From 6b926c459f24c3ff1983d5083a889c96a6bd19d9 Mon Sep 17 00:00:00 2001
From: Davis Vann Bennett <davis.v.bennett@gmail.com>
Date: Wed, 16 Jun 2021 17:47:43 -0400
Subject: [PATCH 1/6] refactor ahead of enabling recursive mode

---
 poetry.lock                         | 200 +++++++++++++++-------------
 src/xarray_multiscale/multiscale.py |  55 ++++----
 tests/test_multiscale.py            |   7 +
 3 files changed, 141 insertions(+), 121 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 10cadfb..5ecb3f1 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -24,17 +24,17 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "attrs"
-version = "20.3.0"
+version = "21.2.0"
 description = "Classes Without Boilerplate"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
 [package.extras]
-dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "furo", "sphinx", "pre-commit"]
-docs = ["furo", "sphinx", "zope.interface"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"]
-tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six"]
+dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit"]
+docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface"]
+tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins"]
 
 [[package]]
 name = "black"
@@ -60,11 +60,15 @@ d = ["aiohttp (>=3.3.2)", "aiohttp-cors"]
 
 [[package]]
 name = "click"
-version = "7.1.2"
+version = "8.0.1"
 description = "Composable command line interface toolkit"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.6"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
 
 [[package]]
 name = "cloudpickle"
@@ -109,7 +113,7 @@ dev = ["pytest (>=5)", "pytest-cov", "coveralls", "black", "mypy", "pylint"]
 
 [[package]]
 name = "dask"
-version = "2021.4.1"
+version = "2021.6.0"
 description = "Parallel PyData with Task Scheduling"
 category = "main"
 optional = false
@@ -124,15 +128,15 @@ toolz = ">=0.8.2"
 
 [package.extras]
 array = ["numpy (>=1.16)"]
-complete = ["bokeh (>=1.0.0,!=2.0.0)", "distributed (>=2021.04.1)", "numpy (>=1.16)", "pandas (>=0.25.0)"]
+complete = ["bokeh (>=1.0.0,!=2.0.0)", "distributed (==2021.06.0)", "numpy (>=1.16)", "pandas (>=0.25.0)"]
 dataframe = ["numpy (>=1.16)", "pandas (>=0.25.0)"]
 diagnostics = ["bokeh (>=1.0.0,!=2.0.0)"]
-distributed = ["distributed (>=2021.04.1)"]
+distributed = ["distributed (==2021.06.0)"]
 test = ["pytest", "pytest-rerunfailures", "pytest-xdist"]
 
 [[package]]
 name = "fasteners"
-version = "0.16"
+version = "0.16.3"
 description = "A python package that provides useful locks."
 category = "dev"
 optional = false
@@ -143,7 +147,7 @@ six = "*"
 
 [[package]]
 name = "fsspec"
-version = "2021.4.0"
+version = "2021.6.0"
 description = "File-system specification"
 category = "main"
 optional = false
@@ -168,7 +172,7 @@ ssh = ["paramiko"]
 
 [[package]]
 name = "importlib-metadata"
-version = "4.0.1"
+version = "4.5.0"
 description = "Read metadata from Python packages"
 category = "dev"
 optional = false
@@ -192,7 +196,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "more-itertools"
-version = "8.7.0"
+version = "8.8.0"
 description = "More routines for operating on iterables, beyond itertools"
 category = "dev"
 optional = false
@@ -224,7 +228,7 @@ python-versions = "*"
 
 [[package]]
 name = "numcodecs"
-version = "0.7.3"
+version = "0.8.0"
 description = "A Python package providing buffer compression and transformation codecs for use in data storage and communication applications."
 category = "dev"
 optional = false
@@ -238,7 +242,7 @@ msgpack = ["msgpack"]
 
 [[package]]
 name = "numpy"
-version = "1.20.2"
+version = "1.20.3"
 description = "NumPy is the fundamental package for array computing with Python."
 category = "main"
 optional = false
@@ -395,7 +399,7 @@ numpy = ">=1.16.5"
 
 [[package]]
 name = "six"
-version = "1.15.0"
+version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
 category = "main"
 optional = false
@@ -427,7 +431,7 @@ python-versions = "*"
 
 [[package]]
 name = "typing-extensions"
-version = "3.7.4.3"
+version = "3.10.0.0"
 description = "Backported and Experimental Type Hints for Python 3.5+"
 category = "main"
 optional = false
@@ -463,7 +467,7 @@ viz = ["matplotlib", "seaborn", "nc-time-axis"]
 
 [[package]]
 name = "zarr"
-version = "2.8.1"
+version = "2.8.3"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python."
 category = "dev"
 optional = false
@@ -508,15 +512,15 @@ atomicwrites = [
     {file = "atomicwrites-1.4.0.tar.gz", hash = "sha256:ae70396ad1a434f9c7046fd2dd196fc04b12f9e91ffb859164193be8b6168a7a"},
 ]
 attrs = [
-    {file = "attrs-20.3.0-py2.py3-none-any.whl", hash = "sha256:31b2eced602aa8423c2aea9c76a724617ed67cf9513173fd3a4f03e3a929c7e6"},
-    {file = "attrs-20.3.0.tar.gz", hash = "sha256:832aa3cde19744e49938b91fea06d69ecb9e649c93ba974535d08ad92164f700"},
+    {file = "attrs-21.2.0-py2.py3-none-any.whl", hash = "sha256:149e90d6d8ac20db7a955ad60cf0e6881a3f20d37096140088356da6c716b0b1"},
+    {file = "attrs-21.2.0.tar.gz", hash = "sha256:ef6aaac3ca6cd92904cdd0d83f629a15f18053ec84e6432106f7a4d04ae4f5fb"},
 ]
 black = [
     {file = "black-20.8b1.tar.gz", hash = "sha256:1c02557aa099101b9d21496f8a914e9ed2222ef70336404eeeac8edba836fbea"},
 ]
 click = [
-    {file = "click-7.1.2-py2.py3-none-any.whl", hash = "sha256:dacca89f4bfadd5de3d7489b7c8a566eee0d3676333fbb50030263894c38c0dc"},
-    {file = "click-7.1.2.tar.gz", hash = "sha256:d2b5255c7c6349bc1bd1e59e08cd12acbbd63ce649f2588755783aa94dfb6b1a"},
+    {file = "click-8.0.1-py3-none-any.whl", hash = "sha256:fba402a4a47334742d782209a7c79bc448911afe1149d07bdabdf480b3e2f4b6"},
+    {file = "click-8.0.1.tar.gz", hash = "sha256:8c04c11192119b1ef78ea049e0a6f0463e4c48ef00a30160c704337586f3ad7a"},
 ]
 cloudpickle = [
     {file = "cloudpickle-1.6.0-py3-none-any.whl", hash = "sha256:3a32d0eb0bc6f4d0c57fbc4f3e3780f7a81e6fee0fa935072884d58ae8e1cc7c"},
@@ -534,28 +538,28 @@ dacite = [
     {file = "dacite-1.6.0.tar.gz", hash = "sha256:d48125ed0a0352d3de9f493bf980038088f45f3f9d7498f090b50a847daaa6df"},
 ]
 dask = [
-    {file = "dask-2021.4.1-py3-none-any.whl", hash = "sha256:344c342d699466c3f742019b7a33caf2472b751f38370b200ede7d2f354aa1e4"},
-    {file = "dask-2021.4.1.tar.gz", hash = "sha256:195e4eeb154222ea7a1c368119b5f321ee4ec9d78531471fe0145a527f744aa8"},
+    {file = "dask-2021.6.0-py3-none-any.whl", hash = "sha256:ac4ec1e622bc220a057ad424ec5303f8ad96a70d672ccdb4ad60bf56d00fa1b7"},
+    {file = "dask-2021.6.0.tar.gz", hash = "sha256:234f62f8e9e5fd60cd669de16ad29b2c7bdad76c3a2ff0ac1eb854e80106f5af"},
 ]
 fasteners = [
-    {file = "fasteners-0.16-py2.py3-none-any.whl", hash = "sha256:74b6847e0a6bb3b56c8511af8e24c40e4cf7a774dfff5b251c260ed338096a4b"},
-    {file = "fasteners-0.16.tar.gz", hash = "sha256:c995d8c26b017c5d6a6de9ad29a0f9cdd57de61ae1113d28fac26622b06a0933"},
+    {file = "fasteners-0.16.3-py2.py3-none-any.whl", hash = "sha256:8408e52656455977053871990bd25824d85803b9417aa348f10ba29ef0c751f7"},
+    {file = "fasteners-0.16.3.tar.gz", hash = "sha256:b1ab4e5adfbc28681ce44b3024421c4f567e705cc3963c732bf1cba3348307de"},
 ]
 fsspec = [
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-    {file = "fsspec-2021.4.0.tar.gz", hash = "sha256:8b1a69884855d1a8c038574292e8b861894c3373282d9a469697a2b41d5289a6"},
+    {file = "fsspec-2021.6.0-py3-none-any.whl", hash = "sha256:e926b66c074ff3fc732d7678187a294e4a0bf112ccb51594fa9dff5a9fbdc4a1"},
+    {file = "fsspec-2021.6.0.tar.gz", hash = "sha256:931961514c67acab86519ca16985491e639ebf992dbc3a1aae24d44202b52426"},
 ]
 importlib-metadata = [
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-    {file = "importlib_metadata-4.0.1.tar.gz", hash = "sha256:8c501196e49fb9df5df43833bdb1e4328f64847763ec8a50703148b73784d581"},
+    {file = "importlib_metadata-4.5.0-py3-none-any.whl", hash = "sha256:833b26fb89d5de469b24a390e9df088d4e52e4ba33b01dc5e0e4f41b81a16c00"},
+    {file = "importlib_metadata-4.5.0.tar.gz", hash = "sha256:b142cc1dd1342f31ff04bb7d022492b09920cb64fed867cd3ea6f80fe3ebd139"},
 ]
 locket = [
     {file = "locket-0.2.1-py2.py3-none-any.whl", hash = "sha256:12b6ada59d1f50710bca9704dbadd3f447dbf8dac6664575c1281cadab8e6449"},
     {file = "locket-0.2.1.tar.gz", hash = "sha256:3e1faba403619fe201552f083f1ecbf23f550941bc51985ac6ed4d02d25056dd"},
 ]
 more-itertools = [
-    {file = "more-itertools-8.7.0.tar.gz", hash = "sha256:c5d6da9ca3ff65220c3bfd2a8db06d698f05d4d2b9be57e1deb2be5a45019713"},
-    {file = "more_itertools-8.7.0-py3-none-any.whl", hash = "sha256:5652a9ac72209ed7df8d9c15daf4e1aa0e3d2ccd3c87f8265a0673cd9cbc9ced"},
+    {file = "more-itertools-8.8.0.tar.gz", hash = "sha256:83f0308e05477c68f56ea3a888172c78ed5d5b3c282addb67508e7ba6c8f813a"},
+    {file = "more_itertools-8.8.0-py3-none-any.whl", hash = "sha256:2cf89ec599962f2ddc4d568a05defc40e0a587fbc10d5989713638864c36be4d"},
 ]
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@@ -578,61 +582,61 @@ mypy-extensions = [
     {file = "mypy_extensions-0.4.3.tar.gz", hash = "sha256:2d82818f5bb3e369420cb3c4060a7970edba416647068eb4c5343488a6c604a8"},
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 toml = [
     {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
@@ -820,9 +832,9 @@ typed-ast = [
     {file = "typed_ast-1.4.3.tar.gz", hash = "sha256:fb1bbeac803adea29cedd70781399c99138358c26d05fcbd23c13016b7f5ec65"},
 ]
 typing-extensions = [
-    {file = "typing_extensions-3.7.4.3-py2-none-any.whl", hash = "sha256:dafc7639cde7f1b6e1acc0f457842a83e722ccca8eef5270af2d74792619a89f"},
-    {file = "typing_extensions-3.7.4.3-py3-none-any.whl", hash = "sha256:7cb407020f00f7bfc3cb3e7881628838e69d8f3fcab2f64742a5e76b2f841918"},
-    {file = "typing_extensions-3.7.4.3.tar.gz", hash = "sha256:99d4073b617d30288f569d3f13d2bd7548c3a7e4c8de87db09a9d29bb3a4a60c"},
+    {file = "typing_extensions-3.10.0.0-py2-none-any.whl", hash = "sha256:0ac0f89795dd19de6b97debb0c6af1c70987fd80a2d62d1958f7e56fcc31b497"},
+    {file = "typing_extensions-3.10.0.0-py3-none-any.whl", hash = "sha256:779383f6086d90c99ae41cf0ff39aac8a7937a9283ce0a414e5dd782f4c94a84"},
+    {file = "typing_extensions-3.10.0.0.tar.gz", hash = "sha256:50b6f157849174217d0656f99dc82fe932884fb250826c18350e159ec6cdf342"},
 ]
 wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
@@ -833,8 +845,8 @@ xarray = [
     {file = "xarray-0.17.0.tar.gz", hash = "sha256:9c2edad2a4e588f9117c666a4249920b9717fb75703b96998cf65fcd4f60551f"},
 ]
 zarr = [
-    {file = "zarr-2.8.1-py3-none-any.whl", hash = "sha256:9e5e3ce14d6b10ddba8de2dad6f636210e719a6ca76569c86eef39702bdaabfb"},
-    {file = "zarr-2.8.1.tar.gz", hash = "sha256:138e5f64bbaf7aece6da1f229b611a7a04742e15358dde49c6e9bae8404a3bf2"},
+    {file = "zarr-2.8.3-py3-none-any.whl", hash = "sha256:9357fb40ce0bffdf81b9aac912c83829618ea367b7898f5a150eecde4cfa1338"},
+    {file = "zarr-2.8.3.tar.gz", hash = "sha256:8aece33269ba3ee2af9320aa528d5fe93f76c30e4ad7fdbfb604b1db3f0d779f"},
 ]
 zipp = [
     {file = "zipp-3.4.1-py3-none-any.whl", hash = "sha256:51cb66cc54621609dd593d1787f286ee42a5c0adbb4b29abea5a63edc3e03098"},
diff --git a/src/xarray_multiscale/multiscale.py b/src/xarray_multiscale/multiscale.py
index 2c3c05e..fa9560b 100644
--- a/src/xarray_multiscale/multiscale.py
+++ b/src/xarray_multiscale/multiscale.py
@@ -1,5 +1,6 @@
 import numpy as np
 import dask.array as da
+import xarray
 from xarray import DataArray
 from typing import Any, List, Optional, Tuple, Union, Sequence, Callable, Dict
 from scipy.interpolate import interp1d
@@ -7,6 +8,7 @@
 from dask.array import coarsen
 
 
+
 def multiscale(
     array: Any,
     reduction: Callable[[Any], Any],
@@ -14,6 +16,7 @@ def multiscale(
     pad_mode: Optional[str] = None,
     preserve_dtype: bool = True,
     chunks: Optional[Union[Sequence[int], Dict[str, int]]] = None,
+    recursive: bool = False,
 ) -> List[DataArray]:
     """
     Lazily generate a multiscale representation of an array
@@ -33,6 +36,8 @@ def multiscale(
 
     chunks: Sequence or Dict of ints, defaults to None. If `chunks` is supplied, all DataArrays are rechunked with these chunks before being returned.
 
+    recursive: boolean, defaults to False. ToDo
+
     Returns a list of DataArrays, one per level of downscaling. These DataArrays have `coords` properties that track the changing offset (if any)
     induced by the downsampling operation. Additionally, the scale factors are stored each DataArray's attrs propery under the key `scale_factors`
     -------
@@ -55,40 +60,19 @@ def multiscale(
     levels = range(
         0, 1 + get_downscale_depth(padded_shape, scale_factors, pad=needs_padding)
     )
-    scales: Tuple[Tuple[int]] = tuple(
+    scales = tuple(
         tuple(s ** l for s in scale_factors) for l in levels
     )
     result = [_ingest_array(array, scales[0])]
-    data = result[0].data
     base_attrs = result[0].attrs
     base_coords = result[0].coords
 
     for scale in scales[1:]:
         downscaled = downscale(
-            data, reduction, scale, pad_mode=pad_mode, preserve_dtype=preserve_dtype
+            result[0], reduction, scale, pad_mode=pad_mode, preserve_dtype=preserve_dtype
         )
+        result.append(downscaled)
 
-        # hideous
-        new_coords = tuple(
-            DataArray(
-                (offset * (base_coords[bc][1] - base_coords[bc][0]))
-                + (base_coords[bc][:s] * sc),
-                name=base_coords[bc].name,
-                attrs=base_coords[bc].attrs,
-            )
-            for s, bc, offset, sc in zip(
-                downscaled.shape, base_coords, get_downsampled_offset(scale), scale
-            )
-        )
-
-        result.append(
-            DataArray(
-                data=downscaled,
-                coords=new_coords,
-                attrs=base_attrs,
-                name=result[0].name,
-            )
-        )
     if chunks is not None:
         if isinstance(chunks, Sequence):
             _chunks = {k: v for k, v in zip(result[0].dims, chunks)}
@@ -209,13 +193,12 @@ def prepad(
                     extended_coords, dims=k, attrs=old_coord.attrs
                 )
         result = DataArray(
-            result, coords=new_coords, dims=array.dims, attrs=array.attrs
-        )
+            result, coords=new_coords, dims=array.dims, attrs=array.attrs)
     return result
 
 
 def downscale(
-    array: Union[np.array, da.array],
+    array: Union[np.array, da.array, xarray.DataArray],
     reduction: Callable,
     scale_factors: Sequence[int],
     pad_mode: Optional[str] = None,
@@ -241,6 +224,7 @@ def downscale(
     Returns the downscaled version of the input as a dask array.
     -------
     """
+
     trim_excess = False
     if pad_mode == None:
         trim_excess = True
@@ -258,6 +242,23 @@ def downscale(
     if preserve_dtype:
         coarsened = coarsened.astype(array.dtype)
 
+    if isinstance(array, xarray.DataArray):
+        base_coords = array.coords
+        new_coords = base_coords
+        if len(base_coords) > 0:
+            new_coords = tuple(
+                DataArray(
+                    (offset * abs(base_coords[bc][1] - base_coords[bc][0]))
+                    + (base_coords[bc][:s] * sc),
+                    name=base_coords[bc].name,
+                    attrs=base_coords[bc].attrs,
+                )
+                for s, bc, offset, sc in zip(
+                    coarsened.shape, base_coords, get_downsampled_offset(scale_factors), scale_factors
+                )
+            )
+        coarsened = DataArray(coarsened, dims=array.dims, coords=new_coords, attrs=array.attrs, name=array.name)
+
     return coarsened
 
 
diff --git a/tests/test_multiscale.py b/tests/test_multiscale.py
index a24cc3e..ce47226 100644
--- a/tests/test_multiscale.py
+++ b/tests/test_multiscale.py
@@ -51,17 +51,24 @@ def test_downscale_2d():
         [[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]], dtype="uint8"
     )
     arr_dask = da.from_array(arr_numpy, chunks=chunks)
+    arr_xarray = DataArray(arr_dask)
 
     downscaled_numpy_float = downscale(
         arr_numpy, np.mean, scale, preserve_dtype=False
     ).compute()
+
     downscaled_dask_float = downscale(
         arr_dask, np.mean, scale, preserve_dtype=False
     ).compute()
 
+    downscaled_xarray_float = downscale(
+        arr_xarray, np.mean, scale, preserve_dtype=False
+    ).compute()
+
     answer_float = np.array([[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]])
     assert np.array_equal(downscaled_numpy_float, answer_float)
     assert np.array_equal(downscaled_dask_float, answer_float)
+    assert np.array_equal(downscaled_xarray_float, answer_float)
 
     downscaled_numpy_int = downscale(
         arr_numpy, np.mean, scale, dtype=arr_numpy.dtype

From 63ddaa727d702c41daa48c6cf3f51332b855a0dd Mon Sep 17 00:00:00 2001
From: Davis Vann Bennett <davis.v.bennett@gmail.com>
Date: Wed, 16 Jun 2021 19:25:31 -0400
Subject: [PATCH 2/6] add recursive mode

---
 src/xarray_multiscale/multiscale.py | 20 +++++++++++---------
 tests/test_multiscale.py            | 26 ++++++++------------------
 2 files changed, 19 insertions(+), 27 deletions(-)

diff --git a/src/xarray_multiscale/multiscale.py b/src/xarray_multiscale/multiscale.py
index fa9560b..2ccddda 100644
--- a/src/xarray_multiscale/multiscale.py
+++ b/src/xarray_multiscale/multiscale.py
@@ -64,21 +64,26 @@ def multiscale(
         tuple(s ** l for s in scale_factors) for l in levels
     )
     result = [_ingest_array(array, scales[0])]
-    base_attrs = result[0].attrs
-    base_coords = result[0].coords
 
-    for scale in scales[1:]:
-        downscaled = downscale(
-            result[0], reduction, scale, pad_mode=pad_mode, preserve_dtype=preserve_dtype
-        )
+    for level in levels[1:]:
+        if recursive:
+            scale = scale_factors
+            downscaled = downscale(result[-1], reduction, scale, pad_mode=pad_mode) 
+        else:
+            scale = scales[level]
+            downscaled = downscale(result[0], reduction, scale, pad_mode=pad_mode)
         result.append(downscaled)
 
+    if preserve_dtype:
+        result = [r.astype(array.dtype) for r in result]
+
     if chunks is not None:
         if isinstance(chunks, Sequence):
             _chunks = {k: v for k, v in zip(result[0].dims, chunks)}
         else:
             _chunks = chunks
         result = [r.chunk(_chunks) for r in result]
+
     return result
 
 
@@ -202,7 +207,6 @@ def downscale(
     reduction: Callable,
     scale_factors: Sequence[int],
     pad_mode: Optional[str] = None,
-    preserve_dtype: bool = True,
     **kwargs,
 ) -> DataArray:
     """
@@ -239,8 +243,6 @@ def downscale(
         **kwargs,
     )
 
-    if preserve_dtype:
-        coarsened = coarsened.astype(array.dtype)
 
     if isinstance(array, xarray.DataArray):
         base_coords = array.coords
diff --git a/tests/test_multiscale.py b/tests/test_multiscale.py
index ce47226..addc8fa 100644
--- a/tests/test_multiscale.py
+++ b/tests/test_multiscale.py
@@ -54,33 +54,20 @@ def test_downscale_2d():
     arr_xarray = DataArray(arr_dask)
 
     downscaled_numpy_float = downscale(
-        arr_numpy, np.mean, scale, preserve_dtype=False
-    ).compute()
+        arr_numpy, np.mean, scale).compute()
 
     downscaled_dask_float = downscale(
-        arr_dask, np.mean, scale, preserve_dtype=False
-    ).compute()
+        arr_dask, np.mean, scale).compute()
 
     downscaled_xarray_float = downscale(
-        arr_xarray, np.mean, scale, preserve_dtype=False
-    ).compute()
+        arr_xarray, np.mean, scale).compute()
 
     answer_float = np.array([[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]])
+    
     assert np.array_equal(downscaled_numpy_float, answer_float)
     assert np.array_equal(downscaled_dask_float, answer_float)
     assert np.array_equal(downscaled_xarray_float, answer_float)
 
-    downscaled_numpy_int = downscale(
-        arr_numpy, np.mean, scale, dtype=arr_numpy.dtype
-    ).compute()
-    downscaled_dask_int = downscale(
-        arr_dask, np.mean, scale, dtype=arr_numpy.dtype
-    ).compute()
-
-    answer_int = answer_float.astype("int")
-    assert np.array_equal(downscaled_numpy_int, answer_int)
-    assert np.array_equal(downscaled_dask_int, answer_int)
-
 
 def test_multiscale():
     ndim = 3
@@ -94,7 +81,7 @@ def test_multiscale():
 
     pyr_trimmed = multiscale(array, np.mean, 2, pad_mode=None)
     pyr_padded = multiscale(array, np.mean, 2, pad_mode="reflect")
-
+    pyr_trimmed_recursive = multiscale(array, np.mean, 2, pad_mode=None, recursive=True)
     assert [p.shape for p in pyr_padded] == [
         shape,
         (5, 5, 5),
@@ -111,4 +98,7 @@ def test_multiscale():
     assert np.array_equal(
         pyr_trimmed[-2].data.mean().compute(), pyr_trimmed[-1].data.compute().mean()
     )
+    assert np.array_equal(
+        pyr_trimmed_recursive[-2].data.mean().compute(), pyr_trimmed_recursive[-1].data.compute().mean()
+    )
     assert np.allclose(pyr_padded[0].data.mean().compute(), 0.17146776406035666)

From fb12bd1b042c4dc43ceae7d911092f6b914e4921 Mon Sep 17 00:00:00 2001
From: Davis Vann Bennett <davis.v.bennett@gmail.com>
Date: Thu, 17 Jun 2021 14:06:29 -0400
Subject: [PATCH 3/6] update docstrings and tests; change 'recursive' to
 'chained' in multiscale signature.

---
 src/xarray_multiscale/multiscale.py | 153 ++++++++++++++++------------
 tests/test_multiscale.py            |   4 +-
 2 files changed, 92 insertions(+), 65 deletions(-)

diff --git a/src/xarray_multiscale/multiscale.py b/src/xarray_multiscale/multiscale.py
index 2ccddda..fdd9ef1 100644
--- a/src/xarray_multiscale/multiscale.py
+++ b/src/xarray_multiscale/multiscale.py
@@ -4,11 +4,9 @@
 from xarray import DataArray
 from typing import Any, List, Optional, Tuple, Union, Sequence, Callable, Dict
 from scipy.interpolate import interp1d
-from dask.array.core import slices_from_chunks, normalize_chunks
 from dask.array import coarsen
 
 
-
 def multiscale(
     array: Any,
     reduction: Callable[[Any], Any],
@@ -16,31 +14,50 @@ def multiscale(
     pad_mode: Optional[str] = None,
     preserve_dtype: bool = True,
     chunks: Optional[Union[Sequence[int], Dict[str, int]]] = None,
-    recursive: bool = False,
+    chained: bool = True,
 ) -> List[DataArray]:
     """
-    Lazily generate a multiscale representation of an array
+    Generate a lazy, coordinate-aware multiscale representation of an array.
 
     Parameters
     ----------
-    array: ndarray to be downscaled.
+    array : numpy array, dask array, or xarray DataArray
+        The array to be downscaled
+
+    reduction : callable
+        A function that aggregates chunks of data over windows. See the documentation of `dask.array.coarsen` for the expected
+        signature of this callable.
 
-    reduction: a function that aggregates data over windows.
+    scale_factors : iterable of ints
+        The desired downscaling factors, one for each axis.
 
-    scale_factors: an iterable of integers that specifies how much to downscale each axis of the array.
+    pad_mode : string or None, default=None
+        How arrays should be padded prior to downscaling in order to ensure that each array dimension
+        is evenly divisible by the respective scale factor. When set to `None` (default), the input will be sliced before downscaling
+        if its dimensions are not divisible by `scale_factors`.
 
-    pad_mode: How (or if) the input should be padded. When set to `None` the input will be trimmed as needed.
+    preserve_dtype : bool, default=True
+        Determines whether the multiresolution arrays are all cast to the same dtype as the input.
 
-    preserve_dtype: boolean, defaults to True, determines whether lower levels of the pyramid are coerced to the same dtype as the input. This assumes that
-    the reduction function accepts a "dtype" kwarg, e.g. numpy.mean(x, dtype='int').
+    chunks : sequence or dict of ints, or None, default=None.
+        If `chunks` is supplied, all output arrays are returned with this chunking. If not None, this
+        argument is passed directly to the `xarray.DataArray.chunk` method of each output array.
 
-    chunks: Sequence or Dict of ints, defaults to None. If `chunks` is supplied, all DataArrays are rechunked with these chunks before being returned.
+    chained : bool, default=True
+        If True (default), the nth downscaled array is generated by applying the reduction function on the n-1th
+        downscaled array with the user-supplied `scale_factors`. This means that the nth downscaled array directly depends on the n-1th
+        downscaled array. Note that nonlinear reductions like the windowed mode may give inaccurate results with `chained` set to True.
 
-    recursive: boolean, defaults to False. ToDo
+        If False, the nth downscaled array is generated by applying the reduction function on the 0th downscaled array
+        (i.e., the input array) with the `scale_factors` raised to the nth power. This means that the nth downscaled array directly
+        depends on the input array.
 
-    Returns a list of DataArrays, one per level of downscaling. These DataArrays have `coords` properties that track the changing offset (if any)
-    induced by the downsampling operation. Additionally, the scale factors are stored each DataArray's attrs propery under the key `scale_factors`
+    Returns
     -------
+    result : list of DataArrays
+        The `coords` attribute of these DataArrays properties that track the changing offset (if any)
+        induced by the downsampling operation. Additionally, the scale factors are stored each DataArray's attrs propery under the key `scale_factors`
+
 
     """
     needs_padding = pad_mode != None
@@ -56,19 +73,16 @@ def multiscale(
     else:
         padded_shape = prepad(array, scale_factors, pad_mode=pad_mode).shape
 
-    # figure out the maximum depth
     levels = range(
         0, 1 + get_downscale_depth(padded_shape, scale_factors, pad=needs_padding)
     )
-    scales = tuple(
-        tuple(s ** l for s in scale_factors) for l in levels
-    )
+    scales = tuple(tuple(s ** l for s in scale_factors) for l in levels)
     result = [_ingest_array(array, scales[0])]
 
     for level in levels[1:]:
-        if recursive:
+        if chained:
             scale = scale_factors
-            downscaled = downscale(result[-1], reduction, scale, pad_mode=pad_mode) 
+            downscaled = downscale(result[-1], reduction, scale, pad_mode=pad_mode)
         else:
             scale = scales[level]
             downscaled = downscale(result[0], reduction, scale, pad_mode=pad_mode)
@@ -80,14 +94,21 @@ def multiscale(
     if chunks is not None:
         if isinstance(chunks, Sequence):
             _chunks = {k: v for k, v in zip(result[0].dims, chunks)}
-        else:
+        elif isinstance(chunks, dict):
             _chunks = chunks
+        else:
+            raise ValueError(
+                f"Chunks must be an instance or a dict, not {type(chunks)}"
+            )
         result = [r.chunk(_chunks) for r in result]
 
     return result
 
 
 def _ingest_array(array: Any, scales: Sequence[int]):
+    """
+    Ingest an array in preparation for downscaling
+    """
     if hasattr(array, "coords"):
         # if the input is a xarray.DataArray, assign a new variable to the DataArray and use the variable
         # `array` to refer to the data property of that array
@@ -101,7 +122,7 @@ def _ingest_array(array: Any, scales: Sequence[int]):
         data = da.asarray(array)
         dims = tuple(f"dim_{d}" for d in range(data.ndim))
         coords = {
-            dim: DataArray(offset + np.arange(s, dtype="float32"), dims=dim)
+            dim: DataArray(offset + np.arange(s, dtype="float"), dims=dim)
             for dim, s, offset in zip(dims, array.shape, get_downsampled_offset(scales))
         }
         name = None
@@ -118,7 +139,13 @@ def even_padding(length: int, window: int) -> int:
     Parameters
     ----------
     length : int
-    window: int
+
+    window : int
+
+    Returns
+    -------
+    int
+        Value that, when added to `length`, results in a sum that is evenly divided by `window`
     """
     return (window - (length % window)) % window
 
@@ -132,8 +159,10 @@ def logn(x: float, n: float) -> float:
     x : float or int.
     n: float or int.
 
-    Returns np.log(x) / np.log(n)
+    Returns
     -------
+    float
+        np.log(x) / np.log(n)
 
     """
     result: float = np.log(x) / np.log(n)
@@ -147,20 +176,25 @@ def prepad(
     rechunk: bool = True,
 ) -> da.array:
     """
-    Pad an array such that its new dimensions are evenly divisible by some integer.
+    Lazily pad an array such that its new dimensions are evenly divisible by some integer.
 
     Parameters
     ----------
-    array: An ndarray that will be padded.
+    array : ndarray
+        Array that will be padded.
 
-    scale_factors: An iterable of integers. The output array is guaranteed to have dimensions that are each evenly divisible
-    by the corresponding scale factor, and chunks that are smaller than or equal to the scale factor (if the array has chunks)
+    scale_factors : Sequence of ints
+        The output array is guaranteed to have dimensions that are each evenly divisible
+        by the corresponding scale factor, and chunks that are smaller than or equal
+        to the scale factor (if the array has chunks)
 
-    mode: String. The edge mode used by the padding routine. See `dask.array.pad` for more documentation.
+    pad_mode : str
+        The edge mode used by the padding routine. This parameter will be passed to
+        `dask.array.pad` as the `mode` keyword.
 
-    Returns a dask array with padded dimensions.
+    Returns
     -------
-
+    dask array
     """
 
     if pad_mode == None:
@@ -198,7 +232,8 @@ def prepad(
                     extended_coords, dims=k, attrs=old_coord.attrs
                 )
         result = DataArray(
-            result, coords=new_coords, dims=array.dims, attrs=array.attrs)
+            result, coords=new_coords, dims=array.dims, attrs=array.attrs
+        )
     return result
 
 
@@ -214,16 +249,22 @@ def downscale(
 
     Parameters
     ----------
-    array: The narray to be downscaled.
+    array : numpy array, dask array, xarray DataArray
+        The array to be downscaled.
 
-    reduction: The function to apply to each window of the array.
+    reduction : callable
+        A function that aggregates chunks of data over windows. See the documentation of `dask.array.coarsen` for the expected
+        signature of this callable.
 
-    scale_factors: A list if ints specifying how much to downscale the array per dimension.
+    scale_factors : iterable of ints
+        The desired downscaling factors, one for each axis.
 
-    trim_excess: A boolean that determines whether the size of the input array should be increased or decreased such that
-    each scale factor tiles its respective array axis. Defaults to False, which will result in the input being padded.
+    trim_excess : bool, default=False
+        Whether the size of the input array should be increased or decreased such that
+        each scale factor tiles its respective array axis. Defaults to False, which will result in the input being padded.
 
-    **kwargs: extra kwargs passed to dask.array.coarsen
+    **kwargs
+        extra kwargs passed to dask.array.coarsen
 
     Returns the downscaled version of the input as a dask array.
     -------
@@ -243,7 +284,6 @@ def downscale(
         **kwargs,
     )
 
-
     if isinstance(array, xarray.DataArray):
         base_coords = array.coords
         new_coords = base_coords
@@ -256,10 +296,19 @@ def downscale(
                     attrs=base_coords[bc].attrs,
                 )
                 for s, bc, offset, sc in zip(
-                    coarsened.shape, base_coords, get_downsampled_offset(scale_factors), scale_factors
+                    coarsened.shape,
+                    base_coords,
+                    get_downsampled_offset(scale_factors),
+                    scale_factors,
                 )
             )
-        coarsened = DataArray(coarsened, dims=array.dims, coords=new_coords, attrs=array.attrs, name=array.name)
+        coarsened = DataArray(
+            coarsened,
+            dims=array.dims,
+            coords=new_coords,
+            attrs=array.attrs,
+            name=array.name,
+        )
 
     return coarsened
 
@@ -324,25 +373,3 @@ def slice_span(sl: slice) -> int:
     Measure the length of a slice
     """
     return sl.stop - sl.start
-
-
-def blocked_pyramid(
-    arr, block_size: Sequence, scale_factors: Sequence[int] = (2, 2, 2), **kwargs
-):
-    full_pyr = multiscale(arr, scale_factors=scale_factors, **kwargs)
-    slices = slices_from_chunks(normalize_chunks(block_size, arr.shape))
-    absolute_block_size = tuple(map(slice_span, slices[0]))
-
-    results = []
-    for idx, sl in enumerate(slices):
-        regions = [
-            tuple(map(downscale_slice, sl, tuple(np.power(scale_factors, exp))))
-            for exp in range(len(full_pyr))
-        ]
-        if tuple(map(slice_span, sl)) == absolute_block_size:
-            pyr = multiscale(arr[sl], scale_factors=scale_factors, **kwargs)
-        else:
-            pyr = [full_pyr[l][r] for l, r in enumerate(regions)]
-        assert len(pyr) == len(regions)
-        results.append((regions, pyr))
-    return results
diff --git a/tests/test_multiscale.py b/tests/test_multiscale.py
index addc8fa..9c1cd49 100644
--- a/tests/test_multiscale.py
+++ b/tests/test_multiscale.py
@@ -81,7 +81,7 @@ def test_multiscale():
 
     pyr_trimmed = multiscale(array, np.mean, 2, pad_mode=None)
     pyr_padded = multiscale(array, np.mean, 2, pad_mode="reflect")
-    pyr_trimmed_recursive = multiscale(array, np.mean, 2, pad_mode=None, recursive=True)
+    pyr_trimmed_unchained = multiscale(array, np.mean, 2, pad_mode=None, chained=False)
     assert [p.shape for p in pyr_padded] == [
         shape,
         (5, 5, 5),
@@ -99,6 +99,6 @@ def test_multiscale():
         pyr_trimmed[-2].data.mean().compute(), pyr_trimmed[-1].data.compute().mean()
     )
     assert np.array_equal(
-        pyr_trimmed_recursive[-2].data.mean().compute(), pyr_trimmed_recursive[-1].data.compute().mean()
+        pyr_trimmed_unchained[-2].data.mean().compute(), pyr_trimmed_unchained[-1].data.compute().mean()
     )
     assert np.allclose(pyr_padded[0].data.mean().compute(), 0.17146776406035666)

From d83db778f3885b5ccc89313704aff0c995d62cce Mon Sep 17 00:00:00 2001
From: Davis Vann Bennett <davis.v.bennett@gmail.com>
Date: Thu, 17 Jun 2021 14:10:57 -0400
Subject: [PATCH 4/6] bump version to reflect some breaking api changes

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7b9fa7e..757f188 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "xarray-multiscale"
-version = "0.1.6"
+version = "0.2.0"
 description = ""
 authors = ["Davis Vann Bennett <davis.v.bennett@gmail.com>"]
 

From a8c8a0bf027188bd6ef714440f0486a4f3afe901 Mon Sep 17 00:00:00 2001
From: Davis Vann Bennett <davis.v.bennett@gmail.com>
Date: Thu, 17 Jun 2021 14:13:12 -0400
Subject: [PATCH 5/6] remove metadata, since this can be handled by other
 existing libraries

---
 tests/test_metadata.py | 78 ------------------------------------------
 1 file changed, 78 deletions(-)
 delete mode 100644 tests/test_metadata.py

diff --git a/tests/test_metadata.py b/tests/test_metadata.py
deleted file mode 100644
index b7078c0..0000000
--- a/tests/test_metadata.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import dask.array as da
-import numpy as np
-from xarray import DataArray
-from xarray_multiscale.metadata.neuroglancer import GroupMeta
-from xarray_multiscale.metadata.util import SpatialTransform, infer_c_or_f_contiguous
-from xarray_multiscale.metadata import neuroglancer, cosem_ome
-import zarr
-from xarray_multiscale.multiscale import multiscale
-
-def test_array_order_inferece():
-    data= np.arange(10)
-    assert infer_c_or_f_contiguous(data) == 'C'
-
-    data = np.arange(10).reshape((2,5))
-    assert infer_c_or_f_contiguous(data) == 'C'
-
-    data = np.arange(10).reshape((2,5), order='F')
-    assert infer_c_or_f_contiguous(data) == 'F'
-
-    data = zarr.zeros((10,10), order='C')
-    assert infer_c_or_f_contiguous(data) == 'C'
-
-    data = zarr.zeros((10,10), order='F')
-    assert infer_c_or_f_contiguous(data) == 'F'
-
-    data = da.zeros((10,10))
-    assert infer_c_or_f_contiguous(data) == 'C'
-
-def test_SpatialTransform():
-    data = DataArray(np.zeros((10,10,10)))
-    transform = SpatialTransform.fromDataArray(data)
-    assert transform == SpatialTransform(axes=['dim_0','dim_1','dim_2'],
-                                         units=[None] * 3, 
-                                         translate=[0.0] * 3, 
-                                         scale=[1.0] * 3)
-
-    coords = [DataArray(np.arange(10), dims=('z'), attrs={'units': 'nm'}), 
-          DataArray(np.arange(10) + 5, dims=('y',), attrs={'units': 'm'}), 
-          DataArray(10 + (np.arange(10) * 10), dims=('x',), attrs={'units': 'km'})]
-
-    data = DataArray(np.zeros((10,10,10)), coords=coords)
-    transform = SpatialTransform.fromDataArray(data)
-    assert transform == SpatialTransform(axes=['z','y','x'],
-                                         units=['nm','m','km'], 
-                                         translate=[0.0, 5.0, 10.0], 
-                                         scale=[1.0, 1.0, 10.0 ] )
-
-    transform = SpatialTransform.fromDataArray(data, reverse_axes=True)
-    assert transform == SpatialTransform(axes=['x','y','z'],
-                                         units=['km','m','nm'], 
-                                         translate=[10.0, 5.0, 0.0], 
-                                         scale=[10.0, 1.0, 1.0 ] )
-
-def test_neuroglancer_metadata():
-    coords = [DataArray(np.arange(16) + .5, dims=('z'), attrs={'units': 'nm'}), 
-          DataArray(np.arange(16) + 1/3, dims=('y',), attrs={'units': 'm'}), 
-          DataArray(10 + (np.arange(16) * 100.1), dims=('x',), attrs={'units': 'km'})]
-
-    data = DataArray(np.zeros((16, 16, 16)), coords=coords)
-    multi = multiscale(data, np.mean, (2,2,2))[:4]
-    neuroglancer_metadata = neuroglancer.GroupMeta.fromDataArraySequence(multi)
-    
-    assert neuroglancer_metadata == neuroglancer.GroupMeta(axes=['x','y','z'],
-                                                            units=['km','m','nm'],
-                                                            scales=[[1,1,1], [2,2,2], [4,4,4], [8,8,8]],
-                                                            pixelResolution=neuroglancer.PixelResolution(dimensions=[100.1, 1.0, 1.0], unit='km'))
-
-def test_cosem_ome():
-    coords = [DataArray(np.arange(16), dims=('z'), attrs={'units': 'nm'}), 
-          DataArray(np.arange(16) + 5, dims=('y',), attrs={'units': 'm'}), 
-          DataArray(10 + (np.arange(16) * 10), dims=('x',), attrs={'units': 'km'})]
-
-    data = DataArray(np.zeros((16, 16, 16)), coords=coords, name='data')
-    multi = multiscale(data, np.mean, (2,2,2))[:2]
-    paths = ['s0', 's1']
-    cosem_ome_group_metadata = cosem_ome.GroupMeta.fromDataArraySequence(multi, paths=paths)
-    scale_metas = [cosem_ome.ScaleMeta(path = p, transform=SpatialTransform.fromDataArray(m)) for p,m in zip(paths, multi)]
-    assert cosem_ome_group_metadata == cosem_ome.GroupMeta(name='data', multiscales=[cosem_ome.MultiscaleMeta(datasets=scale_metas)])
\ No newline at end of file

From 4bb424a3e4fefbcc3d4abac63e8fb0d2d9f15604 Mon Sep 17 00:00:00 2001
From: Davis Vann Bennett <davis.v.bennett@gmail.com>
Date: Thu, 17 Jun 2021 14:14:47 -0400
Subject: [PATCH 6/6] actually remove metadata, and remove unused deps

---
 pyproject.toml                                |  2 -
 src/xarray_multiscale/metadata/__init__.py    |  0
 src/xarray_multiscale/metadata/cosem_ome.py   | 47 ---------------
 .../metadata/neuroglancer.py                  | 37 ------------
 src/xarray_multiscale/metadata/util.py        | 58 -------------------
 5 files changed, 144 deletions(-)
 delete mode 100644 src/xarray_multiscale/metadata/__init__.py
 delete mode 100644 src/xarray_multiscale/metadata/cosem_ome.py
 delete mode 100644 src/xarray_multiscale/metadata/neuroglancer.py
 delete mode 100644 src/xarray_multiscale/metadata/util.py

diff --git a/pyproject.toml b/pyproject.toml
index 757f188..6f604e7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,8 +11,6 @@ mypy = "^0.790"
 scipy = "^1.5.4"
 numpy = "^1.19.4"
 dask = "^^2020.12.0"
-cytoolz = "^0.11.0"
-dacite = "^1.6.0"
 
 [tool.poetry.dev-dependencies]
 pytest = "^5.2"
diff --git a/src/xarray_multiscale/metadata/__init__.py b/src/xarray_multiscale/metadata/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/xarray_multiscale/metadata/cosem_ome.py b/src/xarray_multiscale/metadata/cosem_ome.py
deleted file mode 100644
index 5354731..0000000
--- a/src/xarray_multiscale/metadata/cosem_ome.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from dataclasses import dataclass, asdict
-from typing import Sequence
-from xarray import DataArray
-from .util import BaseMeta, SpatialTransform
-from typing import Optional
-
-
-@dataclass
-class ScaleMeta(BaseMeta):
-    path: str
-    transform: SpatialTransform
-
-
-@dataclass
-class MultiscaleMeta(BaseMeta):
-    datasets: Sequence[ScaleMeta]
-
-
-@dataclass
-class GroupMeta(BaseMeta):
-    name: str
-    multiscales: Sequence[MultiscaleMeta]
-
-    @classmethod
-    def fromDataArraySequence(
-        cls, dataarrays: Sequence[DataArray], paths: Sequence[str]
-    ):
-        name: str = str(dataarrays[0].name)
-        multiscales = [
-            MultiscaleMeta(
-                datasets=[
-                    ScaleMeta(path=path, transform=SpatialTransform.fromDataArray(arr))
-                    for path, arr in zip(paths, dataarrays)
-                ]
-            )
-        ]
-        return cls(name=name, multiscales=multiscales)
-
-
-@dataclass
-class ArrayMeta(BaseMeta):
-    name: Optional[str]
-    transform: SpatialTransform
-
-    @classmethod
-    def fromDataArray(cls, data: DataArray) -> "ArrayMeta":
-        return cls(name=str(data.name), transform=SpatialTransform.fromDataArray(data))
diff --git a/src/xarray_multiscale/metadata/neuroglancer.py b/src/xarray_multiscale/metadata/neuroglancer.py
deleted file mode 100644
index 2329dbb..0000000
--- a/src/xarray_multiscale/metadata/neuroglancer.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from typing import Sequence
-from dataclasses import dataclass
-
-from xarray.core.dataarray import DataArray
-from typing import Union, List
-import numpy as np
-from xarray_multiscale.metadata.util import BaseMeta, SpatialTransform
-
-
-@dataclass
-class PixelResolution(BaseMeta):
-    # fortran-ordered
-    dimensions: Sequence[float]
-    unit: Union[str, None]
-
-
-@dataclass
-class GroupMeta(BaseMeta):
-    # see https://github.com/google/neuroglancer/issues/176#issuecomment-553027775
-    # these properties must be stored in the opposite order of C-contiguous axis indexing
-    axes: Sequence[Union[str, None]]
-    units: Sequence[Union[str, None]]
-    scales: Sequence[Sequence[int]]
-    pixelResolution: PixelResolution
-
-    @classmethod
-    def fromDataArraySequence(cls, dataarrays: Sequence[DataArray]) -> "GroupMeta":
-        transforms = [
-            SpatialTransform.fromDataArray(array, reverse_axes=True)
-            for array in dataarrays
-        ]
-        pixelresolution = PixelResolution(transforms[0].scale, transforms[0].units[0])
-        scales: List[List[int]] = [
-            np.round(np.divide(t.scale, transforms[0].scale)).astype("int").tolist()
-            for t in transforms
-        ]
-        return cls(transforms[0].axes, transforms[0].units, scales, pixelresolution)
diff --git a/src/xarray_multiscale/metadata/util.py b/src/xarray_multiscale/metadata/util.py
deleted file mode 100644
index eaa7ff7..0000000
--- a/src/xarray_multiscale/metadata/util.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from dataclasses import dataclass, asdict
-from typing import Sequence, Union, Dict, Any, Optional
-from xarray import DataArray
-from dacite import from_dict
-
-
-def infer_c_or_f_contiguous(array: Any) -> str:
-    data_order = "C"
-    if hasattr(array, "order"):
-        data_order = array.order
-    elif hasattr(array, "flags"):
-        if array.flags["F_CONTIGUOUS"] and not array.flags["C_CONTIGUOUS"]:
-            data_order = "F"
-    return data_order
-
-
-@dataclass
-class BaseMeta:
-    def asdict(self):
-        return asdict(self)
-
-
-@dataclass
-class SpatialTransform(BaseMeta):
-    axes: Sequence[str]
-    units: Sequence[Union[str, None]]
-    translate: Sequence[float]
-    scale: Sequence[float]
-
-    def __post_init__(self):
-        assert (
-            len(self.axes) == len(self.units) == len(self.translate) == len(self.scale)
-        )
-
-    @classmethod
-    def fromDataArray(
-        cls, dataarray: DataArray, reverse_axes=False
-    ) -> "SpatialTransform":
-        """
-        Generate a spatial transform from a DataArray.
-        """
-
-        orderer = slice(None)
-        if reverse_axes:
-            orderer = slice(-1, None, -1)
-        axes = [str(d) for d in dataarray.dims[orderer]]
-        units = [dataarray.coords[ax].attrs.get("units") for ax in axes]
-        translate = [float(dataarray.coords[ax][0]) for ax in axes]
-        scale = [
-            abs(float(dataarray.coords[ax][1]) - float(dataarray.coords[ax][0]))
-            for ax in axes
-        ]
-
-        return cls(axes=axes, units=units, translate=translate, scale=scale)
-
-    @classmethod
-    def fromDict(cls, d: Dict[str, Any]):
-        return from_dict(cls, d)