Merge pull request #21 from JaneliaSciComp/use-hatch

Use hatch

.gitignore

@@ -42,3 +42,6 @@ pip-wheel-metadata
 
 # numba
 */__pycache__/*
+
+# docs
+site

.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]
+      - id: mypy
+        files: src
+        additional_dependencies:
+          - numpy
+          - typing_extensions
+          # Tests
+          - pytest

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')
+"""
+<xarray.DataArray 's0' (dim_0: 4)> Size: 32B
+array([0, 1, 2, 3])
+Coordinates:
+  * dim_0    (dim_0) float64 32B 0.0 1.0 2.0 3.0
+ 
+<xarray.DataArray 's1' (dim_0: 2)> 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/).

docs/api/chunks.md

@@ -0,0 +1 @@
+::: xarray_multiscale.chunks

docs/api/multiscale.md

@@ -0,0 +1 @@
+::: xarray_multiscale.multiscale

docs/api/reducers.md

@@ -0,0 +1 @@
+::: xarray_multiscale.reducers

docs/api/util.md

@@ -0,0 +1 @@
+::: xarray_multiscale.util