Add marginal plots with option to revert to classic plot (#310)

- Add marginal plots to default plotting settings
- Original plot is available by passing `--classic` to the CLI or `classic=True` to the `DataMorpher`
- Increased the minimum Python version to 3.10
- Increased the minimum Matplotlib version to 3.10
- Increased the minimum NumPy version to 1.23.0
- Increased the minimum pandas version to 2.1

Author: stefmolin

.github/workflows/ci.yml

@@ -44,7 +44,7 @@ jobs:
         fail-fast: false
         matrix:
             os: [macos-latest, ubuntu-latest, windows-latest]
-            python-version: ["3.9", "3.10", "3.11", "3.12", "3.13", "3.14"]
+            python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
 
     env:
       MPLBACKEND: Agg  # non-interactive backend for matplotlib

docs/_static/panda_to_star.gif docs/_static/panda-to-star-classic.gifdocs/_static/panda_to_star.gif renamed to docs/_static/panda-to-star-classic.gif

@@ -44,10 +44,24 @@ This produces the following animation in the newly-created ``morphed_data`` dire
 within your current working directory:
 
 .. figure:: _static/panda-to-star.gif
-   :alt: Morphing the panda dataset into the star shape.
+   :alt: Morphing the panda dataset into the star shape with marginal plots.
    :align: center
 
-   Morphing the panda :class:`.Dataset` into the star :class:`.Shape`.
+   Morphing the panda :class:`.Dataset` into the star :class:`.Shape` with marginal plots.
+
+If you don't want the marginal plots (the histograms on the sides), you can use classic mode:
+
+.. code:: console
+
+   $ data-morph --start-shape panda --target-shape star --classic
+
+Animations generated in classic mode include only the scatter plot and the summary statistics:
+
+.. figure:: _static/panda-to-star-classic.gif
+   :alt: Morphing the panda dataset into the star shape using classic mode.
+   :align: center
+
+   Morphing the panda :class:`.Dataset` into the star :class:`.Shape` using classic mode.
 
 You can smooth the transition with the ``--ease`` or ``--ease-in`` and ``--ease-out`` flags.
 The ``--freeze`` flag allows you to start the animation with the specified number of frames

docs/_static/panda-to-star-eased.gif

@@ -99,7 +99,7 @@ Pass the path to the CSV file to use those points as the starting shape:
 
 .. code:: console
 
-   $ data-morph --start-shape path/to/points.csv --target-shape wide_lines
+   $ data-morph --start-shape path/to/points.csv --target-shape wide_lines --classic
 
 Here is an example animation generated from a custom dataset:
 

docs/_static/panda-to-star.gif

@@ -24,14 +24,13 @@ authors = [
   { name = "Aaron Stevens", email = "[email protected]" },
   { name = "Justin Matejka", email = "[email protected]" },
 ]
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 classifiers = [
   "Development Status :: 4 - Beta",
   "Framework :: Matplotlib",
   "Intended Audience :: Education",
   "Operating System :: OS Independent",
   "Programming Language :: Python :: 3 :: Only",
-  "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",
@@ -45,9 +44,9 @@ dynamic = [
 ]
 
 dependencies = [
-  "matplotlib>=3.7",
-  "numpy>=1.20",
-  "pandas>=1.2",
+  "matplotlib>=3.10",
+  "numpy>=1.23.0",
+  "pandas>=2.1",
   "rich>=13.9.4",
 ]
 

docs/quickstart.rst

@@ -21,7 +21,7 @@ def _validate_2d(data: Iterable[Number], name: str) -> Iterable[Number]:
         The validated data.
     """
     if not (
-        isinstance(data, (tuple, list))
+        isinstance(data, tuple | list)
         and len(data) == 2
         and all(isinstance(x, Number) and not isinstance(x, bool) for x in data)
     ):

docs/tutorials/custom-datasets.rst

@@ -38,7 +38,7 @@ def __init__(
         if isinstance(inclusive, bool):
             inclusive = [inclusive] * 2
         if not (
-            isinstance(inclusive, (tuple, list))
+            isinstance(inclusive, tuple | list)
             and len(inclusive) == 2
             and all(isinstance(x, bool) for x in inclusive)
         ):
@@ -47,19 +47,19 @@ def __init__(
                 ' or a single Boolean value'
             )
 
-        self.x_bounds = (
+        self.x_bounds: Interval = (
             x_bounds.clone()
             if isinstance(x_bounds, Interval)
             else Interval(x_bounds, inclusive[0])
         )
-        """Interval: The bounds for the x direction."""
+        """The bounds for the x direction."""
 
-        self.y_bounds = (
+        self.y_bounds: Interval = (
             y_bounds.clone()
             if isinstance(y_bounds, Interval)
             else Interval(y_bounds, inclusive[1])
         )
-        """Interval: The bounds for the y direction."""
+        """The bounds for the y direction."""
 
         self._bounds = (self.x_bounds, self.y_bounds)
 

pyproject.toml

@@ -198,6 +198,16 @@ def generate_parser() -> argparse.ArgumentParser:
     frame_group = parser.add_argument_group(
         'Animation Configuration', description='Customize aspects of the animation.'
     )
+    frame_group.add_argument(
+        '--classic',
+        default=False,
+        action='store_true',
+        help=(
+            'Whether to plot the original visualization, which consists of a scatter plot '
+            'and the summary statistics. Otherwise, marginal plots will be included in '
+            'addition to the classic plot.'
+        ),
+    )
     frame_group.add_argument(
         '--ease',
         default=False,
@@ -294,6 +304,7 @@ def _morph(
         forward_only_animation=args.forward_only,
         num_frames=100,
         in_notebook=False,
+        classic=args.classic,
         with_median=args.with_median,
     )
 
@@ -409,6 +420,7 @@ def _serialize(args: argparse.Namespace, target_shapes: Sequence[str]) -> None:
             forward_only_animation=args.forward_only,
             num_frames=100,
             in_notebook=False,
+            classic=args.classic,
             with_median=args.with_median,
         )