From 5f8ca7050ed5b82070bf76e667e725a7e93a404d Mon Sep 17 00:00:00 2001 From: Chris Barnes Date: Mon, 5 Oct 2026 09:39:21 +0100 Subject: [PATCH 1/6] Convert marimo notebooks to jupytext --- examples/image.py | 304 ++++++++----------- examples/tutorial.py | 624 +++++++++++++++++---------------------- pyproject.toml | 12 +- uv.lock | 673 ++++++++++++++++++++++++++++++++++++++++++- 4 files changed, 1080 insertions(+), 533 deletions(-) diff --git a/examples/image.py b/examples/image.py index f990266..d93c628 100644 --- a/examples/image.py +++ b/examples/image.py @@ -1,176 +1,128 @@ -import marimo - -__generated_with = "0.23.4" -app = marimo.App(width="medium") - -with app.setup: - import marimo as mo - - -@app.cell(hide_code=True) -def _(): - mo.md(r""" - # Image transformation with `transformnd` - - `transformnd` transforms coordinates, not images, but coordinate transformations can be used to transform images. - Your output (transformed) and source images both have pixels with an `xy` coordinate in their respective image spaces, - and image transformation is simply a case of finding which source pixel to use for each output pixel. - - Here we take a 2-channel fluorescence microscopy image of some cells in 3 dimensions, use scaling information to map those pixels into a real-world space, and then map the pixels of our viewport into the the same space. - - We will refer to the following coordinate spaces and their axes: - - - cells - - Z: 0.29um - - C: membrane/ nuclei label intensity - - Y: 0.26um - - X: 0.26um - - world - - C: membrane/ nuclei label intensity - - Z: 1um - - Y: 1um - - X: 1um - - viewport - - Y: 1px - - X: 1px - - C: red/green/blue intensity - """) - return - - -@app.cell -def _(): - from skimage.data import cells3d - - cells = cells3d() - cells = cells.astype("float64") - cells -= cells.min() - cells /= cells.max() - - print(f"{cells.shape=}") - print(f"{cells.dtype=}") - print(f"{cells.min()=}") - print(f"{cells.max()=}") - return (cells,) - - -@app.cell -def _(): - import transformnd as tnd - from transformnd.transforms import ProjectAxis, MapAxis, Scale - - # Aligned at world origin. - # This would be stored alongside the data. - cells_to_world = tnd.base.TransformSequence( - [ - # Move the color axis to the first position - MapAxis([1, 0, 2, 3]), - # Scale the space axes - Scale([1, 0.29, 0.26, 0.26]), - ], - ) - print(cells_to_world) - - # Aligned at world origin. - # This would be chosen by the viewing application. - viewport_to_world = tnd.base.TransformSequence( - [ - # Create a Z axis - ProjectAxis(created={0}, source_ndim=3), - # Move the color axis to the first position - MapAxis([3, 0, 1, 2]), - # Choose a spatial sampling frequency (here 0.2um isotropic) - Scale([1, 0.2, 0.2, 0.2]), - ], - ) - print(viewport_to_world) - return cells_to_world, viewport_to_world - - -@app.cell(hide_code=True) -def _(): - mo.md(r""" - Both images know how to transform their array indices into the real world. - - We can invert one of those transforms to get a transformation between viewport-space and cell-space. - We can also have a separate transformation to control moving the viewport (useful if we had an interactive viewer). - """) - return - - -@app.cell -def _(cells_to_world, viewport_to_world): - from transformnd.transforms import Translate - - # Shift the viewport within the data, in world measurements. - # This would be controlled by the user as they peruse the data. - viewport_offset = Translate([0, 35 * 0.29, 64 * 0.26, 0.0]) - - viewport_to_cells = viewport_to_world | viewport_offset | ~cells_to_world - return (viewport_to_cells,) - - -@app.cell(hide_code=True) -def _(): - mo.md(r""" - Here we want to get all of the coordinates of our viewport, across all channels, in the shape needed by `transformnd` (number of coordinates x dimensionality of coordinates). - - We then transform that to get the positions of those coordinates within the cells image. - """) - return - - -@app.cell -def _(viewport_to_cells): - import numpy as np - - # 2D YXC image - viewport_shape = (128, 256, 3) - - indices = [np.arange(s, dtype=float) for s in viewport_shape] - grids = np.meshgrid(*indices, indexing="ij") - - # Y, X, C, coords - vp_coords_3d = np.stack(grids, -1) - print(f"{vp_coords_3d.shape=}") - - # Y*X*C, coords - vp_coords = vp_coords_3d.reshape((-1, len(viewport_shape))) - print(f"{vp_coords.shape=}") - - # Z*C*Y*X, coords - cells_coords = viewport_to_cells.apply(vp_coords) - print(f"{cells_coords.shape=}") - return cells_coords, viewport_shape - - -@app.cell(hide_code=True) -def _(): - mo.md(r""" - `scipy.ndimage.map_coordinates` is where the magic happens; looking up our coordinates in the cells image to get the intensities. There's a dask version too! - """) - return - - -@app.cell -def _(cells, cells_coords, viewport_shape): - from scipy.ndimage import map_coordinates - - # transformnd uses `NxD` coordinate arrays; map_coordinates uses `DxN` - cells_vals = map_coordinates(cells, cells_coords.T).T - print(f"{cells_vals.shape=}") - viewport = cells_vals.reshape(viewport_shape) - print(f"{viewport.shape=}") - return (viewport,) - - -@app.cell -def _(viewport): - from matplotlib import pyplot as plt - - plt.imshow(viewport) - return - - -if __name__ == "__main__": - app.run() +# %% [markdown] +# # Image transformation with `transformnd` +# +# `transformnd` transforms coordinates, not images, but coordinate transformations can be used to transform images. +# Your output (transformed) and source images both have pixels with an `xy` coordinate in their respective image spaces, +# and image transformation is simply a case of finding which source pixel to use for each output pixel. +# +# Here we take a 2-channel fluorescence microscopy image of some cells in 3 dimensions, use scaling information to map those pixels into a real-world space, and then map the pixels of our viewport into the the same space. +# +# We will refer to the following coordinate spaces and their axes: +# +# - cells +# - Z: 0.29um +# - C: membrane/ nuclei label intensity +# - Y: 0.26um +# - X: 0.26um +# - world +# - C: membrane/ nuclei label intensity +# - Z: 1um +# - Y: 1um +# - X: 1um +# - viewport +# - Y: 1px +# - X: 1px +# - C: red/green/blue intensity + +# %% +from skimage.data import cells3d + +cells = cells3d() +cells = cells.astype("float64") +cells -= cells.min() +cells /= cells.max() + +print(f"{cells.shape=}") +print(f"{cells.dtype=}") +print(f"{cells.min()=}") +print(f"{cells.max()=}") + +# %% +import transformnd as tnd +from transformnd.transforms import ProjectAxis, MapAxis, Scale + +# Aligned at world origin. +# This would be stored alongside the data. +cells_to_world = tnd.base.TransformSequence( + [ + # Move the color axis to the first position + MapAxis([1, 0, 2, 3]), + # Scale the space axes + Scale([1, 0.29, 0.26, 0.26]), + ], +) +print(cells_to_world) + +# Aligned at world origin. +# This would be chosen by the viewing application. +viewport_to_world = tnd.base.TransformSequence( + [ + # Create a Z axis + ProjectAxis(created={0}, source_ndim=3), + # Move the color axis to the first position + MapAxis([3, 0, 1, 2]), + # Choose a spatial sampling frequency (here 0.2um isotropic) + Scale([1, 0.2, 0.2, 0.2]), + ], +) +print(viewport_to_world) + +# %% [markdown] +# Both images know how to transform their array indices into the real world. +# +# We can invert one of those transforms to get a transformation between viewport-space and cell-space. +# We can also have a separate transformation to control moving the viewport (useful if we had an interactive viewer). + +# %% +from transformnd.transforms import Translate + +# Shift the viewport within the data, in world measurements. +# This would be controlled by the user as they peruse the data. +viewport_offset = Translate([0, 35 * 0.29, 64 * 0.26, 0.0]) + +viewport_to_cells = viewport_to_world | viewport_offset | ~cells_to_world + +# %% [markdown] +# Here we want to get all of the coordinates of our viewport, across all channels, in the shape needed by `transformnd` (number of coordinates x dimensionality of coordinates). +# +# We then transform that to get the positions of those coordinates within the cells image. + +# %% +import numpy as np + +# 2D YXC image +viewport_shape = (128, 256, 3) + +indices = [np.arange(s, dtype=float) for s in viewport_shape] +grids = np.meshgrid(*indices, indexing="ij") + +# Y, X, C, coords +vp_coords_3d = np.stack(grids, -1) +print(f"{vp_coords_3d.shape=}") + +# Y*X*C, coords +vp_coords = vp_coords_3d.reshape((-1, len(viewport_shape))) +print(f"{vp_coords.shape=}") + +# Z*C*Y*X, coords +cells_coords = viewport_to_cells.apply(vp_coords) +print(f"{cells_coords.shape=}") + +# %% [markdown] +# `scipy.ndimage.map_coordinates` is where the magic happens; +# looking up our coordinates in the cells image to get the intensities. +# There's a dask version too! + +# %% +from scipy.ndimage import map_coordinates + +# transformnd uses `NxD` coordinate arrays; map_coordinates uses `DxN` +cells_vals = map_coordinates(cells, cells_coords.T).T +print(f"{cells_vals.shape=}") +viewport = cells_vals.reshape(viewport_shape) +print(f"{viewport.shape=}") + +# %% +from matplotlib import pyplot as plt + +plt.imshow(viewport) diff --git a/examples/tutorial.py b/examples/tutorial.py index dbdbcd5..1dc1ccc 100644 --- a/examples/tutorial.py +++ b/examples/tutorial.py @@ -1,352 +1,272 @@ -import marimo - -__generated_with = "0.23.4" -app = marimo.App() - - -@app.cell -def _(): - import marimo as mo - - return (mo,) - - -@app.cell -def _(mo): - mo.md(""" - # `transformnd` tutorial - - `transformnd` is a python library which provides implementations of coordinate transformations, base classes to ensure a consistent API across implementations, and utilities for combining and working with those coordinate transformations. - - First, define a basic shape, and make a function for plotting it: - """) - return - - -@app.cell -def _(): - import numpy as np - from matplotlib import pyplot as plt - from matplotlib.patches import Polygon - - rng = np.random.default_rng(1991) - - # Array of N coordinates in D dimensions has shape NxD - square = np.array( - [ - [0, 0], - [0, 1], - [1, 1], - [1, 0], - ] - ) - - def plot_shape(**kwargs): - """Draw a labelled polygon from kwargs like name=coords.""" - fig, ax = plt.subplots() - min_ = -1 - max_ = 3 - ax.set_xlim(min_, max_) - ax.set_ylim(min_, max_) - ax.hlines(0, min_, max_, colors="k") - ax.vlines(0, min_, max_, colors="k") - ax.set_aspect("equal") - sq = Polygon(square, closed=False, color="grey", label="original") - ax.add_patch(sq) - - cmap = plt.get_cmap("tab10") - - for idx, (name, coords) in enumerate(kwargs.items()): - poly = Polygon(coords, closed=False, label=name, color=cmap(idx)) - ax.add_patch(poly) - ax.legend() - return fig - - plot_shape() - return np, plot_shape, rng, square - - -@app.cell -def _(mo): - mo.md(""" - Now we'll add some basic transformations. - - Transformations are subclasses of the `Transform` abstract base class. - Once the transformation is instantiated, it can be used just like a function, transforming a numpy array of coordinates into a new numpy array. - """) - return - - -@app.cell -def _(plot_shape, square): - from transformnd.transforms.simple import Scale, Translate - - translate = Translate([1.5, 0]) - scale = Scale([0.5, 2]) - - plot_shape( - translated=translate.apply(square), - scaled=scale.apply(square), - ) - return Scale, Translate, scale, translate - - -@app.cell -def _(mo): - mo.md(""" - ## `TransformSequence` - - Transforms can be chained together into a single transformation, with the `TransformSequence` class. - """) - return - - -@app.cell -def _(plot_shape, scale, square, translate): - from transformnd import TransformSequence - - scale_then_translate = TransformSequence([scale, translate]) - - plot_shape( - translated=translate.apply(square), - scaled=scale.apply(square), - scaled_then_translated=scale_then_translate.apply(square), - ) - return - - -@app.cell -def _(mo): - mo.md(""" - ## Composing and inverting transformations - - `TransformSequences` can also be constructed with the `|` operator. - - Certain transformations can be inverted with the `~` operator. - """) - return - - -@app.cell -def _(plot_shape, scale, square, translate): - translate_scale = translate | scale - # Note that translate -> scale gives different results to scale -> translate above - # because the scaling is done around (0, 0) - - undo_translate_scale = ~translate_scale - # Note that if you want to immediately call an inverted transform, - # you need to use brackets to ensure the inversion happens first: - # - # (~my_transform)(my_coordinates) - - plot_shape( - translated_scaled=translate_scale.apply(square), - undone=undo_translate_scale.apply(translate_scale.apply(square)), - ) - return - - -@app.cell -def _(mo): - mo.md(""" - ## Affine transformations - - A common class of coordinate transformations are affine transformations. - These require a transformation matrix which then multiplies the coordinate array. - `transformnd` provides convenience methods for constructing this transformation matrix out of common operations: translations, scales, reflections, rotations, and shears. - - Because this all uses matrix multiplication under the hood, `AffineTransform` instances can be composed using the matrix multiplication operator `@` rather than needing to apply the transforms one at a time in a `TransformSequence`. - - Note that, as with raw affine transformation matrices, the individual transformations are effectively applied right to left. - That is, to apply a scale, then a rotation, then a translation, use `(translation @ rotation @ scale).apply(coordinates)`. - - Affine transformations are invertible. - """) - return - - -@app.cell -def _(np, plot_shape, square): - from transformnd.transforms.affine import Affine - - sca = Affine.scaling([1.5, 1]) - rot = Affine.rotation2(45) # Only 2D, 3D have convenient constructors - tran = Affine.translation([1, 0]) - sca_rot_tran = tran @ rot @ sca - - plot_shape( - translated=tran.apply(square), - rotated=rot.apply(square), - scaled=sca.apply(square), - translated_rotated_scaled=sca_rot_tran.apply(square), - ) - assert np.allclose( - (tran @ rot @ sca).apply(square), (sca | rot | tran).apply(square) - ) - return - - -@app.cell -def _(mo): - mo.md(""" - ## Spaces and bridging transforms - - A common task in coordinate transformation is to convert coordinates in one space (e.g. a pixel index of an image) into some other space (e.g. a location in "world" space, using the image's resolution and offset). - - Given a set of transforms between known spaces, you can do bridging transforms: - calculating how to get from one space to another by applying some subset of those transforms in sequence. - This uses the `TransformGraph` class (requires `networkx`). - """) - return - - -@app.cell -def _(Scale, Translate): - from transformnd import TransformGraph, Spaced - - g = TransformGraph() - ab = Scale([2, 2]) - bc = Translate([0.5, 1]) - bd = Translate([1, 0.5]) - - g.add_transform(Spaced(ab, "a", "b")) - g.add_transform(Spaced(ab.invert(), "b", "a")) - g.add_transform(Spaced(bc, "b", "c")) - g.add_transform(Spaced(bc.invert(), "c", "b")) - g.add_transform(Spaced(bd, "b", "d")) - g.add_transform(Spaced(bd.invert(), "d", "b")) - - print("Transform sequence from a to c:", g.get_sequence("a", "c")) - print("Transform sequence from d to a:", g.get_sequence("d", "a")) - return - - -@app.cell -def _(mo): - mo.md(""" - ## Making your own `Transform` - - There are many different types of transformation, which have been implemented in many different ways in many different libraries. - `transformnd` makes it easy to bring lots of different implementations under one interface, which can then make use of the same composition tools. - - `Transform` is an abstract base class with a number of utility methods already implemented. - At a minimum, you just need to subclass it and implement the `.apply` method. - If your transformation is only defined for certain dimensionalities, that can also be specified: use the `._validate_coords` method to check that passed-in coordinates are of the right shape. [1] - - You can also optionally implement `.__invert__`, if the transformation is invertible. - - [1]: N.B. `TransformSequence` and `TransformGraph` both ensure that their component transforms have compatible dimensionalities. - """) - return - - -@app.cell -def _(np): - from transformnd import Transform, NDims - - class IsotropicScale2d(Transform): - def __init__(self, factor: float): - # ensure the spaces are handled properly - super().__init__(ndims=NDims(2, 2)) - self.factor = factor - - def apply(self, coords: np.ndarray) -> np.ndarray: - # ensure the coordinates are of the right shape - coords = self._validate_coords(coords) - return coords * self.factor - - def invert(self): - return type(self)( - 1 / self.factor, - ) - - return - - -@app.cell -def _(mo): - mo.md(""" - ## Adapters - - Lots of objects have coordinates, but many other features too. - `transformnd` makes it easy to transform arbitrary python objects like this using adapters. - Basically, you define a subclass of `transformnd.adapters.BaseAdapter` and implement `.apply`, which takes a transform and an object. - This method will get coordinates from the object, and construct a new one from the transformed coordinates and the old object. - - For cases where an object has transformable attributes, there is the `AttrAdapter`, which takes key-value pairs of the name of the transformable attribute, and an adapter which knows how to apply a transform to that attribute. - Cases where the attribute is just a coordinate array can use the `NullAdapter`. - - If you're going to use the same adapter-transform combination a lot, you can use `Adapter.partial` to create a callable representing that combination. - - When writing your own `Adapter`, consider giving the `.apply` method an `in_place` keyword argument if you have the option of modifying the existing object rather than creating a new one. - `AttrAdapter`s which compose over other adapters will save themselves some copying by using this argument intelligently. - """) - return - - -@app.cell -def _(np, rng, translate): - from dataclasses import dataclass - - from transformnd.adapters import AttrAdapter, ReshapeAdapter - - # 2D coordinates arranged in an array - odd_shaped_coordinates = rng.random((5, 3, 2, 4)) - reshape = ReshapeAdapter(2) # the axis containing each coordinate - odd_shaped_transformed = reshape.apply(translate, odd_shaped_coordinates) - assert odd_shaped_coordinates.shape == odd_shaped_transformed.shape - - # Custom class: - - @dataclass - class Town: - houses: np.ndarray - shops: np.ndarray - - my_town = Town( - houses=rng.random((5, 2)), - shops=rng.random((3, 2)), - ) - - # None says "this variable doesn't need adapting for transformation" - # i.e. it's already a numpy array of the correct shape - town_adapter = AttrAdapter(houses=None, shops=None) - translated_town = town_adapter.apply(translate, my_town) - - town_translator = town_adapter.partial(translate) - translated_town_2 = town_translator(translated_town) # noqa: F841 - return - - -@app.cell -def _(mo): - mo.md(""" - `transformnd` includes adapters for instances of `meshio.Mesh`, `pandas.DataFrame` (where certain columns make up a coordinate array), and geometries from `shapely`. - These are only available if you have the requisite dependencies installed. - """) - return - - -@app.cell -def _(Scale, np): - import pandas as pd - - from transformnd.adapters.pandas import PandasAdapter - - df = pd.DataFrame( - np.random.random((3, 5)), - columns=["x", "y", "z", "radius", "weight"], - ) - - print("original") - print(df) - - print("\nscaled") - xyz_adapter = PandasAdapter(["x", "y", "z"]) - scale3d = Scale([1, 10, 100]) - print(xyz_adapter.apply(scale3d, df)) - return - - -if __name__ == "__main__": - app.run() +# %% [markdown] +# # `transformnd` tutorial +# +# `transformnd` is a python library which provides implementations of coordinate transformations, base classes to ensure a consistent API across implementations, and utilities for combining and working with those coordinate transformations. +# +# First, define a basic shape, and make a function for plotting it: + +# %% +import numpy as np +from matplotlib import pyplot as plt +from matplotlib.patches import Polygon + +rng = np.random.default_rng(1991) + +# Array of N coordinates in D dimensions has shape NxD +square = np.array( + [ + [0, 0], + [0, 1], + [1, 1], + [1, 0], + ] +) + + +def plot_shape(**kwargs): + """Draw a labelled polygon from kwargs like name=coords.""" + fig, ax = plt.subplots() + min_ = -1 + max_ = 3 + ax.set_xlim(min_, max_) + ax.set_ylim(min_, max_) + ax.hlines(0, min_, max_, colors="k") + ax.vlines(0, min_, max_, colors="k") + ax.set_aspect("equal") + sq = Polygon(square, closed=False, color="grey", label="original") + ax.add_patch(sq) + + cmap = plt.get_cmap("tab10") + + for idx, (name, coords) in enumerate(kwargs.items()): + poly = Polygon(coords, closed=False, label=name, color=cmap(idx)) + ax.add_patch(poly) + ax.legend() + return fig + + +plot_shape() + +# %% [markdown] +# Now we'll add some basic transformations. +# +# Transformations are subclasses of the `Transform` abstract base class. +# Once the transformation is instantiated, it can be used just like a function, transforming a numpy array of coordinates into a new numpy array. + +# %% +from transformnd.transforms.simple import Scale, Translate + +translate = Translate([1.5, 0]) +scale = Scale([0.5, 2]) + +plot_shape( + translated=translate.apply(square), + scaled=scale.apply(square), +) + +# %% [markdown] +# ## `TransformSequence` +# +# Transforms can be chained together into a single transformation, with the `TransformSequence` class. + +# %% +from transformnd import TransformSequence + +scale_then_translate = TransformSequence([scale, translate]) + +plot_shape( + translated=translate.apply(square), + scaled=scale.apply(square), + scaled_then_translated=scale_then_translate.apply(square), +) + +# %% [markdown] +# ## Composing and inverting transformations +# +# `TransformSequences` can also be constructed with the `|` operator. +# +# Certain transformations can be inverted with the `~` operator. + +# %% +translate_scale = translate | scale +# Note that translate -> scale gives different results to scale -> translate above +# because the scaling is done around (0, 0) + +undo_translate_scale = ~translate_scale +# Note that if you want to immediately call an inverted transform, +# you need to use brackets to ensure the inversion happens first: +# +# (~my_transform)(my_coordinates) + +plot_shape( + translated_scaled=translate_scale.apply(square), + undone=undo_translate_scale.apply(translate_scale.apply(square)), +) + +# %% [markdown] +# ## Affine transformations +# +# A common class of coordinate transformations are affine transformations. +# These require a transformation matrix which then multiplies the coordinate array. +# `transformnd` provides convenience methods for constructing this transformation matrix out of common operations: translations, scales, reflections, rotations, and shears. +# +# Because this all uses matrix multiplication under the hood, `AffineTransform` instances can be composed using the matrix multiplication operator `@` rather than needing to apply the transforms one at a time in a `TransformSequence`. +# +# Note that, as with raw affine transformation matrices, the individual transformations are effectively applied right to left. +# That is, to apply a scale, then a rotation, then a translation, use `(translation @ rotation @ scale).apply(coordinates)`. +# +# Affine transformations are invertible. + +# %% +from transformnd.transforms.affine import Affine + +sca = Affine.scaling([1.5, 1]) +rot = Affine.rotation2(45) # Only 2D, 3D have convenient constructors +tran = Affine.translation([1, 0]) +sca_rot_tran = tran @ rot @ sca + +plot_shape( + translated=tran.apply(square), + rotated=rot.apply(square), + scaled=sca.apply(square), + translated_rotated_scaled=sca_rot_tran.apply(square), +) +assert np.allclose((tran @ rot @ sca).apply(square), (sca | rot | tran).apply(square)) + +# %% [markdown] +# ## Spaces and bridging transforms +# +# A common task in coordinate transformation is to convert coordinates in one space (e.g. a pixel index of an image) into some other space (e.g. a location in "world" space, using the image's resolution and offset). +# +# Given a set of transforms between known spaces, you can do bridging transforms: +# calculating how to get from one space to another by applying some subset of those transforms in sequence. +# This uses the `TransformGraph` class (requires `networkx`). + +# %% +from transformnd import TransformGraph, Spaced + +g = TransformGraph() +ab = Scale([2, 2]) +bc = Translate([0.5, 1]) +bd = Translate([1, 0.5]) + +g.add_transform(Spaced(ab, "a", "b")) +if ba := ab.invert(): + g.add_transform(Spaced(ba, "b", "a")) + +g.add_transform(Spaced(bc, "b", "c")) +if cb := bc.invert(): + g.add_transform(Spaced(cb, "c", "b")) + +g.add_transform(Spaced(bd, "b", "d")) +if db := bd.invert(): + g.add_transform(Spaced(db, "d", "b")) + +print("Transform sequence from a to c:", g.get_sequence("a", "c")) +print("Transform sequence from d to a:", g.get_sequence("d", "a")) + +# %% [markdown] +# ## Making your own `Transform` +# +# There are many different types of transformation, which have been implemented in many different ways in many different libraries. +# `transformnd` makes it easy to bring lots of different implementations under one interface, which can then make use of the same composition tools. +# +# `Transform` is an abstract base class with a number of utility methods already implemented. +# At a minimum, you just need to subclass it and implement the `.apply` method. +# If your transformation is only defined for certain dimensionalities, that can also be specified: use the `._validate_coords` method to check that passed-in coordinates are of the right shape. [1] +# +# You can also optionally implement `.__invert__`, if the transformation is invertible. +# +# [1]: N.B. `TransformSequence` and `TransformGraph` both ensure that their component transforms have compatible dimensionalities. + +# %% +from transformnd import Transform, NDims + + +class IsotropicScale2d(Transform): + def __init__(self, factor: float): + # ensure the spaces are handled properly + super().__init__(ndims=NDims(2, 2)) + self.factor = factor + + def apply(self, coords: np.ndarray) -> np.ndarray: + # ensure the coordinates are of the right shape + coords = self._validate_coords(coords) + return coords * self.factor + + def invert(self): + return type(self)( + 1 / self.factor, + ) + + +# %% [markdown] +# ## Adapters +# +# Lots of objects have coordinates, but many other features too. +# `transformnd` makes it easy to transform arbitrary python objects like this using adapters. +# Basically, you define a subclass of `transformnd.adapters.BaseAdapter` and implement `.apply`, which takes a transform and an object. +# This method will get coordinates from the object, and construct a new one from the transformed coordinates and the old object. +# +# For cases where an object has transformable attributes, there is the `AttrAdapter`, which takes key-value pairs of the name of the transformable attribute, and an adapter which knows how to apply a transform to that attribute. +# Cases where the attribute is just a coordinate array can use the `NullAdapter`. +# +# If you're going to use the same adapter-transform combination a lot, you can use `Adapter.partial` to create a callable representing that combination. +# +# When writing your own `Adapter`, consider giving the `.apply` method an `in_place` keyword argument if you have the option of modifying the existing object rather than creating a new one. +# `AttrAdapter`s which compose over other adapters will save themselves some copying by using this argument intelligently. + +# %% +from dataclasses import dataclass + +from transformnd.adapters import AttrAdapter, ReshapeAdapter + +# 2D coordinates arranged in an array +odd_shaped_coordinates = rng.random((5, 3, 2, 4)) +reshape = ReshapeAdapter(2) # the axis containing each coordinate +odd_shaped_transformed = reshape.apply(translate, odd_shaped_coordinates) +assert odd_shaped_coordinates.shape == odd_shaped_transformed.shape + +# Custom class: + + +@dataclass +class Town: + houses: np.ndarray + shops: np.ndarray + + +my_town = Town( + houses=rng.random((5, 2)), + shops=rng.random((3, 2)), +) + +# None says "this variable doesn't need adapting for transformation" +# i.e. it's already a numpy array of the correct shape +town_adapter = AttrAdapter(houses=None, shops=None) +translated_town = town_adapter.apply(translate, my_town) + +town_translator = town_adapter.partial(translate) +translated_town_2 = town_translator(translated_town) # noqa: F841 + +# %% [markdown] +# `transformnd` includes adapters for instances of `meshio.Mesh`, `pandas.DataFrame` (where certain columns make up a coordinate array), and geometries from `shapely`. +# These are only available if you have the requisite dependencies installed. + +# %% +import pandas as pd + +from transformnd.adapters.pandas import PandasAdapter + +df = pd.DataFrame( + np.random.random((3, 5)), + columns=["x", "y", "z", "radius", "weight"], +) + +print("original") +print(df) + +print("\nscaled") +xyz_adapter = PandasAdapter(["x", "y", "z"]) +scale3d = Scale([1, 10, 100]) +print(xyz_adapter.apply(scale3d, df)) diff --git a/pyproject.toml b/pyproject.toml index 6beb525..949be9b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -78,6 +78,8 @@ lint = [ "pydoclint>=0.8.6", ] doc = [ + "jupytext>=1.19.6", + "nbconvert>=7.17.1", "pdoc>=16.0.0", ] examples = [ @@ -90,6 +92,9 @@ examples = [ bench = [ "pytest-benchmark>=5.2.3", ] +docs = [ + "ipykernel>=7.4.0", +] [build-system] requires = ["uv-build>=0.12.10,<0.13.0"] @@ -107,8 +112,11 @@ addopts = ["--benchmark-skip"] 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changed, 146 insertions(+), 398 deletions(-) diff --git a/examples/image.py b/examples/image.py index d93c628..693a4bf 100644 --- a/examples/image.py +++ b/examples/image.py @@ -1,28 +1,30 @@ # %% [markdown] -# # Image transformation with `transformnd` -# -# `transformnd` transforms coordinates, not images, but coordinate transformations can be used to transform images. -# Your output (transformed) and source images both have pixels with an `xy` coordinate in their respective image spaces, -# and image transformation is simply a case of finding which source pixel to use for each output pixel. -# -# Here we take a 2-channel fluorescence microscopy image of some cells in 3 dimensions, use scaling information to map those pixels into a real-world space, and then map the pixels of our viewport into the the same space. -# -# We will refer to the following coordinate spaces and their axes: -# -# - cells -# - Z: 0.29um -# - C: membrane/ nuclei label intensity -# - Y: 0.26um -# - X: 0.26um -# - world -# - C: membrane/ nuclei label intensity -# - Z: 1um -# - Y: 1um -# - X: 1um -# - viewport -# - Y: 1px -# - X: 1px -# - C: red/green/blue intensity +""" +# Image transformation with `transformnd` + +`transformnd` transforms coordinates, not images, but coordinate transformations can be used to transform images. +Your output (transformed) and source images both have pixels with an `xy` coordinate in their respective image spaces, +and image transformation is simply a case of finding which source pixel to use for each output pixel. + +Here we take a 2-channel fluorescence microscopy image of some cells in 3 dimensions, use scaling information to map those pixels into a real-world space, and then map the pixels of our viewport into the the same space. + +We will refer to the following coordinate spaces and their axes: + +- cells + - Z: 0.29um + - C: membrane/ nuclei label intensity + - Y: 0.26um + - X: 0.26um +- world + - C: membrane/ nuclei label intensity + - Z: 1um + - Y: 1um + - X: 1um +- viewport + - Y: 1px + - X: 1px + - C: red/green/blue intensity +""" # %% from skimage.data import cells3d @@ -68,10 +70,12 @@ print(viewport_to_world) # %% [markdown] -# Both images know how to transform their array indices into the real world. -# -# We can invert one of those transforms to get a transformation between viewport-space and cell-space. -# We can also have a separate transformation to control moving the viewport (useful if we had an interactive viewer). +""" +Both images know how to transform their array indices into the real world. + +We can invert one of those transforms to get a transformation between viewport-space and cell-space. +We can also have a separate transformation to control moving the viewport (useful if we had an interactive viewer). +""" # %% from transformnd.transforms import Translate @@ -83,9 +87,11 @@ viewport_to_cells = viewport_to_world | viewport_offset | ~cells_to_world # %% [markdown] +""" # Here we want to get all of the coordinates of our viewport, across all channels, in the shape needed by `transformnd` (number of coordinates x dimensionality of coordinates). # # We then transform that to get the positions of those coordinates within the cells image. +""" # %% import numpy as np @@ -109,9 +115,11 @@ print(f"{cells_coords.shape=}") # %% [markdown] -# `scipy.ndimage.map_coordinates` is where the magic happens; -# looking up our coordinates in the cells image to get the intensities. -# There's a dask version too! +""" +`scipy.ndimage.map_coordinates` is where the magic happens; +looking up our coordinates in the cells image to get the intensities. +There's a dask version too! +""" # %% from scipy.ndimage import map_coordinates diff --git a/examples/tutorial.py b/examples/tutorial.py index 1dc1ccc..ccfbf87 100644 --- a/examples/tutorial.py +++ b/examples/tutorial.py @@ -1,9 +1,11 @@ # %% [markdown] -# # `transformnd` tutorial -# -# `transformnd` is a python library which provides implementations of coordinate transformations, base classes to ensure a consistent API across implementations, and utilities for combining and working with those coordinate transformations. -# -# First, define a basic shape, and make a function for plotting it: +""" +# `transformnd` tutorial + +`transformnd` is a python library which provides implementations of coordinate transformations, base classes to ensure a consistent API across implementations, and utilities for combining and working with those coordinate transformations. + +First, define a basic shape, and make a function for plotting it: +""" # %% import numpy as np @@ -48,10 +50,12 @@ def plot_shape(**kwargs): plot_shape() # %% [markdown] -# Now we'll add some basic transformations. -# -# Transformations are subclasses of the `Transform` abstract base class. -# Once the transformation is instantiated, it can be used just like a function, transforming a numpy array of coordinates into a new numpy array. +""" +Now we'll add some basic transformations. + +Transformations are subclasses of the `Transform` abstract base class. +Once the transformation is instantiated, it can be used just like a function, transforming a numpy array of coordinates into a new numpy array. +""" # %% from transformnd.transforms.simple import Scale, Translate @@ -65,9 +69,11 @@ def plot_shape(**kwargs): ) # %% [markdown] -# ## `TransformSequence` -# -# Transforms can be chained together into a single transformation, with the `TransformSequence` class. +""" +## `TransformSequence` + +Transforms can be chained together into a single transformation, with the `TransformSequence` class. +""" # %% from transformnd import TransformSequence @@ -81,11 +87,13 @@ def plot_shape(**kwargs): ) # %% [markdown] -# ## Composing and inverting transformations -# -# `TransformSequences` can also be constructed with the `|` operator. -# -# Certain transformations can be inverted with the `~` operator. +""" +## Composing and inverting transformations + +`TransformSequences` can also be constructed with the `|` operator. + +Certain transformations can be inverted with the `~` operator. +""" # %% translate_scale = translate | scale @@ -104,18 +112,20 @@ def plot_shape(**kwargs): ) # %% [markdown] -# ## Affine transformations -# -# A common class of coordinate transformations are affine transformations. -# These require a transformation matrix which then multiplies the coordinate array. -# `transformnd` provides convenience methods for constructing this transformation matrix out of common operations: translations, scales, reflections, rotations, and shears. -# -# Because this all uses matrix multiplication under the hood, `AffineTransform` instances can be composed using the matrix multiplication operator `@` rather than needing to apply the transforms one at a time in a `TransformSequence`. -# -# Note that, as with raw affine transformation matrices, the individual transformations are effectively applied right to left. -# That is, to apply a scale, then a rotation, then a translation, use `(translation @ rotation @ scale).apply(coordinates)`. -# -# Affine transformations are invertible. +""" +## Affine transformations + +A common class of coordinate transformations are affine transformations. +These require a transformation matrix which then multiplies the coordinate array. +`transformnd` provides convenience methods for constructing this transformation matrix out of common operations: translations, scales, reflections, rotations, and shears. + +Because this all uses matrix multiplication under the hood, `AffineTransform` instances can be composed using the matrix multiplication operator `@` rather than needing to apply the transforms one at a time in a `TransformSequence`. + +Note that, as with raw affine transformation matrices, the individual transformations are effectively applied right to left. +That is, to apply a scale, then a rotation, then a translation, use `(translation @ rotation @ scale).apply(coordinates)`. + +Affine transformations are invertible. +""" # %% from transformnd.transforms.affine import Affine @@ -134,13 +144,15 @@ def plot_shape(**kwargs): assert np.allclose((tran @ rot @ sca).apply(square), (sca | rot | tran).apply(square)) # %% [markdown] -# ## Spaces and bridging transforms -# -# A common task in coordinate transformation is to convert coordinates in one space (e.g. a pixel index of an image) into some other space (e.g. a location in "world" space, using the image's resolution and offset). -# -# Given a set of transforms between known spaces, you can do bridging transforms: -# calculating how to get from one space to another by applying some subset of those transforms in sequence. -# This uses the `TransformGraph` class (requires `networkx`). +""" +## Spaces and bridging transforms + +A common task in coordinate transformation is to convert coordinates in one space (e.g. a pixel index of an image) into some other space (e.g. a location in "world" space, using the image's resolution and offset). + +Given a set of transforms between known spaces, you can do bridging transforms: +calculating how to get from one space to another by applying some subset of those transforms in sequence. +This uses the `TransformGraph` class (requires `networkx`). +""" # %% from transformnd import TransformGraph, Spaced @@ -166,18 +178,20 @@ def plot_shape(**kwargs): print("Transform sequence from d to a:", g.get_sequence("d", "a")) # %% [markdown] -# ## Making your own `Transform` -# -# There are many different types of transformation, which have been implemented in many different ways in many different libraries. -# `transformnd` makes it easy to bring lots of different implementations under one interface, which can then make use of the same composition tools. -# -# `Transform` is an abstract base class with a number of utility methods already implemented. -# At a minimum, you just need to subclass it and implement the `.apply` method. -# If your transformation is only defined for certain dimensionalities, that can also be specified: use the `._validate_coords` method to check that passed-in coordinates are of the right shape. [1] -# -# You can also optionally implement `.__invert__`, if the transformation is invertible. -# -# [1]: N.B. `TransformSequence` and `TransformGraph` both ensure that their component transforms have compatible dimensionalities. +""" +## Making your own `Transform` + +There are many different types of transformation, which have been implemented in many different ways in many different libraries. +`transformnd` makes it easy to bring lots of different implementations under one interface, which can then make use of the same composition tools. + +`Transform` is an abstract base class with a number of utility methods already implemented. +At a minimum, you just need to subclass it and implement the `.apply` method. +If your transformation is only defined for certain dimensionalities, that can also be specified: use the `._validate_coords` method to check that passed-in coordinates are of the right shape. [1] + +You can also optionally implement `.__invert__`, if the transformation is invertible. + +[1]: N.B. `TransformSequence` and `TransformGraph` both ensure that their component transforms have compatible dimensionalities. +""" # %% from transformnd import Transform, NDims @@ -201,20 +215,22 @@ def invert(self): # %% [markdown] -# ## Adapters -# -# Lots of objects have coordinates, but many other features too. -# `transformnd` makes it easy to transform arbitrary python objects like this using adapters. -# Basically, you define a subclass of `transformnd.adapters.BaseAdapter` and implement `.apply`, which takes a transform and an object. -# This method will get coordinates from the object, and construct a new one from the transformed coordinates and the old object. -# -# For cases where an object has transformable attributes, there is the `AttrAdapter`, which takes key-value pairs of the name of the transformable attribute, and an adapter which knows how to apply a transform to that attribute. -# Cases where the attribute is just a coordinate array can use the `NullAdapter`. -# -# If you're going to use the same adapter-transform combination a lot, you can use `Adapter.partial` to create a callable representing that combination. -# -# When writing your own `Adapter`, consider giving the `.apply` method an `in_place` keyword argument if you have the option of modifying the existing object rather than creating a new one. -# `AttrAdapter`s which compose over other adapters will save themselves some copying by using this argument intelligently. +""" +## Adapters + +Lots of objects have coordinates, but many other features too. +`transformnd` makes it easy to transform arbitrary python objects like this using adapters. +Basically, you define a subclass of `transformnd.adapters.BaseAdapter` and implement `.apply`, which takes a transform and an object. +This method will get coordinates from the object, and construct a new one from the transformed coordinates and the old object. + +For cases where an object has transformable attributes, there is the `AttrAdapter`, which takes key-value pairs of the name of the transformable attribute, and an adapter which knows how to apply a transform to that attribute. +Cases where the attribute is just a coordinate array can use the `NullAdapter`. + +If you're going to use the same adapter-transform combination a lot, you can use `Adapter.partial` to create a callable representing that combination. + +When writing your own `Adapter`, consider giving the `.apply` method an `in_place` keyword argument if you have the option of modifying the existing object rather than creating a new one. +`AttrAdapter`s which compose over other adapters will save themselves some copying by using this argument intelligently. +""" # %% from dataclasses import dataclass @@ -250,8 +266,10 @@ class Town: translated_town_2 = town_translator(translated_town) # noqa: F841 # %% [markdown] -# `transformnd` includes adapters for instances of `meshio.Mesh`, `pandas.DataFrame` (where certain columns make up a coordinate array), and geometries from `shapely`. -# These are only available if you have the requisite dependencies installed. +""" +`transformnd` includes adapters for instances of `meshio.Mesh`, `pandas.DataFrame` (where certain columns make up a coordinate array), and geometries from `shapely`. +These are only available if you have the requisite dependencies installed. +""" # %% import pandas as pd diff --git a/pyproject.toml b/pyproject.toml index 949be9b..4c039c5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,24 +77,22 @@ lint = [ "types-shapely>=2.1.0.20260408", "pydoclint>=0.8.6", ] -doc = [ - 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run: uv run ruff format --check - marimo: + examples: runs-on: ubuntu-latest steps: - uses: actions/checkout@v7 @@ -23,7 +23,7 @@ jobs: enable-cache: true - run: uv sync --all-extras - uses: extractions/setup-just@v4 - - run: just example-test + - run: just examples-run lint: runs-on: ubuntu-latest diff --git a/.gitignore b/.gitignore index 12f3201..74ce774 100644 --- a/.gitignore +++ b/.gitignore @@ -141,6 +141,3 @@ transformnd/version.py env/ docs/ - -# marimo -__marimo__/ diff --git a/README.md b/README.md index f8e6a29..11461f2 100644 --- a/README.md +++ b/README.md @@ -27,10 +27,6 @@ are of valid type and dimensions. Additionally, `transformnd` provides an interface for transforming types other than NxD numpy arrays, and implements these adapters for a few common types. -See the [tutorial here](https://github.com/clbarnes/transformnd/blob/main/examples/tutorial.py). -It is a [marimo](https://marimo.io) notebook. -Open it with `uv run --group examples marimo edit examples/tutorial.py`. - ## Usage `transformnd` is pre-1.0 and the API is subject to change. diff --git a/examples/.gitignore b/examples/.gitignore new file mode 100644 index 0000000..251d280 --- /dev/null +++ b/examples/.gitignore @@ -0,0 +1,2 @@ +*.ipynb +*.html diff --git a/examples/image.py b/examples/image.py index 693a4bf..3d80d2e 100644 --- a/examples/image.py +++ b/examples/image.py @@ -1,3 +1,18 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: -all +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.19.6 +# kernelspec: +# display_name: python_kernel +# language: python +# name: python_kernel +# --- + # %% [markdown] """ # Image transformation with `transformnd` @@ -88,9 +103,9 @@ # %% [markdown] """ -# Here we want to get all of the coordinates of our viewport, across all channels, in the shape needed by `transformnd` (number of coordinates x dimensionality of coordinates). -# -# We then transform that to get the positions of those coordinates within the cells image. +Here we want to get all of the coordinates of our viewport, across all channels, in the shape needed by `transformnd` (number of coordinates x dimensionality of coordinates). + +We then transform that to get the positions of those coordinates within the cells image. """ # %% diff --git a/examples/tutorial.py b/examples/tutorial.py index ccfbf87..67200f0 100644 --- a/examples/tutorial.py +++ b/examples/tutorial.py @@ -1,3 +1,18 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: -all +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.19.6 +# kernelspec: +# display_name: python_kernel +# language: python +# name: python_kernel +# --- + # %% [markdown] """ # `transformnd` tutorial diff --git a/justfile b/justfile index 172a718..1ba366a 100644 --- a/justfile +++ b/justfile @@ -12,15 +12,17 @@ doc docdir='doc/html': --search \ transformnd mkdir -p {{docdir}}/examples - uv run --group examples marimo export html examples/tutorial.py -o {{docdir}}/examples/tutorial.html - uv run --group examples marimo export html examples/image.py -o {{docdir}}/examples/image.html + uv run --group docs jupytext --to ipynb examples/*.py + uv run --group docs jupyter dejavu --to html examples/*.ipynb --output-dir {{docdir}}/examples + +examples-run: + uv run --group examples jupytext --execute examples/*.py # Run linters and type checkers. lint: uv run --group lint ruff check src tests examples bench uv run --group lint mypy src tests bench uv run --group lint ruff format --check src tests examples bench - uv run --group examples marimo check --strict --ignore-scripts examples/*.py uv run --group lint pydoclint src # Auto-fix format and lints where possible. @@ -36,14 +38,6 @@ format: test: uv run --all-groups --all-extras pytest -v -# Run marimo server for editing examples. -example-edit example: - uv run --group examples marimo edit examples/{{example}}.py - -# Run marimo examples headless. -example-test: - uv run --group examples marimo export session examples --force-overwrite - # Run benchmarks. bench: uv run --group test pytest --benchmark-only diff --git a/pyproject.toml b/pyproject.toml index 4c039c5..755449c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -82,12 +82,12 @@ examples = [ "pandas>=3.0.2", "pooch>=1.9.0", "scikit-image>=0.26.0", + "jupytext>=1.19.6", + "ipykernel>=7.4.0", ] doc = [ "pdoc>=16.0.0", - "jupytext>=1.19.6", "nbconvert>=7.17.1", - "ipykernel>=7.4.0", {include-group = "examples"}, ] bench = [ diff --git a/uv.lock b/uv.lock index 147d173..642149f 100644 --- a/uv.lock +++ b/uv.lock @@ -2397,7 +2397,6 @@ examples = [ { name = "ipykernel" }, { name = "jupytext" }, { name = "matplotlib" }, - { name = "nbconvert" }, { name = "pandas" }, { name = "pooch" }, { name = "scikit-image" }, @@ -2476,7 +2475,6 @@ examples = [ { name = "ipykernel", specifier = ">=7.4.0" }, { name = "jupytext", specifier = ">=1.19.6" }, { name = "matplotlib", specifier = ">=3.10.8" }, - { name = "nbconvert", specifier = ">=7.17.1" }, { name = "pandas", specifier = ">=3.0.2" }, { name = "pooch", specifier = ">=1.9.0" }, { name = "scikit-image", specifier = ">=0.26.0" }, From c2bf238ae41c6df58f4b04fb5c4ba9cf6759049d Mon Sep 17 00:00:00 2001 From: Chris Barnes Date: Mon, 5 Oct 2026 10:37:11 +0100 Subject: [PATCH 4/6] Fix bugs --- justfile | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/justfile b/justfile index 1ba366a..43bbfb1 100644 --- a/justfile +++ b/justfile @@ -3,7 +3,7 @@ default: just --list # Generate documentation, by default under `./doc/html`. -doc docdir='doc/html': +doc docdir='doc/html': install-kernel rm -rf {{docdir}} uv run --group doc pdoc \ --output-directory {{docdir}} \ @@ -12,10 +12,15 @@ doc docdir='doc/html': --search \ transformnd mkdir -p {{docdir}}/examples - uv run --group docs jupytext --to ipynb examples/*.py - uv run --group docs jupyter dejavu --to html examples/*.ipynb --output-dir {{docdir}}/examples + uv run --group doc jupytext --to ipynb examples/*.py + uv run --group doc jupyter dejavu --to html examples/*.ipynb --output-dir {{docdir}}/examples -examples-run: +# Install a jupyter kernel required by examples +install-kernel: + uv run --group doc python -m ipykernel install --name python_kernel + +# Run examples. +examples-run: install-kernel uv run --group examples jupytext --execute examples/*.py # Run linters and type checkers. From af3374db9774836e8e3a51c6b2c45c62a813c759 Mon Sep 17 00:00:00 2001 From: Chris Barnes Date: Mon, 5 Oct 2026 10:39:02 +0100 Subject: [PATCH 5/6] try to fix install-kernel --- justfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/justfile b/justfile index 43bbfb1..d8e65d3 100644 --- a/justfile +++ b/justfile @@ -17,7 +17,7 @@ doc docdir='doc/html': install-kernel # Install a jupyter kernel required by examples install-kernel: - uv run --group doc python -m ipykernel install --name python_kernel + uv run --group doc python -m ipykernel install --name python_kernel --user # Run examples. examples-run: install-kernel From f619e68e28aaff7e0ddef2ad3b575bec0bd96fd2 Mon Sep 17 00:00:00 2001 From: Chris Barnes Date: Mon, 5 Oct 2026 11:03:12 +0100 Subject: [PATCH 6/6] Use just in readthedocs --- .readthedocs.yaml | 5 +---- justfile | 1 + pyproject.toml | 2 ++ uv.lock | 27 +++++++++++++++++++++++++++ 4 files changed, 31 insertions(+), 4 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index f64b450..55744b5 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -15,7 +15,4 @@ build: # this would be preferable but readthedocs' ubuntu doesn't seem to have `just` available as a package # - just doc ${READTHEDOCS_OUTPUT}/html - mkdir -p ${READTHEDOCS_OUTPUT}/html - - uv run --group doc pdoc --output-directory ${READTHEDOCS_OUTPUT}/html --no-include-undocumented --docformat numpy --search transformnd - - mkdir -p ${READTHEDOCS_OUTPUT}/html/examples - - uv run --group examples marimo export html examples/tutorial.py -o ${READTHEDOCS_OUTPUT}/html/examples/tutorial.html - - uv run --group examples marimo export html examples/image.py -o ${READTHEDOCS_OUTPUT}/html/examples/image.html + - uv run just doc ${READTHEDOCS_OUTPUT}/html diff --git a/justfile b/justfile index d8e65d3..334d5a4 100644 --- a/justfile +++ b/justfile @@ -5,6 +5,7 @@ default: # Generate documentation, by default under `./doc/html`. doc docdir='doc/html': 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