diff --git a/.gitignore b/.gitignore index 0c248ee..fd7ce39 100644 --- a/.gitignore +++ b/.gitignore @@ -42,3 +42,6 @@ pip-wheel-metadata # numba */__pycache__/* + +# docs +site \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index db3e79d..34977d2 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,22 +1,33 @@ ci: autoupdate_commit_msg: "chore: update pre-commit hooks" autofix_commit_msg: "style: pre-commit fixes" + autofix_prs: false default_stages: [commit, push] default_language_version: python: python3 repos: - - repo: https://github.com/psf/black - rev: 22.12.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: 'v0.5.4' hooks: - - id: black - language_version: python3.9 + - id: ruff + args: ["--fix", "--show-fixes"] + - id: ruff-format + - repo: https://github.com/codespell-project/codespell + rev: v2.3.0 + hooks: + - id: codespell + args: ["-L", "ba,ihs,kake,nd,noe,nwo,te,fo,zar", "-S", "fixture"] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.6.0 hooks: - id: check-yaml - - repo: https://github.com/charliermarsh/ruff-pre-commit - # Ruff version. - rev: 'v0.0.245' + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.11.0 hooks: - - id: ruff - args: [--fix, --exit-non-zero-on-fix] \ No newline at end of file + - id: mypy + files: src + additional_dependencies: + - numpy + - typing_extensions + # Tests + - pytest \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..b611435 --- /dev/null +++ b/README.md @@ -0,0 +1,39 @@ +# xarray-multiscale + +Simple tools for creating multiscale representations of large images. + +## Installation + +`pip install xarray-multiscale` + +## Motivation + +Many image processing applications benefit from representing images at multiple scales (also known as [image pyramids] (https://en.wikipedia.org/wiki/Pyramid_(image_processing)). This package provides tools for generating lazy multiscale representations of N-dimensional data using [`xarray`](http://xarray.pydata.org/en/stable/) to ensure that the downsampled images have the correct axis coordinates. + +Why are coordinates important for this application? Because a downsampled image is typically scaled and *translated* relative to the source image. Without a coordinate-aware representation of the data, the scaling and translation information is easily lost. + + +## Usage + +Generate a multiscale representation of a numpy array: + +```python +from xarray_multiscale import multiscale, windowed_mean +import numpy as np + +data = np.arange(4) +print(*multiscale(data, windowed_mean, 2), sep='\n') +""" + Size: 32B +array([0, 1, 2, 3]) +Coordinates: + * dim_0 (dim_0) float64 32B 0.0 1.0 2.0 3.0 + + Size: 16B +array([0, 2]) +Coordinates: + * dim_0 (dim_0) float64 16B 0.5 2.5 +""" +``` + +read more in the [project documentation](https://JaneliaSciComp.github.io/xarray-multiscale/). diff --git a/README.rst b/README.rst deleted file mode 100644 index c437b01..0000000 --- a/README.rst +++ /dev/null @@ -1,144 +0,0 @@ -***************** -xarray-multiscale -***************** - -Simple tools for creating multiscale representations of large images. - -Installation -************ -.. code-block:: bash - - pip install xarray-multiscale - -Motivation -********** -Many image processing applications benefit from representing images at multiple scales (also known as `image pyramids `_). This package provides tools for generating lazy multiscale representations of N-dimensional data using `xarray `_ to ensure that the downsampled images have the correct axis coordinates. - -Why are coordinates important for this application? Because a downsampled image is typically scaled and translated relative to the source image, and this scaling and translation information is easily lost without a coordinate-aware representation of the data. - -Implementation -************** -The top-level function `multiscale` takes two main arguments: data to be downsampled, and a reduction function. The reduction function can use any implementation but it should (eagerly) take array data and a tuple of scale factors as inputs and return downsampled data as an output. See examples of reduction functions in `xarray_multiscale.reducers `_. - - -Usage -***** - -Generate a multiscale representation of a numpy array: - -.. code-block:: python - - from xarray_multiscale import multiscale, windowed_mean - import numpy as np - - data = np.arange(4) - multiscale(data, windowed_mean, 2) - -which returns this (a collection of DataArrays, each with decreasing size): - -.. code-block:: python - - [ - array([0, 1, 2, 3]) - Coordinates: - * dim_0 (dim_0) float64 0.0 1.0 2.0 3.0, - - array([0, 2]) - Coordinates: - * dim_0 (dim_0) float64 0.5 2.5] - -By default, the values of the downsampled arrays are cast to the same data type as the input. This behavior can be changed with the ``preserve_dtype`` keyword argument to ``multiscale``. - -Generate a multiscale representation of an ``xarray.DataArray``: - -.. code-block:: python - - from xarray_multiscale import multiscale, windowed_mean - from xarray import DataArray - import numpy as np - - data = np.arange(16).reshape((4,4)) - coords = (DataArray(np.arange(data.shape[0]), dims=('y',), attrs={'units' : 'm'}), - DataArray(np.arange(data.shape[0]), dims=('x',), attrs={'units' : 'm'})) - - arr = DataArray(data, coords) - multiscale(arr, windowed_mean, (2,2)) - -which returns this: - -.. code-block:: python - - [ - array([[ 0, 1, 2, 3], - [ 4, 5, 6, 7], - [ 8, 9, 10, 11], - [12, 13, 14, 15]]) - Coordinates: - * y (y) int64 0 1 2 3 - * x (x) int64 0 1 2 3, - array([[ 2, 4], - [10, 12]]) - Coordinates: - * y (y) float64 0.5 2.5 - * x (x) float64 0.5 2.5] - -Dask arrays work too. Note the control over output chunks via the ``chunks`` keyword argument. - -.. code-block:: python - - from xarray_multiscale import multiscale, windowed_mean - import dask.array as da - - arr = da.random.randint(0, 255, (10,10,10)) - multiscale(arr, windowed_mean, 2, chunks=2) - -returns - -.. code-block:: python - - [ - dask.array - Coordinates: - * dim_0 (dim_0) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 - * dim_1 (dim_1) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 - * dim_2 (dim_2) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0, - - dask.array - Coordinates: - * dim_0 (dim_0) float64 0.5 2.5 4.5 6.5 8.5 - * dim_1 (dim_1) float64 0.5 2.5 4.5 6.5 8.5 - * dim_2 (dim_2) float64 0.5 2.5 4.5 6.5 8.5, - - dask.array - Coordinates: - * dim_0 (dim_0) float64 1.5 5.5 - * dim_1 (dim_1) float64 1.5 5.5 - * dim_2 (dim_2) float64 1.5 5.5] - -Caveats -******* - -* Arrays that are not evenly divisible by the downsampling factors will be trimmed as needed. If this behavior is undesirable, consider padding your array appropriately prior to downsampling. - -* For chunked arrays (e.g., dask arrays), the current implementation divides the input data into *contiguous* chunks. This means that attempting to use downsampling schemes based on sliding windowed smoothing will produce edge artifacts. - -Development -*********** - -This project is devloped using `poetry `_. To develop this codebase, clone the repo and run :code:`poetry install` to install local dependencies. - -For development within a conda environment, create a conda environment with :code:`poetry`, then install dependencies, e.g. - -.. code-block:: bash - - $ git clone https://github.com/d-v-b-/xarray-multiscale - $ conda create -n xarray-multiscale poetry -c conda-forge - $ conda activate xarray-multiscale - $ poetry install - -Run tests with ``pytest`` - diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 0464373..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,19 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/api/chunks.md b/docs/api/chunks.md new file mode 100644 index 0000000..9570338 --- /dev/null +++ b/docs/api/chunks.md @@ -0,0 +1 @@ +::: xarray_multiscale.chunks \ No newline at end of file diff --git a/docs/api/multiscale.md b/docs/api/multiscale.md new file mode 100644 index 0000000..84fff22 --- /dev/null +++ b/docs/api/multiscale.md @@ -0,0 +1 @@ +::: xarray_multiscale.multiscale \ No newline at end of file diff --git a/docs/api/reducers.md b/docs/api/reducers.md new file mode 100644 index 0000000..08bf8be --- /dev/null +++ b/docs/api/reducers.md @@ -0,0 +1 @@ +::: xarray_multiscale.reducers \ No newline at end of file diff --git a/docs/api/util.md b/docs/api/util.md new file mode 100644 index 0000000..b771857 --- /dev/null +++ b/docs/api/util.md @@ -0,0 +1 @@ +::: xarray_multiscale.util \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index 16d85c6..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,63 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -# import os -# import sys -# sys.path.insert(0, os.path.abspath('.')) - - -# -- Project information ----------------------------------------------------- - -project = "xarray-multiscale" -copyright = "2022, Davis Bennett" -author = "Davis Bennett" - -# The full version, including alpha/beta/rc tags -release = "0.3.1" - - -# -- General configuration --------------------------------------------------- - - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", - "sphinx.ext.viewcode", - "sphinx.ext.intersphinx", - "numpydoc", - "sphinx_issues", -] - - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = [] - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = "sphinx_rtd_theme" - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..56fbd09 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,202 @@ +# xarray-multiscale + +Simple tools for creating multiscale representations of large images. + +## Installation + +`pip install xarray-multiscale` + +## Motivation + +Many image processing applications benefit from representing images at multiple scales (also known as [image pyramids](https://en.wikipedia.org/wiki/Pyramid_(image_processing)). This package provides tools for generating lazy multiscale representations of N-dimensional data using [`xarray`](http://xarray.pydata.org/en/stable/) to ensure that the downsampled images have the correct coordinates. + +### Coordinates matter when you downsample images + +It's obvious that downsampling an image applies a scaling transformation, i.e. downsampling increases the distance between image samples. This is the whole purpose of downsampling the image. But it is less obvious that most downsampling operations also apply a *translation transformation* -- downsampling an image (generally) shifts the origin of the output relative to the source. + +In signal processing terms, image downsampling combines an image filtering step (blending local intensities) with a resampling step (sampling intensities at a set of positions in the signal). When you resample an image, you get to choose which points to resample on, and the best choice for most simple downsampling routines is to resample on points that are slightly translated relative to the original image. For simple windowed downsampling, this means that the first element of the downsampled image lies +at the center (i.e., the mean) of the coordinate values of the window. + +We can illustrate this with some simple examples: + +``` +2x windowed downsampling, in one dimension: + +source coordinates: | 0 | 1 | +downsampled coordinates: | 0.5 | +``` + +``` +3x windowed downsampling, in two dimensions: + +source coordinates: | (0,0) | (0,1) | (0,2) | + | (1,0) | (1,1) | (1,2) | + | (2,0) | (2,1) | (2,2) | + +downsampled coordinates: | | + | (1,1) | + | | + +``` + +Another way of thinking about this is that if you downsample an arbitrarily large image to a single value, then the only sensible place to localize that value is at the center of the image. Thus, incrementally downsampling slightly shifts the downsampled image toward that point. + +Why should you care? If you work with images where the coordinates matter (for example, images recorded from scientific instruments), then you should care about keeping track of those coordinates. Tools like numpy or scikit-image make it very easy to ignore the coordinates of your image. These tools model images as simple arrays, and from the array perspective `data[0,0]` and `downsampled_data[0,0]` lie on the same position in space because they take the same array index. However, `downsampled_data[0,0]` is almost certainly shifted relative to `data[0,0]`. Coordinate-blind tools like `scikit-image` force your to track the coordinates on your own, which is a recipe for mistakes. This is the value of `xarray`. By explicitly modelling coordinates alongside data values, `xarray` ensures that you never lose track of where your data comes from, which is why `xarray-multiscale` uses it. + +### Who needs this + +The library `xarray` already supports basic downsampling routines via the [`DataArray.coarsen`](https://docs.xarray.dev/en/stable/user-guide/computation.html#coarsen-large-arrays) API. So if you use `xarray` and just need to compute a windowed mean, then you may not need `xarray-multiscale` at all. But the `DataArray.coarsen` API does not +allow users to provide their own downsampling functions; If you need something like [windowed mode](./api/reducers.md#xarray_multiscale.reducers.windowed_mode) downsampling, or something you wrote yourself, then `xarray-multiscale` should be useful to you. + + +## Usage + +Generate a multiscale representation of a numpy array: + +```python +from xarray_multiscale import multiscale, windowed_mean +import numpy as np + +data = np.arange(4) +print(*multiscale(data, windowed_mean, 2), sep='\n') +""" + Size: 32B +array([0, 1, 2, 3]) +Coordinates: + * dim_0 (dim_0) float64 32B 0.0 1.0 2.0 3.0 + + Size: 16B +array([0, 2]) +Coordinates: + * dim_0 (dim_0) float64 16B 0.5 2.5 +""" +``` + + +By default, the values of the downsampled arrays are cast to the same data type as the input. This behavior can be changed with the ``preserve_dtype`` keyword argument to ``multiscale``: + +```python +from xarray_multiscale import multiscale, windowed_mean +import numpy as np + +data = np.arange(4) +print(*multiscale(data, windowed_mean, 2, preserve_dtype=False), sep="\n") +""" + Size: 32B +array([0, 1, 2, 3]) +Coordinates: + * dim_0 (dim_0) float64 32B 0.0 1.0 2.0 3.0 + + Size: 16B +array([0.5, 2.5]) +Coordinates: + * dim_0 (dim_0) float64 16B 0.5 2.5 +""" +``` + +Anisotropic downsampling is supported: + +```python +from xarray_multiscale import multiscale, windowed_mean +import numpy as np + +data = np.arange(16).reshape((4,4)) +print(*multiscale(data, windowed_mean, (1,2)), sep="\n") +""" + Size: 128B +array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11], + [12, 13, 14, 15]]) +Coordinates: + * dim_0 (dim_0) float64 32B 0.0 1.0 2.0 3.0 + * dim_1 (dim_1) float64 32B 0.0 1.0 2.0 3.0 + + Size: 64B +array([[ 0, 2], + [ 4, 6], + [ 8, 10], + [12, 14]]) +Coordinates: + * dim_0 (dim_0) float64 32B 0.0 1.0 2.0 3.0 + * dim_1 (dim_1) float64 16B 0.5 2.5 +""" +``` + + +Note that `multiscale` returns an `xarray.DataArray`. +The `multiscale` function also accepts `DataArray` objects: + +```python +from xarray_multiscale import multiscale, windowed_mean +from xarray import DataArray +import numpy as np + +data = np.arange(16).reshape((4,4)) +coords = (DataArray(np.arange(data.shape[0]), dims=('y',), attrs={'units' : 'm'}), + DataArray(np.arange(data.shape[0]), dims=('x',), attrs={'units' : 'm'})) + +arr = DataArray(data, coords) +print(*multiscale(arr, windowed_mean, (2,2)), sep="\n") +""" + Size: 128B +array([[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11], + [12, 13, 14, 15]]) +Coordinates: + * y (y) int64 32B 0 1 2 3 + * x (x) int64 32B 0 1 2 3 + + Size: 32B +array([[ 2, 4], + [10, 12]]) +Coordinates: + * y (y) float64 16B 0.5 2.5 + * x (x) float64 16B 0.5 2.5 +""" +``` + +Dask arrays work too. Note the control over output chunks via the ``chunks`` keyword argument. + +```python +from xarray_multiscale import multiscale, windowed_mean +import dask.array as da + +arr = da.random.randint(0, 255, (10,10,10)) +print(*multiscale(arr, windowed_mean, 2, chunks=2), sep="\n") +""" + Size: 8kB +dask.array +Coordinates: + * dim_0 (dim_0) float64 80B 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 + * dim_1 (dim_1) float64 80B 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 + * dim_2 (dim_2) float64 80B 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 + + Size: 1kB +dask.array +Coordinates: + * dim_0 (dim_0) float64 40B 0.5 2.5 4.5 6.5 8.5 + * dim_1 (dim_1) float64 40B 0.5 2.5 4.5 6.5 8.5 + * dim_2 (dim_2) float64 40B 0.5 2.5 4.5 6.5 8.5 + + Size: 64B +dask.array +Coordinates: + * dim_0 (dim_0) float64 16B 1.5 5.5 + * dim_1 (dim_1) float64 16B 1.5 5.5 + * dim_2 (dim_2) float64 16B 1.5 5.5 +""" +``` + +### Caveats + +- Arrays that are not evenly divisible by the downsampling factors will be trimmed as needed. If this behavior is undesirable, consider padding your array appropriately prior to downsampling. +- For chunked arrays (e.g., dask arrays), the current implementation divides the input data into *contiguous* chunks. This means that attempting to use downsampling schemes based on sliding windowed smoothing will produce edge artifacts. +- `multiscale` generates a sequence of arrays of descending size, where the smallest array is the last + +### Development + +This project is developed using [`hatch`](https://hatch.pypa.io/latest/). +Run tests with `hatch run test:pytest`. +Serve docs with `hatch run docs:serve`. diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index e0dd47b..0000000 --- a/docs/index.rst +++ /dev/null @@ -1,20 +0,0 @@ -.. xarray-multiscale documentation master file, created by - sphinx-quickstart on Fri Jan 28 13:45:18 2022. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to xarray-multiscale's documentation! -============================================= - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 25447b8..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=docs -set BUILDDIR=build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/modules.rst b/docs/modules.rst deleted file mode 100644 index 5e9f313..0000000 --- a/docs/modules.rst +++ /dev/null @@ -1,7 +0,0 @@ -src -=== - -.. toctree:: - :maxdepth: 4 - - xarray_multiscale diff --git a/docs/xarray_multiscale.rst b/docs/xarray_multiscale.rst deleted file mode 100644 index 624808c..0000000 --- a/docs/xarray_multiscale.rst +++ /dev/null @@ -1,29 +0,0 @@ -xarray\_multiscale package -========================== - -Submodules ----------- - -xarray\_multiscale.multiscale module ------------------------------------- - -.. automodule:: xarray_multiscale.multiscale - :members: - :undoc-members: - :show-inheritance: - -xarray\_multiscale.reducers module ----------------------------------- - -.. automodule:: xarray_multiscale.reducers - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: xarray_multiscale - :members: - :undoc-members: - :show-inheritance: diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..e949639 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,64 @@ +site_name: xarray-ome-ngff +site_url: https://janeliascicomp.github.io/xarray-multiscale/ +site_author: Davis Bennett +site_description: >- + Multiscale images via Xarray. + +# Repository +repo_name: janeliascicomp/xarray-multiscale +repo_url: https://github.com/janeliascicomp/xarray-multiscale + +# Copyright +copyright: Copyright © 2016 - 2024 HHMI / Janelia +watch: [src] +theme: + features: + - navigation.expand + - content.code.annotate + name: material + palette: + # Palette toggle for light mode + - scheme: default + toggle: + icon: material/brightness-7 + name: Switch to dark mode + + # Palette toggle for dark mode + - scheme: slate + toggle: + icon: material/brightness-4 + name: Switch to light mode + +nav: + - About: index.md + - API: + - multiscale: api/multiscale.md + - reducers: api/reducers.md + - chunks: api/chunks.md + - util: api/util.md + +plugins: +- mkdocstrings: + handlers: + python: + options: + docstring_style: numpy + members_order: source + separate_signature: true + filters: ["!^_"] + docstring_options: + ignore_init_summary: true + merge_init_into_class: true + +markdown_extensions: + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true + - pymdownx.inlinehilite + - pymdownx.snippets + - pymdownx.superfences + - toc: + baselevel: 2 + toc_depth: 4 + permalink: "#" \ No newline at end of file diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 97563e8..0000000 --- a/poetry.lock +++ /dev/null @@ -1,1356 +0,0 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. - -[[package]] -name = "alabaster" -version = "0.7.13" -description = "A configurable sidebar-enabled Sphinx theme" -category = "dev" -optional = false -python-versions = ">=3.6" -files = [ - 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-[tool.poetry.dependencies] -python = "^3.9" -xarray = ">=2022.03.0" -scipy = ">=1.5.4" -numpy = ">=1.19.4" -dask = ">=2020.12.0" - -[tool.poetry.group.dev.dependencies] -Sphinx = "^4.4.0" -sphinx-rtd-theme = "^1.0.0" -numpydoc = "^1.2" -sphinx-issues = "^3.0.1" -pytest-cov = "^3.0.0" -pytest = "^7.1.2" -mypy = "^0.971" -pre-commit = "^3.0.0" +dynamic = ["version"] +description = '' +readme = "README.md" +requires-python = ">=3.8" +license = "MIT" +keywords = [] +authors = [ + { name = "Davis Vann Bennett", email = "davis.v.bennett@gmail.com" }, +] +classifiers = [ + "Development Status :: 4 - Beta", + "Programming Language :: Python", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: Implementation :: PyPy", +] +dependencies = [ + "xarray >=2022.03.0", + "scipy >=1.5.4", + "numpy >=1.19.4", + "dask >=2020.12.0" +] -[build-system] -requires = ["poetry>=0.12"] -build-backend = "poetry.masonry.api" +[project.urls] +Documentation = "https://github.com/janelia-scicomp/xarray-multiscale#readme" +Issues = "https://github.com/janelia-scicomp/xarray-multiscale/issues" +Source = "https://github.com/janelia-scicomp/xarray-multiscale" + +[tool.hatch.version] +path = "src/xarray_multiscale/__about__.py" + +[tool.hatch.envs.test] +dependencies = [ + "coverage", + "pytest", + "pytest-cov", + "pytest-examples == 0.0.12" + ] + +[[tool.hatch.envs.test.matrix]] +python = ["3.9", "3.10", "3.11", "3.12"] + +[tool.hatch.envs.docs] +dependencies = [ + "mkdocs-material == 9.5.30", + "mkdocstrings[python] == 0.25.1", + ] + +[tool.hatch.envs.types] +extra-dependencies = [ + "mypy>=1.0.0", +] +[tool.hatch.envs.types.scripts] +check = "mypy --install-types --non-interactive {args:src/xarray_multiscale tests}" + +[tool.coverage.run] +source_pkgs = ["xarray_multiscale", "tests"] +branch = true +parallel = true +omit = [ + "src/xarray_multiscale/__about__.py", +] + +[tool.coverage.paths] +xarray_multiscale = ["src/xarray_multiscale", "*/xarray-multiscale/src/xarray_multiscale"] +tests = ["tests", "*/xarray-multiscale/tests"] + +[tool.coverage.report] +exclude_lines = [ + "no cov", + "if __name__ == .__main__.:", + "if TYPE_CHECKING:", +] + +[tool.ruff] +line-length = 100 +src = ["src"] +force-exclude = true +extend-exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".mypy_cache", + ".nox", + ".pants.d", + ".ruff_cache", + ".venv", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "venv", + "docs", +] + +[tool.ruff.lint] +extend-select = [ + "B", # flake8-bugbear + "I", # isort + "ISC", + "UP", # pyupgrade + "RSE", + "RUF", +] +ignore = [ + "RUF005", +] + +[tool.mypy] +python_version = "3.10" +ignore_missing_imports = true +namespace_packages = false + +strict = true +warn_unreachable = true -[mypy] -plugins = "numpy.typing.mypy_plugin" \ No newline at end of file +enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] \ No newline at end of file diff --git a/src/xarray_multiscale/__about__.py b/src/xarray_multiscale/__about__.py new file mode 100644 index 0000000..59e069b --- /dev/null +++ b/src/xarray_multiscale/__about__.py @@ -0,0 +1,4 @@ +# SPDX-FileCopyrightText: 2024-present Davis Vann Bennett +# +# SPDX-License-Identifier: MIT +__version__ = "1.1.1" diff --git a/src/xarray_multiscale/__init__.py b/src/xarray_multiscale/__init__.py index bd34801..6f069bb 100644 --- a/src/xarray_multiscale/__init__.py +++ b/src/xarray_multiscale/__init__.py @@ -1,9 +1,19 @@ -__version__ = "1.1.0" -from .multiscale import downscale, multiscale # noqa: F401 -from .reducers import ( # noqa: F401 - windowed_mean, - windowed_mode, +from __future__ import annotations + +from .multiscale import downscale, multiscale +from .reducers import ( windowed_max, + windowed_mean, windowed_min, windowed_rank, ) + +__all__ = [ + "downscale", + "multiscale", + "windowed_mean", + "windowed_mode", + "windowed_max", + "windowed_min", + "windowed_rank", +] diff --git a/src/xarray_multiscale/chunks.py b/src/xarray_multiscale/chunks.py index f0ff0a5..b672302 100644 --- a/src/xarray_multiscale/chunks.py +++ b/src/xarray_multiscale/chunks.py @@ -1,4 +1,9 @@ -from typing import Dict, Hashable, Sequence, Set, Union +from __future__ import annotations + +from typing import TYPE_CHECKING, Hashable, cast + +if TYPE_CHECKING: + from typing import Sequence import dask.array as da import toolz as tz @@ -8,53 +13,61 @@ def normalize_chunks( - array: xarray.DataArray, - chunk_size: Union[str, int, Sequence[int], Dict[Hashable, int]], -) -> Dict[Hashable, int]: + array: xarray.DataArray, chunk_size: str | int | Sequence[int] | dict[Hashable, int] +) -> dict[Hashable, int]: + """ + Given an `xarray.DataArray`, normalize a chunk size against that array. + + Parameters + ---------- + array: xarray.DataArray + An `xarray.DataArray`. + chunk_size: Union[str, int, Sequence[int], dict[Hashable, int]] + A specification of a chunk size. + Returns + ------- + dict[Hashable, int] + An xarray-compatible specification of chunk sizes. + """ + _chunk_size: str | int | Sequence[int] | dict[Hashable, int] if not isinstance(chunk_size, (int, str, dict)): if len(chunk_size) != array.ndim: - raise ValueError( - f""" - Incorrect number of chunks. - Got {len(chunk_size)}, expected {array.ndim} - """ - ) + msg = msg = f"Incorrect number of chunks. Got {len(chunk_size)}, expected {array.ndim}." + raise ValueError(msg) if is_dict_like(chunk_size): # dask's normalize chunks routine assumes dict inputs have integer # keys, so convert dim names to the corresponding integers - + chunk_size = cast(dict[Hashable, int], chunk_size) if len(chunk_size.keys() - set(array.dims)) > 0: - extra: Set[Hashable] = chunk_size.keys() - set(array.dims) - raise ValueError( - f""" - Keys of chunksize must be a subset of array dims. - Got extraneous keys: {extra}. - """ - ) + extra: set[Hashable] = chunk_size.keys() - set(array.dims) + msg = f"Keys of chunksize must be a subset of array dims. Got extraneous keys: {extra}." + raise ValueError(msg) _chunk_size = dict(zip(range(array.ndim), map(tz.first, array.chunks))) _chunk_size.update({array.get_axis_num(d): c for d, c in chunk_size.items()}) - chunk_size = _chunk_size + else: + _chunk_size = chunk_size - new_chunks = map( - tz.first, - da.core.normalize_chunks( - chunk_size, - array.shape, - dtype=array.dtype, - previous_chunks=array.data.chunksize, - ), + new_chunks: tuple[int, ...] = tuple( + map( + tz.first, + da.core.normalize_chunks( + _chunk_size, + array.shape, + dtype=array.dtype, + previous_chunks=array.data.chunksize, + ), + ) ) - result = tuple(new_chunks) - - return {dim: result[array.get_axis_num(dim)] for dim in array.dims} + return {dim: new_chunks[array.get_axis_num(dim)] for dim in array.dims} def align_chunks(array: da.core.Array, scale_factors: Sequence[int]) -> da.core.Array: """ - Ensure that all chunks are divisible by scale_factors + Ensure that all chunks of a dask array are divisible by scale_factors, rechunking the array + if necessary. """ new_chunks = {} for idx, factor in enumerate(scale_factors): diff --git a/src/xarray_multiscale/multiscale.py b/src/xarray_multiscale/multiscale.py index 33cdc75..fe23860 100644 --- a/src/xarray_multiscale/multiscale.py +++ b/src/xarray_multiscale/multiscale.py @@ -1,29 +1,43 @@ -from typing import Any, Dict, Hashable, List, Literal, Sequence, Union +from __future__ import annotations + +from typing import TYPE_CHECKING, Literal + +if TYPE_CHECKING: + from typing import Any, Callable, Hashable, Sequence, TypeAlias import numpy as np import numpy.typing as npt +import xarray from dask.array.core import Array from dask.base import tokenize from dask.core import flatten from dask.highlevelgraph import HighLevelGraph from dask.utils import apply -from xarray import DataArray from xarray_multiscale.chunks import align_chunks, normalize_chunks from xarray_multiscale.reducers import WindowedReducer from xarray_multiscale.util import adjust_shape, broadcast_to_rank, logn -ChunkOption = Literal["preserve", "auto"] +ChunkOption: TypeAlias = Literal["preserve", "auto"] + + +def _default_namer(idx: int) -> str: + """ + The default naming function. Takes an integer index and prepends "s" in front of it. + """ + return f"s{idx}" def multiscale( array: npt.NDArray[Any], reduction: WindowedReducer, - scale_factors: Union[Sequence[int], int], + scale_factors: Sequence[int] | int, preserve_dtype: bool = True, - chunks: Union[str, Sequence[int], Dict[Hashable, int]] = "preserve", + chunks: ChunkOption | Sequence[int] | dict[Hashable, int] = "preserve", chained: bool = True, -) -> List[DataArray]: + namer: Callable[[int], str] = _default_namer, + **kwargs: Any, +) -> list[xarray.DataArray]: """ Generate a coordinate-aware multiscale representation of an array. @@ -72,11 +86,19 @@ def multiscale( power. This means that the nth downscaled array directly depends on the input array. + namer : callable, defaults to `_default_namer` + A function for naming the output arrays. This function should take an integer + index and return a string. The default function simply prepends the string + representation of the integer with the character "s". + + **kwargs: Any + Additional keyword arguments that will be passed to the reduction function. + Returns ------- - result : list of DataArrays + result : list[xarray.DataArray] The first element of this list is the input array, converted to an - `xarray.DataArray`. Each subsquent element of the list is + `xarray.DataArray`. Each subsequent element of the list is the result of downsampling the previous element of the list. The `coords` attributes of these DataArrays track the changing @@ -97,11 +119,11 @@ def multiscale( * dim_0 (dim_0) float64 0.5 2.5] """ scale_factors = broadcast_to_rank(scale_factors, array.ndim) - darray = to_dataarray(array) + darray = to_dataarray(array, name=namer(0)) levels = range(1, downsampling_depth(darray.shape, scale_factors)) - result: List[DataArray] = [darray] + result: list[xarray.DataArray] = [darray] for level in levels: if chained: scale = scale_factors @@ -109,7 +131,9 @@ def multiscale( else: scale = tuple(s**level for s in scale_factors) source = result[0] - result.append(downscale(source, reduction, scale, preserve_dtype)) + downscaled = downscale(source, reduction, scale, preserve_dtype, **kwargs) + downscaled.name = namer(level) + result.append(downscaled) if darray.chunks is not None and chunks != "preserve": new_chunks = [normalize_chunks(r, chunks) for r in result] @@ -118,45 +142,41 @@ def multiscale( return result -def to_dataarray(array: Any) -> DataArray: +def to_dataarray(array: Any, name: str | None = None) -> xarray.DataArray: """ - Convert the input to DataArray if it is not already one. + Convert the input to an `xarray.DataArray` if it is not already one. """ - if isinstance(array, DataArray): + if isinstance(array, xarray.DataArray): data = array.data dims = array.dims # ensure that key order matches dimension order coords = {d: array.coords[d] for d in dims} attrs = array.attrs - name = array.name else: data = array dims = tuple(f"dim_{d}" for d in range(data.ndim)) coords = { - dim: DataArray(np.arange(shape, dtype="float"), dims=dim) + dim: xarray.DataArray(np.arange(shape, dtype="float"), dims=dim) for dim, shape in zip(dims, array.shape) } - name = None attrs = {} - result = DataArray(data=data, coords=coords, dims=dims, attrs=attrs, name=name) + result = xarray.DataArray(data=data, coords=coords, dims=dims, attrs=attrs, name=name) return result def downscale_dask( array: Any, reduction: WindowedReducer, - scale_factors: Union[int, Sequence[int], Dict[int, int]], + scale_factors: Sequence[int], **kwargs: Any, ) -> Any: - + """ + Downscale a dask array. + """ if not np.all((np.array(array.shape) % np.array(scale_factors)) == 0): - raise ValueError( - f""" - Coarsening factors {scale_factors} do not align - with array shape {array.shape}. - """ - ) + msg = f"Coarsening factors {scale_factors} do not align with array shape {array.shape}." + raise ValueError(msg) array = align_chunks(array, scale_factors) name: str = "downscale-" + tokenize(reduction, array, scale_factors) @@ -169,39 +189,30 @@ def downscale_dask( for axis, sizes in enumerate(array.chunks) ) - meta = reduction( - np.empty(scale_factors, dtype=array.dtype), scale_factors, **kwargs - ) + meta = reduction(np.empty(scale_factors, dtype=array.dtype), scale_factors, **kwargs) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[array]) return Array(graph, name, chunks, meta=meta) def downscale( - array: DataArray, + array: xarray.DataArray, reduction: WindowedReducer, scale_factors: Sequence[int], preserve_dtype: bool = True, **kwargs: Any, -) -> Any: - +) -> xarray.DataArray: to_downscale = adjust_shape(array, scale_factors) if to_downscale.chunks is not None: - downscaled_data = downscale_dask( - to_downscale.data, reduction, scale_factors, **kwargs - ) + downscaled_data = downscale_dask(to_downscale.data, reduction, scale_factors, **kwargs) else: - downscaled_data = reduction(to_downscale.data, scale_factors) + downscaled_data = reduction(to_downscale.data, scale_factors, **kwargs) if preserve_dtype: downscaled_data = downscaled_data.astype(array.dtype) downscaled_coords = downscale_coords(to_downscale, scale_factors) - return DataArray( - downscaled_data, downscaled_coords, attrs=array.attrs, dims=array.dims - ) + return xarray.DataArray(downscaled_data, downscaled_coords, attrs=array.attrs, dims=array.dims) -def downscale_coords( - array: DataArray, scale_factors: Sequence[int] -) -> Dict[Hashable, Any]: +def downscale_coords(array: xarray.DataArray, scale_factors: Sequence[int]) -> dict[Hashable, Any]: """ Downscale coordinates by taking the windowed mean of each coordinate array. """ @@ -230,9 +241,11 @@ def downsampling_depth(shape: Sequence[int], scale_factors: Sequence[int]) -> in Parameters ---------- - shape : sequence of positive integers + shape: Sequence[int] + An array shape. - scale_factors : sequence of positive integers + scale_factors : Sequence[int] + Downsampling factors. Examples -------- @@ -244,12 +257,12 @@ def downsampling_depth(shape: Sequence[int], scale_factors: Sequence[int]) -> in 2 """ if len(shape) != len(scale_factors): - raise ValueError( - f""" - Shape (length == {len(shape)} ) and - scale factors (length == {len(scale_factors)}) - do not align.""" + msg = ( + "The shape and scale_factors parameters do not have the same length." + f"Shape={shape} has length {len(shape)}, " + f"but scale_factors={scale_factors} has length {len(scale_factors)}" ) + raise ValueError(msg) _scale_factors = np.array(scale_factors).astype("int") _shape = np.array(shape).astype("int") diff --git a/src/xarray_multiscale/py.typed b/src/xarray_multiscale/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/src/xarray_multiscale/reducers.py b/src/xarray_multiscale/reducers.py index a19980c..7ee4367 100644 --- a/src/xarray_multiscale/reducers.py +++ b/src/xarray_multiscale/reducers.py @@ -1,21 +1,27 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Protocol + +if TYPE_CHECKING: + from typing import Any, Sequence + + import numpy.typing as npt + import math from functools import reduce from itertools import combinations -from typing import Any, Protocol, Sequence, Tuple import numpy as np -from numpy.typing import NDArray from scipy.stats import mode class WindowedReducer(Protocol): def __call__( - self, array: NDArray[Any], window_size: Sequence[int], **kwargs: Any - ) -> NDArray[Any]: - ... + self, array: npt.NDArray[Any], window_size: Sequence[int], **kwargs: Any + ) -> npt.NDArray[Any]: ... -def reshape_windowed(array: NDArray[Any], window_size: Tuple[int]) -> NDArray[Any]: +def reshape_windowed(array: npt.NDArray[Any], window_size: tuple[int, ...]) -> npt.NDArray[Any]: """ Reshape an array to support windowed operations. New dimensions will be added to the array, one for each element of @@ -51,15 +57,15 @@ def reshape_windowed(array: NDArray[Any], window_size: Tuple[int]) -> NDArray[An f"""Length of window_size must match array dimensionality. Got {len(window_size)}, expected {array.ndim}""" ) - new_shape: Tuple[int, ...] = () + new_shape: tuple[int, ...] = () for s, f in zip(array.shape, window_size): new_shape += (s // f, f) return array.reshape(new_shape) def windowed_mean( - array: NDArray[Any], window_size: Tuple[int, ...], **kwargs: Any -) -> NDArray[Any]: + array: npt.NDArray[Any], window_size: tuple[int, ...], **kwargs: Any +) -> npt.NDArray[Any]: """ Compute the windowed mean of an array. @@ -104,13 +110,13 @@ def windowed_mean( [10.5, 12.5]]) """ reshaped = reshape_windowed(array, window_size) - result = reshaped.mean(axis=tuple(range(1, reshaped.ndim, 2)), **kwargs) + result: npt.NDArray[Any] = reshaped.mean(axis=tuple(range(1, reshaped.ndim, 2)), **kwargs) return result def windowed_max( - array: NDArray[Any], window_size: Tuple[int, ...], **kwargs: Any -) -> NDArray[Any]: + array: npt.NDArray[Any], window_size: tuple[int, ...], **kwargs: Any +) -> npt.NDArray[Any]: """ Compute the windowed maximum of an array. @@ -154,13 +160,13 @@ def windowed_max( [13, 15]]) """ reshaped = reshape_windowed(array, window_size) - result = reshaped.max(axis=tuple(range(1, reshaped.ndim, 2)), **kwargs) + result: npt.NDArray[Any] = reshaped.max(axis=tuple(range(1, reshaped.ndim, 2)), **kwargs) return result def windowed_min( - array: NDArray[Any], window_size: Tuple[int, ...], **kwargs: Any -) -> NDArray[Any]: + array: npt.NDArray[Any], window_size: tuple[int, ...], **kwargs: Any +) -> npt.NDArray[Any]: """ Compute the windowed minimum of an array. @@ -204,11 +210,11 @@ def windowed_min( [8, 10]]) """ reshaped = reshape_windowed(array, window_size) - result = reshaped.min(axis=tuple(range(1, reshaped.ndim, 2)), **kwargs) + result: npt.NDArray[Any] = reshaped.min(axis=tuple(range(1, reshaped.ndim, 2)), **kwargs) return result -def windowed_mode(array: NDArray[Any], window_size: Tuple[int, ...]) -> NDArray[Any]: +def windowed_mode(array: npt.NDArray[Any], window_size: tuple[int, ...]) -> npt.NDArray[Any]: """ Compute the windowed mode of an array using either `windowed_mode_countess` or `windowed_mode_scipy` @@ -252,9 +258,7 @@ def windowed_mode(array: NDArray[Any], window_size: Tuple[int, ...]) -> NDArray[ return windowed_mode_scipy(array, window_size) -def windowed_mode_scipy( - array: NDArray[Any], window_size: Tuple[int, ...] -) -> NDArray[Any]: +def windowed_mode_scipy(array: npt.NDArray[Any], window_size: tuple[int, ...]) -> npt.NDArray[Any]: """ Compute the windowed mode of an array using scipy.stats.mode. Input will be coerced to a numpy array. @@ -290,18 +294,24 @@ def windowed_mode_scipy( [ 8, 10]]) """ reshaped = reshape_windowed(array, window_size) - transposed_shape = tuple(range(0, reshaped.ndim, 2)) + tuple( - range(1, reshaped.ndim, 2) - ) + transposed_shape = tuple(range(0, reshaped.ndim, 2)) + tuple(range(1, reshaped.ndim, 2)) transposed = reshaped.transpose(transposed_shape) collapsed = transposed.reshape(tuple(reshaped.shape[slice(0, None, 2)]) + (-1,)) - result = mode(collapsed, axis=collapsed.ndim - 1, keepdims=False).mode + result: npt.NDArray[Any] = mode(collapsed, axis=collapsed.ndim - 1, keepdims=False).mode return result +def _pick(a: npt.NDArray[Any], b: npt.NDArray[Any]) -> Any: + return a * (a == b) + + +def _lor(a: npt.NDArray[Any], b: npt.NDArray[Any]) -> Any: + return a + (a == 0) * b + + def windowed_mode_countless( - array: NDArray[Any], window_size: Tuple[int, ...] -) -> NDArray[Any]: + array: npt.NDArray[Any], window_size: tuple[int, ...] +) -> npt.NDArray[Any]: """ countless downsamples labeled images (segmentations) by finding the mode using vectorized instructions. @@ -327,7 +337,7 @@ def windowed_mode_countless( The window size. The length of ``window_size`` must match the dimensionality of ``array``. - """ # noqa + """ sections = [] mode_of = reduce(lambda x, y: x * y, window_size) @@ -337,19 +347,13 @@ def windowed_mode_countless( part = 1 + array[tuple(np.s_[o::f] for o, f in zip(offset, window_size))] sections.append(part) - def pick(a, b): - return a * (a == b) - - def lor(a, b): - return a + (a == 0) * b - - subproblems = [{}, {}] + subproblems: list[dict[tuple[int, int], npt.ArrayLike]] = [{}, {}] results2 = None for x, y in combinations(range(len(sections) - 1), 2): - res = pick(sections[x], sections[y]) + res = _pick(sections[x], sections[y]) subproblems[0][(x, y)] = res if results2 is not None: - results2 = lor(results2, res) + results2 = _lor(results2, res) # type: ignore[unreachable] else: results2 = res @@ -357,13 +361,13 @@ def lor(a, b): for r in range(3, majority + 1): r_results = None for combo in combinations(range(len(sections)), r): - res = pick(subproblems[0][combo[:-1]], sections[combo[-1]]) + res = _pick(subproblems[0][combo[:-1]], sections[combo[-1]]) # type: ignore[index, arg-type] if combo[-1] != len(sections) - 1: - subproblems[1][combo] = res + subproblems[1][combo] = res # type: ignore[index] if r_results is not None: - r_results = lor(r_results, res) + r_results = _lor(r_results, res) # type: ignore[unreachable] else: r_results = res results.append(r_results) @@ -371,14 +375,14 @@ def lor(a, b): subproblems[1] = {} results.reverse() - final_result = lor(reduce(lor, results), sections[-1]) - 1 + final_result: npt.NDArray[Any] = _lor(reduce(_lor, results), sections[-1]) - 1 # type: ignore[arg-type] return final_result def windowed_rank( - array: NDArray[Any], window_size: Tuple[int, ...], rank: int -) -> NDArray[Any]: + array: npt.NDArray[Any], window_size: tuple[int, ...], rank: int = -1 +) -> npt.NDArray[Any]: """ Compute the windowed rank order filter of an array. Input will be coerced to a numpy array. @@ -389,15 +393,18 @@ def windowed_rank( The array to be downscaled. The array must have a ``reshape`` method. - window_size: Tuple of ints + window_size: tuple[int, ...] The window to use for aggregation. The array is partitioned into non-overlapping regions with size equal to ``window_size``, and the values in each window are sorted to generate the result. - rank: int - The index to take from the sorted values in each window. Must be - between 0 and the product of the elements of ``window_size``, minus one. - Use negative indices to count from the end of the sorted window. + rank: int, default=-1 + The index to take from the sorted values in each window. If non-negative, then + rank must be between 0 and the product of the elements of ``window_size`` minus one, + (inclusive). + Rank may be negative, in which case it denotes an index relative to the end of the sorted + values following normal python indexing rules. + E.g., when rank is -1 (the default), this takes the maxmum value of each window. Returns ------- @@ -416,14 +423,15 @@ def windowed_rank( """ max_rank = np.prod(window_size) - 1 if rank > max_rank or rank < -max_rank - 1: - raise ValueError( - f"Rank must be between either -1 and {-max_rank-1} or 0 and {max_rank} for window_size {window_size}." + msg = ( + f"Invalid rank: {rank} for window_size: {window_size} ", + f"If rank is negative then between either -1 and {-max_rank-1}, inclusive", + f"If rank is non-negtaive, then it must be between 0 and {max_rank}, inclusive.", ) + raise ValueError(msg) reshaped = reshape_windowed(array, window_size) - transposed_shape = tuple(range(0, reshaped.ndim, 2)) + tuple( - range(1, reshaped.ndim, 2) - ) + transposed_shape = tuple(range(0, reshaped.ndim, 2)) + tuple(range(1, reshaped.ndim, 2)) transposed = reshaped.transpose(transposed_shape) collapsed = transposed.reshape(tuple(reshaped.shape[slice(0, None, 2)]) + (-1,)) - result = np.take(np.sort(collapsed, axis=-1), rank, axis=-1) + result: npt.NDArray[Any] = np.take(np.sort(collapsed, axis=-1), rank, axis=-1) return result diff --git a/src/xarray_multiscale/util.py b/src/xarray_multiscale/util.py index 3895dbe..f6056bb 100644 --- a/src/xarray_multiscale/util.py +++ b/src/xarray_multiscale/util.py @@ -1,6 +1,7 @@ from typing import Dict, Sequence, Tuple, Union import numpy as np +import numpy.typing as npt from xarray import DataArray @@ -32,7 +33,7 @@ def adjust_shape(array: DataArray, scale_factors: Sequence[int]) -> DataArray: return result -def logn(x: float, n: float) -> float: +def logn(x: npt.ArrayLike, n: npt.ArrayLike) -> npt.NDArray[np.float64]: """ Compute the logarithm of x base n. @@ -47,7 +48,7 @@ def logn(x: float, n: float) -> float: np.log(x) / np.log(n) """ - result: float = np.log(x) / np.log(n) + result: npt.NDArray[np.float64] = np.log(x) / np.log(n) return result @@ -66,16 +67,19 @@ def broadcast_to_rank( for dim in range(rank): result_dict[dim] = value.get(dim, 1) else: - raise ValueError( - f"""The first argument must be an int, a sequence of ints, - or a dict of ints. Got {type(value)}""" + msg = ( # type: ignore[unreachable] + "The first argument must be an integer, a sequence of integers", + f"or a dictionary of integers. Got {type(value)}", ) + raise ValueError(msg) result = tuple(result_dict.values()) typecheck = tuple(isinstance(val, int) for val in result) if not all(typecheck): bad_values = tuple(result[idx] for idx, val in enumerate(typecheck) if not val) - raise ValueError( - f"""All elements of the first argument of this function - must be ints. Found non-integer values: {bad_values}""" + msg = ( + "All elements of the first argument of this function must be integers. " + f"Got non-integer values: {bad_values}" ) + raise ValueError(msg) + return result diff --git a/tests/test_doc.py b/tests/test_doc.py deleted file mode 100644 index 907fc20..0000000 --- a/tests/test_doc.py +++ /dev/null @@ -1,11 +0,0 @@ -import numpy as np -import xarray as xr - -from xarray_multiscale import multiscale -from xarray_multiscale.reducers import windowed_mean - - -def test_xarray_example(): - data = xr.DataArray(np.zeros((1024, 1024)), dims=("x", "y")) - scaled_data = multiscale(data, windowed_mean, (2, 2)) - assert len(scaled_data) == 10, "Incorrect number of arrays returned" diff --git a/tests/test_docs.py b/tests/test_docs.py new file mode 100644 index 0000000..33bbbf9 --- /dev/null +++ b/tests/test_docs.py @@ -0,0 +1,12 @@ +from __future__ import annotations + +import pytest +from pytest_examples import CodeExample, EvalExample, find_examples + + +@pytest.mark.parametrize("example", find_examples("docs"), ids=str) +def test_docstrings(example: CodeExample, eval_example: EvalExample): + if "test=skip" not in example.prefix_tags(): + eval_example.run_print_check(example) + else: + pytest.skip() diff --git a/tests/test_multiscale.py b/tests/test_multiscale.py index 8b840be..833c194 100644 --- a/tests/test_multiscale.py +++ b/tests/test_multiscale.py @@ -1,6 +1,7 @@ import dask.array as da import numpy as np import pytest +from src.xarray_multiscale.reducers import windowed_rank from xarray import DataArray from xarray.testing import assert_equal @@ -39,18 +40,14 @@ def test_adjust_shape(size, scale): if np.all((old_shape_array % scale_array) == 0): assert np.array_equal(new_shape_array, old_shape_array) else: - assert np.array_equal( - new_shape_array, old_shape_array - (old_shape_array % scale_array) - ) + assert np.array_equal(new_shape_array, old_shape_array - (old_shape_array % scale_array)) def test_downscale_2d(): scale = (2, 1) data = DataArray( - np.array( - [[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]], dtype="uint8" - ), + np.array([[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]], dtype="uint8"), ) answer = DataArray(np.array([[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]])) downscaled = downscale(data, windowed_mean, scale, preserve_dtype=False) @@ -122,9 +119,7 @@ def test_multiscale(ndim: int, chained: bool): cell = np.zeros(np.prod(chunks)).astype("float") cell[0] = 1 cell = cell.reshape(*chunks) - base_array = np.tile(cell, np.ceil(np.divide(shape, chunks)).astype("int"))[ - cropslice - ] + base_array = np.tile(cell, np.ceil(np.divide(shape, chunks)).astype("int"))[cropslice] pyr = multiscale(base_array, windowed_mean, 2, chained=chained) assert [p.shape for p in pyr] == [shape, (4,) * ndim, (2,) * ndim] @@ -133,6 +128,14 @@ def test_multiscale(ndim: int, chained: bool): assert np.array_equal(pyr[0].data, base_array) +@pytest.mark.parametrize("rank", (-1, 0, 1)) +def test_multiscale_rank_kwargs(rank: int): + data = np.arange(16) + window_size = (4,) + pyr = multiscale(data, windowed_rank, window_size, rank=rank) + assert np.array_equal(pyr[1].data, windowed_rank(data, window_size=window_size, rank=rank)) + + def test_chunking(): ndim = 3 shape = (16,) * ndim @@ -143,8 +146,7 @@ def test_chunking(): multi = multiscale(base_array, reducer, scale_factors) expected_chunks = [ - np.floor_divide(chunks, [s**idx for s in scale_factors]) - for idx, m in enumerate(multi) + np.floor_divide(chunks, [s**idx for s in scale_factors]) for idx, m in enumerate(multi) ] expected_chunks = [ x @@ -155,34 +157,23 @@ def test_chunking(): * ndim for x in expected_chunks ] - assert all( - [np.array_equal(m.data.chunksize, e) for m, e in zip(multi, expected_chunks)] - ) + assert all([np.array_equal(m.data.chunksize, e) for m, e in zip(multi, expected_chunks)]) multi = multiscale(base_array, reducer, scale_factors, chunks=chunks) expected_chunks = [ - chunks if np.greater(m.shape, chunks).all() else m.shape - for idx, m in enumerate(multi) + chunks if np.greater(m.shape, chunks).all() else m.shape for idx, m in enumerate(multi) ] - assert all( - [np.array_equal(m.data.chunksize, e) for m, e in zip(multi, expected_chunks)] - ) + assert all([np.array_equal(m.data.chunksize, e) for m, e in zip(multi, expected_chunks)]) chunks = (3, -1, -1) multi = multiscale(base_array, reducer, scale_factors, chunks=chunks) - expected_chunks = [ - (min(chunks[0], m.shape[0]), m.shape[1], m.shape[2]) for m in multi - ] - assert all( - [np.array_equal(m.data.chunksize, e) for m, e in zip(multi, expected_chunks)] - ) + expected_chunks = [(min(chunks[0], m.shape[0]), m.shape[1], m.shape[2]) for m in multi] + assert all([np.array_equal(m.data.chunksize, e) for m, e in zip(multi, expected_chunks)]) chunks = 3 multi = multiscale(base_array, reducer, scale_factors, chunks=chunks) expected_chunks = [tuple(min(chunks, s) for s in m.shape) for m in multi] - assert all( - [np.array_equal(m.data.chunksize, e) for m, e in zip(multi, expected_chunks)] - ) + assert all([np.array_equal(m.data.chunksize, e) for m, e in zip(multi, expected_chunks)]) def test_coords(): @@ -203,3 +194,19 @@ def test_coords(): assert_equal(multi[0], array) assert_equal(multi[1], downscaled) + + +@pytest.mark.parametrize("template", ("default", "{}")) +def test_namer(template): + from xarray_multiscale.multiscale import _default_namer + + if template == "default": + namer = _default_namer + else: + + def namer(v): + return template.format(v) + + data = np.arange(16) + m = multiscale(data, windowed_mean, 2, namer=namer) + assert all(element.name == namer(idx) for idx, element in enumerate(m)) diff --git a/tests/test_reducers.py b/tests/test_reducers.py index cdae328..9909fb9 100644 --- a/tests/test_reducers.py +++ b/tests/test_reducers.py @@ -64,28 +64,19 @@ def test_windowed_mode(): def test_windowed_rank(): - initial_array = np.array([[[1, 2], - [3, 4]], - [[5, 6], - [7, 8]]]) + initial_array = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) larger_array = np.tile(initial_array, (2, 2, 2)) window_size = (2, 2, 2) # 2nd brightest voxel - rank = np.product(window_size) - 2 - answer = np.array([[[7, 7], - [7, 7]], - [[7, 7], - [7, 7]]]) + rank = np.prod(window_size) - 2 + answer = np.array([[[7, 7], [7, 7]], [[7, 7], [7, 7]]]) results = windowed_rank(larger_array, window_size, rank) assert np.array_equal(results, answer) # Test negative rank rank = -8 - answer = np.array([[[1, 1], - [1, 1]], - [[1, 1], - [1, 1]]]) + answer = np.array([[[1, 1], [1, 1]], [[1, 1], [1, 1]]]) results = windowed_rank(larger_array, window_size, rank) assert np.array_equal(results, answer) @@ -108,11 +99,7 @@ def test_reshape_windowed(windows_per_dim: int, window_size: Tuple[int, ...]): assert reshaped.shape[0::2] == (windows_per_dim,) * len(window_size) assert reshaped.shape[1::2] == window_size slice_data = tuple(slice(w) for w in window_size) - slice_reshaped = tuple( - slice(None) if s % 2 else slice(0, 1) for s in range(reshaped.ndim) - ) + slice_reshaped = tuple(slice(None) if s % 2 else slice(0, 1) for s in range(reshaped.ndim)) # because we are reshaping the array, if the first window is correct, all the others # will be correct too - assert np.array_equal( - data[slice_data].squeeze(), reshaped[slice_reshaped].squeeze() - ) + assert np.array_equal(data[slice_data].squeeze(), reshaped[slice_reshaped].squeeze())