ 20

examples/gallery/basemaps/double_y_axes.py

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# Generate two sample Y data from one common X data
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x = np.linspace(1.0, 9.0, num=9)
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y1 = x
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y2 = x ** 2 + 110
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y2 = x**2 + 110
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fig = pygmt.Figure()
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examples/gallery/images/contours.py

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# build the contours underlying data with the function z = x^2 + y^2
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X, Y = np.meshgrid(np.linspace(-10, 10, 50), np.linspace(-10, 10, 50))
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Z = X ** 2 + Y ** 2
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Z = X**2 + Y**2
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x, y, z = X.flatten(), Y.flatten(), Z.flatten()
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examples/projections/nongeo/cartesian_logarithmic.py

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# Create a list of x values 0-100
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xline = np.arange(0, 101)
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# Create a list of y-values that are the square root of the x-values
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yline = xline ** 0.5
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yline = xline**0.5
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# Create a list of x values for every 10 in 0-100
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xpoints = np.arange(0, 101, 10)
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# Create a list of y-values that are the square root of the x-values
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ypoints = xpoints ** 0.5
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ypoints = xpoints**0.5
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fig = pygmt.Figure()
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fig.plot(

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