Adding ArviZ support (#145)

* adding arviz support

* import order

* actually install arviz version

* imports for dev arviz

* adding pre-commit config

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* updating dist build

* adding test for arviz plotting

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.github/workflows/tests.yml

@@ -9,12 +9,16 @@ on:
 
 jobs:
   tests:
-    name: "py${{ matrix.python-version }} on ${{ matrix.os }}"
-    runs-on: ${{ matrix.os }}
+    name: "py${{ matrix.python-version }}; ${{ matrix.arviz-version }}"
+    runs-on: "ubuntu-latest"
     strategy:
       matrix:
-        python-version: [3.7, 3.8]
-        os: [ubuntu-latest, macos-latest]
+        python-version: [3.9]
+        arviz-version:
+          - "arviz~=0.9"
+          - "arviz~=0.10"
+          - "arviz~=0.11"
+          - "https://github.com/arviz-devs/arviz/archive/main.zip"
 
     steps:
       - uses: actions/checkout@v2
@@ -27,14 +31,10 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install -U pip
+          python -m pip install $ARVIZ
           python -m pip install ".[test]"
-
-      - name: Check the style
-        run: |
-          isort -c src/corner
-          isort -c tests
-          black --check src/corner
-          black --check tests
+        env:
+          ARVIZ: ${{ matrix.arviz-version }}
 
       - name: Run the unit tests
         run: python -m pytest -v tests
@@ -53,12 +53,13 @@ jobs:
       - uses: actions/setup-python@v2
         name: Install Python
         with:
-          python-version: "3.8"
+          python-version: "3.9"
 
       - name: Build
         run: |
-          python -m pip install -U pip pep517 twine setuptools_scm
-          python -m pep517.build .
+          python -m pip install -U pip
+          python -m pip install -U build
+          python -m build .
 
       - name: Test the sdist
         run: |

.pre-commit-config.yaml

@@ -0,0 +1,17 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.4.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+        exclude_types: [json, binary]
+  - repo: https://github.com/PyCQA/isort
+    rev: "5.7.0"
+    hooks:
+      - id: isort
+        additional_dependencies: [toml]
+        exclude: docs/tutorials
+  - repo: https://github.com/psf/black
+    rev: "20.8b1"
+    hooks:
+      - id: black

demo.py

@@ -18,19 +18,34 @@
 ndim, nsamples = 3, 50000
 
 # Generate some fake data.
-data1 = np.random.randn(ndim * 4 * nsamples / 5).reshape([4 * nsamples / 5,
-                                                          ndim])
-data2 = (4 * np.random.rand(ndim)[None, :] +
-         np.random.randn(ndim * nsamples / 5).reshape([nsamples / 5, ndim]))
+data1 = np.random.randn(ndim * 4 * nsamples / 5).reshape(
+    [4 * nsamples / 5, ndim]
+)
+data2 = 4 * np.random.rand(ndim)[None, :] + np.random.randn(
+    ndim * nsamples / 5
+).reshape([nsamples / 5, ndim])
 data = np.vstack([data1, data2])
 
 # Plot it.
-figure = corner.corner(data, labels=[r"$x$", r"$y$", r"$\log \alpha$",
-                                     r"$\Gamma \, [\mathrm{parsec}]$"],
-                       quantiles=[0.16, 0.5, 0.84],
-                       show_titles=True, title_kwargs={"fontsize": 12})
-figure.gca().annotate("a demo corner plot",
-                      xy=(1.0, 1.0), xycoords="figure fraction",
-                      xytext=(-20, -10), textcoords="offset points",
-                      ha="right", va="top")
+figure = corner.corner(
+    data,
+    labels=[
+        r"$x$",
+        r"$y$",
+        r"$\log \alpha$",
+        r"$\Gamma \, [\mathrm{parsec}]$",
+    ],
+    quantiles=[0.16, 0.5, 0.84],
+    show_titles=True,
+    title_kwargs={"fontsize": 12},
+)
+figure.gca().annotate(
+    "a demo corner plot",
+    xy=(1.0, 1.0),
+    xycoords="figure fraction",
+    xytext=(-20, -10),
+    textcoords="offset points",
+    ha="right",
+    va="top",
+)
 figure.savefig("demo.png", dpi=300)

docs/conf.py

@@ -36,6 +36,7 @@
 on_rtd = os.environ.get("READTHEDOCS", None) == "True"
 if not on_rtd:
     import sphinx_rtd_theme
+
     html_theme = "sphinx_rtd_theme"
     html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 

docs/pages/custom.rst

@@ -25,14 +25,14 @@ another randomly sampled mode:
 
     import corner
     import numpy as np
-    
+
     ndim, nsamples = 4, 50000
     np.random.seed(1234)
     data1 = np.random.randn(ndim * 4 * nsamples // 5).reshape([4 * nsamples // 5, ndim])
     mean = 4*np.random.rand(ndim)
     data2 = (mean[None, :] + np.random.randn(ndim * nsamples // 5).reshape([nsamples // 5, ndim]))
     samples = np.vstack([data1, data2])
-    
+
     figure = corner.corner(samples)
 
 
@@ -47,22 +47,22 @@ of the second mode.
 
     # This is the true mean of the second mode that we used above:
     value1 = mean
-    
+
     # This is the empirical mean of the sample:
     value2 = np.mean(samples, axis=0)
-    
+
     # Make the base corner plot
     figure = corner.corner(samples)
-    
+
     # Extract the axes
     axes = np.array(figure.axes).reshape((ndim, ndim))
-    
+
     # Loop over the diagonal
     for i in range(ndim):
         ax = axes[i, i]
         ax.axvline(value1[i], color="g")
         ax.axvline(value2[i], color="r")
-        
+
     # Loop over the histograms
     for yi in range(ndim):
         for xi in range(yi):
@@ -81,4 +81,3 @@ of the second mode.
 
 A similar procedure could be used to add anything to the axes that you
 can normally do with matplotlib.
-

docs/pages/quickstart.rst

@@ -17,7 +17,7 @@ its simplest form, you use it like this:
 
     import corner
     import numpy as np
-    
+
     ndim, nsamples = 2, 10000
     np.random.seed(42)
     samples = np.random.randn(ndim * nsamples).reshape([nsamples, ndim])
@@ -34,13 +34,13 @@ The following snippet demonstrates a few more bells and whistles:
 
     # Set up the parameters of the problem.
     ndim, nsamples = 3, 50000
-    
+
     # Generate some fake data.
     np.random.seed(42)
     data1 = np.random.randn(ndim * 4 * nsamples // 5).reshape([4 * nsamples // 5, ndim])
     data2 = (4*np.random.rand(ndim)[None, :] + np.random.randn(ndim * nsamples // 5).reshape([nsamples // 5, ndim]))
     data = np.vstack([data1, data2])
-    
+
     # Plot it.
     figure = corner.corner(data, labels=[r"$x$", r"$y$", r"$\log \alpha$", r"$\Gamma \, [\mathrm{parsec}]$"],
                            quantiles=[0.16, 0.5, 0.84],
@@ -53,4 +53,3 @@ The following snippet demonstrates a few more bells and whistles:
 
 The `API documentation <../api.html>`__ gives more details about all the
 arguments available for customization.
-

docs/pages/sigmas.rst

@@ -39,7 +39,7 @@ We can visualize the difference between sigma definitions:
     import corner
     import numpy as np
     import matplotlib.pyplot as pl
-    
+
     # Generate some fake data from a Gaussian
     np.random.seed(42)
     x = np.random.randn(50000, 2)
@@ -67,5 +67,3 @@ contour compares to the marginalized 68% quantile:
 
 
 .. image:: sigmas_files/sigmas_6_0.png
-
-

setup.py

@@ -23,18 +23,14 @@
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
 ]
-INSTALL_REQUIRES = [
-    "matplotlib>=2.1"
-]
+INSTALL_REQUIRES = ["arviz>=0.9"]
 EXTRA_REQUIRE = {
     "test": [
         "pytest>=3.6",
         "pytest-cov>=2.6.1",
         "black",
         "isort",
         "toml",
-        "scipy",
-        "pandas",
     ],
     "docs": ["sphinx>=1.7.5", "pandoc"],
 }

src/corner/__init__.py

@@ -10,7 +10,7 @@
 __uri__ = "https://corner.readthedocs.io"
 __license__ = "BSD"
 __description__ = "Make some beautiful corner plots"
-__copyright__ = "Copyright 2013-2020 Daniel Foreman-Mackey"
+__copyright__ = "Copyright 2013-2021 Daniel Foreman-Mackey"
 __contributors__ = "https://github.com/dfm/corner.py/graphs/contributors"
 __bibtex__ = __citation__ = """
 @article{corner,