From 24f3acae3961e24f5cabc862b9a450b2bf2bda0e Mon Sep 17 00:00:00 2001
From: R Schwanhold <r.schwanhold@hotmail.de>
Date: Wed, 22 Jan 2020 16:23:08 +0100
Subject: [PATCH] High-level dataset library (#165)

* only log downsampling for each MB ob precessed data

* log every gigabyte instead of every megabyte

* implement PR feedback

* WIP: implement high level dataset api

* WIP: implement TiffMag

* implement test for dataset api

* refactor high level dataset api

* add test data for WKDataset

* improve high level dataset api and add more tests

* use seed in tests to achieve deterministic behaviour

* implement a test for writing out of bounds with a wk_slice

* improve quality of dataset tests

* make naming schema of test files consistent

* implement PR feedback

* rename tiffs in testdata

* Improve error message and reformat code

* add type annotation

* rename Slice to View

* remove comments and reformat code

* add scikit-image (which is needed for skimage) as dependency

* update version of scikit-image

* fix relative paths in tests

* reformat code
---
 poetry.lock                                   | 249 +++++++-
 pyproject.toml                                |   1 +
 .../simple_tiff_dataset/color/1/00000.tif     | Bin 0 -> 21408 bytes
 .../simple_tiff_dataset/color/1/00001.tif     | Bin 0 -> 21408 bytes
 .../simple_tiff_dataset/color/1/00002.tif     | Bin 0 -> 21408 bytes
 .../simple_tiff_dataset/color/1/00003.tif     | Bin 0 -> 21408 bytes
 .../simple_tiff_dataset/color/1/00004.tif     | Bin 0 -> 21408 bytes
 .../simple_tiff_dataset/color/1/00005.tif     | Bin 0 -> 21408 bytes
 .../simple_tiff_dataset/color/1/00006.tif     | Bin 0 -> 21408 bytes
 .../simple_tiff_dataset/color/1/00007.tif     | Bin 0 -> 21408 bytes
 .../simple_tiff_dataset/color/1/00008.tif     | Bin 0 -> 21408 bytes
 .../simple_tiff_dataset/color/1/00009.tif     | Bin 0 -> 21408 bytes
 .../datasource-properties.json                |  40 ++
 testdata/simple_wk_dataset/color/1/header.wkw | Bin 0 -> 16 bytes
 .../simple_wk_dataset/color/1/z0/y0/x0.wkw    | Bin 0 -> 98320 bytes
 .../datasource-properties.json                |  38 ++
 tests/test_dataset.py                         | 532 ++++++++++++++++++
 wkcuber/api/Dataset.py                        | 169 ++++++
 wkcuber/api/Layer.py                          | 130 +++++
 wkcuber/api/MagDataset.py                     | 116 ++++
 wkcuber/api/Properties.py                     | 317 +++++++++++
 wkcuber/api/TiffData/TiffMag.py               | 227 ++++++++
 wkcuber/api/View.py                           | 119 ++++
 23 files changed, 1936 insertions(+), 2 deletions(-)
 create mode 100644 testdata/simple_tiff_dataset/color/1/00000.tif
 create mode 100644 testdata/simple_tiff_dataset/color/1/00001.tif
 create mode 100644 testdata/simple_tiff_dataset/color/1/00002.tif
 create mode 100644 testdata/simple_tiff_dataset/color/1/00003.tif
 create mode 100644 testdata/simple_tiff_dataset/color/1/00004.tif
 create mode 100644 testdata/simple_tiff_dataset/color/1/00005.tif
 create mode 100644 testdata/simple_tiff_dataset/color/1/00006.tif
 create mode 100644 testdata/simple_tiff_dataset/color/1/00007.tif
 create mode 100644 testdata/simple_tiff_dataset/color/1/00008.tif
 create mode 100644 testdata/simple_tiff_dataset/color/1/00009.tif
 create mode 100644 testdata/simple_tiff_dataset/datasource-properties.json
 create mode 100644 testdata/simple_wk_dataset/color/1/header.wkw
 create mode 100644 testdata/simple_wk_dataset/color/1/z0/y0/x0.wkw
 create mode 100644 testdata/simple_wk_dataset/datasource-properties.json
 create mode 100644 tests/test_dataset.py
 create mode 100644 wkcuber/api/Dataset.py
 create mode 100644 wkcuber/api/Layer.py
 create mode 100644 wkcuber/api/MagDataset.py
 create mode 100644 wkcuber/api/Properties.py
 create mode 100644 wkcuber/api/TiffData/TiffMag.py
 create mode 100644 wkcuber/api/View.py

diff --git a/poetry.lock b/poetry.lock
index e56095003..2e14a367a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -126,6 +126,25 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 version = "0.4.3"
 
+[[package]]
+category = "main"
+description = "Composable style cycles"
+name = "cycler"
+optional = false
+python-versions = "*"
+version = "0.10.0"
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+category = "main"
+description = "Decorators for Humans"
+name = "decorator"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*"
+version = "4.4.1"
+
 [[package]]
 category = "main"
 description = "Internationalized Domain Names in Applications (IDNA)"
@@ -134,6 +153,25 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 version = "2.8"
 
+[[package]]
+category = "main"
+description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
+name = "imageio"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "2.6.1"
+
+[package.dependencies]
+numpy = "*"
+pillow = "*"
+
+[package.extras]
+ffmpeg = ["imageio-ffmpeg"]
+fits = ["astropy"]
+full = ["astropy", "gdal", "imageio-ffmpeg", "itk"]
+gdal = ["gdal"]
+itk = ["itk"]
+
 [[package]]
 category = "dev"
 description = "Read metadata from Python packages"
@@ -164,6 +202,17 @@ pyproject = ["toml"]
 requirements = ["pipreqs", "pip-api"]
 xdg_home = ["appdirs (>=1.4.0)"]
 
+[[package]]
+category = "main"
+description = "A fast implementation of the Cassowary constraint solver"
+name = "kiwisolver"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "1.1.0"
+
+[package.dependencies]
+setuptools = "*"
+
 [[package]]
 category = "dev"
 description = "A fast and thorough lazy object proxy."
@@ -172,6 +221,21 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 version = "1.4.3"
 
+[[package]]
+category = "main"
+description = "Python plotting package"
+name = "matplotlib"
+optional = false
+python-versions = ">=3.6"
+version = "3.1.2"
+
+[package.dependencies]
+cycler = ">=0.10"
+kiwisolver = ">=1.0.1"
+numpy = ">=1.11"
+pyparsing = ">=2.0.1,<2.0.4 || >2.0.4,<2.1.2 || >2.1.2,<2.1.6 || >2.1.6"
+python-dateutil = ">=2.1"
+
 [[package]]
 category = "dev"
 description = "McCabe checker, plugin for flake8"
@@ -200,6 +264,30 @@ version = "6.2.0"
 fast = ["fastnumbers (>=2.0.0)"]
 icu = ["PyICU (>=1.0.0)"]
 
+[[package]]
+category = "main"
+description = "Python package for creating and manipulating graphs and networks"
+name = "networkx"
+optional = false
+python-versions = ">=3.5"
+version = "2.4"
+
+[package.dependencies]
+decorator = ">=4.3.0"
+
+[package.extras]
+all = ["numpy", "scipy", "pandas", "matplotlib", "pygraphviz", "pydot", "pyyaml", "gdal", "lxml", "pytest"]
+gdal = ["gdal"]
+lxml = ["lxml"]
+matplotlib = ["matplotlib"]
+numpy = ["numpy"]
+pandas = ["pandas"]
+pydot = ["pydot"]
+pygraphviz = ["pygraphviz"]
+pytest = ["pytest"]
+pyyaml = ["pyyaml"]
+scipy = ["scipy"]
+
 [[package]]
 category = "main"
 description = "Access a multitude of neuroimaging data formats"
@@ -304,7 +392,7 @@ isort = ">=4.2.5,<5"
 mccabe = ">=0.6,<0.7"
 
 [[package]]
-category = "dev"
+category = "main"
 description = "Python parsing module"
 name = "pyparsing"
 optional = false
@@ -336,6 +424,28 @@ version = ">=0.12"
 [package.extras]
 testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]
 
+[[package]]
+category = "main"
+description = "Extensions to the standard Python datetime module"
+name = "python-dateutil"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+version = "2.8.1"
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+category = "main"
+description = "PyWavelets, wavelet transform module"
+name = "pywavelets"
+optional = false
+python-versions = ">=3.5"
+version = "1.1.1"
+
+[package.dependencies]
+numpy = ">=1.13.3"
+
 [[package]]
 category = "main"
 description = "YAML parser and emitter for Python"
@@ -362,6 +472,27 @@ urllib3 = ">=1.21.1,<1.25.0 || >1.25.0,<1.25.1 || >1.25.1,<1.26"
 security = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)"]
 socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7)", "win-inet-pton"]
 
+[[package]]
+category = "main"
+description = "Image processing routines for SciPy"
+name = "scikit-image"
+optional = false
+python-versions = ">=3.6"
+version = "0.16.2"
+
+[package.dependencies]
+PyWavelets = ">=0.4.0"
+imageio = ">=2.3.0"
+matplotlib = ">=2.0.0,<3.0.0 || >3.0.0"
+networkx = ">=2.0"
+pillow = ">=4.3.0"
+scipy = ">=0.19.0"
+
+[package.extras]
+docs = ["sphinx (>=1.3,<1.7.8 || >1.7.8)", "numpydoc (>=0.9)", "sphinx-gallery", "sphinx-copybutton", "pytest-runner", "scikit-learn", "matplotlib (>=3.0.1)", "dask (>=0.15.0)", "cloudpickle (>=0.2.1)"]
+optional = ["simpleitk", "astropy (>=1.2.0)", "tifffile", "qtpy", "pyamg", "dask (>=0.15.0)", "cloudpickle (>=0.2.1)"]
+test = ["pytest (!=3.7.3)", "pytest-cov", "pytest-localserver", "flake8", "codecov"]
+
 [[package]]
 category = "main"
 description = "SciPy: Scientific Library for Python"
@@ -464,7 +595,7 @@ docs = ["sphinx", "jaraco.packaging (>=3.2)", "rst.linker (>=1.9)"]
 testing = ["pathlib2", "contextlib2", "unittest2"]
 
 [metadata]
-content-hash = "fa3824bee7012c6b2f52d0972a77158fb0204c2dde9a12d8f0b0ec3030b13e16"
+content-hash = "273409b6423b78dda553b1088ce6abbf6324ceabfd02f925bbd39f1d35b259aa"
 python-versions = "^3.6"
 
 [metadata.files]
@@ -546,10 +677,22 @@ colorama = [
     {file = "colorama-0.4.3-py2.py3-none-any.whl", hash = "sha256:7d73d2a99753107a36ac6b455ee49046802e59d9d076ef8e47b61499fa29afff"},
     {file = "colorama-0.4.3.tar.gz", hash = "sha256:e96da0d330793e2cb9485e9ddfd918d456036c7149416295932478192f4436a1"},
 ]
+cycler = [
+    {file = "cycler-0.10.0-py2.py3-none-any.whl", hash = "sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d"},
+    {file = "cycler-0.10.0.tar.gz", hash = "sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"},
+]
+decorator = [
+    {file = "decorator-4.4.1-py2.py3-none-any.whl", hash = "sha256:5d19b92a3c8f7f101c8dd86afd86b0f061a8ce4540ab8cd401fa2542756bce6d"},
+    {file = "decorator-4.4.1.tar.gz", hash = "sha256:54c38050039232e1db4ad7375cfce6748d7b41c29e95a081c8a6d2c30364a2ce"},
+]
 idna = [
     {file = "idna-2.8-py2.py3-none-any.whl", hash = "sha256:ea8b7f6188e6fa117537c3df7da9fc686d485087abf6ac197f9c46432f7e4a3c"},
     {file = "idna-2.8.tar.gz", hash = "sha256:c357b3f628cf53ae2c4c05627ecc484553142ca23264e593d327bcde5e9c3407"},
 ]
+imageio = [
+    {file = "imageio-2.6.1-py3-none-any.whl", hash = "sha256:c9763e5c187ecf74091c845626b0bdcc6130a20a0de7a86ae0108e2b5335ed3f"},
+    {file = "imageio-2.6.1.tar.gz", hash = "sha256:f44eb231b9df485874f2ffd22dfd0c3c711e7de076516b9374edea5c65bc67ae"},
+]
 importlib-metadata = [
     {file = "importlib_metadata-1.3.0-py2.py3-none-any.whl", hash = "sha256:d95141fbfa7ef2ec65cfd945e2af7e5a6ddbd7c8d9a25e66ff3be8e3daf9f60f"},
     {file = "importlib_metadata-1.3.0.tar.gz", hash = "sha256:073a852570f92da5f744a3472af1b61e28e9f78ccf0c9117658dc32b15de7b45"},
@@ -558,6 +701,45 @@ isort = [
     {file = "isort-4.3.21-py2.py3-none-any.whl", hash = "sha256:6e811fcb295968434526407adb8796944f1988c5b65e8139058f2014cbe100fd"},
     {file = "isort-4.3.21.tar.gz", hash = "sha256:54da7e92468955c4fceacd0c86bd0ec997b0e1ee80d97f67c35a78b719dccab1"},
 ]
+kiwisolver = [
+    {file = "kiwisolver-1.1.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl", hash = "sha256:7f4dd50874177d2bb060d74769210f3bce1af87a8c7cf5b37d032ebf94f0aca3"},
+    {file = "kiwisolver-1.1.0-cp27-cp27m-macosx_10_6_intel.whl", hash = "sha256:fe51b79da0062f8e9d49ed0182a626a7dc7a0cbca0328f612c6ee5e4711c81e4"},
+    {file = "kiwisolver-1.1.0-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:f790f8b3dff3d53453de6a7b7ddd173d2e020fb160baff578d578065b108a05f"},
+    {file = "kiwisolver-1.1.0-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:f2b22153870ca5cf2ab9c940d7bc38e8e9089fa0f7e5856ea195e1cf4ff43d5a"},
+    {file = "kiwisolver-1.1.0-cp27-cp27mu-manylinux1_i686.whl", hash = "sha256:e8bf074363ce2babeb4764d94f8e65efd22e6a7c74860a4f05a6947afc020ff2"},
+    {file = "kiwisolver-1.1.0-cp27-cp27mu-manylinux1_x86_64.whl", hash = "sha256:05b5b061e09f60f56244adc885c4a7867da25ca387376b02c1efc29cc16bcd0f"},
+    {file = "kiwisolver-1.1.0-cp27-none-win32.whl", hash = "sha256:47b8cb81a7d18dbaf4fed6a61c3cecdb5adec7b4ac292bddb0d016d57e8507d5"},
+    {file = "kiwisolver-1.1.0-cp27-none-win_amd64.whl", hash = "sha256:b64916959e4ae0ac78af7c3e8cef4becee0c0e9694ad477b4c6b3a536de6a544"},
+    {file = "kiwisolver-1.1.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl", hash = "sha256:682e54f0ce8f45981878756d7203fd01e188cc6c8b2c5e2cf03675390b4534d5"},
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+]
 lazy-object-proxy = [
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     {file = "lazy_object_proxy-1.4.3-cp27-cp27m-macosx_10_13_x86_64.whl", hash = "sha256:a2238e9d1bb71a56cd710611a1614d1194dc10a175c1e08d75e1a7bcc250d442"},
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 ]
+matplotlib = [
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+    {file = "matplotlib-3.1.2-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:2d6ab54015a7c0d727c33e36f85f5c5e4172059efdd067f7527f6e5d16ad01aa"},
+    {file = "matplotlib-3.1.2-cp36-cp36m-win32.whl", hash = "sha256:819d4860315468b482f38f1afe45a5437f60f03eaede495d5ff89f2eeac89500"},
+    {file = "matplotlib-3.1.2-cp36-cp36m-win_amd64.whl", hash = "sha256:08ccc8922eb4792b91c652d3e6d46b1c99073f1284d1b6705155643e8046463a"},
+    {file = "matplotlib-3.1.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:1f9e885bfa1b148d16f82a6672d043ecf11197f6c71ae222d0546db706e52eb2"},
+    {file = "matplotlib-3.1.2-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:8cc0e44905c2c8fda5637cad6f311eb9517017515a034247ab93d0cf99f8bb7a"},
+    {file = "matplotlib-3.1.2-cp37-cp37m-win32.whl", hash = "sha256:161dcd807c0c3232f4dcd4a12a382d52004a498174cbfafd40646106c5bcdcc8"},
+    {file = "matplotlib-3.1.2-cp37-cp37m-win_amd64.whl", hash = "sha256:ee59b7bb9eb75932fe3787e54e61c99b628155b0cedc907864f24723ba55b309"},
+    {file = "matplotlib-3.1.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5d2e408a2813abf664bd79431107543ecb449136912eb55bb312317edecf597e"},
+    {file = "matplotlib-3.1.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:d59bb0e82002ac49f4152963f8a1079e66794a4f454457fd2f0dcc7bf0797d30"},
+    {file = "matplotlib-3.1.2-cp38-cp38-win32.whl", hash = "sha256:61c8b740a008218eb604de518eb411c4953db0cb725dd0b32adf8a81771cab9e"},
+    {file = "matplotlib-3.1.2-cp38-cp38-win_amd64.whl", hash = "sha256:80f10af8378fccc136da40ea6aa4a920767476cdfb3241acb93ef4f0465dbf57"},
+    {file = "matplotlib-3.1.2.tar.gz", hash = "sha256:8e8e2c2fe3d873108735c6ee9884e6f36f467df4a143136209cff303b183bada"},
+]
 mccabe = [
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@@ -593,6 +790,10 @@ natsort = [
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     {file = "natsort-6.2.0.tar.gz", hash = "sha256:58c6fb2f355117e88a19808394ec1ed30a2ff881bdd2c81c436952caebd30668"},
 ]
+networkx = [
+    {file = "networkx-2.4-py3-none-any.whl", hash = "sha256:cdfbf698749a5014bf2ed9db4a07a5295df1d3a53bf80bf3cbd61edf9df05fa1"},
+    {file = "networkx-2.4.tar.gz", hash = "sha256:f8f4ff0b6f96e4f9b16af6b84622597b5334bf9cae8cf9b2e42e7985d5c95c64"},
+]
 nibabel = [
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     {file = "nibabel-2.5.1-py3-none-any.whl", hash = "sha256:44678e9ec6151643736329103987c70f4a7b5b251e2ebb7012648365f29e2324"},
@@ -693,6 +894,33 @@ pytest = [
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     {file = "pytest-5.3.2.tar.gz", hash = "sha256:6b571215b5a790f9b41f19f3531c53a45cf6bb8ef2988bc1ff9afb38270b25fa"},
 ]
+python-dateutil = [
+    {file = "python-dateutil-2.8.1.tar.gz", hash = "sha256:73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c"},
+    {file = "python_dateutil-2.8.1-py2.py3-none-any.whl", hash = "sha256:75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a"},
+]
+pywavelets = [
+    {file = "PyWavelets-1.1.1-cp35-cp35m-macosx_10_6_intel.whl", hash = "sha256:35959c041ec014648575085a97b498eafbbaa824f86f6e4a59bfdef8a3fe6308"},
+    {file = "PyWavelets-1.1.1-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:55e39ec848ceec13c9fa1598253ae9dd5c31d09dfd48059462860d2b908fb224"},
+    {file = "PyWavelets-1.1.1-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:c06d2e340c7bf8b9ec71da2284beab8519a3908eab031f4ea126e8ccfc3fd567"},
+    {file = "PyWavelets-1.1.1-cp35-cp35m-win32.whl", hash = "sha256:be105382961745f88d8196bba5a69ee2c4455d87ad2a2e5d1eed6bd7fda4d3fd"},
+    {file = "PyWavelets-1.1.1-cp35-cp35m-win_amd64.whl", hash = "sha256:076ca8907001fdfe4205484f719d12b4a0262dfe6652fa1cfc3c5c362d14dc84"},
+    {file = "PyWavelets-1.1.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:7947e51ca05489b85928af52a34fe67022ab5b81d4ae32a4109a99e883a0635e"},
+    {file = "PyWavelets-1.1.1-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:9e2528823ccf5a0a1d23262dfefe5034dce89cd84e4e124dc553dfcdf63ebb92"},
+    {file = "PyWavelets-1.1.1-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:80b924edbc012ded8aa8b91cb2fd6207fb1a9a3a377beb4049b8a07445cec6f0"},
+    {file = "PyWavelets-1.1.1-cp36-cp36m-win32.whl", hash = "sha256:d510aef84d9852653d079c84f2f81a82d5d09815e625f35c95714e7364570ad4"},
+    {file = "PyWavelets-1.1.1-cp36-cp36m-win_amd64.whl", hash = "sha256:889d4c5c5205a9c90118c1980df526857929841df33e4cd1ff1eff77c6817a65"},
+    {file = "PyWavelets-1.1.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:68b5c33741d26c827074b3d8f0251de1c3019bb9567b8d303eb093c822ce28f1"},
+    {file = "PyWavelets-1.1.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:18a51b3f9416a2ae6e9a35c4af32cf520dd7895f2b69714f4aa2f4342fca47f9"},
+    {file = "PyWavelets-1.1.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:cfe79844526dd92e3ecc9490b5031fca5f8ab607e1e858feba232b1b788ff0ea"},
+    {file = "PyWavelets-1.1.1-cp37-cp37m-win32.whl", hash = "sha256:720dbcdd3d91c6dfead79c80bf8b00a1d8aa4e5d551dc528c6d5151e4efc3403"},
+    {file = "PyWavelets-1.1.1-cp37-cp37m-win_amd64.whl", hash = "sha256:bc5e87b72371da87c9bebc68e54882aada9c3114e640de180f62d5da95749cd3"},
+    {file = "PyWavelets-1.1.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:98b2669c5af842a70cfab33a7043fcb5e7535a690a00cd251b44c9be0be418e5"},
+    {file = "PyWavelets-1.1.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:e02a0558e0c2ac8b8bbe6a6ac18c136767ec56b96a321e0dfde2173adfa5a504"},
+    {file = "PyWavelets-1.1.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:6162dc0ae04669ea04b4b51420777b9ea2d30b0a9d02901b2a3b4d61d159c2e9"},
+    {file = "PyWavelets-1.1.1-cp38-cp38-win32.whl", hash = "sha256:79f5b54f9dc353e5ee47f0c3f02bebd2c899d49780633aa771fed43fa20b3149"},
+    {file = "PyWavelets-1.1.1-cp38-cp38-win_amd64.whl", hash = "sha256:935ff247b8b78bdf77647fee962b1cc208c51a7b229db30b9ba5f6da3e675178"},
+    {file = "PyWavelets-1.1.1.tar.gz", hash = "sha256:1a64b40f6acb4ffbaccce0545d7fc641744f95351f62e4c6aaa40549326008c9"},
+]
 pyyaml = [
     {file = "PyYAML-5.2-cp27-cp27m-win32.whl", hash = "sha256:35ace9b4147848cafac3db142795ee42deebe9d0dad885ce643928e88daebdcc"},
     {file = "PyYAML-5.2-cp27-cp27m-win_amd64.whl", hash = "sha256:ebc4ed52dcc93eeebeae5cf5deb2ae4347b3a81c3fa12b0b8c976544829396a4"},
@@ -710,6 +938,23 @@ requests = [
     {file = "requests-2.22.0-py2.py3-none-any.whl", hash = "sha256:9cf5292fcd0f598c671cfc1e0d7d1a7f13bb8085e9a590f48c010551dc6c4b31"},
     {file = "requests-2.22.0.tar.gz", hash = "sha256:11e007a8a2aa0323f5a921e9e6a2d7e4e67d9877e85773fba9ba6419025cbeb4"},
 ]
+scikit-image = [
+    {file = "scikit-image-0.16.2.tar.gz", hash = "sha256:dd7fbd32da74d4e9967dc15845f731f16e7966cee61f5dc0e12e2abb1305068c"},
+    {file = "scikit_image-0.16.2-cp36-cp36m-macosx_10_6_intel.whl", hash = "sha256:0808ab5f8218d91a1c008036993636535a37efd67a52ab0f2e6e3f4b7e75aeda"},
+    {file = "scikit_image-0.16.2-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:3af3d781ce085573ced37b2b5b9abfd32ce3d4723bd17f37e829025d189b0421"},
+    {file = "scikit_image-0.16.2-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:063d1c20fcd53762f82ee58c29783ae4e8f6fbed445b41b704fa33b6f355729d"},
+    {file = "scikit_image-0.16.2-cp36-cp36m-win32.whl", hash = "sha256:2a54bea469eb1b611bee1ce36e60710f5f94f29205bc5bd67a51793909b1e62b"},
+    {file = "scikit_image-0.16.2-cp36-cp36m-win_amd64.whl", hash = "sha256:2d346d49b6852cffb47cbde995e2696d5b07f688d8c057a0a4548abf3a98f920"},
+    {file = "scikit_image-0.16.2-cp37-cp37m-macosx_10_6_intel.whl", hash = "sha256:8b2b768b02c6b7476f2e16ddd91f827d3817aef73f82cf28bff7a8dcdfd8c55c"},
+    {file = "scikit_image-0.16.2-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:3ad2efa792ab8de5fcefe6f4f5bc1ab64c411cdb5c829ce1526ab3a5a7729627"},
+    {file = "scikit_image-0.16.2-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:2aa962aa82d815606d7dad7f045f5d7ca55c65b4320d47e15a98fc92612c2d6c"},
+    {file = "scikit_image-0.16.2-cp37-cp37m-win32.whl", hash = "sha256:e774377876cb258e8f4d63f7809863f961c98aa02263b3ff54a39483bc6f7d26"},
+    {file = "scikit_image-0.16.2-cp37-cp37m-win_amd64.whl", hash = "sha256:6786b127f33470fd843e644435522fbf43bce05c9f5527946c390ccb9e1cac27"},
+    {file = "scikit_image-0.16.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:a48fb0d34a090b578b87ffebab0fe035295c1945dbc2b28e1a55ea2cf6031751"},
+    {file = "scikit_image-0.16.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:41e28db0136f29ecd305bef0408fdfc64be9d415e54f5099a95555c65f5c1865"},
+    {file = "scikit_image-0.16.2-cp38-cp38-win32.whl", hash = "sha256:0715b7940778ba5d73da3908d60ddf2eb93863f7c394493a522fe56d3859295c"},
+    {file = "scikit_image-0.16.2-cp38-cp38-win_amd64.whl", hash = "sha256:e18d73cc8893e2268b172c29f9aab530faf8cd3b7c11ae0bee3e763d719d35c5"},
+]
 scipy = [
     {file = "scipy-1.4.0-cp35-cp35m-macosx_10_6_intel.whl", hash = "sha256:1b51721e5792c4d722e0cab4daf82187d5f22f2e42c56cd247398a16e4d2f48d"},
     {file = "scipy-1.4.0-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:cdbcfbe97a21fc6b3c55991a7bcd6a278a38bd197e2d274a45611cdc91beb5c0"},
diff --git a/pyproject.toml b/pyproject.toml
index a179e77d1..e4d5264d4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,6 +17,7 @@ cluster_tools = "1.46"
 natsort = "^6.2.0"
 psutil = "^5.6.7"
 nibabel = "^2.5.1"
+scikit-image = "^0.16.2"
 
 [tool.poetry.dev-dependencies]
 pylint = "2.3.1"
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diff --git a/testdata/simple_tiff_dataset/datasource-properties.json b/testdata/simple_tiff_dataset/datasource-properties.json
new file mode 100644
index 000000000..93ecef44f
--- /dev/null
+++ b/testdata/simple_tiff_dataset/datasource-properties.json
@@ -0,0 +1,40 @@
+{
+    "id": {
+        "name": "tiff_dataset_1",
+        "team": ""
+    },
+    "scale": [
+        1
+    ],
+    "dataLayers": [
+        {
+            "name": "color",
+            "category": "color",
+            "elementClass": "uint8",
+            "num_channels": 1,
+            "boundingBox": {
+                "topLeft": [
+                    0,
+                    0,
+                    0
+                ],
+                "width": 265,
+                "height": 265,
+                "depth": 10
+            },
+            "wkwResolutions": [
+                {
+                    "resolution": [
+                        1,
+                        1,
+                        1
+                    ]
+                }
+            ]
+        }
+    ],
+    "grid_shape": [
+        0,
+        0
+    ]
+}
diff --git a/testdata/simple_wk_dataset/color/1/header.wkw b/testdata/simple_wk_dataset/color/1/header.wkw
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diff --git a/testdata/simple_wk_dataset/color/1/z0/y0/x0.wkw b/testdata/simple_wk_dataset/color/1/z0/y0/x0.wkw
new file mode 100644
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diff --git a/testdata/simple_wk_dataset/datasource-properties.json b/testdata/simple_wk_dataset/datasource-properties.json
new file mode 100644
index 000000000..7b13d0bf7
--- /dev/null
+++ b/testdata/simple_wk_dataset/datasource-properties.json
@@ -0,0 +1,38 @@
+{
+    "id": {
+        "name": "simple_wk_dataset",
+        "team": ""
+    },
+    "scale": [
+        1
+    ],
+    "dataLayers": [
+        {
+            "dataFormat": "wkw",
+            "name": "color",
+            "category": "color",
+            "elementClass": "uint8",
+            "num_channels": 3,
+            "boundingBox": {
+                "topLeft": [
+                    0,
+                    0,
+                    0
+                ],
+                "width": 24,
+                "height": 24,
+                "depth": 24
+            },
+            "wkwResolutions": [
+                {
+                    "resolution": [
+                        1,
+                        1,
+                        1
+                    ],
+                    "cube_length": 32
+                }
+            ]
+        }
+    ]
+}
\ No newline at end of file
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
new file mode 100644
index 000000000..3175eeada
--- /dev/null
+++ b/tests/test_dataset.py
@@ -0,0 +1,532 @@
+import filecmp
+
+import numpy as np
+from shutil import rmtree, copytree
+
+from wkcuber.api.Dataset import WKDataset, TiffDataset
+from os import path, makedirs
+
+from wkcuber.api.Layer import Layer
+from wkcuber.api.Properties import TiffProperties
+from wkcuber.mag import Mag
+
+
+def delete_dir(relative_path):
+    if path.exists(relative_path) and path.isdir(relative_path):
+        rmtree(relative_path)
+
+
+def test_create_wk_dataset_with_layer_and_mag():
+    delete_dir("./testoutput/wk_dataset")
+
+    ds = WKDataset.create("./testoutput/wk_dataset", scale=(1, 1, 1))
+    ds.add_layer("color", "color")
+
+    ds.get_layer("color").add_mag("1")
+    ds.get_layer("color").add_mag("2-2-1")
+
+    assert path.exists("./testoutput/wk_dataset/color/1")
+    assert path.exists("./testoutput/wk_dataset/color/2-2-1")
+
+    assert len(ds.properties.data_layers) == 1
+    assert len(ds.properties.data_layers["color"].wkw_magnifications) == 2
+
+
+def test_create_wk_dataset_with_explicit_header_fields():
+    delete_dir("./testoutput/wk_dataset_advanced")
+
+    ds = WKDataset.create("./testoutput/wk_dataset_advanced", scale=(1, 1, 1))
+    ds.add_layer("color", "color", dtype=np.uint16, num_channels=3)
+
+    ds.get_layer("color").add_mag("1", block_len=64, file_len=64)
+    ds.get_layer("color").add_mag("2-2-1")
+
+    assert path.exists("./testoutput/wk_dataset_advanced/color/1")
+    assert path.exists("./testoutput/wk_dataset_advanced/color/2-2-1")
+
+    assert len(ds.properties.data_layers) == 1
+    assert len(ds.properties.data_layers["color"].wkw_magnifications) == 2
+
+    assert ds.properties.data_layers["color"].element_class == np.dtype(np.uint16)
+    assert (
+        ds.properties.data_layers["color"].wkw_magnifications[0].cube_length == 64 * 64
+    )  # mag "1"
+    assert ds.properties.data_layers["color"].wkw_magnifications[0].mag == Mag("1")
+    assert (
+        ds.properties.data_layers["color"].wkw_magnifications[1].cube_length == 32 * 32
+    )  # mag "2-2-1" (defaults are used)
+    assert ds.properties.data_layers["color"].wkw_magnifications[1].mag == Mag("2-2-1")
+
+
+def test_create_tiff_dataset_with_layer_and_mag():
+    # This test would be the same for WKDataset
+
+    delete_dir("./testoutput/tiff_dataset")
+
+    ds = WKDataset.create("./testoutput/tiff_dataset", scale=(1, 1, 1))
+    ds.add_layer("color", Layer.COLOR_TYPE)
+
+    ds.get_layer("color").add_mag("1")
+    ds.get_layer("color").add_mag("2-2-1")
+
+    assert path.exists("./testoutput/tiff_dataset/color/1")
+    assert path.exists("./testoutput/tiff_dataset/color/2-2-1")
+
+    assert len(ds.properties.data_layers) == 1
+    assert len(ds.properties.data_layers["color"].wkw_magnifications) == 2
+
+
+def test_open_wk_dataset():
+    ds = WKDataset("./testdata/simple_wk_dataset")
+
+    assert len(ds.properties.data_layers) == 1
+    assert len(ds.properties.data_layers["color"].wkw_magnifications) == 1
+
+
+def test_open_tiff_dataset():
+    ds = TiffDataset("./testdata/simple_tiff_dataset")
+
+    assert len(ds.properties.data_layers) == 1
+    assert len(ds.properties.data_layers["color"].wkw_magnifications) == 1
+
+
+def test_view_read_with_open():
+    # This test would be the same for TiffDataset
+
+    wk_view = WKDataset("./testdata/simple_wk_dataset/").get_view(
+        "color", "1", size=(32, 32, 32)
+    )
+
+    assert not wk_view._is_opened
+
+    with wk_view.open():
+        assert wk_view._is_opened
+
+        data = wk_view.read((10, 10, 10))
+        assert data.shape == (3, 10, 10, 10)  # three channel
+
+    assert not wk_view._is_opened
+
+
+def test_view_read_without_open():
+    # This test would be the same for TiffDataset
+
+    wk_view = WKDataset("./testdata/simple_wk_dataset/").get_view(
+        "color", "1", size=(32, 32, 32)
+    )
+
+    assert not wk_view._is_opened
+
+    # 'read()' checks if it was already opened. If not, it opens and closes automatically
+    data = wk_view.read((10, 10, 10))
+    assert data.shape == (3, 10, 10, 10)  # three channel
+
+    assert not wk_view._is_opened
+
+
+def test_view_wk_write():
+    delete_dir("./testoutput/simple_wk_dataset/")
+    copytree("./testdata/simple_wk_dataset/", "./testoutput/simple_wk_dataset/")
+
+    wk_view = WKDataset("./testoutput/simple_wk_dataset/").get_view(
+        "color", "1", size=(100, 100, 100)
+    )
+
+    with wk_view.open():
+        np.random.seed(1234)
+        write_data = (np.random.rand(3, 10, 10, 10) * 255).astype(np.uint8)
+
+        wk_view.write(write_data)
+
+        data = wk_view.read((10, 10, 10))
+        assert np.array_equal(data, write_data)
+
+
+def test_view_tiff_write():
+    delete_dir("./testoutput/simple_tiff_dataset/")
+    copytree("./testdata/simple_tiff_dataset/", "./testoutput/simple_tiff_dataset/")
+
+    tiff_view = TiffDataset("./testoutput/simple_tiff_dataset/").get_view(
+        "color", "1", size=(100, 100, 100)
+    )
+
+    with tiff_view.open():
+        np.random.seed(1234)
+        write_data = (np.random.rand(5, 5, 5) * 255).astype(np.uint8)
+
+        tiff_view.write(write_data)
+
+        data = tiff_view.read((5, 5, 5))
+        assert data.shape == (1, 5, 5, 5)  # this dataset has only one channel
+        assert np.array_equal(data, np.expand_dims(write_data, 0))
+
+
+def test_view_tiff_write_out_of_bounds():
+    new_dataset_path = "./testoutput/tiff_view_dataset_out_of_bounds/"
+
+    delete_dir(new_dataset_path)
+    copytree("./testdata/simple_tiff_dataset/", new_dataset_path)
+
+    tiff_view = TiffDataset(new_dataset_path).get_view(
+        "color", "1", size=(100, 100, 100)
+    )
+
+    with tiff_view.open():
+        try:
+            tiff_view.write(
+                np.zeros((200, 200, 5), dtype=np.uint8)
+            )  # this is bigger than the bounding_box
+            raise Exception(
+                "The test 'test_view_tiff_write_out_of_bounds' did not throw an exception even though it should"
+            )
+        except AssertionError:
+            pass
+
+
+def test_view_wk_write_out_of_bounds():
+    new_dataset_path = "./testoutput/wk_view_dataset_out_of_bounds/"
+
+    delete_dir(new_dataset_path)
+    copytree("./testdata/simple_wk_dataset/", new_dataset_path)
+
+    tiff_view = WKDataset(new_dataset_path).get_view("color", "1", size=(100, 100, 100))
+
+    with tiff_view.open():
+        try:
+            tiff_view.write(
+                np.zeros((200, 200, 5), dtype=np.uint8)
+            )  # this is bigger than the bounding_box
+            raise Exception(
+                "The test 'test_view_wk_write_out_of_bounds' did not throw an exception even though it should"
+            )
+        except AssertionError:
+            pass
+
+
+def test_tiff_write_out_of_bounds():
+    new_dataset_path = "./testoutput/simple_tiff_dataset_out_of_bounds/"
+
+    delete_dir(new_dataset_path)
+    copytree("./testdata/simple_tiff_dataset/", new_dataset_path)
+
+    ds = TiffDataset(new_dataset_path)
+    mag_dataset = ds.get_layer("color").get_mag("1")
+
+    assert ds.properties.data_layers["color"].get_bounding_box_size() == (265, 265, 10)
+    mag_dataset.write(
+        np.zeros((300, 300, 15), dtype=np.uint8)
+    )  # this is bigger than the bounding_box
+    assert ds.properties.data_layers["color"].get_bounding_box_size() == (300, 300, 15)
+
+
+def test_wk_write_out_of_bounds():
+    new_dataset_path = "./testoutput/simple_wk_dataset_out_of_bounds/"
+
+    delete_dir(new_dataset_path)
+    copytree("./testdata/simple_wk_dataset/", new_dataset_path)
+
+    ds = WKDataset(new_dataset_path)
+    mag_dataset = ds.get_layer("color").get_mag("1")
+
+    assert ds.properties.data_layers["color"].get_bounding_box_size() == (24, 24, 24)
+    mag_dataset.write(
+        np.zeros((3, 1, 1, 48), dtype=np.uint8)
+    )  # this is bigger than the bounding_box
+    assert ds.properties.data_layers["color"].get_bounding_box_size() == (24, 24, 48)
+
+
+def test_update_new_bounding_box_offset():
+    # This test would be the same for WKDataset
+
+    delete_dir("./testoutput/tiff_dataset")
+
+    ds = TiffDataset.create("./testoutput/tiff_dataset", scale=(1, 1, 1))
+    mag = ds.add_layer("color", Layer.COLOR_TYPE).add_mag("1")
+
+    assert ds.properties.data_layers["color"].bounding_box["topLeft"] == (-1, -1, -1)
+
+    np.random.seed(1234)
+    write_data = (np.random.rand(10, 10, 10) * 255).astype(np.uint8)
+    mag.write(
+        write_data, offset=(10, 10, 10)
+    )  # the write method of MagDataset does always use the relative offset to (0, 0, 0)
+    assert ds.properties.data_layers["color"].bounding_box["topLeft"] == (10, 10, 10)
+
+    mag.write(
+        write_data, offset=(5, 5, 20)
+    )  # the write method of MagDataset does always use the relative offset to (0, 0, 0)
+    assert ds.properties.data_layers["color"].bounding_box["topLeft"] == (5, 5, 10)
+
+
+def test_tiff_write_multi_channel_uint8():
+    dataset_path = "./testoutput/tiff_multichannel/"
+    delete_dir(dataset_path)
+
+    ds_tiff = TiffDataset.create(dataset_path, scale=(1, 1, 1))
+    mag = ds_tiff.add_layer("color", Layer.COLOR_TYPE, num_channels=3).add_mag("1")
+
+    # 10 images (z-layers), each 250x250, dtype=np.uint8
+    data = np.zeros((3, 250, 250, 10), dtype=np.uint8)
+    for h in range(10):
+        for i in range(250):
+            for j in range(250):
+                data[0, i, j, h] = i
+                data[1, i, j, h] = j
+                data[2, i, j, h] = 100
+
+    ds_tiff.get_layer("color").get_mag("1").write(data)
+
+    assert np.array_equal(data, mag.read(size=(250, 250, 10)))
+
+
+def test_wk_write_multi_channel_uint8():
+    dataset_path = "./testoutput/wk_multichannel/"
+    delete_dir(dataset_path)
+
+    ds_tiff = WKDataset.create(dataset_path, scale=(1, 1, 1))
+    mag = ds_tiff.add_layer("color", Layer.COLOR_TYPE, num_channels=3).add_mag("1")
+
+    # 10 images (z-layers), each 250x250, dtype=np.uint8
+    data = np.zeros((3, 250, 250, 10), dtype=np.uint8)
+    for h in range(10):
+        for i in range(250):
+            for j in range(250):
+                data[0, i, j, h] = i
+                data[1, i, j, h] = j
+                data[2, i, j, h] = 100
+
+    ds_tiff.get_layer("color").get_mag("1").write(data)
+
+    assert np.array_equal(data, mag.read(size=(250, 250, 10)))
+
+
+def test_tiff_write_multi_channel_uint16():
+    dataset_path = "./testoutput/tiff_multichannel/"
+    delete_dir(dataset_path)
+
+    ds_tiff = TiffDataset.create(dataset_path, scale=(1, 1, 1))
+    mag = ds_tiff.add_layer(
+        "color", Layer.COLOR_TYPE, num_channels=3, dtype=np.uint16
+    ).add_mag("1")
+
+    # 10 images (z-layers), each 250x250, dtype=np.uint16
+    data = np.zeros((3, 250, 250, 10), dtype=np.uint16)
+    for h in range(10):
+        for i in range(250):
+            for j in range(250):
+                data[0, i, j, h] = i * 256
+                data[1, i, j, h] = j * 256
+                data[2, i, j, h] = 100 * 256
+
+    mag.write(data)
+    written_data = mag.read(size=(250, 250, 10))
+
+    print(written_data.dtype)
+
+    assert np.array_equal(data, written_data)
+
+
+def test_wk_write_multi_channel_uint16():
+    dataset_path = "./testoutput/wk_multichannel/"
+    delete_dir(dataset_path)
+
+    ds_tiff = WKDataset.create(dataset_path, scale=(1, 1, 1))
+    mag = ds_tiff.add_layer(
+        "color", Layer.COLOR_TYPE, num_channels=3, dtype=np.uint16
+    ).add_mag("1")
+
+    # 10 images (z-layers), each 250x250, dtype=np.uint16
+    data = np.zeros((3, 250, 250, 10), dtype=np.uint16)
+    for h in range(10):
+        for i in range(250):
+            for j in range(250):
+                data[0, i, j, h] = i * 256
+                data[1, i, j, h] = j * 256
+                data[2, i, j, h] = 100 * 256
+
+    mag.write(data)
+    written_data = mag.read(size=(250, 250, 10))
+
+    print(written_data.dtype)
+
+    assert np.array_equal(data, written_data)
+
+
+def test_wkw_empty_read():
+    filename = "./testoutput/empty_wk_dataset"
+    delete_dir(filename)
+
+    mag = (
+        WKDataset.create(filename, scale=(1, 1, 1))
+        .add_layer("color", Layer.COLOR_TYPE)
+        .add_mag("1")
+    )
+    data = mag.read(size=(0, 0, 0), offset=(1, 1, 1))
+
+    assert data.shape == (1, 0, 0, 0)
+
+
+def test_tiff_empty_read():
+    filename = "./testoutput/empty_tiff_dataset"
+    delete_dir(filename)
+
+    mag = (
+        TiffDataset.create(filename, scale=(1, 1, 1))
+        .add_layer("color", Layer.COLOR_TYPE)
+        .add_mag("1")
+    )
+    data = mag.read(size=(0, 0, 0), offset=(1, 1, 1))
+
+    assert data.shape == (1, 0, 0, 0)
+
+
+def test_tiff_read_padded_data():
+    filename = "./testoutput/empty_tiff_dataset"
+    delete_dir(filename)
+
+    mag = (
+        TiffDataset.create(filename, scale=(1, 1, 1))
+        .add_layer("color", Layer.COLOR_TYPE, num_channels=3)
+        .add_mag("1")
+    )
+    # there are no tiffs yet, however, this should not fail but pad the data with zeros
+    data = mag.read(size=(10, 10, 10))
+
+    assert data.shape == (3, 10, 10, 10)
+    assert np.array_equal(data, np.zeros((3, 10, 10, 10)))
+
+
+def test_wk_read_padded_data():
+    filename = "./testoutput/empty_wk_dataset"
+    delete_dir(filename)
+
+    mag = (
+        WKDataset.create(filename, scale=(1, 1, 1))
+        .add_layer("color", Layer.COLOR_TYPE, num_channels=3)
+        .add_mag("1")
+    )
+    # there is no data yet, however, this should not fail but pad the data with zeros
+    data = mag.read(size=(10, 10, 10))
+
+    assert data.shape == (3, 10, 10, 10)
+    assert np.array_equal(data, np.zeros((3, 10, 10, 10)))
+
+
+def test_read_and_write_of_properties():
+    destination_path = "./testoutput/read_write_properties/"
+    delete_dir(destination_path)
+    source_file_name = "./testdata/simple_tiff_dataset/datasource-properties.json"
+    destination_file_name = destination_path + "datasource-properties.json"
+
+    imported_properties = TiffProperties._from_json(source_file_name)
+    imported_properties._path = destination_file_name
+    makedirs(destination_path)
+    imported_properties._export_as_json()
+
+    filecmp.cmp(source_file_name, destination_file_name)
+
+
+def test_num_channel_mismatch_assertion():
+    delete_dir("./testoutput/wk_dataset")
+
+    ds = WKDataset.create("./testoutput/wk_dataset", scale=(1, 1, 1))
+    mag = ds.add_layer("color", Layer.COLOR_TYPE, num_channels=1).add_mag(
+        "1"
+    )  # num_channel=1 is also the default
+
+    np.random.seed(1234)
+    write_data = (np.random.rand(3, 10, 10, 10) * 255).astype(np.uint8)  # 3 channels
+
+    try:
+        mag.write(write_data)  # there is a mismatch between the number of channels
+        raise Exception(
+            "The test 'test_num_channel_mismatch_assertion' did not throw an exception even though it should"
+        )
+    except AssertionError:
+        pass
+
+
+def test_get_or_add_layer():
+    # This test would be the same for TiffDataset
+
+    delete_dir("./testoutput/wk_dataset")
+
+    ds = WKDataset.create("./testoutput/wk_dataset", scale=(1, 1, 1))
+
+    assert "color" not in ds.layers.keys()
+
+    # layer did not exist before
+    layer = ds.get_or_add_layer(
+        "color", Layer.COLOR_TYPE, dtype=np.uint8, num_channels=1
+    )
+    assert "color" in ds.layers.keys()
+    assert layer.name == "color"
+
+    # layer did exist before
+    layer = ds.get_or_add_layer(
+        "color", Layer.COLOR_TYPE, dtype=np.uint8, num_channels=1
+    )
+    assert "color" in ds.layers.keys()
+    assert layer.name == "color"
+
+    try:
+        # layer did exist before but with another 'dtype' (this would work the same for 'category' and 'num_channels')
+        layer = ds.get_or_add_layer(
+            "color", Layer.COLOR_TYPE, dtype=np.uint16, num_channels=1
+        )
+
+        raise Exception(
+            "The test 'test_get_or_add_layer' did not throw an exception even though it should"
+        )
+    except AssertionError:
+        pass
+
+
+def test_get_or_add_mag_for_wk():
+    delete_dir("./testoutput/wk_dataset")
+
+    layer = WKDataset.create("./testoutput/wk_dataset", scale=(1, 1, 1)).add_layer(
+        "color", Layer.COLOR_TYPE
+    )
+
+    assert "1" not in layer.mags.keys()
+
+    # The mag did not exist before
+    mag = layer.get_or_add_mag("1", block_len=32, file_len=32, block_type=1)
+    assert "1" in layer.mags.keys()
+    assert mag.name == "1"
+
+    # The mag did exist before
+    layer.get_or_add_mag("1", block_len=32, file_len=32, block_type=1)
+    assert "1" in layer.mags.keys()
+    assert mag.name == "1"
+
+    try:
+        # mag did exist before but with another 'block_len' (this would work the same for 'file_len' and 'block_type')
+        mag = layer.get_or_add_mag("1", block_len=64, file_len=32, block_type=1)
+
+        raise Exception(
+            "The test 'test_get_or_add_layer' did not throw an exception even though it should"
+        )
+    except AssertionError:
+        pass
+
+
+def test_get_or_add_mag_for_tiff():
+    delete_dir("./testoutput/wk_dataset")
+
+    layer = TiffDataset.create("./testoutput/wk_dataset", scale=(1, 1, 1)).add_layer(
+        "color", Layer.COLOR_TYPE
+    )
+
+    assert "1" not in layer.mags.keys()
+
+    # The mag did not exist before
+    mag = layer.get_or_add_mag("1")
+    assert "1" in layer.mags.keys()
+    assert mag.name == "1"
+
+    # The mag did exist before
+    layer.get_or_add_mag("1")
+    assert "1" in layer.mags.keys()
+    assert mag.name == "1"
diff --git a/wkcuber/api/Dataset.py b/wkcuber/api/Dataset.py
new file mode 100644
index 000000000..8da76905d
--- /dev/null
+++ b/wkcuber/api/Dataset.py
@@ -0,0 +1,169 @@
+from shutil import rmtree
+from abc import ABC, abstractmethod
+from os import makedirs, path
+from os.path import join, normpath, basename
+from pathlib import Path
+import numpy as np
+
+from wkcuber.api.Properties import WKProperties, TiffProperties, Properties
+from wkcuber.api.Layer import Layer, WKLayer, TiffLayer
+from wkcuber.api.View import View
+
+
+class AbstractDataset(ABC):
+    @abstractmethod
+    def __init__(self, dataset_path):
+        properties = self._get_properties_type()._from_json(
+            join(dataset_path, Properties.FILE_NAME)
+        )
+        self.layers = {}
+        self.path = Path(properties.path).parent
+        self.properties = properties
+
+        # construct self.layer
+        for layer_name in self.properties.data_layers:
+            layer = self.properties.data_layers[layer_name]
+            self.add_layer(
+                layer.name, layer.category, layer.element_class, layer.num_channels
+            )
+            for resolution in layer.wkw_magnifications:
+                self.layers[layer_name].setup_mag(resolution.mag.to_layer_name())
+
+    @classmethod
+    def create_with_properties(cls, properties):
+        dataset_path = path.dirname(properties.path)
+        # create directories on disk and write datasource-properties.json
+        try:
+            makedirs(dataset_path)
+            properties._export_as_json()
+        except OSError:
+            raise FileExistsError("Creation of Dataset {} failed".format(dataset_path))
+
+        # initialize object
+        return cls(dataset_path)
+
+    @classmethod
+    @abstractmethod
+    def create(cls, dataset_path, scale):
+        pass
+
+    def downsample(self, layer, target_mag_shape, source_mag):
+        raise NotImplemented()
+
+    def get_properties(self) -> Properties:
+        return self.properties
+
+    def get_layer(self, layer_name) -> Layer:
+        if layer_name not in self.layers.keys():
+            raise IndexError(
+                "The layer {} is not a layer of this dataset".format(layer_name)
+            )
+        return self.layers[layer_name]
+
+    def add_layer(self, layer_name, category, dtype=np.dtype("uint8"), num_channels=1):
+        # normalize the value of dtype in case the parameter was passed as a string
+        dtype = np.dtype(dtype)
+
+        if layer_name in self.layers.keys():
+            raise IndexError(
+                "Adding layer {} failed. There is already a layer with this name".format(
+                    layer_name
+                )
+            )
+        self.layers[layer_name] = self._create_layer(layer_name, dtype, num_channels)
+        self.properties._add_layer(layer_name, category, dtype.name, num_channels)
+        return self.layers[layer_name]
+
+    def get_or_add_layer(
+        self, layer_name, category, dtype=None, num_channels=None
+    ) -> Layer:
+        if layer_name in self.layers.keys():
+            assert self.properties.data_layers[layer_name].category == category, (
+                f"Cannot get_or_add_layer: The layer '{layer_name}' already exists, but the categories do not match. "
+                + f"The category of the existing layer is '{self.properties.data_layers[layer_name].category}' "
+                + f"and the passed parameter is '{category}'."
+            )
+            assert dtype is None or self.layers[layer_name].dtype == np.dtype(dtype), (
+                f"Cannot get_or_add_layer: The layer '{layer_name}' already exists, but the dtypes do not match. "
+                + f"The dtype of the existing layer is '{self.layers[layer_name].dtype}' "
+                + f"and the passed parameter is '{dtype}'."
+            )
+            assert (
+                num_channels is None
+                or self.layers[layer_name].num_channels == num_channels
+            ), (
+                f"Cannot get_or_add_layer: The layer '{layer_name}' already exists, but the number of channels do not match. "
+                + f"The number of channels of the existing layer are '{self.layers[layer_name].num_channels}' "
+                + f"and the passed parameter is '{num_channels}'."
+            )
+            return self.layers[layer_name]
+        else:
+            return self.add_layer(layer_name, category, dtype, num_channels)
+
+    def delete_layer(self, layer_name):
+        if layer_name not in self.layers.keys():
+            raise IndexError(
+                f"Removing layer {layer_name} failed. There is no layer with this name"
+            )
+        del self.layers[layer_name]
+        self.properties._delete_layer(layer_name)
+        # delete files on disk
+        rmtree(join(self.path, layer_name))
+
+    def get_view(self, layer_name, mag_name, size, global_offset=(0, 0, 0)) -> View:
+        layer = self.get_layer(layer_name)
+        mag = layer.get_mag(mag_name)
+        mag_file_path = path.join(self.path, layer.name, mag.name)
+
+        return mag.get_view(mag_file_path, size=size, global_offset=global_offset)
+
+    def _create_layer(self, layer_name, dtype, num_channels) -> Layer:
+        raise NotImplementedError
+
+    @abstractmethod
+    def _get_properties_type(self):
+        pass
+
+
+class WKDataset(AbstractDataset):
+    @classmethod
+    def create(cls, dataset_path, scale):
+        name = basename(normpath(dataset_path))
+        properties = WKProperties(join(dataset_path, Properties.FILE_NAME), name, scale)
+        return WKDataset.create_with_properties(properties)
+
+    def __init__(self, dataset_path):
+        super().__init__(dataset_path)
+        assert isinstance(self.properties, WKProperties)
+
+    def to_tiff_dataset(self, new_dataset_path):
+        raise NotImplementedError  # TODO; implement
+
+    def _create_layer(self, layer_name, dtype, num_channels) -> Layer:
+        return WKLayer(layer_name, self, dtype, num_channels)
+
+    def _get_properties_type(self):
+        return WKProperties
+
+
+class TiffDataset(AbstractDataset):
+    @classmethod
+    def create(cls, dataset_path, scale):
+        name = basename(normpath(dataset_path))
+        properties = TiffProperties(
+            join(dataset_path, Properties.FILE_NAME), name, scale
+        )
+        return TiffDataset.create_with_properties(properties)
+
+    def __init__(self, dataset_path):
+        super().__init__(dataset_path)
+        assert isinstance(self.properties, TiffProperties)
+
+    def to_wk_dataset(self, new_dataset_path):
+        raise NotImplementedError  # TODO; implement
+
+    def _create_layer(self, layer_name, dtype, num_channels) -> Layer:
+        return TiffLayer(layer_name, self, dtype, num_channels)
+
+    def _get_properties_type(self):
+        return TiffProperties
diff --git a/wkcuber/api/Layer.py b/wkcuber/api/Layer.py
new file mode 100644
index 000000000..4ac0a4b14
--- /dev/null
+++ b/wkcuber/api/Layer.py
@@ -0,0 +1,130 @@
+from shutil import rmtree
+from os.path import join
+from os import makedirs
+from wkw import wkw
+
+from wkcuber.api.MagDataset import MagDataset, WKMagDataset, TiffMagDataset
+from wkcuber.mag import Mag
+
+
+class Layer:
+
+    COLOR_TYPE = "color"
+    SEGMENTATION_TYPE = "segmentation"
+
+    def __init__(self, name, dataset, dtype, num_channels):
+        self.name = name
+        self.dataset = dataset
+        self.dtype = dtype
+        self.num_channels = num_channels
+        self.mags = {}
+
+        full_path = join(dataset.path, name)
+        makedirs(full_path, exist_ok=True)
+
+    def get_mag(self, mag) -> MagDataset:
+        if mag not in self.mags.keys():
+            raise IndexError("The mag {} is not a mag of this layer".format(mag))
+        return self.mags[mag]
+
+    def delete_mag(self, mag):
+        if mag not in self.mags.keys():
+            raise IndexError(
+                "Deleting mag {} failed. There is no mag with this name".format(mag)
+            )
+
+        del self.mags[mag]
+        self.dataset.properties._delete_mag(self.name, mag)
+        # delete files on disk
+        full_path = join(self.dataset.path, self.name, mag)
+        rmtree(full_path)
+
+    def _create_dir_for_mag(self, mag):
+        full_path = join(self.dataset.path, self.name, mag)
+        makedirs(full_path, exist_ok=True)
+
+    def _assert_mag_does_not_exist_yet(self, mag):
+        if mag in self.mags.keys():
+            raise IndexError(
+                "Adding mag {} failed. There is already a mag with this name".format(
+                    mag
+                )
+            )
+
+
+class WKLayer(Layer):
+    def add_mag(self, mag, block_len=32, file_len=32, block_type=1) -> WKMagDataset:
+        # normalize the name of the mag
+        mag = Mag(mag).to_layer_name()
+
+        self._assert_mag_does_not_exist_yet(mag)
+        self._create_dir_for_mag(mag)
+
+        self.mags[mag] = WKMagDataset.create(self, mag, block_len, file_len, block_type)
+        self.dataset.properties._add_mag(self.name, mag, block_len * file_len)
+
+        return self.mags[mag]
+
+    def get_or_add_mag(
+        self, mag, block_len=None, file_len=None, block_type=None
+    ) -> WKMagDataset:
+        # normalize the name of the mag
+        mag = Mag(mag).to_layer_name()
+
+        if mag in self.mags.keys():
+            assert (
+                block_len is None or self.mags[mag].header.block_len == block_len
+            ), f"Cannot get_or_add_mag: The mag {mag} already exists, but the block lengths do not match"
+            assert (
+                file_len is None or self.mags[mag].header.file_len == file_len
+            ), f"Cannot get_or_add_mag: The mag {mag} already exists, but the file lengths do not match"
+            assert (
+                block_type is None or self.mags[mag].header.block_type == block_type
+            ), f"Cannot get_or_add_mag: The mag {mag} already exists, but the block types do not match"
+            return self.get_mag(mag)
+        else:
+            return self.add_mag(mag, block_len, file_len, block_type)
+
+    def setup_mag(self, mag):
+        # This method is used to initialize the mag when opening the Dataset. This does not create e.g. the wk_header.
+
+        # normalize the name of the mag
+        mag = Mag(mag).to_layer_name()
+
+        self._assert_mag_does_not_exist_yet(mag)
+
+        with wkw.Dataset.open(join(self.dataset.path, self.name, mag)) as wkw_dataset:
+            wk_header = wkw_dataset.header
+
+        self.mags[mag] = WKMagDataset(
+            self, mag, wk_header.block_len, wk_header.file_len, wk_header.block_type
+        )
+        self.dataset.properties._add_mag(
+            self.name, mag, wk_header.block_len * wk_header.file_len
+        )
+
+
+class TiffLayer(Layer):
+    def add_mag(self, mag) -> MagDataset:
+        # normalize the name of the mag
+        mag = Mag(mag).to_layer_name()
+
+        self._assert_mag_does_not_exist_yet(mag)
+        self._create_dir_for_mag(mag)
+
+        self.mags[mag] = TiffMagDataset.create(self, mag)
+        self.dataset.properties._add_mag(self.name, mag)
+
+        return self.mags[mag]
+
+    def get_or_add_mag(self, mag) -> MagDataset:
+        # normalize the name of the mag
+        mag = Mag(mag).to_layer_name()
+
+        if mag in self.mags.keys():
+            return self.get_mag(mag)
+        else:
+            return self.add_mag(mag)
+
+    def setup_mag(self, mag):
+        self.add_mag(mag)
diff --git a/wkcuber/api/MagDataset.py b/wkcuber/api/MagDataset.py
new file mode 100644
index 000000000..bfed0018d
--- /dev/null
+++ b/wkcuber/api/MagDataset.py
@@ -0,0 +1,116 @@
+from os.path import join
+
+from wkw import wkw
+import numpy as np
+
+import wkcuber.api as api
+from wkcuber.api.View import WKView, TiffView
+from wkcuber.api.TiffData.TiffMag import TiffMagHeader
+
+
+class MagDataset:
+    def __init__(self, layer, name):
+        self.layer = layer
+        self.name = name
+        self.header = self.get_header()
+
+        file_path = join(self.layer.dataset.path, self.layer.name, self.name)
+        size = self.layer.dataset.properties.data_layers[
+            self.layer.name
+        ].get_bounding_box_size()
+        offset = self.layer.dataset.properties.data_layers[
+            self.layer.name
+        ].get_bounding_box_offset()
+        self.view = self.get_view(
+            file_path, size, global_offset=(0, 0, 0), is_bounded=False
+        )
+
+    def open(self):
+        self.view.initialize()
+
+    def close(self):
+        self.view.close()
+
+    def read(self, size, offset=(0, 0, 0)) -> np.array:
+        return self.view.read(size, offset)
+
+    def write(self, data, offset=(0, 0, 0)):
+        self._assert_valid_num_channels(data.shape)
+        self.view.write(data, offset)
+        layer_properties = self.layer.dataset.properties.data_layers[self.layer.name]
+        current_offset = layer_properties.get_bounding_box_offset()
+        current_size = layer_properties.get_bounding_box_size()
+
+        new_offset = (
+            offset
+            if current_offset == (-1, -1, -1)
+            else tuple(min(x) for x in zip(current_offset, offset))
+        )
+        total_size = tuple(max(x) for x in zip(current_size, data.shape[-3:]))
+        self.view.size = total_size
+
+        self.layer.dataset.properties._set_bounding_box_of_layer(
+            self.layer.name, new_offset, total_size
+        )
+
+    def get_header(self):
+        raise NotImplementedError
+
+    def get_view(self, mag_file_path, size, global_offset, is_bounded=True):
+        raise NotImplementedError
+
+    def _assert_valid_num_channels(self, write_data_shape):
+        num_channels = self.layer.num_channels
+        if len(write_data_shape) == 3:
+            assert (
+                num_channels == 1
+            ), f"The number of channels of the dataset ({num_channels}) does not match the number of channels of the passed data (1)"
+        else:
+            assert (
+                num_channels == 3
+            ), f"The number of channels of the dataset ({num_channels}) does not match the number of channels of the passed data ({write_data_shape[0]})"
+
+
+class WKMagDataset(MagDataset):
+    def __init__(self, layer, name, block_len, file_len, block_type):
+        self.block_len = block_len
+        self.file_len = file_len
+        self.block_type = block_type
+        super().__init__(layer, name)
+
+    def get_header(self) -> wkw.Header:
+        return wkw.Header(
+            voxel_type=self.layer.dtype,
+            num_channels=self.layer.num_channels,
+            version=1,
+            block_len=self.block_len,
+            file_len=self.file_len,
+            block_type=self.block_type,
+        )
+
+    def get_view(self, mag_file_path, size, global_offset, is_bounded=True) -> WKView:
+        return WKView(mag_file_path, self.header, size, global_offset, is_bounded)
+
+    @classmethod
+    def create(cls, layer, name, block_len, file_len, block_type):
+        mag_dataset = cls(layer, name, block_len, file_len, block_type)
+        wkw.Dataset.create(
+            join(layer.dataset.path, layer.name, mag_dataset.name), mag_dataset.header
+        )
+
+        return mag_dataset
+
+
+class TiffMagDataset(MagDataset):
+    def get_header(self) -> TiffMagHeader:
+        return TiffMagHeader(
+            dtype=self.layer.dtype, num_channels=self.layer.num_channels
+        )
+
+    def get_view(self, mag_file_path, size, global_offset, is_bounded=True) -> TiffView:
+        return TiffView(mag_file_path, self.header, size, global_offset, is_bounded)
+
+    @classmethod
+    def create(cls, layer, name):
+        mag_dataset = cls(layer, name)
+        return mag_dataset
diff --git a/wkcuber/api/Properties.py b/wkcuber/api/Properties.py
new file mode 100644
index 000000000..4b2ab51c5
--- /dev/null
+++ b/wkcuber/api/Properties.py
@@ -0,0 +1,317 @@
+import json
+import numpy as np
+
+from wkcuber.mag import Mag
+
+
+class Resolution:
+    def _to_json(self) -> dict:
+        pass
+
+    @classmethod
+    def _from_json(cls, json_data):
+        pass
+
+
+class TiffResolution(Resolution):
+    def __init__(self, mag):
+        self._mag = Mag(mag)
+
+    def _to_json(self) -> dict:
+        return {"resolution": self.mag.to_array()}
+
+    @classmethod
+    def _from_json(cls, json_data):
+        return cls(json_data["resolution"])
+
+    @property
+    def mag(self) -> Mag:
+        return self._mag
+
+
+class WkResolution(Resolution):
+    def __init__(self, mag, cube_length):
+        self._mag = Mag(mag)
+        self._cube_length = cube_length
+
+    def _to_json(self) -> dict:
+        return {"resolution": self.mag.to_array(), "cube_length": self.cube_length}
+
+    @classmethod
+    def _from_json(cls, json_data):
+        return cls(json_data["resolution"], json_data["cube_length"])
+
+    @property
+    def mag(self) -> Mag:
+        return self._mag
+
+    @property
+    def cube_length(self) -> int:
+        return self._cube_length
+
+
+class Properties:
+
+    FILE_NAME = "datasource-properties.json"
+
+    def __init__(self, path, name, scale, team="", data_layers=None):
+        self._path = path
+        self._name = name
+        self._team = team
+        self._scale = scale
+        if data_layers is None:
+            self._data_layers = {}
+        else:
+            self._data_layers = data_layers
+
+    @classmethod
+    def _from_json(cls, path):
+        pass
+
+    def _export_as_json(self):
+        pass
+
+    def _add_layer(self, layer_name, category, element_class, num_channels=1):
+        # this layer is already in data_layers in case we reconstruct the dataset from a datasource-properties.json
+        if layer_name not in self.data_layers:
+            new_layer = LayerProperties(
+                layer_name, category, element_class, num_channels
+            )
+            self.data_layers[layer_name] = new_layer
+            self._export_as_json()
+
+    def _delete_layer(self, layer_name):
+        del self.data_layers[layer_name]
+        self._export_as_json()
+
+    def _delete_mag(self, layer_name, mag):
+        self._data_layers[layer_name]._delete_resolution(mag)
+        self._export_as_json()
+
+    def _set_bounding_box_of_layer(self, layer_name, offset, size):
+        self._data_layers[layer_name]._set_bounding_box_size(size)
+        self._data_layers[layer_name]._set_bounding_box_offset(offset)
+        self._export_as_json()
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    @property
+    def path(self) -> str:
+        return self._path
+
+    @property
+    def team(self) -> str:
+        return self._team
+
+    @property
+    def scale(self) -> tuple:
+        return self._scale
+
+    @property
+    def data_layers(self) -> dict:
+        return self._data_layers
+
+
+class WKProperties(Properties):
+    @classmethod
+    def _from_json(cls, path) -> Properties:
+        with open(path) as datasource_properties:
+            data = json.load(datasource_properties)
+
+            # reconstruct data_layers
+            data_layers = {}
+            for layer in data["dataLayers"]:
+                data_layers[layer["name"]] = LayerProperties._from_json(
+                    layer, WkResolution
+                )
+
+            return cls(
+                path, data["id"]["name"], data["scale"], data["id"]["team"], data_layers
+            )
+
+    def _export_as_json(self):
+        data = {
+            "id": {"name": self.name, "team": self.team},
+            "scale": self.scale,
+            "dataLayers": [
+                self.data_layers[layer_name]._to_json()
+                for layer_name in self.data_layers
+            ],
+        }
+        with open(self.path, "w") as outfile:
+            json.dump(data, outfile, indent=4, separators=(",", ": "))
+
+    def _add_mag(self, layer_name, mag, cube_length):
+        # this mag is already in wkw_magnifications in case we reconstruct the dataset from a datasource-properties.json
+        if not any(
+            [
+                res.mag == Mag(mag)
+                for res in self.data_layers[layer_name].wkw_magnifications
+            ]
+        ):
+            self._data_layers[layer_name]._add_resolution(
+                WkResolution(mag, cube_length)
+            )
+            self._export_as_json()
+
+
+class TiffProperties(Properties):
+    def __init__(self, path, name, scale, team="", data_layers=None, grid_shape=(0, 0)):
+        super().__init__(path, name, scale, team, data_layers)
+        self._grid_shape = grid_shape
+
+    @classmethod
+    def _from_json(cls, path) -> Properties:
+        with open(path) as datasource_properties:
+            data = json.load(datasource_properties)
+
+            # reconstruct data_layers
+            data_layers = {}
+            for layer in data["dataLayers"]:
+                data_layers[layer["name"]] = LayerProperties._from_json(
+                    layer, TiffResolution
+                )
+
+            return cls(
+                path,
+                data["id"]["name"],
+                data["scale"],
+                data["id"]["team"],
+                data_layers,
+                data["grid_shape"],
+            )
+
+    def _export_as_json(self):
+        data = {
+            "id": {"name": self.name, "team": self.team},
+            "scale": self.scale,
+            "dataLayers": [
+                self.data_layers[layer_name]._to_json()
+                for layer_name in self.data_layers
+            ],
+            "grid_shape": self.grid_shape,
+        }
+        with open(self.path, "w") as outfile:
+            json.dump(data, outfile, indent=4, separators=(",", ": "))
+
+    @property
+    def grid_shape(self) -> tuple:
+        return self._grid_shape
+
+    def _add_mag(self, layer_name, mag):
+        # this mag is already in wkw_magnifications in case we reconstruct the dataset from a datasource-properties.json
+        if not any(
+            [
+                res.mag == Mag(mag)
+                for res in self.data_layers[layer_name].wkw_magnifications
+            ]
+        ):
+            self.data_layers[layer_name]._add_resolution(TiffResolution(mag))
+            self._export_as_json()
+
+
+class LayerProperties:
+    def __init__(
+        self,
+        name,
+        category,
+        element_class,
+        num_channels,
+        bounding_box=None,
+        resolutions=None,
+    ):
+        self._name = name
+        self._category = category
+        self._element_class = element_class
+        self._num_channels = num_channels
+        self._bounding_box = bounding_box or {
+            "topLeft": (-1, -1, -1),
+            "width": 0,
+            "height": 0,
+            "depth": 0,
+        }
+        self._wkw_magnifications = resolutions or []
+
+    def _to_json(self):
+        return {
+            "name": self.name,
+            "category": self.category,
+            "elementClass": self.element_class,
+            "num_channels": self.num_channels,
+            "boundingBox": {}
+            if self.bounding_box is None
+            else {
+                "topLeft": self.bounding_box["topLeft"],
+                "width": self.bounding_box["width"],
+                "height": self.bounding_box["height"],
+                "depth": self.bounding_box["depth"],
+            },
+            "wkwResolutions": [r._to_json() for r in self.wkw_magnifications],
+        }
+
+    @classmethod
+    def _from_json(cls, json_data, resolution_type):
+        # create LayerProperties without resolutions
+        layer_properties = cls(
+            json_data["name"],
+            json_data["category"],
+            json_data["elementClass"],
+            json_data["num_channels"],
+            json_data["boundingBox"],
+        )
+
+        # add resolutions to LayerProperties
+        for resolution in json_data["wkwResolutions"]:
+            layer_properties._add_resolution(resolution_type._from_json(resolution))
+
+        return layer_properties
+
+    def _add_resolution(self, resolution):
+        self._wkw_magnifications.append(resolution)
+
+    def _delete_resolution(self, resolution):
+        self._wkw_magnifications.delete(resolution)
+
+    def get_bounding_box_size(self) -> tuple:
+        return (
+            self.bounding_box["width"],
+            self.bounding_box["height"],
+            self.bounding_box["depth"],
+        )
+
+    def get_bounding_box_offset(self) -> tuple:
+        return tuple(self.bounding_box["topLeft"])
+
+    def _set_bounding_box_size(self, size):
+        self._bounding_box["width"] = size[0]
+        self._bounding_box["height"] = size[1]
+        self._bounding_box["depth"] = size[2]
+
+    def _set_bounding_box_offset(self, offset):
+        self._bounding_box["topLeft"] = offset
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    @property
+    def category(self) -> str:
+        return self._category
+
+    @property
+    def element_class(self):
+        return self._element_class
+
+    @property
+    def num_channels(self) -> int:
+        return self._num_channels
+
+    @property
+    def bounding_box(self) -> dict:
+        return self._bounding_box
+
+    @property
+    def wkw_magnifications(self) -> dict:
+        return self._wkw_magnifications
diff --git a/wkcuber/api/TiffData/TiffMag.py b/wkcuber/api/TiffData/TiffMag.py
new file mode 100644
index 000000000..de7b5a50d
--- /dev/null
+++ b/wkcuber/api/TiffData/TiffMag.py
@@ -0,0 +1,227 @@
+from typing import Optional, List, Generator
+
+from skimage import io
+import numpy as np
+import os
+from re import findall
+from glob import iglob
+from itertools import zip_longest
+
+
+def replace_coordinate(pattern: str, coord_id: str, coord: int) -> str:
+    occurrences = findall("{" + coord_id + "+}", pattern)
+    for occurrence in occurrences:
+        number_of_digits = len(occurrence) - 2
+        if number_of_digits > 1:
+            format_str = "0" + str(number_of_digits) + "d"
+        else:
+            format_str = "d"
+        pattern = pattern.replace(occurrence, format(coord, format_str), 1)
+    return pattern
+
+
+def to_file_name(z) -> str:
+    return replace_coordinate("{zzzzz}.tif", "z", z)
+
+
+def detect_value(
+    pattern_element: str,
+    ls_item: str,
+    dim: str,
+    ignore_dims: Optional[List[str]] = None,
+) -> List[int]:
+    if ignore_dims is not None:
+        for ignore_dim in ignore_dims:
+            pattern_element = pattern_element.replace("{" + ignore_dim, ignore_dim)
+            pattern_element = pattern_element.replace(ignore_dim + "}", ignore_dim)
+
+    if "{" + dim in pattern_element and dim + "}" in pattern_element:
+        open_position = pattern_element.find("{" + dim)
+        close_position = pattern_element.find(dim + "}")
+        try:
+            substring = ls_item[open_position:close_position]
+            return [int(substring)]
+        except ValueError:
+            raise ValueError(
+                f"Failed to autodetect tile ranges, there were files not matching the pattern: {ls_item} does not match {pattern_element}"
+            )
+    return []
+
+
+class TiffMag:
+    def __init__(self, root, header):
+        x_range = [0]  # currently tiled tiffs are not supported
+        y_range = [0]  # currently tiled tiffs are not supported
+
+        self.root = root
+        self.tiffs = dict()
+        self.dtype = header.dtype
+        self.num_channels = header.num_channels
+
+        pattern = "{zzzzz}.tif"
+
+        z_range = [
+            detect_value(pattern, file_name, dim="z")[0]
+            for file_name in self.list_files()
+        ]
+
+        for z in z_range:
+            self.tiffs[z] = TiffReader.open(
+                self.get_file_name_for_layer(z)
+            )  # open is lazy
+
+    def read(self, off, shape) -> np.array:
+        if not self.has_only_one_channel():
+            # modify the shape to also include the num_channels
+            shape = tuple(shape) + tuple([self.num_channels])
+
+        data = np.zeros(shape=shape, dtype=self.dtype)
+        for i, (z, offset, size) in enumerate(
+            self.calculate_relevant_slices(off, shape)
+        ):
+            if z in self.tiffs:
+                data[:, :, i] = np.array(self.tiffs[z].read(), self.dtype)[
+                    offset[0] : offset[0] + size[0], offset[1] : offset[1] + size[1]
+                ]
+            else:
+                shape_without_z = shape[:2] + shape[3:]
+                data[:, :, i] = np.zeros(shape_without_z, self.dtype)
+
+        if self.has_only_one_channel():
+            # convert data into shape with dedicated num_channels (len(data.shape) == 4)
+            # this only effects data where the num_channel is 1 and therefore len(data.shape) was 3
+            # this makes it easier to handle both, multi-channel and single-channel, similar
+            data = np.expand_dims(data, 3)
+
+        # reformat array to have the channels as the first index (similar to wkw)
+        data = np.moveaxis(data, -1, 0)
+        return data
+
+    def write(self, off, data):
+        # convert data into shape with dedicated num_channels (len(data.shape) == 4)
+        # this only effects data where the num_channel is 1 and therefore len(data.shape) was 3
+        # this makes it easier to handle both, multi-channel and single-channel, similar
+        data = data.reshape((-1,) + data.shape[-3:])
+
+        # reformat array to have the channels as the first index (similar to wkw)
+        data = np.moveaxis(data, 0, -1)
+
+        self.assert_correct_data_format(data)
+
+        for i, (z, offset, _) in enumerate(
+            self.calculate_relevant_slices(off, data.shape)
+        ):
+            # initialize images for z_layers that did not exist before
+            if z not in self.tiffs:
+                total_shape = [
+                    sum(x)
+                    for x in zip_longest(data[:, :, i].shape, offset, fillvalue=0)
+                ]
+                if self.has_only_one_channel():
+                    # Convert single-channel data into the expected format
+                    # E.g. convert shape (300, 300, 1) into (300, 300)
+                    total_shape = tuple(total_shape)[:-1]
+
+                self.tiffs[z] = TiffReader.init_tiff(
+                    np.zeros(total_shape, self.dtype), self.get_file_name_for_layer(z)
+                )
+
+            # write new pixel data into the image
+            pixel_data = (
+                data[:, :, i] if not self.has_only_one_channel() else data[:, :, i, 0]
+            )
+
+            self.tiffs[z].merge_with_image(pixel_data, offset)
+
+    def compress(self, dst_path: str, compress_files: bool = False):
+        raise NotImplementedError
+
+    def list_files(self):
+        file_paths = list(iglob(os.path.join(self.root, "*.tif")))
+
+        for file_path in file_paths:
+            yield os.path.relpath(os.path.normpath(file_path), self.root)
+
+    def close(self):
+        return
+
+    def calculate_relevant_slices(self, offset, shape):
+        for z in range(offset[2] + 1, offset[2] + shape[2] + 1):
+            yield tuple(
+                (z, offset[0:2], shape[0:2])
+            )  # return tuple of important z layers an the x-y offset (without z offset) and the size (without z length)
+
+    def has_only_one_channel(self) -> bool:
+        return self.num_channels == 1
+
+    def assert_correct_data_format(self, data):
+        if not len(data.shape) == 4:
+            raise AttributeError(
+                "The shape of the provided data does not match the expected shape."
+            )
+        if not data.shape[3] == self.num_channels:
+            raise AttributeError(
+                f"The shape of the provided data does not match the expected shape. (Expected {self.num_channels} channels)"
+            )
+        if not np.dtype(data.dtype) == self.dtype:
+            raise AttributeError(
+                f"The type of the provided data does not match the expected type. (Expected np.array of type {self.dtype.name})"
+            )
+
+    def get_file_name_for_layer(self, z) -> str:
+        return os.path.join(self.root, to_file_name(z))
+
+    @staticmethod
+    def open(root: str, header=None):
+        if header is None:
+            header = TiffMagHeader()
+        return TiffMag(root, header)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, type, value, tb):
+        self.close()
+
+
+class TiffMagHeader:
+    def __init__(self, pattern="{z}.tif", dtype=np.dtype("uint8"), num_channels=1):
+        self.pattern = pattern
+        self.dtype = np.dtype(dtype)
+        self.num_channels = num_channels
+
+
+class TiffReader:
+    def __init__(self, file_name):
+        self.file_name = file_name
+
+    @classmethod
+    def init_tiff(cls, pixels, file_name):
+        tr = TiffReader(file_name)
+        tr.write(pixels)
+        return tr
+
+    @classmethod
+    def open(cls, file_name):
+        return cls(file_name)
+
+    def read(self) -> np.array:
+        return io.imread(self.file_name)
+
+    def write(self, pixels):
+        io.imsave(self.file_name, pixels, check_contrast=False)
+
+    def merge_with_image(self, foreground_pixels, offset):
+        background_pixels = self.read()
+        bg_shape = background_pixels.shape
+        fg_shape = foreground_pixels.shape
+
+        fg_shape_with_off = [sum(x) for x in zip_longest(fg_shape, offset, fillvalue=0)]
+        total_shape = [max(x) for x in zip(bg_shape, fg_shape_with_off)]
+        new_image = np.zeros(total_shape, dtype=background_pixels.dtype)
+
+        new_image[0 : bg_shape[0], 0 : bg_shape[1]] = background_pixels
+        new_image[
+            offset[0] : fg_shape_with_off[0], offset[1] : fg_shape_with_off[1]
+        ] = foreground_pixels
+        self.write(new_image)
diff --git a/wkcuber/api/View.py b/wkcuber/api/View.py
new file mode 100644
index 000000000..d2e79b42a
--- /dev/null
+++ b/wkcuber/api/View.py
@@ -0,0 +1,119 @@
+import numpy as np
+from wkw import Dataset
+
+from wkcuber.api.TiffData.TiffMag import TiffMag
+
+
+class View:
+    def __init__(
+        self,
+        path_to_mag_dataset,
+        header,
+        size,
+        global_offset=(0, 0, 0),
+        is_bounded=True,
+    ):
+        self.dataset = None
+        self.path = path_to_mag_dataset
+        self.header = header
+        self.size = size
+        self.global_offset = global_offset
+        self.is_bounded = is_bounded
+        self._is_opened = False
+
+    def open(self):
+        raise NotImplemented()
+
+    def close(self):
+        if not self._is_opened:
+            raise Exception("Cannot close View: the view is not opened")
+        else:
+            self.dataset.close()
+            self.dataset = None
+            self._is_opened = False
+
+    def write(self, data, offset=(0, 0, 0)):
+        # assert the size of the parameter data is not in conflict with the attribute self.size
+        assert_non_negative_offset(offset)
+        self.assert_bounds(offset, data.shape[-3:])
+
+        # calculate the absolute offset
+        absolute_offset = tuple(sum(x) for x in zip(self.global_offset, offset))
+
+        if not self._is_opened:
+            self.open()
+
+        self.dataset.write(absolute_offset, data)
+
+        if not self._is_opened:
+            self.close()
+
+    def read(self, size=None, offset=(0, 0, 0)) -> np.array:
+        was_opened = self._is_opened
+        size = size or self.size
+
+        # assert the parameter size is not in conflict with the attribute self.size
+        self.assert_bounds(offset, size)
+
+        # calculate the absolute offset
+        absolute_offset = tuple(sum(x) for x in zip(self.global_offset, offset))
+
+        if not was_opened:
+            self.open()
+
+        data = self.dataset.read(absolute_offset, size)
+
+        if not was_opened:
+            self.close()
+
+        return data
+
+    def check_bounds(self, offset, size) -> bool:
+        for s1, s2, off in zip(self.size, size, offset):
+            if s2 + off > s1 and self.is_bounded:
+                return False
+        return True
+
+    def assert_bounds(self, offset, size):
+        if not self.check_bounds(offset, size):
+            raise AssertionError(
+                f"Writing out of bounds: The passed parameter 'size' {size} exceeds the size of the current view ({self.size})"
+            )
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, type, value, tb):
+        self.close()
+
+
+class WKView(View):
+    def open(self):
+        if self._is_opened:
+            raise Exception("Cannot open view: the view is already opened")
+        else:
+            self.dataset = Dataset.open(
+                self.path
+            )  # No need to pass the header to the wkw.Dataset
+            self._is_opened = True
+        return self
+
+
+class TiffView(View):
+    def open(self):
+        if self._is_opened:
+            raise Exception("Cannot open view: the view is already opened")
+        else:
+            self.dataset = TiffMag.open(self.path, self.header)
+            self._is_opened = True
+        return self
+
+
+def assert_non_negative_offset(offset):
+    all_positive = all(i >= 0 for i in offset)
+    if not all_positive:
+        raise Exception(
+            "All elements of the offset need to be positive: %s" % "("
+            + ",".join(map(str, offset))
+            + ")"
+        )